Analysis of the Correlation Between Postoperative Temperature Trajectory and Prognosis After Cardiac Surgery: A retrospective analysis of the MIMIC-IV database

Analysis of the Correlation Between Postoperative Temperature Trajectory and Prognosis After Cardiac Surgery: A retrospective analysis of the MIMIC-IV database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of the Correlation Between Postoperative Temperature Trajectory and Prognosis After Cardiac Surgery: A retrospective analysis of the MIMIC-IV database Yujie Fan, Hao Yuan, Zefeng Yang, Jiayao Wei, Longteng Nan, Qiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7320249/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 Hypothermia following cardiac surgery can result in negative postoperative outcomes. The goal of this study was to determine the trend of temperature changes in elderly cardiac surgery patients within three days after surgery, as well as to assess its impact on mortality and poor clinical outcomes. Methods This retrospective cohort study selected elderly patients who underwent cardiac surgery from the MIMIC-IV (Medical Information Mart for Intensive Care IV) database. The Latent Class Trajectory Model (LCTM) was employed to classify heterogeneous patterns of temperature changes in patients following cardiac surgery over a three-day period. Then, disparities in survival across the trajectory groups were analyzed using Kaplan-Meier survival curves. The Cox regression model was used to analyze the relationship between patients' temperature trajectories post-cardiac surgery and their risk of death within one year. A subgroup analysis was performed to identify interaction factors and evaluate the robustness of this finding. Results A total of 1,995 cardiac surgery patients were included in the analysis. All patients were over 65 years of age. Five distinct temperature trajectory groups were identified: Group 1 (293 patients, 14.69%); Group 2 (318 patients, 15.94%); Group 3 (86 patients, 4.31%); Group 4 (892 patients, 44.71%); and Group 5 (406 patients, 20.35%). Kaplan-Meier survival analysis revealed that patients in Group 3 had higher in-hospital and one-year mortality rates than the other groups. Patients in Group 1 had lower in-hospital and one-year mortality rates. Subgroup analysis showed that the one-year mortality rate was higher in Group 3 patients and remained stable across different complication groups. Conclusion Distinguishing different temperature trajectories could help identify patient subgroups at varying risk levels for adverse outcomes after cardiac surgery. This would be a clinically meaningful way to categorize patients. Trial registration : Retrospectively registered. Post-cardiac surgery trajectory analysis temperature Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The growing prevalence of cardiovascular diseases, fueled by an aging global population, has led to an increased demand for cardiac surgical procedures. Approximately two million people undergo heart surgery worldwide each year( 1 ). Mortality rates have decreased due to advances in surgical techniques and perioperative management( 2 ), However, mortality rates after cardiac surgery remain higher than those after other surgical procedures. Mortality rates after valve replacement and coronary artery bypass grafting have been reported to be 2–3% and 4%, respectively( 3 ). Higher mortality rates are associated with more complex or combined procedures and high-risk patients( 4 , 5 ). People are paying more attention to assessing the risk of death from heart surgery. Temperature is a fundamental indicator of overall health( 6 ). Unhealthy lifestyle factors, including high-calorie diets, chronic stress, sleep deprivation, type 2 diabetes, and undiagnosed medical conditions such as latent infections, are associated with temperature changes and reduced survival( 7 , 8 ). Maintaining a constant temperature is necessary for normal physiological function( 9 ). In medical settings, temperature measurements are periodically taken to evaluate patients' physical conditions( 6 ).Temperature is a nonlinear function of several variables, such as age, health status, sex, ambient temperature, and circadian cycle time( 10 ).As a special type of surgery, cardiac surgery greatly impacts blood circulation and physiology. Hypothermia after cardiac surgery is associated with a significantly increased risk of death( 11 , 12 ) and may be related to systemic failure leading to peripheral hypoperfusion( 13 ). LCTM is a sophisticated statistical technique used to identify groups with different trends over time and to explore their characteristics of these trends within individual subgroups( 14 – 16 ). Typically, two to seven categories can be described, as will be detailed later. At least seven model structures can be fitted( 14 , 15 ).This study aims to use LCTM to identify different temperature trajectory patterns in cardiac surgery patients, realize population clustering, and accurately assess the association between temperature changes and the risk of death in these patients. These findings could inform clinical practice by helping to identify high-risk populations for targeted treatment and early intervention. Methods Database The Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic-iv.mit.edu/) was used to collect the study data. MIMIC-IV, a multi-parameter, structured, single-center ICU database, was released in 2003. This initiative will not affect clinical care, and the patients in the database cannot be identified; therefore, individual patient consent or ethical approval is not required(17).Research team member Yujie Fan was granted access to the website and was responsible for data collection（ID number：13908252）. Our study also adhered to the principles outlined in the Declaration of Helsinki and followed transparent reporting guidelines for multivariate prognostic or diagnostic models (18). Patients The inclusion criteria were as follows: First, 8,560 patients were identified based on their surgery dates, the MIMIC-IV database, and International Classification of Diseases (ICD) codes. Post-cardiac surgery patients were excluded if they met any of the following criteria: 1) absence of multiple temperature measurements within 72 hours after admission for cardiac surgery, 2) missing survival information, 3) age <65 years, and 4) postoperative hospital stay <72 hours. For patients with multiple procedures, only the first procedure was included in the analysis. Observational variables The primary study variable was the lowest temperature recorded within three days after surgery. The other variables included: (1) age, gender, and race; (2) laboratory test results from patients within 24 hours after cardiac surgery; (3) comorbidities, including myocardial infarction, malignant tumors, diabetes mellitus, congestive heart failure (CHF), and Cerebrovascular disease (CD); (4) medications or treatments, including norepinephrine, cardiopulmonary bypass for cardiac surgery, and vasopressin; (5) outcome variables, including in-hospital mortality and survival information at one, five, and ten years after admission. Navicat Premier 17.0 software and Structured Query Language (SQL) were used to extract metrics from the MIMIC-IV database. Data processing Variables with more than 20% missing values will be deleted. Missing values in variables with a loss rate of less than 20% will be replaced using multiple imputation. Outliers will be identified using the interquartile range (IQR) and treated as missing values. Statistical analysis Continuous variables are expressed as the mean ± standard deviation (SD) or the median and interquartile range (IQR). Categorical variables are reported as numbers and percentages (n (%)). We used the Wilcoxon rank-sum test for continuous variables and the chi-square or Fisher's exact test for categorical variables. Using Kaplan-Meier survival curves, we examined the relationship between temperature trajectories and patient mortality over time. We constructed Cox proportional hazards regression models to evaluate the impact of temperature trajectories on survival outcomes. Three models were developed with progressive adjustment for covariates. Model 1 involved univariate analysis and did not adjust for any covariates. Model 2 was adjusted for sex, age, and race. Model 3 included the adjustments of Model 2, as well as adjustments for heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide, congestive heart failure (CHF), cerebrovascular disease (CD), and malignant tumor complications. To ascertain the optimal number of classes, a series of models were constructed, ranging from two to seven classes. The evaluation of LCTM fit was conducted by employing log-likelihood, entropy, and information criteria. Lower Akaike and Bayesian information criterion values are indicative of a superior model fit. The entropy value (range: 0–1) is indicative of the model's efficacy in classifying individuals into their respective categories. Typically, an entropy value greater than 0.7 is indicative of high classification accuracy, with higher entropy values corresponding to a superior model fit. To ensure model stability, the sample size for each category should exceed 2% of the total study population. The model's goodness of fit was verified by ensuring that the average posterior probability of all taxonomic members was at least 70%. The clinical interpretability of the model was also taken into consideration. Results Trajectory grouping based on the LCTM model The goodness-of-fit statistics for the LCTM model are displayed in Table 1 . The entropy values obtained were all greater than 0.7. The AIC and BIC values exhibited a continuous decrease from the class 1 model to the class 7 model, and the rate of decline demonstrated an inflection point in the class 5 model. After a thorough evaluation of the available models, the Class 5 model was selected on the basis of its clinical interpretability. Consequently, the fifth class model was regarded as the final model. Table 1 Fit statistics for different number of trajectory groups. Number of classes Log Likelihood npm BIC AIC Entropy Relative entropy %class1 %class2 %class3 %class4 %class5 %class6 %class7 1 4120.368 7 8293.926 8254.74 1 1 100 2 3754.774 12 7600.728 7533.547 403.614 0.708 45.81454 54.18546 3 3495.889 17 7120.951 7025.779 513.764 0.766 11.17794 37.29323 51.52882 4 3364.738 22 6896.64 6773.476 817.967 0.704 30.97744 23.40852 41.30326 4.310777 5 3261.206 27 6727.569 6576.412 891.245 0.722 14.68672 15.93985 4.310777 44.71178 20.35088 6 3225.815 32 6694.779 6515.63 956.597 0.732 13.13283 14.98747 21.75439 5.413534 40.30075 4.411028 7 3194.61 37 6670.361 6463.221 1132.62 0.708 6.115288 12.98246 22.80702 2.857143 10.62657 38.64662 5.964912 Log Likelihood: Logarithm of the likelihood function; npm: number of free parameters; BIC: Bayesian Information Criterion; AIC: Akaike Information Criterion. The temperature trajectories of the five models are shown in Fig. 1. LCTM-5 classified the development cohort into five categories: class 1 [293 (14.69%); class 2 [318 (15.94%); class 3 [86 (4.31%); class 4 [892 (44.71%); and class 5 [406 (20.35%)]. In Class 1, the initial lowest temperature was below 35°C, followed by rapid rewarming. The Class 2 is the low-grade stable group, whose lowest temperature remained at approximately 35.9°C. The Class 3 is the low-grade rebound group, which is characterized by an initial lowest temperature below 36°C, followed by a process of first decreasing and then rebounding. In the Class 4 of moderate-temperature stable groups, the lowest recorded temperature was maintained at a moderate level of approximately 36.4°C. In Class 5, the lowest temperature in moderate temperature rise groups commenced from a comparatively modest initial value and ascended to approximately 36.5°C, exhibiting a persistent upward tendency. Characteristics of temperature trajectories The initial sample size for the study was determined to be 8,560 patients. Following the application of inclusion and exclusion criteria, a total of 1,995 patients were ultimately enrolled in the study. The baseline characteristics of patients stratified based on the five trajectory classes are listed in Table 3. The study population consisted of patients over the age of 65, with a median age of 76.81 years. Most of the patients were male (1,255, or 62.9%) and white (1,542, or 77.3%). Of the study subjects, 595 (29.8%) had a history of myocardial infarction; 712 (35.7%) were identified as having congestive heart failure (CHF); 276 (13.8%) were diagnosed with chronic kidney disease (CKD); 496 (24.9%) had been diagnosed with chronic lung disease; and 747 (37.4%) had been diagnosed with diabetes mellitus. Meanwhile, 63 patients (3.2%) underwent continuous renal replacement therapy, 133 patients (6.7%) used vasopressin, 359 patients (18%) used norepinephrine during hospitalization, and 526 patients (26.4%) underwent cardiac surgery with extracorporeal circulation assistance. Table 3 Descriptive characteristics of