| 1 | Machine learning predicts unpredicted deaths with high accuracy following hepatopancreatic surgery显示文摘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. | Kota Sahara Anghela Z.Paredes Diamantis I.Tsilimigras Kazunari Sasaki Amika Moro JMadison Hyer Rittal Mehta Syeda A.Farooq Lu Wu Itaru Endo Timothy M.Pawlik | 2021 | Hepatobiliary Surgery and Nutrition2021，10，1： | 1 |
| 2 | Using the win ratio to compare laparoscopic versus open liver resection for colorectal cancer liver metastases显示文摘Background:We sought to assess the overall benefit of laparoscopic versus open hepatectomy for treatment of colorectal liver metastases(CRLMs)using the win ratio,a novel methodological approach.Methods:CRLM patients undergoing curative-intent resection in 2001-2018 were identified from an international multi-institutional database.Patients were paired and matched based on age,number and size of lesions,lymph node status and receipt of preoperative chemotherapy.The win ratio was calculated based on margin status,severity of postoperative complications,90-day mortality,time to recurrence,and time to death.Results:Among 962 patients,the majority underwent open hepatectomy(n=832,86.5%),while a minority underwent laparoscopic hepatectomy(n=130,13.5%).Among matched patient-to-patient pairs,the odds of the patient undergoing laparoscopic resection“winning”were 1.77[WR:1.77,95%confidence interval(CI):1.42-2.34].The win ratio favored laparoscopic hepatectomy independent of low(WR:2.94,95%CI:1.20-6.39),medium(WR:1.56,95%CI:1.16-2.10)or high(WR:7.25,95%CI:1.13-32.0)tumor burden,as well as unilobar(WR:1.71,95%CI:1.25-2.31)or bilobar(WR:4.57,95%CI:2.36-8.64)disease.The odds of“winning”were particularly pronounced relative to short-term outcomes(i.e.,90-day mortality and severity of postoperative complications)(WR:4.06,95%CI:2.33-7.78).Conclusions:Patients undergoing laparoscopic hepatectomy had 77%increased odds of“winning”.Laparoscopic liver resection should be strongly considered as a preferred approach to resection in CRLM patients. | Alessandro Paro J.Madison Hyer Brandon S.Avery Diamantis I.Tsilimigras Fabio Bagante Alfredo Guglielmi Andrea Ruzzenente Sorin Alexandrescu George Poultsides Kazunari Sasaki Federico Aucejo Timothy M.Pawlik | 2023 | Hepatobiliary Surgery and Nutrition2023，12，5： | 0 |
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