Development and validation of a risk prediction model for new-onset ventricular arrhythmias after coronary artery bypass grafting: A retrospective observational study

Development and validation of a risk prediction model for new-onset ventricular arrhythmias after coronary artery bypass grafting: A retrospective observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a risk prediction model for new-onset ventricular arrhythmias after coronary artery bypass grafting: A retrospective observational study Zuochen Xue, Shan Sun, Yutian Shi, Peigen Yang, Jiayi Sun, Aijuan Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6557885/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background To examine the risk factors for in-hospital ventricular arrhythmias (VA) in coronary artery disease (CAD) patients after coronary artery bypass grafting (CABG) and develop a nomogram to predict the risk of VA occurrence. Methods This retrospective study involved the analysis of the clinical data (using the MIMIC-IV database) of 5,267 patients who underwent CABG. The risk factors for in-hospital VA were identified using the least absolute shrinkage and selection parameter (LASSO) and multivariate logistic regression analyses. Based on the outcomes, a risk prediction model was then developed. Results The nomogram was constructed using eight predictive indicators: congestive heart failure (CHF), atrial fibrillation (AFib), base excess, systolic blood pressure (SBP), white blood cell (WBC) count, use of milrinone or dobutamine, continuous renal replacement therapy (CRRT), and logistic organ dysfunction system(LODS) score. According to the internal validation results, the model demonstrated a good predictive ability, with area under the curve (AUC) values of 0.777 and 0.743 in the training and validation sets, respectively. Furthermore, the calibration curve revealed that the model’s predicted values were in good agreement with the actual observed values. Moreover, the clinical decision curve analysis (DCA) showed that the model had a significant clinical net benefit at diagnostic thresholds of 0.1–0.95 and 0.1–0.8 in the training and validation sets, respectively. Conclusion Herein, we developed a risk prediction model for VA occurrence. The model demonstrated good discrimination, calibration, and clinical applicability, ensuring early VA prediction, which could facilitate risk stratification, enhance patient monitoring and management post-CABG, and reduce VA incidence in high-risk patients. Coronary artery disease Coronary artery bypass graft Ventricular arrhythmias Prediction model MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coronary atherosclerotic heart disease, commonly known as Coronary artery disease (CAD), is a type of cardiovascular disease (CVD) that occurs when coronary arteries become narrowed or obstructed, leading to myocardial ischemia, hypoxia, and necrosis. According to reports, the global CAD incidence is quite high, making it one of the leading causes of death worldwide [ 1 ]. For patients with multivessel or complex CAD, coronary artery bypass grafting (CABG) is the primary revascularization strategy[ 2 ]. Although CABG offers the benefits of reduced short-term mortality and improved long-term outcomes for patients[ 3 , 4 ], its multiple iatrogenic complications such as myocardial infarction (MI), arrhythmias, stroke, and acute renal failure (ARF) cannot be overlooked[ 5 ]. Notably, ventricular arrhythmias [VA; including ventricular tachycardia (VT) and ventricular fibrillation(VF)], some of the most severe complications post-CABG, are a leading cause of sudden cardiac death[ 6 ]. Herein, we aimed to develop a prediction model to assess the risk of VA in patients post-CABG to aid clinicians in implementing proactive preventive and therapeutic measures for reducing in-hospital mortality and improving patient outcomes. Materials and Methods This research followed the reporting standards set forth by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines(Supplementary File 1). Data source Data used herein were extracted from the publicly available Medical Information Mart for Intensive Care (MIMIC-IV version 2.2) database. Specifically, medical records, laboratory results, demographic information, and International Classification of Diseases (ICD) codes for patients admitted to the Intensive Care Units (ICUs) of Beth Israel Deaconess Medical Center between 2008 and 2019 were extracted. Having completed the CITI course and passed the examination, the author (Zuochen Xue) had the certification to access the database (Record ID 53090145). Notably, since this study involved the analysis of a third-party anonymized public database, Institutional Review Board (IRB) approval was exempted. To safeguard their privacy, the identities of the patients were kept secret. Study design and data extraction This retrospective study involved the analysis of the clinical data of CAD patients who underwent CABG during their hospital stay. Baseline clinical data for patients upon ICU admission were extracted using SQL queries, including demographic characteristics, vital signs, laboratory tests, medication and device treatments, comorbidities, and critical illness scoring system results. For vital signs and laboratory tests, we used the measurements obtained at the time of the patient's first ICU admission. Additionally, we recorded the number of patients who received pharmacological and device treatments, such as vasoactive agents and CRRT, prior to the onset of ventricular tachycardia. The exclusion criteria were as follows: (1) Patients with missing rhythm records; and (2)Patients with pre-existing VA before CABG. Notably, in the MIMIC-IV database, rhythm descriptions were recorded in the chartevents table. Moreover, the primary endpoints were non-sustained VT, sustained VT, and VF. Figure 1 shows the study design flowchart. Statistical analysis The variables extracted from the MIMIC-IV database were merged using the Stata 17.0 statistical software. Variables with > 20% missing data were excluded. At the same time, mean imputation was used for outliers and variables with < 5% missing values, whereas multiple imputation was employed for variables with 5–20% missing values(Supplementary Table 1). Baseline data were grouped per VA occurrence. For continuous variables, normally and non-normally distributed data were expressed as mean ± standard deviation (SD) and medians and quartiles [M (Q1, Q3)], respectively. Depending on the variable distribution, independent sample t-tests or Mann-Whitney U tests were used for comparisons. Categorical variables were presented as frequencies (percentages) and compared using chi-square tests. For internal validation, the caret package in R 4.1.2 was used to randomly split the 5,267 CABG patients into two groups: Training (N = 3,689) and validation (N = 1,578) sets. Variables with p < 0.1 in the baseline data were further screened via least absolute shrinkage and selection operator (LASSO) regression (glmnet package). Based on the inflexion points as "cut-off values" from receiver operating characteristic (ROC) curves, continuous variables such as vital signs and laboratory tests were converted into binary variables to further enhance clinical relevance. Blood potassium and glucose levels, potentially having a nonlinear relationship with outcome variables, were transformed into categorical variables based on "inflexion points" derived from restricted cubic splines (RCS). The optimal Lambda parameter was selected via 10-fold cross-validation, with the Lambda.1se value as the best variable selection model. The variables finally selected based on likely association with VA risk post-CABG included congestive heart failure (CHF), atrial fibrillation (AFib), lactate, base excess, systolic blood pressure (SBP), white blood cell (WBC) count, use of milrinone or dobutamine, continuous renal replacement therapy (CRRT), and logistic organ dysfunction system(LODS) score. The R ‘rms’ package was used to conduct multivariate logistic regression with VA occurrence and the LASSO-selected risk factors as dependent and independent variables, respectively. A backward stepwise regression method was then employed to construct the most stable prediction model and create a nomogram. The R ‘pROC’ package was utilized to plot ROC curves for the training and validation sets, with the area under the curve (AUC) values determined and used to assess the model’s discriminatory ability. Furthermore, the R ‘rms’ package was used to generate calibration curves, which, in combination with the Hosmer–Lemeshow test, were used to assess the model’s calibration ability. Finally, clinical decision curves were plotted using the R ‘rmda’ package to determine the model’s clinical applicability. Results Study population characteristics Herein,5,267 subjects (comprising 4,053 males and 1,214 females) were included. Among them, 425 developed in-hospital VA post-CABG, resulting in an incidence rate of 8.07% (425/5267*100). Notably, the patients were randomly assigned to the training (N=3,689) and validation (N=1,578) sets in a 7:3 ratio. For vital signs such as blood pressure, heart rate, as well as laboratory indicators including blood gas analysis and routine blood tests, we used the measurements obtained at the time of the patient's first ICU admission. Additionally, we recorded the number of patients who received pharmacological and device-based treatments (e.g., CRRT) prior to the onset of ventricular tachycardia.Compared to those without VA, VA patients had significantly longer hospital stays and a higher 30-day mortality rate (6.12%) (median 10.41 days vs. median 7.05 days, P < 0.001; 6.12% vs. 1.05%, P < 0.001). Table 1 details the patients’ baseline characteristics. Table 1 Baseline characteristics of the study population (grouped by VA occurrence and randomly assigned to training and validation Sets) Variables E ntire P atient C oh ort (n = 5267) Patients without VA (n = 4842) Patients with VA (n = 425) P Validation set (n = 1578) Training set (n = 3689) P Age, M (Q₁, Q₃), year 69.18 (61.95, 76.16) 68.81 (61.70, 75.94) 73.26 (65.71, 78.65) <.001 69.07 (61.69, 76.33) 69.20 (62.07, 76.12) 0.658 Gender, Male, n (%) 4053 (76.95) 3727 (76.97) 326 (76.71) 0.9 1193 (75.60) 2860 (77.53) 0.128 Race, n (%) 0.696 1 White 4028 (76.48) 3693 (76.27) 335 (78.82) 1206 (76.43) 2822 (76.50) Black 158 (3.00) 147 (3.04) 11 (2.59) 48 (3.04) 110 (2.98) Asian 97 (1.84) 90 (1.86) 7 (1.65) 29 (1.84) 68 (1.84) Others 984 (18.68) 912 (18.84) 72 (16.94) 295 (18.69) 689 (18.68) BMI, M (Q₁, Q₃), kg/m2 29.03 (25.69, 31.84) 28.98 (25.69, 31.83) 29.26 (25.66, 31.95) 0.799 29.01 (25.69, 31.67) 29.03 (25.69, 31.88) 0.485 Congestive Heart Failure, n (%) 1296 (24.61) 1086 (22.43) 210 (49.41) <.001 398 (25.22) 898 (24.34) 0.497 Acute Myocardial Infarction, n(%) 644 (12.23) 555 (11.46) 89 (20.94) <.001 188 (11.91) 456 (12.36) 0.65 Atrial Fibrillation , n (%) 2219 (42.13) 1934 (39.94) 285 (67.06) <.001 664 (42.08) 1555 (42.15) 0.96 Hypertension , n (%) 4579 (86.94) 4197 (86.68) 382 (89.88) 0.06 1369 (86.76) 3210 (87.02) 0.798 Diabetes, n (%) 2223 (42.21) 2049 (42.32) 174 (40.94) 0.582 651 (41.25) 1572 (42.61) 0.361 Hyperlipidemia, n (%) 4162 (79.02) 3830 (79.10) 332 (78.12) 0.634 1269 (80.42) 2893 (78.42) 0.103 Liver Disease, n (%) 12 (0.23) 10 (0.21) 2 (0.47) 0.252 3 (0.19) 9 (0.24) 0.952 Peripheral Vascular Disease, n (%) 705 (13.39) 612 (12.64) 93 (21.88) <.001 239 (15.15) 466 (12.63) 0.014 Cerebrovascular Disease, n (%) 544 (10.33) 479 (9.89) 65 (15.29) <.001 179 (11.34) 179 (11.34) 0.113 Chronic Pulmonary Disease, n (%) 1008 (19.14) 911 (18.81) 97 (22.82) 0.044 322 (20.41) 686 (18.60) 0.126 Malignant Cancer, n (%) 143 (2.72) 124 (2.56) 19 (4.47) 0.02 36 (2.28) 107 (2.90) 0.205 Heart Rate, M (Q₁, Q₃), bpm 81 (76, 87) 81(76, 87) 82 (77, 88) 0.069 81 (76, 87) 81 (76, 87) 0.638 Temperature, M (Q₁, Q₃), ℃ 36.7(36.5, 36.9) 36.7(36.5, 36.9) 36.7(36.5, 36.8) 0.125 36.7(36.5, 36.9) 36.7(36.5, 36.9) 0.5 SBP, M (Q₁, Q₃), mmHg 112(107, 117) 112(107, 117) 109 (103, 116) <.001 111 (106, 117) 112 (107, 117) 0.114 DBP, M (Q₁, Q₃), mmHg 56 (52 61) 57 (52, 6) 55(51, 60) <.001 56 (52, 61.) 