Development and Validation of a Predictive Model for Survival Outcomes in Patients with Paroxysmal versus Persistent Atrial Fibrillation: A Retrospective Cohort Study Based on the MIMIC-IV Database

Development and Validation of a Predictive Model for Survival Outcomes in Patients with Paroxysmal versus Persistent Atrial Fibrillation: A Retrospective Cohort Study Based on the MIMIC-IV Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Predictive Model for Survival Outcomes in Patients with Paroxysmal versus Persistent Atrial Fibrillation: A Retrospective Cohort Study Based on the MIMIC-IV Database Haoran Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6891214/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Atrial fibrillation (AF) has been implicated in increasing all-cause mortality among patients in intensive care unit (ICU), with paroxysmal atrial fibrillation (PAF) often progressing over time to persistent atrial fibrillation (PersAF), which carries an even higher risk of death compared to PAF. Our study aims to analyze the survival disparities between patients with PAF and PersAF, and to a comprehensive model to predict the impact of life-threatening comorbidities on AF patients' prognosis in the ICU. This endeavor is geared towards facilitating early assessment and timely intervention for AF patients, ultimately improving their clinical outcomes. Methods: Data were retrieved from the MIMIC-IV database for patients aged ≥ 18 years admitted to the ICU for the first time between 2008 and 2019. A total of 12,130 AF patients were identified and split into a training cohort (n = 8,491) and a validation cohort (n = 3,639). Cox regression analysis was performed to identify independent predictors of 90-day mortality. A nomogram was developed to predict survival probabilities at 30, 60, and 90 days. Kaplan-Meier survival curves were generated to visually compare survival outcomes between patients with PAF and PersAF. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and Decision Curve Analysis (DCA). Results: The mean age of the study population was 74.60 ± 12.05 years, with 40.63% females. Independent predictors of 90-day mortality included age, persistent AF, cerebral infarction, intracranial injury, chronic heart failure (CHF), acute kidney failure (AKF), severe sepsis, cardiogenic shock, acute respiratory distress syndrome (ARDS), malignant neoplasm, and acute renal failure (ARF). Antiplatelet therapy and anticoagulants were protective factors. The nomogram demonstrated excellent discriminatory performance with AUC values ranging from 0.80 to 0.84. Calibration curves and DCA confirmed the model's reliability and clinical usefulness. Kaplan-Meier curves showed higher survival rates in patients with PAF compared to those with PersAF. Conclusion: The developed and validated nomogram has demonstrated sufficient accuracy in predicting the risk of all-cause mortality and identifying prognostic factors in patients with atrial fibrillation (AF) admitted to the intensive care unit (ICU) for the first time. atrial fibrillation（AF) intensive care unit（ICU) mortality predictive model predictor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION AF is a prevalent cardiac arrhythmia characterized by rapid and irregular electrical activity within the atria, accompanied by the loss of coordinated mechanical contraction. This condition frequently precipitates complications such as heart failure and thromboembolic events, particularly strokes, thereby augmenting the risk of mortality. Numerous studies have consistently demonstrated that the patients with AF in the ICU exhibit significantly higher all-cause mortality rates compared to the non-AF patients, compounding the complexity and severity of their illness and markedly increasing the therapeutic challenge. Notably, Zhang HD et al. conducted a Kaplan-Meier analysis on 31,562 ICU patients, revealing a significantly lower one-year survival rate among patients with new-onset AF ( p < 0.001) [ 1 ]. This finding underscores the profound impact of AF on survival outcomes in the critically ill population. AF has been established as an independent predictor of poor outcomes in critically ill patients, and we firmly believe that AF, in conjunction with other risk factors, interacts to exacerbate the risk of death in these patients. Numerous studies have explored this, for instance: Huang T et al. found that the ratio of high lactate to albumin can predict in-hospital mortality in ICU patients with AF [ 2 ]; Pan LY et al. indicated that a high level of the ratio of red cell distribution width to albumin is associated with increased in-hospital mortality of the patients with AF [ 3 ]; and Meng H et al. revealed that AF poses a significant risk for adverse outcomes in sepsis patients [ 4 ]. However, these studies primarily focused on the relationship between a single factor and the prognosis of AF patients. Our study aims to develop a more comprehensive prediction model, which takes into account common fatal diseases and conditions in ICU patients and is specifically designed for the AF population. Through 90-day survival analysis of all AF patients, we observed a significant difference in outcomes between PAF and PersAF, with the latter carrying a higher risk of death. While this phenomenon has been sparsely addressed in previous research, Wetterslev M et al. suggested that the ultimate cause of death in ICU patients with AF is more closely related to the severity of their underlying diseases, and the 90-day mortality rate is not significantly associated with AF itself [ 5 ]. Another study by Baroutidou A et al., a retrospective cohort analysis of 1,052 patients, assessed the prognostic impact of different AF patterns, including newly diagnosed, paroxysmal, and persistent or permanent AF, but the sample size was relatively small [ 6 ]. Our study, however, is based on detailed data from tens of thousands of AF patients. Given that PAF often progresses to PersAF, it is crucial to highlight their distinct dangers. While both forms pose significant risks, PAF's intermittent nature offers a unique advantage: it allows for more flexible timing of therapeutic interventions. This adaptability in treatment timing enhances the efficacy of interventions, leading to improved outcomes compared to PersAF. With an expanding array of treatment options for AF, including pharmacological therapy, electrical cardioversion, and catheter ablation, we firmly believe that early diagnosis and intervention are pivotal in effectively improving the prognosis of patients with AF. METHODS AND MATERIALS Dataset Selection and Splitting This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database ( https://mimic.mit.edu/ ), which contains de-identified clinical data from patients admitted to the intensive care units (ICUs) of Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, between 2008 and 2019. The MIMIC-IV database(certification number: 69652196) has received ethical approval from the Institutional Review Boards (IRBs) of BIDMC and the Massachusetts Institute of Technology (MIT), thereby waiving the need for additional ethical consent for this study. As all protected health information was de-identified, individual patient consent was not required. Based on the MIMIC-IV database, we retrieved records of patients aged ≥ 18 years who were admitted to the ICU for the first time between 2008 and 2019. Using SQL language, we filtered out patients with AF who had an ICD-10 code of I48, where I480 is classified as PAF, and I481 to I489 (including I481, I482, I483, I484, I485, I486, I487, I488, I489) are considered as PersAF. Additionally, the ‘heart_rhythm’ field during hospitalization was used to further confirm the type of cardiac arrhythmia, ensuring accurate classification of paroxysmal and persistent AF. Furthermore, by aggregating all ICD diagnosis codes for each AF patient through SQL queries, we obtained their baseline characteristics, comorbidities, common critical illnesses or statuses among ICU patients, and death information. For instance, a patient identified with AF through ICD-10 code I48 would undergo automated aggregation of all diagnosis codes across hospital encounters. This reveals comorbidities such as: Chronic heart failure (I50), Coronary artery disease (I25.1), Hypertension (I10), Nicotine dependence (Z87.891) and so on. To address missing data while preserving dataset integrity, we employed multiple imputation by chained equations (MICE). This method iteratively models missing values using observed data distributions, generating multiple imputed datasets that capture uncertainty in estimations. Results were consolidated using Rubin's rules to ensure valid statistical inference. The MICE approach was chosen for its capacity to handle diverse missing data patterns and maintain variable relationships critical for subsequent Cox regression modeling and nomogram development. We maximized statistical power, preserved data relationships, and enhanced the generalizability of our findings. In our Cox regression models, patients who were still alive at the end of the observation period (90 days) or who were discharged from the ICU before death were treated as right-censored observations. Ultimately, a total of 12,130 patients were identified, with 8,491 patients assigned to the training cohort and 3,639 to the validation cohort, split in a 7:3 ration. Statistical Analysis We employed R software (version 4.4.2) to perform a comprehensive statistical analysis on data retrieved from the MIMIC-IV database. A two-tailed P-value threshold of < 0.05 was established to denote statistical significance. All analyses were conducted in accordance with stringent methodological standards to guarantee the robustness and generalizability of our conclusions. Continuous variables adhering to a normal distribution were summarized as mean ± standard deviation (SD), while non-normally distributed variables were expressed as median with interquartile range (IQR). Categorical variables were presented as absolute counts accompanied by their respective percentages. Continuous variables were compared between the paroxysmal AF and persistent AF cohorts using the independent t-test for normally distributed data or the Mann-Whitney U test for non-normal distributions. Categorical variables were evaluated using the Chi-square (χ²) test, with Fisher's exact test applied when expected cell frequencies fell below 5. All variables initially underwent univariate Cox proportional hazards regression analysis to identify potential risk factors for all-cause mortality. Variables demonstrating statistical significance ( p < 0.05) were subsequently incorporated into a multivariate Cox regression model to ascertain independent predictors of mortality. A nomogram was constructed utilizing the key predictors identified to facilitate clinical interpretation and mortality risk stratification. The predictive performance of the model was rigorously assessed through internal validation. Specifically, the area under the receiver operating characteristic curve (AUC) was calculated for both training and validation cohorts to evaluate discriminatory power. Calibration curves were generated to assess the alignment between predicted survival probabilities and observed outcomes, while Decision Curve Analysis (DCA) was employed to quantify the net clinical benefit of the model across varying risk thresholds, thereby elucidating optimal clinical applications (Fig. 1 ). RESULTS Baseline Clinical Characteristics Based on a meticulous analysis of the MIMIC-IV database from 2008–2019, our study focused on the survival outcomes of adult patients with AF during their first ICU admission. It is well-established that AF can elevate the risk of all-cause mortality, with PersAF conferring a higher mortality risk compared to PAF. Table 1 presents the baseline clinical characteristics of all AF patients included in this study. Among the 12,130 patients with AF, the mean real age was 74.60 ± 12.05 years, with no significant difference between the validation cohort (n = 3,639) and the training cohort (n = 8,491) ( p = 0.816). Gender distribution was balanced, with 40.63% females and 59.37% males. Regarding BMI, the majority of patients fell within the 20 to 39 range, with no significant difference across cohorts ( p = 0.412). In terms of past medical history, hypertension was present in 40.75% of patients, T2DM in 28.42%, hyperlipidemia in 55.40%, and OSAS in 11.77%. Alcohol abuse and nicotine dependence were relatively uncommon, affecting 1.36% and 33.62% of patients, respectively. Cardiovascular procedures and interventions, such as presence of PCI or CABG, were noted in 7.09% and 5.94% of patients, respectively. The presence of PHV was rare, observed in only 2.00% of patients. Comorbidities were prevalent, with CHD affecting 34.65% of patients, AMI in 11.35%, CHF in 37.82%, AKF in 28.24%, and CKD in 22.87%. Other notable comorbidities included AF itself, with PAF in 27.64% and PersAF in 72.36%, hypothyroidism in 14.20%, cerebral infarction in 7.22%, COPD in 11.71%, and SPH in 9.54%. In terms of treatment regimens, antiplatelet therapy was used in 18.65% of patients, while anticoagulants were prescribed in 39.10%. A small proportion of patients (1.86%) were dependent on renal dialysis. Table 1 Baseline Demographics and Clinical Characteristics of Patients. Variables Total (n = 12130) Validation cohort (n = 3639) Training cohort (n = 8491) Statistic P Age, Mean ± SD 74.60 ± 12.05 74.56 ± 12.02 74.61 ± 12.06 t=-0.23 0.816 Gender, n (%) χ²=0.00 0.949 Female 4928 (40.63) 1480 (40.67) 3448 (40.61) Male 7202 (59.37) 2159 (59.33) 5043 (59.39) BMI, n (%) χ²=2.87 0.412 19 or Less 2656 (21.90) 764 (20.99) 1892 (22.28) 20 to 29 3028 (24.96) 915 (25.14) 2113 (24.89) 30 to 39 3391 (27.96) 1020 (28.03) 2371 (27.92) 40 or Greater 3055 (25.19) 940 (25.83) 2115 (24.91) Past History Hypertension, n (%) χ²=0.79 0.373 No 7187 (59.25) 2134 (58.64) 5053 (59.51) Yes 4943 (40.75) 1505 (41.36) 3438 (40.49) T2DM, n (%) χ²=0.00 0.996 No 8683 (71.58) 2605 (71.59) 6078 (71.58) Yes 3447 (28.42) 1034 (28.41) 2413 (28.42) Hyperlipidemia, n (%) χ²=0.99 0.319 No 5410 (44.60) 1648 (45.29) 3762 (44.31) Yes 6720 (55.40) 1991 (54.71) 4729 (55.69) OSAS, n(%) χ²=0.22 0.640 No 10702 (88.23) 3203 (88.02) 7499 (88.32) Yes 1428 (11.77) 436 (11.98) 992 (11.68) Alcohol Abuse, n (%) χ²=1.24 0.266 No 11965 (98.64) 3583 (98.46) 8382 (98.72) Yes 165 (1.36) 56 (1.54) 109 (1.28) Nicotine Dependence, n (%) χ²=0.10 0.750 No 8052 (66.38) 2408 (66.17) 5644 (66.47) Presence Of PCI, n (%) χ²=0.10 0.757 No 11270 (92.91) 3385 (93.02) 7885 (92.86) Yes 860 (7.09) 254 (6.98) 606 (7.14) Presence Of PHV, n (%) χ²=4.76 0.029 No 11888 (98.00) 3551 (97.58) 8337 (98.19) Yes 242 (2.00) 88 (2.42) 154 (1.81) Yes 4078 (33.62) 1231 (33.83) 2847 (33.53) Presence Of CABG, n (%) χ²=0.53 0.466 No 11409 (94.06) 3414 (93.82) 7995 (94.16) Yes 721 (5.94) 225 (6.18) 496 (5.84) Comorbidity AF, n (%) χ²=3.12 0.077 Paroxysmal AF 3353 (27.64) 966 (26.55) 2387 (28.11) Persistent AF 8777 (72.36) 2673 (73.45) 6104 (71.89) Hypothyroidism, n (%) χ²=2.16 0.142 No 10407 (85.80) 3148 (86.51) 7259 (85.49) Yes 1723 (14.20) 491 (13.49) 1232 (14.51) Cerebral Infarction, n (%) χ²=3.05 0.081 No 11254 (92.78) 3399 (93.40) 7855 (92.51) Yes 876 (7.22) 240 (6.60) 636 (7.49) Intracranial Injury, n (%) χ²=0.00 0.975 No 11679 (96.28) 3504 (96.29) 8175 (96.28) Yes 451 (3.72) 135 (3.71) 316 (3.72) COPD, n (%) χ²=5.78 0.016 No 10710 (88.29) 3174 (87.22) 7536 (88.75) Yes 1420 (11.71) 465 (12.78) 955 (11.25) SPH, n (%) χ²=0.00 0.952 No 10973 (90.46) 3291 (90.44) 7682 (90.47) Yes 1157 (9.54) 348 (9.56) 809 (9.53) CHD, n (%) χ²=1.37 0.242 No 7927 (65.35) 2350 (64.58) 5577 (65.68) Yes 4203 (34.65) 1289 (35.42) 2914 (34.32) AMI, n (%) χ²=0.20 0.657 No 10753 (88.65) 3233 (88.84) 7520 (88.56) Yes 1377 (11.35) 406 (11.16) 971 (11.44) CHF, n (%) χ²=1.86 0.172 No 7542 (62.18) 2296 (63.09) 5246 (61.78) Yes 4588 (37.82) 1343 (36.91) 3245 (38.22) AKF, n (%) χ²=0.13 0.718 No 8704 (71.76) 2603 (71.53) 6101 (71.85) Yes 3426 (28.24) 1036 (28.47) 2390 (28.15) CKD, n (%) χ²=0.42 0.515 No 9356 (77.13) 2793 (76.75) 6563 (77.29) Yes 2774 (22.87) 846 (23.25) 1928 (22.71) Acute Pancreatitis, n (%) χ²=0.59 0.441 No 11965 (98.64) 3585 (98.52) 8380 (98.69) Yes 165 (1.36) 54 (1.48) 111 (1.31) Gastrointestinal Hemorrhage, n (%) χ²=0.48 0.488 No 12021 (99.10) 3603 (99.01) 8418 (99.14) Yes 109 (0.90) 36 (0.99) 73 (0.86) Malignant Neoplasm, n (%) χ²=0.09 0.770 No 11854 (97.72) 3554 (97.66) 8300 (97.75) Yes 276 (2.28) 85 (2.34) 191 (2.25) Critically Ill Severe Sepsis, n (%) χ²=3.06 0.080 No 11163 (92.03) 3325 (91.37) 7838 (92.31) Yes 967 (7.97) 314 (8.63) 653 (7.69) Hypovolemic Shock, n (%) χ²=0.20 0.651 No 12016 (99.06) 3607 (99.12) 8409 (99.03) Yes 114 (0.94) 32 (0.88) 82 (0.97) Cardiogenic Shock, n (%) χ²=0.02 0.884 No 11605 (95.67) 3483 (95.71) 8122 (95.65) Yes 525 (4.33) 156 (4.29) 369 (4.35) Traumatic Shock, n (%) χ²=0.03 0.852 No 12108 (99.82) 3632 (99.81) 8476 (99.82) Yes 22 (0.18) 7 (0.19) 15 (0.18) APA, n (%) χ²=4.38 0.036 No 9603 (79.17) 2838 (77.99) 6765 (79.67) Yes 2527 (20.83) 801 (22.01) 1726 (20.33) ARDS, n (%) χ²=0.51 0.474 No 12015 (99.05) 3601 (98.96) 8414 (99.09) Yes 115 (0.95) 38 (1.04) 77 (0.91) ARF, n (%) χ²=0.27 0.603 No 10245 (84.46) 3083 (84.72) 7162 (84.35) Yes 1885 (15.54) 556 (15.28) 1329 (15.65) Medication Antiplatelets, n (%) χ²=0.08 0.776 No 9868 (81.35) 2966 (81.51) 6902 (81.29) Yes 2262 (18.65) 673 (18.49) 1589 (18.71) Anticoagulants, n (%) χ²=1.47 0.225 No 7387 (60.90) 2246 (61.72) 5141 (60.55) Yes 4743 (39.10) 1393 (38.28) 3350 (39.45) Dependence On Renal Dialysis, n (%) χ²=0.00 0.977 No 11904 (98.14) 3571 (98.13) 8333 (98.14) Yes 226 (1.86) 68 (1.87) 158 (1.86) t: t-test, χ²: Chi-square test; SD: standard deviation; BMI: Body Mass Index; T2DM: Type 2 Diabetes Mellitus; OSAS: Obstructive Sleep Apnea Syndrome; PCI: Percutaneous Coronary Intervention; PHV: Prosthetic Heart Valve; CABG: Coronary Artery Bypass Grafting; AF: Atrial Fibrillation; COPD: Chronic Obstructive Pulmonary Disease; SPH: Secondary Pulmonary Hypertension; CHD: Coronary Heart Disease; AMI: Acute Myocardial Infarction; CHF: Congestive Heart Failure; AKF: Acute Kidney Failure; CKD: Chronic Kidney Disease; APA: Acute Posthemorrhagic Anemia; ARDS: Acute Respiratory Distress Syndrome; ARF: Acute Respiratory Failure. COX Regression Analysis As presented in Table 2 , we conducted a meticulous Cox regression analysis to investigate the risk factors associated with 90-day mortality in adult patients with AF during their first ICU admission, utilizing data from the MIMIC-IV database. This analysis aimed to identify the independent predictors of mortality among a range of potential variables. Initially, a univariate Cox regression analysis was performed on fifteen candidate risk factors. This preliminary step revealed thirteen variables that exhibited significant associations with mortality, including age, gender, BMI, hypertension, T2DM, hyperlipidemia, OSAS, alcohol abuse, nicotine dependence, presence of PCI, presence of CABG, PersAF, and the presence of various comorbidities such as CHD, AMI, CHF, AKF, CKD, and severe sepsis. Subsequently, a multivariate Cox regression analysis was carried out to further refine and identify the independent predictors of 90-day mortality. This analysis, which utilized various selection methods including forward stepwise selection, backward stepwise selection, forward-backward stepwise selection, and P < 0.05 significance threshold, yielded a final model comprising several key variables. The independent predictors of 90-day mortality, as determined by the multivariate Cox regression analysis, were age, PersAF, cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm and ARF. Notably, the use of antiplatelet therapy and anticoagulants were found to be protective factors, associated with reduced mortality risk. Table 2 Univariate and multivariate Cox regression models for risk factors of all-cause mortality in patients with atrial fibrillation. Variables Univariate analysis Multivariate analysis HR (95%CI) P HR (95%CI) P Real Age, Mean ± SD 1.04 (1.04 ~ 1.05) < .001 1.05 (1.04 ~ 1.06) < .001 Gender, n (%) Female 1.00 (Reference) Male 0.88 (0.79 ~ 0.98) 0.018 BMI, n (%) 19 or Less 1.00 (Reference) 20 to 29 1.13 (0.98 ~ 1.31) 0.100 30 to 39 0.82 (0.71 ~ 0.96) 0.012 40 or Greater 0.81 (0.69 ~ 0.95) 0.009 Hypertension, n (%) No 1.00 (Reference) Yes 0.61 (0.55 ~ 0.69) < .001 T2DM, n (%) No 1.00 (Reference) Yes 1.10 (0.98 ~ 1.23) 0.119 Hyperlipidemia, n (%) No 1.00 (Reference) Yes 0.81 (0.73 ~ 0.90) < .001 OSAS, n(%) No 1.00 (Reference) Yes 0.63 (0.52 ~ 0.76) < .001 Alcohol Abuse, n (%) No 1.00 (Reference) Yes 1.21 (0.79 ~ 1.87) 0.382 Nicotine Dependence, n (%) No 1.00 (Reference) Yes 0.94 (0.84 ~ 1.06) 0.322 Presence Of PCI, n (%) No 1.00 (Reference) Yes 1.02 (0.83 ~ 1.25) 0.841 Presence Of PHV, n (%) No 1.00 (Reference) Yes 1.10 (0.75 ~ 1.61) 0.614 Presence Of CABG, n (%) No 1.00 (Reference) Yes 1.58 (1.31 ~ 1.91) < .001 AF, n (%) Paroxysmal AF 1.00 (Reference) 1.00 (Reference) Persistent AF 1.45 (1.28 ~ 1.65) < .001 1.41 (1.24 ~ 1.61) < .001 Hypothyroidism, n (%) No 1.00 (Reference) Yes 1.19 (1.03 ~ 1.37) 0.015 Cerebral Infarction, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 2.09 (1.79 ~ 2.44) < .001 2.03 (1.74 ~ 2.38) < .001 Intracranial Injury, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 1.35 (1.05 ~ 1.73) 0.017 1.68 (1.30 ~ 2.15) < .001 COPD, n (%) No 1.00 (Reference) Yes 1.26 (1.08 ~ 1.47) 0.003 SPH, n (%) No 1.00 (Reference) Yes 1.75 (1.51 ~ 2.03) < .001 CHD, n (%) No 1.00 (Reference) Yes 0.93 (0.83 ~ 1.04) 0.226 AMI, n (%) No 1.00 (Reference) Yes 1.82 (1.59 ~ 2.09) < .001 CHF, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 1.78 (1.60 ~ 1.98) < .001 1.29 (1.15 ~ 1.44) < .001 AKF, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 3.30 (2.96 ~ 3.67) < .001 1.79 (1.59 ~ 2.01) < .001 CKD, n (%) No 1.00 (Reference) Yes 1.71 (1.53 ~ 1.92) < .001 Acute Pancreatitis, n (%) No 1.00 (Reference) Yes 1.42 (0.95 ~ 2.10) 0.085 Gastrointestinal Hemorrhage, n (%) No 1.00 (Reference) Yes 2.85 (1.98 ~ 4.12) < .001 Malignant Neoplasm, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 3.10 (2.48 ~ 3.88) < .001 3.35 (2.67 ~ 4.19) < .001 Severe Sepsis, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 5.70 (5.04 ~ 6.43) < .001 2.74 (2.38 ~ 3.15) < .001 Hypovolemic Shock, n (%) No 1.00 (Reference) Yes 3.38 (2.44 ~ 4.69) < .001 Cardiogenic Shock, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 3.42 (2.89 ~ 4.04) < .001 2.10 (1.76 ~ 2.52) < .001 Traumatic Shock, n (%) No 1.00 (Reference) Yes 3.10 (1.39 ~ 6.90) 0.006 APA, n (%) No 1.00 (Reference) Yes 0.88 (0.77 ~ 1.01) 0.064 ARDS, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 4.83 (3.57 ~ 6.52) < .001 4.46 (3.22 ~ 6.18) < .001 ARF, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 4.06 (3.64 ~ 4.52) < .001 2.30 (2.03 ~ 2.61) < .001 Antiplatelets, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 0.58 (0.50 ~ 0.69) < .001 0.65 (0.55 ~ 0.77) < .001 Anticoagulants, n (%) No 1.00 (Reference) 1.00 (Reference) Yes 0.85 (0.76 ~ 0.95) 0.003 0.78 (0.70 ~ 0.88) < .001 HR: Hazards Ratio; CI: Confidence Interval. Model Training and Validation The nomogram depicted in Fig. 2 is a visual tool used to predict survival probabilities at 30, 60, and 90 days for AF patients firstly admitted to the ICU. Each predictor variable is assigned points based on their relative contribution to mortality risk. The total points calculated by summing the points for all predictors correspond to a linear predictor score, which is then used to estimate survival probabilities. Figure 3 displays the ROC curves for the training and validation cohorts at 30-day, 60-day, and 90-day time points. The ROC curves illustrate the model's ability to distinguish between patients who survive and those who do not at each time horizon. The AUC values, ranging from 0.80 to 0.84, indicate excellent discriminatory performance of the model. Figure 4 presents the calibration curves for the training and validation cohorts at 30-day, 60-day, and 90-day time points. These curves compare the observed proportions of patients who survive with the predicted probabilities derived from the nomogram. A well-calibrated model should have its calibration curve closely align with the diagonal line, representing perfect calibration. The observed curves closely follow the diagonal, indicating that the predicted survival probabilities closely match the actual outcomes, thus validating the model's reliability and accuracy. Figure 5 shows the DCA for the training and validation cohorts at 30-day, 60-day, and 90-day time points. The blue curves represent the net benefit of using the prognostic nomogram, while the red curves indicate the net benefit of the assumption that all patients will either experience the event (all die) or none will (all survive). The DCA curves demonstrate that using the nomogram provides a significant net benefit compared to the extreme strategies across a wide range of threshold probabilities, highlighting the clinical usefulness of the model in aiding decision-making for AF patients admitted to the ICU. Survival Analysis The Kaplan-Meier Survival Curves (Fig. 6 ) visually depict the difference in survival rates between patients with PAF and PersAF. The higher survival rates observed for patients with PAF suggest that this subgroup has a better prognosis compared to those with PersAF. The steeper decline in survival for patients with PersAF highlights the more severe nature and poorer outcomes associated with this type of AF. The consistency of the findings between the training and validation cohorts strengthens the evidence that the observed differences in survival rates are robust and not mere artifacts of the data. DISCUSSION Model Performance and Validation Our study provides comprehensive insights into the survival outcomes of AF patients during their first ICU admission, leveraging the extensive data from the MIMIC-IV database. The findings underscore the critical role of PersAF in significantly elevating the risk of all-cause mortality, as compared to PAF. Through rigorous statistical analysis, we identified independent predictors of 90-day mortality, including age, type of AF, and the presence of comorbidities such as cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm, and ARF. Notably, the use of antiplatelet therapy and anticoagulants emerged as protective factors, associated with reduced mortality risk. A key contribution of our study lies in the development and validation of a comprehensive prediction model designed to assess 30, 60, and 90-day survival probabilities for AF patients admitted to the ICU. Our nomogram, which integrates a wide array of demographic, clinical, and treatment-related factors, exhibits outstanding discriminatory performance, with an area under the ROC curve AUC ranging between 0.80 and 0.84. This level of precision is on par with, and sometimes even surpasses, other established risk prediction tools tailored for AF patients. Comparison with Existing Studies Our model builds upon and extends the existing literature, such as the work by Verhaeghe, J., et al. [ 7 ], who emphasized the importance of generalizable and calibrated machine learning models for real-time AF risk prediction in ICU patients. Similar to the approach taken by Verhaeghe, J., et al., we assessed the calibration of our model's predicted probabilities using Expected Calibration Error (ECE) and Expected Signed Calibration Error (ESCE) metrics. Our model demonstrated good calibration, indicating that the predicted risks closely align with the actual outcomes. Additionally, our model's use of robust statistical methods and its validation on independent external datasets (MIMIC-IV and GUH) suggest that it is generalizable across different hospitals and care standards. This generalizability is crucial for the widespread adoption and impact of our model in clinical practice. One of the key strengths of our model lies in its comparison with established risk scores such as CHA2DS2-VASc and HAS-BLED. Similar to the findings of Fox et al. [ 8 ] in their study of the GARFIELD-AF risk tool, our model outperformed CHA2DS2-VASc in predicting all-cause mortality. This finding highlights the importance of considering a broader spectrum of risk factors beyond those included in traditional scores. Moreover, by incorporating the effect of oral anticoagulation (OAC) therapy, our model aligns with the GARFIELD-AF risk tool in enabling clinicians to make informed decisions regarding anticoagulation strategies, which is crucial for optimizing patient outcomes. Our findings corroborate those of Chen et al., who developed a LASSO-Cox model to predict all-cause mortality in AF patients, achieving comparable AUC values (0.842 in the training set and 0.854 in the validation set) over a 365-day period post-discharge [ 9 ]. Similarly, our constructed nomogram, based on eight independent features including age, type of AF, and the presence of comorbidities, offers a visually intuitive and user-friendly tool with an AUC ranging from 0.80 to 0.84, demonstrating exceptional discriminative ability. Both models highlight the potential of machine learning in capturing intricate patterns