Association between estimated pulse wave velocity and 30-day mortality in critically ill patients with atrial fibrillation: a cohort study based on the MIMIC-IV database

Association between estimated pulse wave velocity and 30-day mortality in critically ill patients with atrial fibrillation: a 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 Association between estimated pulse wave velocity and 30-day mortality in critically ill patients with atrial fibrillation: a cohort study based on the MIMIC-IV database Shuyang Dai, Bingjie Li, Zongshan Zhang, Yingli Qiao, Poshi Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7412924/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background and objective: Estimated pulse wave velocity (ePWV) is widely recognized as a practical surrogate marker for carotid-femoral pulse wave velocity (cfPWV) and has been validated as a prognostic indicator for a variety of diseases. Nevertheless, the association between ePWV and the risk of 30-day mortality in intensive care unit (ICU) patients diagnosed with atrial fibrillation (AF) remains insufficiently understood. This study aims to investigate the correlation between ePWV and 30-day mortality in critically ill patients with AF. Methods A retrospective analysis was conducted utilizing data from the MIMIC-IV database, focusing on ICU patients with AF. Participants were categorized into two groups based on an ePWV cutoff value determined through receiver operating characteristic (ROC) curve analysis. The relationship between ePWV levels and 30-day mortality was evaluated using Kaplan-Meier survival analysis, Cox proportional-hazards models, and restricted cubic spline (RCS) regression. Additionally, subgroup analyses were performed to assess the influence of ePWV on 30-day mortality across various patient subgroups. Results A cohort of 9,179 critically ill patients with AF was analyzed. The 30-day all-cause mortality rate was observed to be 15.47%. Upon adjusting for confounding variables, ePWV persisted as an independent risk factor for 30-day mortality ( P <0.001). RCS modeling revealed a non-linear association between ePWV and 30-day all-cause mortality (non-linear P <0.001). Conclusions The study identified ePWV as an independent predictor of 30-day mortality among critically ill patients with AF. Early identification of high-risk patients through ePWV assessment may enable timely interventions and enhance clinical outcomes. Clinical trial number Not applicable. Atrial fibrillation Intensive care unit Estimated pulse wave velocity 30-day mortality Predictor Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Atrial fibrillation(AF) is one of the most prevalent cardiovascular arrhythmia, and its incidence is increasing globally[1]. Approximately 1–3% of the general population is affected by this condition, with a higher prevalence observed among elderly individuals, males, and those with comorbidities[2–6]. In 2019, global epidemiological data indicated that there were approximately 59.7 million individuals affected by AF, with an estimated 4.72 million new cases reported during that year[7]. AF is linked to a range of serious complications and constitutes a significant contributor to the global burden of cardiovascular diseases[8–11]. Despite advancements in current treatment approaches, the management of AF continues to pose significant challenges, primarily due to the intricate pathophysiology of the condition and the heterogeneous responses observed among patients. Therefore, the identification of reliable prognostic markers is of critical importance in mitigating complications and enhancing clinical outcomes. Estimated pulse wave velocity (ePWV) serves as a convenient surrogate marker for carotid-femoral pulse wave velocity (cfPWV)[12]. Research has demonstrated that ePWV is significantly associated with the prognosis of various clinical conditions[13–16]. It remains uncertain whether ePWV is associated with the prognosis of critically ill patients diagnosed with AF. To address this knowledge gap, the present study conducted a retrospective cohort analysis using data from patients with AF extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary objective was to investigate the potential association between ePWV and short-term mortality in this patient population. Furthermore, this research aims to facilitate the development of a straightforward and reliable prognostic assessment tool, which may support the formulation of novel strategies to improve outcomes for critically ill patients with AF. Method Data sources The MIMIC-IV database is a large, publicly accessible repository that contains comprehensive medical records of over 50,000 patients admitted to the intensive care units at Beth Israel Deaconess Medical Center[17]. MIMIC-IV also includes extensive clinical data such as patients’ demographic characteristics, vital signs, comorbidities, laboratory test results, and clinical outcomes. The data utilized in this study were fully anonymized and adhered to the requirements of the Health Insurance Portability and Accountability Act (HIPAA). The research protocol was approved by the Institutional Review Board (IRB) of Beth Israel Deaconess Medical Center. The authors gained access to the database following the successful completion of the required training and certification program (Record Number: 14012091). Prior to data extraction and analysis, all patient identifiers were removed to ensure confidentiality. Study population and data collection In this investigation, we initially identified 18,805 critically ill patients with AF who were first admitted to ICU between 2008 and 2019. We excluded 2,996 patients with length of ICU stay shorter than 24 hours, 144 patients without records of mean arterial pressure (MAP), and 6,486 patients lacking body-mass index (BMI) data. Ultimately, 9,179 patients with critical AF were retained and then classified into low- and high-ePWV groups according to the optimal cut-off derived from the ROC curve. The patient selection flowchart is presented in Figure 1. Data extraction was performed with PostgreSQL (v13.7.2) and Navicat Premium (v16) by executing structured query language (SQL) scripts. Potential confounders considered in this study included: (1) baseline demographic information: age, sex, race, body mass index (BMI); (2) vital signs: systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP); (3) Comorbidities: chronic kidney disease(CKD), type 2 diabetes mellitus(T2DM), heart failure(HF), myocardial infarction(MI), stroke, hypertension, liver cirrhosis(LC), chronic obstructive pulmonary disease (COPD); (4) Treatments and Medications: mechanical ventilation(MV), antibiotics, corticosteroids, antihypertensive agents utilisation; (5) Laboratory parameters: white blood cell(WBC), red blood cell (RBC), hemoglobin, platelet, glucose, anion gap(AG), potassium, calcium; (6) Illness-severity scores: Acute Physiology ScoreIII (APSIII), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS). Missing data were present, however, for every variable the proportion of missing values did not exceed 5%. We retained the missing values without imputation. ePWV was calculated using the formula[14]:ePWV=9.587−0.402×age+4.560×10-3×age²−2.621×10-5×age²×MAP+3.176×10-3×age×MAP−1.832×10-2×MAP.The primary outcome was 30-day mortality after ICU admission. Statistical analysis To determine the distribution of the measurement data, normality tests were conducted. The results indicated that the measurement data did not follow a normal distribution and were therefore summarized using the median (P25, P75). For intergroup comparisons, the Kruskal-Wallis rank sum test was employed. Categorical variables were presented as frequencies and percentages (%), and group differences were assessed using the chi-square test. Kaplan-Meier survival analysis was performed based on stratification into low and high ePWV groups, with the log-rank test used to evaluate differences between survival curves. The association between ePWV and the 30-day mortality risk among atrial fibrillation patients was examined using the Cox proportional hazards model, with results expressed as hazard ratios (HR) and 95% confidence intervals (CI). Restricted cubic splines(RCS) were applied to explore the potential nonlinear relationship between ePWV and clinical outcomes in AF patients. Subgroup analyses were also conducted to assess the robustness and consistency of the findings. A two-sided P < 0.05 was considered statistically significant. All statistical analyses were performed using STATA 17.0, R version 4.3.2, and DecisionLinnc 1.0 software. DecisionLinnc 1.0 is a data analysis platform that integrates multiple programming language environments and provides a visual interface for data processing and analytical tasks[18]. Results Baseline demographic and clinical characteristics A total of 9,179 patients were enrolled and categorized based on