J-Shaped association between heart rate and in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational study

J-Shaped association between heart rate and in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational study | 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 J-Shaped association between heart rate and in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational study Kai Zhang, Yu Han, Fangming Gu, Zhaoxuan Gu, Jiaying liang, Jiayu Zhao, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3427589/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite extensive evidence linking heart rate (HR) to the risk of all-cause mortality, little attention has been given to exploring this association in patients with congestive heart failure (CHF). This study aimed to assess the relationship between HR and in-hospital mortality in CHF patients using data from a large clinical population-based sample. Methods This retrospective observational study utilized the Medical Information Mart for Intensive Care IV database to extract all relevant data. In-hospital mortality served as the primary outcome measure. Data analyses involved restricted cubic spline regression, piecewise logistic regression, and multiple logistic regression models. Additionally, subgroup analysis was performed to examine the robustness of the main findings. Results The study included 15,983 participants with CHF, aged 72.9 ± 13.4 years. After adjusting for all factors, with each unit increase in HR, there was a 1% risk increase of patient death (95% confidence interval: 1.01 ~ 1.01, P < 0.001). Compared with individuals with HR Q2 (72–81 beats per minute (bpm) ), the adjusted OR values for HR and in-hospital mortality in Q1 (≤ 72 bpm), Q3 (81–93 bpm), and Q4 (＞93 bpm) were 1.18 (95% CI: 0.99 ~ 1.41, p = 0.07), 1.24 (95% CI: 1.04 ~ 1.47, p = 0.014), and 1.64 (95% CI: 1.39 ~ 1.94, p < 0.001), respectively. A dose-response relationship revealed an J-shaped curve between HR and the risk of in-hospital mortality, with an inflection point at approximately 76 bpm. Stratified analyses confirmed the robustness of this correlation. Conclusions In patients with CHF, there exists a J-shaped relationship between heart rate and in-hospital mortality, with an inflection point at 76 bpm. Nonetheless, further investigation through large randomized controlled trials is warranted in the future. Heart rate In-hospital mortality congestive heart failure association generalized additive model subgroup analysis Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Congestive heart failure (CHF) is a severe clinical syndrome characterized by abnormal cardiac structure and/or function[ 1 ] [ 2 ] [ 3 ], leading to the ventricles' inability to maintain normal cardiac output or increase output to meet higher peripheral demand [ 4 ]. It is the leading cause of mortality in developed countries and exhibits a rising prevalence[ 5 ]. Importantly, CHF is not an isolated disease but rather a multifaceted phenomenon that manifests as various other heart diseases progress to an advanced stage, resulting in relatively high morbidity and mortality[ 6 ]. Typically, CHF patients require admission to the intensive care unit (ICU) for treatment, with approximately 10%-15% of hospitalized heart failure patients in the USA being admitted to the ICU [ 7 ]. [ 8 ]. Despite significant medical advancements, the mortality rate among heart failure patients remains high [ 9 ]. Consequently, CHF presents a significant clinical challenge, necessitating active identification of new risk factors and the development of corresponding treatment strategies. In recent years, heart rate (HR) has garnered substantial attention as a readily available vital sign that holds crucial prognostic information[ 10 ]. The heart rate (HR) reflects the integration of inputs from the autonomic, cardiorespiratory, and adrenal systems and is readily measurable in clinical practice [ 11 , 12 ]. It has long been established as a vital health indicator [ 13 ]. While much research has concentrated on the association between elevated heart rate and mortality in congestive heart failure, the impact of low heart rate remains uncertain. Elevated resting heart rate is a predictor of increased morbidity and mortality across genders, regardless of the presence of cardiovascular diseases [ 14 ], as supported by studies involving patients with coronary artery disease, acute myocardial infarction, and heart failure [ 15 ] [ 16 ] [ 17 ]. Nevertheless, there is a scarcity of comprehensive longitudinal investigations that evaluate in-hospital mortality risks across the heart rate spectrum, particularly within the general population with sinus rhythm and atrial fibrillation, while considering serum chloride levels. Anh L. Bui's study demonstrated that patients with higher heart rates tended to be younger and have fewer comorbidities. The relationship between in-hospital mortality and heart rate exhibited a J-shaped curve, with the lowest mortality rates observed within the range of 70 to 75 beats per minute [ 18 ]. Despite this, relevant research on patients with congestive heart failure is currently lacking. Therefore, the primary objective of this study was to investigate the association between low and high heart rate and in-hospital mortality in a substantial cohort of American adults with congestive heart failure admitted to the intensive care unit. Method Data source: This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Data for this investigation were extracted from the Medical Information Mart for Intensive Care (MIMIC-IV) database. MIMIC is a collaborative initiative between the Beth Israel Deaconess Medical Center and the Laboratory for Computational Physiology at the Massachusetts Institute of Technology[ 19 ], providing comprehensive clinical data, encompassing medical records, drug therapies, laboratory results, patient characteristics, and International Classification of Diseases (ICD) disease codes [ 20 ]. The data extraction was carried out by the author (Kai Zhang), who completed the "Protecting Human Research Participants" training course on the National Institutes of Health (NIH) website and obtained approval for research purposes (certification number: 11639604) [ 21 ]. The usage of the MIMIC-IV database was sanctioned by the institutional review boards of the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center (Boston, MA, United States) [ 22 ] [ 23 ]. The study received approval from the Beth Israel Deaconess Medical Center Institutional Review Board, and the need for patient consent was waived due to the retrospective and de-identified nature of the data[ 24 ] [ 25 ]. Study population In this study, we utilized ICD-9 and ICD-10 codes for disease identification, focusing on adult patients with congestive heart failure (CHF) during their initial admission to the intensive care unit (ICU). Patients adhering to the diagnostic criteria of the European Society of Cardiology (ESC) for CHF were eligible for inclusion [ 26 ]. Diagnostic information was extracted from the 'diagnoses_icd' and 'd_icd_diagnoses' tables in the database. For reference, Table S1 in the Supporting Information provides the International Classification of Diseases (ICD) code specific to CHF. We initially enrolled 16,012 patients from the MIMIC-IV dataset. Subsequently, we excluded individuals under the age of 18 (n = 12) and those lacking outcome data post-ICU admission (n = 17). Ultimately, 15,983 patients constituted our study cohort, as illustrated in Fig. 1 . Expose and outcome Data retrieval from the database was accomplished using structured query language with PostgreSQL (version 13). Among the patients, several had multiple heart rate (HR) measurements, but only the initial HR measurement upon hospital admission was retained. The recorded initial heart rate was treated as a continuous variable, and patients were categorized into four groups based on their heart rate quartiles on the first day of ICU admission: Q1 (≤ 72 bpm), Q2 (72–81 bpm), Q3 (81–93 bpm), and Q4 (> 93 bpm). Our primary outcome of interest was in-hospital mortality, identified as a binary indicator variable in the discharge records. ICU mortality was determined exclusively during the first ICU admission. Covariates Based on the previous literature and clinical experience, the selected covariates were obtained as follows: (1) Demographic variables: sex, age, and race.; (2) Comorbidities: chronic obstructive pulmonary disease (COPD), diabetes, hepatic failure (HepF), acute myocardial infarction (AMI), Melanosis coli (MC), and diabetes; (3) Medical procedures: Ventilation and intubation. (4) Medication usage: Norepinephrine, dopamine, epinephrine, phenylephrine, and vasopressin. (5) Basic vital signs: Temperature, respiratory rate, and systolic blood pressure (SBP). (6) Blood biochemical indicators: Anion gap (AG), blood urea nitrogen (BUN), chloride, creatinine, hemoglobin (Hb), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), platelet count, potassium, sodium, red blood cell distribution width (RDW), red blood cell (RBC) count, and white blood cell (WBC) count. (7) Sequential Organ Failure Assessment Score(SOFA) Statistical analyses The study conducted an analysis of Heart Rate (HR) as both continuous and categorical variables, categorized into quartiles. Normally-distributed continuous variables were reported as mean ± SD, while non-normally-distributed continuous variables were presented as medians with interquartile range (IQR). Normally distributed variables were compared using Student's t-test, and non-normally distributed variables were compared using the Mann-Whitney U test. Statistical significance was assessed using an analysis of variance (ANOVA) or a Kruskal-Wallis test to examine group differences. The association between HR and in-hospital mortality was investigated using multivariate logistic regression, while adjusting for covariates. Odds ratios (ORs) and 95% confidence intervals (95%CIs) were calculated. Model 1 represented the crude model without adjusted covariates, and subsequent models (Model 2, Model 3, Model 4, and Model 5) were adjusted for different sets of variables. To assess the non-linear relationship between HR and in-hospital mortality, smooth curve fitting (penalized spline method) and Restricted cubic spline regression were utilized. The inflection point was determined through two-piecewise logistic regression and a recursive algorithm. A significance level of P < 0.05 was considered statistically significant. Subgroup analysis was performed to test for sensitivity, and an interaction test was employed to determine if there were relevant differences in effect across subgroups. All statistical analyses were conducted using R software (version 4.1.1) and Free Statistics software (version 1.7). P values < 0.05 (two-sided) were considered statistically significant. The reporting of this cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Results Baseline characteristics of selected participants Table 1 presents baseline characteristics of the study population, focusing on clinical aspects. The study subjects were categorized into quartiles based on their baseline heart rate levels: Q1 (≤ 72 bpm), Q2 (72–81 bpm), Q3 (81–93 bpm), and Q4 (＞93 bpm). The study enrolled a total of 15,983 subjects, comprising 8,733 men and 7,250 women, with a median age of 72.9 ± 13.4 years. Table 1 Characteristics of the study population (N = 15983) Variables Total (n = 15983) Q1 (n = 3912) Q2 (n = 3709) Q3 (n = 4241) Q4 (n = 4121) P value a Age, Mean ± SD 72.9 ± 13.4 76.0 ± 12.0 73.4 ± 12.4 71.9 ± 13.4 70.4 ± 15.0 < 0.001 Gender, n (%) 0.004 Male 8733 (54.6) 2178 (55.7) 2089 (56.3) 2300 (54.2) 2166 (52.6) Female 7250 (45.4) 1734 (44.3) 1620 (43.7) 1941 (45.8) 1955 (47.4) Race, n (%) 0.029 White 11056 (69.2) 2769 (70.8) 2582 (69.6) 2931 (69.1) 2774 (67.3) Black 2124 (13.3) 484 (12.4) 472 (12.7) 567 (13.4) 601 (14.6) Other 2803 (17.5) 659 (16.8) 655 (17.7) 743 (17.5) 746 (18.1) Medication situation Norepinephrine, n (%) < 0.001 No 12095 (75.7) 3162 (80.8) 2877 (77.6) 3184 (75.1) 2872 (69.7) Yes 3888 (24.3) 750 (19.2) 832 (22.4) 1057 (24.9) 1249 (30.3) Dopamine, n (%) 0.831 No 15504 (97.0) 3788 (96.8) 3605 (97.2) 4114 (97) 3997 (97) Yes 479 ( 3.0) 124 (3.2) 104 (2.8) 127 (3) 124 (3) Epinephrine, n (%) < 0.001 No 15245 (95.4) 3818 (97.6) 3525 (95) 3995 (94.2) 3907 (94.8) Yes 738 ( 4.6) 94 (2.4) 184 (5) 246 (5.8) 214 (5.2) Phenylephrine, n (%) < 0.001 No 14781 (92.5) 3721 (95.1) 3488 (94) 3920 (92.4) 3652 (88.6) Yes 1202 ( 7.5) 191 (4.9) 221 (6) 321 (7.6) 469 (11.4) Vasopressin, n (%) < 0.001 No 14817 (92.7) 3753 (95.9) 3498 (94.3) 3922 (92.5) 3644 (88.4) Yes 1166 ( 7.3) 159 (4.1) 211 (5.7) 319 (7.5) 477 (11.6) Medical Procedures Vent, n (%) < 0.001 No 2275 (14.2) 679 (17.4) 546 (14.7) 566 (13.3) 484 (11.7) Yes 13708 (85.8) 3233 (82.6) 3163 (85.3) 3675 (86.7) 3637 (88.3) Intubated, n (%) < 0.001 No 11232 (70.3) 2878 (73.6) 2416 (65.1) 2927 (69) 3011 (73.1) Yes 4751 (29.7) 1034 (26.4) 1293 (34.9) 1314 (31) 1110 (26.9) complicating disease COPD, n (%) < 0.001 No 9674 (60.5) 2475 (63.3) 2238 (60.3) 2538 (59.8) 2423 (58.8) Yes 6309 (39.5) 1437 (36.7) 1471 (39.7) 1703 (40.2) 1698 (41.2) HepF, n (%) 0.152 No 15545 (97.3) 3810 (97.4) 3623 (97.7) 4120 (97.1) 3992 (96.9) Yes 438 ( 2.7) 102 (2.6) 86 (2.3) 121 (2.9) 129 (3.1) MC, n (%) < 0.001 No 14374 (89.9) 3592 (91.8) 3375 (91) 3837 (90.5) 3570 (86.6) Yes 1609 (10.1) 320 (8.2) 334 (9) 404 (9.5) 551 (13.4) Diabetes, n (%) < 0.001 No 8920 (55.8) 2106 (53.8) 2023 (54.5) 2334 (55) 2457 (59.6) Yes 7063 (44.2) 1806 (46.2) 1686 (45.5) 1907 (45) 1664 (40.4) AMI, n (%) < 0.001 No 10926 (68.4) 2616 (66.9) 2445 (65.9) 2851 (67.2) 3014 (73.1) Yes 5057 (31.6) 1296 (33.1) 1264 (34.1) 1390 (32.8) 1107 (26.9) vital signs Temperature, Mean ± SD 36.7 ± 0.5 36.6 ± 0.5 36.7 ± 0.5 36.8 ± 0.5 36.9 ± 0.5 < 0.001 Respiratory Rate, Mean ± SD 19.9 ± 3.8 18.5 ± 3.1 19.3 ± 3.3 20.0 ± 3.6 21.8 ± 4.2 < 0.001 SBP, Mean ± SD 116.2 ± 17.0 119.8 ± 17.6 117.1 ± 16.5 115.5 ± 16.8 112.7 ± 16.3 < 0.001 Blood biochemical indicators AG, Mean ± SD 15.3 ± 4.4 15.1 ± 4.0 14.8 ± 4.3 15.2 ± 4.4 16.0 ± 4.6 < 0.001 BUN, Mean ± SD 36.4 ± 25.9 40.1 ± 28.4 35.5 ± 25.3 35.2 ± 24.8 34.9 ± 24.6 < 0.001 Calcium, Mean ± SD 8.5 ± 0.8 8.5 ± 0.8 8.5 ± 0.8 8.4 ± 0.8 8.4 ± 0.9 < 0.001 Chloride, Mean ± SD 102.2 ± 7.0 102.1 ± 6.8 102.8 ± 7.0 102.2 ± 7.0 101.6 ± 7.1 < 0.001 Creatinine, Mean ± SD 1.9 ± 1.8 2.0 ± 1.7 1.9 ± 1.8 1.9 ± 1.9 1.8 ± 1.8 < 0.001 Hb, Mean ± SD 10.2 ± 2.2 10.2 ± 2.1 10.0 ± 2.1 10.2 ± 2.2 10.5 ± 2.2 < 0.001 MCH, Mean ± SD 29.5 ± 2.8 29.7 ± 2.7 29.6 ± 2.8 29.5 ± 2.8 29.4 ± 2.9 < 0.001 MCHC, Mean ± SD 32.3 ± 1.7 32.4 ± 1.7 32.4 ± 1.7 32.3 ± 1.7 32.2 ± 1.8 < 0.001 MCV, Mean ± SD 91.5 ± 7.4 91.8 ± 7.1 91.5 ± 7.4 91.2 ± 7.4 91.6 ± 7.8 0.003 Platelet, Mean ± SD 209.2 ± 101.8 197.8 ± 85.4 202.2 ± 97.0 209.5 ± 101.2 225.8 ± 117.4 < 0.001 Potassium, Mean ± SD 4.3 ± 0.8 4.3 ± 0.8 4.3 ± 0.8 4.3 ± 0.8 4.3 ± 0.8 0.138 Sodium, Mean ± SD 138.1 ± 5.3 138.1 ± 5.4 138.4 ± 5.1 138.1 ± 5.2 137.9 ± 5.6 0.002 RBC, Mean ± SD 3.5 ± 0.8 3.5 ± 0.7 3.4 ± 0.7 3.5 ± 0.8 3.6 ± 0.8 < 0.001 RDW, Mean ± SD 15.8 ± 2.4 15.7 ± 2.2 15.6 ± 2.3 15.8 ± 2.3 16.1 ± 2.6 < 0.001 WBC, Mean ± SD 11.9 ± 8.7 10.6 ± 7.2 11.7 ± 8.5 12.3 ± 9.0 13.0 ± 9.6 < 0.001 Hstatus, n (%) < 0.001 survival 13995 (87.6) 3520 (90) 3363 (90.7) 3745 (88.3) 3367 (81.7) death 1988 (12.4) 392 (10) 346 (9.3) 496 (11.7) 754 (18.3) SOFA, Mean ± SD 3.4 ± 3.0 3.3 ± 2.8 3.5 ± 2.9 3.5 ± 3.0 3.6 ± 3.2 < 0.001 Abbreviations: %, weighted proportion.; Hstatus: hospital status; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; HepF, hepatic failure; AMI, acute myocardial infarction; SOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap; BUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell; RDW, red blood cell distribution width; WBC, white blood cell count. Q1(≤ 72bpm)Q2(72-81bpm)Q3(81-93bpm)Q4(＞93bpm) a P values of multiple comparisons were corrected by the False Discovery Rate method. b Q1-Q4: according to Heart Rate. Additionally, the analysis indicates that individuals with higher heart rate levels tend to exhibit the following characteristics: younger age, a lower proportion of white participants, lower systolic blood pressure (SBP), lower blood urea nitrogen (BUN), lower blood calcium and creatinine levels, lower mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC), fewer diabetes complications, and increased use of Norepinephrine, Phenylalanine, Vasopressin, and Vent support. Furthermore, these individuals showed a higher incidence of COPD complications, multiple organ complications, elevated body temperature, respiratory rate, platelet count, white blood cell count (WBC), and SOFA score. Association between heart rate and In-hospital mortality in patients with congestive heart failure Table 2 shows the association between heart rate and In-hospital mortality. Analyzing heart rate as a continuous variable, each unit increase in heart rate was associated with a ∼1% increase in the risk of in-hospital mortality (OR, 1.01; 95% CI, 1.01 ~ 1.01, p < 0.001). When heart rate consumption was analyzed using quartiles, there was a significant positively association between heart rate and In-hospital mortality after adjusting for potential confounders. Compared with individuals with heart rate Q2 (72–81 times per minute), the adjusted OR values for heart rate and In-hospital mortality in Q1 (≤ 72 times per minute), Q3 (81–93 times per minute), and Q4 (＞93 times per minute) were 1.18 (95% CI: 0.99 ~ 1.41, p = 0.07), 1.24 (95% CI: 1.04 ~ 1.47, p = 0.014), and 1.64 (95% CI: 1.39 ~ 1.94, p < 0.001) (Table 3), respectively. The overall trend was statistically significant (P trend test < 0.001). Table 2 Multivariable logistic regression to assess the association of Heart Rate with In-hospital mortality rate Model 1 Model 2 Model 3 Model 4 Model 5 Heart Rate OR_95CI P value OR_95CI P value OR_95CI P value OR_95CI P value OR_95CI P value continuous variable 1.02 (1.02 ~ 1.02) < 0.001 1.02 (1.02 ~ 1.03) < 0.001 1.02 (1.02 ~ 1.03) < 0.001 1.01 (1.01 ~ 1.01) < 0.001 1.01 (1.01 ~ 1.01) < 0.001 Categorical variable Q1(≤ 72) 1.08 (0.93 ~ 1.26) 0.307 1.01 (0.86 ~ 1.17) 0.93 1.01 (0.87 ~ 1.18) 0.882 1.17 (0.98 ~ 1.39) 0.082 1.18 (0.99 ~ 1.41) 0.07 Q2(72–81) 1(Ref) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q3(81–93) 1.29 (1.11 ~ 1.49) 0.001 1.34 (1.16 ~ 1.56) < 0.001 1.35 (1.16 ~ 1.56) < 0.001 1.21 (1.02 ~ 1.43) 0.027 1.24 (1.04 ~ 1.47) 0.014 Q4(＞93) 2.18 (1.9 ~ 2.49) < 0.001 2.38 (2.07 ~ 2.73) < 0.001 2.39 (2.08 ~ 2.74) < 0.001 1.59 (1.35 ~ 1.88) < 0.001 1.64 (1.39 ~ 1.94) < 0.001 P for tread < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Abbreviations: the unit of Heart Rate is bpm, %, weighted proportion. CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; HepF, hepatic failure; AMI, acute myocardial infarction; bpm, beats per minute; SOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap; BUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell; RDW, red blood cell distribution width; WBC, white blood cell count. CI: confidence interval;OR: odds ratios, Ref: reference Model 1: No adjustment Model 2: Adjusted for demographic variables( sex, age, race) Model 3: Adjusted for demographic variables, comorbidities (COPD, AMI, MC, HepF, diabetes) Model 4: Adjusted for demographic variables, comorbidities, Medical Procedures(Vent, Intubated), Medication situation(Norepinephrine Dopamine Epinephrine Phenylephrine Vasopressin),Basic vital signs(Temperature Respiratory Rate SBP),Blood biochemical indicators(AG BUN Chloride Creatinine, Hb MCH MCHC MCV Platelet Potassium Sodium RBC RDW WBC) Model 5: Adjusted for demographic variables, comorbidities, Medical Procedures, Medication situation, Basic vital signs, Blood biochemical indicators, SOFA Table 3 Threshold effect analysis of relationship of Heart Rate with In-hospital mortality rate. Adjusted OR_95CI P value Two model Heart Rate ≤ 76 bpm 0.982 (0.965 ~ 0.999) 0.0371 Heart Rate ≥ 76 bpm 1.015 (1.009 ~ 1.02) < 0.001 Likelihood Ratio test - 0.001 Adjusted for demographic variables (sex, age, race), Concomitant disease(COPD,AMI,MC, HepF, diabetes), Medical Procedures(Vent, Intubated), Medication situation(Norepinephrine Dopamine Epinephrine Phenylephrine Vasopressin),Basic vital signs(Temperature Respiratory Rate SBP),Blood biochemical indicators(AG BUN Chloride Creatinine Hb MCH MCHC MCV Platelet Potassium Sodium RBC RDW WBC), SOFA Abbreviations: %, weighted proportion. CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; HepF, hepatic failure; AMI, acute myocardial infarction; bpm, beats per minute; SOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap; BUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell; RDW, red blood cell distribution width; WBC, white blood cell count. CI: confidence interval;OR: odds ratios, Ref: reference Dose–Response Relationships In this study, we utilized restricted cubic spline models (Fig. 2 ) to explore nonlinear relationships. Our findings reveal a J-shaped association between heart rate (HR) and in-hospital mortality in patients diagnosed with congestive heart failure, with adjustments made for potential confounding variables. Below a threshold of 76 bpm, we observed a negative correlation between HR and the risk of in-hospital mortality (OR = 0.982 [95% CI 0.965–0.999], p = 0.0371). Conversely, beyond this threshold, exceeding 76 bpm, there is a significant increase in the risk of in-hospital mortality (OR = 1.015 [95% CI 1.009–1.02], p < 0.001) (Table 3). Subgroup analysis To investigate potential modifications by confounding factors, we performed subgroup analyses using stratification variables: Age, Sex, Race, Nor epinephrine, Dopamine, Epinephrine, Phenylalanine, Vasopression, COPD, AMI, MC, diabetes, and hepatic failure. However, no significant interactions between the HR and these stratified variables were detected (P > 0.05). The summarized results of the subgroup analyses and interactions can be found in the Fig. 3 . Discussion In this research, we investigated the prognostic significance of heart rate (HR) as a readily accessible vital sign[ 10 ]. Specifically, we focused on its J-shaped relationship with in-hospital mortality in ICU patients with congestive heart failure (CHF), identifying the optimal heart rate to be 76 bpm. Both higher and lower heart rates were associated with an increased risk of mortality. Subgroup analysis corroborated the overall findings for this patient cohort. The significance of heart rate in prognosis has been extensively demonstrated across various diseases. In this study, we have shown a noteworthy association between higher heart rates and increased mortality rates, which corroborates previous research[ 27 ]. Surprisingly, we also observed a rise in mortality among patients with lower heart rates, with the nadir at 76 bpm presenting the lowest mortality. This finding contrasts with previous reports from the Framingham Study, which indicated a progressive increase in mortality with resting heart rate[ 28 ]. Similarly, the Goteborg Primary Prevention Trial [ 29 ] and the NHEFS Cohort[ 30 ] both found an escalation in all-cause and cardiovascular mortality with increasing heart rate, surpassing 84 bpm. It is important to consider that during the late 80s, beta-receptor blockade was not yet established as a standard treatment for patients with congestive heart failure (CHF). Therefore, the discrepancies in the observed results may be attributed to the limited adoption of β blockers on a large scale. Our study extends upon these previous findings by investigating the relationship between heart rate levels, both high and low, and mortality in hospitalized heart failure patients in the United States. In a retrospective analysis of the MIMIC-Ⅳ database, our logistic regression analysis demonstrates that high and low heart rates during CHF in ICU patients are associated with reduced in-hospital mortality risk, with the optimal heart rate range for lowest mortality risk identified as approximately 72–81 bpm. To address the non-linear relationship between heart rate and in-hospital mortality in individuals with heart failure, this study employed smooth curve fitting and generalized additive models. Subgroup analyses were additionally conducted to assess the consistency of the primary findings. The association between heart rate and in-hospital mortality follows a J-shaped pattern, indicating an inflection point at 76 bpm. Notably, the risk of in-hospital mortality decreases with increasing heart rate; however, among subjects with a population heart rate of 76 bpm or higher, the risk of in-hospital mortality increases with heart rate. These results suggest a potential beneficial effect of maintaining heart rate within a stable range around 76 bpm for CHF patients in the ICU. These findings underscore the importance of considering both low and high heart rate levels in clinical practice. This implies that healthcare professionals should vigilantly monitor the blood pressure of congestive heart failure patients in the ICU, and timely and effective interventions may enhance patient prognosis. Despite a partial understanding of the underlying mechanism, several plausible explanations exist for the elevated mortality risk associated with heart rate. Speculation regarding a fundamental pathophysiologic relationship between higher heart rate and the development[ 31 ] [ 32 ] or exacerbation of heart failure (HF)[ 33 ] [ 34 , 35 ] has encompassed factors such as myocardial energetic considerations and favorable alterations in arterial afterload through heart rate reduction[ 36 ] [ 37 ]. The SHIFT trial has implicated heart rate in the causal pathway of HF progression, identifying it as a potentially modifiable risk factor[ 38 ]. Changes in resting heart rate are normal physiological adaptations that maintain adequate cardiac output. However, in patients with underlying HF, an excessively fast heart rate may become pathophysiological. A faster heart rate increases the myocardial demand for oxygen and shortens diastole, thereby limiting the time available for oxygenated blood to flow through the coronary arteries, leading to insufficient myocardial perfusion [ 39 ].Consequently, the combination of increased oxygen demand and reduced perfusion time creates hypoxic conditions in the myocardium, further exacerbating an already failing heart [ 39 ]. The specific source of benefit from heart rate reduction, such as reduced myocardial oxygen consumption and improved myocardial efficiency, reduced total afterload, or other potential explanations, remains to be determined [ 40 ]. Subsequent basic experiments are necessary to address these issues in the future. The employed methodology in this study offers several notable advantages. Firstly, previous investigations on risk and prognostic factors for congestive heart failure (CHF) have been limited by small sample sizes. To our knowledge, this study is the first attempt to analyze patients with CHF using the MIMIC-IV database. Secondly, a smoothing function analysis was applied to address potential data analysis contingencies, enabling a comprehensive understanding of the association between heart rate and in-hospital mortality. Additionally, to minimize the influence of confounding factors inherent in observational studies, logistic regression analysis was employed with multiple models, and subgroup analyses were conducted with appropriate grouping. The study has several limitations. First, its retrospective research design may compromise the validity of our findings, underscoring the necessity for validation through prospective case-control studies in the future. Second, the incompleteness of publicly available databases restricted our access to certain factors, such as Beta Blocker usage. We intend to address this limitation in future investigations by employing a more comprehensive database. Finally, the exclusive inclusion of American participants may constrain the generalizability of our results to other populations. Therefore, it is essential to exercise caution when extrapolating our findings in light of this limitation. Given these constraints, the imperative for well-designed multicenter controlled trials to corroborate our present findings is evident. Conclusions In conclusion, our study unveils a J-shaped correlation between heart rate and in-hospital mortality in this specific patient population. To substantiate and reinforce these results, future research should employ prospective, randomized, controlled study designs. Declarations Acknowledgements: We appreciate Dr. Jie Liu of the Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital for statistics, study deign consultations and editing the manuscript. Author Contributions: KZ contributed as First authors of this manuscript. YH, FMG, ZXG JYL and JYZ were responsible for the concept and design of the study. YQZ, MG and ZYH explain the analysis. TYC, YFG, RH, TZL, DC and BL are responsible for data recovery. K Z, D C and B L is the primary corresponding author. All authors critically revised the important intellectual content of the paper and approved the final draft. Data availability: The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. To obtain the application executable files, please contact the author Kai Zhang by email [email protected] Disclosure: Funding Statement: The study has no Foundation. Conflict of Interest: The authors declare no conflict of interest. Approval date of Registry and the Registration No. of the study/trial: N/A Animal Studies: N/A Ethics approval and consent to participate The establishment of this database was approved by the Massachusetts Institute of Technology (Cambridge, MA, USA) and Beth Israel Deaconess Medical Center (Boston, MA, USA), and informed consents were exempted due to all patients’ data were anonymized before the data were obtained. We also complied with all relevant ethical regulations regarding the use of the data in our study. All reports adhered to the guidelines for Strengthening the Reporting of Observational Studies in Epidemiology and the Declaration of Helsinki. Competing interests The authors declare that they have no competing interests. References Yang, F.J., et al., Anti-cytomegalovirus IgG antibody titer is positively associated with advanced T cell differentiation and coronary artery disease in end-stage renal disease. Immun Ageing, 2018. 15 : p. 15. Tang, Y., et al., A retrospective cohort study on the association between early coagulation disorder and short-term all-cause mortality of critically ill patients with congestive heart failure. Front Cardiovasc Med, 2022. 9 : p. 999391. Becari, C., et al., Elastase-2, an angiotensin II-generating enzyme, contributes to increased angiotensin II in resistance arteries of mice with myocardial infarction. Br J Pharmacol, 2017. 174 (10): p. 1104-1115. Hanft, L.M., C.A. Emter, and K.S. McDonald, Cardiac myofibrillar contractile properties during the progression from hypertension to decompensated heart failure. Am J Physiol Heart Circ Physiol, 2017. 313 (1): p. H103-h113. Lu, Z., et al., Oxidative stress regulates left ventricular PDE5 expression in the failing heart. Circulation, 2010. 121 (13): p. 1474-83. Moyehodie, Y.A., et al., Time to Death and Its Determinant Factors Among Patients With Chronic Heart Failure in Northwest Ethiopia: A Retrospective Study at Selected Referral Hospitals. Front Cardiovasc Med, 2022. 9 : p. 817074. Acharya, P., et al., Incidence, Predictors, and Outcomes of In-Hospital Cardiac Arrest in COVID-19 Patients Admitted to Intensive and Non-Intensive Care Units: Insights From the AHA COVID-19 CVD Registry. J Am Heart Assoc, 2021. 10 (16): p. e021204. 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Wong, A.I., et al., Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA Netw Open, 2021. 4 (11): p. e2131674. Luo, C., et al., A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure. J Transl Med, 2022. 20 (1): p. 136. Hou, N., et al., Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med, 2020. 18 (1): p. 462. Ge, X., et al., A Novel Blood Inflammatory Indicator for Predicting Deterioration Risk of Mild Traumatic Brain Injury. Front Aging Neurosci, 2022. 14 : p. 878484. Rojas, J.C., et al., Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data. Ann Am Thorac Soc, 2018. 15 (7): p. 846-853. O'Donnell, T.F.X., et al., Weekend Effect in Carotid Endarterectomy. Stroke, 2018. 49 (12): p. 2945-2952. Li, J., et al., Nocturnal Mean Arterial Pressure Rising Is Associated With Mortality in the Intensive Care Unit: A Retrospective Cohort Study. J Am Heart Assoc, 2019. 8 (19): p. e012388. Ponikowski, P., et al., 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail, 2016. 