overall participants and by temperature trajectory Variables level Overall(n=1995) Group1(n=293) Group2(n=318) Group3(n=86) Group4(n=892) Group5(n=406) P- value Gender Female 740(37.1) 141(48.1) 130(40.9) 36(41.9) 289(32.4) 144(35.5) <0.001 Male 1255(62.9) 152(51.9) 188(59.1) 50(58.1) 603(67.6) 262(64.5) Age(years) 76.81(7.33) 77.70(7.20) 77.74(7.40) 76.92(6.55) 76.16(7.51) 76.84(6.99) 0.002 Race Other 453(22.7) 74(25.3) 49(15.4) 17(19.8) 207(23.2) 106(26.1) 0.007 White 1542(77.3) 219(74.7) 269(84.6) 69(80.2) 685(76.8) 300(73.9) Weight 80.00(68.60,92.90) 75.20(63.50,87.00) 80.00(66.93,93.38) 79.35(67.05,88.90) 81.70(70.00,94.85) 81.10(70.05,94.00) <0.001 Height 170.00(160.00,176.50) 168.00(160.00,173.00) 168.00(160.00,175.00) 168.00(160.00,175.00) 170.00(163.00,178.00) 170.00(163.00,178.00) <0.001 Heart rate 80.00(73.00,85.00) 80.00(72.00,85.00) 80.00(75.00,87.75) 80.00(75.00,89.50) 80.00(71.00,85.00) 80.00(74.00,84.00) <0.001 Sbp 112.00(100.00,126.00) 113.00(100.00,124.00) 110.00(98.25,123.00) 108.00(98.00,118.00) 115.00(102.00,129.00) 110.00(99.00,122.75) <0.001 Dbp 55.00(49.00,63.00) 57.00(50.00,65.00) 55.00(49.00,61.00) 54.00(46.00,60.88) 55.00(49.00,63.00) 56.00(50.00,62.00) 0.005 Resp rate 15.00(14.00,17.00) 15.00(14.00,16.00) 14.00(13.00,16.00) 15.00(14.00,16.00) 16.00(14.00,18.00) 15.75(14.00,16.00) <0.001 SpO 2 100.00(99.00,100.00) 100.00(100.00,100.00) 100.00(99.00,100.00) 100.00(99.00,100.00) 100.00(98.00,100.00) 100.00(99.00,100.00) <0.001 Inr 1.30(1.20,1.50) 1.40(1.20,1.60) 1.30(1.20,1.50) 1.30(1.10,1.40) 1.30(1.20,1.50) 1.40(1.20,1.50) <0.001 Pt 14.80(13.10,16.40) 15.20(13.60,16.90) 14.80(13.50,16.17) 14.40(13.12,15.85) 14.40(12.70,16.22) 15.15(13.60,16.88) <0.001 Ptt 33.90(29.30,51.00) 35.20(30.10,51.50) 34.85(29.50,52.02) 34.10(28.50,54.15) 33.55(28.90,53.95) 33.10(29.90,44.80) 0.374 Chloride 107.00(104.00,110.00) 109.00(105.00,112.00) 108.00(103.00,111.00) 106.00(103.00,111.00) 107.00(103.00,109.00) 108.00(105.00,110.00) <0.001 Bicarbonate 23.00(22.00,25.00) 23.00(21.00,25.00) 24.00(22.00,26.00) 24.00(23.00,26.00) 24.00(22.00,26.00) 23.00(21.00,25.00) <0.001 Bun 18.00(14.00,25.00) 18.00(13.00,23.00) 20.00(15.00,28.00) 19.00(15.00,25.50) 18.00(14.00,25.00) 17.50(14.00,24.00) 0.001 Potassium 4.20(3.90,4.50) 4.20(3.90,4.50) 4.30(3.90,4.60) 4.20(3.90,4.60) 4.20(3.90,4.60) 4.20(3.90,4.50) 0.555 Creatinine 0.90(0.70,1.20) 0.80(0.70,1.10) 1.00(0.80,1.20) 1.00(0.72,1.30) 0.90(0.70,1.20) 0.90(0.70,1.10) <0.001 Wbc 9.70(7.40,13.20) 9.60(7.00,12.70) 9.60(7.30,12.78) 10.05(8.22,12.60) 9.75(7.30,13.43) 10.00(7.90,13.90) 0.257 Mchc 33.20(32.20,34.10) 33.20(32.30,33.90) 33.60(32.50,34.40) 33.60(32.32,34.70) 33.00(32.10,33.90) 33.10(32.20,34.10) <0.001 Rdw 13.80(13.10,14.90) 13.80(13.10,14.70) 14.05(13.33,15.30) 14.20(13.70,15.20) 13.70(13.00,14.90) 13.80(13.20,14.80) <0.001 Rbc 3.19(2.76,3.68) 3.08(2.60,3.59) 3.09(2.69,3.65) 3.24(2.82,3.83) 3.30(2.86,3.77) 3.08(2.70,3.57) <0.001 Platelet 152.00(119.00,197.00) 136.00(107.00,183.00) 160.00(126.25,212.75) 174.00(128.75,218.00) 156.50(124.00,201.00) 143.00(115.00,184.75) <0.001 Mcv 91.00(88.00,95.00) 92.00(88.00,95.00) 90.00(87.00,93.00) 90.00(87.00,93.00) 92.00(88.00,95.00) 92.00(88.00,95.00) <0.001 Hemoglobin 9.60(8.30,11.00) 9.30(8.00,10.80) 9.20(8.00,11.00) 9.65(8.33,11.28) 9.90(8.60,11.22) 9.30(8.10,10.80) <0.001 Hematocrit 29.00(25.20,33.50) 28.30(24.30,32.70) 27.65(24.52,32.50) 28.85(25.20,34.30) 29.90(26.50,34.30) 28.45(25.00,32.27) <0.001 Lactate 1.30(1.00,1.70) 1.30(1.00,1.80) 1.20(0.90,1.60) 1.20(1.00,1.67) 1.35(1.00,1.80) 1.30(1.00,1.60) <0.001 PH 7.41(7.38,7.44) 7.41(7.38,7.45) 7.41(7.38,7.44) 7.42(7.39,7.46) 7.40(7.37,7.43) 7.41(7.38,7.44) <0.001 oxygenation index 277.00(188.00,369.00) 311.00(221.00,393.00) 277.50(194.50,367.00) 265.00(191.00,331.75) 265.50(176.00,358.00) 274.50(181.00,371.00) <0.001 PCO 2 40.00(37.00,44.00) 39.00(36.00,43.00) 41.00(37.00,45.00) 38.00(35.25,42.00) 41.00(37.00,45.00) 40.00(37.00,43.00) <0.001 Dopamine used 0.255 No 1943(97.4) 289(98.6) 306(96.2) 83(96.5) 866(97.1) 399(98.3) Yes 52(2.6) 4(1.4) 12(3.8) 3(3.5) 26(2.9) 7(1.7) Vasopressin used 0.032 No 1862(93.3) 272(92.8) 292(91.8) 75(87.2) 847(95.0) 376(92.6) Yes 133(6.7) 21(7.2) 26(8.2) 11(12.8) 45(5.0) 30(7.4) Crrt 0.003 No 1932(96.8) 285(97.3) 303(95.3) 78(90.7) 873(97.9) 393(96.8) Yes 63(3.2) 8(2.7) 15(4.7) 8(9.3) 19(2.1) 13(3.2) Delirium 0.138 No 1662(83.3) 237(80.9) 280(88.1) 70(81.4) 741(83.1) 334(82.3) Yes 333(16.7) 56(19.1) 38(11.9) 16(18.6) 151(16.9) 72(17.7) Myocardial Infarct 0.046 No 1400(70.2) 226(77.1) 210(66.0) 59(68.6) 624(70.0) 281(69.2) Yes 595(29.8) 67(22.9) 108(34.0) 27(31.4) 268(30.0) 125(30.8) Congestive Heart failure 0.001 No 1283(64.3) 212(72.4) 177(55.7) 54(62.8) 572(64.1) 268(66.0) Yes 712(35.7) 81(27.6) 141(44.3) 32(37.2) 320(35.9) 138(34.0) Peripheral Vascular disease 0.14 No 1639(82.2) 228(77.8) 259(81.4) 68(79.1) 739(82.8) 345(85.0) Yes 356(17.8) 65(22.2) 59(18.6) 18(20.9) 153(17.2) 61(15.0) CD 0.557 No 1719(86.2) 256(87.4) 265(83.3) 73(84.9) 775(86.9) 350(86.2) Yes 276(13.8) 37(12.6) 53(16.7) 13(15.1) 117(13.1) 56(13.8) Dementia 0.503 No 1967(98.6) 291(99.3) 315(99.1) 85(98.8) 875(98.1) 401(98.8) Yes 28(1.4) 2(0.7) 3(0.9) 1(1.2) 17(1.9) 5(1.2) Chronic pulmonary disease 0.179 No 1499(75.1) 225(76.8) 225(70.8) 71(82.6) 671(75.2) 307(75.6) Yes 496(24.9) 68(23.2) 93(29.2) 15(17.4) 221(24.8) 99(24.4) Rheumatic Disease 0.646 No 1902(95.3) 275(93.9) 307(96.5) 82(95.3) 851(95.4) 387(95.3) Yes 93(4.7) 18(6.1) 11(3.5) 4(4.7) 41(4.6) 19(4.7) Peptic ulcer Disease 0.234 No 1983(99.4) 289(98.6) 316(99.4) 85(98.8) 890(99.8) 403(99.3) Yes 12(0.6) 4(1.4) 2(0.6) 1(1.2) 2(0.2) 3(0.7) Mild liver disease 0.084 No 1934(96.9) 291(99.3) 310(97.5) 83(96.5) 861(96.5) 389(95.8) Yes 61(3.1) 2(0.7) 8(2.5) 3(3.5) 31(3.5) 17(4.2) Diabetes 0.004 No 1248(62.6) 208(71.0) 197(61.9) 52(60.5) 526(59.0) 265(65.3) Yes 747(37.4) 85(29.0) 121(38.1) 34(39.5) 366(41.0) 141(34.7) Diabetes mellitus without complications 0.101 No 1434(71.9) 226(77.1) 230(72.3) 58(67.4) 621(69.6) 299(73.6) Yes 561(28.1) 67(22.9) 88(27.7) 28(32.6) 271(30.4) 107(26.4) Diabetes mellitus with complications 0.215 No 1758(88.1) 268(91.5) 277(87.1) 78(90.7) 774(86.8) 361(88.9) Yes 237(11.9) 25(8.5) 41(12.9) 8(9.3) 118(13.2) 45(11.1) Paraplegia 0.949 No 1962(98.3) 289(98.6) 313(98.4) 85(98.8) 875(98.1) 400(98.5) Yes 33(1.7) 4(1.4) 5(1.6) 1(1.2) 17(1.9) 6(1.5) Renal disease 0.608 No 1489(74.6) 221(75.4) 226(71.1) 66(76.7) 669(75.0) 307(75.6) Yes 506(25.4) 72(24.6) 92(28.9) 20(23.3) 223(25.0) 99(24.4) Malignant cancer 0.432 No 1915(96.0) 280(95.6) 305(95.9) 86(100.0) 855(95.9) 389(95.8) Yes 80(4.0) 13(4.4) 13(4.1) 0(0.0) 37(4.1) 17(4.2) Severe liver disease 0.378 No 1985(99.5) 293(100.0) 315(99.1) 86(100.0) 886(99.3) 405(99.8) Yes 10(0.5) 0(0.0) 3(0.9) 0(0.0) 6(0.7) 1(0.2) Metastatic solid tumor 0.783 No 1985(99.5) 292(99.7) 316(99.4) 86(100.0) 886(99.3) 405(99.8) Yes 10(0.5) 1(0.3) 2(0.6) 0(0.0) 6(0.7) 1(0.2) Extracorporeal circulation Auxiliary to Open heart surgery <0.001 No 1469(73.6) 206(70.3) 180(56.6) 50(58.1) 732(82.1) 301(74.1) Yes 526(26.4) 87(29.7) 138(43.4) 36(41.9) 160(17.9) 105(25.9) Norepinephrine used 0.009 No 1636(82.0) 236(80.5) 257(80.8) 59(68.6) 742(83.2) 342(84.2) Yes 359(18.0) 57(19.5) 61(19.2) 27(31.4) 150(16.8) 64(15.8) Sbp, Systolic blood pressure; Dbp, Diastolic blood pressure; SpO2, Oxygen saturation; Inr, International normalized ratio; Pt, Prothrombin time; Ptt, Partial thromboplastin time; Wbc, White blood cell count; Mchc, Mean corpuscular hemoglobin concentratio Table 3 about here. Compared to the other groups, Class 1 exhibited lower creatinine levels. A comparative analysis revealed that Class 3 had lower systolic blood pressure, chloride levels, mean corpuscular hemoglobin, and oxygenation index than the other groups. Conversely, the white blood cell count, red blood cell distribution width, platelet count, blood pH, proportion of patients using vasopressin and norepinephrine, proportion of patients with and without diabetic complications, and proportion of patients receiving continuous renal replacement therapy (CRRT) were higher. The Table 3 illustrates the fundamental characteristics of the aforementioned five classes. Univariate and multivariate analysis Fig. 2 illustrates the Kaplan-Meier survival curves of disparate temperature trajectory groups at one year, five years, and ten years. The one-year mortality rate was found to be the lowest in Class 1, while Class 3 exhibited the highest rate. Table 2 shows the baseline characteristics of survivors and non-survivors. Then, potential risk factors with p < 0.05 were included as covariates in the multivariate Cox regression analysis. Table 2 Baseline characteristics of the Survivors and Non-survivors Variables Overall Survival Non-survivors p N 1995 1818 177 Gender Female 740(37.1) 665(36.6) 75(42.4) 0.149 Male 1255(62.9) 1153(63.4) 102(57.6) Age ( years ) 76.81(7.33) 76.43(7.16) 80.74(7.87) <0.001 Race Other 453(22.7) 415(22.8) 38(21.5) 0.751 White 1542(77.3) 1403(77.2) 139(78.5) Heart rate 79.18(11.46) 78.81(11.13) 82.95(13.93) <0.001 Sbp 114.53(20.85) 114.37(20.49) 116.26(24.26) 0.25 Dbp 56.38(11.44) 56.47(11.25) 55.47(13.30) 0.268 Resp rate 15.61(4.01) 15.50(3.88) 16.83(5.00) <0.001 Hemoglobin 9.84(1.99) 9.85(2.01) 9.70(1.81) 0.323 Creatinine 1.11(0.88) 1.08(0.84) 1.48(1.15) <0.001 Potassium 4.25(0.52) 4.25(0.52) 4.26(0.59) 0.723 Pt 15.21(5.09) 15.17(5.11) 15.71(4.86) 0.174 Ptt 44.94(26.84) 43.94(25.67) 55.22(35.31) <0.001 Rbc 3.27(0.69) 3.27(0.69) 3.28(0.64) 0.807 Platelet 165.69(68.29) 164.00(66.62) 183.10(81.66) <0.001 Wbc 10.98(5.86) 11.02(5.89) 10.65(5.50) 0.423 Lactate 1.45(1.04) 1.45(1.05) 1.47(0.91) 0.82 PH 7.41(0.05) 7.41(0.05) 7.40(0.07) 0.169 PCO2 40.72(6.06) 40.58(5.85) 42.18(7.77) 0.001 CHF No 1283(64.3) 1229(67.6) 54(30.5) <0.001 Yes 712(35.7) 589(32.4) 123(69.5) Myocardial infarct No 1400(70.2) 1284(70.6) 116(65.5) 0.184 Yes 595(29.8) 534(29.4) 61(34.5) CD No 1719(86.2) 1580(86.9) 139(78.5) 0.003 Yes 276(13.8) 238(13.1) 38(21.5) Malignant cancer No 1915(96.0) 1752(96.4) 163(92.1) 0.01 Yes 80(4.0) 66(3.6) 14(7.9) Diabetes No 1248(62.6) 1135(62.4) 113(63.8) 0.773 Yes 747(37.4) 683(37.6) 64(36.2) Extracorporeal circulation Auxiliary to Open heart surgery No 1469(73.6) 1328(73.0) 141(79.7) 0.069 Yes 526(26.4) 490(27.0) 36(20.3) Sbp, Systolic blood pressure; Dbp, Diastolic blood pressure; Pt, Prothrombin time; Ptt, Partial thromboplastin time; Rbc, Red blood cell count; Wbc, White blood cell count; PH, potential of hydrogen in blood; Pco2, Partial pressure of carbon dioxide. Fig. 3 shows the final results of three models, each of which focuses on one-year and in-hospital patient mortality. Models 1-3 were derived from multivariable Cox regression models. Model 1 centered exclusively on the temperature trajectory. Model 2 was adjusted for gender, age, and race. The Model 3 covariates were adjusted for the following: sex, age, race, heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide, CHF, CD, and malignant tumor complications. In the analysis employing Class 1 temperature trajectories as the reference, patients with Class 3 temperature trajectories had the highest risk. Subsequent to the implementation of comprehensive adjustments to account for the presence of various confounders, the initial trend remained consistent. Subgroup analyses Based on our clinical experience, we selected the following factors as variables for a stratified subgroup analysis: age over 80 years, the presence of complications from heart failure or cerebrovascular disease, or diabetes. Fig. 4 shows the forest plot of the patient subgroup analysis. Patients on trajectory 3 had a higher one-year mortality rate, while patients on trajectory 1 had the lowest rate. This remained stable across different age groups and patients with various underlying conditions. Discussion We present a method for identifying subphenotypes in cardiac surgery patients based on their temperature trajectories. Using trajectory modeling, we identified and validated five patient groups with distinct temperature trajectories patterns. Significant demographic and physiological differences were found between these groups, as well as notable differences in mortality rates.Subsequent analysis revealed substantial disparities among the study groups with respect to age, race, heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide, CHF complications, CD complications, and malignant tumor complications. These findings are of significant importance in the context of understanding the heterogeneity of elderly patients following cardiac surgery, and they may serve as a foundation for future research endeavors. The identification of subphenotypes may result in the development of more personalized management strategies( 19 ). The ubiquity of longitudinal temperature trajectories renders them a readily accessible auxiliary means for identifying clinically relevant postoperative cardiac diagnoses. Temperature management is crucial for patients' recovery after surgery, especially for critically ill patients undergoing cardiac surgery. According to the DeFoe