and interactions among risk factors often overlooked by conventional risk scores. Our nomogram aligns with existing literature, including Chen et al.'s emphasis on the adverse prognosis associated with persistent AF [ 9 ] and Wang et al.'s predictive nomogram for in-hospital mortality in AF patients in the CCU [ 10 ]. Similar to other studies, we identified age, comorbidities, and clinical indices such as SAPSII, RDW, and urine output as important predictors of in-hospital mortality in patients with coexisting heart failure (HF) and AF [ 11 ]. These factors have consistently been shown to be associated with poor prognosis in both HF and AF patients, aligning with previous research that indicates the combination of HF and AF poses a significant risk for increased mortality. For instance, a study by Guan et al. [ 12 ] identified several risk factors for 1-year mortality in this patient population, including age, sex, New York Heart Association (NYHA) cardiac function class, history of myocardial infarction, and laboratory parameters such as albumin, triglycerides, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and blood urea nitrogen (BUN) levels. Our model included age, sex, NYHA class III or IV, history of myocardial infarction, and these same laboratory markers, underscoring their consistent prognostic value. Similar to the study by Yan et al. [ 11 ], which used both internal and external validation sets to validate their prediction model, our comprehensive validation strategy ensures that our model is not overfitted to a specific dataset. Our study, in conjunction with the findings of Paludan-Müller et al. [ 13 ], emphasizes the pivotal role of age at diagnosis in predicting the prognosis of AF patients. Analyzing data from the MIMIC-IV database, we found that AF, particularly PersAF, significantly elevates the risk of all-cause mortality, with age serving as an independent predictor of adverse outcomes. Notably, younger patients with AF exhibit disproportionately higher hazard ratios for cardiovascular events and mortality compared to older counterparts, echoing the nationwide cohort study by Paludan-Müller et al., which reported hazard ratios of 8.90 for cardiomyopathy, 8.64 for heart failure, 2.18 for ischaemic stroke, and 2.74 for mortality in individuals ≤ 50 years old. These results underscore the particularly detrimental effects of early-onset AF, associated with shortened life expectancy and increased morbidity, with an estimated average loss of 9.2 life years among those ≤ 50 years. In parallel, Li et al. [ 14 ] reported a significant global increase in the burden of AF/AFL over the past three decades, particularly affecting older populations and women, and highlighted an upward trend in low-middle and low sociodemographic index (SDI) regions despite overall decreasing global net drift mortality. This trend, coupled with identified risk factors such as high body mass index, hypertension, smoking, alcohol consumption in developed countries, and lead exposure in developing regions, underscores the multifaceted nature of AF/AFL and the urgent need for comprehensive, risk-differentiated, and cost-effective management strategies to address inequities and treatment gaps across different socioeconomic strata. An interesting parallel emerges from the genetic risk score study by Ebana Y et al., which pinpointed eight genetic variants linked to the risk of cerebral infarction (CI) in AF patients [ 15 ]. Although traditional risk scores like CHADS2 and CHA2DS2-VASc are commonly employed to gauge stroke risk in AF patients, Ebana et al. revealed that their genetic risk score (GRS) independently correlated with CI risk, emphasizing the potential role of genetic factors in enhancing risk stratification. This underscores the necessity for timely management strategies to prevent AF progression and related complications, as genetic variants may help identify high-risk AF patients, paving the way for more personalized and targeted interventions. These findings resonate with those of Ivănescu et al. [ 16 ], who conducted a thorough review on the utility of stroke risk scores in predicting severe outcomes in AF patients. Both studies highlight that elevated CHADS2 and CHA2DS2-VASc scores are associated with increased mortality rates, confirming their utility in pinpointing patients at higher risk for all-cause death. While Ivănescu et al. explored the broader application of these scores beyond thromboembolic risk assessment, our study delved into their implications within the ICU setting, where the complex interplay of multiple comorbidities and critical illnesses markedly influences patient outcomes. Our findings are consistent with those reported by Hussain et al. [ 17 ], who investigated the timing of AF diagnosis in oncology patients and its impact on mortality, alongside the CHA2DS2-VASc score and cancer therapeutics. Hussain et al. [ 17 ] noted that the initial diagnosis of AF in cancer patients often occurred at or shortly after cancer diagnosis, especially in older patients and those receiving cardiotoxic treatments. Importantly, pre-existing AF or a diagnosis within three years of cancer diagnosis was associated with a poor prognosis, with AF diagnosis significantly linked to death during this 'early phase'. Conversely, the CHA2DS2-VASc score, used for stroke risk stratification in AF patients, was only associated with mortality in the 'late phase', beyond three years after cancer diagnosis. These observations underscore the intricate relationship between AF and cancer, emphasizing the need for personalized risk assessment and management. In agreement, a nationwide Dutch study by Chen et al. [ 18 ] also found that concurrent AF and cancer adversely affected survival outcomes, highlighting the bidirectional association between AF and cancer, as well as variations in AF risk across different cancer types. Additionally, our results are corroborated by a nationwide population-based study by Jakobsen et al. [ 19 ], which revealed an elevated incidence of AF across all major cancer subtypes, suggesting a potential connection between malignancy and AF development. This link further complicates the prognostic outlook for AF patients, particularly those with cancer diagnoses. In line with the findings of Jamal et al. [ 20 ], our study also highlights the adverse effects of AF on the clinical outcomes of patients with ARDS. While there was no statistically significant difference in adjusted all-cause mortality between ARDS patients with and without AF, the presence of AF was associated with a higher prevalence of comorbidities and increased odds of several adverse events, including acute myocardial infarction, cardiogenic shock, pressor use, acute kidney injury, permanent pacemaker implantation, cardiac arrest, and the need for mechanical circulatory support. Furthermore, AF was linked to a longer length of hospital stay and higher inflation-adjusted costs, underscoring the substantial economic burden associated with this arrhythmia in critically ill patients. These findings emphasize the need for comprehensive risk assessment and aggressive management strategies in AF patients, particularly those with comorbid conditions like ARDS. Future research should focus on elucidating the underlying mechanisms linking AF to adverse outcomes in ARDS and exploring novel therapeutic approaches to further optimize patient care. Consistent with the study by Schupp et al. [ 21 ], which examined the prognostic impact of preexisting and new-onset AF in patients with septic or cardiogenic shock, we found no significant association between the presence of AF (either preexisting or new-onset) and 30-day all-cause mortality in our broader cohort of critically ill AF patients. However, our study extended the analysis to 90-day mortality and identified independent predictors that include age, type of AF, and the presence of comorbidities such as cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm, and ARF. Consistent with prior research, such as the study by Wang G et al., which reported an AKF incidence of 8.0% among AF patients [ 22 ]. The observed association between AKF and an increased risk of major adverse cardiovascular events underscores its prognostic significance in AF patients. Furthermore, the retrospective study by Bo et al. [ 23 ] demonstrates that AKF in AF patients is associated with a higher risk of mortality, but also reveals a potential benefit of oral anticoagulation therapy (OACs) in reducing 30-day mortality, despite a prolonged length of stay in both the hospital and ICU. These findings emphasize the critical role of clinicians in vigilant monitoring of renal function and early recognition and management of AKF in AF patients, particularly those in critical care settings. Appropriate anticoagulation therapy, balanced against the potential for bleeding complications and considering individual patient characteristics, may play a pivotal role in improving outcomes for these vulnerable patients [ 23 ]. The significance of our study lies in the identification of OACs as a protective factor against mortality in septic patients with AF. This finding echoes the observations made by Ge et al. [ 24 ], who reported a notably higher 30-day survival rate in septic AF patients receiving OACs compared to those who did not (81.59% vs. 58.10%; P < 0.001). Despite the potential for an increased length of stay in the ICU and hospital, the improvement in survival underscores the clinical value of OACs in this context, possibly due to their mitigation of systemic clotting activation associated with sepsis, thereby reducing the risk of adverse cardiovascular events. Consistent with previous research, our findings reinforce the crucial role of anticoagulation therapy in improving survival outcomes in AF patients, as evidenced by the study by Calderon et al. [ 25 ], which found that new oral anticoagulants (NOACs) offered significant protection against stroke and all-cause mortality compared to vitamin K antagonists (VKAs) and untreated patients. The findings from the systematic review and meta-analysis by Benz et al. [ 26 ] align with our observations, emphasizing that antiplatelet therapy may modestly reduce stroke risk in AF patients not receiving oral anticoagulation. This underscores the importance of a thorough risk-benefit assessment when prescribing antiplatelet therapy in AF patients, particularly considering their overall treatment regimen and comorbidities. Consistent with the findings of Camm et al. [ 27 ] and the growing body of evidence, our study, which analyzed the survival outcomes of AF patients during their first ICU admission using the MIMIC-IV database, adds support to the increasing role of rhythm control in AF management. Despite the historical focus on rate control, early rhythm control using safe and effective therapies, such as antiarrhythmic drugs and AF ablation, is increasingly recognized for its potential to reduce adverse cardiovascular outcomes, including AF-related deaths, heart failure, and strokes. Our findings align with the EARLY-AF trial by Kirchhof et al. [ 28 ], demonstrating that early rhythm-control therapy was associated with a lower risk of adverse cardiovascular outcomes among patients with early AF and cardiovascular conditions. Furthermore, consistent with Ravi et al.'s [ 29 ] results, which showed a significant reduction in all-cause mortality with catheter ablation compared to medical therapy alone, our analysis also found that PersAF significantly increases the risk of all-cause mortality. Additionally, echoing Akerström et al.'s findings [ 30 ], our study revealed that catheter ablation was associated with a significant reduction in the risk of all-cause mortality and stroke, with a reported 42% lower risk of the composite endpoint of all-cause mortality or stroke in patients undergoing catheter ablation compared to medically managed patients. Our study similarly demonstrated the heightened risk of all-cause mortality with PersAF and suggested that catheter ablation, among other therapeutic strategies, may contribute to improving survival outcomes in this high-risk population. The protective effect of catheter ablation observed across these studies underscores the importance of considering this interventional approach in the management of AF patients, particularly those at high risk of adverse outcomes. Limitations and Future Directions One key limitation of the model is its reliance on retrospective MIMIC-IV data, which may not capture the full complexity of real-world clinical scenarios, and its performance in prospective studies with real-time data remains unknown. Additionally, the model focuses solely on demographic and clinical factors at admission, neglecting dynamic changes in patients' health during their ICU stay and the impact of genetic factors on AF risk and outcomes. Furthermore, it lacks external validation and does not consider information on AF catheter ablation treatment, which are crucial for enhancing predictive accuracy, risk stratification, and personalized treatment approaches. Integrating longitudinal, real-time data, genetic information, and AF treatment data could significantly improve the model's effectiveness. CONCLUSIONS Our study analyzed survival outcomes of AF patients during their first ICU admission using the MIMIC-IV database. We found that PersAF significantly increases the risk of all-cause mortality. Independent predictors of 90-day mortality including age, type of AF, and the presence of comorbidities such as cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm, and ARF. Antiplatelet therapy and anticoagulants are protective. Kaplan-Meier curves show higher survival rates in PAF versus PersAF. We developed and validated a nomogram predicting 30, 60, and 90-day survival probabilities with excellent performance (AUC 0.80–0.84). The model is reliable, accurate, and clinically useful, as confirmed by calibration curves and DCA. Abbreviations AKF - Acute Kidney Failure AMI - Acute Myocardial Infarction APA - Acute Posthemorrhagic Anemia ARDS - Acute Respiratory Distress Syndrome ARF - Acute Respiratory Failure AF - Atrial Fibrillation AUC - Area Under the Receiver Operating Characteristic Curve BMI - Body Mass Index CABG - Coronary Artery Bypass Grafting CHD - Coronary Heart Disease CHF - Congestive Heart Failure CI - Confidence Interval CKD - Chronic Kidney Disease COPD - Chronic Obstructive Pulmonary Disease DCA - Decision Curve Analysis HR - Hazard Ratio ICU - Intensive Care Unit MIMIC-IV - Medical Information Mart for Intensive Care IV OSAS - Obstructive Sleep Apnea Syndrome PAF - Paroxysmal Atrial Fibrillation PersAF - Persistent Atrial Fibrillation PCI - Percutaneous Coronary Intervention PHV - Prosthetic Heart Valve SD - Standard Deviation SPH - Secondary Pulmonary Hypertension T2DM - Type 2 Diabetes Mellitus Declarations Ethics approval and consent to participate : Not applicable. Consent for publication : Not applicable. Availability of data and materials : The datasets analyzed in this study were derived from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic.mit.edu/docs/，certification number: 69652196). Competing interests : The authors declare that they have no competing interests. Funding : No funding. Authors' contributions : Haoran Chen, as the first author and corresponding author of this study, conceived and designed the study, collected and analyzed the data, built and validated the predictive model, and wrote and revised the manuscript. H.C. is responsible for the integrity of the work as a whole, from inception to the finished article. Acknowledgements : I would like to express my sincere gratitude to my supervisor, Dr. Yuan He, for her invaluable guidance and unwavering support throughout this research. As an Associate Researcher at the Cardiovascular Disease Research Laboratory, Guangdong Medical University Affiliated Hospital, Dr. He’s profound expertise in the pathogenesis and intervention strategies of cardiovascular diseases has profoundly enriched the academic rigor and innovation of this work. Her meticulous mentorship, critical insights, and dedication to fostering scientific excellence have been instrumental in shaping the direction and outcomes of this study. I am deeply honored to have benefited from her wisdom and professionalism. This acknowledgment is made with her permission. Clinical trial number : not applicable. References Zhang HD, Ding L, Mi LJ, et al. Impact of New-Onset Atrial Fibrillation on Mortality in Critically Ill Patients. Clin Epidemiol. 