Completed Study Validates Machine Learning Model for VTE Prediction in ICU Patients — Clinical Decision Support Tool Shows Promise
247 Health News Editorial
·28 May 2026
·4 min read
·clinical-trialsA completed study of 12,061 ICU patients has validated a machine learning model for predicting venous thromboembolism risk. This ICU-specific tool could improve risk stratification and thromboprophylaxis decisions in critically ill patients.
Originally reported by ClinicalTrials.gov
References
- 1.Australian and New Zealand Intensive Care Society. (2023). ANZICS Statement on Artificial Intelligence and Machine Learning in Intensive Care. ANZICS.
- 2.Barbar, S., Noventa, F., Rossetto, V., Ferrari, A., Brandolin, B., Perlati, M., ... & Prandoni, P. (2010). A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua Prediction Score. Journal of Thrombosis and Haemostasis, 8(11), 2450-2457. https://doi.org/10.1111/j.1538-7836.2010.03781.x
- 3.Beijing Tsinghua Chang Gung Hospital. (2024). Development and Validation of an Interpretable Machine Learning Model for Predicting Venous Thromboembolism in ICU Patients. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07596264
- 4.Caprini, J. A. (2005). Thrombosis risk assessment as a guide to quality patient care. Disease-a-Month, 51(2-3), 70-78. https://doi.org/10.1016/j.disamonth.2005.02.003
- 5.Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD). BMJ, 350, g7594. https://doi.org/10.1136/bmj.g7594
- 6.Cook, D., Crowther, M., Meade, M., Rabbat, C., Griffith, L., Schiff, D., ... & Guyatt, G. (2005). Deep venous thrombosis in medical-surgical critically ill patients: prevalence, incidence, and risk factors. Critical Care Medicine, 33(7), 1565-1571. https://doi.org/10.1097/01.CCM.0000171207.95319.B2
- 7.National Health and Medical Research Council. (2023). Australian Code for the Responsible Conduct of Research. NHMRC.
- 8.Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814259
- 9.Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y
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247 Health News Editorial
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