56 (52, 61) 0.832 Lactate, M (Q₁, Q₃), mmol/L 2.50 (2.00, 3.30) 2.50 (2.00, 3.20) 2.88 (2.20, 4.60) <.001 2.60 (2.00, 3.30) 2.50 (2.00, 3.20) 0.277 Table 1 (continued) Variables E ntire P atient C oh ort (n = 5267) Patients without VA (n = 4842) Patients with VA (n = 425) P Validation set (n = 1578) Training set (n = 3689) P PH, M (Q₁, Q₃) 7.31 (7.28, 7.34) 7.32 (7.28, 7.35) 7.30 (7.25, 7.34) <.001 7.31 (7.28, 7.34) 7.32 (7.28, 7.34) 0.284 PO 2 , M (Q₁, Q₃), mmHg 99.00 (82.00, 123.00) 100.00 (83.00, 123.00) 89.00 (74.00, 108.00) <.001 100.00 (82.00, 122.00) 99.00 (82.00, 123.00) 0.801 PCO 2 , M (Q₁, Q₃), mmHg 48.00 (44.00, 52.00) 48.00 (44.00, 51.00) 48.62 (44.00, 54.00) 0.003 48.00 (44.00, 52.00) 48.00 (44.00, 52.00) 0.864 Base excess, M (Q₁, Q₃), mmol/L -3.00 (-5.00, -1.00) -3.00 (-4.00, -1.00) -4.00 (-6.00, -2.00) <.001 -3.00 (-5.00, -1.00) -3.00 (-5.00, -1.00) 0.113 Aniongap, M (Q₁, Q₃), mmol/L 13.00 (11.00, 15.00) 13.00 (11.00, 15.00) 14.00 (12.00, 17.00) <.001 13.00 (11.00, 15.00) 13.00 (11.00, 15.00) 0.213 WBC, M (Q₁, Q₃),K/μL 15.70 (12.40, 19.70) 15.50 (12.30, 19.40) 18.00 (13.50, 22.80) <.001 15.90 (12.50, 19.80) 15.50 (12.40, 19.60) 0.173 Hematocrit, M (Q₁, Q₃), % 26.60 (23.70, 29.80) 26.70 (23.80, 29.90) 25.70 (23.20, 28.70) <.001 26.60 (23.70, 29.80) 26.60 (23.80, 29.80) 0.952 Hemoglobin, M (Q₁, Q₃), g/L 9.00 (8.00, 10.10) 9.10 (8.00, 10.20) 8.60 (7.70, 9.70) <.001 9.00 (8.00, 10.10) 9.00 (8.00, 10.10) 0.78 Platelets, M (Q₁, Q₃), K/μL 183.00 (143.00, 227.00) 182.50 (143.00, 226.00) 189.00 (144.00, 238.00) 0.041 184.00 (142.00, 227.00) 183.00 (143.00, 228.00) 0.778 BUN, M (Q₁, Q₃),mg/dl 18.00 (14.00, 23.00) 17.00 (14.00, 22.00) 21.00 (16.00, 30.00) <.001 18.00 (14.00, 23.00) 17.00 (14.00, 23.00) 0.694 Creatinine, M (Q₁, Q₃), mg/dl 1.00 (0.80, 1.20) 1.00 (0.80, 1.20) 1.10 (0.90, 1.60) <.001 1.00 (0.80, 1.20) 1.00 (0.80, 1.20) 0.309 Glucose, M (Q₁, Q₃), mg/dl 124.00 (108.00, 143.00) 124.00 (108.00, 142.00) 131.00 (111.00, 154.00) <.001 124.00 (108.00, 142.00) 124.00 (108.00, 144.00) 0.896 Sodium, M (Q₁, Q₃), mmol/L 139.00 (138.00, 141.00) 139.00 (138.00, 141.00) 140.00 (138.00, 142.00) 0.008 139.00 (138.00, 141.00) 139.00 (138.00, 141.00) 0.312 Potassium, M (Q₁, Q₃), mmol/L 4.10 (3.80, 4.40) 4.10 (3.90, 4.40) 4.10 (3.70, 4.40) 0.002 4.10 (3.90, 4.40) 4.10 (3.80, 4.40) 0.822 Bicarbonate, M (Q₁, Q₃), mmol/L 22.00 (21.00, 24.00) 22.00 (21.00, 24.00) 22.00 (20.00, 23.00) <.001 22.00 (21.00, 24.00) 22.00 (21.00, 24.00) 0.289 MCHC, M (Q₁, Q₃), g/dl 33.53 (32.70, 34.40) 33.53 (32.70, 34.40) 33.40 (32.50, 34.20) 0.001 33.53 (32.70, 34.40) 33.53 (32.70, 34.40) 0.623 MCV, M (Q₁, Q₃), fl 90.44 (87.00, 94.00) 90.44 (87.00, 94.00) 91.00 (88.00, 94.00) 0.047 90.44 (87.00, 94.00) 90.44 (87.00, 94.00) 0.861 RDW, M (Q₁, Q₃), % 13.50 (12.90, 14.20) 13.50 (12.90, 14.20) 13.80 (13.20, 14.60) <.001 13.50 (12.90, 14.30) 13.50 (12.90, 14.20) 0.401 OASIS, M (Q₁, Q₃) 31 (26, 36) 31 (26, 36) 33(29, 39) <.001 31 (26, 36) 31 (26, 36) 0.65 SAPSII, M (Q₁, Q₃) 35 (29, 42) 34.00 (29, 42) 39 (32, 48) <.001 35 (29, 42) 35 (29, 42) 0.58 LODS, M (Q₁, Q₃) 4 (3, 6) 4 (3, 6) 6 (4, 8) <.001 4 (3, 6) 4(3, 6) 0.502 Number of grafts, n (%) 0.048 0.294 1 1498 (28.44) 1395 (28.81) 103 (24.24) 435 (27.57) 1063 (28.82) 2 1956 (37.14) 1800 (37.17) 156 (36.71) 597 (37.83) 1359 (36.84) 3 1399 (26.56) 1278 (26.39) 121 (28.47) 435 (27.57) 964 (26.13) ≥4 414 (7.86) 369 (7.62) 45 (10.59) 111 (7.03) 303 (8.21) Use of milrinone or dobutamine, n (%) 372 (7.06) 243 (5.02) 129 (30.35) <.001 108 (6.84) 264 (7.16) 0.685 Vasoactive agents , n (%) 4186 (79.48) 3820 (78.89) 366 (86.12) <.001 1243 (78.77) 2943 (79.78) 0.407 IABP, n (%) 284 (5.39) 228 (4.71) 56 (13.18) <.001 92 (5.83) 192 (5.20) 0.357 Table 1 (continued) Variables E ntire P atient C oh ort (n = 5267) Patients without VA (n = 4842) Patients with VA (n = 425) P Validation set (n = 1578) Training set (n = 3689) P CRRT, n (%) 121 (2.30) 65 (1.34) 56 (13.18) <.001 41 (2.60) 80 (2.17) 0.34 Length of hospitalization, M (Q₁, Q₃), days 7.25 (5.34, 10.48) 7.05 (5.30, 10.02) 10.41 (6.96, 17.90) <.001 7.28 (5.33, 10.59) 7.24 (5.36, 10.46) 0.715 Mortality in 30 days, n (%) 77(1.46) 51(1.05) 26(6.12) <.001 26 (1.65) 51(1.38) 0.463 Abbreviations: BMI: body mass index; SBP/DBP: systolic/diastolic blood pressure; PO 2 :partial oxygen pressure; PCO 2 : partial carbon dioxide pressure; WBC: white blood cell; BUN: blood urea nitrogen; MCHC: mean corpuscular hemoglobin concentration; MCV: mean corpuscular volume; RDW: red blood cell distribution width; OASIS: oxford acute severity of illness score; SAPS II: simplified acute physiology score II; LODS: logistic organ dysfunction system; IABP: intra-aortic balloon pump; and CRRT: continuous renal replacement therapy LASSO Regression for Risk Factor Selection in the Training Set Given the large number of variables included herein and the potential correlations between them, LASSO regression was employed for dimensionality reduction and variable selection among the 37 baseline variables with a p-value < 0.1. Based on the inflexion points as "cut-off values" derived from ROC curves[7] (Supplementary Fig. 1), continuous variables such as laboratory tests and vital signs were converted into binary variables to enhance the clinical relevance of the final risk prediction model. Given that blood potassium and glucose levels may exhibit a nonlinear relationship with outcome variables[8, 9], we used RCS to categorize blood potassium levels into two groups ( <4.1 mmol/L, and ≥4.1 mmol/L)and glucose levels into two groups (<124 mg/dL, and ≥124 mg/dL) based on the "inflexion points" (Fig. 2). These variables were subsequently included in the LASSO regression model as categorical variables[10]. Furthermore, the number of CABGs was treated as an unordered categorical variable. The final list of variables with non-zero regression coefficients included CHF, AFib, lactate, base excess, SBP, WBC count, use of milrinone or dobutamine, CRRT, and LODS score (Fig. 3). Prediction model construction The nine variables selected via LASSO regression were included in a multivariate logistic regression model. Following that, a prediction model was constructed using the backward stepwise logistic regression (LR) method. Notably, the model was optimized via akaike information criterion (AIC) minimization. The final model included the following eight variables: CHF, AFib, base excess (cut-off value = -4.5 mmol/L), SBP (cut-off value = 105 mmHg), WBC count (cut-off value = 18 K/µL), use of milrinone or dobutamine, CRRT, and LODS score. This model was used to predict the risk of VA (Table 2) and was visualized using a nomogram (Fig. 4). Table 2 Logistic regression analysis of VA risk factors in patients post-CABG Variables Odds ratio 95% CI P-value CHF 1.587 1.188～2.119 0.002 AFib 2.256 1.711～2.973 <0.001 SBP 0.574 0.425～0.775 <0.001 WBC 1.854 1.419～2.423 <0.001 Base excess 0.717 0.540～0.953 0.022 CRRT 4.126 2.409～7.066 <0.001 LODS 1.066 1.012～1.123 0.016 Use of milrinone or dobutamine 3.252 2.283～4.631 <0.001 Abbreviations: CHF: congestive heart Failure; AFib: atrial fibrillation Validation of the prediction model According to the ROC curve analysis results, the prediction model had a greater AUC than any individual risk factor. The AUC values for the training and validation sets were 0.777 (95% CI: 0.747-0.807) and 0.743 (95% CI: 0.698-0.789), respectively, indicating the model’s good discrimination ability (Fig. 5). The model's calibration was assessed using calibration curves and the Hosmer-Lemeshow test. For the training set, the Hosmer-Lemeshow test yielded a P-value of 0.730 and a Brier score of 0.060. On the other hand, the validation set had a P-value of 0.057 and a Brier score was 0.073. These results suggested no statistically significant differences between predicted values and actual observed values, demonstrating good consistency (Fig. 6). The clinical decision curve analysis (DCA)revealed that the prediction model offered a significant clinical net benefit at diagnostic thresholds of 0.1-0.95 and 0.1-0.8 in the training and validation sets, respectively (Fig. 7). Discussion As earlier stated, VAs, common severe iatrogenic complications post-CABG, are a major cause of sudden cardiac death. Herein, VA incidence, which was 8.07%, correlated with longer hospital stays and a higher 30-day mortality rate. Notably, CAD, a common cause of malignant arrhythmias[11], myocardial injury and remodeling, and scar formation, can serve as a substrate for VA[12]. Additionally, excessive sympathetic nervous system (SNS) activation, myocardial electrophysiological changes[13], hypothermia during CABG, inflammation, ischemia-reperfusion injury (IRI), and postoperative myocardial edema could further contribute to VA onset[14]. Therefore, early assessment of VA risk in patients who underwent CABG is crucial for promptly designing and implementing preventive and therapeutic measures, thus addressing or correcting risk factors, reducing mortality risk, and improving outcomes. Notably, ischemic cardiomyopathy (ICM) has been established as a primary cause of heart failure (HF)[15]. Furthermore, changes in cardiac structure and function, diuretic use-induced electrolyte imbalances, renin-angiotensin-aldosterone system (RAAS) activation, autonomic dysregulation, myocardial fibrosis, and increased cardiac load could significantly elevate the risk of sudden cardiac death and VA in HF patients[16]. It is also noteworthy that VA patients often exhibit a high AFib incidence. According to the ARIC and CHS studies, patients with AFib are at a 1.5-fold higher risk of sudden cardiac death and VA[17]. Furthermore, AFib-induced alterations in myocardial ion channels and myocardial remodeling, coupled with rapid heart rates, leading to shortened myocardial refractory periods and recurrent long-short cycle variations, may provoke arrhythmias[18]. Low base excess, an indicator of metabolic acidosis, often results from hypoxemia, inadequate tissue perfusion, and lactate accumulation, and could lead to electrolyte imbalances and reduced myocardial contractility[19]. Additionally, hypotension, which is characterized by reduced cardiac function and blood volume, could adversely affect tissue perfusion, thus contributing to arrhythmias occurrence[20]. In CAD patients, WBCs, as common inflammatory markers, are often associated with plaque inflammation, which could increase the risk of thrombosis and ischemic injury[21]. Additionally, an elevated WBC count suggests the presence of infections. Moreover, Le Li et al. discovered that VAs are a common complication in patients with sepsis[22]. Although milrinone and dobutamine are often used to enhance cardiac contractility during cardiogenic shock (CS) and refractory heart failure (RHF)treatment, their impact on VAs remains unclear. Furthermore, milrinone could increase postoperative mortality and the incidence of arrhythmias[23]. This phenomenon could be attributed to its elevation effect on intracellular calcium levels, thus promoting cellular depolarization and increasing the risk of arrhythmias[24]. According to research, chronic kidney disease (CKD) patients and individuals undergoing dialysis are at a higher risk of VAs and sudden cardiac death[25]. Notably, kidney disease progression could lead to abnormalities in myocardial repolarization, such as QT interval prolongation. Furthermore, changes in potassium and calcium concentrations during blood purification, as well as fluctuations in blood pressure and volume, can disrupt the electro-mechanical balance of myocardial cells. The LODS, OASIS, and SAPS II are commonly used scoring tools in the ICU to assess the severity of illnesses and predict outcomes. Herein, we discovered that the LODS score is more significantly involved in predicting VA risk. Clinical Significance: Herein, differential analysis was employed for initial variable screening, continuous variables were transformed into categorical variables, and LASSO regression was utilized for dimensionality reduction and variable selection. Following that, multivariate logistic regression analysis was performed after which a stepwise backward selection method was used to develop a prediction model for VA post-CABG. The model was then validated for stability, with the ROC, calibration, and clinical decision curves confirming its good discrimination and calibration abilities and clinical utility. Moreover, we deduced that the model could be applied on a broader scale as the eight features it comprised are readily available in clinical practice. Overall, early VA prediction could aid in risk stratification for CABG patients, monitoring of vital signs, proactively correcting risk factors, and mitigating adverse effects of certain medications, thus reducing VA incidence in high-risk patients. Limitations: This study had some limitations. First, it involves a retrospective analysis of patients’ clinical data, necessitating additional prospective research to further validate its findings. Second, due to missing data, indicators such as troponin, brain natriuretic peptide (BNP), and left ventricular ejection fraction (LVEF) were excluded, potentially affecting the model's accuracy. Conclusion This study employed a nomogram to verify eight indicators, including CHF, AFib, base excess, SBP, WBC count, use of milrinone or dobutamine, CRRT, and LODS score. Notably, these indicators are very meaningful in identifying VA risk post-CABG and could be leveraged to ensure early VA prediction, thus facilitating risk stratification, enhancing patient monitoring and management post-CABG, and reducing VA incidence in high-risk patients. Abbreviations VA ventricular arrhythmias CAD coronary artery disease CABG coronary artery bypass grafting LASSO least absolute shrinkage and selection parameter CHF congestive heart failure AFib atrial fibrillation SBP systolic blood pressure WBC white blood cell CRRT continuous renal replacement therapy LODS logistic organ dysfunction system AUC area under the curve DCA decision curve analysis ROC receiver operating characteristic BMI body mass index DBP diastolic blood pressure BUN blood urea nitrogen SAPS II simplified acute physiology score II IABP intra-aortic balloon pump Declarations Acknowledgments We express our gratitude to the entire team at the MIT Laboratory for Computational Physiology and Beth Israel Deaconess Medical Center for their efforts in developing the MIMIC IV database. Authors’ Contributions Zuochen Xue and Aijuan Cheng designed the study. Zuochen Xue wrote the manuscript. Shan Sun, Yutian Shi, Peigen Yang and Jiayi Sun collected, analyzed, and interpreted the data. Aijuan Cheng conducted a thorough review, made edits, and provided approval for the manuscript. All authors have read and consented to the final version of the manuscript. Funding This research was funded by the Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-055B). Data availability The datasets utilized in this study are derived from the MIMIC-IV database, which can be accessed at the following link: https://physionet.org/content/mimiciv/2.2. Ethics approval and consent to participate The Beth Israel Women’s Deaconess Medical Center and the MIT Institutional Review Board granted authorization for access to the MIMIC database. Furthermore, all patient information contained within the database has been anonymized, eliminating the need for informed consent. We completed the necessary online courses and exams to gain access to the database(ID: 53090145). Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Nedkoff L, Briffa T, Zemedikun D, Herrington S, Wright FL. Global Trends in Atherosclerotic Cardiovascular Disease. Clin Ther. 2023;45:1087–91. Windecker S, Neumann F-J, Jüni P, Sousa-Uva M, Falk V. Considerations for the choice between coronary artery bypass grafting and percutaneous coronary intervention as revascularization strategies in major categories of patients with stable multivessel coronary artery disease: an accompanying article of the task force of the 2018 ESC/EACTS guidelines on myocardial revascularization. Eur Heart J. 2019;40:204–12. Laukkanen JA, Kunutsor SK, Niemelä M, Kervinen K, Thuesen L, Mäkikallio TH. All-cause mortality and major cardiovascular outcomes comparing percutaneous coronary angioplasty versus coronary artery bypass grafting in the treatment of unprotected left main stenosis: a meta-analysis of short-term and long-term randomised trials. Open Heart. 2017;4:e000638. Hua T, Vlahos A, Shariat MH, Payne D, Redfearn D. Predicting adverse cardiovascular outcomes in post-coronary artery bypass grafting patients using novel ECG frequency analysis of the QRS complex. Ann Noninvasive Electrocardiol. 2021;26:e12822. Hausenloy DJ, Candilio L, Evans R, Ariti C, Jenkins DP, Kolvekar S, et al. Remote Ischemic Preconditioning and Outcomes of Cardiac Surgery. N Engl J Med. 2015;373:1408–17. Narayan SM, Wang PJ, Daubert JP. New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic. J Am Coll Cardiol. 2019;73:70–88. Liu Y, Li X, Yin Z, Lu P, Ma Y, Kai J, et al. Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer. Front Oncol. 2020;10:571169. Jorairahmadi S, Javaherforooshzadeh F, Jannatmakan F, Soltani F, Shidel Zadeh L. Evaluation of the Relationship Between Changes in Potassium Concentration and Arrhythmia During Coronary Artery Bypass Grafting Surgery. Anesthesiol Pain Med. 2022;12. Su Y, Fan W, Liu Y, Hong K. Glycemic variability and in-hospital death of critically ill patients and the role of ventricular arrhythmias. Cardiovasc Diabetol. 2023;22:134. Huang G, Jin Q, Mao Y. Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study. J Med Internet Res. 2023;25:e46891. Deo R, Albert CM. Epidemiology and Genetics of Sudden Cardiac Death. Circulation. 2012;125:620–37. Sourour N, Riveland E, Næsgaard P, Kjekshus H, Larsen AI, Røsjø H et al. Associations Between Biomarkers of Myocardial Injury and Systemic Inflammation and Risk of Incident Ventricular Arrhythmia. JACC Clin Electrophysiol. 2024;:S2405500X24002950. Waldron NH, Fudim M, Mathew JP, Piccini JP. Neuromodulation for the Treatment of Heart Rhythm Disorders. JACC Basic Transl Sci. 2019;4:546–62. Zahara R, Santoso A, Barano AZ. Myocardial Fluid Balance and Pathophysiology of Myocardial Edema in Coronary Artery Bypass Grafting. Cardiol Res Pract. 2020;2020:1–10. Erdogan O, Karaayvaz E, Erdogan T, Panc C, Sarıkaya R, Oncul A, et al. A new biomarker that predicts ventricular arrhythmia in patients with ischemic dilated cardiomyopathy: Galectin-3. Rev Port Cardiol Engl Ed. 2021;40:829–35. Peichl P, Rafaj A, Kautzner J. Management of ventricular arrhythmias in heart failure: Current perspectives. Heart Rhythm O2. 2021;2:796–806. Chen LY, Sotoodehnia N, Bůžková P, Lopez FL, Yee LM, Heckbert SR, et al. Atrial Fibrillation and the Risk of Sudden Cardiac Death: The Atherosclerosis Risk in Communities Study and Cardiovascular Health Study. JAMA Intern Med. 2013;173:29. Rusnak J, Behnes M, Reiser L, Schupp T, Bollow A, Reichelt T, et al. Atrial fibrillation increases the risk of recurrent ventricular tachyarrhythmias in implantable cardioverter defibrillator recipients. Arch Cardiovasc Dis. 2021;114:443–54. Huang Y, Ao T, Zhen P, Hu M. Association between the anion gap and mortality in critically ill patients with influenza: A cohort study. Heliyon. 2024;10:e35199. Arundel C, Lam PH, Gill GS, Patel S, Panjrath G, Faselis C, et al. Systolic Blood Pressure and Outcomes in Patients With Heart Failure With Reduced Ejection Fraction. J Am Coll Cardiol. 2019;73:3054–63. Chen J-H, Tseng C-L, Tsai S-H, Chiu W-T. Initial serum glucose level and white blood cell predict ventricular arrhythmia after first acute myocardial infarction. Am J Emerg Med. 2010;28:418–23. Li L, Zhang Z, Zhou L, Zhang Z, Xiong Y, Hu Z, et al. Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis. Eur Heart J - Digit Health. 2023;4:245–53. Ren Y, Li L, Peng T, Tan Y, Sun Y, Cheng G, et al. The effect of milrinone on mortality in adult patients who underwent CABG surgery: a systematic review of randomized clinical trials with a meta-analysis and trial sequential analysis. BMC Cardiovasc Disord. 2020;20:328. Kaakeh Y, Overholser BR, Lopshire JC, Tisdale JE. Drug-Induced Atr Fibrillation: Drugs. 2012;72:1617–30. Weidner K, Behnes M, Schupp T, Rusnak J, Reiser L, Taton G, et al. Prognostic impact of chronic kidney disease and renal replacement therapy in ventricular tachyarrhythmias and aborted cardiac arrest. Clin Res Cardiol. 2019;108:669–82. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1: Supplemental Figure 1. Transform continuous variables into categorical variables using cut-off values derived from the ROC curve. Supplementary Table 1. Approaches for managing missing data in variables. Supplementary File 1. STROBE Statement. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Jun, 2025 Editor invited by journal 12 May, 2025 Editor assigned by journal 08 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 29 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6557885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449787818,"identity":"b0a15fed-5e9d-4dd0-ba4b-cee1fd35c378","order_by":0,"name":"Zuochen Xue","email":"","orcid":"","institution":"Tianjin Chest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zuochen","middleName":"","lastName":"Xue","suffix":""},{"id":449787819,"identity":"73eb3a7a-21da-454b-881c-2d11622b57a7","order_by":1,"name":"Shan Sun","email":"","orcid":"","institution":"Tianjin Chest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Sun","suffix":""},{"id":449787820,"identity":"930fbea0-cf04-42f9-9586-e9aefbc43006","order_by":2,"name":"Yutian Shi","email":"","orcid":"","institution":"Tianjin Chest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yutian","middleName":"","lastName":"Shi","suffix":""},{"id":449787821,"identity":"d994047b-06ef-41f7-9790-961e2a479b4a","order_by":3,"name":"Peigen Yang","email":"","orcid":"","institution":"Tianjin Chest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peigen","middleName":"","lastName":"Yang","suffix":""},{"id":449787822,"identity":"4ceda287-f282-4d5d-962f-7bbca0531e5a","order_by":4,"name":"Jiayi Sun","email":"","orcid":"","institution":"Tianjin Chest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Sun","suffix":""},{"id":449787823,"identity":"72b1b8b1-9452-44e2-aed4-5c45193650aa","order_by":5,"name":"Aijuan Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIElEQVRIie2Rv0rEQBCHd1nINatpN0TOV1gJHILBZ4kENk38Ux0WIgHhqmAd8Cl8AicsxCbetSkstLG64kQ50tzhJDZX5MKVgvsVswMzH7+BJcRg+IuI38qaCotrf4gPttZuCs2zUnl8R6WF6b2JPkvbvkexH+6K9++b44F8fgHgloqeaCnJYqxxlHSHvBaRd1AIJsuLAAT3z1NSSppNNY6gU5EiHrnCQgViCVKoVmF4IY6CLcrl0hVrVGZzCYHUEW+UVa8SW87nBJUKUyDQQavQHkVUauTSe8Gcai7zBNRRCsVVnk4jLqpuxc7CD6de3ob7s9j7Wq39w0GmH9/q8cnQzrqVBsYJCTeCof1MvnUfoTUhpxvBSd+ywWAw/EN+APy4YnjNXz7WAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Chest Hospital","correspondingAuthor":true,"prefix":"","firstName":"Aijuan","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-04-29 15:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6557885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6557885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82070381,"identity":"f154a40a-fdbb-423d-84ac-ef8c03358251","added_by":"auto","created_at":"2025-05-06 13:11:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":842453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design flowchart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/ef618e06f7a53fcdc9cc70f2.png"},{"id":82070377,"identity":"7d5bff9c-cad5-4e05-8ad3-b20a48ef6167","added_by":"auto","created_at":"2025-05-06 13:11:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":301601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRCS demonstrating the relationship between blood potassium (a) and glucose (b) concentrations and VA occurrence.