ArticleBMC emergency medicine2021
Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model.
Article in BMC emergency medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.Journal of thrombosis and thrombolysis · 2026Pooled it
- Artificial intelligence in computational modeling of thrombosis: Bridging mechanistic insights and clinical translation.Journal of thrombosis and thrombolysis · 2026Pooled it
- Machine learning prediction models for deep vein thrombosis in hospitalized patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- The Caprini score for venous thromboembolism risk assessment: A scoping review of applications, validation, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Challenges in Venous Thromboembolism Risk Stratification After Pancreatic and Liver Cancer Surgery Using Claims Data.Journal of surgical oncology · 2026Article
- Accuracy of nurses' Caprini risk assessment for venous thromboembolism: a simulation-based observational study in China.BMC nursing · 2026Article
- A comparative analysis of three risk assessment scales for predicting venous thromboembolism in traumatic brain injury patients.Scientific reports · 2025Article
- The predicted value for venous thromboembolism based on a modified model of caprini combined with fasting blood glucose and D-dimer: a retrospective study.BMC musculoskeletal disorders · 2025Article
- A Pharmacist-Led VTE Management Program in Orthopedic Surgery Associated with Reduced Venous Thromboembolism and Increased Pharmacological Prophylaxis: A Retrospective Study.Journal of multidisciplinary healthcare · 2025Article
- Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records: A Retrospective Cohort Study.Clinical epidemiology · 2025Article
- Identification of key risk factors for venous thromboembolism in urological inpatients based on the Caprini scale and interpretable machine learning methods.Thrombosis journal · 2024Article
- Padua prediction score may be inappropriate for VTE risk assessment in hospitalized patients with acute respiratory conditions: A Chinese single-center cohort study.International journal of cardiology. Heart & vasculature · 2023Article
- A nomogram to predict postoperative deep vein thrombosis in patients with femoral fracture: a retrospective study.Journal of orthopaedic surgery and research · 2023Article
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
- Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.Frontiers in cardiovascular medicine · 2023Article
- Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients.Asia-Pacific journal of oncology nursing · 2022Article
- Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.Scientific reports · 2022Article
- An abbreviated Caprini model for VTE risk assessment in trauma.Journal of thrombosis and thrombolysis · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVenous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms.
methodsWe retrospectively reviewed emergency trauma patients who were admitted to a trauma center in a tertiary hospital from September 2019 to March 2020. The data in the patient's electronic health record (EHR) and the Caprini score were extracted, combined with multiple feature screening methods and the random forest (RF) algorithm to constructs the VTE prediction model, and compares the prediction performance of (1) using only Caprini score; (2) using EHR data to build a machine learning model; (3) using EHR data and Caprini score to build a machine learning model. True Positive Rate (TPR), False Positive Rate (FPR), Area Under Curve (AUC), accuracy, and precision were reported.
resultsThe Caprini score shows a good VTE prediction effect on the trauma hospitalized population when the cut-off point is 11 (TPR = 0.667, FPR = 0.227, AUC = 0.773), The best prediction model is LASSO+RF model combined with Caprini Score and other five features extracted from EHR data (TPR = 0.757, FPR = 0.290, AUC = 0.799).
conclusionThe Caprini score has good VTE prediction performance in trauma patients, and the use of machine learning methods can further improve the prediction performance.
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