Mode of Fixation and Survivorship in Primary Total Knee Arthroplasty in the American Joint Replacement Registry.

BACKGROUND: A recent rapid increase in cementless total knee arthroplasty (TKA) has been noted in the American Joint Replacement Registry (AJRR). The purpose of our study was to compare TKA survivorship based on the mode of fixation reported to the AJRR in the Medicare population. METHODS: Primary TKAs from Medicare patients submitted to AJRR from 2012 to 2022 were analyzed. The Medicare and AJRR databases were merged. Cox regression stratified by sex compared revision outcomes (all-cause, infection, mechanical loosening, and fracture) for cemented, cementless, and hybrid fixation, controlling for age and the Charlson comorbidity index (CCI). RESULTS: A total of 634,470 primary TKAs were analyzed. Cementless TKAs were younger (71.8 versus 73.1 years, P < .001) than cemented TKAs and more frequently utilized in men (8.2 versus 5.8% women, P < .001). Regional differences were noted, with cementless fixation more common in the Northeast (10.5%) and South (9.2%) compared to the West (4.4%) and Midwest (4.3%) (P < .001). No significant differences were identified in all-cause revision rates in men or women ≥ 65 for cemented, cementless, or hybrid TKA after adjusting for age and CCI. Significantly lower revision for fracture was identified for cemented compared to cementless and hybrid fixation in women ≥ 65 after adjusting for age and CCI (P = .0169). CONCLUSIONS: No survivorship advantage for all-cause revision was noted based on the mode of fixation in men or women ≥ 65 after adjusting for age and CCI. A significantly lower revision rate for fractures was noted in women ≥ 65 utilizing cemented fixation. Cementless fixation in primary TKA should be used with caution in elderly women.

Indication for Proximal Femoral Replacement Is Associated With Risk of Failure.

BACKGROUND: Proximal femoral replacement (PFR) is reserved as a salvage procedure after failed total hip arthroplasty (THA) or after wide margin resection of tumors involving the proximal femur. Although failure of the PFR construct remains a significant problem, indication has not previously been investigated as a risk factor for failure. METHODS: This study retrospectively evaluated patients who underwent PFR over a consecutive 15-year period for primary sarcoma or metastatic disease of the proximal femur, compared with conversion to PFR after failed THA. PFR failure was defined as recurrent prosthetic dislocations, periprosthetic fracture, aseptic loosening, or infection that ultimately resulted in revision surgery. RESULTS: Overall, 99 patients were evaluated, including 58 in the neoplasm and 41 in the failed THA cohorts. Failed THA patients were older (P < .001), with a greater proportion having comorbid hypertension (P = .008), cardiac disease (P = .014), and history of prior ipsilateral and intracapsular surgeries (P < .001). The failure rate was significantly higher in failed THA patients (39.0% vs 10.3%; P < .001) with significantly shorter implant survivorship on Kaplan-Meier analysis (P = .003). A multivariate Cox proportional hazards model showed that THA failure was the only independent predictor for PFR failure (hazard ratio: 4.26, 95% confidence interval: 1.66-10.94; P = .003). CONCLUSION: This study revealed significantly worse PFR implant survivorship in patients undergoing PFR for the indication of failed THA compared with neoplasm. Although the underlying etiology of this relationship remains to be explicitly outlined, poor bone quality and soft tissue integrity, multiple prior surgeries, and comorbid conditions are likely contributing factors.

Diagnostic Performance of Artificial Intelligence for Detection of Anterior Cruciate Ligament and Meniscus Tears: A Systematic Review.

PURPOSE: To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the efficacy to human clinical experts. METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried in November 2019 for research articles pertaining to AI use for detection of ACL and meniscus tears. Information regarding AI model, prediction accuracy/area under the curve (AUC), sample sizes of testing/training sets, and imaging modalities were recorded. RESULTS: A total of 11 AI studies were identified: 5 investigated ACL tears, 5 investigated meniscal tears, and 1 investigated both. The AUC of AI models for detecting ACL tears ranged from 0.895 to 0.980, and the prediction accuracy ranged from 86.7% to 100%. Of these studies, 3 compared AI models to clinical experts. Two found no significant differences in diagnostic capability, whereas one found that radiologists had a significantly greater sensitivity for detecting ACL tears (P = .002) and statistically similar specificity and accuracy. Of the 5 studies investigating the meniscus, the AUC for AI models ranged from 0.847 to 0.910 and prediction accuracy ranged from 75.0% to 90.0%. Of these studies, 2 compared AI models with clinical experts. One found no significant differences in diagnostic accuracy, whereas one found that the AI model had a significantly lower specificity (P = .003) and accuracy (P = .015) than radiologists. Two studies reported that the addition of AI models significantly increased the diagnostic performance of clinicians compared to their efforts without these models. CONCLUSIONS: AI prediction capabilities were excellent and may enhance the diagnosis of ACL and meniscal pathology; however, AI did not outperform clinical experts. CLINICAL RELEVANCE: AI models promise to improve diagnosing certain pathologies as well as or better than human experts, are excellent for detecting ACL and meniscus tears, and may enhance the diagnostic capabilities of human experts; however, when compared with these experts, they may not offer any significant advantage.

Systematic Review of Sleep Quality Before and After Arthroscopic Rotator Cuff Repair: Are Improvements Experienced and Maintained?

BACKGROUND: Poor sleep quality is prevalent among patients with rotator cuff tears (RCTs) and negatively influences the potential for healing and quality of life. However, there is a paucity of literature describing the magnitude and timing of changes in sleep quality after arthroscopic rotator cuff repair (RCR). PURPOSE: (1) To evaluate the prevalence of poor sleep quality in patients undergoing arthroscopic RCR and (2) to determine the timing and magnitude of changes in sleep quality after RCR. STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: PubMed, OVID/Medline, and Cochrane databases were queried in January 2020 for literature investigating the prevalence of poor sleep quality in patients with RCTs or changes in sleep quality after arthroscopic RCR. Data pertaining to study characteristics, risk of bias, sleep quality assessments, and clinical outcomes were extracted. A qualitative analysis of the prevalence of poor sleep quality and changes in sleep quality was performed. RESULTS: A total of 8 studies (1034 patients) were included. The mean Pittsburgh Sleep Quality Index (PSQI) ranged from 5.2 to 15.0 preoperatively among all studies, while the frequency of patients experiencing poor sleep quality ranged from 40.8% to 89.0% in 4 studies. Four studies reported the mean PSQI at a minimum of 6 months postoperatively, which ranged from 4.2 to 7.1. Four studies did not report the PSQI score or the proportion of patients who experienced poor postoperative sleep quality. One study evaluated the PSQI at 12 months postoperatively, which decreased to 4.2 from 5.8 at 6 months. One study evaluated the PSQI at 24 months postoperatively, which decreased to 5.5 from 6.2 at 6 months. CONCLUSION: Patients with RCTs have a high prevalence of poor sleep quality. Consistent improvements in sleep quality are observed in the 6 months after arthroscopic RCR, but there is limited evidence based on the available data to characterize changes in sleep quality beyond this time. More evidence is needed to characterize changes in sleep quality beyond 6 months and how these changes are perceived by this patient population.