the optimal ePWV cut-off value of 11.97 m/s into a low-ePWV group (< 11.97 m/s) and a high-ePWV group (≥ 11.97 m/s) (Table 1). The median age was 74 years (19, 99); 5,935 (64.66%) were male. Compared with the low-ePWV group, the high-ePWV group exhibited significantly higher values for age, APS III score, prevalence of CKD, HF, and MI, as well as higher RBC and PLT counts, glucose and calcium levels, and corticosteroid utilization (all P < 0.05). Conversely, the proportions of males, SOFA scores, prevalence of T2DM, hypertension, and LC, WBC , HBG , and potassium concentrations, and antibiotic and antihypertensive medication use were significantly lower in the high-ePWV group (all P < 0.05). With respect to the primary endpoint, 30-day mortality differed significantly between the two groups ( P < 0.001). Specifically, mortality was 12.26% in the low-ePWV group versus 22.59% in the high-ePWV group. Table 1 Comparison of patients’baseline information Variable Overall(n=9179) Low(n=6328) High(n=2851) P Age,years 74 (19-99) 70 (19-88) 85 (46-99) <0.001 BMI，kg/m 2 28.069 (12.63-45.128) 29.006 (12.63-45.128) 26.158 (12.82-45.014) <0.001 Sex, n(%) male 5935 (64.66) 4327 (68.38) 1608 (56.40) <0.001 female 3244 (35.34) 2001 (31.62) 1243 (43.60) Race, n(%) Black 475 (5.17) 321 (5.07) 154 (5.40) 0.023 White 7555 (82.31) 5175 (81.78) 2380 (83.48) Others 1149 (12.52) 832 (13.15) 317 (11.12) SOFA 5 (0-22) 5 (0-22) 5 (0-20) <0.001 APSIII 43 (3-171) 42 (3-171) 47 (8-148) <0.001 SIRS (%) 0 64 (0.70) 43 (0.68) 21 (0.74) <0.001 1 823 (8.97) 520 (8.22) 303 (10.63) 2 2772 (30.20) 1849 (29.22) 923 (32.37) 3 3844 (41.88) 2709 (42.81) 1135 (39.81) 4 1676 (18.26) 1207 (19.07) 469 (16.45) CKD, n(%) 2269 (24.72) 1340 (21.18) 929 (32.59) <0.001 T2DM, n(%) 2880 (31.38) 2104 (33.25) 776 (27.22) <0.001 HF, n(%) 3959 (43.13) 2487 (39.30) 1472 (51.63) <0.001 MI, n(%) 1060 (11.55) 695 (10.98) 365 (12.80) 0.013 stroke, n(%) 5211 (56.77) 3629 (57.35) 1582 (55.49) 0.101 hypertension, n(%) 4077 (44.42) 2885 (45.59) 1192 (41.81) 0.001 LC, n(%) 410 (4.47) 348 (5.50) 62 (2.17) <0.001 COPD, n(%) 1588 (17.30) 1071 (16.92) 517 (18.13) 0.165 WBC, ×10 9 /L 11.5 (0.1-302.5) 11.8 (0.1-248.6) 10.7 (0.2-302.5) 0.018 RBC, ×10 12 /L 3.32 (1-6.99) 3.28 (1.08-6.99) 3.44 (1-6.31) <0.001 platelet, ×10 9 /L 163 (5-1121) 157 (5-865) 177 (9-1121) <0.001 hemoglobin, g/L 10 (3.1-19.8) 9.9 (3.4-19.8) 10.3 (3.1-19.1) <0.001 AG, mmol/L 13 (1-57) 13 (2-57) 14 (1-35) <0.001 glucose,mg/dL 125 (25-2044) 124 (25-987) 128 (30-2044) 0.003 potassium, mmom/L 4.2 (1.7-10) 4.3 (1.8-9.8) 4.2 (1.7-10) <0.001 calcium, mmol/L 8.3 (2.4-27.5) 8.3 (2.4-27.5) 8.4 (4-12.3) <0.001 MV, n(%) 7861 (92.74) 5492 (94.11) 2369 (89.73) 0.161 antibiotics, n(%) 7915 (86.23) 5556 (87.80) 2359 (82.74) <0.001 corticosteroids, n(%) 1777 (19.36) 1158 (18.30) 619 (21.71) <0.001 antihypertensive, n(%) 8195 (89.28) 5730 (90.55) 2465 (86.46) <0.001 SBP，mmHg 114 (101-131) 110 (98-124) 126 (110-144) <0.001 DBP，mmHg 63 (53-74) 61 (52-70) 69 (58-84) 0.309 MAP，mmHg 76 (67-87) 73 (65-83) 84 (72.5-99) 0.052 ePWV(m/s) 10.57（8.85-12.51） 9.51(8.25-10.68) 13.52(12.66-14.63) <0.001 30-day mortality, n(%) 1420 (15.47) 776 (12.26) 644 (22.59) <0.001 BMI, body mass index; SOFA, Sequential Organ Failure Assessment; APSIII, Acute Physiology ScoreIII (APSIII); SIRS, Systemic Inflammatory Response Syndrome; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; HF, heart failure, MI, myocardial infarction; LC, liver cirrhosis; COPD, chronic obstructive pulmonary disease; WBC, white blood cells; RBC, red blood cells; AG, anion gape; MV, mechanical ventilation; MAP, mean arterial pressure; SBP, systolic pressure; DBP, diastolic pressure; ePWV, estimated pulse wave velocity. Survival curve analysis of ePWV and 30-day ACM in patients with atrial fibrillation A total of 9,179 patients were included in this study, of whom 1,420 died within 30 days, resulting in a mortality rate of 14.47%. Patients were stratified into two groups based on ePWV levels, and the Kaplan-Meier survival curve was employed to assess the difference in mortality between these groups. The results indicated that the 30-day survival rate was significantly lower in the high ePWV group (77.41%) compared to the low ePWV group (87.74%), with a statistically significant difference (log-rank test, P < 0.001), as illustrated in Figure 2. Cox regression analysis of mortality risk in AF patients across different ePWV levels Univariate cox regression analysis was conducted for 30-day mortality, and the results were summarized in Table 2. Table 3 presents a comparison from one group to another using a multifactor Cox regression model. In the unadjusted model, the mortality risk in the high ePWV group was significantly higher than in the low ePWV group (HR = 1.97; 95% CI: 1.78 – 2.19; P < 0.001). In Model 3, after adjusting for additional covariates, the high ePWV group still exhibited a significantly higher mortality risk compared to the low ePWV group (HR = 1.70; 95% CI: 1.51 – 1.91; P <0.001). Furthermore, the covariate-adjusted restricted cubic spline (RCS) model revealed a nonlinear association between ePWV levels and 30-day mortality risk (nonlinear P < 0.001), as illustrated in Figure 3. The 30-day mortality risk among AF patients increased progressively with higher ePWV levels ( P <0.001). Subgroup analysis To further substantiate the relationship between 30-day mortality and ePWV, subgroup analyses were executed,partitioned by clinical conditions such as hypertension, T2DM, HF, MI and AKI. As depicted in Figure 4, the out[1]comes of these subgroup examinations align closely with the overarching results. Importantly, ePWV exhibited marked disparities in 28-day mortality rates, regardless of the presence or absence of hypertension, T2DM, HF, MI and AKI, with statistical significance (p<0.001). The interactions within these subgroups did not reach statistical significance, indicating that the prognostic impact of ePWV is consistent across various patient subpopulations. ( P for interaction >0.05). Table 2 Univariate cox regression analysis for 30-day mortality. Variable HR(95%CI) P Age 1.03（1.02-1.03） <0.001 BMI 0.97（0.96-0.98） <0.001 Sex male 0.72（0.65-0.80） <0.001 female 1.00(Reference) Race Black 1.00(Reference) White 0.69（0.56-0.84） <0.001 Others 0.59（0.46-0.76） <0.001 SOFA 1.19（1.17-1.20） <0.001 APSIII 1.03（1.03-1.03） <0.001 SIRS 0 1.00(Reference) 1 1.00（0.40-2.49） 0.997 2 1.6（0.66-3.87） 0.297 3 2.08（0.86-5.02） 0.102 4 3.42（1.41-8.25） 0.006 CKD 1.55（1.39-1.74） <0.001 T2DM 1.12（1.00-1.26） 0.037 HF 1.55（1.40-1.72） <0.001 MI 1.41（1.22-1.63） <0.001 hypertension 0.60（0.54-0.67） <0.001 LC 2.87（2.42-3.40） <0.001 WBC 1.01（1.00-1.01） <0.001 RBC 1.41（1.25-1.63） <0.001 platelet 1.00（1.00-1.00） <0.001 hemoglobin 1.06（1.04-1.09） <0.001 AG 1.11（1.11-1.12） <0.001 glucose 1.00（1.00-1.00） <0.001 potassium 1.15（1.07-1.24） <0.001 calcium 0.95（0.88-1.01） 0.112 antibiotics 1.81（1.50-2.19） <0.001 corticosteroids 2.60（2.33-2.90） <0.001 antihypertensive 0.35（0.31-0.40） <0.001 BMI, body mass index; SOFA, Sequential Organ Failure Assessment; APSIII, Acute Physiology ScoreIII (APSIII); SIRS, Systemic Inflammatory Response Syndrome; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; HF, heart failure; MI, myocardial infarction; LC, liver cirrhosis; WBC, white blood cells; RBC, red blood cells; AG, anion gape. Table 3 Cox Proportional Hazards Regression Analysis of 30-Day Mortality Group Model 1 Model 2 Model 3 HR(95%CI) P HR(95%CI) P HR(95%CI) P Low（n=6328） 1.00(Reference) 1.00(Reference) 1.00(Reference) High(n=2851) 1.97（1.78-2.19） <0.001 1.83（1.64-2.04） <0.001 1.70（1.51-1.91） <0.001 Model 1: Unadjusted; Model 2: Adjusted for sex, race, and BMI; Model 3: Adjusted for all covariates. HR, hazard ratio; CI, confidence interval. Discussion This research delves into the relationship between ePWV and the likelihood of dying within 30 days in patients suffering from AF. Our results indicate that ePWV could be a prognostic marker independently for 30-day mortality in this distinct patients.Even after adjusting for potential confounding factors, elevated ePWV levels continued to exhibit a robust predictive capacity for 30-day mortality. Furthermore, among patients with AF, a non-linear association was observed between ePWV and the predicted 30-day mortality risk (non-linear p < 0.001). ePWV serves as a simplified alternative indicator to cfPWV. The clinical application of cfPWV is limited due to its complex operational procedure, which requires coordination among multiple professionals[19]。