Sepsis remains one of the most formidable challenges in modern intensive care, a syndrome in which the body’s response to infection spirals into widespread inflammation, organ dysfunction, and too often death. Yet even for patients who survive the initial septic crisis, a silent threat frequently lurks in the background: venous thromboembolism, or VTE, a condition encompassing deep vein thrombosis and pulmonary embolism that can strike without warning and turn a stabilizing ICU course into a fatal one. Now, a team of researchers in China has developed and validated a machine learning approach that may give clinicians an earlier and more accurate way to identify which sepsis patients in the intensive care unit are most likely to develop these dangerous blood clots. The retrospective study, published in BMC Infectious Diseases, drew on real-world clinical data from a major tertiary hospital and compared six different predictive algorithms, with one clear winner emerging: a model built on extreme gradient boosting, widely known as XGBoost.
The clinical stakes of the problem are considerable. Patients with sepsis occupy a peculiar intersection of risk factors for thrombosis. The same inflammatory storm that characterizes sepsis activates the coagulation cascade, endothelial damage is common, and prolonged immobility during intensive care compounds the danger. Traditional risk assessment tools, such as the widely used Padua and Caprini scores, were developed largely for general hospitalized populations and, according to the study’s authors, have shown limited effectiveness when applied to sepsis patients, whose physiology differs substantially from that of the average medical or surgical ward patient. This gap in predictive accuracy has real consequences: anticoagulation prophylaxis, the standard preventive treatment for VTE, carries its own risks of bleeding, and clinicians need to know precisely which patients warrant aggressive prevention. A tool that can reliably stratify thrombotic risk in this population could meaningfully shift the balance of preventive care.
To build their models, the research team, led by Chenglong Liang and Chen Zhou of the First Affiliated Hospital of Wenzhou Medical University, along with colleagues at several affiliated institutions, retrospectively collected data from ICU sepsis patients treated at the hospital. The dataset comprised 1,824 patients, of whom 235 developed venous thromboembolism during their ICU stay, an incidence that underscores just how prevalent the complication is in this population. The researchers divided the data temporally rather than randomly, a methodological choice designed to mimic real-world deployment. Patients admitted between 2021 and 2023 were split into a training cohort containing 70 percent of cases and a validation cohort containing the remaining 30 percent, while patients admitted from January to June 2024 served as an entirely independent test cohort, providing a rigorous check on whether the models could generalize to patients admitted after the model-building period.
Before any modeling could begin, the team confronted two of the most common pitfalls in clinical machine learning: irrelevant or redundant predictors and severely imbalanced classes. Because only a minority of sepsis patients develop VTE, a naive model could achieve superficially high accuracy simply by predicting that no one is at risk. To counter this, the researchers applied the Synthetic Minority Over-sampling Technique, or SMOTE, to the training cohort, generating synthetic examples of the minority class, patients who did develop VTE, so that the algorithms could learn the distinguishing features of both groups rather than defaulting to the majority. Feature selection proceeded in stages: univariate logistic regression was first used to screen candidate predictors, after which stepwise regression based on the Akaike Information Criterion and the least absolute shrinkage and selection operator, known as Lasso, refined the variable set. The Lasso method, in particular, shrinks the coefficients of uninformative variables toward zero, effectively performing automated variable pruning while guarding against overfitting.
The result of this selection process was a compact and clinically interpretable set of six predictors: patient age, length of stay in the ICU, duration of hospitalization before ICU admission, use of glucocorticoids, use of analgesic medications, and serum D-dimer levels. Each of these variables makes intuitive clinical sense. Older patients tend to have more compromised vascular systems and reduced mobility. Longer ICU stays mean more time exposed to the prothrombotic conditions of critical illness. Pre-ICU hospitalization duration may reflect disease severity and prolonged immobilization before intensive management began. Glucocorticoids, staples of sepsis management in many protocols, are known to influence coagulation pathways, and analgesic use may serve as a proxy for both disease burden and immobility. D-dimer, a fibrin degradation product routinely measured in critically ill patients, is a direct biochemical marker of active clot formation and breakdown, making it perhaps the most mechanistically direct of all the predictors.
Armed with these six features, the researchers constructed and compared six machine learning models spanning the spectrum of complexity from classical statistics to modern ensemble methods: logistic regression, decision tree, random forest, support vector machine, gradient boosting machine, and extreme gradient boosting. Each model was evaluated with a battery of standard performance metrics, including receiver operating characteristic curves and the area under them (AUC), calibration curves, positive and negative predictive values, true positive and true negative rates, accuracy, F1-score, and the Brier score, which quantifies the overall accuracy of probabilistic predictions. The AUC, a measure of a model’s ability to discriminate between patients who will and will not develop VTE, served as the primary basis for comparison, with values closer to 1.0 indicating stronger discrimination.