study, cardiac biomarker assessment reveals greater myocardial damage in the hypothermia group( 20 , 21 ). The Nam study found that the all-cause mortality rate of moderate to severe hypothermia in off-pump CABG patients was more than twice that of normothermic patients. Mild hypothermia was also found to be an unsatisfactory outcome at the 47-month follow-up( 22 ). A critical issue arises during the rewarming phase. While restoring normal temperature is essential for recovery, it can also lead to reperfusion injury( 23 ). Thermoregulatory processes occur in the hypothalamic preoptic nucleus and activate subsequent effectors. Under basal conditions, the liver and heart are the main organs involved in thermogenesis( 24 ). Hypothermia redistributes blood flow through vasoconstriction, which reduces blood volume to the skin and subcutaneous muscles and thereby reduces heat loss( 25 ). Hypothermia progressively deteriorates cardiovascular function and affects cardiac conduction. Decreased pacemaker cell activity leads to bradycardia, and the reduced transmembrane resting potential increases the risk of atrial and ventricular arrhythmias( 25 ). Moderate hypothermia prolongs repolarization and causes arrhythmia( 26 ).At the cellular level, low temperatures lead to enzyme inhibition and reduced ATP production( 23 ).To maintain temperature, the body induces peripheral vasoconstriction as blood flow is redistributed. This would increase systemic vascular resistance and decrease cardiac output, thereby reducing coronary perfusion and peripheral perfusion, which would worsen metabolic acidosis and myocardial ischemia( 23 ). Taking a temperature reading at a single time point inevitably increases the risk of bias( 27 ). Temperature trajectories can more accurately reflect patients' body conditions than single temperature readings. Changes in temperature can easily trigger acute cardiovascular outcomes( 28 ). The warming process following hypothermia tends to increase oxygen demand( 29 ).This may lead to myocardial ischemia( 29 ).Regarding the effect of drugs on temperature, one study showed that acetaminophen administration did not significantly affect temperature trajectories( 30 ). The white blood cell count of class 3 was higher than that of the other groups. Thermoregulation is closely related to the immune system( 31 , 32 ). Hypothermia can produce significant physiological changes, such as a left shift in the oxygenation curve, decreased coagulation function, and arrhythmia. These changes may lead to tissue hypoxia, multiple organ dysfunction, and fluid resuscitation failure( 33 ).Hypothermia can also lead to a range of adverse consequences, such as bleeding, infection, arrhythmia, blood coagulation disorders and kidney failure( 23 , 34 ).Hypothermia also causes alterations in the steady-state enzymatic activity of coagulation factors, leading to coagulopathy( 25 , 35 ). The age of surgical patients can affect prognosis( 36 ). Elderly patients have lower subcutaneous fat content, a lower basal metabolic rate, and decreased thermoregulatory function of the central nervous system. They are also more susceptible to hypothermia( 37 ). The decrease in temperature with age is believed to be caused by a slowing of the metabolic rate and a reduced ability to regulate temperature in response to environmental changes, such as seasonal changes ( 6 ). Being over 80 years old was an independent predictor of 30-day mortality after aortic valve replacement and significantly impacts long-term survival( 38 ). Patients with a better prognosis had lower creatinine levels( 39 ).This may be due to early aggressive fluid resuscitation, which alleviates renal hypoperfusion and preserves glomerular filtration( 40 ). One study also revealed a significant sex-based difference in creatinine levels. A greater proportion of males were in the high creatinine group than in the low creatinine group. This suggests that sex hormones may be related to creatinine levels. Testosterone can increase creatinine levels, while estradiol can decrease them( 41 ). Two previous studies showed that the difference in cardiopulmonary bypass time was not statistically significant for prognosis( 38 , 39 ). This is consistent with our results. Although most cardiac procedures involving CPB use intentional hypothermia, patients experience hypothermia less frequently after cardiac surgery than after noncardiac surgery. This may be due to the body being actively warmed up after cardiopulmonary bypass( 42 ). We generated predictive models for in-hospital mortality and one-year mortality outcomes, identifying general variables as risk factors. These factors include sex, age, race, heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide(PaCO2), CHF, CD, and malignancy complications. Physicians can use these variables in their clinical practice. Several studies have shown that women have a higher percentage of subcutaneous body fat, which correlates with lower mean skin temperatures. Some theories suggest that these temperature differences are related to female hormone levels( 43 , 44 ). Patients with diabetes who undergo cardiac surgery experience increased perioperative complications, higher in-hospital mortality, and lower long-term survival rates( 45 , 46 ). Compared with younger patients, preoperative complications, especially CHF, are more prevalent in older patients( 38 ). Hypothermia can impair platelet function( 23 ). A reduction of 1–2°C in temperature causes reversible impairment of platelet aggregation by decreasing thromboxane A3 and negatively affecting platelet formation( 47 ). This study has several strengths. First, the LCTM model was used to categorize and analyze the three-day minimum temperature trajectories of cardiac surgery patients. A series of models were established to adjust for various confounding factors and reduce the risk of temperature bias resulting from observing only one time point. These stable, reliable results can inform clinical decision-making for cardiac surgery patients. Second, the results of this study may be useful for early identification of high-risk populations after cardiac surgery. Early intervention could then be implemented to reduce disease burden and mortality. This study uses real-world data to create a larger, more ethnically diverse population study. Additionally, temperature is a more intuitive, convenient, and inexpensive indicator than laboratory indicators. This study is the first to examine long-term temperature patterns in cardiac surgery patients. The study identified five stable subgroups of patients based on their temperature trajectories. However, our findings have several limitations. First, the retrospective, single-center design of the study makes selection bias inevitable. We examined the temperature trajectories of patients admitted to this center after cardiac surgery. This analysis revealed a high-risk subgroup that had not been reported previously. While our LCTM-5 model can classify various temperature trajectories, it was developed and validated using three-day lowest temperature data. Therefore, its application to predicting long-term temperature trajectories is limited. Nevertheless, our primary objective was to analyze three-day temperature trajectories and identify subcategories of patients so they could be classified and receive intervention as soon as possible. Finally, temperature is the result of a combination of factors, not all of which were considered in this study. Further studies are needed to determine whether these factors influence temperature dynamic trajectories. Nonetheless, our goal was to deepen our understanding of post-cardiac disease, and we succeeded in identifying a subgroup of robust post-cardiac patients with unique clinical features and significantly different outcomes. This study provides valuable insights into the evolution of cardiac surgery and paves the way for future research on predicting and treating early postoperative subclasses. Conclusions We identified five robust and clinically meaningful subgroups of temperature changes in elderly patients after cardiac surgery. We found that in-hospital and long-term mortality rates varied depending on the trajectory of these changes. One subgroup experienced an initial minimum temperature below 36°C, followed by a decline and subsequent rebound. This subgroup was associated with the highest risk of death. Another subgroup had an initial minimum temperature below 35°C, followed by rapid rewarming. This subgroup had the lowest risk of death. Previous studies that considered temperature at a single time point overlooked this finding. Temperature trajectory groups can identify patient subphenotypes and facilitate personalized improvements in patient management after cardiac surgery. Abbreviations LCTM: Latent Class Trajectory Model ICD: International Classification of Diseases SD: Standard deviation IQR: Interquartile range CRRT: Continuous renal replacement therapy CABG: Coronary artery bypass graft PaCO 2 : Arterial partial pressure of carbon dioxide MIMIC-IV: Medical Information Mart for Intensive Care-IV CHF: Congestive heart failure CD : Cerebrovascular disease Declarations Ethics approval and consent to participate The model was developed and validated based on public database. After completing the Collaborative Institutional Training Initiative programme, we got permission to access the database. This study used public deidentification databases, so there is no need to obtain the informed consent and approval of the Institutional Review Board. Participants gave informed consent to participate in the study before taking part. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are available in the [Medical Information Mart for Intensive Care (MIMIC)-IV] repository, [https://mimic.mit.edu/docs/iv/]. Competing interests The authors declare that they have no competing interests. Funding No external funding. Authors' contributions Yujie Fan played a key role in collecting and analyzing the data, and in drafting the manuscript. Hao Yuan and Zefeng Yang played a pivotal role in extracting the data and designing the study. Jiayao Wei and Longteng Nan revised the manuscript for intellectual content. Qiang Li oversaw the entire project, providing guidance and contributing to the study's design and review. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Shi P, Rui S, Meng Q. 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Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review. J Cardiothorac Vasc Anesth. 2024;38(1):248-67. Volpi S, Rajah T, Ali JM. Rationale and strategies for improving glycaemic control in diabetic patients undergoing cardiac surgery: a narrative review. J Thorac Dis. 2024;16(11):8088-102. Sabbag IP, Hohmann FB, Assunção MSC, de Freitas Chaves RC, Corrêa TD, Menezes PFL, et al. Postoperative hypothermia following non-cardiac high-risk surgery: A prospective study of temporal patterns and risk factors. PLoS One. 2021;16(11):e0259789. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7320249","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501452877,"identity":"b5e67666-b82a-45a5-8b92-be8f3f8518f9","order_by":0,"name":"Yujie Fan","email":"","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Fan","suffix":""},{"id":501452878,"identity":"242e48dc-5d73-47e8-9084-c68bcef92764","order_by":1,"name":"Hao Yuan","email":"","orcid":"","institution":"Xuzhou No.1 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Yuan","suffix":""},{"id":501452879,"identity":"f427e2ff-8d4c-4c1a-a438-3af95f4bd5ce","order_by":2,"name":"Zefeng Yang","email":"","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Yang","suffix":""},{"id":501452880,"identity":"53d4c497-ee57-482d-8f89-80a73c7920f7","order_by":3,"name":"Jiayao Wei","email":"","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiayao","middleName":"","lastName":"Wei","suffix":""},{"id":501452881,"identity":"a4d422ca-10b8-4141-8060-7d336fe23271","order_by":4,"name":"Longteng Nan","email":"","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Longteng","middleName":"","lastName":"Nan","suffix":""},{"id":501452882,"identity":"bffcdde2-34e3-4130-99fb-9cc365bd6097","order_by":5,"name":"Qiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACPmYGhgNAWo4fKsDYQEgLG1SLsWQD0VqgdOKGA0RrYWfeeODnjlrGzefPmG7mYbCR3XCA+dkD/A5jKzjYe+Y4s9mNtLTbPAxpxhsOsJkb4NfCY3CAt+0Ym9kN5mNALYeBLuRhkyCk5eDftmM8xv0H24Ba/hOn5TBvW42EAUMyyJYDxGhhKzgs23bAQALol5tzDJKNZx5mM8OrhZ//8OaPb9vq6vv7z5jdeFNhJ9t3vPkZXi1AAAqew0hsZgLqocrqCCsbBaNgFIyCkQsARx5ILaQY62AAAAAASUVORK5CYII=","orcid":"","institution":"The Second Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-07 15:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7320249/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7320249/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89377993,"identity":"893268cf-e499-4011-98d6-06af016e7120","added_by":"auto","created_at":"2025-08-19 11:28:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1438259,"visible":true,"origin":"","legend":"\u003cp\u003eFive trajectories of body temperature based on LCTM. The dotted lines indicate the 95% confidence interval for each trajectory.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7320249/v1/0dd3a0983ae2d0e6582a30d9.png"},{"id":89377994,"identity":"8fbd32f2-4e57-4538-bc43-3fdb3b37af6d","added_by":"auto","created_at":"2025-08-19 11:28:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":886095,"visible":true,"origin":"","legend":"\u003cp\u003eThe survival curves were calculated using the Kaplan-Meier method. (A) One year, (B) five years, (C) ten years.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7320249/v1/abcb28b5ea194b4feb01dfa8.png"},{"id":89377995,"identity":"a189aa75-321f-4533-8cae-34d9ca65a42b","added_by":"auto","created_at":"2025-08-19 11:28:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1036767,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate COX analysis was performed using logistic regression analysis to identify risk factors for in-hospital death and 1-year mortality in patients.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7320249/v1/739a6297ac33c88b8744abd6.png"},{"id":89377996,"identity":"665a92b6-432c-4204-8174-80b385bf443d","added_by":"auto","created_at":"2025-08-19 11:28:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1394640,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis using forest plots was performed to explore the relationship between one-year mortality and body temperature trajectories. CHF: congestive heart failure, CD : Cerebrovascular disease.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7320249/v1/27549bf53befe994ce3a67e6.png"},{"id":92952807,"identity":"0be45615-4e7e-4983-900e-fd2ad6eaa1b2","added_by":"auto","created_at":"2025-10-07 13:24:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8291014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7320249/v1/c85b72a5-bd3f-4baf-a2a1-1e3afc932a65.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAnalysis of the Correlation Between Postoperative Temperature Trajectory and Prognosis After Cardiac Surgery: A retrospective analysis of the MIMIC-IV database\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe growing prevalence of cardiovascular diseases, fueled by an aging global population, has led to an increased demand for cardiac surgical procedures. Approximately two million people undergo heart surgery worldwide each year(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Mortality rates have decreased due to advances in surgical techniques and perioperative management(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), However, mortality rates after cardiac surgery remain higher than those after other surgical procedures. Mortality rates after valve replacement and coronary artery bypass grafting have been reported to be 2\u0026ndash;3% and 4%, respectively(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Higher mortality rates are associated with more complex or combined procedures and high-risk patients(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). People are paying more attention to assessing the risk of death from heart surgery.\u003c/p\u003e\u003cp\u003eTemperature is a fundamental indicator of overall health(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Unhealthy lifestyle factors, including high-calorie diets, chronic stress, sleep deprivation, type 2 diabetes, and undiagnosed medical conditions such as latent infections, are associated with temperature changes and reduced survival(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Maintaining a constant temperature is necessary for normal physiological function(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In medical settings, temperature measurements are periodically taken to evaluate patients' physical conditions(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).Temperature is a nonlinear function of several variables, such as age, health status, sex, ambient temperature, and circadian cycle time(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).As a special type of surgery, cardiac surgery greatly impacts blood circulation and physiology. Hypothermia after cardiac surgery is associated with a significantly increased risk of death(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and may be related to systemic failure leading to peripheral hypoperfusion(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLCTM is a sophisticated statistical technique used to identify groups with different trends over time and to explore their characteristics of these trends within individual subgroups(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Typically, two to seven categories can be described, as will be detailed later. At least seven model structures can be fitted(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).This study aims to use LCTM to identify different temperature trajectory patterns in cardiac surgery patients, realize population clustering, and accurately assess the association between temperature changes and the risk of death in these patients. These findings could inform clinical practice by helping to identify high-risk populations for targeted treatment and early intervention.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eDatabase\u003c/h2\u003e\n\u003cp\u003eThe Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic-iv.mit.edu/) was used to collect the study data. MIMIC-IV, a multi-parameter, structured, single-center ICU database, was released in 2003. This initiative will not affect clinical care, and the patients in the database cannot be identified; therefore, individual patient consent or ethical approval is not required(17).Research team member Yujie Fan was granted access to the website and was responsible for data collection（ID number：13908252）. Our study also adhered to the principles outlined in the Declaration of Helsinki and followed transparent reporting guidelines for multivariate prognostic or diagnostic models (18).\u003c/p\u003e\n\u003ch2\u003ePatients\u003c/h2\u003e\n\u003cp\u003eThe inclusion criteria were as follows: First, 8,560 patients were identified based on their surgery dates, the MIMIC-IV database, and International Classification of Diseases (ICD) codes. Post-cardiac surgery patients were excluded if they met any of the following criteria: 1) absence of multiple temperature measurements within 72 hours after admission for cardiac surgery, 2) missing survival information, 3) age \u0026lt;65 years, and 4) postoperative hospital stay \u0026lt;72 hours. For patients with multiple procedures, only the first procedure was included in the analysis.\u003c/p\u003e\n\u003ch2\u003eObservational variables\u003c/h2\u003e\n\u003cp\u003eThe primary study variable was the lowest temperature recorded within three days after surgery. The other variables included: (1) age, gender, and race; (2) laboratory test results from patients within 24 hours after cardiac surgery; (3) comorbidities, including myocardial infarction, malignant tumors, diabetes mellitus, congestive heart failure (CHF), and Cerebrovascular disease (CD); (4) medications or treatments, including norepinephrine, cardiopulmonary bypass for cardiac surgery, and vasopressin; (5) outcome variables, including in-hospital mortality and survival information at one, five, and ten years after admission. Navicat Premier 17.0 software and Structured Query Language (SQL) were used to extract metrics from the MIMIC-IV database.\u003c/p\u003e\n\u003ch2\u003eData processing\u003c/h2\u003e\n\u003cp\u003eVariables with more than 20% missing values will be deleted. Missing values in variables with a loss rate of less than 20% will be replaced using multiple imputation. Outliers will be identified using the interquartile range (IQR) and treated as missing values.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eContinuous variables are expressed as the mean \u0026plusmn; standard deviation (SD) or the median and interquartile range (IQR). Categorical variables are reported as numbers and percentages (n (%)). We used the Wilcoxon rank-sum test for continuous variables and the chi-square or Fisher\u0026apos;s exact test for categorical variables. Using Kaplan-Meier survival curves, we examined the relationship between temperature trajectories and patient mortality over time. We constructed Cox proportional hazards regression models to evaluate the impact of temperature trajectories on survival outcomes. Three models were developed with progressive adjustment for covariates. Model 1 involved univariate analysis and did not adjust for any covariates. Model 2 was adjusted for sex, age, and race. Model 3 included the adjustments of Model 2, as well as adjustments for heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide, congestive heart failure (CHF), cerebrovascular disease (CD), and malignant tumor complications.\u003c/p\u003e\n\u003cp\u003eTo ascertain the optimal number of classes, a series of models were constructed, ranging from two to seven classes. The evaluation of LCTM fit was conducted by employing log-likelihood, entropy, and information criteria. Lower Akaike and Bayesian information criterion values are indicative of a superior model fit. The entropy value (range: 0\u0026ndash;1) is indicative of the model\u0026apos;s efficacy in classifying individuals into their respective categories. Typically, an entropy value greater than 0.7 is indicative of high classification accuracy, with higher entropy values corresponding to a superior model fit. To ensure model stability, the sample size for each category should exceed 2% of the total study population. The model\u0026apos;s goodness of fit was verified by ensuring that the average posterior probability of all taxonomic members was at least 70%. The clinical interpretability of the model was also taken into consideration.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eTrajectory grouping based on the LCTM model\u003c/h2\u003e\n\u003cp\u003eThe goodness-of-fit statistics for the LCTM model are displayed in \u003cstrong\u003eTable 1\u003c/strong\u003e. The entropy values obtained were all greater than 0.7. The AIC and BIC values exhibited a continuous decrease from the class 1 model to the class 7 model, and the rate of decline demonstrated an inflection point in the class 5 model. After a thorough evaluation of the available models, the Class 5 model was selected on the basis of its clinical interpretability. Consequently, the fifth class model was regarded as the final model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFit statistics for different number of trajectory groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eof classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLog\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLikelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003enpm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.708\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e6.115288\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e12.98246\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e22.80702\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2.857143\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e10.62657\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e38.64662\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5.964912\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLog Likelihood: Logarithm of the likelihood function; npm: number of free parameters; BIC: Bayesian Information Criterion; AIC: Akaike Information Criterion.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe temperature trajectories of the five models are shown in Fig. 1. LCTM-5 classified the development cohort into five categories: class 1 [293 (14.69%); class 2 [318 (15.94%); class 3 [86 (4.31%); class 4 [892 (44.71%); and class 5 [406 (20.35%)]. In Class 1, the initial lowest temperature was below 35\u0026deg;C, followed by rapid rewarming. The Class 2 is the low-grade stable group, whose lowest temperature remained at approximately 35.9\u0026deg;C. The Class 3 is the low-grade rebound group, which is characterized by an initial lowest temperature below 36\u0026deg;C, followed by a process of first decreasing and then rebounding. In the Class 4 of moderate-temperature stable groups, the lowest recorded temperature was maintained at a moderate level of approximately 36.4\u0026deg;C. In Class 5, the lowest temperature in moderate temperature rise groups commenced from a comparatively modest initial value and ascended to approximately 36.5\u0026deg;C, exhibiting a persistent upward tendency.