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Background: Falls are the leading cause of injury-related death among older Americans. While some research has found that insomnia heightens falls, health care resource utilization (HCRU) and costs, the impact of insomnia treatments on fall risk, mortality, HCRU and costs in the elderly population, which could be of substantial interest to payers, has not been fully elucidated. This study evaluated the risk of falls and related consequences among adults ≥ 65 years of age treated with common prescription medications for insomnia compared with non-sleep disordered controls.Methods: This was a retrospective cohort analysis of deidentified Medicare claims from January 2011 through December 2017. Medicare beneficiaries treated for insomnia receiving zolpidem extended-release, zolpidem immediate-release, trazodone, or benzodiazepines were matched with non-sleep disordered controls. The main outcomes were falls, mortality, healthcare resource utilization (HCRU), and medical costs during the 12 months following the earliest fill date for the insomnia medication of interest. Generalized linear models controlled for several key covariates, including age, race, sex, geographic region and Charlson Comorbidity Index score.Results: The study included 1,699,913 Medicare beneficiaries (59.9% female, mean age 75 years). Relative to controls, adjusted analyses showed that beneficiaries receiving insomnia medication experienced over twice as many falls (odds ratio [OR] = 2.34, 95% CI: 2.31-2.36). In adjusted analyses, patients receiving benzodiazepines or trazodone had the greatest risk. Crude all-cause mortality rates were 15-times as high for the insomnia-treated as controls. Compared with controls, beneficiaries receiving insomnia treatment demonstrated higher estimated adjusted mean number of inpatient, outpatient, and emergency department visits and longer length of inpatient stay. All-cause total adjusted mean costs were higher among insomnia treated patients ($967 vs $454).Conclusions: Individuals receiving insomnia treatment had an increased risk of falls and mortality and higher HCRU and costs compared with matched beneficiaries without sleep disorders. Trazodone and benzodiazepines were associated with the greatest risk of falls. This analysis suggests that significant risks are associated with common, older generation insomnia medication treatments in the elderly. Nonetheless, these results should be interpreted with caution as the use of these medications may be indicative of underlying morbidity with potential for residual confounding. [ABSTRACT FROM AUTHOR]

Introduction: Falls are a common cause for morbidity and mortality among patients taking prescription insomnia medication. The objective of this study is to compare the risk of falls, all-cause healthcare resource utilization (HCRU), and costs among patients treated with commonly used, older generation insomnia medications and non-sleep-disordered controls.Methods: This retrospective cohort study used the IBM® MarketScan® Commercial and Medicare Supplemental Databases to identify patients aged at least 18 years treated with commonly prescribed medications for insomnia (zolpidem, trazodone, benzodiazepines) between 1 January 2012 and 30 September 2017. The insomnia-treated cohort were age- and sex-matched (1:1) to non-sleep-disordered controls. Odds ratios (ORs) compared risk of falls in each cohort, adjusting for covariates. Costs were adjusted to 2018 dollars, the most recent year for the study data.Results: Relative to matched controls (n = 313,086), the insomnia-treated cohort had a higher rate of falls (3.34% vs. 1.33%), and higher risk of falls [OR = 2.36 (95% confidence interval 2.27-2.44)]. Relative to other index treatments, patients treated with trazodone had the greatest risk of falls. Compared with matched controls, the estimated mean number of inpatient visits, emergency department visits, outpatient visits, and mean length of inpatient stay were all significantly higher among patients treated for insomnia. Such patients incurred greater total costs per patient per month than matched controls ($2100 versus $888; estimated mean ratio, 2.36; 95% CI 2.35-2.38; p < 0.0001).Conclusions: Relative to matched controls, the insomnia-treated cohort showed higher risk of falls with greater HCRU and costs. Each outcome measured was highest among patients treated with trazodone, relative to other index treatments. Findings suggest the need for new treatment options to optimize quality of care for patients with insomnia. [ABSTRACT FROM AUTHOR]

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights., Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure., Study Design: Cohort study; Level of evidence, 3., Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance., Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR., Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies., Registration: NCT00625885 (ClinicalTrials.gov identifier)., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: This study was funded by the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant 5R01-AR060846). See Supplemental Material for individual disclosures. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto., (© The Author(s) 2024.)