• Haoyan Zhong, Jashvant Poeran, Alex Gu, Lauren A Wilson, Gonzalez Della ValleAlejandroADepartment of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA., Stavros G Memtsoudis, and Jiabin Liu.
• Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, New York, USA.
• Reg Anesth Pain Med. 2021 Sep 1; 46 (9): 779-783.

BackgroundWith 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.MethodsThis 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.ResultsApplying 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.ConclusionsMachine 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.© American Society of Regional Anesthesia & Pain Medicine 2021. No commercial re-use. See rights and permissions. Published by BMJ.

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