Most risk assessment tools assume that the impact of risk factors is linear and cumulative. Using novel machine-learning
techniques, we sought to design an interactive, nonlinear risk calculator
for Emergency Surgery
All ES patients in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) 2007 to 2013 database were included (derivation cohort). Optimal Classification Trees
(OCT) were leveraged to train machine-learning
algorithms to predict postoperative mortality
, and 18 specific complications (eg, sepsis, surgical site infection). Unlike classic heuristics (eg, logistic regression), OCT is adaptive and reboots itself with each variable, thus accounting for nonlinear interactions among variables. An application [Predictive OpTimal Trees in Emergency Surgery
)] 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 with the American Society of Anesthesiologists (ASA), Emergency Surgery
Score (ESS), and ACS-NSQIP calculators’ performance.
Based on 382,960 ES patients, comprehensive decision-making algorithms were derived, and POTTER
was created where the provider's answer to a question interactively dictates the subsequent question. For any specific patient, the number of questions needed to predict mortality
ranged from 4 to 11. The mortality
c-statistic was 0.9162, higher than ASA (0.8743), ESS (0.8910), and ACS (0.8975). The morbidity
c-statistics was similarly the highest (0.8414).
is a highly accurate and user-friendly ES risk calculator
with the potential to continuously improve accuracy with ongoing machine-learning
might prove useful as a tool for bedside preoperative counseling of ES patients and families.