Abnormal preoperative leukocyte counts and postoperative complications following total shoulder arthroplasty

Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database

INTRODUCTION

Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository.

MATERIALS AND METHODS

We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility.

RESULTS

The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age.

CONCLUSIONS

Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.

Components of metabolic syndrome as significant risk factors for postoperative complications following total shoulder arthroplasty: hypertension, diabetes, and obesity

Background

Metabolic syndrome (MetS) is a known risk factor for adverse postoperative outcomes. However, the literature surrounding the effects of MetS on orthopedic surgery outcomes following total shoulder arthroplasty (TSA) remains understudied. The purpose of this study is to investigate the effect of MetS on postoperative 30-day adverse outcomes following TSA.

Methods

The American College of Surgeons National Surgical Quality Improvement Program database was queried for all patients who underwent TSA between 2015 and 2020. After exclusion criteria, patients were divided into MetS and no MetS cohorts. MetS patients were defined as presence of hypertension, diabetes, and body mass index > 30 kg/m2. Bivariate logistic regression was used to compare patient demographics, comorbidities, and complications. Multivariate logistic regression, adjusted for all significant patient demographics and comorbidities, was used to identify the complications independently associated with MetS.

Results

A total of 26,613 patients remained after exclusion criteria, with 23,717 (89.1%) in the no MetS cohort and 2896 (10.9%) in the MetS cohort. On multivariate analysis, MetS was found to be an independent predictor of postoperative pneumonia (odds ratio [OR] 1.61, 95% confidence interval [CI] 1.02-2.55; P = .042), renal insufficiency (OR 4.09, 95% CI 1.67-10.00; P = .002), acute renal failure (OR 4.17, 95% CI 1.13-15.31; P = .032), myocardial infarction (OR 2.11, 95% CI 1.21-3.69; P = .009), nonhome discharge (OR 1.41, 95% CI 1.24-1.60; P < .001), and prolonged hospital stay > 3 days (OR 1.44, 95% CI 1.25-1.66; P < .001).

Conclusion

MetS was identified as an independent risk factor for postoperative pneumonia, renal insufficiency, acute renal failure, myocardial infarction, nonhome discharge, and prolonged hospital stay following TSA. These findings encourage physicians to medically optimize MetS patients prior to surgery to limit adverse outcomes.