Health Technology/clinical-trials

Large-Scale Observational Study Develops Machine Learning Model to Predict Post-Surgical Acute Kidney Injury — Clinical Validation Underway

247 Health News Editorial

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4 Apr 2026

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4 min read

·clinical-trials

A large-scale Chinese study is developing machine learning models to predict postoperative acute kidney injury in non-cardiac surgery patients. The research incorporates frailty assessment as a novel predictor and could revolutionise perioperative risk stratification.

Originally reported by ClinicalTrials.gov

References

  1. 1.Gameiro, J., Fonseca, J. A., Marques, F., & Lopes, J. A. (2020). Acute kidney injury in major abdominal surgery: incidence, risk factors, pathogenesis and outcomes. Annals of Intensive Care, 10(1), 66. https://doi.org/10.1186/s13613-020-00673-0
  2. 2.Hobson, C., Ozrazgat-Baslanti, T., Kuxhausen, A., Thottakkara, P., Efron, P. A., Moore, F. A., Moldawer, L. L., Segal, M. S., & Bihorac, A. (2015). Cost and mortality associated with postoperative acute kidney injury. Annals of Surgery, 261(6), 1207-1214. https://doi.org/10.1097/SLA.0000000000000732
  3. 3.Kellum, J. A., Lameire, N., & KDIGO AKI Guideline Work Group. (2013). Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary. Critical Care, 17(1), 204. https://doi.org/10.1186/cc11454
  4. 4.Kheterpal, S., Tremper, K. K., Heung, M., Rosenberg, A. L., Englesbe, M., Shanks, A. M., & Campbell, D. A. (2009). Development and validation of an acute kidney injury risk index for patients undergoing general surgery. Anesthesiology, 110(3), 505-515. https://doi.org/10.1097/ALN.0b013e3181979440
  5. 5.Lin, H. S., Watts, J. N., Peel, N. M., & Hubbard, R. E. (2016). Frailty and post-operative outcomes in older surgical patients: a systematic review. BMC Geriatrics, 16(1), 157. https://doi.org/10.1186/s12877-016-0329-8
  6. 6.Meersch, M., Schmidt, C., & Zarbock, A. (2017). Prevention of perioperative acute kidney injury. Current Opinion in Anaesthesiology, 30(1), 64-75. https://doi.org/10.1097/ACO.0000000000000439
  7. 7.Therapeutic Goods Administration. (2023). Software as a Medical Device: Guidance for manufacturers. https://www.tga.gov.au/resources/resource/guidance/software-medical-device-guidance-manufacturers
  8. 8.Thongprayoon, C., Cheungpasitporn, W., & Kashani, K. (2016). Serum creatinine level, a surrogate marker for muscle mass, predicts mortality in critically ill patients. Journal of Thoracic Disease, 8(5), 1075-1088. https://doi.org/10.21037/jtd.2016.05.36
  9. 9.Zhu, L. (2024). A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment. ClinicalTrials.gov Identifier: NCT07030166. https://clinicaltrials.gov/study/NCT07030166
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247 Health News Editorial

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