Anaesthesia, critical care & pain medicine
September 21, 2026 · via Europe PMC
Multicenter External Validation of the PIPRA Model for Postoperative Delirium in Older Adults.
Schmutz N, Reeve K, Gaudet JG, +9 more, Steiner LA.
AI summary — for doctors, generated from the abstract only
In external multicenter validation, the PIPRA model predicted postoperative delirium with acceptable discrimination (AUC 0.73) but imperfect calibration.
What was studied
A prospective cohort of 1179 patients aged 60 or older having noncardiac, non-intracranial surgery at four hospitals in Switzerland and Germany, validating the PIPRA machine learning model.
Key findings
- Delirium occurred in 14.8%
- AUC 0.73 vs 0.80 in the development dataset
- AUC 0.64 in patients receiving structured prevention and 0.76 without prevention
- Calibration was imperfect, especially at higher risks
Clinical relevance
PIPRA can stratify delirium risk, but absolute risk estimates should not be used directly without local recalibration.
Who should care
Limitations
Calibration problems and reduced performance in patients receiving prevention programs.
PIPRA reasonably ranks delirium risk but needs local recalibration before relying on its numbers.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Postoperative delirium (POD) is a complication affecting up to 50% of older surgical patients. Early identification of at-risk patients is critical for targeted prevention.
Methods
In this prospective cohort study, we validated the performance of the PIPRA machine-learning model for predicting POD across four hospitals in Switzerland and Germany. POD incidence (defined as a 4AT score of ≥4 points or an ICDSC score of ≥4) was assessed twice daily for 5 days in 1,179 consecutive patients aged 60 years or older undergoing noncardiac, non-intracranial surgery. Risk of POD was estimated using the PIPRA algorithm. A prespecified subgroup analysis was conducted in patients receiving a structured delirium prevention program, and predictive performance was assessed using discrimination (AUC), calibration, and the Brier score.
Main results
174 patients (14.8%) developed POD. PIPRA discriminated with an AUC of 0.73 (95% CI: 0.69-0.77), compared to the AUC of the original development dataset (0.80 (95% CI: 0.77-0.82)). In patients receiving structured POD prevention, PIPRA discriminated with an AUC of 0.64 (95% CI: 0.55-0.73), and in patients with no prevention, it discriminated with an AUC of 0.76 (95% CI: 0.72-0.81).
Conclusions
In this large, multicenter cohort, the PIPRA model demonstrated acceptable, albeit not perfect, discrimination for predicting POD in older surgical patients and was able to stratify risk in current perioperative practice. However, the imperfect calibration, particularly at higher risks, limits the direct use of absolute risk estimates. Local recalibration and dedicated implementation studies are required before considering its general implementation to guide targeted delirium prevention.
DOI
10.1016/j.accpm.2026.101915
PMID
42767372
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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