2024;16:811–22. 10.2147/CLEP.S485411 . Published 2024 Nov 21. Huang T, Lin S. 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In-hospital acute kidney injury and atrial fibrillation: incidence, risk factors, and outcome. Ren Fail. 2021;43(1):949–57. 10.1080/0886022X.2021.1939049 . Bo D, Wang X, Wang Y. Survival benefits of oral anticoagulation therapy in acute kidney injury patients with atrial fibrillation: a retrospective study from the MIMIC-IV database. BMJ Open. 2023;13(1):e069333. 10.1136/bmjopen-2022-069333 . Published 2023 Jan 2. Ge G, Bo D, Jiang R, Zhao W, Lu Y. Oral anticoagulants increased 30-day survival in sepsis patients complicated with atrial fibrillation: a retrospective analysis from MIMIC-IV database. Front Cardiovasc Med. 2024;11:1322045. Published 2024 Jan 18. 10.3389/fcvm.2024.1322045 Calderon JM, Martinez F, Diaz J, et al. Real-World Data of Anticoagulant Treatment in Non-valvular Atrial Fibrillation. Front Cardiovasc Med. 2022;8:733300. 10.3389/fcvm.2021.733300 . Published 2022 Jan 21. Benz AP, Johansson I, Dewilde WJM, et al. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor invited by journal 11 Jul, 2025 Editor assigned by journal 03 Jul, 2025 Submission checks completed at journal 02 Jul, 2025 First submitted to journal 02 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6891214","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502161715,"identity":"5d33b15b-b79d-4ffc-b715-6871dd832068","order_by":0,"name":"Haoran Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACAyA+AEbsjY0PP5Cmhedws7EEsVoguiTS2wR4iNFizt778MCPmjvy5pIP2xgkGOzkdBsIaLHsOW5wsOfYM8OdsxPbHhQwJBubHSDksBtpDAd4Gw4zbrid2G4gwXAgcRsxWg7+bThsv+HmwTYJHmK1HAbakrjhBiOxWs4cYzgsc+xw8oYzicBANiDGL8fbmD++qTlsu+H48YcPP1TYyRHUgm4CacpHwSgYBaNgFOAAAKTUTAY2LR7EAAAAAElFTkSuQmCC","orcid":"","institution":"Maoming People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-14 01:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6891214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6891214/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-05264-9","type":"published","date":"2025-11-03T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89562656,"identity":"6f797e3e-0fdc-49d6-8f06-1c4653d0b772","added_by":"auto","created_at":"2025-08-21 10:26:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49390,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Survival Analysis and Mortality Risk Prediction Model for First-Time ICU Admissions of Adult Atrial Fibrillation Patients in the MIMIC-IV Database\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/5b87875f581599b9bf9d430d.jpg"},{"id":89562657,"identity":"8083e913-3374-47b7-b73b-2e5803e8eac1","added_by":"auto","created_at":"2025-08-21 10:26:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54914,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram summarizing key predictors of mortality in adult patients with atrial fibrillation admitted to the ICU for the first time. Each predictor variable is assigned points, and the total points are used to derive the linear predictor score, which subsequently determines survival probabilities at 30, 60, and 90 days.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/7999c7b9f7f814d28414fd98.jpg"},{"id":89562660,"identity":"636eddb0-d932-462e-be11-3c31b84905ed","added_by":"auto","created_at":"2025-08-21 10:26:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51750,"visible":true,"origin":"","legend":"\u003cp\u003eCurves A, C, and E represent the 30-day, 60-day, and 90-day ROC curves for the training cohort, respectively. Curves B, D, and F correspond to the 30-day, 60-day, and 90-day ROC curves for the validation cohort. Each curve illustrates the model's predictive performance at different time points, with AUC values ranging from 0.80 to 0.84, indicating the model's ability to predict all-cause mortality in atrial fibrillation patients across various time horizons.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/a99575e04e61b50af85490b5.jpg"},{"id":89562658,"identity":"3b5768c3-54e0-44d6-be97-e4344603f4b2","added_by":"auto","created_at":"2025-08-21 10:26:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47265,"visible":true,"origin":"","legend":"\u003cp\u003ePanels A, C, and E represent the calibration curves for the training cohort at 30-day, 60-day, and 90-day time points, respectively. Conversely, Panels B, D, and F correspond to the validation cohort at the same time points. The diagonal line signifies perfect calibration, with the observed proportions plotted against the predicted probabilities, indicating the model's performance in predicting survival probabilities.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/9727003c51ca18a89c271674.jpg"},{"id":89565380,"identity":"ba9d6786-5993-486b-873f-ac7003e54e8c","added_by":"auto","created_at":"2025-08-21 10:42:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39761,"visible":true,"origin":"","legend":"\u003cp\u003ePanels A, C, and E show the 30-day, 60-day, and 90-day DCA curves for the training cohort, respectively. Panels B, D, and F depict the corresponding curves for the validation cohort. Each panel illustrates the balance between the benefits of the predictive model and the potential risks of overdiagnosis or overtreatment at different threshold probabilities. The blue curves represent the model's performance, while the red curves indicate the baseline performance.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/23c2fedcc0d7a6c3ec0d4af9.jpg"},{"id":89564361,"identity":"d70af4a8-9a80-4ef3-9d1a-5220f0708a8d","added_by":"auto","created_at":"2025-08-21 10:34:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48132,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curves for Patients with PAF versus PersAF. A: Training Cohort; B: Validation Cohort. These curves depict the survival probabilities over time for patients with PAF and PersAF. The p values indicate the statistical significance of the difference between the survival probabilities for the two groups, with lower p values suggesting greater differences.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/5b78ac86beac0f131ab540e1.jpg"},{"id":95563996,"identity":"3aeafdfa-6f6c-4409-8991-6d26aef40aab","added_by":"auto","created_at":"2025-11-10 16:06:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1989716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6891214/v1/73bc4cc0-0aa0-4fcc-88b8-7837e33d4a8f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Predictive Model for Survival Outcomes in Patients with Paroxysmal versus Persistent Atrial Fibrillation: A Retrospective Cohort Study Based on the MIMIC-IV Database","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAF is a prevalent cardiac arrhythmia characterized by rapid and irregular electrical activity within the atria, accompanied by the loss of coordinated mechanical contraction. This condition frequently precipitates complications such as heart failure and thromboembolic events, particularly strokes, thereby augmenting the risk of mortality. Numerous studies have consistently demonstrated that the patients with AF in the ICU exhibit significantly higher all-cause mortality rates compared to the non-AF patients, compounding the complexity and severity of their illness and markedly increasing the therapeutic challenge. Notably, Zhang HD et al. conducted a Kaplan-Meier analysis on 31,562 ICU patients, revealing a significantly lower one-year survival rate among patients with new-onset AF (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This finding underscores the profound impact of AF on survival outcomes in the critically ill population.\u003c/p\u003e\u003cp\u003eAF has been established as an independent predictor of poor outcomes in critically ill patients, and we firmly believe that AF, in conjunction with other risk factors, interacts to exacerbate the risk of death in these patients. Numerous studies have explored this, for instance: Huang T et al. found that the ratio of high lactate to albumin can predict in-hospital mortality in ICU patients with AF [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; Pan LY et al. indicated that a high level of the ratio of red cell distribution width to albumin is associated with increased in-hospital mortality of the patients with AF [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; and Meng H et al. revealed that AF poses a significant risk for adverse outcomes in sepsis patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, these studies primarily focused on the relationship between a single factor and the prognosis of AF patients.\u003c/p\u003e\u003cp\u003eOur study aims to develop a more comprehensive prediction model, which takes into account common fatal diseases and conditions in ICU patients and is specifically designed for the AF population. Through 90-day survival analysis of all AF patients, we observed a significant difference in outcomes between PAF and PersAF, with the latter carrying a higher risk of death. While this phenomenon has been sparsely addressed in previous research, Wetterslev M et al. suggested that the ultimate cause of death in ICU patients with AF is more closely related to the severity of their underlying diseases, and the 90-day mortality rate is not significantly associated with AF itself [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Another study by Baroutidou A et al., a retrospective cohort analysis of 1,052 patients, assessed the prognostic impact of different AF patterns, including newly diagnosed, paroxysmal, and persistent or permanent AF, but the sample size was relatively small [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our study, however, is based on detailed data from tens of thousands of AF patients.\u003c/p\u003e\u003cp\u003eGiven that PAF often progresses to PersAF, it is crucial to highlight their distinct dangers. While both forms pose significant risks, PAF's intermittent nature offers a unique advantage: it allows for more flexible timing of therapeutic interventions. This adaptability in treatment timing enhances the efficacy of interventions, leading to improved outcomes compared to PersAF. With an expanding array of treatment options for AF, including pharmacological therapy, electrical cardioversion, and catheter ablation, we firmly believe that early diagnosis and intervention are pivotal in effectively improving the prognosis of patients with AF.\u003c/p\u003e"},{"header":"METHODS AND MATERIALS","content":"\u003cp\u003e\u003cb\u003eDataset Selection and Splitting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mimic.mit.edu/\u003c/span\u003e\u003cspan address=\"https://mimic.mit.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contains de-identified clinical data from patients admitted to the intensive care units (ICUs) of Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, between 2008 and 2019. The MIMIC-IV database(certification number: 69652196) has received ethical approval from the Institutional Review Boards (IRBs) of BIDMC and the Massachusetts Institute of Technology (MIT), thereby waiving the need for additional ethical consent for this study. As all protected health information was de-identified, individual patient consent was not required.\u003c/p\u003e\u003cp\u003eBased on the MIMIC-IV database, we retrieved records of patients aged ≥ 18 years who were admitted to the ICU for the first time between 2008 and 2019. Using SQL language, we filtered out patients with AF who had an ICD-10 code of I48, where I480 is classified as PAF, and I481 to I489 (including I481, I482, I483, I484, I485, I486, I487, I488, I489) are considered as PersAF. Additionally, the ‘heart_rhythm’ field during hospitalization was used to further confirm the type of cardiac arrhythmia, ensuring accurate classification of paroxysmal and persistent AF. Furthermore, by aggregating all ICD diagnosis codes for each AF patient through SQL queries, we obtained their baseline characteristics, comorbidities, common critical illnesses or statuses among ICU patients, and death information. For instance, a patient identified with AF through ICD-10 code I48 would undergo automated aggregation of all diagnosis codes across hospital encounters. This reveals comorbidities such as: Chronic heart failure (I50), Coronary artery disease (I25.1), Hypertension (I10), Nicotine dependence (Z87.891) and so on.\u003c/p\u003e\u003cp\u003eTo address missing data while preserving dataset integrity, we employed multiple imputation by chained equations (MICE). This method iteratively models missing values using observed data distributions, generating multiple imputed datasets that capture uncertainty in estimations. Results were consolidated using Rubin's rules to ensure valid statistical inference. The MICE approach was chosen for its capacity to handle diverse missing data patterns and maintain variable relationships critical for subsequent Cox regression modeling and nomogram development. We maximized statistical power, preserved data relationships, and enhanced the generalizability of our findings.