Background: Patient selection for outpatient total joint arthroplasty (TJA) is important for optimizing patient outcomes. This study develops machine learning models that may aid in patient selection for outpatient TJA based on medical comorbidities and demographic factors. Methods: This study queried elective total knee arthroplasty (TKA) and total hip arthroplasty (THA) cases during 2010-2018 in the American College of Surgeons National Surgical Quality Improvement Program. Artificial neural network models predicted same-day discharge and length of stay (LOS) fewer than 2 days (short LOS). Multiple linear and logistic regression analyses were used to identify variables significantly associated with predicted outcomes. Results: A total of 284,731 TKA cases and 153,053 THA cases met inclusion criteria. For TKA, prediction of short LOS had an area under the receiver operating characteristic curve (AUC) of 0.767 and accuracy of 84.1%; prediction of same-day discharge had an AUC of 0.802 and accuracy of 89.2%. For THA, prediction of short LOS had an AUC of 0.757 and accuracy of 70.6%; prediction of same-day discharge had an AUC of 0.814 and accuracy of 78.8%. Conclusion: This study developed machine learning models for aiding patient selection for outpatient TJA, through accurately predicting short LOS or outpatient vs inpatient cases. As outpatient TJA expands, it will be important to optimize preoperative patient selection and effectively screen surgical candidates from a broader patient population. Incorporating models such as these into electronic medical records could aid in decision-making and resource planning in real time.

With current emphasis on preoperative templating of anatomical and reverse shoulder arthroplasty (aTSA and rTSA, respectively), patients often receive thin slice (<1.0 mm) computerized tomography (CT) scans of the operative shoulder, which includes about two-thirds of the ipsilateral lung. The purpose of this study is to evaluate the prevalence and management of incidentally detected pulmonary nodules on preoperative CT scans for shoulder arthroplasty. In this single-center retrospective study, we queried records of aTSA and rTSA patients from 2015 to 2020 who received preoperative CT imaging of the shoulder. Compared to patients with negative CT findings, there were significantly more females (63.8% vs. 46.4%; P = .011), COPD (13.0% vs. 4.7%; P = .015), and asthma (18.8% vs. 6.9%; P = .003) among the patients with incidental nodules on CT. Binary logistic regression confirmed that female sex (odds ratio = 2.00; 95% CI = 1.04 to 3.88; P = .037), COPD history (OR = 3.02; 95% CI = 1.05 to 8.65; P = .040), and asthma history (OR = 3.17; 95% CI = 1.30 to 7.77; P = .011) were significantly associated with an incidental nodule finding. Incidental pulmonary nodules found on shoulder arthroplasty preoperative CT scans are often low risk in size with low risk of malignancy, and do not require further workup. This study may provide guidance to orthopedic surgeons on how to manage patients with incidental pulmonary nodules to increase chances of early cancer detection, avoid unnecessary referrals, reduce potentially harmful radiation exposure of serial CT scans, and improve cost efficiency.

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