Decreasing trend of inpatient mortality rates of aseptic versus septic revision total hip arthroplasty: an analysis of 681,034 cases

Neighborhood Socioeconomic Disadvantage is Associated With Increased Health Care Utilization After Septic and Aseptic Revision Total Hip Arthroplasty

BACKGROUND

A greater area deprivation index (ADI), a tool that gauges socioeconomic disadvantage at the neighborhood level, is associated with worse health care outcomes following primary total hip arthroplasty. However, its association with revision total hip arthroplasty (rTHA) is unknown. This study aimed to determine the association between ADI and rates of postoperative health care resource utilization following rTHA.

METHODS

A total of 996 patients who underwent rTHA between 2016 and 2022 were enrolled in a prospective study. The primary outcomes assessed were nonhome discharge disposition (DD), length of stay (LOS) ≥ three days, 90-day emergency department (ED) visits, and 90-day hospital readmissions. The ADI was calculated using the patient's home address at the time of surgery, with greater ADI indicating greater socioeconomic disadvantage. We evaluated the mediation effect of patient race on ADI and postoperative health care utilization using a multivariable logistic regression model.

RESULTS

A higher median ADI was revealed for patients who experienced nonhome discharge (P = 0.001), extended LOS (P < 0.001), and ED readmission within 90 days of surgery (P = 0.045). When comparing septic versus aseptic rTHA patients, there were significant differences in health care resource utilization but no difference in ADI between the two groups. For aseptic rTHA, ADI significantly mediated the effect of race on both nonhome DD and LOS ≥ 3 (41 and 46% mediation, respectively). In septic rTHA, ADI mediated 31.1% of the effect of race on nonhome DD, but showed minimal mediation effect on LOS. The mediation effect of ADI on ED admission and hospital readmission was minimal for both groups.

CONCLUSIONS

Higher ADI scores are associated with increased health care utilization after rTHA, including longer hospital stays and more nonhome discharges. The ADI significantly mediates the effect of race on these outcomes, particularly in aseptic rTHA cases, suggesting that neighborhood socioeconomic factors play a crucial role in previously observed racial disparities.

Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84-0.97), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60-611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.