Short-Term Radiographic Outcomes of Bone Versus Metallic Augmented, Central Screw Type Baseplate in Reverse Total Shoulder Arthroplasty: Matched Case-Control Study.

Background: Although glenoid bone grafting and metallic augmented baseplates have demonstrated success in restoring the glenohumeral joint line in the recent literature, there remain no consensus guidelines defining the use of one versus the other. Methods: Between 2017 and 2020, 15 primary RTSA with screw-in metallically augmented glenoid baseplates were identified and 2:1 matched by age, sex, and body mass index with primary bony-augmented glenoid baseplate patients. Patients with previous glenoid implantation or fracture were excluded. Charts, routine radiographic series (Grashey, Scapula Y, Axillary lateral), and 3-dimensional computed tomography (3D CT) scans were retrospectively reviewed. Structural patient-specific metal or bony augmentation was indicated based on preoperative glenoid morphology as identified by 3D CT. Aseptic failure was identified as hardware breakage and/or shift in glenoid baseplate component position. Results: There were 45 eligible cases with mean age of 65.7 years (range 44-85 years) and 65.5 years (range 42-82 years) for the metallic-augmented and bone graft group, respectively. Correspondingly, mean follow up was 22.6 months (range 12-53 months), and 27.3 months (range 11-53 months). At latest follow up, there were no baseplate failures in the metallic augment group and 2 baseplate failures (7%) in the bone graft group at a mean of 42.5 months (range 32-53 months) postoperatively. Mean age of the bone failure group was 64.5 years (range 64-65 years). Conclusion: Contemporary reversed shoulder arthroplasty glenoid baseplate designs appear to have low incidence of failure. Further analysis is necessary to determine if a critical degree of glenoid retroversion or inclination is preferable with a specific form of augmentation. Level of Evidence: III; Retrospective Cohort Comparison.

The Problem With Predicting Uncommon Events: The Critical Effect of Prevalence in Test Performance.

We used the Bayesian model to show the relationship between prevalence and the test's negative and positive predictive value. We used the above principle to understand the utility of biomarkers for acute rejection under different pretest probability of rejection. Given the test's sensitivity and specificity, the disease prevalence affects the predictive value of the test; the clinical decision to get any test should be considered while understanding the prevalence of disease and cost, risks, benefits of the tests, and available alternatives.

Current State of Evidence on Kidney Transplantation: How Fragile Are the Results?

BACKGROUND: The study aims is to use the fragility index (FI) to examine the strength of evidence of randomized controlled trials (RCTs) published in the last decade on kidney transplantation. METHODS: We searched MEDLINE for studies on kidney transplantation. We included the RCTs that compared 2 groups with 1:1 randomization and reported significant P values (<0.05) for a dichotomous outcome and were published in the top 10 transplant journals. We calculated the FI; a calculation used to determine the minimum number of subjects needed to change from a nonevent to an event to make the study results nonsignificant (P ≥ 0.05). RESULTS: Fifty-seven RCTs met our inclusion criteria. The median sample size was 100 participants in each arm, the median number of events was 16 (interquartile range, 8-30) in the intervention group. Among the included trials, 79% were industry-funded, 93% involved medications, and the majority were open label. The median FI was 3 (interquartile range, 1-11). In 43% of the trials, the number of patients reported lost to follow-up was higher than or equal to the FI. Only 4% of the RCTs imputed a value for the missing dichotomous outcome. Furthermore, the median number of subjects who discontinued the trial because of adverse effects was 21, which was greater than the FI in 60% of the RCTs. CONCLUSIONS: The arbitrary classification of results into "significant" and "nonsignificant" based on P value <0.05 should perhaps be interpreted with the help of other statistical parameters and FI is one of them.