\u003c/p\u003e\n\u003ch2\u003eCharacteristics of temperature trajectories\u003c/h2\u003e\n\u003cp\u003eThe initial sample size for the study was determined to be 8,560 patients. Following the application of inclusion and exclusion criteria, a total of 1,995 patients were ultimately enrolled in the study. The baseline characteristics of patients stratified based on the five trajectory classes are listed in Table 3. The study population consisted of patients over the age of 65, with a median age of 76.81 years. Most of the patients were male (1,255, or 62.9%) and white (1,542, or 77.3%). Of the study subjects, 595 (29.8%) had a history of myocardial infarction; 712 (35.7%) were identified as having congestive heart failure (CHF); 276 (13.8%) were diagnosed with chronic kidney disease (CKD); 496 (24.9%) had been diagnosed with chronic lung disease; and 747 (37.4%) had been diagnosed with diabetes mellitus. Meanwhile, 63 patients (3.2%) underwent continuous renal replacement therapy, 133 patients (6.7%) used vasopressin, 359 patients (18%) used norepinephrine during hospitalization, and 526 patients (26.4%) underwent cardiac surgery with extracorporeal circulation assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e Descriptive characteristics of overall participants and by temperature trajectory\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall(n=1995)\u003c/strong\u003e\u003c/p\u003e\n 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style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.00(14.00,17.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.00(14.00,16.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.00(13.00,16.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.00(14.00,16.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.00(14.00,18.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.75(14.00,16.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.55(28.90,53.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.10(29.90,44.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.374\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChloride\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e107.00(104.00,110.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e109.00(105.00,112.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n 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style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.41(7.38,7.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.41(7.38,7.45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.41(7.38,7.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.42(7.39,7.46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.40(7.37,7.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.41(7.38,7.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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\u003cp\u003e\u003cstrong\u003e265.50(176.00,358.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e274.50(181.00,371.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCO\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.00(37.00,44.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.00(36.00,43.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.00(37.00,45.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.00(35.25,42.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.00(37.00,45.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.00(37.00,43.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDopamine used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e133(6.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21(7.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26(8.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11(12.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45(5.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30(7.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e1662(83.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e237(80.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e280(88.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70(81.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e741(83.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e334(82.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e333(16.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56(19.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38(11.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16(18.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e151(16.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72(17.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMyocardial\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInfarct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1400(70.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e226(77.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e210(66.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59(68.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e624(70.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e281(69.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e595(29.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n 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failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1283(64.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e212(72.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e177(55.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e54(62.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e572(64.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e268(66.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e712(35.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e81(27.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e141(44.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32(37.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e1639(82.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e228(77.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e259(81.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68(79.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e739(82.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e345(85.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e356(17.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e65(22.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59(18.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18(20.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e153(17.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61(15.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.557\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1719(86.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e256(87.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e265(83.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e73(84.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e775(86.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e350(86.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e276(13.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37(12.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e53(16.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13(15.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e117(13.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56(13.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDementia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.503\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1967(98.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e291(99.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e315(99.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85(98.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e875(98.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e401(98.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e5(1.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic pulmonary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003edisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e225(76.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e225(70.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71(82.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e671(75.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e307(75.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003eRheumatic\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.646\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1902(95.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e275(93.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e307(96.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e82(95.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e851(95.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e387(95.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93(4.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18(6.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11(3.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4(4.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41(4.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19(4.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptic ulcer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.234\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1983(99.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e289(98.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e316(99.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85(98.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e890(99.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e403(99.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e3(0.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild liver\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003edisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.084\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1934(96.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e291(99.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e310(97.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e83(96.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e861(96.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e389(95.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61(3.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2(0.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8(2.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3(3.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31(3.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17(4.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1248(62.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e208(71.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e197(61.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e52(60.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e526(59.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e265(65.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e747(37.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85(29.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e121(38.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34(39.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e366(41.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e141(34.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus without complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.101\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1434(71.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e226(77.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e230(72.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e58(67.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e621(69.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e299(73.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e561(28.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e67(22.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e88(27.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28(32.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e271(30.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e107(26.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus with complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.215\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1758(88.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e268(91.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e277(87.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e78(90.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e774(86.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e361(88.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e237(11.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25(8.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41(12.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8(9.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e118(13.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45(11.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParaplegia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.949\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1962(98.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e289(98.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e313(98.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85(98.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e875(98.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e400(98.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33(1.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4(1.