Development and Validation of a Predictive Model for 6-Month Mortality in Pulmonary Embolism: The Prognostic Value of the Systemic Inflammation Response Index (SIRI) 

Development and Validation of a Predictive Model for 6-Month Mortality in Pulmonary Embolism: The Prognostic Value of the Systemic Inflammation Response Index (SIRI) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Predictive Model for 6-Month Mortality in Pulmonary Embolism: The Prognostic Value of the Systemic Inflammation Response Index (SIRI) Zhiye Wu, Xuemei Wu, Xinhuang Hou, Weizhi Chen, Jun Lin, Qiaoyi Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9158089/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Pulmonary embolism (PE) remains a major cause of cardiovascular mortality, yet existing risk stratification tools predominantly focus on short-term outcomes. In this study, we set out to develop and externally validate a predictive model for 6-month all-cause mortality in PE patients, by integrating routine clinical parameters with systemic inflammatory indices to improve prognostic assessment. Methods We conducted a retrospective analysis of patients with PE from two independent cohorts. The MIMIC-IV database served as the training set for variable selection using least absolute shrinkage and selection operator (LASSO) regression. External validation was performed using data from the First Affiliated Hospital of Fujian Medical University. Model performance was assessed through discrimination (area under the receiver operating characteristic curve [AUC]), calibration (Hosmer–Lemeshow test and calibration plots), and decision curve analysis (DCA). Variable importance was further evaluated using random forest models and SHapley Additive exPlanations (SHAP) values. Results The training cohort comprised 817 patients from the MIMIC-IV database, with a 6-month mortality rate of 21.3% (n = 174). LASSO regression initially selected 16 predictors. External validation in the local cohort (n = 217, 6-month mortality: 16.5%) refined these to five core predictors: length of stay, respiratory rate, oxygen saturation (SpO 2 ), atrial fibrillation, and the systemic inflammation response index (SIRI). The parsimonious model demonstrated robust discrimination (AUC = 0.81, 95% confidence interval [CI]: 0.74–0.89), excellent calibration (Hosmer–Lemeshow P = 0.781), and positive net clinical benefit across a range of threshold probabilities. SHAP analysis identified SIRI and SpO 2 as the most influential predictors. Conclusion This simplified model incorporating five readily available clinical and inflammatory variables accurately predicts 6-month mortality in patients with PE. This model may facilitate early risk stratification and guide clinical decision-making in diverse healthcare settings. Pulmonary Embolism Mortality Systemic Inflammation Prognosis Nomograms Risk Stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pulmonary embolism (PE) represents a significant global health burden, with an estimated annual incidence ranging from 39 to 115 per 100,000 individuals [ 1 – 4 ] . Despite advances in diagnostic modalities and therapeutic interventions, PE-associated mortality remains substantial, particularly in the months following the index event [ 5 – 8 ] .Crucially, evidence suggests that the majority of PE-related deaths occur within the first six months, with the risk stabilizing thereafter; beyond this period, long-term mortality is primarily driven by underlying comorbidities rather than the index PE itself [ 4 ] . Accurate and timely risk stratification is therefore paramount for optimizing patient management. Contemporary risk stratification in PE predominantly relies on tools such as the Pulmonary Embolism Severity Index and its simplified version [ 1 , 4 , 9 ] . While these scores demonstrate robust performance in predicting 30-day mortality, their utility in forecasting longer-term outcomes is less well established [ 10 ] . Furthermore, these instruments do not incorporate emerging prognostic indicators, such as markers of systemic inflammation, which may provide valuable insights into the underlying pathophysiology and disease trajectory. A growing body of evidence has implicated systemic inflammation as a key determinant of adverse clinical outcomes in various cardiovascular diseases, including venous thromboembolism [ 11 , 12 ] . Composite inflammatory indices derived from routine complete blood counts, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the more recently described systemic inflammation response index (SIRI), offer practical and reproducible measures of the host inflammatory state. SIRI integrates information from three leukocyte lineages, offering a more comprehensively reflection of the complex interplay between innate immunity, thrombosis, and endothelial dysfunction [ 13 , 14 ] . In recent years, non-invasive inflammatory biomarkers have received attention as potential tools for assessing disease severity and predicting prognosis. Biomarkers such as C-reactive protein (CRP), NLR, PLR, and systemic immune inflammation index (SII) have been investigated in various diseases including PE. Previous evidence suggests that elevated NLR and PLR are promising inflammatory biomarkers for predicting acute PE [ 15 – 17 ] . While several studies have demonstrated the prognostic value of these measures in predicting mortality, some reports have yielded inconsistent or conflicting results [ 18 , 19 ] . To date, the incremental prognostic value of SIRI relative to established clinical predictors in PE has not been rigorously evaluated in externally validated, multivariable predictive models [ 15 ] . To address this gap, we conducted a dual-center retrospective study with the aim of identifying clinical and inflammatory predictors of 6-month all-cause mortality in patients with PE, using Least absolute shrinkage and selection operator (LASSO) regression on a large, publicly available database (MIMIC-IV). Additionally, we sought to externally validate and refine these predictors in an independent Chinese cohort. To enhance model interpretability and assess the importance of variables, we employed machine learning techniques, including random forest and SHapley Additive exPlanations (SHAP) analysis. Finally, we aimed to develop a simple, clinically applicable nomogram for individualized risk assessment. Materials and Methods Study Design and Data Sources This study was a retrospective prediction model study based on two independent cohorts. The training set data were obtained from the MIMIC-IV database in the United States. The local cohort data were derived from the electronic medical record system of the First Affiliated Hospital of Fujian Medical University, collected from [January 2015] to [December 2022]. This study adhered to the Declaration of Helsinki. Due to the retrospective and anonymized nature of the data, the use of the MIMIC-IV database was approved by the Institutional Review Board, and the use of local data was approved by the Ethics Committee of our hospital with a waiver of informed consent. Study Population Inclusion criteria:(1)patients diagnosed with PE using lung computed tomography pulmonary angiography (CTPA); (2) age ≥18 years; (3) complete hospitalization records. Exclusion criteria: (1) non-first admission; (2) length of hospital stay 5 times the upper limit of normal, or estimated glomerular filtration rate (eGFR) <15 mL/min/1.73m2; (4) insufficient follow-up data. Outcome The primary outcome was all-cause mortality within 6 months after admission. Survival status was determined through hospital medical records, outpatient follow-up records, or telephone follow-up. Time to death was defined as the number of days from the admission date to the date of death. Data Collection and Variable Definitions Demographics and Clinical Characteristics were extracted from electronic medical records. These included age, sex, body mass index (BMI), length of stay (LOS), temperature (T), systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO 2 ). Comorbidities and Lifestyle Factors included smoking history, alcohol consumption, hypertension, syncope, liver dysfunction, renal dysfunction, ischemic stroke, congestive heart failure (CHF), malignancy, chronic lung disease, and atrial fibrillation (AF). All comorbidities were determined based on ICD codes or discharge diagnoses and were represented as binary variables. Laboratory Variables were collected from the first laboratory tests performed within 24 hours of admission, including white blood cell count (WBC), neutrophil count (NUE), lymphocyte count (LC), monocyte count (MONO), platelet count (PLT), C-reactive protein (CRP), and albumin (ALB). Based on these, the following composite inflammatory indices were calculated: Neutrophil-to-lymphocyte ratio (NLR) = NUE / LC; Derived NLR (d-NLR) = NUE / (WBC – NUE); Lymphocyte-to-monocyte ratio (LMR) = LC / MONO; Platelet-to-lymphocyte ratio (PLR) = PLT / LC; Systemic immune-inflammation index (SII) = PLT × NUE / LC; Systemic inflammation response index (SIRI) = NUE × MONO / LC; Aggregate index of systemic inflammation (AISI) = NUE × MONO × PLT / LC. All laboratory variables were analyzed as continuous variables. Statistical Analysis Continuous variables were presented as mean ± standard deviation (for normally distributed data) or median (interquartile range) (for non-normally distributed data). Group comparisons for continuous variables were performed using the independent samples t-test or Mann-Whitney U test, as appropriate based on normality of distribution.Categorical variables were expressed as frequencies and percentages and were compared using the chi-square test or Fisher’s exact test. A two-sided P-value <0.05 was considered statistically significant. There were no missing values in either the MIMIC-IV database or the local data. To identify potential predictors of 6-month mortality from the MIMIC-IV database, LASSO regression was employed. LASSO adds an L1 penalty term to the loss function, which can shrink some regression coefficients to zero, thereby enabling automatic variable selection and model regularization. Candidate variables included all demographic, clinical, comorbidity, and inflammatory indices (a total of 38 variables). Continuous variables were standardized