However, ePWV can be estimated soely based on the patient's age and blood pressure. In certain critical clinical scenarios, its performance in detecting arterial stiffness is comparable to that of cfPWV[20]. Therefore, to conserve limited medical resources, ePWV can serve as a simplified alternative to cfPWV. Cui identified a statistically significant association between ePWV and increased mortality risk associated with AKI during ICU stays[21]. Wei demonstrated that ePWV serves as an independent predictor of 28-day mortality among patients with sepsis-associated AKI[22]. ePWV has been identified as an independent predictor of both short-term and long-ter[13]m mortality among critically ill patients with coronary heart disease. Integrating ePWV into conventional risk assessment models can substantially enhance the predictive accuracy of all-cause mortality within one year as well as during hospitalization for this patient population[23]. Chen conducted an 11-year follow-up study and found that elevated ePWV is associated with an increased risk of new-onset AF. Among the overall population, the incidence of new-onset AF in the medium, medium-high, and high ePWV groups was 1.64-fold, 1.92-fold, and 2.64-fold higher, respectively, compared to the low ePWV group[13]. There may be a causal relationship between arterial stiffness and AF. Research on atrial tissue has demonstrated that atherosclerosis can influence the progression of atrial fibrillation[24]. In addition, atherosclerosis and its associated risk factors contribute to structural and electrical remodeling of the atrium. Atrial fibrosis is recognized as both a consequence and a promoter of the initiation and progression of atrial fibrillation[25]. Furthermore, emerging evidence indicates that greater severity of atherosclerosis may lead to left ventricular hypertrophy, impaired left ventricular diastolic function, and left atrial enlargement, all of which can elevate the risk of developing atrial fibrillation[26]. In this study, based on a retrospective cohort of 9,179 ICU patients diagnosed with AF from the MIMIC-IV database, we confirmed that the 30-day all-cause mortality rate was 22.6% among patients with ePWV ≥ 11.97 m/s, which was significantly higher than that of patients in the low ePWV group. Multivariate Cox regression analysis revealed that ePWV was an independent risk factor for 30-day mortality (HR = 1.70, 95% CI: 1.51–1.91), with a non-linear association observed. Subgroup analyses demonstrated consistent results across various stratifications, indicating the robustness of the findings. Therefore, ePWV, which can be rapidly calculated at the bedside, may serve as a non-invasive and independent predictor for early mortality risk among ICU patients with AF, enabling timely identification of high-risk individuals and facilitating optimized clinical interventions. Nevertheless, this study has several limitations. As the data were derived from an observational cohort within the MIMIC-IV database, potential confounding and selection biases may exist. Although multivariate Cox regression and restricted cubic spline (RCS) analyses were employed to adjust for known confounders, residual or unmeasured confounding factors cannot be entirely ruled out. Additionally, the MIMIC-IV database primarily includes data from patients in the United States, which may limit the generalizability of the findings to AF patients in other regions or countries. Furthermore, this study focused solely on the initial ePWV measurement following ICU admission and did not account for potential dynamic changes in ePWV over time. Given that ePWV may fluctuate in response to changes in a patient’s clinical condition, future research should investigate the longitudinal behavior of ePWV and further validate its predictive value and underlying mechanisms. Conclusion This study represents the first to identify a significant nonlinear relationship between ePWV and 30-day mortality among AF patients admitted to the ICU. These findings indicate that ePWV may function as an independent and novel biomarker for prognostic evaluation within this patient cohort, offering a theoretical foundation for early risk stratification and tailored therapeutic interventions. Further research is warranted to confirm the generalizability of these results through multicenter cohort studies, as well as to investigate the underlying pathophysiological mechanisms, including the interplay between arterial stiffness, atrial fibrillation, and multi-organ dysfunction. Abbreviations AF atrial fibrillation ICU intensive care unit ePWV estimated pulse wave velocity ROC receiver operating characteristic RCS restricted cubic spline BMI body mass index MAP mean arterial pressure SBP systolic pressure DBP diastolic pressure SOFA Sequential Organ Failure Assessment APSIII Acute Physiology ScoreIII (APSIII) SIRS Systemic Inflammatory Response Syndrome CKD chronic kidney disease T2DM type 2 diabetes mellitus HF heart failure MI myocardial infarction LC liver cirrhosis COPD chronic obstructive pulmonary disease WBC white blood cells RBC red blood cells AG anion gape MV mechanical ventilation Declarations Acknowledgements Not applicable. Author contributions PX: designed and conceptualized this study, SD: analyzed the data and wrote the manuscript, BL: drew the images, ZZ and YQ: checked the manuscript. Funding The author declares that this research and the publication of this paper were supported by relevant funding. This work was supported by the Intramural Fund of Fuwai Central China Cardiovascular Hospital (Project No.: FWQN240005). Data availability The datasets generated during and/or analyzed during the current study are available in the MIMIC-IV database, https://physionet.org/content/mimiciv/3.0. Ethics approval and consent to participate The requirement of ethical approval for this was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital, because the data was accessed from MIMIC-IV (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests.Publisher’s note. References Murphy A, Banerjee A, Breithardt G, Camm AJ, Commerford P, Freedman B, et al. The World Heart Federation Roadmap for Nonvalvular Atrial Fibrillation. Glob Heart. 2017;12:273–84. https://doi.org/10.1016/j.gheart.2017.01.015. Nielsen JC, Lin Y-J, de Oliveira Figueiredo MJ, Sepehri Shamloo A, Alfie A, Boveda S, et al. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population. 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Association between preoperative baseline pulse pressure and estimated pulse wave velocity and acute renal failure and mortality following colorectal surgery. A single-centre observational study. Rev Esp Anestesiol Reanim (Engl Ed). 2021;68:564–75. https://doi.org/10.1016/j.redare.2021.02.004. Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10:1. https://doi.org/10.1038/s41597-022-01899-x. Team, D. C. DecisionLinnc is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. 2023. Jin W, Chowienczyk P, Alastruey J. Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms. PLoS One. 2021;16:e0245026. https://doi.org/10.1371/journal.pone.0245026. Greve SV, Laurent S, Olsen MH. Estimated Pulse Wave Velocity Calculated from Age and Mean Arterial Blood Pressure. Pulse (Basel). 2017;4:175–9. https://doi.org/10.1159/000453073. Cui X, Hu Y, Li D, Lu M, Zhang Z, Kan D, et al. Association between estimated pulse wave velocity and in-hospital mortality of patients with acute kidney injury: a retrospective cohort analysis of the MIMIC-IV database. Ren Fail. 2024;46:2313172. https://doi.org/10.1080/0886022X.2024.2313172. Wei L, Cui X, Lv Y, Zhang F, Wu J. The relationship between estimated pulse wave velocity and 28-day mortality in patients with sA-AKI: a retrospective cohort analysis of the MIMIC-IV database. Ren Fail. 2025;47:2507162. https://doi.org/10.1080/0886022X.2025.2507162. Gu Y, Han X, Liu J, Li Y, Li Z, Zhang W, et al. Prognostic significance of the estimated pulse wave velocity in critically ill patients with coronary heart disease: analysis from the MIMIC‑IV database. Eur Heart J Qual Care Clin Outcomes. 2024;:qcae076. https://doi.org/10.1093/ehjqcco/qcae076. Abed HS, Samuel CS, Lau DH, Kelly DJ, Royce SG, Alasady M, et al. Obesity results in progressive atrial structural and electrical remodeling: implications for atrial fibrillation. Heart Rhythm. 2013;10:90–100. https://doi.org/10.1016/j.hrthm.2012.08.043. Lage JGB, Bortolotto AL, Bortolotto LA, Verardino RGS, Pessente GD, Bihan DCSL, et al. Association between Arterial Stiffness and Higher Burden of Atrial Arrhythmia in Elderly Hypertensive Patients without Atrial Fibrillation. Arq Bras Cardiol. 2024;121:e20240251. https://doi.org/10.36660/abc.20240251. Mascarenhas LA, Ji Y, Wang W, Inciardi RM, Parikh RR, Eaton AA, et al. Association of central arterial stiffness with atrial myopathy: the Atherosclerosis Risk in Communities (ARIC) study. Hypertens Res. 2024;47:2902–13. https://doi.org/10.1038/s41440-024-01831-3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 01 Oct, 2025 Editor invited by journal 04 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 03 Sep, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7412924","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531531151,"identity":"cc862c03-64d7-4354-9b2a-a883a6f8e5e9","order_by":0,"name":"Shuyang Dai","email":"","orcid":"","institution":"Heart Center of Henan Provincial People′s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shuyang","middleName":"","lastName":"Dai","suffix":""},{"id":531531152,"identity":"ed7e4f6e-3534-4509-ba6f-1d43695bd528","order_by":1,"name":"Bingjie 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1","display":"","copyAsset":false,"role":"figure","size":25745,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the selection of patients from the MIMIC-IV database.