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/32ffc5dc4223869b80f55bb0.png"},{"id":82073176,"identity":"c9600aeb-7cc4-408f-b950-7e08d3ef4385","added_by":"auto","created_at":"2025-05-06 13:27:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":780589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCoefficient path (a) and cross-validation plot (b)for LASSO regression.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/26684c838394743e53678fc3.png"},{"id":82070929,"identity":"49646d20-ab98-40df-9f67-86bc016169e0","added_by":"auto","created_at":"2025-05-06 13:19:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":466062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Risk factors including CHF, AF, SBP, WBC, base excess, use of milrinone or dobutamine, CRRT, and LODS for the nomogram prediction model; and (b) A dynamic nomogram used as an example.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/7ea14905de8e9655b144b155.png"},{"id":82070384,"identity":"45e05c2e-146c-48f9-8602-294b57f358ff","added_by":"auto","created_at":"2025-05-06 13:11:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1245857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves.\u003c/strong\u003e (a) In the training set, the AUC value for the prediction model was 0.777 (95% CI: 0.747-0.807), and those for individual variables in predicting VAs were 0.628, 0.638, 0.582, 0.610, 0.609, 0.632, 0.564, and 0.669;(b) On the other hand, in the validation set, the AUC value for the prediction model was 0.743 (95% CI: 0.698-0.789), and those for individual variables in predicting VAs were 0.628, 0.630, 0.570, 0.595, 0.536, 0.615, 0.549, and 0.680.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/7cfb19a72b626b4b90840e89.png"},{"id":82070387,"identity":"562b33ee-aba2-4179-bcf4-7d8586d04790","added_by":"auto","created_at":"2025-05-06 13:11:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":156074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the predictive model.\u003c/strong\u003e The diagonal line represents the ideal model’s perfect prediction. The solid line represents the performance of the training set (a) and the validation set (b), both close to the diagonal line, indicating a better prediction performance.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/109e8cb2d4f032e373483801.png"},{"id":82070389,"identity":"0ffb0fe4-c046-4757-8401-4336d705f0fb","added_by":"auto","created_at":"2025-05-06 13:11:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":256868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe predictive model’s clinical decision curves. \u003c/strong\u003eWith the net clinical benefit greater than the “all intervention” and “no intervention” scenarios at diagnostic thresholds of 0.1-0.95 in the training set (a) and 0.1-0.8 in the validation set (b).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/8e0f820418560d7d3d7d1d0f.png"},{"id":82075535,"identity":"a9fdde6e-9fe5-4443-a7a1-37033519c01c","added_by":"auto","created_at":"2025-05-06 13:43:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4753621,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/8c7eff5e-5b2b-4fc5-b790-dffd41cf69e4.pdf"},{"id":82070385,"identity":"7f664e94-f6ec-40df-946d-d4d1ba2c4e65","added_by":"auto","created_at":"2025-05-06 13:11:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1959178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Supplemental Figure 1.\u003c/strong\u003e Transform continuous variables into categorical variables using cut-off values derived from the ROC curve. \u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eApproaches for managing missing data in variables. \u003cstrong\u003eSupplementary File 1. \u003c/strong\u003eSTROBE Statement.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6557885/v1/bcf5abfd05e632178a293043.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a risk prediction model for new-onset ventricular arrhythmias after coronary artery bypass grafting: A retrospective observational study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary atherosclerotic heart disease, commonly known as Coronary artery disease (CAD), is a type of cardiovascular disease (CVD) that occurs when coronary arteries become narrowed or obstructed, leading to myocardial ischemia, hypoxia, and necrosis. According to reports, the global CAD incidence is quite high, making it one of the leading causes of death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For patients with multivessel or complex CAD, coronary artery bypass grafting (CABG) is the primary revascularization strategy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although CABG offers the benefits of reduced short-term mortality and improved long-term outcomes for patients[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], its multiple iatrogenic complications such as myocardial infarction (MI), arrhythmias, stroke, and acute renal failure (ARF) cannot be overlooked[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, ventricular arrhythmias [VA; including ventricular tachycardia (VT) and ventricular fibrillation(VF)], some of the most severe complications post-CABG, are a leading cause of sudden cardiac death[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Herein, we aimed to develop a prediction model to assess the risk of VA in patients post-CABG to aid clinicians in implementing proactive preventive and therapeutic measures for reducing in-hospital mortality and improving patient outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e This research followed the reporting standards set forth by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines(Supplementary File 1).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eData used herein were extracted from the publicly available Medical Information Mart for Intensive Care (MIMIC-IV version 2.2) database. Specifically, medical records, laboratory results, demographic information, and International Classification of Diseases (ICD) codes for patients admitted to the Intensive Care Units (ICUs) of Beth Israel Deaconess Medical Center between 2008 and 2019 were extracted. Having completed the CITI course and passed the examination, the author (Zuochen Xue) had the certification to access the database (Record ID 53090145). Notably, since this study involved the analysis of a third-party anonymized public database, Institutional Review Board (IRB) approval was exempted. To safeguard their privacy, the identities of the patients were kept secret.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and data extraction\u003c/h3\u003e\n\u003cp\u003eThis retrospective study involved the analysis of the clinical data of CAD patients who underwent CABG during their hospital stay. Baseline clinical data for patients upon ICU admission were extracted using SQL queries, including demographic characteristics, vital signs, laboratory tests, medication and device treatments, comorbidities, and critical illness scoring system results. For vital signs and laboratory tests, we used the measurements obtained at the time of the patient's first ICU admission. Additionally, we recorded the number of patients who received pharmacological and device treatments, such as vasoactive agents and CRRT, prior to the onset of ventricular tachycardia. The exclusion criteria were as follows: (1) Patients with missing rhythm records; and (2)Patients with pre-existing VA before CABG. Notably, in the MIMIC-IV database, rhythm descriptions were recorded in the chartevents table. Moreover, the primary endpoints were non-sustained VT, sustained VT, and VF. Figure\u0026nbsp;1 shows the study design flowchart.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe variables extracted from the MIMIC-IV database were merged using the Stata 17.0 statistical software. Variables with \u0026gt;\u0026thinsp;20% missing data were excluded. At the same time, mean imputation was used for outliers and variables with \u0026lt;\u0026thinsp;5% missing values, whereas multiple imputation was employed for variables with 5\u0026ndash;20% missing values(Supplementary Table\u0026nbsp;1). Baseline data were grouped per VA occurrence. For continuous variables, normally and non-normally distributed data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and medians and quartiles [M (Q1, Q3)], respectively. Depending on the variable distribution, independent sample t-tests or Mann-Whitney U tests were used for comparisons. Categorical variables were presented as frequencies (percentages) and compared using chi-square tests.\u003c/p\u003e \u003cp\u003eFor internal validation, the caret package in R 4.1.2 was used to randomly split the 5,267 CABG patients into two groups: Training (N\u0026thinsp;=\u0026thinsp;3,689) and validation (N\u0026thinsp;=\u0026thinsp;1,578) sets. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the baseline data were further screened via least absolute shrinkage and selection operator (LASSO) regression (glmnet package). Based on the inflexion points as \"cut-off values\" from receiver operating characteristic (ROC) curves, continuous variables such as vital signs and laboratory tests were converted into binary variables to further enhance clinical relevance. Blood potassium and glucose levels, potentially having a nonlinear relationship with outcome variables, were transformed into categorical variables based on \"inflexion points\" derived from restricted cubic splines (RCS). The optimal Lambda parameter was selected via 10-fold cross-validation, with the Lambda.1se value as the best variable selection model. The variables finally selected based on likely association with VA risk post-CABG included congestive heart failure (CHF), atrial fibrillation (AFib), lactate, base excess, systolic blood pressure (SBP), white blood cell (WBC) count, use of milrinone or dobutamine, continuous renal replacement therapy (CRRT), and logistic organ dysfunction system(LODS) score. The R \u0026lsquo;rms\u0026rsquo; package was used to conduct multivariate logistic regression with VA occurrence and the LASSO-selected risk factors as dependent and independent variables, respectively. A backward stepwise regression method was then employed to construct the most stable prediction model and create a nomogram. The R \u0026lsquo;pROC\u0026rsquo; package was utilized to plot ROC curves for the training and validation sets, with the area under the curve (AUC) values determined and used to assess the model\u0026rsquo;s discriminatory ability. Furthermore, the R \u0026lsquo;rms\u0026rsquo; package was used to generate calibration curves, which, in combination with the Hosmer\u0026ndash;Lemeshow test, were used to assess the model\u0026rsquo;s calibration ability. Finally, clinical decision curves were plotted using the R \u0026lsquo;rmda\u0026rsquo; package to determine the model\u0026rsquo;s clinical applicability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy population characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHerein,5,267 subjects (comprising 4,053 males and 1,214 females) were included. Among them, 425 developed in-hospital VA post-CABG, resulting in an incidence rate of 8.07% (425/5267*100). Notably, the patients were randomly assigned to the training (N=3,689) and validation (N=1,578) sets in a 7:3 ratio. For vital signs such as blood pressure, heart rate, as well as laboratory indicators including blood gas analysis and routine blood tests, we used the measurements obtained at the time of the patient\u0026apos;s first ICU admission. Additionally, we recorded the number of patients who received pharmacological and device-based treatments (e.g., CRRT) prior to the onset of ventricular tachycardia.Compared to those without VA, VA patients had significantly longer hospital stays and a higher 30-day mortality rate (6.12%) (median 10.41 days vs. median 7.05 days, P \u0026lt; 0.001; 6.12% vs. 1.05%, P \u0026lt; 0.001). Table 1 details the patients\u0026rsquo; baseline characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Baseline characteristics of the study population (grouped by VA occurrence and randomly assigned to training and validation Sets)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003entire P\u003c/strong\u003e\u003cstrong\u003eatient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eoh\u003c/strong\u003e\u003cstrong\u003eort (n = 5267)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewithout\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n = 4842)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewith\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n = 425)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation set (n = 1578)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining set (n = 3689)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eAge, M (Q₁, Q₃), year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e69.18 (61.95, 76.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e68.81 (61.70, 75.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e73.26 (65.71, 78.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e69.07 (61.69, 76.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e69.20 (62.07, 76.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eGender, Male, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e4053 (76.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e3727 (76.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e326 (76.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e1193 (75.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e2860 (77.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e4028 (76.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e3693 (76.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e335 (78.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e1206 (76.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e2822 (76.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e158 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e147 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e11 (2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e48 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e110 (2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e97 (1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e90 (1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e7 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e29 (1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e68 (1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e984 (18.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e912 (18.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e72 (16.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e295 (18.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e689 (18.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eBMI, M (Q₁, Q₃), kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e29.03 (25.69, 31.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e28.98 (25.69, 31.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e29.26 (25.66, 31.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e29.01 (25.69, 31.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e29.03 (25.69, 31.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eCongestive Heart Failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e1296 (24.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e1086 (22.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e210 (49.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e398 (25.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e898 (24.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eAcute\u0026nbsp;Myocardial Infarction, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e644 (12.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e555 (11.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e89 (20.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e188 (11.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e456 (12.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eAtrial Fibrillation\u003c/p\u003e\n \u003cp\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e2219 (42.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e1934 (39.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e285 (67.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e664 (42.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e1555 (42.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e4579 (86.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e4197 (86.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e382 (89.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e1369 (86.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e3210 (87.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e2223 (42.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e2049 (42.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e174 (40.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e651 (41.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e1572 (42.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e4162 (79.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e3830 (79.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e332 (78.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e1269 (80.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e2893 (78.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eLiver Disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e12 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e10 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e2 (0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e3 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e9 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003ePeripheral Vascular Disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e705 (13.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e612 (12.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e93 (21.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e239 (15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e466 (12.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eCerebrovascular Disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e544 (10.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e479 (9.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e65 (15.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e179 (11.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e179 (11.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eChronic Pulmonary Disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e1008 (19.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e911 (18.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e97 (22.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e322 (20.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e686 (18.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eMalignant Cancer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e143 (2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e124 (2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e19 (4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e36 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e107 (2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eHeart Rate, M (Q₁, Q₃), bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e81 (76, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e81(76, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e82 (77, 88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e81 (76, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e81 (76, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eTemperature, M (Q₁, Q₃), ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e36.7(36.5, 36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e36.7(36.5, 36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e36.7(36.5, 36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e36.7(36.5, 36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e36.7(36.5, 36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eSBP, M (Q₁, Q₃), mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e112(107, 117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e112(107, 117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e109 (103, 116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e111 (106, 117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e112 (107, 117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eDBP, M (Q₁, Q₃), mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e56 (52 61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e57 (52, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e55(51, 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e56 (52, 61.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e56 (52, 61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.1548%;\"\u003e\n \u003cp\u003eLactate, M (Q₁, Q₃), mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0628%;\"\u003e\n \u003cp\u003e2.50 (2.00, 3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8996%;\"\u003e\n \u003cp\u003e2.50 (2.00, 3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2497%;\"\u003e\n \u003cp\u003e2.88 (2.20, 4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5551%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9707%;\"\u003e\n \u003cp\u003e2.60 (2.00, 3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.947%;\"\u003e\n \u003cp\u003e2.50 (2.00, 3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1604%;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 (continued)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003entire P\u003c/strong\u003e\u003cstrong\u003eatient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eoh\u003c/strong\u003e\u003cstrong\u003eort (n = 5267)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewithout\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n = 4842)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewith\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n = 425)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation set (n = 1578)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining set (n = 3689)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ePH, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.31 (7.28, 7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.32 (7.28, 7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.30 (7.25, 7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.31 (7.28, 7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7.32 (7.28, 7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e, M (Q₁, Q₃), mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e99.00 (82.00, 123.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e100.00 (83.00, 123.