Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis.
10.11817/j.issn.1672-7347.2025.250191
- Author:
Hongwei DUAN
1
;
Huaizheng LIU
2
;
Chuanzheng SUN
3
;
Jing QI
3
Author Information
1. Department of Emergency, Third Xiangya Hospital, Central South University, Changsha 410013, China. 228311092@csu.edu.cn.
2. Department of Emergency, Third Xiangya Hospital, Central South University, Changsha 410013, China. lhz3385@csu.edu.cn.
3. Department of Emergency, Third Xiangya Hospital, Central South University, Changsha 410013, China.
- Publication Type:English Abstract
- Keywords:
deep vein thrombosis;
extreme gradient boosting;
in-hospital mortality;
machine learning model;
nomogram model;
risk factors;
sepsis
- MeSH:
Humans;
Sepsis/complications*;
Machine Learning;
Nomograms;
Venous Thrombosis/complications*;
Retrospective Studies;
Hospital Mortality;
Male;
Female;
Middle Aged;
Aged;
Intensive Care Units;
Prognosis;
Bayes Theorem
- From:
Journal of Central South University(Medical Sciences)
2025;50(6):1013-1029
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability.
METHODS:This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI).
RESULTS:Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58).
CONCLUSIONS:Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