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7412924/v1/d3bd69723aa954e0c4ac0039.png"},{"id":94046764,"identity":"00dddf01-cfcb-42fd-b0ff-2962e6ded533","added_by":"auto","created_at":"2025-10-21 23:08:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73183,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for 30-day mortality in the high and low ePWV groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7412924/v1/34028123e62349fff446eb85.png"},{"id":94046765,"identity":"1781cd5e-8029-4a2f-9749-0a201d43bb6d","added_by":"auto","created_at":"2025-10-21 23:08:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30527,"visible":true,"origin":"","legend":"\u003cp\u003eRCS curve for the ePWV hazard ratio and 30-day mortality\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7412924/v1/3145b655bb5522a5bea0a958.png"},{"id":94046766,"identity":"c976b4b0-3238-4c44-9e96-9b84903d9592","added_by":"auto","created_at":"2025-10-21 23:08:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":132697,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of hazard ratios for the association between ePWV and 30-day mortality across different subgroups. BMI, body mass index; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; HF, heart failure; MI, myocardial infarction; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7412924/v1/a8320e831d20370c27394aa9.png"},{"id":94048630,"identity":"25a32c39-77ca-42aa-89ec-b2655753a52b","added_by":"auto","created_at":"2025-10-21 23:24:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1031141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7412924/v1/62cb5304-8820-4187-9c8c-feec014c24dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between estimated pulse wave velocity and 30-day mortality in critically ill patients with atrial fibrillation: a cohort study based on the MIMIC-IV database","fulltext":[{"header":"Background","content":"\u003cp\u003eAtrial fibrillation(AF) is one of the most prevalent cardiovascular arrhythmia, and its incidence is increasing globally[1]. Approximately 1\u0026ndash;3% of the general population is affected by this condition, with a higher prevalence observed among elderly individuals, males, and those with comorbidities[2\u0026ndash;6]. In 2019, global epidemiological data indicated that there were approximately 59.7\u0026nbsp;million individuals affected by AF, with an estimated 4.72\u0026nbsp;million new cases reported during that year[7]. AF is linked to a range of serious complications and constitutes a significant contributor to the global burden of cardiovascular diseases[8\u0026ndash;11]. Despite advancements in current treatment approaches, the management of AF continues to pose significant challenges, primarily due to the intricate pathophysiology of the condition and the heterogeneous responses observed among patients. Therefore, the identification of reliable prognostic markers is of critical importance in mitigating complications and enhancing clinical outcomes.\u003c/p\u003e\u003cp\u003eEstimated pulse wave velocity (ePWV) serves as a convenient surrogate marker for carotid-femoral pulse wave velocity (cfPWV)[12]. Research has demonstrated that ePWV is significantly associated with the prognosis of various clinical conditions[13\u0026ndash;16]. It remains uncertain whether ePWV is associated with the prognosis of critically ill patients diagnosed with AF. To address this knowledge gap, the present study conducted a retrospective cohort analysis using data from patients with AF extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary objective was to investigate the potential association between ePWV and short-term mortality in this patient population. Furthermore, this research aims to facilitate the development of a straightforward and reliable prognostic assessment tool, which may support the formulation of novel strategies to improve outcomes for critically ill patients with AF.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eData sources \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV database is a large, publicly accessible repository that contains comprehensive medical records of over 50,000 patients admitted to the intensive care units at Beth Israel Deaconess Medical Center[17]. MIMIC-IV also includes extensive clinical data such as patients\u0026rsquo; demographic characteristics, vital signs, comorbidities, laboratory test results, and clinical outcomes. The data utilized in this study were fully anonymized and adhered to the requirements of the Health Insurance Portability and Accountability Act (HIPAA). The research protocol was approved by the Institutional Review Board (IRB) of Beth Israel Deaconess Medical Center. The authors gained access to the database following the successful completion of the required training and certification program (Record Number: 14012091). Prior to data extraction and analysis, all patient identifiers were removed to ensure confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this investigation, we initially identified 18,805 critically ill patients with AF who were first admitted to ICU between 2008 and 2019. We excluded 2,996 patients with length of \u0026nbsp;ICU stay shorter than 24 hours, 144 patients without records of mean arterial pressure (MAP), and 6,486 patients lacking body-mass index (BMI) data. Ultimately, 9,179 patients with critical AF were retained and then classified into low- and high-ePWV groups according to the optimal cut-off derived from the ROC curve. The patient selection flowchart is presented in Figure 1. Data extraction was performed with PostgreSQL (v13.7.2) and Navicat Premium (v16) by executing structured query language (SQL) scripts. Potential confounders considered in this study included: (1) baseline demographic information: age, sex, race, body mass index (BMI); (2) vital signs: systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP); (3) Comorbidities: chronic kidney disease(CKD), type 2 diabetes mellitus(T2DM), heart failure(HF), myocardial infarction(MI), stroke, hypertension, liver cirrhosis(LC), chronic obstructive pulmonary disease (COPD); (4) Treatments and Medications: mechanical ventilation(MV), antibiotics, corticosteroids, antihypertensive agents utilisation; (5) Laboratory parameters: white blood cell(WBC), red blood cell (RBC), hemoglobin, platelet, glucose, anion gap(AG), potassium, calcium; (6) Illness-severity scores: Acute Physiology ScoreIII (APSIII), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS). Missing data were present, however, for every variable the proportion of missing values did not exceed 5%. We retained the missing values without imputation. ePWV was calculated using the formula[14]:ePWV=9.587\u0026minus;0.402\u0026times;age+4.560\u0026times;10-3\u0026times;age\u0026sup2;\u0026minus;2.621\u0026times;10-5\u0026times;age\u0026sup2;\u0026times;MAP+3.176\u0026times;10-3\u0026times;age\u0026times;MAP\u0026minus;1.832\u0026times;10-2\u0026times;MAP.The primary outcome was 30-day mortality after ICU admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the distribution of the measurement data, normality tests were conducted. The results indicated that the measurement data did not follow a normal distribution and were therefore summarized using the median (P25, P75). For intergroup comparisons, the Kruskal-Wallis rank sum test was employed. Categorical variables were presented as frequencies and percentages (%), and group differences were assessed using the chi-square test. Kaplan-Meier survival analysis was performed based on stratification into low and high ePWV groups, with the log-rank test used to evaluate differences between survival curves. The association between ePWV and the 30-day mortality risk among atrial fibrillation patients was examined using the Cox proportional hazards model, with results expressed as hazard ratios (HR) and 95% confidence intervals (CI). Restricted cubic splines(RCS) were applied to explore the potential nonlinear relationship between ePWV and clinical outcomes in AF patients. Subgroup analyses were also conducted to assess the robustness and consistency of the findings. A two-sided \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant. All statistical analyses were performed using STATA 17.0, R version 4.3.2, and DecisionLinnc 1.0 software. DecisionLinnc 1.0 is a data analysis platform that integrates multiple programming language environments and provides a visual interface for data processing and analytical tasks[18].