before modeling, and categorical variables were coded as 0/1. Ten-fold cross-validation was used to select the optimal penalty parameter λ, with the criterion of minimizing binomial deviance. The final model was determined by selecting the non-zero coefficient variables corresponding to lambda.1se (the largest λ within one standard error of the minimum binomial deviance), balancing model goodness-of-fit and parsimony. LASSO analysis was performed using the “glmnet” package in R software, and cross-validation error curves and coefficient path plots were generated. The 16 variables selected by LASSO were then incorporated into a multivariable logistic regression model using the local data, and the apparent discrimination (AUC) of the original model was calculated. To correct for potential over-optimism, internal validation was performed using bootstrap resampling (1000 repetitions). In each bootstrap sample, the model fitting process was repeated, and the AUC on that bootstrap sample and its AUC when applied to the original data were calculated. The difference between these two values represented the optimism index for that sample. The final optimism-corrected AUC was calculated as the apparent AUC minus the mean optimism index. Two machine learning methods, Random Forest and SHAP, were used for variable importance assessment. The “randomForest” package was used to build a random forest classification model on the local data, with ntree set to 500 and mtry set to the square root of the number of variables (default). The Mean Decrease Gini was used as the metric for variable importance. The “fastshap” package was used to calculate SHAP values for each variable for each sample, reflecting their marginal contribution to the predicted probability. The mean absolute SHAP value was used to measure global importance, and SHAP beeswarm plots were generated to visualize the direction and distribution of variable effects. The SHAP analysis was based on the random forest model, with the prediction function returning the probability of death, and the number of simulations (nsim) set to 50. Concurrently, LASSO regression was re-run on the local dataset to verify the stability of the reduced variable set. Based on the selected core variables, a logistic regression model was re-fitted on the local data. A nomogram was plotted using the nomogram function from the “rms” package to visualize the model. The AUC and its 95% confidence interval were calculated. A calibration plot was drawn to compare the agreement between predicted probabilities and observed frequencies, and the Hosmer-Lemeshow goodness-of-fit test was performed. Decision Curve Analysis (DCA) was conducted using the “rmda” package to evaluate the net benefit of the model across a range of threshold probabilities, comparing it with the “treat-all” and “treat-none” strategies. The DeLong test was used to compare the difference in AUC between the reduced model (with 5 variables) and the original 16-variable model, with a two-sided P-value <0.05 considered statistically significant. All statistical analyses were performed using R software (version 4.2.3, R Foundation for Statistical Computing, Vienna, Austria). Result We included 817 patients with pulmonary embolism who had complete data from the MIMIC-IV database. Among them, 154 patients died within 3 months (18.8%) and 174 patients died within 6 months (21.3%). After comparing the clinical parameters between patients who died within 6 months and those who survived, we found that patients who died within 6 months were older. In terms of mean vital signs, patients who died within 6 months had lower SBP, DBP, and SpO 2 , but a higher respiratory rate. Regarding comorbidities, patients who died within 6 months had a higher prevalence of liver dysfunction, renal dysfunction, CHF, malignancy, and AF. In addition, we calculated several inflammatory indices, and the results indicated that multiple inflammatory indices were significantly elevated in patients who died within 6 months compared with survivors ( Table 1 ). Through LASSO regression in a binomial logistic regression model, the optimal penalty parameter λ was selected using 10-fold cross-validation. The final model was determined by balancing prediction accuracy and model simplicity. After selecting λ, variables with non-zero coefficients were retained as the final selected predictors. From 814 patients in the MIMIC-IV database, 16 predictors associated with 180-day mortality were identified, including: age, LOS, temperature, respiratory rate, blood oxygen saturation, smoking history, alcohol history, liver dysfunction, renal dysfunction, congestive heart failure, malignancy, AF, NUE, LC, NLR, and SIRI. These variables will be used for constructing a predictive model in a subsequent cohort at our hospital ( Figure 1 ). In the local dataset, a total of 217 patients with PE were included, among whom 36 patients (16.5%) died within 6 months. The mean age was 61.31 ± 16.0 years, and the average LOS was 14.7 ± 13.8 days. After comparing the clinical parameters between PE patients who died within 6 months and those who survived, we found that patients who died within 6 months had higher HR and RR, lower SpO 2 levels, and a higher proportion of AF. In addition, multiple inflammatory indices were significantly elevated in patients who died within 6 months compared with survivors ( Table 2 ). From the MIMIC-IV cohort, LASSO regression identified 16 potential predictors of 6-month mortality. These variables were subsequently entered into a multivariable logistic regression model using the local hospital data. The initial model demonstrated good discrimination with an AUC of 0.83 (95% CI: 0.76–0.89). To correct for optimism inherent in model development, we performed 1000 bootstrap resamples, yielding an optimism-corrected AUC of 0.74 ( Figure 2 ), indicating that the model retained acceptable discriminative ability after internal validation. We employed random forest and SHAP analyses to assess variable importance and confirm the robustness of the selected predictors. In the random forest model, the top five variables by mean decrease Gini were LOS, SIRI, SpO 2 , NLR, and age ( Figure 3A ), while SHAP summary plots revealed that SIRI, SpO 2 , and LOS exhibited the highest mean absolute SHAP values, indicating their dominant contributions to the prediction ( Figure 3B ). Notably, AF also emerged as an important factor in the SHAP analysis. The concordance among LASSO, random forest, and SHAP methods underscores the stability and relevance of these five variables. The final reduced model, comprising LOS, respiratory rate, SpO 2 , AF, and SIRI, achieved an optimism-corrected AUC of 0.751 after bootstrap validation, suggesting that the simplification did not compromise predictive performance. Concurrently, to enhance model parsimony and clinical applicability, we conducted a second LASSO regression directly on the local dataset. With 10-fold cross-validation and the lambda.1se criterion, five variables were retained: LOS, RR, SpO 2 , AF, and SIRI ( Figure 4 ). Based on the five selected predictors, we constructed a multivariable logistic regression model to predict 6-month mortality in the local cohort ( Table 3 ). A nomogram was developed to visualize the model (Figure 5) , providing an intuitive tool for clinicians to estimate individualized risk by summing the points assigned to each predictor. The model demonstrated good discrimination, with an AUC of 0.81 (95% CI: 0.74–0.89; Figure 6A ). Calibration of the model was assessed using a calibration plot, which showed close agreement between predicted probabilities and observed outcomes (Figure 6B) ; the Hosmer–Lemeshow test yielded a non-significant χ 2 value of 4.78 (P = 0.781), further supporting good calibration. DCA was performed to evaluate the clinical utility of the model across a range of threshold probabilities (Figure 6C) . The DCA demonstrated that the model provided a positive net benefit compared with either the “treat-all” or “treat-none” strategies. Compared to the previously mentioned combination model of 16 variables (AUC = 0.83), the simplified model with five variables did not show a statistically significant difference in discrimination after delong test (Z = -0.78, p = 0.44). Collectively, these validation metrics indicate that the parsimonious model based on five readily available variables is both discriminative and well-calibrated, with favorable clinical net benefit. Discussion In this dual-center retrospective prognostic study, we developed and externally validated a predictive model for 6-month all-cause mortality in patients with PE. The final model, comprising five readily available variables—LOS, RR, SpO 2 , AF , and SIRI—demonstrated robust discrimination, excellent calibration, and favorable clinical utility in an independent validation cohort. These findings have several important implications for clinical practice and future research. Prognostic Value of Systemic Inflammation. SIRI reflects the complex and dynamic nature of the systemic inflammatory response in patients by combining neutrophil, monocyte, and lymphocyte counts. Neutrophils contribute to thrombus formation and stabilization through the release of neutrophil extracellular traps and tissue factor [20, 21] . Monocytes, upon activation, differentiate into macrophages and secrete pro-inflammatory cytokines, perpetuating the inflammatory cascade [22, 23] . Conversely, lymphopenia is a well-recognized consequence of stress-induced cortisol release and apoptosis, serving as a marker of physiological exhaustion [24, 25] . SIRI provides a more holistic assessment of the inflammatory milieu than simpler ratios such as NLR or PLR, By capturing information from all three lineages. Our results align with and extend prior studies demonstrating associations between SIRI and adverse outcomes in sepsis, cancer, and cardiovascular disease [26-32] , and suggest that SIRI warrants consideration for inclusion in future iterations of PE risk scores. Clinical Predictors and Pathophysiological Plausibility. The inclusion of AF, RR, SpO 2 , and LOS in the final model aligns with the evolving understanding of PE pathophysiology and prognosis, reflecting their multifaceted roles in both the acute and long-term clinical course of the disease. AF is a known risk factor for PE and may also serve as a marker of underlying cardiac dysfunction or hemodynamic compromise, both of which are associated with worse outcomes [33-35] . Tachypnea and hypoxemia at presentation are cardinal manifestations of right ventricular dysfunction and ventilation-perfusion mismatch, reflecting the severity of the embolic burden and its hemodynamic consequences [2, 6] . LOS, while inherently a post-admission variable, can be conceptualized as a composite surrogate for disease severity, in-hospital complications, and the overall clinical trajectory. Its inclusion in models predicting post-discharge mortality has precedent in the literature [36, 37] and captures prognostic information not fully accounted for by admission variables alone. Model Performance and Parsimony. The simplified five-variable model exhibited comparable discriminatory performance to the initial 16-variable model, underscoring the principle of parsimony in predictive modeling. The use of LASSO regression for variable selection, coupled with machine learning-based feature importance analysis (random forest and SHAP), enhanced the transparency and generalizability of our approach by mitigating the risk of overfitting. The consistency of variable importance across these distinct methodologies—with SIRI, SpO 2 , and LOS consistently ranking highest—strengthens confidence in the robustness of the selected predictors. The resulting nomogram provides a practical, visual tool for clinicians to estimate individualized 6-month mortality risk at the bedside using variables routinely captured in electronic health records. Comparison with Existing Risk Scores. Direct comparison with existing tools such as PESI or sPESI is limited by differing outcome timeframe (30 days vs. 6 months) and predictor compositions. Nevertheless, our model demonstrates favorable discriminative performance relative to published short-term PESI metrics [6, 38, 39] . A key advantage of our approach is the incorporation of SIRI, a dynamic and modifiable biomarker that reflects therapy response and enables longitudinal risk monitoring. Limitations. Several limitations merit acknowledgment. First, the retrospective design introduces the potential for selection bias and unmeasured confounding, despite our use of rigorous statistical methods to adjust for measured confounders. Second, the sample size of the external validation cohort, while adequate for primary analyses, precluded robust subgroup analyses. Third, we were unable to include potentially important imaging variables, such as right ventricular dysfunction assessed by echocardiography or computed tomography, which may have incremental prognostic value. Fourth, the model was developed and validated in hospital-based cohorts; its performance in outpatient or emergency department settings remains to be established. Finally, while we performed internal validation using bootstrapping, external validation in diverse geographic and ethnic populations is essential before widespread clinical implementation. Conclusions In summary, we have developed and externally validated a parsimonious predictive model for 6-month mortality in patients with PE, based on five readily available variables. The model demonstrates robust discrimination, calibration, and clinical utility. The prominence of SIRI underscores the critical role of systemic inflammation in determining long-term prognosis following PE. This simple, practical tool may facilitate early identification of high-risk patients, inform clinical decision-making. Declarations Competing interests The authors declare that they have no conflict of interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. Ethics approval This study was carried out in strict compliance with the Declaration of Helsinki. It received approval from the Ethics Committees of The First Affiliated Hospital of Fujian Medical University. Given the retrospective and anonymized characteristics of the data, the necessity for obtaining informed consent was waived. Data Availability Statement The training set data used in this study are publicly available from the MIMIC-IV database (https://physionet.org/content/mimiciv/2.2/). The external validation data (local cohort) used in this study are from the electronic medical record system of The First Affiliated Hospital of Fujian Medical University and are not publicly available due to ethical restrictions and patient privacy protection requirements. 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NT Pro-BNP can be used as a risk predictor of clinical atrial fibrillation with or without left atrial enlargement [J]. Clin Cardiol, 2022, 45 (1):68. Jiménez D, Rodríguez C, León F, et al. Randomised controlled trial of a prognostic assessment and management pathway to reduce the length of hospital stay in normotensive patients with acute pulmonary embolism [J]. Eur Respir J, 2022, 59 (2) Donadini M P, Dentali F, Castellaneta M, et al. Pulmonary embolism prognostic factors and length of hospital stay: A cohort study [J]. Thromb Res, 2017, 156:155. Hariharan P, Takayesu J K, Kabrhel C. Association between the Pulmonary Embolism Severity Index (PESI) and short-term clinical deterioration [J]. Thromb Haemost, 2011, 105 (4):706. Posch F, Ay C. Symptoms, signs, suspicion and setting: a PESI score for cancer-associated pulmonary embolism? [J]. Eur Respir J, 2017, 49 (1) Tables Table 1. Comparison of Clinical Characteristics of Pulmonary Embolism Patients in the MIMIC-IV Database for Death and Survival within 6 Months Characteristic Alive (n=640) Death (n=174) P-value Age (years) 62.00 (50.25, 72.00) 69.00 (57.75, 78.00) <0.001* Sex n (%) 0.175 Male 346 (54.0%) 84 (48.3%) Female 294 (46.0%) 90 (51.7%) BMI (kg/m 2 ) 28.23 (23.78, 34.09) 27.55 (23.53, 32.83) 0.215 LOS (days) 17.00 (9.00, 28.00) 12.00 (6.00, 20.00) <0.001* T (℃) 37.00 (36.75, 37.33) 36.95 (36.66, 37.29) 0.100 SBP (mmHg) 118.82 (110.28, 130.16) 111.47 (102.65, 120.09) <0.001* DBP (mmHg) 68.90 (61.84, 76.20) 63.51 (56.25, 71.44) <0.001* HR (bpm) 90.71 (80.59, 99.55) 91.79 (83.07, 100.81) 0.062 RR (bpm) 20.60 (18.23, 23.20) 22.51 (20.36, 25.17) <0.001* SpO 2 (%) 96.63 (95.45, 97.73) 95.89 (94.05, 97.18) <0.001* Smoking n (%) 240 (37.5%) 50 (28.7%) 0.032* Drinking n (%) 64 (10.0%) 9 (5.1%) 0.052 Hypertension n (%) 377 (58.9%) 105 (60.3%) 0.732 Syncope n (%) 6 (0.9%) 2 (1.1%) 0.682 Liver dysfunction n (%) 79 (12.3%) 51 (29.3%) <0.001* Renal dysfunction n (%) 73 (11.4%) 35 (20.1%) 0.003* Ischemic stroke n (%) 52 (8.1%) 16 (9.1%) 0.644 CHF n (%) 156 (24.3%) 60 (34.4%) 0.007* Malignancy n (%) 132 (20.6%) 64 (36.8%) <0.001* Chronic lung disease n (%) 139 (21.7%) 47 (27.0%) 0.140 Atrial fibrillation n (%) 104 (16.2%) 45 (25.8%) 0.004* WBC 12.20 (8.70, 15.80) 14.05 (9.00, 19.83) 0.006* NUE 9.27 (6.36, 12.66) 11.11 (7.13, 16.10) 0.001* LC 1.23 (0.77, 1.93) 0.93 (0.55, 1.48) <0.001* MONO 0.69 (0.42, 1.04) 0.70 (0.40, 1.22) 0.446 PLT 217.00 (153.25, 295.00) 203.00 (119.50, 301.00) 0.162 NLR 7.12 (4.14, 12.23) 11.38 (6.59, 20.81) <0.001* d_NLR 6.47 (3.77, 11.27) 10.60 (6.07, 19.51) <0.001* LMR 1.97(1.04, 3.03) 1.36 (0.74, 2.15) <0.001* PLR 18.76 (10.75, 38.61) 27.80 (13.11, 58.25) <0.001* SII 1434.79 (727.60, 3012.23) 2289.98 (1097.98, 4985.90) <0.001* SIRI 39.77 (24.25, 73.76) 59.43 (35.10, 111.77) <0.001* AISI 250.78 (107.93, 560.50) 373.11 (149.34, 1145.16) <0.001* *P<0.05 Table 2. Baseline Clinical Characteristics of Pulmonary Embolism Patients in the Local Cohort Stratified by 6-Month Mortality Characteristic Alive (n=181) Death (n=36) P-value Age (years) 64.0 (49.5, 73.0) 67.5 (53.3, 75.0) 0.158 Sex 0.119 Female 106 (58.6%) 16 (44.4%) Male 75 (41.4%) 20 (55.6%) BMI (kg/m 2 ) 23.6±3.3 22.9±3.1 0.233 LOS (days) 10.0 (16.0, 36.0) 16.5 (10.5, 30.8) <0.001* T (℃) 36.5 (36.3, 36.7) 36.5 (36.3, 36.8) 0.461 SBP (mmHg) 126 (116.0, 137.5) 125.0 (107.8, 138.0) 0.223 DBP (mmHg) 76.0 (68.0, 84.0) 75.0 (69.3, 82.0) 0.533 HR (bpm) 76.0 (70.0, 80.5) 85 (75.3, 94.0) <0.001* RR (bpm) 20.0 (19.0, 20.0) 20.0 (20.0, 24.0) 0.003* SpO 2 (%) 97.0 (96.0, 98.0) 94.0 (90.3, 96.0) <0.001* Smoking n (%) 44 (24.3%) 12 (33.3%) 0.258 Drinking n (%) 31 (17.1%) 4 (14.1%) 0.370 Hypertension n (%) 71 (32.9%) 20 (55.6%) 0.070 Syncope n (%) 22 (12.2%) 7 (19.4%) 0.365 Liver dysfunction n (%) 28 (15.5%) 10 (27.8%) 0.076 Renal dysfunction n (%) 16 (8.8%) 6 (16.7%) 0.263 Ischemic stroke n (%) 5 (2.8%) 17 (2.8%) 0.996 CHF n (%) 22 (12.2%) 7 (19.4%) 0.365 Malignancy n (%) 8 (4.4%) 5 (13.9%) 0.072 Chronic lung disease n (%) 40 (22.1%) 8 (22.2%) 0.987 Atrial fibrillation n (%) 8 (4.4%) 8 (22.2%) <0.001* WBC 7.03 (5.83, 9.48) 8.4 (5.76, 10.47) 0.203 NUE 4.84 (3.74, 7.05 ) 6.03 (4.58, 8.23) 0.051 LC 1.43 (1.05, 1.99 ) 1.07 (0.67, 1.61 ) 0.002 MONO 0.41 (0.30, 0.54 ) 0.47 (0.30, 0.69 ) 0.089 PLT 226 (176.5,287) 157 (133, 231.75) <0.001* NLR 3.36 (2.22,5.60 ) 5.45 (2.99, 11.45 ) <0.001* d_NLR 2.16 (1.55,3.49 ) 3.24 (2.04, 5.42 ) <0.001* LMR 3.58 (2.39,5.00 ) 2.30 (1.33, 3.32 ) <0.001* PLR 157.02 (105.71, 238.79) 178.35 (98.49, 269.24) 0.559 SII 770.43 (472.02, 1447.38) 1113.92 (445.86, 1978.62) 0.173 SIRI 1.35 (0.82, 2.49) 2.89 (1.25,5.35） <0.001* AISI 319.84 (169.11, 642.16) 478.99 (175.33, 1295.38) 0.061 *P<0.05 Table 3. Univariate and Multivariate Logistic Regression Analyses Exploring 6-Month Mortality in Pulmonary Embolism Patients Characteristic Univariate Logistic Regression Multivariate Logistic Regression OR (95% CI) P-value OR (95% CI) P-value LOS (days) 1.06 (1.03, 1.09) <0.001* 1.04 (1.01, 1.07) 0.014* RR (bpm) 1.33 (1.15, 1.54) <0.001* 1.15 (0.97, 1.36) 0.121 SpO 2 (%) 0.85 (0.78, 0.92) <0.001* 0.88 (0.80, 0.96) 0.004* Atrial fibrillation 5.16 (2.18, 12.21) <0.001* 3.70 (1.32, 10.34) 0.013* SIRI 1.18 (1.05, 1.31) 0.004 1.10 (0.98, 1.22) 0.104 *P<0.05 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 18 Mar, 2026 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-9158089","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625403891,"identity":"1e1d0f0b-bf65-43d5-bbe5-b67591f36056","order_by":0,"name":"Zhiye Wu","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiye","middleName":"","lastName":"Wu","suffix":""},{"id":625403893,"identity":"95c797cf-191b-4d77-9cc7-c8608215e257","order_by":1,"name":"Xuemei