Surgical risk is simply not linear: Derivation and validation of a novel and interactive machine-learning Predictive OpTimization Trees in Emergency surgery Risk (POTTER) calculator
Haytham M. Kaafarani*1, Dimitris Bertsimas*2, Jack Dunn*3, George Velmahos1
1Massachusetts General Hospital & Harvard Medical School, Boston, MA;2Massachusetts Institute of Technology, Boston, MA;3Massacusetts Institute of Technology, Boston, MA
Introduction: Most risk assessment tools presume that the impact of risk factors is linear and cumulative. Using novel machine-learning techniques, we sought to design an interactive non-linear risk-calculator for Emergency Surgery (ES).
Methods: All ES patients in the ACS-NSQIP 2007-2013 database were included (derivation cohort). Optimization Classification Trees (OCT) were leveraged to train machine-learning algorithms to predict postoperative mortality, morbidity, and 20 specific complications (e.g. sepsis, surgical site infection). Unlike classic heuristics (e.g. logistic regression), OCT is adaptive and reboots itself with each variable thus accounting for non-linear interactions among variables. An application (POTTER) was then designed as the algorithms' interactive and user-friendly interface. POTTER performance was measured (c-statistic) using the 2014 ACS-NSQIP database (validation cohort) and compared to the ASA, ESS, and ACS-NSQIP calculators' performance.
Results: Out of 382,960 ES patients, comprehensive decision-making algorithms were derived, and POTTER was created where the provider's answer to a question dictates the subsequent question [Figure 1]. For any specific patient, the number of questions needed to predict any outcome ranged from 4 to 10. The mortality c-statistic was 0.9199, higher than ASA (0.8743), ESS (0.8910) and ACS (0.8979). The morbidity c-statistics was similarly the highest (0.8511).
Conclusion: We thus reveal POTTER, a highly-accurate ES risk calculator. POTTER might prove useful as an evidence-based, adaptive and user-friendly tool for bedside preoperative counseling of ES patients and families.
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