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5(1.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(1.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17(1.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6(1.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRenal disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.608\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1489(74.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e221(75.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e226(71.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e66(76.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e669(75.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e307(75.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e506(25.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72(24.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e92(28.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20(23.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e223(25.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99(24.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.432\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1915(96.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e280(95.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e305(95.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e86(100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e855(95.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e389(95.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80(4.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13(4.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13(4.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0(0.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37(4.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17(4.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere liver disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.378\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1985(99.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e293(100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e315(99.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e86(100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e886(99.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e405(99.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10(0.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0(0.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3(0.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0(0.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6(0.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(0.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastatic solid tumor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.783\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1985(99.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e292(99.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e316(99.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e86(100.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e886(99.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e405(99.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10(0.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(0.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2(0.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0(0.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6(0.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(0.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtracorporeal circulation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAuxiliary to\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOpen heart surgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1469(73.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e206(70.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e180(56.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50(58.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e732(82.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e301(74.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e526(26.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e87(29.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e138(43.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36(41.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e160(17.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e105(25.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorepinephrine used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1636(82.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e236(80.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e257(80.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59(68.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e742(83.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e342(84.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e359(18.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57(19.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61(19.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27(31.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e150(16.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e64(15.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSbp, Systolic blood pressure; Dbp, Diastolic blood pressure; SpO2, Oxygen saturation; Inr, International normalized ratio; Pt, Prothrombin time; Ptt, Partial thromboplastin time; Wbc, White blood cell count; Mchc, Mean corpuscular hemoglobin concentratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eabout here.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to the other groups, Class 1 exhibited lower creatinine levels. A comparative analysis revealed that Class 3 had lower systolic blood pressure, chloride levels, mean corpuscular hemoglobin, and oxygenation index than the other groups. Conversely, the white blood cell count, red blood cell distribution width, platelet count, blood pH, proportion of patients using vasopressin and norepinephrine, proportion of patients with and without diabetic complications, and proportion of patients receiving continuous renal replacement therapy (CRRT) were higher. The Table 3 illustrates the fundamental characteristics of the aforementioned five classes.\u003c/p\u003e\n\u003ch2\u003eUnivariate and multivariate analysis\u003c/h2\u003e\n\u003cp\u003eFig. 2 illustrates the Kaplan-Meier survival curves of disparate temperature trajectory groups at one year, five years, and ten years. The one-year mortality rate was found to be the lowest in Class 1, while\u0026nbsp;Class 3\u0026nbsp;exhibited the highest rate.\u003c/p\u003e\n\u003cp\u003eTable 2 shows the baseline characteristics of survivors and non-survivors. Then, potential risk factors with p \u0026lt; 0.05 were included as covariates in the multivariate Cox regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBaseline characteristics of the Survivors and Non-survivors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNon-survivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e740(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e665(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e75(42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1255(62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1153(63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e102(57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAge\u003cstrong\u003e(\u003c/strong\u003eyears\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e76.81(7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e76.43(7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e80.74(7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e453(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e415(22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e38(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1542(77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1403(77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e139(78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e79.18(11.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e78.81(11.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e82.95(13.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eSbp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e114.53(20.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e114.37(20.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e116.26(24.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eDbp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56.38(11.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56.47(11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e55.47(13.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eResp rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15.61(4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15.50(3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e16.83(5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.84(1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.85(2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.70(1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.11(0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.08(0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.48(1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.25(0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.25(0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.26(0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15.21(5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15.17(5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15.71(4.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePtt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e44.94(26.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e43.94(25.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e55.22(35.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRbc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.27(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.27(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.28(0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePlatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e165.69(68.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e164.00(66.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e183.10(81.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eWbc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e10.98(5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e11.02(5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e10.65(5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.45(1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.45(1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.47(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7.41(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7.41(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7.40(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e40.72(6.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e40.58(5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e42.18(7.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;CHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1283(64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1229(67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e54(30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e712(35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e589(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e123(69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eMyocardial infarct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1400(70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1284(70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e116(65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e595(29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e534(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e61(34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1719(86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1580(86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e139(78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e276(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e238(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e38(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eMalignant cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1915(96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1752(96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e163(92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e80(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e66(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e14(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1248(62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1135(62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e113(63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e747(37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e683(37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e64(36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eExtracorporeal circulation\u003c/p\u003e\n \u003cp\u003eAuxiliary to\u003c/p\u003e\n \u003cp\u003eOpen heart surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1469(73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1328(73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e141(79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e526(26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e490(27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e36(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSbp, Systolic blood pressure; Dbp, Diastolic blood pressure; Pt, Prothrombin time; Ptt, Partial thromboplastin time; Rbc, Red blood cell count; Wbc, White blood cell count; PH, potential of hydrogen in blood; Pco2, Partial pressure of carbon dioxide.