\u003c/p\u003e\u003cp\u003eIn our Cox regression models, patients who were still alive at the end of the observation period (90 days) or who were discharged from the ICU before death were treated as right-censored observations. Ultimately, a total of 12,130 patients were identified, with 8,491 patients assigned to the training cohort and 3,639 to the validation cohort, split in a 7:3 ration.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe employed R software (version 4.4.2) to perform a comprehensive statistical analysis on data retrieved from the MIMIC-IV database. A two-tailed P-value threshold of \u0026lt; 0.05 was established to denote statistical significance. All analyses were conducted in accordance with stringent methodological standards to guarantee the robustness and generalizability of our conclusions.\u003c/p\u003e\u003cp\u003eContinuous variables adhering to a normal distribution were summarized as mean ± standard deviation (SD), while non-normally distributed variables were expressed as median with interquartile range (IQR). Categorical variables were presented as absolute counts accompanied by their respective percentages.\u003c/p\u003e\u003cp\u003eContinuous variables were compared between the paroxysmal AF and persistent AF cohorts using the independent t-test for normally distributed data or the Mann-Whitney U test for non-normal distributions. Categorical variables were evaluated using the Chi-square (χ²) test, with Fisher's exact test applied when expected cell frequencies fell below 5.\u003c/p\u003e\u003cp\u003eAll variables initially underwent univariate Cox proportional hazards regression analysis to identify potential risk factors for all-cause mortality. Variables demonstrating statistical significance (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) were subsequently incorporated into a multivariate Cox regression model to ascertain independent predictors of mortality.\u003c/p\u003e\u003cp\u003eA nomogram was constructed utilizing the key predictors identified to facilitate clinical interpretation and mortality risk stratification. The predictive performance of the model was rigorously assessed through internal validation. Specifically, the area under the receiver operating characteristic curve (AUC) was calculated for both training and validation cohorts to evaluate discriminatory power. Calibration curves were generated to assess the alignment between predicted survival probabilities and observed outcomes, while Decision Curve Analysis (DCA) was employed to quantify the net clinical benefit of the model across varying risk thresholds, thereby elucidating optimal clinical applications (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eBaseline Clinical Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on a meticulous analysis of the MIMIC-IV database from 2008\u0026ndash;2019, our study focused on the survival outcomes of adult patients with AF during their first ICU admission. It is well-established that AF can elevate the risk of all-cause mortality, with PersAF conferring a higher mortality risk compared to PAF. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline clinical characteristics of all AF patients included in this study. Among the 12,130 patients with AF, the mean real age was 74.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05 years, with no significant difference between the validation cohort (n\u0026thinsp;=\u0026thinsp;3,639) and the training cohort (n\u0026thinsp;=\u0026thinsp;8,491) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.816). Gender distribution was balanced, with 40.63% females and 59.37% males. Regarding BMI, the majority of patients fell within the 20 to 39 range, with no significant difference across cohorts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.412). In terms of past medical history, hypertension was present in 40.75% of patients, T2DM in 28.42%, hyperlipidemia in 55.40%, and OSAS in 11.77%. Alcohol abuse and nicotine dependence were relatively uncommon, affecting 1.36% and 33.62% of patients, respectively. Cardiovascular procedures and interventions, such as presence of PCI or CABG, were noted in 7.09% and 5.94% of patients, respectively. The presence of PHV was rare, observed in only 2.00% of patients. Comorbidities were prevalent, with CHD affecting 34.65% of patients, AMI in 11.35%, CHF in 37.82%, AKF in 28.24%, and CKD in 22.87%. Other notable comorbidities included AF itself, with PAF in 27.64% and PersAF in 72.36%, hypothyroidism in 14.20%, cerebral infarction in 7.22%, COPD in 11.71%, and SPH in 9.54%. In terms of treatment regimens, antiplatelet therapy was used in 18.65% of patients, while anticoagulants were prescribed in 39.10%. A small proportion of patients (1.86%) were dependent on renal dialysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Demographics and Clinical Characteristics of Patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12130)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3639)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTraining cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8491)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.56\u0026thinsp;\u0026plusmn;\u0026thinsp;12.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.61\u0026thinsp;\u0026plusmn;\u0026thinsp;12.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4928 (40.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1480 (40.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3448 (40.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7202 (59.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2159 (59.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5043 (59.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19 or Less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2656 (21.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e764 (20.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1892 (22.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20 to 29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3028 (24.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e915 (25.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2113 (24.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 to 39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3391 (27.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1020 (28.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2371 (27.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40 or Greater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3055 (25.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e940 (25.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2115 (24.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePast History\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7187 (59.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2134 (58.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5053 (59.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4943 (40.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1505 (41.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3438 (40.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2DM, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8683 (71.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2605 (71.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6078 (71.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3447 (28.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1034 (28.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2413 (28.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5410 (44.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1648 (45.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3762 (44.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6720 (55.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1991 (54.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4729 (55.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOSAS, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10702 (88.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3203 (88.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7499 (88.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1428 (11.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e436 (11.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e992 (11.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Abuse, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11965 (98.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3583 (98.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8382 (98.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165 (1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNicotine Dependence, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8052 (66.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2408 (66.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5644 (66.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence Of PCI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11270 (92.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3385 (93.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7885 (92.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e860 (7.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e254 (6.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e606 (7.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence Of PHV, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11888 (98.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3551 (97.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8337 (98.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e154 (1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4078 (33.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1231 (33.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2847 (33.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence Of CABG, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11409 (94.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3414 (93.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7995 (94.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e721 (5.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225 (6.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e496 (5.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParoxysmal AF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3353 (27.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e966 (26.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2387 (28.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersistent AF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8777 (72.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2673 (73.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6104 (71.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothyroidism, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10407 (85.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3148 (86.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7259 (85.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1723 (14.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e491 (13.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1232 (14.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebral Infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11254 (92.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3399 (93.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7855 (92.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e876 (7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (6.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e636 (7.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntracranial Injury, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11679 (96.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3504 (96.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8175 (96.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e451 (3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e316 (3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10710 (88.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3174 (87.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7536 (88.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1420 (11.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e465 (12.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e955 (11.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPH, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10973 (90.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3291 (90.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7682 (90.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1157 (9.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e348 (9.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e809 (9.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7927 (65.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2350 (64.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5577 (65.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4203 (34.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1289 (35.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2914 (34.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10753 (88.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3233 (88.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7520 (88.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1377 (11.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406 (11.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e971 (11.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7542 (62.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2296 (63.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5246 (61.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4588 (37.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1343 (36.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3245 (38.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8704 (71.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2603 (71.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6101 (71.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3426 (28.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1036 (28.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2390 (28.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9356 (77.