Long-term costs to Medicare associated with endovascular and open repairs of infrarenal and complex abdominal aortic aneurysms.

OBJECTIVE: The vast majority of patients with abdominal aortic aneurysms (AAAs) undergoing repairs receive endovascular interventions (EVARs) instead of open operations (OARs). Although EVARs have better short-term outcomes, OARs have improved longer-term durability and require less radiographic follow-up and monitoring, which may have significant implications on health care economics surrounding provision of AAA care nationally. Herein, we compared costs associated with EVAR and OAR of both infrarenal and complex AAAs. METHODS: We examined patients undergoing index elective EVARs or OARs of infrarenal and complex AAAs in the 2014-2019 Vascular Quality Initiative-Vascular Implant Surveillance and Interventional Outcomes Network (VQI-VISION) dataset. We defined overall costs as the aggregated longitudinal costs associated with: (1) the index surgery; (2) reinterventions; and (3) imaging tests. We evaluated overall costs up to 5 years after infrarenal AAA repair and 3 years for complex AAA repair. Multivariable regressions adjusted for case-mix when evaluating cost differences between EVARs vs OARs. RESULTS: We identified 23,746 infrarenal AAA repairs (8.7% OAR, 91% EVAR) and 2279 complex AAA repairs (69% OAR, 31% EVAR). In both cohorts, patients undergoing EVARs were more likely to be older and have more comorbidities. The cost for the index procedure for EVARs relative to OARs was lower for infrarenal AAAs ($32,440 vs $37,488; P < .01) but higher among complex AAAs ($48,870 vs $44,530; P < .01). EVARs had higher annual imaging and reintervention costs during each of the 5 postoperative years for infrarenal aneurysms and the 3 postoperative years for complex aneurysms. Among patients undergoing infrarenal AAA repairs who survived 5 years, the total 5-year cost of EVARs was similar to that of OARs ($35,858 vs $34,212; -$223 [95% confidence interval (CI), -$3042 to $2596]). For complex AAA repairs, the total cost at 3 years of EVARs was greater than OARs ($64,492 vs $42,212; +$9860 [95% CI, $5835-$13,885]). For patients receiving EVARs for complex aneurysms, physician-modified endovascular grafts had higher index procedure costs ($55,835 vs $47,064; P < .01) although similar total costs on adjusted analyses (+$1856 [95% CI, -$7997 to $11,710]; P = .70) relative to Zenith fenestrated endovascular grafts among those that were alive at 3 years. CONCLUSIONS: Longer-term costs associated with EVARs are lower for infrarenal AAAs but higher for complex AAAs relative to OARs, driven by reintervention and imaging costs. Further analyses to characterize the financial viability of EVARs for both infrarenal and complex AAAs should evaluate hospital margins and anticipated changes in costs of devices.

Predicting Contralateral Surgery for Trapeziometacarpal Arthrosis Within 5 Years.

PURPOSE: Symptomatic trapeziometacarpal (TMC) joint arthritis is a common cause of hand pain. It is unknown how many patients ultimately elect to have bilateral surgery for TMC arthritis. In this study, we assessed the frequency and predictive factors for contralateral TMC surgery in patients who underwent prior TMC surgery. METHODS: We identified 712 patients who underwent primary surgery for TMC arthritis with a follow-up period of 5 years. We collected demographic, surgical, and follow-up data. Prediction models for contralateral surgery using a training and testing data set were created with multivariable logistic regression and random forest classifier algorithms. RESULTS: At the time of initial surgery, 230 patients had bilateral thumb pain (32%), but only 153 patients ultimately had an operation for TMC arthritis on the contralateral side within 5 years (21% of 712 total patients and 67% of 230 patients with bilateral pain). Common predictive factors between both models for contralateral surgery were younger age (odds ratio [OR] = 0.95; 95% confidence interval [CI], 0.93-0.98), bilateral thumb pain (OR = 3.76; 95% CI, 2.52-5.65), and anxiety disorders (OR = 1.84; 95% CI, 1.11-3.03). CONCLUSIONS: In our study, we found that the rate of contralateral surgery was 21% in patients who underwent prior TMC surgery. Predictive factors for future contralateral surgery included younger age, bilateral thumb pain, and anxiety disorder at the time of initial surgery. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic II.

Systematic review of machine-learning models in orthopaedic trauma.

Aims: Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods: A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results: A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion: The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.