Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty

Abstract

Purpose Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA. Methods A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis. ResultsThe strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The ive machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net beneits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. ConclusionThe study indings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use. Level of evidence IV.

Preoperative Predictors of Prolonged Opioid Use in the 6 Months Following Total Knee Arthroplasty

“…Previously published predictive models for extended postoperative opioid use-all based on retrospective data-show an average AUC of 0.76 for preoperative opioid use. [62][63][64][65][66][67][68][69][70][71][72][73] Our prospective models show that at 6 weeks post-TKA, preoperative opioid use is a less accurate predictor (AUC = 0.64) than prior retrospective models indicate, highlighting the importance of psychosocial and pain-related predictors at this time point. In contrast, at 6-month follow-up, preoperative opioid use is an even better prospective predictor than prior work would suggest (AUC = 0.90), and the addition of the above phenotypic characteristics improves it even further.…”

“…Many large cross-sectional predictive algorithm studies confirm that prior opioid use is frequently the strongest predictor of prolonged postoperative use. [62][63][64][65] Our prospective findings indicate that patient psychosocial and pain phenotypic characteristics predict greater opioid use at postoperative follow-up above and beyond that accounted for by preoperative opioid use. These findings have implications for developing clinically useful opioid stratification algorithms.…”

Narcotic Utilization After Cleft Lip Repair: Does Local Anesthetic Choice Matter?

“…14 Creating standardized postoperative patient care pathways will hopefully help alleviate racial bias in opioid administration and prescribing. Machine learning has also been showing promise in helping to analyze opioid utilization patterns to classify patients at risk for opioid abuse, [53][54][55] which will hopefully offset providers attempting to classify patient risk for dependence. There are several analytic and ethical challenges of incorporating data on opioid use and misuse of parents/guardians that confound the application of this technology to opioid prescription in young children.…”

The Utility of Machine Learning Algorithms for the Prediction of Early Revision Surgery After Primary Total Hip Arthroplasty

“…However, this is a common limitation for predictive modeling studies in this research area due to the lack of statistical power for all non-osteoarthritis-related surgical indications. 43 Fourth, all data for analysis were collected from electronical medical records; thus, any error in these electronic patient charts may affect the study findings. However, the use of electronic medical records has seen a widespread use for clinical outcome research for patients with total hip and knee arthroplasty.…”