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2793 (76.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6563 (77.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2774 (22.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e846 (23.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1928 (22.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute Pancreatitis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11965 (98.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3585 (98.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8380 (98.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165 (1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111 (1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal Hemorrhage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12021 (99.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3603 (99.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8418 (99.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant Neoplasm, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11854 (97.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3554 (97.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8300 (97.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e276 (2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191 (2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCritically Ill\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Sepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11163 (92.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3325 (91.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7838 (92.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e967 (7.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e314 (8.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e653 (7.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypovolemic Shock, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12016 (99.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3607 (99.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8409 (99.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiogenic Shock, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11605 (95.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3483 (95.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8122 (95.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e525 (4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156 (4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e369 (4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic Shock, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12108 (99.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3632 (99.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8476 (99.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9603 (79.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2838 (77.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6765 (79.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2527 (20.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e801 (22.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1726 (20.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARDS, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12015 (99.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3601 (98.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8414 (99.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10245 (84.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3083 (84.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7162 (84.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1885 (15.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e556 (15.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1329 (15.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelets, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9868 (81.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2966 (81.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6902 (81.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2262 (18.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e673 (18.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1589 (18.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnticoagulants, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7387 (60.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2246 (61.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5141 (60.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4743 (39.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1393 (38.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3350 (39.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependence On Renal Dialysis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11904 (98.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3571 (98.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8333 (98.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226 (1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158 (1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003et: t-test, χ\u0026sup2;: Chi-square test; SD: standard deviation; BMI: Body Mass Index; T2DM: Type 2 Diabetes Mellitus; OSAS: Obstructive Sleep Apnea Syndrome; PCI: Percutaneous Coronary Intervention; PHV: Prosthetic Heart Valve; CABG: Coronary Artery Bypass Grafting; AF: Atrial Fibrillation; COPD: Chronic Obstructive Pulmonary Disease; SPH: Secondary Pulmonary Hypertension; CHD: Coronary Heart Disease; AMI: Acute Myocardial Infarction; CHF: Congestive Heart Failure; AKF: Acute Kidney Failure; CKD: Chronic Kidney Disease; APA: Acute Posthemorrhagic Anemia; ARDS: Acute Respiratory Distress Syndrome; ARF: Acute Respiratory Failure.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCOX Regression Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we conducted a meticulous Cox regression analysis to investigate the risk factors associated with 90-day mortality in adult patients with AF during their first ICU admission, utilizing data from the MIMIC-IV database. This analysis aimed to identify the independent predictors of mortality among a range of potential variables. Initially, a univariate Cox regression analysis was performed on fifteen candidate risk factors. This preliminary step revealed thirteen variables that exhibited significant associations with mortality, including age, gender, BMI, hypertension, T2DM, hyperlipidemia, OSAS, alcohol abuse, nicotine dependence, presence of PCI, presence of CABG, PersAF, and the presence of various comorbidities such as CHD, AMI, CHF, AKF, CKD, and severe sepsis. Subsequently, a multivariate Cox regression analysis was carried out to further refine and identify the independent predictors of 90-day mortality. This analysis, which utilized various selection methods including forward stepwise selection, backward stepwise selection, forward-backward stepwise selection, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance threshold, yielded a final model comprising several key variables. The independent predictors of 90-day mortality, as determined by the multivariate Cox regression analysis, were age, PersAF, cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm and ARF. Notably, the use of antiplatelet therapy and anticoagulants were found to be protective factors, associated with reduced mortality risk.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression models for risk factors of all-cause mortality in patients with atrial fibrillation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal Age, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04 (1.04\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05 (1.04\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.79\u0026thinsp;~\u0026thinsp;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19 or Less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20 to 29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.13 (0.98\u0026thinsp;~\u0026thinsp;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 to 39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.71\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40 or Greater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81 (0.69\u0026thinsp;~\u0026thinsp;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61 (0.55\u0026thinsp;~\u0026thinsp;0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2DM, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10 (0.98\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81 (0.73\u0026thinsp;~\u0026thinsp;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOSAS, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.63 (0.52\u0026thinsp;~\u0026thinsp;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Abuse, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21 (0.79\u0026thinsp;~\u0026thinsp;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNicotine Dependence, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94 (0.84\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence Of PCI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02 (0.83\u0026thinsp;~\u0026thinsp;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence Of PHV, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10 (0.75\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence Of CABG, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58 (1.31\u0026thinsp;~\u0026thinsp;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParoxysmal AF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersistent AF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45 (1.28\u0026thinsp;~\u0026thinsp;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41 (1.24\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothyroidism, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.19 (1.03\u0026thinsp;~\u0026thinsp;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebral Infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.09 (1.79\u0026thinsp;~\u0026thinsp;2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.03 (1.74\u0026thinsp;~\u0026thinsp;2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntracranial Injury, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.35 (1.05\u0026thinsp;~\u0026thinsp;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68 (1.30\u0026thinsp;~\u0026thinsp;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26 (1.08\u0026thinsp;~\u0026thinsp;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPH, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75 (1.51\u0026thinsp;~\u0026thinsp;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93 (0.83\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82 (1.59\u0026thinsp;~\u0026thinsp;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78 (1.60\u0026thinsp;~\u0026thinsp;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.29 (1.15\u0026thinsp;~\u0026thinsp;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.30 (2.96\u0026thinsp;~\u0026thinsp;3.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.79 (1.59\u0026thinsp;~\u0026thinsp;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.71 (1.53\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute Pancreatitis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42 (0.95\u0026thinsp;~\u0026thinsp;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal Hemorrhage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.85 (1.98\u0026thinsp;~\u0026thinsp;4.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant Neoplasm, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.10 (2.48\u0026thinsp;~\u0026thinsp;3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.35 (2.67\u0026thinsp;~\u0026thinsp;4.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Sepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.70 (5.04\u0026thinsp;~\u0026thinsp;6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.74 (2.38\u0026thinsp;~\u0026thinsp;3.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypovolemic Shock, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.38 (2.44\u0026thinsp;~\u0026thinsp;4.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiogenic Shock, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.42 (2.89\u0026thinsp;~\u0026thinsp;4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.10 (1.76\u0026thinsp;~\u0026thinsp;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic Shock, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.10 (1.39\u0026thinsp;~\u0026thinsp;6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.77\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARDS, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.83 (3.57\u0026thinsp;~\u0026thinsp;6.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.46 (3.22\u0026thinsp;~\u0026thinsp;6.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.06 (3.64\u0026thinsp;~\u0026thinsp;4.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.30 (2.03\u0026thinsp;~\u0026thinsp;2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelets, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58 (0.50\u0026thinsp;~\u0026thinsp;0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65 (0.55\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnticoagulants, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.76\u0026thinsp;~\u0026thinsp;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78 (0.70\u0026thinsp;~\u0026thinsp;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eHR: Hazards Ratio; CI: Confidence Interval.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Training and Validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe nomogram depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is a visual tool used to predict survival probabilities at 30, 60, and 90 days for AF patients firstly admitted to the ICU. Each predictor variable is assigned points based on their relative contribution to mortality risk. The total points calculated by summing the points for all predictors correspond to a linear predictor score, which is then used to estimate survival probabilities. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the ROC curves for the training and validation cohorts at 30-day, 60-day, and 90-day time points. The ROC curves illustrate the model's ability to distinguish between patients who survive and those who do not at each time horizon. The AUC values, ranging from 0.80 to 0.84, indicate excellent discriminatory performance of the model. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the calibration curves for the training and validation cohorts at 30-day, 60-day, and 90-day time points. These curves compare the observed proportions of patients who survive with the predicted probabilities derived from the nomogram. A well-calibrated model should have its calibration curve closely align with the diagonal line, representing perfect calibration. The observed curves closely follow the diagonal, indicating that the predicted survival probabilities closely match the actual outcomes, thus validating the model's reliability and accuracy. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the DCA for the training and validation cohorts at 30-day, 60-day, and 90-day time points. The blue curves represent the net benefit of using the prognostic nomogram, while the red curves indicate the net benefit of the assumption that all patients will either experience the event (all die) or none will (all survive). The DCA curves demonstrate that using the nomogram provides a significant net benefit compared to the extreme strategies across a wide range of threshold probabilities, highlighting the clinical usefulness of the model in aiding decision-making for AF patients admitted to the ICU.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Kaplan-Meier Survival Curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) visually depict the difference in survival rates between patients with PAF and PersAF. The higher survival rates observed for patients with PAF suggest that this subgroup has a better prognosis compared to those with PersAF. The steeper decline in survival for patients with PersAF highlights the more severe nature and poorer outcomes associated with this type of AF. The consistency of the findings between the training and validation cohorts strengthens the evidence that the observed differences in survival rates are robust and not mere artifacts of the data.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cb\u003eModel Performance and Validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study provides comprehensive insights into the survival outcomes of AF patients during their first ICU admission, leveraging the extensive data from the MIMIC-IV database. The findings underscore the critical role of PersAF in significantly elevating the risk of all-cause mortality, as compared to PAF. Through rigorous statistical analysis, we identified independent predictors of 90-day mortality, including age, type of AF, and the presence of comorbidities such as cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm, and ARF. Notably, the use of antiplatelet therapy and anticoagulants emerged as protective factors, associated with reduced mortality risk. A key contribution of our study lies in the development and validation of a comprehensive prediction model designed to assess 30, 60, and 90-day survival probabilities for AF patients admitted to the ICU. Our nomogram, which integrates a wide array of demographic, clinical, and treatment-related factors, exhibits outstanding discriminatory performance, with an area under the ROC curve AUC ranging between 0.80 and 0.84. This level of precision is on par with, and sometimes even surpasses, other established risk prediction tools tailored for AF patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison with Existing Studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur model builds upon and extends the existing literature, such as the work by Verhaeghe, J., et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], who emphasized the importance of generalizable and calibrated machine learning models for real-time AF risk prediction in ICU patients. Similar to the approach taken by Verhaeghe, J., et al., we assessed the calibration of our model's predicted probabilities using Expected Calibration Error (ECE) and Expected Signed Calibration Error (ESCE) metrics. Our model demonstrated good calibration, indicating that the predicted risks closely align with the actual outcomes. Additionally, our model's use of robust statistical methods and its validation on independent external datasets (MIMIC-IV and GUH) suggest that it is generalizable across different hospitals and care standards. This generalizability is crucial for the widespread adoption and impact of our model in clinical practice.\u003c/p\u003e\u003cp\u003eOne of the key strengths of our model lies in its comparison with established risk scores such as CHA2DS2-VASc and HAS-BLED. Similar to the findings of Fox et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] in their study of the GARFIELD-AF risk tool, our model outperformed CHA2DS2-VASc in predicting all-cause mortality. This finding highlights the importance of considering a broader spectrum of risk factors beyond those included in traditional scores. Moreover, by incorporating the effect of oral anticoagulation (OAC) therapy, our model aligns with the GARFIELD-AF risk tool in enabling clinicians to make informed decisions regarding anticoagulation strategies, which is crucial for optimizing patient outcomes.\u003c/p\u003e\u003cp\u003eOur findings corroborate those of Chen et al., who developed a LASSO-Cox model to predict all-cause mortality in AF patients, achieving comparable AUC values (0.842 in the training set and 0.854 in the validation set) over a 365-day period post-discharge [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, our constructed nomogram, based on eight independent features including age, type of AF, and the presence of comorbidities, offers a visually intuitive and user-friendly tool with an AUC ranging from 0.80 to 0.84, demonstrating exceptional discriminative ability. Both models highlight the potential of machine learning in capturing intricate patterns and interactions among risk factors often overlooked by conventional risk scores. Our nomogram aligns with existing literature, including Chen et al.'s emphasis on the adverse prognosis associated with persistent AF [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Wang et al.'s predictive nomogram for in-hospital mortality in AF patients in the CCU [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilar to other studies, we identified age, comorbidities, and clinical indices such as SAPSII, RDW, and urine output as important predictors of in-hospital mortality in patients with coexisting heart failure (HF) and AF [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These factors have consistently been shown to be associated with poor prognosis in both HF and AF patients, aligning with previous research that indicates the combination of HF and AF poses a significant risk for increased mortality. For instance, a study by Guan et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] identified several risk factors for 1-year mortality in this patient population, including age, sex, New York Heart Association (NYHA) cardiac function class, history of myocardial infarction, and laboratory parameters such as albumin, triglycerides, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and blood urea nitrogen (BUN) levels. Our model included age, sex, NYHA class III or IV, history of myocardial infarction, and these same laboratory markers, underscoring their consistent prognostic value. Similar to the study by Yan et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which used both internal and external validation sets to validate their prediction model, our comprehensive validation strategy ensures that our model is not overfitted to a specific dataset.\u003c/p\u003e\u003cp\u003eOur study, in conjunction with the findings of Paludan-M\u0026uuml;ller et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], emphasizes the pivotal role of age at diagnosis in predicting the prognosis of AF patients. Analyzing data from the MIMIC-IV database, we found that AF, particularly PersAF, significantly elevates the risk of all-cause mortality, with age serving as an independent predictor of adverse outcomes. Notably, younger patients with AF exhibit disproportionately higher hazard ratios for cardiovascular events and mortality compared to older counterparts, echoing the nationwide cohort study by Paludan-M\u0026uuml;ller et al., which reported hazard ratios of 8.90 for cardiomyopathy, 8.64 for heart failure, 2.18 for ischaemic stroke, and 2.74 for mortality in individuals\u0026thinsp;\u0026le;\u0026thinsp;50 years old. These results underscore the particularly detrimental effects of early-onset AF, associated with shortened life expectancy and increased morbidity, with an estimated average loss of 9.2 life years among those\u0026thinsp;\u0026le;\u0026thinsp;50 years. In parallel, Li et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reported a significant global increase in the burden of AF/AFL over the past three decades, particularly affecting older populations and women, and highlighted an upward trend in low-middle and low sociodemographic index (SDI) regions despite overall decreasing global net drift mortality. This trend, coupled with identified risk factors such as high body mass index, hypertension, smoking, alcohol consumption in developed countries, and lead exposure in developing regions, underscores the multifaceted nature of AF/AFL and the urgent need for comprehensive, risk-differentiated, and cost-effective management strategies to address inequities and treatment gaps across different socioeconomic strata.\u003c/p\u003e\u003cp\u003eAn interesting parallel emerges from the genetic risk score study by Ebana Y et al., which pinpointed eight genetic variants linked to the risk of cerebral infarction (CI) in AF patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although traditional risk scores like CHADS2 and CHA2DS2-VASc are commonly employed to gauge stroke risk in AF patients, Ebana et al. revealed that their genetic risk score (GRS) independently correlated with CI risk, emphasizing the potential role of genetic factors in enhancing risk stratification. This underscores the necessity for timely management strategies to prevent AF progression and related complications, as genetic variants may help identify high-risk AF patients, paving the way for more personalized and targeted interventions. These findings resonate with those of Ivănescu et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], who conducted a thorough review on the utility of stroke risk scores in predicting severe outcomes in AF patients. Both studies highlight that elevated CHADS2 and CHA2DS2-VASc scores are associated with increased mortality rates, confirming their utility in pinpointing patients at higher risk for all-cause death. While Ivănescu et al. explored the broader application of these scores beyond thromboembolic risk assessment, our study delved into their implications within the ICU setting, where the complex interplay of multiple comorbidities and critical illnesses markedly influences patient outcomes.\u003c/p\u003e\u003cp\u003eOur findings are consistent with those reported by Hussain et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who investigated the timing of AF diagnosis in oncology patients and its impact on mortality, alongside the CHA2DS2-VASc score and cancer therapeutics. Hussain et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] noted that the initial diagnosis of AF in cancer patients often occurred at or shortly after cancer diagnosis, especially in older patients and those receiving cardiotoxic treatments. Importantly, pre-existing AF or a diagnosis within three years of cancer diagnosis was associated with a poor prognosis, with AF diagnosis significantly linked to death during this 'early phase'. Conversely, the CHA2DS2-VASc score, used for stroke risk stratification in AF patients, was only associated with mortality in the 'late phase', beyond three years after cancer diagnosis. These observations underscore the intricate relationship between AF and cancer, emphasizing the need for personalized risk assessment and management. In agreement, a nationwide Dutch study by Chen et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] also found that concurrent AF and cancer adversely affected survival outcomes, highlighting the bidirectional association between AF and cancer, as well as variations in AF risk across different cancer types. Additionally, our results are corroborated by a nationwide population-based study by Jakobsen et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which revealed an elevated incidence of AF across all major cancer subtypes, suggesting a potential connection between malignancy and AF development. This link further complicates the prognostic outlook for AF patients, particularly those with cancer diagnoses.\u003c/p\u003e\u003cp\u003eIn line with the findings of Jamal et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], our study also highlights the adverse effects of AF on the clinical outcomes of patients with ARDS. While there was no statistically significant difference in adjusted all-cause mortality between ARDS patients with and without AF, the presence of AF was associated with a higher prevalence of comorbidities and increased odds of several adverse events, including acute myocardial infarction, cardiogenic shock, pressor use, acute kidney injury, permanent pacemaker implantation, cardiac arrest, and the need for mechanical circulatory support. Furthermore, AF was linked to a longer length of hospital stay and higher inflation-adjusted costs, underscoring the substantial economic burden associated with this arrhythmia in critically ill patients. These findings emphasize the need for comprehensive risk assessment and aggressive management strategies in AF patients, particularly those with comorbid conditions like ARDS. Future research should focus on elucidating the underlying mechanisms linking AF to adverse outcomes in ARDS and exploring novel therapeutic approaches to further optimize patient care.\u003c/p\u003e\u003cp\u003eConsistent with the study by Schupp et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which examined the prognostic impact of preexisting and new-onset AF in patients with septic or cardiogenic shock, we found no significant association between the presence of AF (either preexisting or new-onset) and 30-day all-cause mortality in our broader cohort of critically ill AF patients. However, our study extended the analysis to 90-day mortality and identified independent predictors that include age, type of AF, and the presence of comorbidities such as cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm, and ARF.