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e89.00 (74.00, 108.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e100.00 (82.00, 122.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e99.00 (82.00, 123.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ePCO\u003csub\u003e2\u003c/sub\u003e, M (Q₁, Q₃), mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e48.00 (44.00, 52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e48.00 (44.00, 51.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e48.62 (44.00, 54.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e48.00 (44.00, 52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e48.00 (44.00, 52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eBase excess, M (Q₁, Q₃), mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-3.00 (-5.00, -1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-3.00 (-4.00, -1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-4.00 (-6.00, -2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-3.00 (-5.00, -1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-3.00 (-5.00, -1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAniongap, M (Q₁, Q₃), mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13.00 (11.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13.00 (11.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e14.00 (12.00, 17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.00 (11.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.00 (11.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eWBC, M (Q₁, Q₃),K/\u0026mu;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e15.70 (12.40, 19.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e15.50 (12.30, 19.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e18.00 (13.50, 22.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.90 (12.50, 19.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.50 (12.40, 19.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHematocrit, M (Q₁, Q₃), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e26.60 (23.70, 29.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e26.70 (23.80, 29.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e25.70 (23.20, 28.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e26.60 (23.70, 29.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26.60 (23.80, 29.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHemoglobin, M (Q₁, Q₃), g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9.00 (8.00, 10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9.10 (8.00, 10.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8.60 (7.70, 9.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.00 (8.00, 10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.00 (8.00, 10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ePlatelets, M (Q₁, Q₃), K/\u0026mu;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e183.00 (143.00, 227.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e182.50 (143.00, 226.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e189.00 (144.00, 238.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e184.00 (142.00, 227.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e183.00 (143.00, 228.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eBUN, M (Q₁, Q₃),mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e18.00 (14.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e17.00 (14.00, 22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e21.00 (16.00, 30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e18.00 (14.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e17.00 (14.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCreatinine, M (Q₁, Q₃), mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00 (0.80, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.00 (0.80, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.10 (0.90, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.00 (0.80, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.00 (0.80, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eGlucose, M (Q₁, Q₃), mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e124.00 (108.00, 143.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e124.00 (108.00, 142.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e131.00 (111.00, 154.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e124.00 (108.00, 142.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e124.00 (108.00, 144.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSodium, M (Q₁, Q₃), mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e139.00 (138.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e139.00 (138.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e140.00 (138.00, 142.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e139.00 (138.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e139.00 (138.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003ePotassium, M (Q₁, Q₃), mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.10 (3.80, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.10 (3.90, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.10 (3.70, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.10 (3.90, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.10 (3.80, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eBicarbonate, M (Q₁, Q₃), mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e22.00 (21.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e22.00 (21.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e22.00 (20.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e22.00 (21.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e22.00 (21.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMCHC, M (Q₁, Q₃), g/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e33.53 (32.70, 34.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e33.53 (32.70, 34.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e33.40 (32.50, 34.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e33.53 (32.70, 34.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e33.53 (32.70, 34.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMCV, M (Q₁, Q₃), fl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e90.44 (87.00, 94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e90.44 (87.00, 94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e91.00 (88.00, 94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e90.44 (87.00, 94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e90.44 (87.00, 94.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eRDW, M (Q₁, Q₃), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13.50 (12.90, 14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13.50 (12.90, 14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.80 (13.20, 14.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.50 (12.90, 14.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.50 (12.90, 14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOASIS, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e31 (26, 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e31 (26, 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e33(29, 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e31 (26, 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e31 (26, 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSAPSII, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e35 (29, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e34.00 (29, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e39 (32, 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e35 (29, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e35 (29, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLODS, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4 (3, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4 (3, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6 (4, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4 (3, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4(3, 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNumber of grafts, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1498 (28.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1395 (28.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e103 (24.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e435 (27.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1063 (28.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1956 (37.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1800 (37.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e156 (36.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e597 (37.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1359 (36.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1399 (26.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1278 (26.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e121 (28.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e435 (27.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e964 (26.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e414 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e369 (7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e45 (10.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e111 (7.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e303 (8.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eUse of milrinone or dobutamine, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e372 (7.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e243 (5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e129 (30.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e108 (6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e264 (7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eVasoactive agents\u003c/p\u003e\n \u003cp\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4186 (79.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3820 (78.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e366 (86.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1243 (78.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2943 (79.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIABP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e284 (5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e228 (4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e56 (13.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e92 (5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e192 (5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 (continued)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003entire P\u003c/strong\u003e\u003cstrong\u003eatient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eoh\u003c/strong\u003e\u003cstrong\u003eort (n = 5267)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewithout\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n = 4842)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewith\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n = 425)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation set (n = 1578)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining set (n = 3689)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCRRT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e121 (2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e65 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e56 (13.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e41 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e80 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLength of hospitalization, M (Q₁, Q₃), days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.25 (5.34, 10.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.05 (5.30, 10.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.41 (6.96, 17.