Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction

Yining Lu, Enrico Forlenza, Matthew R. Cohn, Ophelie Lavoie-Gagne, Ryan R. Wilbur, Bryant M. Song, Aaron J. Krych, Brian Forsythe

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Overnight admission following anterior cruciate ligament reconstruction has implications on clinical outcomes as well as cost benefit, yet there are few validated risk calculators for reliable identification of appropriate candidates. The purpose of this study is to develop and validate a machine learning algorithm that can effectively identify patients requiring admission following elective anterior cruciate ligament (ACL) reconstruction. Methods: A retrospective review of a national surgical outcomes database was performed to identify patients who underwent elective ACL reconstruction 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), linear discriminant classifier (LDA), and adaptive boosting algorithms (AdaBoost), and an additional model was produced as a weighted ensemble of the four final algorithms. Results: Overall, of the 4,709 patients included, 531 patients (11.3%) required at least one overnight stay following ACL reconstruction. The factors determined most important for identification of candidates for inpatient admission were operative time, anesthesia type, age, gender, and BMI. Smoking history, history of COPD, and history of coagulopathy were identified as less important variables. The following factors supported overnight admission: operative time > 200 min, age < 35.8 or > 53.5 years, male gender, BMI < 25 or > 31.2 kg/m2, positive smoking history, history of COPD and the presence of preoperative coagulopathy. The ensemble model achieved the best performance based on discrimination assessed via internal validation (AUC = 0.76), calibration, and decision curve analysis. The model was integrated into a web-based open-access application able to provide both predictions and explanations. Conclusion: Modifiable risk factors identified by the model such as increased BMI, operative time, anesthesia type, and comorbidities can help clinicians optimize preoperative status to prevent costs associated with unnecessary admissions. If externally validated in independent populations, this algorithm could use these inputs to guide preoperative screening and risk stratification to identify patients requiring overnight admission for observation following ACL reconstruction. Level of evidence: IV.

Original languageEnglish (US)
JournalKnee Surgery, Sports Traumatology, Arthroscopy
DOIs
StateAccepted/In press - 2020

Keywords

  • ACL
  • ACL reconstruction
  • Anterior cruciate ligament
  • Machine learning

ASJC Scopus subject areas

  • Surgery
  • Orthopedics and Sports Medicine

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