Pre-Interventional Risk Assessment in The Elderly (PIRATE): Development of a scoring system to predict 30-day mortality using data of the Peri-Interventional Outcome Study in the Elderly.

Risk assessment before interventions in elderly patients becomes more and more vital due to an increasing number of elderly patients requiring surgery. Existing risk scores are often not tailored to marginalized groups such as patients aged 80 years or older. We aimed to develop an easy-to-use and readily applicable risk assessment tool that implements pre-interventional predictors of 30-day mortality in elderly patients (≥80 years) undergoing interventions under anesthesia. Using Cox regression analysis, we compared different sets of predictors by taking into account their ease of availability and by evaluating predictive accuracy. Coefficient estimates were utilized to set up a scoring system that was internally validated. Model building and evaluation were based on data from the Peri-Interventional Outcome Study in the Elderly (POSE), which was conducted as a European multicenter, observational prospective cohort study. Our risk assessment tool, named PIRATE, contains three predictors assessable at admission (urgency, severity and living conditions). Discriminatory power, as measured by the concordance index, was 0.75. The estimated prediction error, as measured by the Brier score, was 0.036 (covariate-free reference model: 0.043). PIRATE is an easy-to-use risk assessment tool that helps stratifying elderly patients undergoing interventions with anesthesia at increased risk of mortality. PIRATE is readily available and applies to a wide variety of settings. In particular, it covers patients needing elective or emergency surgery and undergoing in-hospital or day-case surgery. Also, it applies to all types of interventions, from minor to major. It may serve as a basis for multidisciplinary and informed shared decision-making.

Copyright: © 2023 Schenk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PMID: 38127877