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline demographic and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 9,179 patients were enrolled and categorized based on the optimal ePWV cut-off value of 11.97 m/s into a low-ePWV group (\u0026lt; 11.97 m/s) and a high-ePWV group (\u0026ge; 11.97 m/s) (Table 1). The median age was 74 years (19, 99); 5,935 (64.66%) were male. Compared with the low-ePWV group, the high-ePWV group exhibited significantly higher values for age, APS III score, prevalence of CKD, HF, and MI, as well as higher RBC and PLT counts, glucose and calcium levels, and corticosteroid utilization (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Conversely, the proportions of males, SOFA scores, prevalence of T2DM, hypertension, and LC, WBC , HBG , and potassium concentrations, and antibiotic and antihypertensive medication use were significantly lower in the high-ePWV group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). With respect to the primary endpoint, 30-day mortality differed significantly between the two groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Specifically, mortality was 12.26% in the low-ePWV group versus 22.59% in the high-ePWV group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u0026nbsp; Comparison of patients\u0026rsquo;baseline information\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall(n=9179)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow(n=6328)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh(n=2851)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eAge,years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e74 (19-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e70 (19-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e85 (46-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eBMI，kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e28.069 (12.63-45.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e29.006 (12.63-45.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e26.158 (12.82-45.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eSex, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5935 (64.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4327 (68.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1608 (56.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3244 (35.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2001 (31.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1243 (43.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eRace, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e475 (5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e321 (5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e154 (5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7555 (82.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5175 (81.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2380 (83.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1149 (12.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e832 (13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e317 (11.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5 (0-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5 (0-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e5 (0-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eAPSIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e43 (3-171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e42 (3-171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e47 (8-148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eSIRS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e64 (0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e43 (0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e21 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e823 (8.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e520 (8.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e303 (10.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2772 (30.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1849 (29.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e923 (32.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3844 (41.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2709 (42.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1135 (39.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1676 (18.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1207 (19.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e469 (16.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eCKD, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2269 (24.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1340 (21.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e929 (32.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eT2DM, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2880 (31.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2104 (33.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e776 (27.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eHF, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3959 (43.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2487 (39.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1472 (51.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eMI, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1060 (11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e695 (10.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e365 (12.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003estroke, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5211 (56.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e3629 (57.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1582 (55.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ehypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4077 (44.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2885 (45.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1192 (41.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eLC, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e410 (4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e348 (5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e62 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eCOPD, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1588 (17.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1071 (16.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e517 (18.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e11.5 (0.1-302.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e11.8 (0.1-248.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e10.7 (0.2-302.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eRBC, \u0026times;10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3.32 (1-6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e3.28 (1.08-6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e3.44 (1-6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eplatelet, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e163 (5-1121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e157 (5-865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e177 (9-1121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ehemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10 (3.1-19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e9.9 (3.4-19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e10.3 (3.1-19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eAG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e13 (1-57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e13 (2-57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e14 (1-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eglucose,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e125 (25-2044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e124 (25-987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e128 (30-2044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003epotassium, mmom/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4.2 (1.7-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4.3 (1.8-9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e4.2 (1.7-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ecalcium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e8.3 (2.4-27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e8.3 (2.4-27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e8.4 (4-12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eMV, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7861 (92.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5492 (94.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2369 (89.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eantibiotics, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e7915 (86.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5556 (87.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2359 (82.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003ecorticosteroids, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1777 (19.