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7.28 (5.33, 10.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7.24 (5.36, 10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMortality in 30 days, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e77(1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e51(1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e26(6.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e26 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e51(1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BMI: body mass index; SBP/DBP: systolic/diastolic blood pressure; PO\u003csub\u003e2\u003c/sub\u003e:partial oxygen pressure; PCO\u003csub\u003e2\u003c/sub\u003e: partial carbon dioxide pressure; WBC: white blood cell; BUN: blood urea nitrogen; MCHC: mean corpuscular hemoglobin concentration; MCV: mean corpuscular volume; RDW: red blood cell distribution width; OASIS: oxford acute severity of illness score; SAPS II: simplified acute physiology score II; LODS: logistic organ dysfunction system; IABP: intra-aortic balloon\u0026nbsp;pump; and CRRT: continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLASSO Regression for Risk Factor Selection in the Training Set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the large number of variables included herein and the potential correlations between them, LASSO regression was employed for dimensionality reduction and variable selection among the 37 baseline variables with a p-value \u0026lt; 0.1. Based on the inflexion points as \u0026quot;cut-off values\u0026quot; derived from ROC curves[7] (Supplementary Fig. 1), continuous variables such as laboratory tests and vital signs were converted into binary variables to enhance the clinical relevance of the final risk prediction model. \u0026nbsp;Given that blood potassium and glucose levels may exhibit a nonlinear relationship with outcome variables[8, 9], we used RCS to categorize blood potassium levels into two groups ( \u0026lt;4.1 mmol/L, and \u0026ge;4.1 mmol/L)and glucose levels into two groups (\u0026lt;124 mg/dL, and \u0026ge;124 mg/dL) based on the \u0026quot;inflexion points\u0026quot; (Fig. 2). These variables were subsequently included in the LASSO regression model as categorical variables[10]. Furthermore, the number of CABGs was treated as an unordered categorical variable. The final list of variables with non-zero regression coefficients included CHF, AFib, lactate, base excess, SBP, WBC count, use of milrinone or dobutamine, CRRT, and LODS score (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction model construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nine variables selected via LASSO regression were included in a multivariate logistic regression model. Following that, a prediction model was constructed using the backward stepwise logistic regression (LR) method. Notably, the model was optimized via akaike information criterion (AIC) minimization. The final model included the following eight variables: CHF, AFib, base excess (cut-off value = -4.5 mmol/L), SBP (cut-off value = 105 mmHg), WBC count (cut-off value = 18 K/\u0026micro;L), use of milrinone or dobutamine, CRRT, and LODS score. This model was used to predict the risk of VA (Table 2) and was visualized using a nomogram (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Logistic regression analysis of VA risk factors in patients post-CABG\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"423\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e1.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e1.188～2.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAFib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e2.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e1.711～2.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e0.425～0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e1.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e1.419～2.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBase excess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e0.540～0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e4.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e2.409～7.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLODS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e1.012～1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUse of milrinone or dobutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5485%;\"\u003e\n \u003cp\u003e3.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.513%;\"\u003e\n \u003cp\u003e2.283～4.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CHF: congestive heart Failure; AFib: atrial fibrillation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the ROC curve analysis results, the prediction model had a greater AUC than any individual risk factor. The AUC values for the training and validation sets were 0.777 (95% CI: 0.747-0.807) and 0.743 (95% CI: 0.698-0.789), respectively, indicating the model\u0026rsquo;s good discrimination ability (Fig. 5).\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s calibration was assessed using calibration curves and the Hosmer-Lemeshow test. For the training set, the Hosmer-Lemeshow test yielded a P-value of 0.730 and a Brier score of 0.060. On the other hand, the validation set had a P-value of 0.057 and a Brier score was 0.073. These results suggested no statistically significant differences between predicted values and actual observed values, demonstrating good consistency (Fig. 6).\u003c/p\u003e\n\u003cp\u003eThe clinical decision curve analysis (DCA)revealed that the prediction model offered a significant clinical net benefit at diagnostic thresholds of 0.1-0.95 and 0.1-0.8 in the training and validation sets, respectively (Fig. 7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs earlier stated, VAs, common severe iatrogenic complications post-CABG, are a major cause of sudden cardiac death. Herein, VA incidence, which was 8.07%, correlated with longer hospital stays and a higher 30-day mortality rate. Notably, CAD, a common cause of malignant arrhythmias[11], myocardial injury and remodeling, and scar formation, can serve as a substrate for VA[12]. Additionally, excessive sympathetic nervous system (SNS) activation, myocardial electrophysiological changes[13], hypothermia during CABG, inflammation, ischemia-reperfusion injury (IRI), and postoperative myocardial edema could further contribute to VA onset[14]. Therefore, early assessment of VA risk in patients who underwent CABG is crucial for promptly designing and implementing preventive and therapeutic measures, thus addressing or correcting risk factors, reducing mortality risk, and improving outcomes.\u003c/p\u003e\n\u003cp\u003eNotably, ischemic cardiomyopathy (ICM) has been established as a primary cause of heart failure (HF)[15]. Furthermore, changes in cardiac structure and function, diuretic use-induced electrolyte imbalances, renin-angiotensin-aldosterone system (RAAS) activation, autonomic dysregulation, myocardial fibrosis, and increased cardiac load could significantly elevate the risk of sudden cardiac death and VA in HF patients[16].\u003c/p\u003e\n\u003cp\u003eIt is also noteworthy that VA patients often exhibit a high AFib incidence. According to the ARIC and CHS studies, patients with AFib are at a 1.5-fold higher risk of sudden cardiac death and VA[17]. Furthermore, AFib-induced alterations in myocardial ion channels and myocardial remodeling, coupled with rapid heart rates, leading to shortened myocardial refractory periods and recurrent long-short cycle variations, may provoke arrhythmias[18].\u003c/p\u003e\n\u003cp\u003eLow base excess, an indicator of metabolic acidosis, often results from hypoxemia, inadequate tissue perfusion, and lactate accumulation, and could lead to electrolyte imbalances and reduced myocardial contractility[19]. Additionally, hypotension, which is characterized by reduced cardiac function and blood volume, could adversely affect tissue perfusion, thus contributing to arrhythmias occurrence[20].\u003c/p\u003e\n\u003cp\u003eIn CAD patients, WBCs, as common inflammatory markers, are often associated with plaque inflammation, which could increase the risk of thrombosis and ischemic injury[21]. Additionally, an elevated WBC count suggests the presence of infections. Moreover, Le Li et al. discovered that VAs are a common complication in patients with sepsis[22].\u003c/p\u003e\n\u003cp\u003eAlthough milrinone and dobutamine are often used to enhance cardiac contractility during cardiogenic shock (CS) and refractory heart failure (RHF)treatment, their impact on VAs remains unclear. Furthermore, milrinone could increase postoperative mortality and the incidence of arrhythmias[23]. This phenomenon could be attributed to its elevation effect on intracellular calcium levels, thus promoting cellular depolarization and increasing the risk of arrhythmias[24].\u003c/p\u003e\n\u003cp\u003eAccording to research, chronic kidney disease (CKD) patients and individuals undergoing dialysis are at a higher risk of VAs and sudden cardiac death[25]. Notably, kidney disease progression could lead to abnormalities in myocardial repolarization, such as QT interval prolongation. Furthermore, changes in potassium and calcium concentrations during blood purification, as well as fluctuations in blood pressure and volume, can disrupt the electro-mechanical balance of myocardial cells.\u003c/p\u003e\n\u003cp\u003eThe LODS, OASIS, and SAPS II are commonly used scoring tools in the ICU to assess the severity of illnesses and predict outcomes. Herein, we discovered that the LODS score is more significantly involved in predicting VA risk.\u003c/p\u003e\n\u003cp\u003eClinical Significance: Herein, differential analysis was employed for initial variable screening, continuous variables were transformed into categorical variables, and LASSO regression was utilized for dimensionality reduction and variable selection. Following that, multivariate logistic regression analysis was performed after which a stepwise backward selection method was used to develop a prediction model for VA post-CABG. The model was then validated for stability, with the ROC, calibration, and clinical decision curves confirming its good discrimination and calibration abilities and clinical utility. Moreover, we deduced that the model could be applied on a broader scale as the eight features it comprised are readily available in clinical practice. Overall, early VA prediction could aid in risk stratification for CABG patients, monitoring of vital signs, proactively correcting risk factors, and mitigating adverse effects of certain medications, thus reducing VA incidence in high-risk patients.\u003c/p\u003e\n\u003cp\u003eLimitations: This study had some limitations. First, it involves a retrospective analysis of patients\u0026rsquo; clinical data, necessitating additional prospective research to further validate its findings. Second, due to missing data, indicators such as troponin, brain natriuretic peptide (BNP), and left ventricular ejection fraction (LVEF) were excluded, potentially affecting the model\u0026apos;s accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed a nomogram to verify eight indicators, including CHF, AFib, base excess, SBP, WBC count, use of milrinone or dobutamine, CRRT, and LODS score. Notably, these indicators are very meaningful in identifying VA risk post-CABG and could be leveraged to ensure early VA prediction, thus facilitating risk stratification, enhancing patient monitoring and management post-CABG, and reducing VA incidence in high-risk patients.