Wu","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuemei","middleName":"","lastName":"Wu","suffix":""},{"id":625403894,"identity":"1cf09365-a5d2-4eac-96e8-650aeea6e08d","order_by":2,"name":"Xinhuang Hou","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinhuang","middleName":"","lastName":"Hou","suffix":""},{"id":625403900,"identity":"51735748-61d3-410d-ac61-e85dcc5649d9","order_by":3,"name":"Weizhi Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weizhi","middleName":"","lastName":"Chen","suffix":""},{"id":625403902,"identity":"7368c1ae-21b1-41a8-a2e8-a9cae82abfcb","order_by":4,"name":"Jun Lin","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Lin","suffix":""},{"id":625403904,"identity":"5cf72520-5567-4e9a-9dbe-afde66ab40e6","order_by":5,"name":"Qiaoyi Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACfmb+j4//VEjw8LM3EKlFsr3B2IDnjIWMZM8BIrUYnDlgJsHbVmFjMCOBWC03EhIkJNskeAwkH2+8wVBjE03YYTcSDhgYnJPgMZdOK7ZgOJaW20BIC9+NxIaEhDIJHsvZOWYSjA2HCWthuJHMcOAAG9BhN88QqUXgzDHGxgaQX27wEKlFsr2HmZnhjASPZA/QLwnE+IWfmYf9N0NFnT0/++GNNz7U2BDhFyRgIJFAinKIFlJ1jIJRMApGwcgAANZ5PYKOpSrfAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qiaoyi","middleName":"","lastName":"Wu","suffix":""},{"id":625403907,"identity":"8ba57457-df16-4665-a3f5-340f8ef4545f","order_by":6,"name":"Xiaoyan Ji","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2026-03-18 10:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9158089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9158089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107448610,"identity":"87d33ee7-7fee-4a50-8847-ef29c42dc8c0","added_by":"auto","created_at":"2026-04-21 14:58:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO regression analysis of factors associated with 6-month mortality after pulmonary embolism in the MIMIC-IV database.\u003c/strong\u003e (A) Coefficient profiles of the 16 variables included in the LASSO regression model. (B) Partial likelihood deviance plotted against the log-transformed tuning parameter λ.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/9fb8478c0c1fd65da5894db7.png"},{"id":107448605,"identity":"c8e2c684-bce3-43f7-84a7-d831da55b508","added_by":"auto","created_at":"2026-04-21 14:58:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve of the model constructed using 16 variables derived from the MIMIC-IV database in the local cohort, along with the ROC curve after bootstrap internal validation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/9fc3506a33b99589e1a42d75.png"},{"id":107489394,"identity":"50ff178a-7643-4ceb-939d-295ec52da92d","added_by":"auto","created_at":"2026-04-22 02:47:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature Importance Analysis Based on Random Forest and SHAP Methods.\u003c/strong\u003e (A) Variable importance from the random forest model, with features ranked in descending order according to the mean decrease in Gini (mean absolute value); (B) SHAP summary plot of variable importance, with features ranked in descending order according to the mean absolute SHAP value.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/14e0d7a670c1ad25b2373579.png"},{"id":107448606,"identity":"3f9970d0-3dc3-43ab-814c-83acb4ec59d2","added_by":"auto","created_at":"2026-04-21 14:58:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO regression analysis of factors among 16 variables derived from the MIMIC-IV database associated with 6-month mortality in local cohort.\u003c/strong\u003e (A) Coefficient profiles of the 5 variables included in the LASSO regression model. (B) Partial likelihood deviance plotted against the log-transformed tuning parameter λ.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/cda5a0c3b7c08af9bf8d941d.png"},{"id":108005609,"identity":"9fa7172f-c32a-4c16-ba0d-fc5f27c219b6","added_by":"auto","created_at":"2026-04-28 12:43:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":77112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for Predicting 6-Month Mortality After Pulmonary Embolism in the Local Cohort.\u003c/strong\u003e The total score corresponds to the probability at the bottom, summing each value of the variable.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/d8e064c1d2c9a5a4fe46b529.png"},{"id":107448609,"identity":"840d1a58-4b21-4eed-9d3e-63328f8978a6","added_by":"auto","created_at":"2026-04-21 14:58:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of the Predictive Performance of this Model.\u003c/strong\u003e (A) Receiver Operating Characteristic Curve; (B) Calibration Plots; (C) Decision Curve Analysis Curve. The horizontal and vertical coordinates represent the threshold probability and net benefit, respectively. The graph contains three types of lines: the blue line represent baseline model; the gray and black lines represent two extreme examples. All samples on the horizontal line are negative, none are intervened, and no benefits are gained.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/92eefb0f407717c58b2ff6bb.png"},{"id":108807082,"identity":"e6d19bda-2bd6-4256-ac3f-58259749eaec","added_by":"auto","created_at":"2026-05-08 15:30:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":884986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9158089/v1/2f7fb522-aba6-441e-921b-a7d981bf5d2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Predictive Model for 6-Month Mortality in Pulmonary Embolism: The Prognostic Value of the Systemic Inflammation Response Index (SIRI) ","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary embolism (PE) represents a significant global health burden, with an estimated annual incidence ranging from 39 to 115 per 100,000 individuals\u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite advances in diagnostic modalities and therapeutic interventions, PE-associated mortality remains substantial, particularly in the months following the index event\u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.Crucially, evidence suggests that the majority of PE-related deaths occur within the first six months, with the risk stabilizing thereafter; beyond this period, long-term mortality is primarily driven by underlying comorbidities rather than the index PE itself\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Accurate and timely risk stratification is therefore paramount for optimizing patient management.\u003c/p\u003e \u003cp\u003eContemporary risk stratification in PE predominantly relies on tools such as the Pulmonary Embolism Severity Index and its simplified version\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. While these scores demonstrate robust performance in predicting 30-day mortality, their utility in forecasting longer-term outcomes is less well established\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, these instruments do not incorporate emerging prognostic indicators, such as markers of systemic inflammation, which may provide valuable insights into the underlying pathophysiology and disease trajectory.\u003c/p\u003e \u003cp\u003eA growing body of evidence has implicated systemic inflammation as a key determinant of adverse clinical outcomes in various cardiovascular diseases, including venous thromboembolism\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Composite inflammatory indices derived from routine complete blood counts, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the more recently described systemic inflammation response index (SIRI), offer practical and reproducible measures of the host inflammatory state. SIRI integrates information from three leukocyte lineages, offering a more comprehensively reflection of the complex interplay between innate immunity, thrombosis, and endothelial dysfunction\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In recent years, non-invasive inflammatory biomarkers have received attention as potential tools for assessing disease severity and predicting prognosis. Biomarkers such as C-reactive protein (CRP), NLR, PLR, and systemic immune inflammation index (SII) have been investigated in various diseases including PE. Previous evidence suggests that elevated NLR and PLR are promising inflammatory biomarkers for predicting acute PE\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. While several studies have demonstrated the prognostic value of these measures in predicting mortality, some reports have yielded inconsistent or conflicting results\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. To date, the incremental prognostic value of SIRI relative to established clinical predictors in PE has not been rigorously evaluated in externally validated, multivariable predictive models\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address this gap, we conducted a dual-center retrospective study with the aim of identifying clinical and inflammatory predictors of 6-month all-cause mortality in patients with PE, using Least absolute shrinkage and selection operator (LASSO) regression on a large, publicly available database (MIMIC-IV). Additionally, we sought to externally validate and refine these predictors in an independent Chinese cohort. To enhance model interpretability and assess the importance of variables, we employed machine learning techniques, including random forest and SHapley Additive exPlanations (SHAP) analysis. Finally, we aimed to develop a simple, clinically applicable nomogram for individualized risk assessment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a retrospective prediction model study based on two independent cohorts. The training set data were obtained from the MIMIC-IV database in the United States. The local cohort data were derived from the electronic medical record system of the First Affiliated Hospital of Fujian Medical University, collected from [January 2015] to [December 2022]. This study adhered to the Declaration of Helsinki. Due to the retrospective and anonymized nature of the data, the use of the MIMIC-IV database was approved by the Institutional Review Board, and the use of local data was approved by the Ethics Committee of our hospital\u0026nbsp;with a waiver of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Inclusion criteria:(1)patients diagnosed with PE using lung computed tomography pulmonary angiography (CTPA); (2) age \u0026ge;18 years; (3) complete hospitalization records. Exclusion criteria: (1) non-first admission; (2) length of hospital