18 (8): p. 891-975. Custodis, F., et al., Resting heart rate is an independent predictor of all-cause mortality in the middle aged general population. Clin Res Cardiol, 2016. 105 (7): p. 601-12. Kannel, W.B., et al., Heart rate and cardiovascular mortality: the Framingham Study. Am Heart J, 1987. 113 (6): p. 1489-94. Wilhelmsen, L., et al., The multifactor primary prevention trial in Göteborg, Sweden. Eur Heart J, 1986. 7 (4): p. 279-88. Gillum, R.F., D.M. Makuc, and J.J. Feldman, Pulse rate, coronary heart disease, and death: the NHANES I Epidemiologic Follow-up Study. Am Heart J, 1991. 121 (1 Pt 1): p. 172-7. Ho, J.E., et al., Long-term cardiovascular risks associated with an elevated heart rate: the Framingham Heart Study. J Am Heart Assoc, 2014. 3 (3): p. e000668. Opdahl, A., et al., Resting heart rate as predictor for left ventricular dysfunction and heart failure: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol, 2014. 63 (12): p. 1182-1189. Lechat, P., et al., Heart rate and cardiac rhythm relationships with bisoprolol benefit in chronic heart failure in CIBIS II Trial. Circulation, 2001. 103 (10): p. 1428-33. Metra, M., et al., Influence of heart rate, blood pressure, and beta-blocker dose on outcome and the differences in outcome between carvedilol and metoprolol tartrate in patients with chronic heart failure: results from the COMET trial. Eur Heart J, 2005. 26 (21): p. 2259-68. Gullestad, L., et al., What resting heart rate should one aim for when treating patients with heart failure with a beta-blocker? Experiences from the Metoprolol Controlled Release/Extended Release Randomized Intervention Trial in Chronic Heart Failure (MERIT-HF). J Am Coll Cardiol, 2005. 45 (2): p. 252-9. Levine, H.J., Optimum heart rate of large failing hearts. Am J Cardiol, 1988. 61 (8): p. 633-6. Kelly, R.P., et al., Effective arterial elastance as index of arterial vascular load in humans. Circulation, 1992. 86 (2): p. 513-21. Böhm, M., et al., Heart rate as a risk factor in chronic heart failure (SHIFT): the association between heart rate and outcomes in a randomised placebo-controlled trial. Lancet, 2010. 376 (9744): p. 886-94. Bhakat, B., et al., A Prospective Study to Evaluate the Possible Role of Cholecalciferol Supplementation on Autoimmunity in Hashimoto's Thyroiditis. J Assoc Physicians India, 2023. 71 (1): p. 1. Levine, H.J., Rest heart rate and life expectancy. J Am Coll Cardiol, 1997. 30 (4): p. 1104-6. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted 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-3427589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":239259107,"identity":"ec6d34d4-210c-4805-ab24-1140020b9e39","order_by":0,"name":"Kai 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13:14:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3427589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3427589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44609873,"identity":"ae8d96a4-02a7-432a-bc76-868b445f30d6","added_by":"auto","created_at":"2023-10-14 00:15:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62352,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3427589/v1/a4a916a9e063f202b3f96f80.png"},{"id":44609871,"identity":"bb7aabc6-d6bf-4f62-9fd4-7bbdce04b7d8","added_by":"auto","created_at":"2023-10-14 00:15:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106535,"visible":true,"origin":"","legend":"\u003cp\u003eDose–Response Relationships between Heart Rate with In-hospital mortality rate odds ratio.\u003c/p\u003e\n\u003cp\u003eSolid and dashed lines represent\u0026nbsp;the predicted value and 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eAdjusted for demographic variables\u0026nbsp;(sex, age, race) Concomitant\u0026nbsp;disease(COPD, AMI, MC, HepF, diabetes), comorbidities, Medical Procedures(Vent, Intubated) , Medication situation(Norepinephrine Dopamine Epinephrine Phenylephrine Vasopressin),Basic vital signs(Temperature Respiratory Rate\u0026nbsp;SBP), Blood biochemical indicators(AG BUN\u0026nbsp;Chloride Creatinine, Hb MCH MCHC MCV Platelet Potassium Sodium RBC WBC), SOFA. Only 99% of the data is shown.\u003c/p\u003e\n\u003cp\u003eAbbreviations: %, weighted proportion. CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; HepF, hepatic failure; AMI, acute myocardial infarction; APSIII, Acute Physiology III; SOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap; BUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell; RDW, red blood cell distribution width; WBC, white blood cell count. CI: confidence interval; OR: odds ratios，Ref: reference\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427589/v1/ba0177161d45b2f9e491612c.jpg"},{"id":44609872,"identity":"d7180ea8-d287-4842-8225-855799025cac","added_by":"auto","created_at":"2023-10-14 00:15:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158439,"visible":true,"origin":"","legend":"\u003cp\u003eStratifiedanalyses of the association between Heart Rate with In-hospital mortality rate.\u003c/p\u003e\n\u003cp\u003eNote: The p value for interaction represents the likelihood of interaction between the Heart Rate with In-hospital mortality rate.\u003c/p\u003e\n\u003cp\u003eAbbreviations: OR, odd ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3427589/v1/f02cdf5357f79fc966d192f6.jpg"},{"id":46796481,"identity":"c7b09236-69b5-48c2-a94e-8d580b6b95d8","added_by":"auto","created_at":"2023-11-20 18:52:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":653416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3427589/v1/df385606-47e2-4b61-a096-4af4c60e72bf.pdf"},{"id":44609874,"identity":"206c1867-4809-4d13-9d6d-097e05195f3b","added_by":"auto","created_at":"2023-10-14 00:15:24","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15842,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3427589/v1/ef533c171dadecf2b5be81e6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"J-Shaped association between heart rate and in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCongestive heart failure (CHF) is a severe clinical syndrome characterized by abnormal cardiac structure and/or function[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], leading to the ventricles' inability to maintain normal cardiac output or increase output to meet higher peripheral demand [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is the leading cause of mortality in developed countries and exhibits a rising prevalence[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Importantly, CHF is not an isolated disease but rather a multifaceted phenomenon that manifests as various other heart diseases progress to an advanced stage, resulting in relatively high morbidity and mortality[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Typically, CHF patients require admission to the intensive care unit (ICU) for treatment, with approximately 10%-15% of hospitalized heart failure patients in the USA being admitted to the ICU [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite significant medical advancements, the mortality rate among heart failure patients remains high [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, CHF presents a significant clinical challenge, necessitating active identification of new risk factors and the development of corresponding treatment strategies. In recent years, heart rate (HR) has garnered substantial attention as a readily available vital sign that holds crucial prognostic information[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe heart rate (HR) reflects the integration of inputs from the autonomic, cardiorespiratory, and adrenal systems and is readily measurable in clinical practice [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has long been established as a vital health indicator [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While much research has concentrated on the association between elevated heart rate and mortality in congestive heart failure, the impact of low heart rate remains uncertain. Elevated resting heart rate is a predictor of increased morbidity and mortality across genders, regardless of the presence of cardiovascular diseases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], as supported by studies involving patients with coronary artery disease, acute myocardial infarction, and heart failure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, there is a scarcity of comprehensive longitudinal investigations that evaluate in-hospital mortality risks across the heart rate spectrum, particularly within the general population with sinus rhythm and atrial fibrillation, while considering serum chloride levels. Anh L. Bui's study demonstrated that patients with higher heart rates tended to be younger and have fewer comorbidities. The relationship between in-hospital mortality and heart rate exhibited a J-shaped curve, with the lowest mortality rates observed within the range of 70 to 75 beats per minute [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this, relevant research on patients with congestive heart failure is currently lacking. Therefore, the primary objective of this study was to investigate the association between low and high heart rate and in-hospital mortality in a substantial cohort of American adults with congestive heart failure admitted to the intensive care unit.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source:\u003c/h2\u003e \u003cp\u003e This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Data for this investigation were extracted from the Medical Information Mart for Intensive Care (MIMIC-IV) database. MIMIC is a collaborative initiative between the Beth Israel Deaconess Medical Center and the Laboratory for Computational Physiology at the Massachusetts Institute of Technology[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], providing comprehensive clinical data, encompassing medical records, drug therapies, laboratory results, patient characteristics, and International Classification of Diseases (ICD) disease codes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The data extraction was carried out by the author (Kai Zhang), who completed the \"Protecting Human Research Participants\" training course on the National Institutes of Health (NIH) website and obtained approval for research purposes (certification number: 11639604) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The usage of the MIMIC-IV database was sanctioned by the institutional review boards of the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center (Boston, MA, United States) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The study received approval from the Beth Israel Deaconess Medical Center Institutional Review Board, and the need for patient consent was waived due to the retrospective and de-identified nature of the data[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn this study, we utilized ICD-9 and ICD-10 codes for disease identification, focusing on adult patients with congestive heart failure (CHF) during