Health Technology/clinical-trials

Completed Study Validates Machine Learning Model for VTE Prediction in ICU Patients — Clinical Decision Support Tool Shows Promise

247 Health News Editorial

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28 May 2026

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4 min read

·clinical-trials

A 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. 1.Australian and New Zealand Intensive Care Society. (2023). ANZICS Statement on Artificial Intelligence and Machine Learning in Intensive Care. ANZICS.
  2. 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. 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. 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. 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. 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. 7.National Health and Medical Research Council. (2023). Australian Code for the Responsible Conduct of Research. NHMRC.
  8. 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. 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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