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Area of Science:

  • Medical research
  • Surgical outcomes
  • Clinical prediction

Background:

  • Accuracy of predicting outcomes after major lower limb amputation (MLLA) by healthcare professionals (HCPs) and risk prediction tools is uncertain.
  • Evaluating the precision of predicting short-term (30-day) mortality, morbidity, and revisional surgery post-MLLA is crucial.

Purpose of the Study:

  • To assess the accuracy of HCPs and risk prediction tools in forecasting 30-day mortality, morbidity, and MLLA revision.
  • To compare the predictive performance of HCPs against established risk prediction tools.

Main Methods:

  • The PERCEIVE study, an international multicenter trial, included 537 adult patients undergoing MLLA.
  • Surgeons and anesthetists' preoperative predictions of 30-day outcomes were recorded and compared with probabilities from risk prediction tools.
  • Accuracy was evaluated using measures of discrimination, calibration, and overall performance.

Main Results:

  • HCPs demonstrated acceptable discrimination for mortality (C-statistic 0.758) and MLLA revision (C-statistic 0.756), but poor discrimination for morbidity (C-statistic 0.616).
  • HCPs consistently overpredicted the risk of all outcomes.
  • Most risk prediction tools showed inferior discrimination or calibration compared to HCPs, with SORT version 2 (incorporating HCP predictions) showing superior calibration and overall performance for mortality prediction.

Conclusions:

  • HCPs accurately predict mortality and MLLA revision but are less accurate for morbidity, often overestimating risks.
  • Many existing risk prediction tools perform worse than HCPs in terms of discrimination and calibration.
  • A statistical tool integrating HCP estimations represents the most effective method for predicting mortality after MLLA.