XGBoost emerged as the top performer in both the validation and test cohorts, demonstrating that its sophisticated approach, which builds an ensemble of decision trees sequentially, with each new tree trained to correct the errors of the ensemble so far, captured subtle patterns in the sepsis data that simpler models missed. In the validation cohort, the XGBoost model achieved an AUC of 0.837, with a 95 percent confidence interval of 0.79 to 0.883, placing it in the range commonly regarded as good clinical discrimination. Its performance held up reasonably well in the temporally separated test cohort, where the AUC was 0.792 (95 percent CI 0.716 to 0.869). For comparison, the other five models were also tested, but none matched XGBoost’s combination of sensitivity and specificity across the two evaluation sets.
The detailed performance metrics in each cohort reveal both the strengths and the realistic limitations of the tool. In the validation cohort, XGBoost correctly identified 73.3 percent of patients who went on to develop VTE (the true positive rate, or sensitivity) and correctly cleared 75.9 percent of those who did not (the true negative rate, or specificity). In the test cohort, sensitivity rose to 82.4 percent while specificity dipped to 70.2 percent. The negative predictive values are particularly striking: 94.8 percent in the validation cohort and 97.4 percent in the test cohort, meaning that when the model predicts a patient is at low risk, that reassurance is very often correct. Positive predictive values, at 32.4 percent and 22.8 percent respectively, were more modest, a mathematical consequence of the relatively low baseline incidence of VTE, but this matters less in a screening context where the goal is to cast a reasonably wide net while avoiding overwhelming false alarms.
The clinical implications of a high negative predictive value deserve emphasis. In an ICU setting, where every intervention carries cost and risk, a model that can reliably rule out VTE risk in the majority of patients would allow clinicians to concentrate prophylactic attention, whether that means more aggressive anticoagulation, enhanced surveillance imaging, or mechanical measures such as compression devices, on the smaller group flagged as high risk. The authors conclude that the XGBoost model demonstrated superior discriminatory ability compared with the other models and has the potential to assist clinical healthcare professionals in identifying high-risk VTE patients among those with sepsis in the ICU. Because the study was retrospective, the next step for this line of research would naturally be prospective validation, ideally in multi-center settings, to confirm that the model’s performance translates across different hospitals, patient mixes, and clinical practices.
The study also reflects a broader trend in critical care medicine: the migration of machine learning from academic curiosity to bedside decision support. Ensemble tree methods like XGBoost have become workhorses of clinical prediction because they handle nonlinear relationships, interactions between variables, and heterogeneous data types with minimal manual feature engineering. What distinguishes the current work is its disciplined methodology, temporal data splitting to test real-world generalization, principled feature selection through Lasso and information-criterion-based stepwise regression, class balancing with SMOTE, and evaluation across a comprehensive set of metrics rather than a single headline number. The research was supported by the National Natural Science Foundation of China and several Zhejiang provincial programs, and the study was approved by the Ethics Review Committee of the First Affiliated Hospital of Wenzhou Medical University, with informed consent waived due to its retrospective nature. As hospitals worldwide grapple with rising sepsis admissions and the accumulating evidence that VTE significantly worsens outcomes in these patients, tools of this kind may soon become a quiet but consequential part of the intensive care workflow, flagging the vulnerable before the clot forms.
Subject of Research: Machine learning-based prediction of venous thromboembolism risk in ICU patients with sepsis
Subject of Research: Medicine
Article Title: Machine learning models for predicting the risk of venous thromboembolism in ICU sepsis patients: a retrospective study
Article References: Liang, C., Zhou, C., Wang, B., Wu, J., Wang, Y., Meng, J., Kim, S. J., Zhang, X., Quan, S., & Pan, J. (2026). Machine learning models for predicting the risk of venous thromboembolism in ICU sepsis patients: a retrospective study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14315-1
Image Credits: AI Generated
DOI: 10.1186/s12879-026-14315-1
Keywords: ICU, sepsis, venous thromboembolism, machine learning, XGBoost, predictive model, D-dimer, prognosis, retrospective study, risk prediction
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Ophelia Keating. (September 5, 2026). AI models predict blood clot risk in septic ICU patients. Scienmag. https://scienmag.com/ai-models-predict-blood-clot-risk-in-septic-icu-patients/
Ophelia Keating. “AI models predict blood clot risk in septic ICU patients.” Scienmag, 5 September 2026, https://scienmag.com/ai-models-predict-blood-clot-risk-in-septic-icu-patients/. Accessed 5 September 2026.
Ophelia Keating. “AI models predict blood clot risk in septic ICU patients.” Scienmag. September 5, 2026. https://scienmag.com/ai-models-predict-blood-clot-risk-in-septic-icu-patients/
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