stay \u0026lt;24 hours; (3) severe hepatic or renal dysfunction, defined as alanine aminotransferase (ALT) or aspartate aminotransferase (AST) \u0026gt;5 times the upper limit of normal, or estimated glomerular filtration rate (eGFR) \u0026lt;15 mL/min/1.73m2; (4) insufficient follow-up data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality within 6 months after admission. Survival status was determined through hospital medical records, outpatient follow-up records, or telephone follow-up. Time to death was defined as the number of days from the admission date to the date of death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Variable Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographics and Clinical Characteristics\u003c/strong\u003e were extracted from electronic medical records. These included age, sex, body mass index (BMI), length of stay (LOS), temperature (T), systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidities and Lifestyle Factors\u003c/strong\u003e included smoking history, alcohol consumption, hypertension, syncope, liver dysfunction, renal dysfunction, ischemic stroke, congestive heart failure (CHF), malignancy, chronic lung disease, and atrial fibrillation (AF). All comorbidities were determined based on ICD codes or discharge diagnoses and were represented as binary variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory Variables\u003c/strong\u003e were collected from the first laboratory tests performed within 24 hours of admission, including white blood cell count (WBC), neutrophil count (NUE), lymphocyte count (LC), monocyte count (MONO), platelet count (PLT), C-reactive protein (CRP), and albumin (ALB). Based on these, the following composite inflammatory indices were calculated: Neutrophil-to-lymphocyte ratio (NLR) = NUE / LC; Derived NLR (d-NLR) = NUE / (WBC \u0026ndash; NUE); Lymphocyte-to-monocyte ratio (LMR) = LC / MONO; Platelet-to-lymphocyte ratio (PLR) = PLT / LC; Systemic immune-inflammation index (SII) = PLT \u0026times; NUE / LC; Systemic inflammation response index (SIRI) = NUE \u0026times; MONO / LC; Aggregate index of systemic inflammation (AISI) = NUE \u0026times; MONO \u0026times; PLT / LC. All laboratory variables were analyzed as continuous variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were presented as mean \u0026plusmn; standard deviation (for normally distributed data) or median (interquartile range) (for non-normally distributed data). Group comparisons for continuous variables were performed using the independent samples t-test or Mann-Whitney U test, as appropriate based on normality of distribution.Categorical variables were expressed as frequencies and percentages and were compared using the chi-square test or Fisher\u0026rsquo;s exact test. A two-sided P-value \u0026lt;0.05 was considered statistically significant. There were no missing values in either the MIMIC-IV database or the local data.\u003c/p\u003e\n\u003cp\u003eTo identify potential predictors of 6-month mortality from the MIMIC-IV database, LASSO regression was employed. LASSO adds an L1 penalty term to the loss function, which can shrink some regression coefficients to zero, thereby enabling automatic variable selection and model regularization. Candidate variables included all demographic, clinical, comorbidity, and inflammatory indices (a total of 38 variables). Continuous variables were standardized before modeling, and categorical variables were coded as 0/1. Ten-fold cross-validation was used to select the optimal penalty parameter \u0026lambda;, with the criterion of minimizing binomial deviance. The final model was determined by selecting the non-zero coefficient variables corresponding to lambda.1se (the largest \u0026lambda; within one standard error of the minimum binomial deviance), balancing model goodness-of-fit and parsimony. LASSO analysis was performed using the \u0026ldquo;glmnet\u0026rdquo; package in R software, and cross-validation error curves and coefficient path plots were generated.\u003c/p\u003e\n\u003cp\u003eThe 16 variables selected by LASSO were then incorporated into a multivariable logistic regression model using the local data, and the apparent discrimination (AUC) of the original model was calculated. To correct for potential over-optimism, internal validation was performed using bootstrap resampling (1000 repetitions). In each bootstrap sample, the model fitting process was repeated, and the AUC on that bootstrap sample and its AUC when applied to the original data were calculated. The difference between these two values represented the optimism index for that sample. The final optimism-corrected AUC was calculated as the apparent AUC minus the mean optimism index.\u003c/p\u003e\n\u003cp\u003eTwo machine learning methods, Random Forest and SHAP, were used for variable importance assessment. The \u0026ldquo;randomForest\u0026rdquo; package was used to build a random forest classification model on the local data, with ntree set to 500 and mtry set to the square root of the number of variables (default). The Mean Decrease Gini was used as the metric for variable importance. The \u0026ldquo;fastshap\u0026rdquo; package was used to calculate SHAP values for each variable for each sample, reflecting their marginal contribution to the predicted probability. The mean absolute SHAP value was used to measure global importance, and SHAP beeswarm plots were generated to visualize the direction and distribution of variable effects. The SHAP analysis was based on the random forest model, with the prediction function returning the probability of death, and the number of simulations (nsim) set to 50. Concurrently, LASSO regression was re-run on the local dataset to verify the stability of the reduced variable set.\u003c/p\u003e\n\u003cp\u003eBased on the selected core variables, a logistic regression model was re-fitted on the local data. A nomogram was plotted using the nomogram function from the \u0026ldquo;rms\u0026rdquo; package to visualize the model. The AUC and its 95% confidence interval were calculated. A calibration plot was drawn to compare the agreement between predicted probabilities and observed frequencies, and the Hosmer-Lemeshow goodness-of-fit test was performed. Decision Curve Analysis (DCA) was conducted using the \u0026ldquo;rmda\u0026rdquo; package to evaluate the net benefit of the model across a range of threshold probabilities, comparing it with the \u0026ldquo;treat-all\u0026rdquo; and \u0026ldquo;treat-none\u0026rdquo; strategies. The DeLong test was used to compare the difference in AUC between the reduced model (with 5 variables) and the original 16-variable model, with a two-sided P-value \u0026lt;0.05 considered statistically significant.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.2.3, R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eWe included 817 patients with pulmonary embolism who had complete data from the MIMIC-IV database. Among them, 154 patients died within 3 months (18.8%) and 174 patients died within 6 months (21.3%). After comparing the clinical parameters between patients who died within 6 months and those who survived, we found that patients who died within 6 months were older. In terms of mean vital signs, patients who died within 6 months had lower SBP, DBP, and SpO\u003csub\u003e2\u003c/sub\u003e, but a higher respiratory rate. Regarding comorbidities, patients who died within 6 months had a higher prevalence of liver dysfunction, renal dysfunction, CHF, malignancy, and AF. In addition, we calculated several inflammatory indices, and the results indicated that multiple inflammatory indices were significantly elevated in patients who died within 6 months compared with survivors (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThrough LASSO regression in a binomial logistic regression model, the optimal penalty parameter \u0026lambda; was selected using 10-fold cross-validation. The final model was determined by balancing prediction accuracy and model simplicity. After selecting \u0026lambda;, variables with non-zero coefficients were retained as the final selected predictors. From 814 patients in the MIMIC-IV database, 16 predictors associated with 180-day mortality were identified, including: age, LOS, temperature, respiratory rate, blood oxygen saturation, smoking history, alcohol history, liver dysfunction, renal dysfunction, congestive heart failure, malignancy, AF, NUE, LC, NLR, and SIRI. These variables will be used for constructing a predictive model in a subsequent cohort at our hospital (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn the local dataset, a total of 217 patients with PE were included, among whom 36 patients (16.5%) died within 6 months. The mean age was 61.31 \u0026plusmn; 16.0 years, and the average LOS was 14.7 \u0026plusmn; 13.8 days. After comparing the clinical parameters between PE patients who died within 6 months and those who survived, we found that patients who died within 6 months had higher HR and RR, lower SpO\u003csub\u003e2\u003c/sub\u003e levels, and a higher proportion of AF. In addition, multiple inflammatory indices were significantly elevated in patients who died within 6 months compared with survivors (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFrom the MIMIC-IV cohort, LASSO regression identified 16 potential predictors of 6-month mortality. These variables were subsequently entered into a multivariable logistic regression model using the local hospital data. The initial model demonstrated good discrimination with an AUC of 0.83 (95% CI: 0.76\u0026ndash;0.89). To correct for optimism inherent in model development, we performed 1000 bootstrap resamples, yielding an optimism-corrected AUC of 0.74 (\u003cstrong\u003eFigure 2\u003c/strong\u003e), indicating that the model retained acceptable discriminative ability after internal validation.