Predicting Postoperative Delirium in Older Patients: a multicenter retrospective cohort study (Preprint)
Bibliographic record
Abstract
BACKGROUND Elective surgeries for older adults are increasing. Machine learning could enhance risk assessment, influencing surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early detection and management of postoperative delirium (POD). OBJECTIVE This study aims to assess machine learning models' predictive ability for POD, focusing on adding neuropsychological assessments before surgery. METHODS This retrospective cohort study analyzed data from the multicenter PAWEL and PAWEL-R studies, encompassing older patients (≥70 years) undergoing elective surgeries from July 2017 to April 2019. A total of 1624 patients were included, with POD diagnosis made before discharge. Data included demographics, clinical, surgical, and neuropsychological features collected pre- and perioperatively. Machine learning model performance was evaluated using the area under the receiver operating characteristic curve (AUC), with permutation testing for significance and SHapley Additive exPlanations to identify effective neuropsychological assessments. RESULTS In this cohort of 1624 patients, 52.3% (N=850) were male, with a mean (s.d.) age of 77.9 (4.9) years. Predicting POD before surgery achieved an AUC of 0.786. Incorporating all pre- and perioperative features into the model yielded a slightly higher AUC of 0.806, with no statistically significant difference observed (P= .193). Notably, cognitive factors alone were not strong predictors (AUC=0.611). However, specific tests within neuropsychological assessments, such as the Montreal Cognitive Assessment memory subdomain and Trail Making Test Part B, were found to be crucial for prediction. CONCLUSIONS Preoperative risk prediction for POD can increase risk awareness in presurgical assessment and improve postoperative management in older patients with a high risk for delirium. CLINICALTRIAL The study was registered with the German Clinical Trials Register under the identifiers DRKS12797 and DRKS13311. INTERNATIONAL REGISTERED REPORT RR2-2019 Jan 21;20(1):71. doi: 10.1186/s13063-018-3148-8
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".