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 3 shows the final results of three models, each of which focuses on one-year and in-hospital patient mortality. Models 1-3 were derived from multivariable Cox regression models. Model 1 centered exclusively on the temperature trajectory. Model 2 was adjusted for gender, age, and race. The Model 3 covariates were adjusted for the following: sex, age, race, heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide, CHF, CD, and malignant tumor complications. In the analysis employing Class 1 temperature trajectories as the reference, patients with Class 3 temperature trajectories had the highest risk. Subsequent to the implementation of comprehensive adjustments to account for the presence of various confounders, the initial trend remained consistent.\u003c/p\u003e\n\u003ch2\u003eSubgroup analyses\u003c/h2\u003e\n\u003cp\u003eBased on our clinical experience, we selected the following factors as variables for a stratified subgroup analysis: age over 80 years, the presence of complications from heart failure or cerebrovascular disease, or diabetes. Fig. 4\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eshows the forest plot of the patient subgroup analysis. Patients on trajectory 3 had a higher one-year mortality rate, while patients on trajectory 1 had the lowest rate. This remained stable across different age groups and patients with various underlying conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe present a method for identifying subphenotypes in cardiac surgery patients based on their temperature trajectories. Using trajectory modeling, we identified and validated five patient groups with distinct temperature trajectories patterns. Significant demographic and physiological differences were found between these groups, as well as notable differences in mortality rates.Subsequent analysis revealed substantial disparities among the study groups with respect to age, race, heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide, CHF complications, CD complications, and malignant tumor complications. These findings are of significant importance in the context of understanding the heterogeneity of elderly patients following cardiac surgery, and they may serve as a foundation for future research endeavors. The identification of subphenotypes may result in the development of more personalized management strategies(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The ubiquity of longitudinal temperature trajectories renders them a readily accessible auxiliary means for identifying clinically relevant postoperative cardiac diagnoses.\u003c/p\u003e\u003cp\u003eTemperature management is crucial for patients' recovery after surgery, especially for critically ill patients undergoing cardiac surgery. According to the DeFoe study, cardiac biomarker assessment reveals greater myocardial damage in the hypothermia group(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The Nam study found that the all-cause mortality rate of moderate to severe hypothermia in off-pump CABG patients was more than twice that of normothermic patients. Mild hypothermia was also found to be an unsatisfactory outcome at the 47-month follow-up(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A critical issue arises during the rewarming phase. While restoring normal temperature is essential for recovery, it can also lead to reperfusion injury(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThermoregulatory processes occur in the hypothalamic preoptic nucleus and activate subsequent effectors. Under basal conditions, the liver and heart are the main organs involved in thermogenesis(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Hypothermia redistributes blood flow through vasoconstriction, which reduces blood volume to the skin and subcutaneous muscles and thereby reduces heat loss(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Hypothermia progressively deteriorates cardiovascular function and affects cardiac conduction. Decreased pacemaker cell activity leads to bradycardia, and the reduced transmembrane resting potential increases the risk of atrial and ventricular arrhythmias(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Moderate hypothermia prolongs repolarization and causes arrhythmia(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).At the cellular level, low temperatures lead to enzyme inhibition and reduced ATP production(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).To maintain temperature, the body induces peripheral vasoconstriction as blood flow is redistributed. This would increase systemic vascular resistance and decrease cardiac output, thereby reducing coronary perfusion and peripheral perfusion, which would worsen metabolic acidosis and myocardial ischemia(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTaking a temperature reading at a single time point inevitably increases the risk of bias(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Temperature trajectories can more accurately reflect patients' body conditions than single temperature readings. Changes in temperature can easily trigger acute cardiovascular outcomes(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The warming process following hypothermia tends to increase oxygen demand(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).This may lead to myocardial ischemia(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).Regarding the effect of drugs on temperature, one study showed that acetaminophen administration did not significantly affect temperature trajectories(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The white blood cell count of class 3 was higher than that of the other groups. Thermoregulation is closely related to the immune system(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Hypothermia can produce significant physiological changes, such as a left shift in the oxygenation curve, decreased coagulation function, and arrhythmia. These changes may lead to tissue hypoxia, multiple organ dysfunction, and fluid resuscitation failure(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).Hypothermia can also lead to a range of adverse consequences, such as bleeding, infection, arrhythmia, blood coagulation disorders and kidney failure(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).Hypothermia also causes alterations in the steady-state enzymatic activity of coagulation factors, leading to coagulopathy(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe age of surgical patients can affect prognosis(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Elderly patients have lower subcutaneous fat content, a lower basal metabolic rate, and decreased thermoregulatory function of the central nervous system. They are also more susceptible to hypothermia(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The decrease in temperature with age is believed to be caused by a slowing of the metabolic rate and a reduced ability to regulate temperature in response to environmental changes, such as seasonal changes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Being over 80 years old was an independent predictor of 30-day mortality after aortic valve replacement and significantly impacts long-term survival(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatients with a better prognosis had lower creatinine levels(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).This may be due to early aggressive fluid resuscitation, which alleviates renal hypoperfusion and preserves glomerular filtration(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). One study also revealed a significant sex-based difference in creatinine levels. A greater proportion of males were in the high creatinine group than in the low creatinine group. This suggests that sex hormones may be related to creatinine levels. Testosterone can increase creatinine levels, while estradiol can decrease them(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Two previous studies showed that the difference in cardiopulmonary bypass time was not statistically significant for prognosis(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This is consistent with our results. Although most cardiac procedures involving CPB use intentional hypothermia, patients experience hypothermia less frequently after cardiac surgery than after noncardiac surgery. This may be due to the body being actively warmed up after cardiopulmonary bypass(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe generated predictive models for in-hospital mortality and one-year mortality outcomes, identifying general variables as risk factors. These factors include sex, age, race, heart rate, respiratory rate, partial thromboplastin time, platelet count, arterial partial pressure of carbon dioxide(PaCO2), CHF, CD, and malignancy complications. Physicians can use these variables in their clinical practice. Several studies have shown that women have a higher percentage of subcutaneous body fat, which correlates with lower mean skin temperatures. Some theories suggest that these temperature differences are related to female hormone levels(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Patients with diabetes who undergo cardiac surgery experience increased perioperative complications, higher in-hospital mortality, and lower long-term survival rates(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Compared with younger patients, preoperative complications, especially CHF, are more prevalent in older patients(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Hypothermia can impair platelet function(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A reduction of 1\u0026ndash;2\u0026deg;C in temperature causes reversible impairment of platelet aggregation by decreasing thromboxane A3 and negatively affecting platelet formation(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has several strengths. First, the LCTM model was used to categorize and analyze the three-day minimum temperature trajectories of cardiac surgery patients. A series of models were established to adjust for various confounding factors and reduce the risk of temperature bias resulting from observing only one time point. These stable, reliable results can inform clinical decision-making for cardiac surgery patients. Second, the results of this study may be useful for early identification of high-risk populations after cardiac surgery. Early intervention could then be implemented to reduce disease burden and mortality. This study uses real-world data to create a larger, more ethnically diverse population study. Additionally, temperature is a more intuitive, convenient, and inexpensive indicator than laboratory indicators.\u003c/p\u003e\u003cp\u003eThis study is the first to examine long-term temperature patterns in cardiac surgery patients. The study identified five stable subgroups of patients based on their temperature trajectories. However, our findings have several limitations. First, the retrospective, single-center design of the study makes selection bias inevitable. We examined the temperature trajectories of patients admitted to this center after cardiac surgery. This analysis revealed a high-risk subgroup that had not been reported previously. While our LCTM-5 model can classify various temperature trajectories, it was developed and validated using three-day lowest temperature data. Therefore, its application to predicting long-term temperature trajectories is limited. Nevertheless, our primary objective was to analyze three-day temperature trajectories and identify subcategories of patients so they could be classified and receive intervention as soon as possible. Finally, temperature is the result of a combination of factors, not all of which were considered in this study. Further studies are needed to determine whether these factors influence temperature dynamic trajectories. Nonetheless, our goal was to deepen our understanding of post-cardiac disease, and we succeeded in identifying a subgroup of robust post-cardiac patients with unique clinical features and significantly different outcomes. This study provides valuable insights into the evolution of cardiac surgery and paves the way for future research on predicting and treating early postoperative subclasses.