Presurgical Screening Improves Risk Prediction for Delirium in Elective Surgery of Older Patients: The PAWEL RISK Study
Bibliographic record
Abstract
Introduction: The number of elective surgeries for patients who are over 70 years of age is continuously growing. At the same time, postoperative delirium (POD) is common in older patients (5–60%) depending on predisposing risk factors, such as multimorbidity, cognitive impairment, neurodegenerative disorders and other dementing disorders, and precipitating factors, such as duration of surgery. Knowledge of individual risk profiles prior to elective surgery may help to identify patients at increased risk for development of POD. In this study, clinical and cognitive risk factors for POD were investigated in patients undergoing various elective cardiac and non-cardiac surgeries. Methods: The PAWEL study is a prospective, interventional trial on delirium prevention. At baseline, 880 inpatients at five surgical centers were recruited for sub-sample PAWEL-R. Multimodal assessments included clinical renal function, medication, American Society of Anesthesiologists (ASA) Physical Status Classification System, geriatric and cognitive assessments, which comprised the Montreal Cognitive Assessment Scale (MoCA), Trail-making Test, and Digit Span backward. Delirium incidence was monitored postoperatively by the Confusion Assessment Method (CAM) and a chart review for up to a week or until discharge. Multivariate regression models and Chi-square Automatic Interaction Detectors (CHAID) analyses were performed using delirium incidence as the primary outcome. Results: Eighteen risk factors were investigated in elective cardiovascular and orthopedic or general surgery. A total of 208 out of 880 patients (24%) developed POD. A global regression model that included all risk variables predicted delirium incidence with high accuracy (AUC = 0.81; 95% CI 0.77, 0.85). A simpler model (clinical and cognitive variables; model CLIN-COG) of 10 factors that only included surgery type, multimorbidity, renal failure, polypharmacy, ASA, cut-to-suture time, and cognition (MoCA, Digit Span backward, and preexisting dementia), however, exhibited similar predictive accuracy (AUC = 0.80; 95% CI 0.76, 0.84). Conclusion: The risk of developing POD can be estimated by preoperative assessments, such as ASA classification, expected cut-to-suture time, and short cognitive screenings. This rather efficient approach predicted POD risk over all types of surgery. Thus, a basic risk assessment including a cognitive screen can help to stratify patients at low, medium, or high POD risk to provide targeted prevention and/or management strategies for patients at risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".