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1158 (18.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e619 (21.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eantihypertensive, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e8195 (89.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5730 (90.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e2465 (86.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eSBP，mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e114 (101-131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e110 (98-124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e126 (110-144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eDBP，mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e63 (53-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e61 (52-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e69 (58-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eMAP，mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e76 (67-87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e73 (65-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e84 (72.5-99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eePWV(m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"156\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e10.57（8.85-12.51）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e9.51(8.25-10.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e13.52(12.66-14.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e30-day mortality, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1420 (15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e776 (12.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e644 (22.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBMI, body mass index; SOFA, Sequential Organ Failure Assessment; APSIII, Acute Physiology ScoreIII (APSIII); SIRS, Systemic Inflammatory Response Syndrome; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; HF, heart failure, MI, myocardial infarction; LC, liver cirrhosis; COPD, chronic obstructive pulmonary disease; WBC, white blood cells; RBC, red blood cells; AG, anion gape; MV, mechanical ventilation; MAP, mean arterial pressure; SBP, systolic pressure; DBP, diastolic pressure; ePWV, estimated pulse wave velocity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival curve analysis of ePWV and 30-day ACM in patients with atrial fibrillation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 9,179 patients were included in this study, of whom 1,420 died within 30 days, resulting in a mortality rate of 14.47%. Patients were stratified into two groups based on ePWV levels, and the Kaplan-Meier survival curve was employed to assess the difference in mortality between these groups. The results indicated that the 30-day survival rate was significantly lower in the high ePWV group (77.41%) compared to the low ePWV group (87.74%), with a statistically significant difference (log-rank test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), as illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCox regression analysis of mortality risk in AF patients across different ePWV levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate cox regression analysis was conducted for 30-day mortality, and the results were summarized in Table 2. Table 3 presents a comparison from one group to another using a multifactor Cox regression model. In the unadjusted model, the mortality risk in the high ePWV group was significantly higher than in the low ePWV group (HR = 1.97; 95% CI: 1.78 \u0026ndash; 2.19; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). In Model 3, after adjusting for additional covariates, the high ePWV group still exhibited a significantly higher mortality risk compared to the low ePWV group (HR = 1.70; 95% CI: 1.51 \u0026ndash; 1.91; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Furthermore, the covariate-adjusted restricted cubic spline (RCS) model revealed a nonlinear association between ePWV levels and 30-day mortality risk (nonlinear P \u0026lt; 0.001), as illustrated in Figure 3. The 30-day mortality risk among AF patients increased progressively with higher ePWV levels (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further substantiate the relationship between 30-day mortality and ePWV, subgroup analyses were executed,partitioned by clinical conditions such as hypertension, T2DM, HF, MI and AKI. As depicted in Figure 4, the out[1]comes of these subgroup examinations align closely with the overarching results. Importantly, ePWV exhibited marked disparities in 28-day mortality rates, regardless of the presence or absence of hypertension, T2DM, HF, MI and AKI, with statistical significance (p\u0026lt;0.001). The interactions within these subgroups did not reach statistical significance, indicating that the prognostic impact of ePWV is consistent across various patient subpopulations. (\u003cem\u003eP\u003c/em\u003e for interaction \u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026ensp;Univariate cox regression analysis for 30-day mortality.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"317\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.03（1.02-1.03）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.97（0.96-0.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.72（0.65-0.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.69（0.56-0.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.59（0.46-0.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.19（1.17-1.20）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eAPSIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.03（1.03-1.03）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eSIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00（0.40-2.49）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.6（0.66-3.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2.08（0.86-5.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3.42（1.41-8.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.55（1.39-1.74）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.12（1.00-1.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.55（1.40-1.72）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.41（1.22-1.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.60（0.54-0.67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2.87（2.42-3.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.01（1.00-1.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.41（1.25-1.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eplatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00（1.00-1.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ehemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.06（1.04-1.09）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.11（1.11-1.12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eglucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00（1.00-1.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003epotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.15（1.07-1.24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ecalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.95（0.88-1.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eantibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.81（1.50-2.19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ecorticosteroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e2.60（2.33-2.90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eantihypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.35（0.31-0.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBMI, body mass index; SOFA, Sequential Organ Failure Assessment; APSIII, Acute Physiology ScoreIII (APSIII); SIRS, Systemic Inflammatory Response Syndrome; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; HF, heart failure; MI, myocardial infarction; LC, liver cirrhosis; WBC, white blood cells; RBC, red blood cells; AG, anion gape.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; Cox Proportional Hazards Regression Analysis of 30-Day Mortality\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"494\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low（n=6328）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eHigh(n=2851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.97（1.78-2.19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.83（1.64-2.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.70（1.51-1.91）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel 1: Unadjusted; Model 2: Adjusted for sex, race, and BMI; Model 3: Adjusted for all covariates. HR, hazard ratio; CI, confidence interval.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research delves into the relationship between ePWV and the likelihood of dying within 30 days in patients suffering from AF. Our results indicate that ePWV could be a prognostic marker independently for 30-day mortality in this distinct patients.Even after adjusting for potential confounding factors, elevated ePWV levels continued to exhibit a robust predictive capacity for 30-day mortality. Furthermore, among patients with AF, a non-linear association was observed between ePWV and the predicted 30-day mortality risk (non-linear p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eePWV serves as a simplified alternative indicator to cfPWV. The clinical application of cfPWV is limited due to its complex operational procedure, which requires coordination among multiple professionals[19]。However, ePWV can be estimated soely based on the patient's age and blood pressure. In certain critical clinical scenarios, its performance in detecting arterial stiffness is comparable to that of cfPWV[20]. Therefore, to conserve limited medical resources, ePWV can serve as a simplified alternative to cfPWV. Cui identified a statistically significant association between ePWV and increased mortality risk associated with AKI during ICU stays[21]. Wei demonstrated that ePWV serves as an independent predictor of 28-day mortality among patients with sepsis-associated AKI[22]. ePWV has been identified as an independent predictor of both short-term and long-ter[13]m mortality among critically ill patients with coronary heart disease. Integrating ePWV into conventional risk assessment models can substantially enhance the predictive accuracy of all-cause mortality within one year as well as during hospitalization for this patient population[23]. Chen conducted an 11-year follow-up study and found that elevated ePWV is associated with an increased risk of new-onset AF. Among the overall population, the incidence of new-onset AF in the medium, medium-high, and high ePWV groups was 1.64-fold, 1.92-fold, and 2.64-fold higher, respectively, compared to the low ePWV group[13]. There may be a causal relationship between arterial stiffness and AF. Research on atrial tissue has demonstrated that atherosclerosis can influence the progression of atrial fibrillation[24]. In addition, atherosclerosis and its associated risk factors contribute to structural and electrical remodeling of the atrium. Atrial fibrosis is recognized as both a consequence and a promoter of the initiation and progression of atrial fibrillation[25]. Furthermore, emerging evidence indicates that greater severity of atherosclerosis may lead to left ventricular hypertrophy, impaired left ventricular diastolic function, and left atrial enlargement, all of which can elevate the risk of developing atrial fibrillation[26].\u003c/p\u003e\u003cp\u003eIn this study, based on a retrospective cohort of 9,179 ICU patients diagnosed with AF from the MIMIC-IV database, we confirmed that the 30-day all-cause mortality rate was 22.6% among patients with ePWV\u0026thinsp;\u0026ge;\u0026thinsp;11.97 m/s, which was significantly higher than that of patients in the low ePWV group. Multivariate Cox regression analysis revealed that ePWV was an independent risk factor for 30-day mortality (HR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.51\u0026ndash;1.91), with a non-linear association observed. Subgroup analyses demonstrated consistent results across various stratifications, indicating the robustness of the findings. Therefore, ePWV, which can be rapidly calculated at the bedside, may serve as a non-invasive and independent predictor for early mortality risk among ICU patients with AF, enabling timely identification of high-risk individuals and facilitating optimized clinical interventions.\u003c/p\u003e\u003cp\u003eNevertheless, this study has several limitations. As the data were derived from an observational cohort within the MIMIC-IV database, potential confounding and selection biases may exist. Although multivariate Cox regression and restricted cubic spline (RCS) analyses were employed to adjust for known confounders, residual or unmeasured confounding factors cannot be entirely ruled out. Additionally, the MIMIC-IV database primarily includes data from patients in the United States, which may limit the generalizability of the findings to AF patients in other regions or countries. Furthermore, this study focused solely on the initial ePWV measurement following ICU admission and did not account for potential dynamic changes in ePWV over time. Given that ePWV may fluctuate in response to changes in a patient\u0026rsquo;s clinical condition, future research should investigate the longitudinal behavior of ePWV and further validate its predictive value and underlying mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents the first to identify a significant nonlinear relationship between ePWV and 30-day mortality among AF patients admitted to the ICU. These findings indicate that ePWV may function as an independent and novel biomarker for prognostic evaluation within this patient cohort, offering a theoretical foundation for early risk stratification and tailored therapeutic interventions. Further research is warranted to confirm the generalizability of these results through multicenter cohort studies, as well as to investigate the underlying pathophysiological mechanisms, including the interplay between arterial stiffness, atrial fibrillation, and multi-organ dysfunction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAF \u0026nbsp;atrial fibrillation\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp;intensive care unit\u003c/p\u003e\n\u003cp\u003eePWV \u0026nbsp;estimated pulse wave velocity\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp;receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp;restricted cubic spline\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eMAP \u0026nbsp;mean arterial pressure\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp;systolic pressure\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp;diastolic pressure\u003c/p\u003e\n\u003cp\u003eSOFA \u0026nbsp;Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eAPSIII \u0026nbsp;Acute Physiology ScoreIII (APSIII)\u003c/p\u003e\n\u003cp\u003eSIRS \u0026nbsp;Systemic Inflammatory Response Syndrome\u003c/p\u003e\n\u003cp\u003eCKD \u0026nbsp;chronic kidney disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM \u0026nbsp;type 2 diabetes mellitus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHF \u0026nbsp;heart failure\u003c/p\u003e\n\u003cp\u003eMI \u0026nbsp;myocardial infarction\u003c/p\u003e\n\u003cp\u003eLC \u0026nbsp;liver cirrhosis\u003c/p\u003e\n\u003cp\u003eCOPD chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp;white blood cells\u003c/p\u003e\n\u003cp\u003eRBC \u0026nbsp;red blood cells\u003c/p\u003e\n\u003cp\u003eAG anion gape\u003c/p\u003e\n\u003cp\u003eMV \u0026nbsp;mechanical ventilation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePX:\u0026nbsp;designed and conceptualized this study, SD: analyzed the data and wrote the manuscript,\u0026nbsp;BL:\u0026nbsp;drew the images,\u0026nbsp;ZZ\u0026nbsp;and YQ: checked the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that this research and the publication of this paper were supported by relevant funding. This work was supported by the Intramural Fund of Fuwai Central China Cardiovascular Hospital (Project No.: FWQN240005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available in the MIMIC-IV database, https://physionet.org/content/mimiciv/3.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement of ethical approval for this was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital, because the data was accessed from MIMIC-IV (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.Publisher\u0026rsquo;s note.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMurphy A, Banerjee A, Breithardt G, Camm AJ, Commerford P, Freedman B, et al. The World Heart Federation Roadmap for Nonvalvular Atrial Fibrillation. Glob Heart. 2017;12:273\u0026ndash;84. https://doi.org/10.1016/j.gheart.2017.01.015.\u003c/li\u003e\n\u003cli\u003eNielsen JC, Lin Y-J, de Oliveira Figueiredo MJ, Sepehri Shamloo A, Alfie A, Boveda S, et al. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population. Heart Rhythm. 2020;17:e269\u0026ndash;316. https://doi.org/10.1016/j.hrthm.2020.05.004.\u003c/li\u003e\n\u003cli\u003eKirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J. 2016;37:2893\u0026ndash;962. https://doi.org/10.1093/eurheartj/ehw210.\u003c/li\u003e\n\u003cli\u003eMareev YV, Polyakov DS, Vinogradova NG, Fomin IV, Mareev VY, Belenkov YN, et al. Epidemiology of atrial fibrillation in a representative sample of the European part of the Russian Federation. Analysis of EPOCH-CHF study. Kardiologiia. 2022;62:12\u0026ndash;9. https://doi.org/10.18087/cardio.2022.4.n1997.\u003c/li\u003e\n\u003cli\u003ede Burgos-Lunar C, Del Cura-Gonz\u0026aacute;lez I, C\u0026aacute;rdenas-Valladolid J, G\u0026oacute;mez-Campelo P, Ab\u0026aacute;nades-Herranz JC, L\u0026oacute;pez-de Andr\u0026eacute;s A, et al. Real-world data in primary care: validation of diagnosis of atrial fibrillation in primary care electronic medical records and estimated prevalence among consulting patients\u0026rsquo;. BMC Prim Care. 2023;24:4. https://doi.org/10.1186/s12875-022-01961-y.