Health Technology/clinical-trials

Brazilian Researchers Complete Landmark Study on Predicting Post-ICU Recovery — Machine Learning Model Shows Promise for Clinical Decision-Making

247 Health News Editorial

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23 June 2026

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4 min read

·clinical-trials

Brazilian researchers have completed a landmark study of over 2,000 ICU patients, developing machine learning models to predict functional decline and return-to-work outcomes after critical illness. The research addresses a critical gap in post-ICU care planning and could transform rehabilitation decision-making.

Originally reported by ClinicalTrials.gov

References

  1. 1.Griffiths, J., Hatch, R. A., Bishop, J., Morgan, K., Jenkinson, C., Cuthbertson, B. H., & Brett, S. J. (2013). An exploration of social and economic outcome and associated health-related quality of life after critical illness in general intensive care unit survivors. Critical Care Medicine, 41(10), 2282-2291. https://doi.org/10.1097/CCM.0b013e318291f9c3
  2. 2.Hospital de Clinicas de Porto Alegre. (2024). Development and validation of a machine learning model for predicting functional decline and return to work after critical illness. ClinicalTrials.gov. NCT07616388. https://clinicaltrials.gov/study/NCT07616388
  3. 3.Mehlhorn, J., Freytag, A., Rehberg, S., Grensemann, J., Puppe, F., & Quintel, M. (2014). Rehabilitation interventions for postintensive care syndrome: a systematic review. Critical Care Medicine, 42(5), 1263-1271. https://doi.org/10.1097/CCM.0000000000000148
  4. 4.Needham, D. M., Davidson, J., Cohen, H., Hopkins, R. O., Weinert, C., Wunsch, H., ... & Harvey, M. A. (2012). Improving long-term outcomes after discharge from intensive care unit: report from a stakeholders' conference. Critical Care Medicine, 40(2), 502-509. https://doi.org/10.1097/CCM.0b013e318232da75
  5. 5.Rawal, G., Yadav, S., & Kumar, R. (2017). Post-intensive care syndrome: an overview. Journal of Translational Internal Medicine, 5(2), 90-92. https://doi.org/10.1515/jtim-2016-0016
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247 Health News Editorial

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