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe identified five robust and clinically meaningful subgroups of temperature changes in elderly patients after cardiac surgery. We found that in-hospital and long-term mortality rates varied depending on the trajectory of these changes. One subgroup experienced an initial minimum temperature below 36\u0026deg;C, followed by a decline and subsequent rebound. This subgroup was associated with the highest risk of death. Another subgroup had an initial minimum temperature below 35\u0026deg;C, followed by rapid rewarming. This subgroup had the lowest risk of death. Previous studies that considered temperature at a single time point overlooked this finding. Temperature trajectory groups can identify patient subphenotypes and facilitate personalized improvements in patient management after cardiac surgery.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLCTM: Latent Class Trajectory Model\u003c/p\u003e\n\u003cp\u003eICD: International Classification of Diseases\u003c/p\u003e\n\u003cp\u003eSD: Standard deviation\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile range\u003c/p\u003e\n\u003cp\u003eCRRT: Continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003eCABG: Coronary artery bypass graft\u003c/p\u003e\n\u003cp\u003ePaCO\u003csub\u003e2\u003c/sub\u003e: Arterial partial pressure of carbon dioxide\u003c/p\u003e\n\u003cp\u003eMIMIC-IV: Medical Information Mart for Intensive Care-IV\u003c/p\u003e\n\u003cp\u003eCHF: Congestive heart failure\u003c/p\u003e\n\u003cp\u003eCD : Cerebrovascular disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe model was developed and validated based on public database. After completing the Collaborative Institutional Training Initiative programme, we got permission to access the database. This study used public deidentification databases, so there is no need to obtain the informed consent and approval of the Institutional Review Board. Participants gave informed consent to participate in the study before taking part.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the [Medical Information Mart for Intensive Care (MIMIC)-IV] repository, [https://mimic.mit.edu/docs/iv/].\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo external funding.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eYujie Fan\u0026nbsp;played a key role in collecting and analyzing the data, and in drafting the manuscript.\u0026nbsp;Hao Yuan\u0026nbsp;and\u0026nbsp;Zefeng Yang\u0026nbsp;played a pivotal role in extracting the data and designing the study.\u0026nbsp;Jiayao Wei\u0026nbsp;and\u0026nbsp;Longteng Nan\u0026nbsp;revised the manuscript for intellectual content.\u0026nbsp;Qiang Li\u0026nbsp;oversaw the entire project, providing guidance and contributing to the study\u0026apos;s design and review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShi P, Rui S, Meng Q. Association between serum creatinine-to-albumin ratio and 28-day mortality in intensive care unit patients following cardiac surgery: analysis of mimic-iv data. BMC Cardiovasc Disord. 2025;25(1):100.\u003c/li\u003e\n\u003cli\u003eChen W, Yu P, Chen C, Cai S, Chen J, Zheng C, et al. Association Between the Red Blood Cell Distribution Width and 30-Day Mortality in Intensive Care Patients Undergoing Cardiac Surgery: A Retrospective Observational Study Based on the Medical Information Mart for Intensive Care-IV Database. Ann Lab Med. 2024;44(5):401-9.\u003c/li\u003e\n\u003cli\u003eKim S, Park JH, Lim H, Lee H, Song SW. Association of Delta Neutrophil Index with the 30-day Mortality in Adult Cardiac Surgical Patients. 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Perfusion. 2003;18(2):127-33.\u003c/li\u003e\n\u003cli\u003eLuo W, Cao L, Wang C. Low body temperature and mortality in critically ill patients with coronary heart disease: a retrospective analysis from MIMIC-IV database. Eur J Med Res. 2023;28(1):614.\u003c/li\u003e\n\u003cli\u003eNam K, Jo WY, Kwon SM, Kang P, Cho YJ, Jeon Y, et al. Association Between Postoperative Body Temperature and All-Cause Mortality After Off-Pump Coronary Artery Bypass Graft Surgery: A Retrospective Observational Study. Anesth Analg. 2020;130(5):1381-8.\u003c/li\u003e\n\u003cli\u003eOprita B, Olaru I, Botezatu L, Diaconu AE, Oprita R. Management of Severe Hypothermia: Challenges and Advanced Strategies. J Clin Med. 2025;14(5).\u003c/li\u003e\n\u003cli\u003eOsilla EV, Marsidi JL, Shumway KR, Sharma S. Physiology, Temperature Regulation. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright \u0026copy; 2025, StatPearls Publishing LLC.; 2025.\u003c/li\u003e\n\u003cli\u003eSavioli G, Ceresa IF, Bavestrello Piccini G, Gri N, Nardone A, La Russa R, et al. Hypothermia: Beyond the Narrative Review-The Point of View of Emergency Physicians and Medico-Legal Considerations. J Pers Med. 2023;13(12).\u003c/li\u003e\n\u003cli\u003eDietrichs ES, McGlynn K, Allan A, Connolly A, Bishop M, Burton F, et al. Moderate but not severe hypothermia causes pro-arrhythmic changes in cardiac electrophysiology. Cardiovasc Res. 2020;116(13):2081-90.\u003c/li\u003e\n\u003cli\u003eShen Y, Lou Y, Zhu S. Hyperthermia is a predictor of high mortality in patients with sepsis. Crit Care. 2020;24(1):543.\u003c/li\u003e\n\u003cli\u003eKawashima C, Matsuzawa Y, Akiyama E, Konishi M, Suzuki H, Hashiba K, et al. Prolonged Fever After ST-Segment Elevation Myocardial Infarction and Long-Term Cardiac Outcomes. J Am Heart Assoc. 2017;6(7).\u003c/li\u003e\n\u003cli\u003eWang YC, Huang HH, Lin PC, Wang MJ, Huang CH. Hypothermia is an independent risk factor for prolonged ICU stay in coronary artery bypass surgery: an observational study. Sci Rep. 2023;13(1):4626.\u003c/li\u003e\n\u003cli\u003eYoung P, Saxena M, Bellomo R, Freebairn R, Hammond N, van Haren F, et al. Acetaminophen for Fever in Critically Ill Patients with Suspected Infection. N Engl J Med. 2015;373(23):2215-24.\u003c/li\u003e\n\u003cli\u003eRehman T, deBoisblanc BP. Persistent fever in the ICU. Chest. 2014;145(1):158-65.\u003c/li\u003e\n\u003cli\u003eBhavani SV, Spicer A, Sinha P, Malik A, Lopez-Espina C, Schmalz L, et al. Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories. Intensive Care Med. 2024;50(12):2094-104.\u003c/li\u003e\n\u003cli\u003eXu F, Zhang C, Liu C, Bi S, Gu J. Relationship Between First 24-h Mean Body Temperature and Clinical Outcomes of Post-cardiac Surgery Patients. Front Cardiovasc Med. 2021;8:746228.\u003c/li\u003e\n\u003cli\u003eKim JH, Nagy \u0026Aacute;, Putzu A, Belletti A, Biondi-Zoccai G, Likhvantsev VV, et al. Therapeutic Hypothermia in Critically Ill Patients: A Systematic Review and Meta-Analysis of High Quality Randomized Trials. Crit Care Med. 2020;48(7):1047-54.\u003c/li\u003e\n\u003cli\u003eLi L, Chen X, Ma W, Li Y. The effects of hypothermia in thrombosis: a systematic review and meta-analysis. Ann Palliat Med. 2021;10(9):9564-71.\u003c/li\u003e\n\u003cli\u003eAriyaratnam P, Ananthasayanam A, Moore J, Vijayan A, Hong V, Loubani M. Prediction of Postoperative Outcomes and Long-Term Survival in Cardiac Surgical Patients Using the Intensive Care National Audit \u0026amp; Research Centre Score. J Cardiothorac Vasc Anesth. 2019;33(11):3022-7.\u003c/li\u003e\n\u003cli\u003eTan R, Chen Y, Yang D, Long X, Ma H, Yang C. Risk factors for postoperative hypothermia in non-cardiac surgery patients: a systematic review and meta-analysis. BMC Anesthesiol. 2025;25(1):223.\u003c/li\u003e\n\u003cli\u003eMistiaen W, Deblier I, Dossche K, Vanermen A. Clinical Outcomes after Surgical Aortic Valve Replacement in 681 Octogenarians: A Single-Center Real-World Experience Comparing the Old Patients with the Very Old Patients. Geriatrics (Basel). 2024;9(2).\u003c/li\u003e\n\u003cli\u003eGriffin BR, Bronsert M, Reece TB, Pal JD, Cleveland JC, Fullerton DA, et al. Creatinine elevations from baseline at the time of cardiac surgery are associated with postoperative complications. J Thorac Cardiovasc Surg. 2022;163(4):1378-87.\u003c/li\u003e\n\u003cli\u003eZhu W, Xie X, Shu Z, Li L, Hu G, Song H. Prognostic Significance of the Serum Creatinine Level During the Shock Stage in Severe Burn Patients: A 10-Year Retrospective Study. Mediators Inflamm. 2025;2025:8876691.\u003c/li\u003e\n\u003cli\u003eMaheshwari A, Dines V, Saul D, Nippoldt T, Kattah A, Davidge-Pitts C. The Effect of Gender-Affirming Hormone Therapy on Serum Creatinine in Transgender Individuals. Endocr Pract. 2022;28(1):52-7.\u003c/li\u003e\n\u003cli\u003eLiu H, Wang X, Liu S, Cong S, Lu Y, Yang Y, et al. Postoperative hypothermia after total aortic arch replacement in acute type A aortic dissection-multivariate analysis and risk identification for postoperative hypothermia occurrence. J Thorac Dis. 2020;12(12):7089-96.\u003c/li\u003e\n\u003cli\u003eSalamunes ACC, Stadnik AMW, Neves EB. The effect of body fat percentage and body fat distribution on skin surface temperature with infrared thermography. J Therm Biol. 2017;66:1-9.\u003c/li\u003e\n\u003cli\u003eNeves EB, Salamunes ACC, de Oliveira RM, Stadnik AMW. Effect of body fat and gender on body temperature distribution. J Therm Biol. 2017;70(Pt B):1-8.\u003c/li\u003e\n\u003cli\u003eThongsuk Y, Hwang NC. Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review. J Cardiothorac Vasc Anesth. 2024;38(1):248-67.\u003c/li\u003e\n\u003cli\u003eVolpi S, Rajah T, Ali JM. Rationale and strategies for improving glycaemic control in diabetic patients undergoing cardiac surgery: a narrative review. J Thorac Dis. 2024;16(11):8088-102.\u003c/li\u003e\n\u003cli\u003eSabbag IP, Hohmann FB, Assun\u0026ccedil;\u0026atilde;o MSC, de Freitas Chaves RC, Corr\u0026ecirc;a TD, Menezes PFL, et al. Postoperative hypothermia following non-cardiac high-risk surgery: A prospective study of temporal patterns and risk factors. PLoS One. 2021;16(11):e0259789.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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},"keywords":"Post-cardiac surgery, trajectory analysis, temperature","lastPublishedDoi":"10.21203/rs.3.rs-7320249/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7320249/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Hypothermia following cardiac surgery can result in negative postoperative outcomes. The goal of this study was to determine the trend of temperature changes in elderly cardiac surgery patients within three days after surgery, as well as to assess its impact on mortality and poor clinical outcomes.\u003c/p\u003e\n\u003cp\u003eMethods This retrospective cohort study selected elderly patients who underwent cardiac surgery from the MIMIC-IV (Medical Information Mart for Intensive Care IV) database. The Latent Class Trajectory Model (LCTM) was employed to classify heterogeneous patterns of temperature changes in patients following cardiac surgery over a three-day period. Then, disparities in survival across the trajectory groups were analyzed using Kaplan-Meier survival curves. The Cox regression model was used to analyze the relationship between patients' temperature trajectories post-cardiac surgery and their risk of death within one year. A subgroup analysis was performed to identify interaction factors and evaluate the robustness of this finding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 1,995 cardiac surgery patients were included in the analysis. All patients were over 65 years of age. Five distinct temperature trajectory groups were identified: Group 1 (293 patients, 14.69%); Group 2 (318 patients, 15.94%); Group 3 (86 patients, 4.31%); Group 4 (892 patients, 44.71%); and Group 5 (406 patients, 20.35%). Kaplan-Meier survival analysis revealed that patients in Group 3 had higher in-hospital and one-year mortality rates than the other groups. Patients in Group 1 had lower in-hospital and one-year mortality rates. Subgroup analysis showed that the one-year mortality rate was higher in Group 3 patients and remained stable across different complication groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eDistinguishing different temperature trajectories could help identify patient subgroups at varying risk levels for adverse outcomes after cardiac surgery. This would be a clinically meaningful way to categorize patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration :\u003c/strong\u003eRetrospectively registered.\u003c/p\u003e","manuscriptTitle":"Analysis of the Correlation Between Postoperative Temperature Trajectory and Prognosis After Cardiac Surgery: A retrospective analysis of the MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 11:28:51","doi":"10.21203/rs.3.rs-7320249/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":"e111a344-1dc5-487b-b9ff-52aedddae7f6","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T13:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 11:28:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7320249","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7320249","identity":"rs-7320249","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}