\u003c/p\u003e\n\u003cp\u003eWe employed random forest and SHAP analyses to assess variable importance and confirm the robustness of the selected predictors. In the random forest model, the top five variables by mean decrease Gini were LOS, SIRI, SpO\u003csub\u003e2\u003c/sub\u003e, NLR, and age (\u003cstrong\u003eFigure 3A\u003c/strong\u003e), while SHAP summary plots revealed that SIRI, SpO\u003csub\u003e2\u003c/sub\u003e, and LOS exhibited the highest mean absolute SHAP values, indicating their dominant contributions to the prediction (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). Notably, AF also emerged as an important factor in the SHAP analysis. The concordance among LASSO, random forest, and SHAP methods underscores the stability and relevance of these five variables. The final reduced model, comprising LOS, respiratory rate, SpO\u003csub\u003e2\u003c/sub\u003e, AF, and SIRI, achieved an optimism-corrected AUC of 0.751 after bootstrap validation, suggesting that the simplification did not compromise predictive performance. Concurrently, to enhance model parsimony and clinical applicability, we conducted a second LASSO regression directly on the local dataset. With 10-fold cross-validation and the lambda.1se criterion, five variables were retained: LOS, RR, SpO\u003csub\u003e2\u003c/sub\u003e, AF, and SIRI (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the five selected predictors, we constructed a multivariable logistic regression model to predict 6-month mortality in the local cohort (\u003cstrong\u003eTable 3\u003c/strong\u003e). A nomogram was developed to visualize the model\u0026nbsp;\u003cstrong\u003e(Figure 5)\u003c/strong\u003e, providing an intuitive tool for clinicians to estimate individualized risk by summing the points assigned to each predictor. The model demonstrated good discrimination, with an AUC of 0.81 (95% CI: 0.74\u0026ndash;0.89;\u0026nbsp;\u003cstrong\u003eFigure 6A\u003c/strong\u003e). Calibration of the model was assessed using a calibration plot, which showed close agreement between predicted probabilities and observed outcomes\u0026nbsp;\u003cstrong\u003e(Figure 6B)\u003c/strong\u003e; the Hosmer\u0026ndash;Lemeshow test yielded a non-significant \u0026chi;\u003csup\u003e2\u003c/sup\u003e value of 4.78 (P = 0.781), further supporting good calibration. DCA was performed to evaluate the clinical utility of the model across a range of threshold probabilities\u0026nbsp;\u003cstrong\u003e(Figure 6C)\u003c/strong\u003e. The DCA demonstrated that the model provided a positive net benefit compared with either the \u0026ldquo;treat-all\u0026rdquo; or \u0026ldquo;treat-none\u0026rdquo; strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to the previously mentioned combination model of 16 variables (AUC = 0.83), the simplified model with five variables did not show a statistically significant difference in discrimination after delong test (Z = -0.78, p = 0.44). Collectively, these validation metrics indicate that the parsimonious model based on five readily available variables is both discriminative and well-calibrated, with favorable clinical net benefit.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this dual-center retrospective prognostic study, we developed and externally validated a predictive model for 6-month all-cause mortality in patients with PE. The final model, comprising five readily available variables\u0026mdash;LOS, RR, SpO\u003csub\u003e2\u003c/sub\u003e, AF , and SIRI\u0026mdash;demonstrated robust discrimination, excellent calibration, and favorable clinical utility in an independent validation cohort. These findings have several important implications for clinical practice and future research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic Value of Systemic Inflammation.\u003c/strong\u003e SIRI reflects the complex and dynamic nature of the systemic inflammatory response in patients by combining neutrophil, monocyte, and lymphocyte counts. Neutrophils contribute to thrombus formation and stabilization through the release of neutrophil extracellular traps and tissue factor\u003csup\u003e[20, 21]\u003c/sup\u003e. Monocytes, upon activation, differentiate into macrophages and secrete pro-inflammatory cytokines, perpetuating the inflammatory cascade\u003csup\u003e[22, 23]\u003c/sup\u003e. Conversely, lymphopenia is a well-recognized consequence of stress-induced cortisol release and apoptosis, serving as a marker of physiological exhaustion\u003csup\u003e[24, 25]\u003c/sup\u003e. SIRI provides a more holistic assessment of the inflammatory milieu than simpler ratios such as NLR or PLR, By capturing information from all three lineages. Our results align with and extend prior studies demonstrating associations between SIRI and adverse outcomes in sepsis, cancer, and cardiovascular disease\u003csup\u003e[26-32]\u003c/sup\u003e, and suggest that SIRI warrants consideration for inclusion in future iterations of PE risk scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Predictors and Pathophysiological Plausibility.\u003c/strong\u003e The inclusion of AF, RR, SpO\u003csub\u003e2\u003c/sub\u003e, and LOS in the final model aligns with the evolving understanding of PE pathophysiology and prognosis, reflecting their multifaceted roles in both the acute and long-term clinical course of the disease. AF is a known risk factor for PE and may also serve as a marker of underlying cardiac dysfunction or hemodynamic compromise, both of which are associated with worse outcomes\u003csup\u003e[33-35]\u003c/sup\u003e. Tachypnea and hypoxemia at presentation are cardinal manifestations of right ventricular dysfunction and ventilation-perfusion mismatch, reflecting the severity of the embolic burden and its hemodynamic consequences\u003csup\u003e[2, 6]\u003c/sup\u003e. LOS, while inherently a post-admission variable, can be conceptualized as a composite surrogate for disease severity, in-hospital complications, and the overall clinical trajectory. Its inclusion in models predicting post-discharge mortality has precedent in the literature\u003csup\u003e[36, 37]\u003c/sup\u003e and captures prognostic information not fully accounted for by admission variables alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Performance and Parsimony.\u0026nbsp;\u003c/strong\u003eThe simplified five-variable model exhibited comparable discriminatory performance to the initial 16-variable model, underscoring the principle of parsimony in predictive modeling. The use of LASSO regression for variable selection, coupled with machine learning-based feature importance analysis (random forest and SHAP), enhanced the transparency and generalizability of our approach by mitigating the risk of overfitting. The consistency of variable importance across these distinct methodologies\u0026mdash;with SIRI, SpO\u003csub\u003e2\u003c/sub\u003e, and LOS consistently ranking highest\u0026mdash;strengthens confidence in the robustness of the selected predictors. The resulting nomogram provides a practical, visual tool for clinicians to estimate individualized 6-month mortality risk at the bedside using variables routinely captured in electronic health records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Existing Risk Scores.\u0026nbsp;\u003c/strong\u003eDirect comparison with existing tools such as PESI or sPESI is limited by differing outcome timeframe (30 days vs. 6 months) and predictor compositions. Nevertheless, our model demonstrates favorable discriminative performance relative to published short-term PESI metrics\u003csup\u003e[6, 38, 39]\u003c/sup\u003e . A key advantage of our approach is the incorporation of SIRI, a dynamic and modifiable biomarker that reflects therapy response and enables longitudinal risk monitoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations.\u0026nbsp;\u003c/strong\u003eSeveral limitations merit acknowledgment. First, the retrospective design introduces the potential for selection bias and unmeasured confounding, despite our use of rigorous statistical methods to adjust for measured confounders. Second, the sample size of the external validation cohort, while adequate for primary analyses, precluded robust subgroup analyses. Third, we were unable to include potentially important imaging variables, such as right ventricular dysfunction assessed by echocardiography or computed tomography, which may have incremental prognostic value. Fourth, the model was developed and validated in hospital-based cohorts; its performance in outpatient or emergency department settings remains to be established. Finally, while we performed internal validation using bootstrapping, external validation in diverse geographic and ethnic populations is essential before widespread clinical implementation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we have developed and externally validated a parsimonious predictive model for 6-month mortality in patients with PE, based on five readily available variables. The model demonstrates robust discrimination, calibration, and clinical utility. The prominence of SIRI underscores the critical role of systemic inflammation in determining long-term prognosis following PE. This simple, practical tool may facilitate early identification of high-risk patients, inform clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in strict compliance with the Declaration of Helsinki. It received approval from the Ethics Committees of The First Affiliated Hospital of Fujian Medical University. Given the retrospective and anonymized characteristics of the data, the necessity for obtaining informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe training set data used in this study are publicly available from the MIMIC-IV database (https://physionet.org/content/mimiciv/2.2/). The external validation data (local cohort) used in this study are from the electronic medical record system of The First Affiliated Hospital of Fujian Medical University and are not publicly available due to ethical restrictions and patient privacy protection requirements. De-identified individual participant data that underpin the results reported in this study are available from the corresponding authors upon reasonable request and with the approval of the Ethics Committees of The First Affiliated Hospital of Fujian Medical University.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKonstantinides S V, Meyer G, Becattini C, et al. 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS) [J]. Eur Heart J, 2020, 41 (4):543.\u003c/li\u003e\n \u003cli\u003eHuisman M V, Barco S, Cannegieter S C, et al. Pulmonary embolism [J]. Nat Rev Dis Primers, 2018, 4:18028.\u003c/li\u003e\n \u003cli\u003eJare\u0026ntilde;o Esteban J J, de Miguel D\u0026iacute;ez J, Fern\u0026aacute;ndez Bermejo L A. Pulmonary Embolism and Comorbidity [J]. 