\u003c/li\u003e\n\u003cli\u003eLi J, Xin Y, Li J, Zhou L, Qiu H, Shen A, et al. Association of haemoglobin glycation index with outcomes in patients with acute coronary syndrome: results from an observational cohort study in China. Diabetol Metab Syndr. 2022;14:162. https://doi.org/10.1186/s13098-022-00926-6.\u003c/li\u003e\n\u003cli\u003eMa Q, Zhu J, Zheng P, Zhang J, Xia X, Zhao Y, et al. 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JACC Clin Electrophysiol. 2021;7:594\u0026ndash;603. https://doi.org/10.1016/j.jacep.2020.11.021.\u003c/li\u003e\n\u003cli\u003eSingh SM, Abdel-Qadir H, Pang A, Fang J, Koh M, Dorian P, et al. Population Trends in All-Cause Mortality and Cause Specific-Death With Incident Atrial Fibrillation. J Am Heart Assoc. 2020;9:e016810. https://doi.org/10.1161/JAHA.120.016810.\u003c/li\u003e\n\u003cli\u003eFreedman B, Hindricks G, Banerjee A, Baranchuk A, Ching CK, Du X, et al. World Heart Federation Roadmap on Atrial Fibrillation - A 2020 Update. Glob Heart. 2021;16:41. https://doi.org/10.5334/gh.1023.\u003c/li\u003e\n\u003cli\u003eGreve SV, Laurent S, Olsen MH. Estimated Pulse Wave Velocity Calculated from Age and Mean Arterial Blood Pressure. Pulse (Basel). 2017;4:175\u0026ndash;9. https://doi.org/10.1159/000453073.\u003c/li\u003e\n\u003cli\u003eChen C, Bao W, Chen C, Chen L, Wang L, Gong H. Association between estimated pulse wave velocity and all-cause mortality in patients with coronary artery disease: a cohort study from NHANES 2005-2008. BMC Cardiovasc Disord. 2023;23:412. https://doi.org/10.1186/s12872-023-03435-0.\u003c/li\u003e\n\u003cli\u003eHuang H, Bu X, Pan H, Yang S, Cheng W, Shubhra QTH, et al. Estimated pulse wave velocity is associated with all-cause and cardio-cerebrovascular disease mortality in stroke population: Results from NHANES (2003-2014). Front Cardiovasc Med. 2023;10:1140160. https://doi.org/10.3389/fcvm.2023.1140160.\u003c/li\u003e\n\u003cli\u003eTownsend RR, Anderson AH, Chirinos JA, Feldman HI, Grunwald JE, Nessel L, et al. Association of Pulse Wave Velocity With Chronic Kidney Disease Progression and Mortality: Findings From the CRIC Study (Chronic Renal Insufficiency Cohort). Hypertension. 2018;71:1101\u0026ndash;7. https://doi.org/10.1161/HYPERTENSIONAHA.117.10648.\u003c/li\u003e\n\u003cli\u003eRipoll\u0026eacute;s-Melchor J, Fern\u0026aacute;ndez Dorado F, Rubio Aguilera AI, Criado Camargo A, Chico Garc\u0026iacute;a M, Abad-Motos A, et al. Association between preoperative baseline pulse pressure and estimated pulse wave velocity and acute renal failure and mortality following colorectal surgery. A single-centre observational study. Rev Esp Anestesiol Reanim (Engl Ed). 2021;68:564\u0026ndash;75. https://doi.org/10.1016/j.redare.2021.02.004.\u003c/li\u003e\n\u003cli\u003eJohnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10:1. https://doi.org/10.1038/s41597-022-01899-x.\u003c/li\u003e\n\u003cli\u003eTeam, D. C. DecisionLinnc is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. 2023.\u003c/li\u003e\n\u003cli\u003eJin W, Chowienczyk P, Alastruey J. Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms. PLoS One. 2021;16:e0245026. https://doi.org/10.1371/journal.pone.0245026.\u003c/li\u003e\n\u003cli\u003eGreve SV, Laurent S, Olsen MH. Estimated Pulse Wave Velocity Calculated from Age and Mean Arterial Blood Pressure. Pulse (Basel). 2017;4:175\u0026ndash;9. https://doi.org/10.1159/000453073.\u003c/li\u003e\n\u003cli\u003eCui X, Hu Y, Li D, Lu M, Zhang Z, Kan D, et al. Association between estimated pulse wave velocity and in-hospital mortality of patients with acute kidney injury: a retrospective cohort analysis of the MIMIC-IV database. Ren Fail. 2024;46:2313172. https://doi.org/10.1080/0886022X.2024.2313172.\u003c/li\u003e\n\u003cli\u003eWei L, Cui X, Lv Y, Zhang F, Wu J. The relationship between estimated pulse wave velocity and 28-day mortality in patients with sA-AKI: a retrospective cohort analysis of the MIMIC-IV database. Ren Fail. 2025;47:2507162. https://doi.org/10.1080/0886022X.2025.2507162.\u003c/li\u003e\n\u003cli\u003eGu Y, Han X, Liu J, Li Y, Li Z, Zhang W, et al. Prognostic significance of the estimated pulse wave velocity in critically ill patients with coronary heart disease: analysis from the MIMIC‑IV database. Eur Heart J Qual Care Clin Outcomes. 2024;:qcae076. https://doi.org/10.1093/ehjqcco/qcae076.\u003c/li\u003e\n\u003cli\u003eAbed HS, Samuel CS, Lau DH, Kelly DJ, Royce SG, Alasady M, et al. Obesity results in progressive atrial structural and electrical remodeling: implications for atrial fibrillation. Heart Rhythm. 2013;10:90\u0026ndash;100. https://doi.org/10.1016/j.hrthm.2012.08.043.\u003c/li\u003e\n\u003cli\u003eLage JGB, Bortolotto AL, Bortolotto LA, Verardino RGS, Pessente GD, Bihan DCSL, et al. Association between Arterial Stiffness and Higher Burden of Atrial Arrhythmia in Elderly Hypertensive Patients without Atrial Fibrillation. Arq Bras Cardiol. 2024;121:e20240251. https://doi.org/10.36660/abc.20240251.\u003c/li\u003e\n\u003cli\u003eMascarenhas LA, Ji Y, Wang W, Inciardi RM, Parikh RR, Eaton AA, et al. Association of central arterial stiffness with atrial myopathy: the Atherosclerosis Risk in Communities (ARIC) study. Hypertens Res. 2024;47:2902\u0026ndash;13. https://doi.org/10.1038/s41440-024-01831-3.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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, Intensive care unit, Estimated pulse wave velocity, 30-day mortality, Predictor","lastPublishedDoi":"10.21203/rs.3.rs-7412924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7412924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and objective:\u003c/strong\u003e Estimated pulse wave velocity (ePWV) is widely recognized as a practical surrogate marker for carotid-femoral pulse wave velocity (cfPWV) and has been validated as a prognostic indicator for a variety of diseases. Nevertheless, the association between ePWV and the risk of 30-day mortality in intensive care unit (ICU) patients diagnosed with atrial fibrillation (AF) remains insufficiently understood. This study aims to investigate the correlation between ePWV and 30-day mortality in critically ill patients with AF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA retrospective analysis was conducted utilizing data from the MIMIC-IV database, focusing on ICU patients with AF. Participants were categorized into two groups based on an ePWV cutoff value determined through receiver operating characteristic (ROC) curve analysis. The relationship between ePWV levels and 30-day mortality was evaluated using Kaplan-Meier survival analysis, Cox proportional-hazards models, and restricted cubic spline (RCS) regression. Additionally, subgroup analyses were performed to assess the influence of ePWV on 30-day mortality across various patient subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA cohort of 9,179 critically ill patients with AF was analyzed. The 30-day all-cause mortality rate was observed to be 15.47%. Upon adjusting for confounding variables, ePWV persisted as an independent risk factor for 30-day mortality (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). RCS modeling revealed a non-linear association between ePWV and 30-day all-cause mortality (non-linear \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e The study identified ePWV as an independent predictor of 30-day mortality among critically ill patients with AF. Early identification of high-risk patients through ePWV assessment may enable timely interventions and enhance clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e \u0026nbsp;Not applicable.\u003c/p\u003e","manuscriptTitle":"Association between estimated pulse wave velocity and 30-day mortality in critically ill patients with atrial fibrillation: a cohort study based on the MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:08:15","doi":"10.21203/rs.3.rs-7412924/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"214926629255785550794680079059077117694","date":"2025-10-17T06:08:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T07:35:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T18:25:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T09:29:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-04T03:03:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-09-04T02:55:16+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":"e9c7601e-9ed0-48e7-abe2-d8716046928c","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T23:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 23:08:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7412924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7412924","identity":"rs-7412924","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}