their initial admission to the intensive care unit (ICU). Patients adhering to the diagnostic criteria of the European Society of Cardiology (ESC) for CHF were eligible for inclusion [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Diagnostic information was extracted from the 'diagnoses_icd' and 'd_icd_diagnoses' tables in the database. For reference, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Supporting Information provides the International Classification of Diseases (ICD) code specific to CHF. We initially enrolled 16,012 patients from the MIMIC-IV dataset. Subsequently, we excluded individuals under the age of 18 (n\u0026thinsp;=\u0026thinsp;12) and those lacking outcome data post-ICU admission (n\u0026thinsp;=\u0026thinsp;17). Ultimately, 15,983 patients constituted our study cohort, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExpose and outcome\u003c/h2\u003e \u003cp\u003eData retrieval from the database was accomplished using structured query language with PostgreSQL (version 13). Among the patients, several had multiple heart rate (HR) measurements, but only the initial HR measurement upon hospital admission was retained. The recorded initial heart rate was treated as a continuous variable, and patients were categorized into four groups based on their heart rate quartiles on the first day of ICU admission: Q1 (\u0026le;\u0026thinsp;72 bpm), Q2 (72\u0026ndash;81 bpm), Q3 (81\u0026ndash;93 bpm), and Q4 (\u0026gt;\u0026thinsp;93 bpm). Our primary outcome of interest was in-hospital mortality, identified as a binary indicator variable in the discharge records. ICU mortality was determined exclusively during the first ICU admission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eBased on the previous literature and clinical experience, the selected covariates were obtained as follows: (1) Demographic variables: sex, age, and race.; (2) Comorbidities: chronic obstructive pulmonary disease (COPD), diabetes, hepatic failure (HepF), acute myocardial infarction (AMI), Melanosis coli (MC), and diabetes; (3) Medical procedures: Ventilation and intubation. (4) Medication usage: Norepinephrine, dopamine, epinephrine, phenylephrine, and vasopressin. (5) Basic vital signs: Temperature, respiratory rate, and systolic blood pressure (SBP). (6) Blood biochemical indicators: Anion gap (AG), blood urea nitrogen (BUN), chloride, creatinine, hemoglobin (Hb), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), platelet count, potassium, sodium, red blood cell distribution width (RDW), red blood cell (RBC) count, and white blood cell (WBC) count. (7) Sequential Organ Failure Assessment Score(SOFA)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe study conducted an analysis of Heart Rate (HR) as both continuous and categorical variables, categorized into quartiles. Normally-distributed continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, while non-normally-distributed continuous variables were presented as medians with interquartile range (IQR). Normally distributed variables were compared using Student's t-test, and non-normally distributed variables were compared using the Mann-Whitney U test. Statistical significance was assessed using an analysis of variance (ANOVA) or a Kruskal-Wallis test to examine group differences.\u003c/p\u003e \u003cp\u003eThe association between HR and in-hospital mortality was investigated using multivariate logistic regression, while adjusting for covariates. Odds ratios (ORs) and 95% confidence intervals (95%CIs) were calculated. Model 1 represented the crude model without adjusted covariates, and subsequent models (Model 2, Model 3, Model 4, and Model 5) were adjusted for different sets of variables.\u003c/p\u003e \u003cp\u003eTo assess the non-linear relationship between HR and in-hospital mortality, smooth curve fitting (penalized spline method) and Restricted cubic spline regression were utilized. The inflection point was determined through two-piecewise logistic regression and a recursive algorithm. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Subgroup analysis was performed to test for sensitivity, and an interaction test was employed to determine if there were relevant differences in effect across subgroups.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 4.1.1) and Free Statistics software (version 1.7). P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) were considered statistically significant. The reporting of this cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eBaseline characteristics of selected participants\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents baseline characteristics of the study population, focusing on clinical aspects. The study subjects were categorized into quartiles based on their baseline heart rate levels: Q1 (\u0026le;\u0026thinsp;72 bpm), Q2 (72\u0026ndash;81 bpm), Q3 (81\u0026ndash;93 bpm), and Q4 (＞93 bpm). The study enrolled a total of 15,983 subjects, comprising 8,733 men and 7,250 women, with a median age of 72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of the study population (N\u0026thinsp;=\u0026thinsp;15983)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;15983)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;3912)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;3709)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;4241)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;4121)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGender, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8733 (54.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2178 (55.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2089 (56.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2300 (54.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2166 (52.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7250 (45.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1734 (44.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1620 (43.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1941 (45.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1955 (47.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRace, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11056 (69.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2769 (70.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2582 (69.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2931 (69.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2774 (67.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2124 (13.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e484 (12.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e472 (12.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e567 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e601 (14.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2803 (17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e659 (16.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e655 (17.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e743 (17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e746 (18.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMedication situation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNorepinephrine, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12095 (75.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3162 (80.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2877 (77.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3184 (75.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2872 (69.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3888 (24.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e750 (19.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e832 (22.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1057 (24.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1249 (30.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDopamine, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15504 (97.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3788 (96.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3605 (97.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4114 (97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3997 (97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e479 ( 3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (3.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127 (3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEpinephrine, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15245 (95.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3818 (97.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3525 (95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3995 (94.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3907 (94.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e738 ( 4.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94 (2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184 (5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e246 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e214 (5.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePhenylephrine, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14781 (92.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3721 (95.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3488 (94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3920 (92.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3652 (88.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1202 ( 7.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191 (4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221 (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e321 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e469 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVasopressin, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14817 (92.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3753 (95.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3498 (94.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3922 (92.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3644 (88.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1166 ( 7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e211 (5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e319 (7.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e477 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMedical Procedures\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVent, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2275 (14.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e679 (17.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e546 (14.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e566 (13.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e484 (11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13708 (85.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3233 (82.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3163 (85.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3675 (86.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3637 (88.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntubated, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11232 (70.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2878 (73.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2416 (65.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2927 (69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3011 (73.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4751 (29.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1034 (26.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1293 (34.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1314 (31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1110 (26.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ecomplicating disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCOPD, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9674 (60.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2475 (63.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2238 (60.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2538 (59.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2423 (58.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6309 (39.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1437 (36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1471 (39.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1703 (40.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1698 (41.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHepF, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15545 (97.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3810 (97.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3623 (97.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4120 (97.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3992 (96.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e438 ( 2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMC, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14374 (89.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3592 (91.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3375 (91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3837 (90.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3570 (86.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1609 (10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e320 (8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e334 (9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e404 (9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e551 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8920 (55.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2106 (53.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023 (54.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2334 (55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2457 (59.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7063 (44.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1806 (46.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1686 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1907 (45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1664 (40.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAMI, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10926 (68.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2616 (66.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2445 (65.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2851 (67.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3014 (73.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5057 (31.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1296 (33.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1264 (34.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1390 (32.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1107 (26.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evital signs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRespiratory Rate, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSBP, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBlood biochemical indicators\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAG, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBUN, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCalcium, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChloride, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCreatinine, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHb, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCH, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCHC, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCV, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlatelet, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e209.2\u0026thinsp;\u0026plusmn;\u0026thinsp;101.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197.8\u0026thinsp;\u0026plusmn;\u0026thinsp;85.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202.2\u0026thinsp;\u0026plusmn;\u0026thinsp;97.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e209.5\u0026thinsp;\u0026plusmn;\u0026thinsp;101.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e225.8\u0026thinsp;\u0026plusmn;\u0026thinsp;117.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePotassium, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSodium, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRBC, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRDW, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHstatus, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esurvival\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13995 (87.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3520 (90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3363 (90.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3745 (88.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3367 (81.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edeath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1988 (12.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e392 (10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e346 (9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e496 (11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e754 (18.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOFA, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eAbbreviations: %, weighted proportion.; Hstatus: hospital status; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eHepF, hepatic failure; AMI, acute\u0026nbsp;myocardial\u0026nbsp;infarction;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eSOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eBUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003ehemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eRDW, red blood cell distribution width; WBC, white blood cell count.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eQ1(\u0026le;\u0026thinsp;72bpm)Q2(72-81bpm)Q3(81-93bpm)Q4(＞93bpm)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;P\u0026nbsp;values of multiple comparisons were corrected by the False Discovery Rate method.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;Q1-Q4: according to Heart Rate.\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\u003eAdditionally, the analysis indicates that individuals with higher heart rate levels tend to exhibit the following characteristics: younger age, a lower proportion of white participants, lower systolic blood pressure (SBP), lower blood urea nitrogen (BUN), lower blood calcium and creatinine levels, lower mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC), fewer diabetes complications, and increased use of Norepinephrine, Phenylalanine, Vasopressin, and Vent support. Furthermore, these individuals showed a higher incidence of COPD complications, multiple organ complications, elevated body temperature, respiratory rate, platelet count, white blood cell count (WBC), and SOFA score.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eAssociation between heart rate and In-hospital mortality in patients with congestive heart failure\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the association between heart rate and In-hospital mortality. Analyzing heart rate as a continuous variable, each unit increase in heart rate was associated with a \u0026sim;1% increase in the risk of in-hospital mortality (OR, 1.01; 95% CI, 1.01\u0026thinsp;~\u0026thinsp;1.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When heart rate consumption was analyzed using quartiles, there was a significant positively association between heart rate and In-hospital mortality after adjusting for potential confounders. Compared with individuals with heart rate Q2 (72\u0026ndash;81 times per minute), the adjusted OR values for heart rate and In-hospital mortality in Q1 (\u0026le;\u0026thinsp;72 times per minute), Q3 (81\u0026ndash;93 times per minute), and Q4 (＞93 times per minute) were 1.18 (95% CI: 0.99\u0026thinsp;~\u0026thinsp;1.41, p\u0026thinsp;=\u0026thinsp;0.07), 1.24 (95% CI: 1.04\u0026thinsp;~\u0026thinsp;1.47, p\u0026thinsp;=\u0026thinsp;0.014), and 1.64 (95% CI: 1.39\u0026thinsp;~\u0026thinsp;1.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;3), respectively. The overall trend was statistically significant (P trend test\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable logistic regression to assess the association of Heart Rate with In-hospital mortality rate\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 5\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart Rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR_95CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR_95CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR_95CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR_95CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR_95CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003econtinuous variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (1.02\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (1.02\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 (1.02\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCategorical variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1(\u0026le;\u0026thinsp;72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 (0.93\u0026thinsp;~\u0026thinsp;1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 (0.86\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 (0.87\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.882\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17 (0.98\u0026thinsp;~\u0026thinsp;1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (0.99\u0026thinsp;~\u0026thinsp;1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2(72\u0026ndash;81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1(Ref)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1(Ref)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1(Ref)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1(Ref)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1(Ref)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3(81\u0026ndash;93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29 (1.11\u0026thinsp;~\u0026thinsp;1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34 (1.16\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35 (1.16\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (1.02\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.24 (1.04\u0026thinsp;~\u0026thinsp;1.