Background:Machine learning to predict morbidity and mortality-especially in a population traditionally considered low risk-has not been previously examined.We sought to characterize the incidence of death among patie...Background:Machine learning to predict morbidity and mortality-especially in a population traditionally considered low risk-has not been previously examined.We sought to characterize the incidence of death among patients with a low estimated morbidity and mortality risk based on the National Surgical Quality Improvement Program(NSQIP)estimated probability(EP),as well as develop a machine learning model to identify individuals at risk for“unpredicted death”(UD)among patients undergoing hepatopancreatic(HP)procedures.Methods:The NSQIP database was used to identify patients who underwent elective HP surgery between 2012-2017.The risk of morbidity and mortality was stratified into three tiers(low,intermediate,or high estimated)using a k-means clustering method with bin sorting.A machine learning classification tree and multivariable regression analyses were used to predict 30-day mortality with a 10-fold cross validation.C statistics were used to compare model performance.Results:Among 63,507 patients who underwent an HP procedure,median patient age was 63(IQR:54-71)years.Patients underwent either pancreatectomy(n=38,209,60.2%)or hepatic resection(n=25,298,39.8%).Patients were stratified into three tiers of predicted morbidity and mortality risk based on the NSQIP EP:low(n=36,923,58.1%),intermediate(n=23,609,37.2%)and high risk(n=2,975,4.7%).Among 36,923 patients with low estimated risk of morbidity and mortality,237 patients(0.6%)experienced a UD.According to the classification tree analysis,age was the most important factor to predict UD(importance 16.9)followed by preoperative albumin level(importance:10.8),disseminated cancer(importance:6.5),preoperative platelet count(importance:6.5),and sex(importance 5.9).Among patients deemed to be low risk,the c-statistic for the machine learning derived prediction model was 0.807 compared with an AUC of only 0.662 for the NSQIP EP.Conclusions:A prognostic model derived using machine learning methodology performed better than the NSQIP EP in predicting 30-day UD among low risk patients undergoing HP surgery.展开更多
文摘Background:Machine learning to predict morbidity and mortality-especially in a population traditionally considered low risk-has not been previously examined.We sought to characterize the incidence of death among patients with a low estimated morbidity and mortality risk based on the National Surgical Quality Improvement Program(NSQIP)estimated probability(EP),as well as develop a machine learning model to identify individuals at risk for“unpredicted death”(UD)among patients undergoing hepatopancreatic(HP)procedures.Methods:The NSQIP database was used to identify patients who underwent elective HP surgery between 2012-2017.The risk of morbidity and mortality was stratified into three tiers(low,intermediate,or high estimated)using a k-means clustering method with bin sorting.A machine learning classification tree and multivariable regression analyses were used to predict 30-day mortality with a 10-fold cross validation.C statistics were used to compare model performance.Results:Among 63,507 patients who underwent an HP procedure,median patient age was 63(IQR:54-71)years.Patients underwent either pancreatectomy(n=38,209,60.2%)or hepatic resection(n=25,298,39.8%).Patients were stratified into three tiers of predicted morbidity and mortality risk based on the NSQIP EP:low(n=36,923,58.1%),intermediate(n=23,609,37.2%)and high risk(n=2,975,4.7%).Among 36,923 patients with low estimated risk of morbidity and mortality,237 patients(0.6%)experienced a UD.According to the classification tree analysis,age was the most important factor to predict UD(importance 16.9)followed by preoperative albumin level(importance:10.8),disseminated cancer(importance:6.5),preoperative platelet count(importance:6.5),and sex(importance 5.9).Among patients deemed to be low risk,the c-statistic for the machine learning derived prediction model was 0.807 compared with an AUC of only 0.662 for the NSQIP EP.Conclusions:A prognostic model derived using machine learning methodology performed better than the NSQIP EP in predicting 30-day UD among low risk patients undergoing HP surgery.
