The Centers for Medicare & Medicaid Services currently incentivizes hospitals to reduce postdischarge adverse events such as unplanned hospital readmissions for patients who underwent total joint arthroplasty (TJA). This study aimed to predict 90-day TJA readmissions from our comprehensive electronic health record data and routinely collected patient-reported outcome measures. We retrospectively queried all TJA-related readmissions in our tertiary care center between 2016 and 2019. A total of 104-episode care characteristics and preoperative patient-reported outcome measures were used to develop several machine learning models for prediction performance evaluation and comparison. For interpretability, a logistic regression model was built to investigate the statistical significance, magnitudes, and directions of associations between risk factors and readmission. Given the significant imbalanced outcome (5.8% of patients were readmitted), our models robustly predicted the outcome, yielding areas under the receiver operating characteristic curves over 0.8, recalls over 0.5, and precisions over 0.5. In addition, the logistic regression model identified risk factors predicting readmission: diabetes, preadmission medication prescriptions (ie, nonsteroidal anti-inflammatory drug, corticosteroid, and narcotic), discharge to a skilled nursing facility, and postdischarge care behaviors within 90 days. Notably, low self-reported confidence to carry out social activities accurately predicted readmission. A machine learning model can help identify patients who are at substantially increased risk of a readmission after TJA. This finding may allow for health-care providers to increase resources targeting these patients. In addition, a poor response to the “social activities” question may be a useful indicator that predicts a significant increased risk of readmission after TJA.

Background: Hip Disability and Osteoarthritis Outcome Score (HOOS), HOOS-Joint Replacement (JR), HOOS Physical Function (PS), and HOOS-12 item scale have been suggested as reliable and valid instruments for assessing hip disability. However, factorial validity, invariance across subgroups, and repeated measures of the scale across different populations have not been well supported in the literature. Purpose: The primary study objectives were to: (1) assess model fit and psychometric properties of the original 40-item HOOS scale, (2) assess model fit of the HOOS-JR, (3) assess model fit of the HOOS-PS, and (4) assess model fit of the HOOS-12. A secondary objective was to perform multigroup invariance testing across physical activity level and hip pathology subgroups for models that met recommended fit indices. Study Design: Cross-Sectional Study Methods: Individual confirmatory factor analyses (CFAs) were conducted for the HOOS, HOOS-JR, HOOS-PS, and HOOS-12. Additionally, multigroup invariance testing (i.e., activity level, injury type) was conducted on the HOOS-JR and HOOS-PS. Results: Model fit indices did not meet contemporary recommendations for the HOOS and the HOOS-12. Model fit indices for the HOOS-JR and the HOOS-PS met some, but not all, contemporary recommendations. Invariance criteria was met for the HOOS-JR and HOOS-PS. Conclusion: The scale structure of the HOOS and HOOS-12 were not supported; however, preliminary evidence to support the scale structure of the HOOS-JR and HOOS-PS was found. Clinicians and researchers who utilize the scales should do so with caution due to their limitations and untested properties until further research establishes the full psychometric properties of these scales and recommendations for their continued use.

With modern advancements in surgical techniques and rapid recovery protocols, incidence of outpatient total joint arthroplasty (TJA) is increasing. Previous literature has historically focused on cost, safety, and clinical outcomes, with few studies investigating patient expectations and experiences. The aim of this study was to survey preoperative patient expectations related to outpatient TJA surgery compared with perioperative perceptions and experience. Prospective study of patients undergoing outpatient total hip or knee arthroplasty at a single Tertiary Academic center. Preoperative and postoperative surveys were administered during routine clinic visits. One hundred and six patients completed preoperative surveys; 79 completed postoperative surveys and were included in the final data analysis. Fifty (63.3 %) patients reported being aware of outpatient TJA prior to undergoing the procedure. There was no difference between preoperative anticipated pain control and postoperative perceived pain control (6.64 vs. 6.88, p = 0.77). Most postoperative patients (N = 56, 70.9 %) rated outpatient surgery as "much better" or "better" than expected. Most postoperative patients (N = 68, 86 %) would opt to have outpatient surgery again. Fifty-two (65.8 %) of postoperative patients believed outpatient surgery sped up their postoperative rehabilitation. For most patients, the outpatient surgical experience met or exceeded expectations. Nearly 90 % of patients would prefer to have outpatient surgery in the future, further supporting the continued migration of elective arthroplasty away from inpatient sites of care.

The purpose of this study was to investigate the reliability and validity of the Clinometer^™ smartphone application. Intrarater and interrater reliability were calculated for devices (i.e., goniometer, Clinometer^™, bubble inclinometer) using intraclass correlation coefficient, standard error of measurement, and minimal detectable change. Interinstrument reliability and Clinometer^™ validity were also assessed. Intrarater reliability was good (intraclass correlation coefficients = .70 − .83) and interrater reliability was moderate (intraclass correlation coefficient = .63 − .72) across devices. Interinstrument reliability and Clinometer^™ validity were supported across analyses. The Clinometer^™ is comparable to a goniometer or bubble inclinometer for measuring prone knee flexion.

The aim of this study was to improve understanding of hospital length of stay (LOS) in patients undergoing total joint arthroplasty (TJA) in a high-efficiency, hospital-based pathway. We retrospectively reviewed 1401 consecutive primary and revision TJA patients across 67 patient and preoperative care characteristics from 2016 to 2019 from the institutional electronic health records. A machine learning approach, testing multiple models, was used to assess predictors of LOS. The median LOS was 1 day; outpatients accounted for 16.5%, 1-day inpatient stays for 38.0%, 2-day stays for 26.4%, and 3-days or more for 19.1%. Patients characteristically fell into 1 of 3 broad categories that contained relatively similar characteristics: outpatient (0-day LOS), short stay (1- to 2-day LOS), and prolonged stay (3 days or greater). The random forest models suggested that a lower Risk Assessment and Prediction Tool score, unplanned admission or hospital transfer, and a medical history of cardiovascular disease were associated with an increased LOS. Documented narcotic use for surgery preparation prior to hospitalization and preoperative corticosteroid use were factors independently associated with a decreased LOS. After TJA, most patients have either an outpatient or short-stay hospital episode. Patients who stay 2 days do not differ substantially from patients who stay 1 day, while there is a distinct group that requires prolonged admission. Our machine learning models support a better understanding of the patient factors associated with different hospital LOS categories for TJA, demonstrating the potential for improved health policy decisions and risk stratification for centers caring for complex patients.