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4(＞93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.18 (1.9\u0026thinsp;~\u0026thinsp;2.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.38 (2.07\u0026thinsp;~\u0026thinsp;2.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.39 (2.08\u0026thinsp;~\u0026thinsp;2.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59 (1.35\u0026thinsp;~\u0026thinsp;1.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64 (1.39\u0026thinsp;~\u0026thinsp;1.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP for tread\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eAbbreviations: the unit of Heart Rate is bpm, %, weighted proportion. CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eHepF, hepatic failure; AMI, acute\u0026nbsp;myocardial\u0026nbsp;infarction;\u0026nbsp;bpm, beats per minute;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eSOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eBUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003ehemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eRDW, red blood cell distribution width; WBC, white blood cell count.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eCI: confidence interval;OR: odds ratios, Ref: reference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eModel 1: No adjustment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eModel 2: Adjusted for demographic variables( sex, age, race)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eModel 3: Adjusted for demographic variables, comorbidities (COPD, AMI, MC, HepF, diabetes)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eModel 4: Adjusted for demographic variables, comorbidities, Medical Procedures(Vent, Intubated), Medication situation(Norepinephrine Dopamine Epinephrine Phenylephrine Vasopressin),Basic vital signs(Temperature Respiratory Rate SBP),Blood biochemical indicators(AG BUN Chloride Creatinine, Hb MCH MCHC MCV Platelet Potassium Sodium RBC RDW WBC)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eModel 5: Adjusted for demographic variables, comorbidities, Medical Procedures, Medication situation, Basic vital signs, Blood biochemical indicators, SOFA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 3\u003c/p\u003e\n\u003cp\u003eThreshold effect analysis of relationship of Heart Rate with In-hospital mortality rate.\u003c/p\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 369px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 389px;\" align=\"left\"\u003e\n\u003cp\u003eAdjusted OR_95CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 156px;\" align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 758px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTwo model\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 156px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 369px;\" align=\"left\"\u003e\n\u003cp\u003eHeart Rate\u0026thinsp;\u0026le;\u0026thinsp;76 bpm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 389px;\" align=\"left\"\u003e\n\u003cp\u003e0.982 (0.965\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 156px;\" align=\"left\"\u003e\n\u003cp\u003e0.0371\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 369px;\" align=\"left\"\u003e\n\u003cp\u003eHeart Rate\u0026thinsp;\u0026ge;\u0026thinsp;76 bpm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 389px;\" align=\"left\"\u003e\n\u003cp\u003e1.015 (1.009\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 156px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 369px;\" align=\"left\"\u003e\n\u003cp\u003eLikelihood Ratio test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 389px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 156px;\" align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 960px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAdjusted for demographic variables (sex, age, race), Concomitant disease(COPD,AMI,MC, HepF, diabetes), Medical Procedures(Vent, Intubated), Medication situation(Norepinephrine Dopamine Epinephrine Phenylephrine Vasopressin),Basic vital signs(Temperature Respiratory Rate SBP),Blood biochemical indicators(AG BUN Chloride Creatinine Hb MCH MCHC MCV Platelet Potassium Sodium RBC RDW WBC), SOFA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAbbreviations: %, weighted proportion. CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eHepF, hepatic failure; AMI, acute\u0026nbsp;myocardial\u0026nbsp;infarction;\u0026nbsp;bpm, beats per minute;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSOFA, Sequential Organ Failure Assessment; SBP, systolic blood pressure; AG, anion gap;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBUN, blood urea nitrogen; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ehemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRDW, red blood cell distribution width; WBC, white blood cell count.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 969.938px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCI: confidence interval;OR: odds ratios, Ref: reference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eDose\u0026ndash;Response Relationships\u003c/h2\u003e\n\u003cp\u003eIn this study, we utilized restricted cubic spline models (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) to explore nonlinear relationships. Our findings reveal a J-shaped association between heart rate (HR) and in-hospital mortality in patients diagnosed with congestive heart failure, with adjustments made for potential confounding variables. Below a threshold of 76 bpm, we observed a negative correlation between HR and the risk of in-hospital mortality (OR\u0026thinsp;=\u0026thinsp;0.982 [95% CI 0.965\u0026ndash;0.999], p\u0026thinsp;=\u0026thinsp;0.0371). Conversely, beyond this threshold, exceeding 76 bpm, there is a significant increase in the risk of in-hospital mortality (OR\u0026thinsp;=\u0026thinsp;1.015 [95% CI 1.009\u0026ndash;1.02], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\n\u003cp\u003eTo investigate potential modifications by confounding factors, we performed subgroup analyses using stratification variables: Age, Sex, Race, Nor epinephrine, Dopamine, Epinephrine, Phenylalanine, Vasopression, COPD, AMI, MC, diabetes, and hepatic failure. However, no significant interactions between the HR and these stratified variables were detected (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The summarized results of the subgroup analyses and interactions can be found in the Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this research, we investigated the prognostic significance of heart rate (HR) as a readily accessible vital sign[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Specifically, we focused on its J-shaped relationship with in-hospital mortality in ICU patients with congestive heart failure (CHF), identifying the optimal heart rate to be 76 bpm. Both higher and lower heart rates were associated with an increased risk of mortality. Subgroup analysis corroborated the overall findings for this patient cohort.\u003c/p\u003e \u003cp\u003eThe significance of heart rate in prognosis has been extensively demonstrated across various diseases. In this study, we have shown a noteworthy association between higher heart rates and increased mortality rates, which corroborates previous research[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Surprisingly, we also observed a rise in mortality among patients with lower heart rates, with the nadir at 76 bpm presenting the lowest mortality. This finding contrasts with previous reports from the Framingham Study, which indicated a progressive increase in mortality with resting heart rate[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similarly, the Goteborg Primary Prevention Trial [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and the NHEFS Cohort[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] both found an escalation in all-cause and cardiovascular mortality with increasing heart rate, surpassing 84 bpm. It is important to consider that during the late 80s, beta-receptor blockade was not yet established as a standard treatment for patients with congestive heart failure (CHF). Therefore, the discrepancies in the observed results may be attributed to the limited adoption of β blockers on a large scale. Our study extends upon these previous findings by investigating the relationship between heart rate levels, both high and low, and mortality in hospitalized heart failure patients in the United States. In a retrospective analysis of the MIMIC-Ⅳ database, our logistic regression analysis demonstrates that high and low heart rates during CHF in ICU patients are associated with reduced in-hospital mortality risk, with the optimal heart rate range for lowest mortality risk identified as approximately 72\u0026ndash;81 bpm.\u003c/p\u003e \u003cp\u003eTo address the non-linear relationship between heart rate and in-hospital mortality in individuals with heart failure, this study employed smooth curve fitting and generalized additive models. Subgroup analyses were additionally conducted to assess the consistency of the primary findings. The association between heart rate and in-hospital mortality follows a J-shaped pattern, indicating an inflection point at 76 bpm. Notably, the risk of in-hospital mortality decreases with increasing heart rate; however, among subjects with a population heart rate of 76 bpm or higher, the risk of in-hospital mortality increases with heart rate. These results suggest a potential beneficial effect of maintaining heart rate within a stable range around 76 bpm for CHF patients in the ICU. These findings underscore the importance of considering both low and high heart rate levels in clinical practice. This implies that healthcare professionals should vigilantly monitor the blood pressure of congestive heart failure patients in the ICU, and timely and effective interventions may enhance patient prognosis.