PT - JOURNAL ARTICLE AU - Zhong, Haoyan AU - Poeran, Jashvant AU - Gu, Alex AU - Wilson, Lauren A AU - Gonzalez Della Valle, Alejandro AU - Memtsoudis, Stavros G AU - Liu, Jiabin TI - Machine learning approaches in predicting ambulatory same day discharge patients after total hip arthroplasty AID - 10.1136/rapm-2021-102715 DP - 2021 Sep 01 TA - Regional Anesthesia & Pain Medicine PG - 779--783 VI - 46 IP - 9 4099 - http://rapm.bmj.com/content/46/9/779.short 4100 - http://rapm.bmj.com/content/46/9/779.full SO - Reg Anesth Pain Med2021 Sep 01; 46 AB - Background With continuing financial and regulatory pressures, practice of ambulatory total hip arthroplasty is increasing. However, studies focusing on selection of optimal candidates are burdened by limitations related to traditional statistical approaches. Hereby we aimed to apply machine learning algorithm to identify characteristics associated with optimal candidates.Methods This retrospective cohort study included elective total hip arthroplasty (n=63 859) recorded in National Surgical Quality Improvement Program dataset from 2017 to 2018. The main outcome was length of stay. A total of 40 candidate variables were considered. We applied machine learning algorithms (multivariable logistic regression, artificial neural networks, and random forest models) to predict length of stay=0 day. Models’ accuracies and area under the curve were calculated.Results Applying machine learning models to compare length of stay=0 day to length of stay=1–3 days cases, we found area under the curve of 0.715, 0.762, and 0.804, accuracy of 0.65, 0.73, and 0.81 for logistic regression, artificial neural networks, and random forest model, respectively. Regarding the most important predictive features, anesthesia type, body mass index, age, ethnicity, white blood cell count, sodium level, and alkaline phosphatase were highlighted in machine learning models.Conclusions Machine learning algorithm exhibited acceptable model quality and accuracy. Machine learning algorithms highlighted the as yet unrecognized impact of laboratory testing on future patient ambulatory pathway assignment.Data may be obtained from a third party and are not publicly available. The data are acquired from American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP): https://www.facs.org/Quality-Programs/ACS-NSQIP.