\u003c/p\u003e\u003cp\u003eConsistent with prior research, such as the study by Wang G et al., which reported an AKF incidence of 8.0% among AF patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The observed association between AKF and an increased risk of major adverse cardiovascular events underscores its prognostic significance in AF patients. Furthermore, the retrospective study by Bo et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] demonstrates that AKF in AF patients is associated with a higher risk of mortality, but also reveals a potential benefit of oral anticoagulation therapy (OACs) in reducing 30-day mortality, despite a prolonged length of stay in both the hospital and ICU. These findings emphasize the critical role of clinicians in vigilant monitoring of renal function and early recognition and management of AKF in AF patients, particularly those in critical care settings. Appropriate anticoagulation therapy, balanced against the potential for bleeding complications and considering individual patient characteristics, may play a pivotal role in improving outcomes for these vulnerable patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe significance of our study lies in the identification of OACs as a protective factor against mortality in septic patients with AF. This finding echoes the observations made by Ge et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], who reported a notably higher 30-day survival rate in septic AF patients receiving OACs compared to those who did not (81.59% vs. 58.10%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite the potential for an increased length of stay in the ICU and hospital, the improvement in survival underscores the clinical value of OACs in this context, possibly due to their mitigation of systemic clotting activation associated with sepsis, thereby reducing the risk of adverse cardiovascular events. Consistent with previous research, our findings reinforce the crucial role of anticoagulation therapy in improving survival outcomes in AF patients, as evidenced by the study by Calderon et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which found that new oral anticoagulants (NOACs) offered significant protection against stroke and all-cause mortality compared to vitamin K antagonists (VKAs) and untreated patients. The findings from the systematic review and meta-analysis by Benz et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] align with our observations, emphasizing that antiplatelet therapy may modestly reduce stroke risk in AF patients not receiving oral anticoagulation. This underscores the importance of a thorough risk-benefit assessment when prescribing antiplatelet therapy in AF patients, particularly considering their overall treatment regimen and comorbidities.\u003c/p\u003e\u003cp\u003eConsistent with the findings of Camm et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and the growing body of evidence, our study, which analyzed the survival outcomes of AF patients during their first ICU admission using the MIMIC-IV database, adds support to the increasing role of rhythm control in AF management. Despite the historical focus on rate control, early rhythm control using safe and effective therapies, such as antiarrhythmic drugs and AF ablation, is increasingly recognized for its potential to reduce adverse cardiovascular outcomes, including AF-related deaths, heart failure, and strokes. Our findings align with the EARLY-AF trial by Kirchhof et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], demonstrating that early rhythm-control therapy was associated with a lower risk of adverse cardiovascular outcomes among patients with early AF and cardiovascular conditions. Furthermore, consistent with Ravi et al.'s [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] results, which showed a significant reduction in all-cause mortality with catheter ablation compared to medical therapy alone, our analysis also found that PersAF significantly increases the risk of all-cause mortality. Additionally, echoing Akerstr\u0026ouml;m et al.'s findings [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], our study revealed that catheter ablation was associated with a significant reduction in the risk of all-cause mortality and stroke, with a reported 42% lower risk of the composite endpoint of all-cause mortality or stroke in patients undergoing catheter ablation compared to medically managed patients. Our study similarly demonstrated the heightened risk of all-cause mortality with PersAF and suggested that catheter ablation, among other therapeutic strategies, may contribute to improving survival outcomes in this high-risk population. The protective effect of catheter ablation observed across these studies underscores the importance of considering this interventional approach in the management of AF patients, particularly those at high risk of adverse outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOne key limitation of the model is its reliance on retrospective MIMIC-IV data, which may not capture the full complexity of real-world clinical scenarios, and its performance in prospective studies with real-time data remains unknown. Additionally, the model focuses solely on demographic and clinical factors at admission, neglecting dynamic changes in patients' health during their ICU stay and the impact of genetic factors on AF risk and outcomes. Furthermore, it lacks external validation and does not consider information on AF catheter ablation treatment, which are crucial for enhancing predictive accuracy, risk stratification, and personalized treatment approaches. Integrating longitudinal, real-time data, genetic information, and AF treatment data could significantly improve the model's effectiveness.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur study analyzed survival outcomes of AF patients during their first ICU admission using the MIMIC-IV database. We found that PersAF significantly increases the risk of all-cause mortality. Independent predictors of 90-day mortality including age, type of AF, and the presence of comorbidities such as cerebral infarction, intracranial injury, CHF, AKF, severe sepsis, cardiogenic shock, ARDS, malignant neoplasm, and ARF. Antiplatelet therapy and anticoagulants are protective. Kaplan-Meier curves show higher survival rates in PAF versus PersAF. We developed and validated a nomogram predicting 30, 60, and 90-day survival probabilities with excellent performance (AUC 0.80\u0026ndash;0.84). The model is reliable, accurate, and clinically useful, as confirmed by calibration curves and DCA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKF - Acute Kidney Failure\u003c/p\u003e\n\u003cp\u003eAMI - Acute Myocardial Infarction\u003c/p\u003e\n\u003cp\u003eAPA - Acute Posthemorrhagic Anemia\u003c/p\u003e\n\u003cp\u003eARDS - Acute Respiratory Distress Syndrome\u003c/p\u003e\n\u003cp\u003eARF - Acute Respiratory Failure\u003c/p\u003e\n\u003cp\u003eAF - Atrial Fibrillation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC - Area Under the Receiver Operating Characteristic Curve\u003c/p\u003e\n\u003cp\u003eBMI - Body Mass Index\u003c/p\u003e\n\u003cp\u003eCABG - Coronary Artery Bypass Grafting\u003c/p\u003e\n\u003cp\u003eCHD - Coronary Heart Disease\u003c/p\u003e\n\u003cp\u003eCHF - Congestive Heart Failure\u003c/p\u003e\n\u003cp\u003eCI - Confidence Interval\u003c/p\u003e\n\u003cp\u003eCKD - Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003eCOPD - Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eDCA - Decision Curve Analysis\u003c/p\u003e\n\u003cp\u003eHR - Hazard Ratio\u003c/p\u003e\n\u003cp\u003eICU - Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eMIMIC-IV - Medical Information Mart for Intensive Care IV\u003c/p\u003e\n\u003cp\u003eOSAS - Obstructive Sleep Apnea Syndrome\u003c/p\u003e\n\u003cp\u003ePAF - Paroxysmal Atrial Fibrillation\u003c/p\u003e\n\u003cp\u003ePersAF - Persistent Atrial Fibrillation\u003c/p\u003e\n\u003cp\u003ePCI - Percutaneous Coronary Intervention\u003c/p\u003e\n\u003cp\u003ePHV - Prosthetic Heart Valve\u003c/p\u003e\n\u003cp\u003eSD - Standard Deviation\u003c/p\u003e\n\u003cp\u003eSPH - Secondary Pulmonary Hypertension\u003c/p\u003e\n\u003cp\u003eT2DM - Type 2 Diabetes Mellitus\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e: The datasets analyzed in this study were derived from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic.mit.edu/docs/，certification number: 69652196).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/strong\u003e: Haoran Chen, as the first author and corresponding author of this study, conceived and designed the study, collected and analyzed the data, built and validated the predictive model, and wrote and revised the manuscript. H.C. is responsible for the integrity of the work as a whole, from inception to the finished article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e: I would like to express my sincere gratitude to my supervisor, Dr. Yuan He, for her invaluable guidance and unwavering support throughout this research. As an Associate Researcher at the Cardiovascular Disease Research Laboratory, Guangdong Medical University Affiliated Hospital, Dr. He’s profound expertise in the pathogenesis and intervention strategies of cardiovascular diseases has profoundly enriched the academic rigor and innovation of this work. Her meticulous mentorship, critical insights, and dedication to fostering scientific excellence have been instrumental in shaping the direction and outcomes of this study. I am deeply honored to have benefited from her wisdom and professionalism. This acknowledgment is made with her permission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang HD, Ding L, Mi LJ, et al. Impact of New-Onset Atrial Fibrillation on Mortality in Critically Ill Patients. Clin Epidemiol. 2024;16:811\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/CLEP.S485411\u003c/span\u003e\u003cspan address=\"10.2147/CLEP.S485411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2024 Nov 21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang T, Lin S. Usefulness of lactate to albumin ratio for predicting in-hospital mortality in atrial fibrillation patients admitted to the intensive care unit: a retrospective analysis from MIMIC-IV database [published correction appears in. 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Eur Heart J. 2024;45(24):2119\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehae216\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehae216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"atrial fibrillation（AF), intensive care unit（ICU), mortality, predictive model, predictor","lastPublishedDoi":"10.21203/rs.3.rs-6891214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6891214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAtrial fibrillation (AF) has been implicated in increasing all-cause mortality among patients in intensive care unit (ICU), with paroxysmal atrial fibrillation (PAF) often progressing over time to persistent atrial fibrillation (PersAF), which carries an even higher risk of death compared to PAF. Our study aims to analyze the survival disparities between patients with PAF and PersAF, and to a comprehensive model to predict the impact of life-threatening comorbidities on AF patients' prognosis in the ICU. This endeavor is geared towards facilitating early assessment and timely intervention for AF patients, ultimately improving their clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData were retrieved from the MIMIC-IV database for patients aged ≥ 18 years admitted to the ICU for the first time between 2008 and 2019. A total of 12,130 AF patients were identified and split into a training cohort (n = 8,491) and a validation cohort (n = 3,639). Cox regression analysis was performed to identify independent predictors of 90-day mortality. A nomogram was developed to predict survival probabilities at 30, 60, and 90 days. Kaplan-Meier survival curves were generated to visually compare survival outcomes between patients with PAF and PersAF. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and Decision Curve Analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe mean age of the study population was 74.60 ± 12.05 years, with 40.63% females. Independent predictors of 90-day mortality included age, persistent AF, cerebral infarction, intracranial injury, chronic heart failure (CHF), acute kidney failure (AKF), severe sepsis, cardiogenic shock, acute respiratory distress syndrome (ARDS), malignant neoplasm, and acute renal failure (ARF). Antiplatelet therapy and anticoagulants were protective factors. The nomogram demonstrated excellent discriminatory performance with AUC values ranging from 0.80 to 0.84. Calibration curves and DCA confirmed the model's reliability and clinical usefulness. Kaplan-Meier curves showed higher survival rates in patients with PAF compared to those with PersAF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe developed and validated nomogram has demonstrated sufficient accuracy in predicting the risk of all-cause mortality and identifying prognostic factors in patients with atrial fibrillation (AF) admitted to the intensive care unit (ICU) for the first time.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Predictive Model for Survival Outcomes in Patients with Paroxysmal versus Persistent Atrial Fibrillation: A Retrospective Cohort Study Based on the MIMIC-IV Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 10:25:58","doi":"10.21203/rs.3.rs-6891214/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T09:09:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T03:00:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T03:32:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40134365833976515531599840158964382299","date":"2025-08-25T01:34:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53510786799030123201659503778807356210","date":"2025-08-24T09:34:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T09:17:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T17:19:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T11:41:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-02T16:55:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-02T16:41:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c45b223-160d-4e8a-9caa-bf5a27d714af","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:00:59+00:00","versionOfRecord":{"articleIdentity":"rs-6891214","link":"https://doi.org/10.1186/s12872-025-05264-9","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-11-03 15:57:27","publishedOnDateReadable":"November 3rd, 2025"},"versionCreatedAt":"2025-08-21 10:25:58","video":"","vorDoi":"10.1186/s12872-025-05264-9","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05264-9","workflowStages":[]},"version":"v1","identity":"rs-6891214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6891214","identity":"rs-6891214","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}