\u003c/p\u003e \u003cp\u003eDespite a partial understanding of the underlying mechanism, several plausible explanations exist for the elevated mortality risk associated with heart rate. Speculation regarding a fundamental pathophysiologic relationship between higher heart rate and the development[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] or exacerbation of heart failure (HF)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] has encompassed factors such as myocardial energetic considerations and favorable alterations in arterial afterload through heart rate reduction[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The SHIFT trial has implicated heart rate in the causal pathway of HF progression, identifying it as a potentially modifiable risk factor[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Changes in resting heart rate are normal physiological adaptations that maintain adequate cardiac output. However, in patients with underlying HF, an excessively fast heart rate may become pathophysiological. A faster heart rate increases the myocardial demand for oxygen and shortens diastole, thereby limiting the time available for oxygenated blood to flow through the coronary arteries, leading to insufficient myocardial perfusion [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].Consequently, the combination of increased oxygen demand and reduced perfusion time creates hypoxic conditions in the myocardium, further exacerbating an already failing heart [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The specific source of benefit from heart rate reduction, such as reduced myocardial oxygen consumption and improved myocardial efficiency, reduced total afterload, or other potential explanations, remains to be determined [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Subsequent basic experiments are necessary to address these issues in the future.\u003c/p\u003e \u003cp\u003eThe employed methodology in this study offers several notable advantages. Firstly, previous investigations on risk and prognostic factors for congestive heart failure (CHF) have been limited by small sample sizes. To our knowledge, this study is the first attempt to analyze patients with CHF using the MIMIC-IV database. Secondly, a smoothing function analysis was applied to address potential data analysis contingencies, enabling a comprehensive understanding of the association between heart rate and in-hospital mortality. Additionally, to minimize the influence of confounding factors inherent in observational studies, logistic regression analysis was employed with multiple models, and subgroup analyses were conducted with appropriate grouping.\u003c/p\u003e \u003cp\u003eThe study has several limitations. First, its retrospective research design may compromise the validity of our findings, underscoring the necessity for validation through prospective case-control studies in the future. Second, the incompleteness of publicly available databases restricted our access to certain factors, such as Beta Blocker usage. We intend to address this limitation in future investigations by employing a more comprehensive database. Finally, the exclusive inclusion of American participants may constrain the generalizability of our results to other populations. Therefore, it is essential to exercise caution when extrapolating our findings in light of this limitation. Given these constraints, the imperative for well-designed multicenter controlled trials to corroborate our present findings is evident.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study unveils a J-shaped correlation between heart rate and in-hospital mortality in this specific patient population. To substantiate and reinforce these results, future research should employ prospective, randomized, controlled study designs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe appreciate Dr. Jie Liu of the Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital for statistics, study deign consultations and editing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eKZ contributed as First authors of this manuscript. YH, FMG, ZXG JYL and JYZ were responsible for the concept and design of the study. YQZ, MG and ZYH explain the analysis. TYC, YFG, RH, TZL, DC and BL are responsible for data recovery. K Z, D C and B L is the primary corresponding author. All authors critically revised the important intellectual content of the paper and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. To obtain the application executable files, please contact the author Kai Zhang by email \u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e The study has no Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval date of Registry and the Registration No. of the study/trial:\u003c/strong\u003e N/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal Studies:\u003c/strong\u003e N/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe establishment of this database was approved by the Massachusetts Institute of Technology (Cambridge, MA, USA) and Beth Israel Deaconess Medical Center (Boston, MA, USA), and informed consents were exempted due to all patients\u0026rsquo; data were anonymized before the data were obtained. We also complied with all relevant ethical regulations regarding the use of the data in our study. All reports adhered to the guidelines for Strengthening the Reporting of Observational Studies in Epidemiology and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang, F.J., et al., \u003cem\u003eAnti-cytomegalovirus IgG antibody titer is positively associated with advanced T cell differentiation and coronary artery disease in end-stage renal disease.\u003c/em\u003e Immun Ageing, 2018. \u003cstrong\u003e15\u003c/strong\u003e: p. 15.\u003c/li\u003e\n\u003cli\u003eTang, Y., et al., \u003cem\u003eA retrospective cohort study on the association between early coagulation disorder and short-term all-cause mortality of critically ill patients with congestive heart failure.\u003c/em\u003e Front Cardiovasc Med, 2022. \u003cstrong\u003e9\u003c/strong\u003e: p. 999391.\u003c/li\u003e\n\u003cli\u003eBecari, C., et al., \u003cem\u003eElastase-2, an angiotensin II-generating enzyme, contributes to increased angiotensin II in resistance arteries of mice with myocardial infarction.\u003c/em\u003e Br J Pharmacol, 2017. \u003cstrong\u003e174\u003c/strong\u003e(10): p. 1104-1115.\u003c/li\u003e\n\u003cli\u003eHanft, L.M., C.A. 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Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.\u003c/em\u003e Eur J Heart Fail, 2016. \u003cstrong\u003e18\u003c/strong\u003e(8): p. 891-975.\u003c/li\u003e\n\u003cli\u003eCustodis, F., et al., \u003cem\u003eResting heart rate is an independent predictor of all-cause mortality in the middle aged general population.\u003c/em\u003e Clin Res Cardiol, 2016. \u003cstrong\u003e105\u003c/strong\u003e(7): p. 601-12.\u003c/li\u003e\n\u003cli\u003eKannel, W.B., et al., \u003cem\u003eHeart rate and cardiovascular mortality: the Framingham Study.\u003c/em\u003e Am Heart J, 1987. \u003cstrong\u003e113\u003c/strong\u003e(6): p. 1489-94.\u003c/li\u003e\n\u003cli\u003eWilhelmsen, L., et al., \u003cem\u003eThe multifactor primary prevention trial in G\u0026ouml;teborg, Sweden.\u003c/em\u003e Eur Heart J, 1986. \u003cstrong\u003e7\u003c/strong\u003e(4): p. 279-88.\u003c/li\u003e\n\u003cli\u003eGillum, R.F., D.M. Makuc, and J.J. Feldman, \u003cem\u003ePulse rate, coronary heart disease, and death: the NHANES I Epidemiologic Follow-up Study.\u003c/em\u003e Am Heart J, 1991. \u003cstrong\u003e121\u003c/strong\u003e(1 Pt 1): p. 172-7.\u003c/li\u003e\n\u003cli\u003eHo, J.E., et al., \u003cem\u003eLong-term cardiovascular risks associated with an elevated heart rate: the Framingham Heart Study.\u003c/em\u003e J Am Heart Assoc, 2014. \u003cstrong\u003e3\u003c/strong\u003e(3): p. e000668.\u003c/li\u003e\n\u003cli\u003eOpdahl, A., et al., \u003cem\u003eResting heart rate as predictor for left ventricular dysfunction and heart failure: MESA (Multi-Ethnic Study of Atherosclerosis).\u003c/em\u003e J Am Coll Cardiol, 2014. \u003cstrong\u003e63\u003c/strong\u003e(12): p. 1182-1189.\u003c/li\u003e\n\u003cli\u003eLechat, P., et al., \u003cem\u003eHeart rate and cardiac rhythm relationships with bisoprolol benefit in chronic heart failure in CIBIS II Trial.\u003c/em\u003e Circulation, 2001. \u003cstrong\u003e103\u003c/strong\u003e(10): p. 1428-33.\u003c/li\u003e\n\u003cli\u003eMetra, M., et al., \u003cem\u003eInfluence of heart rate, blood pressure, and beta-blocker dose on outcome and the differences in outcome between carvedilol and metoprolol tartrate in patients with chronic heart failure: results from the COMET trial.\u003c/em\u003e Eur Heart J, 2005. \u003cstrong\u003e26\u003c/strong\u003e(21): p. 2259-68.\u003c/li\u003e\n\u003cli\u003eGullestad, L., et al., \u003cem\u003eWhat resting heart rate should one aim for when treating patients with heart failure with a beta-blocker? 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This study aimed to assess the relationship between HR and in-hospital mortality in CHF patients using data from a large clinical population-based sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective observational study utilized the Medical Information Mart for Intensive Care IV database to extract all relevant data. In-hospital mortality served as the primary outcome measure. Data analyses involved restricted cubic spline regression, piecewise logistic regression, and multiple logistic regression models. Additionally, subgroup analysis was performed to examine the robustness of the main findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 15,983 participants with CHF, aged 72.9 ± 13.4 years. After adjusting for all factors, with each unit increase in HR, there was a 1% risk increase of patient death (95% confidence interval: 1.01 ~ 1.01, P \u0026lt; 0.001). Compared with individuals with HR Q2 (72–81 beats per minute (bpm) ), the adjusted OR values for HR and in-hospital mortality in Q1 (≤ 72 bpm), Q3 (81–93 bpm), and Q4 (＞93 bpm) were 1.18 (95% CI: 0.99 ~ 1.41, p = 0.07), 1.24 (95% CI: 1.04 ~ 1.47, p = 0.014), and 1.64 (95% CI: 1.39 ~ 1.94, p \u0026lt; 0.001), respectively. A dose-response relationship revealed an J-shaped curve between HR and the risk of in-hospital mortality, with an inflection point at approximately 76 bpm. Stratified analyses confirmed the robustness of this correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn patients with CHF, there exists a J-shaped relationship between heart rate and in-hospital mortality, with an inflection point at 76 bpm. Nonetheless, further investigation through large randomized controlled trials is warranted in the future.\u003c/p\u003e","manuscriptTitle":"J-Shaped association between heart rate and in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-14 00:15:19","doi":"10.21203/rs.3.rs-3427589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4631f8af-9fc3-4ecb-a2b3-ecd77a7b6b5f","owner":[],"postedDate":"October 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-20T18:44:27+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-14 00:15:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3427589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3427589","identity":"rs-3427589","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}