A Novel Machine Learning Predictive Tool Assessing Outpatient or Inpatient Designation for Medicare Patients Undergoing Total Knee Arthroplasty

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5 days), body mass index (BMI) (> 33.21 kg/m2), and operation time (> 93 min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept =  - 0.11; RF, slope = 0.93, intercept =  - 0.12; HGB, slope = 0.90, intercept =  - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

Outpatient Total Joint Arthroplasty: Are We Closing the Racial Disparities Gap?

INTRODUCTION

As ne arly half of all total joint arthroplasty (TJA) procedures are projected to be performed in the outpatient setting by 2026, the impact of this trend on health disparities remains to be explored. This study investigated the racial/ethnic differences in the proportion of TJA performed as outpatient as well as the impact of outpatient surgery on 30-day complication and readmission rates.

METHODS

The ACS National Surgical Quality Improvement Program was retrospectively reviewed for all patients who underwent primary, elective total hip and knee arthroplasty (THA, TKA) between 2011 and 2018. The proportion of TJA performed as an outpatient, 30-day complications, and 30-day readmission among African American, Hispanic, Asian, Native American/Alaskan, and Hawaiian/Pacific Islander patients were each compared to White patients (control group). Analyses were performed for each racial/ethnic group separately. A general linear model (GLM) was used to calculate the odds ratios for receiving TJA in an outpatient vs. inpatient setting while adjusting for age, gender, body mass index (BMI), functional status, and comorbidities.

RESULTS

In total, 170,722 THAs and 285,920 TKAs were analyzed. Compared to White patients, non-White patients had higher likelihood of THA or TKA performed as an outpatient (OR 1.31 and 1.24 respectively for African American patients, OR 1.65 and 1.76 respectively for Hispanic patients, and OR 1.66 and 1.59 respectively for Asian patients, p < 0.001). Outpatient surgery did not lead to increased complications in any of the study groups compared to inpatient surgery (p > 0.05). However, readmission rates were significantly higher for outpatient TKA in all the study groups compared to inpatient TKA (OR range 2.47-10.15, p < 0.001). Complication and readmission rates were similar between inpatient and outpatient THA for all the study groups.

CONCLUSION

While this study demonstrated higher proportion of TJA performed as an outpatient among most non-White racial/ethnic groups, this observation should be tempered with the increased readmission rates observed in outpatient TKA, which could further the disparities gap in health outcomes.

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review

Background

There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care.

Methods

A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted.

Results

Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level.

Conclusions

High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.