Development and Validation of a Prediction Model for Postoperative Delirium
Laixi Li, Ningning Qiao, Xiaojuan Yang, Xiaohui Yang · 2026
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Description
Laixi Li and colleagues build a machine learning model to flag delirium risk after surgery in a 2026 study across centers. Three thousand surgical patients were split into training, internal testing and outside check groups drawn from several hospitals. Eight models were compared on sorting power, calibration and explanation quality. The goal was to flag high-risk patients early. Flagged patients could then receive preventive bundles, such as orientation support, sleep protection and drug review, before confusion sets in.
Random forest did best across all three groups while keeping error scores low. Six predictors drove the model, spanning starting cognition, nutrition status, anesthesia risk class, age, dementia history and intensive care admission. A clear score of this kind could make screening uniform where nursing time is scarce and delirium often goes unnoticed.
Catalog record
| CREATOR | Laixi Li, Ningning Qiao, Xiaojuan Yang, Xiaohui Yang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |