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A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment

Authors not listed

Lanyue Zhu · 2025

Dysbiosis Human Randomized controlled trial

Abstract

Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured.

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Source: ClinicalTrials.gov
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Fetched: 2026-10-17 MST

Cite this

APA

Anonymous. (2025). A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment. <em>Lanyue Zhu</em>. https://clinicaltrials.gov/study/NCT07030166

Vancouver

Anonymous. A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment. Lanyue Zhu. 2025.

BibTeX

@misc{anon2025AMachi, title = {A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment}, author = {Anonymous}, journal = {Lanyue Zhu}, year = {2025}, }