Machine Learning Model Identifies Increased Operative Time and Greater BMI as Predictors for Overnight Admission After Outpatient Hip Arthroscopy

Bryant M. Song, Yining Lu, Ryan R. Wilbur, Ophelie Lavoie-Gagne, Ayoosh Pareek, Brian Forsythe, Aaron J. Krych

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The purposes of this study were to identify patient characteristics and risk factors for overnight admission following outpatient hip arthroscopy and to develop a machine learning algorithm that can effectively identify patients requiring admission following elective hip arthroscopy. Methods: A retrospective review of a prospectively collected national surgical outcomes database was performed to identify patients who underwent elective outpatient hip arthroscopy from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the four final algorithms. Results: Overall, 1,276 patients were included. The median age was 43 years, and 64.2% (819) were female. Of the included patients, 109 (8.5%) required an overnight stay following elective outpatient hip arthroscopy. The most important factors for inpatient admission were increasing operative time, general anesthesia, age extremes, male gender, greater body mass index (BMI), American Society of Anesthesiologists classification >1, and the following preoperative lab values outside of normal ranges: sodium, platelet count, hematocrit, and leukocyte count. The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = .71), calibration, and decision curve analysis. The model was integrated into a Web-based open-access application able to provide both personalized predictions and explanations. Conclusion: A machine learning algorithm developed based on preoperative features identified increasing operative time, age extremes, greater BMI, sodium, hematocrit, platelets, and leukocyte count as the most important variables associated with inpatient admission with fair validity.

Original languageEnglish (US)
JournalArthroscopy, Sports Medicine, and Rehabilitation
DOIs
StateAccepted/In press - 2021

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Public Health, Environmental and Occupational Health
  • Rehabilitation
  • Physical Therapy, Sports Therapy and Rehabilitation

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