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IL-10 sensing by lung interstitial macrophages prevents bacterial dysbiosis-driven pulmonary inflammation and maintains immune homeostasis [J]. Immunity, 2025, 58 (5):1306.\u003c/li\u003e\n \u003cli\u003eYan Q, Liu S, Sun Y, et al. CC chemokines Modulate Immune responses in Pulmonary Hypertension [J]. J Adv Res, 2024, 63:171.\u003c/li\u003e\n \u003cli\u003eOhta K, Yamashita N. Apoptosis of eosinophils and lymphocytes in allergic inflammation [J]. J Allergy Clin Immunol, 1999, 104 (1):14.\u003c/li\u003e\n \u003cli\u003eScaffidi C, Kirchhoff S, Krammer P H, et al. Apoptosis signaling in lymphocytes [J]. Curr Opin Immunol, 1999, 11 (3):277.\u003c/li\u003e\n \u003cli\u003eRu S, Luo Y. The association and prognostic value of systemic inflammatory response index with short and long-term mortality in patients with sepsis [J]. Medicine (Baltimore), 2023, 102 (29):e33967.\u003c/li\u003e\n \u003cli\u003eYang F R, Li H L, Hu X W, et al. Association between the systemic inflammation response index and mortality in cancer survivors based on NHANES 2001-2018 [J]. Sci Rep, 2025, 15 (1):15151.\u003c/li\u003e\n \u003cli\u003eZhou Y, Dai M, Zhang Z. Prognostic Significance of the Systemic Immune-Inflammation Index (SII) in Patients With Small Cell Lung Cancer: A Meta-Analysis [J]. Front Oncol, 2022, 12:814727.\u003c/li\u003e\n \u003cli\u003eNakamoto S, Ohtani Y, Sakamoto I, et al. Systemic Immune-Inflammation Index Predicts Tumor Recurrence after Radical Resection for Colorectal Cancer [J]. Tohoku J Exp Med, 2023, 261 (3):229.\u003c/li\u003e\n \u003cli\u003eJiang P, Chen J, Li J. Association of the systemic immune-inflammatory index and systemic inflammatory response index with all-cause and cardiovascular mortality in individuals with metabolic inflammatory syndrome [J]. Eur J Med Res, 2025, 30 (1):444.\u003c/li\u003e\n \u003cli\u003eZheng H, Wu K, Zheng H, et al. High systemic inflammation response index and increased cardiovascular risk and mortality in MASLD: A prospective cohort study [J]. JHEP Rep, 2025, 7 (12):101602.\u003c/li\u003e\n \u003cli\u003eChen Y, Lian W, Wu L, et al. Joint association of estimated glucose disposal rate and systemic inflammation response index with mortality in cardiovascular-kidney-metabolic syndrome stage 0-3: a nationwide prospective cohort study [J]. Cardiovasc Diabetol, 2025, 24 (1):147.\u003c/li\u003e\n \u003cli\u003eAlotaibi M, Yang J Z, Papamatheakis D G, et al. Cardiopulmonary exercise test to detect cardiac dysfunction from pulmonary vascular disease [J]. Respir Res, 2024, 25 (1):121.\u003c/li\u003e\n \u003cli\u003eKerr B, Brandon L. Atrial Fibrillation, thromboembolic risk, and the potential role of the natriuretic peptides, a focus on BNP and NT-proBNP - A narrative review [J]. Int J Cardiol Heart Vasc, 2022, 43:101132.\u003c/li\u003e\n \u003cli\u003eZhao X, Li H, Liu C, et al. NT Pro-BNP can be used as a risk predictor of clinical atrial fibrillation with or without left atrial enlargement [J]. Clin Cardiol, 2022, 45 (1):68.\u003c/li\u003e\n \u003cli\u003eJim\u0026eacute;nez D, Rodr\u0026iacute;guez C, Le\u0026oacute;n F, et al. Randomised controlled trial of a prognostic assessment and management pathway to reduce the length of hospital stay in normotensive patients with acute pulmonary embolism [J]. Eur Respir J, 2022, 59 (2)\u003c/li\u003e\n \u003cli\u003eDonadini M P, Dentali F, Castellaneta M, et al. Pulmonary embolism prognostic factors and length of hospital stay: A cohort study [J]. Thromb Res, 2017, 156:155.\u003c/li\u003e\n \u003cli\u003eHariharan P, Takayesu J K, Kabrhel C. Association between the Pulmonary Embolism Severity Index (PESI) and short-term clinical deterioration [J]. Thromb Haemost, 2011, 105 (4):706.\u003c/li\u003e\n \u003cli\u003ePosch F, Ay C. Symptoms, signs, suspicion and setting: a PESI score for cancer-associated pulmonary embolism? [J]. Eur Respir J, 2017, 49 (1)\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Comparison of Clinical Characteristics of Pulmonary Embolism Patients in the MIMIC-IV Database for Death and Survival within 6 Months\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive (n=640)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath (n=174)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e62.00 (50.25, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e69.00 (57.75, 78.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSex n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e346 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e84 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e294 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e90 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e28.23 (23.78, 34.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e27.55 (23.53, 32.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eLOS (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e17.00 (9.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e12.00 (6.00, 20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eT (℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e37.00 (36.75, 37.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e36.95 (36.66, 37.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e118.82 (110.28, 130.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e111.47 (102.65, 120.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e68.90 (61.84, 76.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e63.51 (56.25, 71.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eHR (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e90.71 (80.59, 99.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e91.79 (83.07, 100.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eRR (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e20.60 (18.23, 23.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e22.51 (20.36, 25.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e96.63 (95.45, 97.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e95.89 (94.05, 97.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSmoking n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e240 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e50 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eDrinking n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e64 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eHypertension n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e377 (58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e105 (60.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSyncope n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eLiver dysfunction n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e79 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e51 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eRenal dysfunction n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e73 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e35 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eIschemic stroke n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e52 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e16 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eCHF n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e156 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e60 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eMalignancy n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e132 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e64 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eChronic lung disease n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e139 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e47 (27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eAtrial fibrillation n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e104 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e45 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e12.20 (8.70, 15.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e14.05 (9.00, 19.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eNUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9.27 (6.36, 12.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11.11 (7.13, 16.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.23 (0.77, 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.93 (0.55, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eMONO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.69 (0.42, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.70 (0.40, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e217.00 (153.25, 295.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e203.00 (119.50, 301.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.12 (4.14, 12.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11.38 (6.59, 20.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003ed_NLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6.47 (3.77, 11.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e10.60 (6.07, 19.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.97(1.04, 3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.36 (0.74, 2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e18.76 (10.75, 38.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e27.80 (13.11, 58.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1434.79 (727.60, 3012.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2289.98 (1097.98, 4985.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e39.77 (24.25, 73.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e59.43 (35.10, 111.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eAISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e250.78 (107.93, 560.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e373.11 (149.34, 1145.