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eVA ventricular arrhythmias\u003c/p\u003e\n\u003cp\u003eCAD coronary artery disease\u003c/p\u003e\n\u003cp\u003eCABG coronary artery bypass grafting\u003c/p\u003e\n\u003cp\u003eLASSO least absolute shrinkage and selection parameter\u003c/p\u003e\n\u003cp\u003eCHF congestive heart failure\u003c/p\u003e\n\u003cp\u003eAFib atrial fibrillation\u003c/p\u003e\n\u003cp\u003eSBP systolic blood pressure\u003c/p\u003e\n\u003cp\u003eWBC white blood cell\u003c/p\u003e\n\u003cp\u003eCRRT continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003eLODS logistic organ dysfunction system\u003c/p\u003e\n\u003cp\u003eAUC area under the curve\u003c/p\u003e\n\u003cp\u003eDCA decision curve analysis\u003c/p\u003e\n\u003cp\u003eROC receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eDBP diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eBUN blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eSAPS II simplified acute physiology score II\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIABP intra-aortic balloon pump\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the entire team at the MIT Laboratory for Computational Physiology and Beth Israel Deaconess Medical Center for their efforts in developing the MIMIC IV database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; \u0026nbsp;Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZuochen Xue and Aijuan Cheng designed the study. Zuochen Xue wrote the manuscript. Shan Sun, Yutian Shi, Peigen Yang and Jiayi Sun collected, analyzed, and interpreted the data. Aijuan Cheng conducted a thorough review, made edits, and provided approval for the manuscript. All authors have read and consented to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-055B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study are derived from the MIMIC-IV database, which can be accessed at the following link: https://physionet.org/content/mimiciv/2.2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Beth Israel Women\u0026rsquo;s Deaconess Medical Center and the MIT Institutional Review Board granted authorization for access to the MIMIC database. Furthermore, all patient information contained within the database has been anonymized, eliminating the need for informed consent. We completed the necessary online courses and exams to gain access to the database(ID: 53090145).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNedkoff L, Briffa T, Zemedikun D, Herrington S, Wright FL. Global Trends in Atherosclerotic Cardiovascular Disease. Clin Ther. 2023;45:1087\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWindecker S, Neumann F-J, J\u0026uuml;ni P, Sousa-Uva M, Falk V. Considerations for the choice between coronary artery bypass grafting and percutaneous coronary intervention as revascularization strategies in major categories of patients with stable multivessel coronary artery disease: an accompanying article of the task force of the 2018 ESC/EACTS guidelines on myocardial revascularization. Eur Heart J. 2019;40:204\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaukkanen JA, Kunutsor SK, Niemel\u0026auml; M, Kervinen K, Thuesen L, M\u0026auml;kikallio TH. All-cause mortality and major cardiovascular outcomes comparing percutaneous coronary angioplasty versus coronary artery bypass grafting in the treatment of unprotected left main stenosis: a meta-analysis of short-term and long-term randomised trials. Open Heart. 2017;4:e000638.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua T, Vlahos A, Shariat MH, Payne D, Redfearn D. Predicting adverse cardiovascular outcomes in post-coronary artery bypass grafting patients using novel ECG frequency analysis of the QRS complex. Ann Noninvasive Electrocardiol. 2021;26:e12822.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausenloy DJ, Candilio L, Evans R, Ariti C, Jenkins DP, Kolvekar S, et al. Remote Ischemic Preconditioning and Outcomes of Cardiac Surgery. N Engl J Med. 2015;373:1408\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarayan SM, Wang PJ, Daubert JP. New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic. J Am Coll Cardiol. 2019;73:70\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Li X, Yin Z, Lu P, Ma Y, Kai J, et al. Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer. Front Oncol. 2020;10:571169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorairahmadi S, Javaherforooshzadeh F, Jannatmakan F, Soltani F, Shidel Zadeh L. Evaluation of the Relationship Between Changes in Potassium Concentration and Arrhythmia During Coronary Artery Bypass Grafting Surgery. Anesthesiol Pain Med. 2022;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu Y, Fan W, Liu Y, Hong K. Glycemic variability and in-hospital death of critically ill patients and the role of ventricular arrhythmias. Cardiovasc Diabetol. 2023;22:134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang G, Jin Q, Mao Y. Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study. J Med Internet Res. 2023;25:e46891.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeo R, Albert CM. Epidemiology and Genetics of Sudden Cardiac Death. Circulation. 2012;125:620\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSourour N, Riveland E, N\u0026aelig;sgaard P, Kjekshus H, Larsen AI, R\u0026oslash;sj\u0026oslash; H et al. Associations Between Biomarkers of Myocardial Injury and Systemic Inflammation and Risk of Incident Ventricular Arrhythmia. JACC Clin Electrophysiol. 2024;:S2405500X24002950.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaldron NH, Fudim M, Mathew JP, Piccini JP. Neuromodulation for the Treatment of Heart Rhythm Disorders. JACC Basic Transl Sci. 2019;4:546\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahara R, Santoso A, Barano AZ. Myocardial Fluid Balance and Pathophysiology of Myocardial Edema in Coronary Artery Bypass Grafting. Cardiol Res Pract. 2020;2020:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdogan O, Karaayvaz E, Erdogan T, Panc C, Sarıkaya R, Oncul A, et al. A new biomarker that predicts ventricular arrhythmia in patients with ischemic dilated cardiomyopathy: Galectin-3. Rev Port Cardiol Engl Ed. 2021;40:829\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeichl P, Rafaj A, Kautzner J. Management of ventricular arrhythmias in heart failure: Current perspectives. Heart Rhythm O2. 2021;2:796\u0026ndash;806.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen LY, Sotoodehnia N, Bůžkov\u0026aacute; P, Lopez FL, Yee LM, Heckbert SR, et al. Atrial Fibrillation and the Risk of Sudden Cardiac Death: The Atherosclerosis Risk in Communities Study and Cardiovascular Health Study. JAMA Intern Med. 2013;173:29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusnak J, Behnes M, Reiser L, Schupp T, Bollow A, Reichelt T, et al. Atrial fibrillation increases the risk of recurrent ventricular tachyarrhythmias in implantable cardioverter defibrillator recipients. Arch Cardiovasc Dis. 2021;114:443\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Ao T, Zhen P, Hu M. Association between the anion gap and mortality in critically ill patients with influenza: A cohort study. Heliyon. 2024;10:e35199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArundel C, Lam PH, Gill GS, Patel S, Panjrath G, Faselis C, et al. Systolic Blood Pressure and Outcomes in Patients With Heart Failure With Reduced Ejection Fraction. J Am Coll Cardiol. 2019;73:3054\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J-H, Tseng C-L, Tsai S-H, Chiu W-T. Initial serum glucose level and white blood cell predict ventricular arrhythmia after first acute myocardial infarction. Am J Emerg Med. 2010;28:418\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Zhang Z, Zhou L, Zhang Z, Xiong Y, Hu Z, et al. Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis. Eur Heart J - Digit Health. 2023;4:245\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Y, Li L, Peng T, Tan Y, Sun Y, Cheng G, et al. The effect of milrinone on mortality in adult patients who underwent CABG surgery: a systematic review of randomized clinical trials with a meta-analysis and trial sequential analysis. BMC Cardiovasc Disord. 2020;20:328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaakeh Y, Overholser BR, Lopshire JC, Tisdale JE. Drug-Induced Atr Fibrillation: Drugs. 2012;72:1617\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeidner K, Behnes M, Schupp T, Rusnak J, Reiser L, Taton G, et al. Prognostic impact of chronic kidney disease and renal replacement therapy in ventricular tachyarrhythmias and aborted cardiac arrest. Clin Res Cardiol. 2019;108:669\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary artery disease, Coronary artery bypass graft, Ventricular arrhythmias, Prediction model, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-6557885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6557885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eTo examine the risk factors for in-hospital ventricular arrhythmias (VA) in coronary artery disease (CAD) patients after coronary artery bypass grafting (CABG) and develop a nomogram to predict the risk of VA occurrence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e This retrospective study involved the analysis of the clinical data (using the MIMIC-IV database) of 5,267 patients who underwent CABG. The risk factors for in-hospital VA were identified using the least absolute shrinkage and selection parameter (LASSO) and multivariate logistic regression analyses. Based on the outcomes, a risk prediction model was then developed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e The nomogram was constructed using eight predictive indicators: congestive heart failure (CHF), atrial fibrillation (AFib), base excess, systolic blood pressure (SBP), white blood cell (WBC) count, use of milrinone or dobutamine, continuous renal replacement therapy (CRRT), and logistic organ dysfunction system(LODS) score. According to the internal validation results, the model demonstrated a good predictive ability, with area under the curve (AUC) values of 0.777 and 0.743 in the training and validation sets, respectively. Furthermore, the calibration curve revealed that the model’s predicted values were in good agreement with the actual observed values. Moreover, the clinical decision curve analysis (DCA) showed that the model had a significant clinical net benefit at diagnostic thresholds of 0.1–0.95 and 0.1–0.8 in the training and validation sets, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Herein, we developed a risk prediction model for VA occurrence. The model demonstrated good discrimination, calibration, and clinical applicability, ensuring early VA prediction, which could facilitate risk stratification, enhance patient monitoring and management post-CABG, and reduce VA incidence in high-risk patients.\u003c/p\u003e","manuscriptTitle":"Development and validation of a risk prediction model for new-onset ventricular arrhythmias after coronary artery bypass grafting: A retrospective observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 13:11:06","doi":"10.21203/rs.3.rs-6557885/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-10T10:47:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-12T18:48:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-09T03:05:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-09T03:02:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-04-29T15:14:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cf7ba86-b1a0-442a-a0ac-0a2eea2b6b7e","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T10:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 13:11:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6557885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6557885","identity":"rs-6557885","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}