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\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\u003e*P\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Baseline Clinical Characteristics of Pulmonary Embolism Patients in the Local Cohort Stratified by 6-Month Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive (n=181)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath (n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e64.0 (49.5, 73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e67.5 (53.3, 75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e106 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e16 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.6\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.9\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOS (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.0 (16.0, 36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.5 (10.5, 30.8)\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 valign=\"top\"\u003e\n \u003cp\u003eT (℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.5 (36.3, 36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.5 (36.3, 36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126 (116.0, 137.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.0 (107.8, 138.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.0 (68.0, 84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.0 (69.3, 82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.0 (70.0, 80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85 (75.3, 94.0)\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 valign=\"top\"\u003e\n \u003cp\u003eRR (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.0 (19.0, 20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.0 (20.0, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.0 (96.0, 98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.0 (90.3, 96.0)\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 valign=\"top\"\u003e\n \u003cp\u003eSmoking n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDrinking n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSyncope n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiver dysfunction n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRenal dysfunction n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIschemic stroke n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHF n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMalignancy n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic lung disease n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtrial fibrillation n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.03 (5.83, 9.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.4 (5.76, 10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.84 (3.74, 7.05 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.03 (4.58, 8.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43 (1.05, 1.99 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07 (0.67, 1.61 )\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 valign=\"top\"\u003e\n \u003cp\u003eMONO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41 (0.30, 0.54 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47 (0.30, 0.69 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e226 (176.5,287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157 (133, 231.75)\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 valign=\"top\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.36 (2.22,5.60 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.45 (2.99, 11.45 )\u0026nbsp;\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 valign=\"top\"\u003e\n \u003cp\u003ed_NLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16 (1.55,3.49 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.24 (2.04, 5.42 )\u0026nbsp;\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 valign=\"top\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.58 (2.39,5.00 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.30 (1.33, 3.32 )\u0026nbsp;\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 valign=\"top\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157.02 (105.71, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;238.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178.35 (98.49, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;269.24)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e770.43 (472.02, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1447.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1113.92 (445.86, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1978.62)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35 (0.82, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.89 (1.25,5.35）\u0026nbsp;\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 valign=\"top\"\u003e\n \u003cp\u003eAISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e319.84 (169.11, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 642.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e478.99 (175.33, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1295.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*P\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Univariate and Multivariate Logistic Regression Analyses Exploring 6-Month Mortality in Pulmonary Embolism Patients\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLOS\u0026nbsp;(days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.06 (1.03, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e1.04 (1.01, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eRR (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.33 (1.15, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e1.15 (0.97, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.85 (0.78, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e0.88 (0.80, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5.16 (2.18, 12.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e3.70 (1.32, 10.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSIRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.18 (1.05, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e1.10 (0.98, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*P\u0026lt;0.05\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"thrombosis-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"thrj","sideBox":"Learn more about [Thrombosis Journal](http://thrombosisjournal.biomedcentral.com/)","snPcode":"12959","submissionUrl":"https://submission.nature.com/new-submission/12959/3","title":"Thrombosis Journal","twitterHandle":"@Thrombosis_J","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary Embolism, Mortality, Systemic Inflammation, Prognosis, Nomograms, Risk Stratification","lastPublishedDoi":"10.21203/rs.3.rs-9158089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9158089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePulmonary embolism (PE) remains a major cause of cardiovascular mortality, yet existing risk stratification tools predominantly focus on short-term outcomes. In this study, we set out to develop and externally validate a predictive model for 6-month all-cause mortality in PE patients, by integrating routine clinical parameters with systemic inflammatory indices to improve prognostic assessment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis of patients with PE from two independent cohorts. The MIMIC-IV database served as the training set for variable selection using least absolute shrinkage and selection operator (LASSO) regression. External validation was performed using data from the First Affiliated Hospital of Fujian Medical University. Model performance was assessed through discrimination (area under the receiver operating characteristic curve [AUC]), calibration (Hosmer\u0026ndash;Lemeshow test and calibration plots), and decision curve analysis (DCA). Variable importance was further evaluated using random forest models and SHapley Additive exPlanations (SHAP) values.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe training cohort comprised 817 patients from the MIMIC-IV database, with a 6-month mortality rate of 21.3% (n\u0026thinsp;=\u0026thinsp;174). LASSO regression initially selected 16 predictors. External validation in the local cohort (n\u0026thinsp;=\u0026thinsp;217, 6-month mortality: 16.5%) refined these to five core predictors: length of stay, respiratory rate, oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e), atrial fibrillation, and the systemic inflammation response index (SIRI). The parsimonious model demonstrated robust discrimination (AUC\u0026thinsp;=\u0026thinsp;0.81, 95% confidence interval [CI]: 0.74\u0026ndash;0.89), excellent calibration (Hosmer\u0026ndash;Lemeshow P\u0026thinsp;=\u0026thinsp;0.781), and positive net clinical benefit across a range of threshold probabilities. SHAP analysis identified SIRI and SpO\u003csub\u003e2\u003c/sub\u003e as the most influential predictors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis simplified model incorporating five readily available clinical and inflammatory variables accurately predicts 6-month mortality in patients with PE. This model may facilitate early risk stratification and guide clinical decision-making in diverse healthcare settings.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Predictive Model for 6-Month Mortality in Pulmonary Embolism: The Prognostic Value of the Systemic Inflammation Response Index (SIRI) ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 14:58:50","doi":"10.21203/rs.3.rs-9158089/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-18T11:20:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204350822640626535332894608308012015320","date":"2026-04-16T11:48:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T15:44:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T13:16:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T13:16:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Thrombosis Journal","date":"2026-03-18T10:12:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"thrombosis-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"thrj","sideBox":"Learn more about [Thrombosis Journal](http://thrombosisjournal.biomedcentral.com/)","snPcode":"12959","submissionUrl":"https://submission.nature.com/new-submission/12959/3","title":"Thrombosis Journal","twitterHandle":"@Thrombosis_J","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"078fd287-4d81-48d4-bd1a-b24ecae841a2","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T14:58:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 14:58:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9158089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9158089","identity":"rs-9158089","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}