Effectiveness of Remdesivir Treatment Protocols Among Patients Hospitalized with COVID-19: A Target Trial Emulation.

BACKGROUND: Remdesivir is recommended for certain hospitalized patients with COVID-19. However, these recommendations are based on evidence from small randomized trials, early observational studies, or expert opinion. Further investigation is needed to better inform treatment guidelines with regard to the effectiveness of remdesivir among these patients. METHODS: We emulated a randomized target trial using chargemaster data from 333 US hospitals from 1 May 2020 to 31 December 2021. We compared three treatment protocols: remdesivir within 2 days of hospital admission, no remdesivir within the first 2 days of admission, and no remdesivir ever. We used baseline comorbidities recorded from encounters up to 12 months before admission and identified the use of in-hospital medications, procedures, and oxygen supplementation from charges. We estimated the cumulative incidence of mortality or mechanical ventilation/extracorporeal membrane oxygenation with an inverse probability of censoring weighted estimator. We conducted analyses in the total population as well as in subgroups stratified by level of oxygen supplementation. RESULTS: A total of 274,319 adult patients met the eligibility criteria for the study. Thirty-day in-hospital mortality risk differences for patients adhering to the early remdesivir protocol were -3.1% (95% confidence interval = -3.5%, -2.7%) compared to no early remdesivir and -3.7% (95% confidence interval -4.2%, -3.2%) compared to never remdesivir, with the strongest effect in patients needing high-flow oxygen. For mechanical ventilation/extracorporeal membrane oxygenation, risk differences were minimal. CONCLUSIONS: We estimate that, among hospitalized patients with COVID-19, remdesivir treatment within 2 days of admission reduced 30-day in-hospital mortality, particularly for patients receiving supplemental oxygen on the day of admission.

Lipid Testing Trends Before and After Hospitalization for Myocardial Infarction Among Adults in the United States, 2008-2019.

Background: Routine monitoring of low-density lipoprotein cholesterol (LDL-C) identifies patients who may benefit from modifying lipid-lowering therapies (LLT). However, the extent to which LDL-C testing is occurring in clinical practice is unclear, specifically among patients hospitalized for a myocardial infarction (MI). Methods: Using US commercial claims data, we identified patients with an incident MI hospitalization between 01/01/2008-03/31/2019. LDL-C testing was assessed in the year before admission (pre-MI) and the year after discharge (post-MI). Changes in LDL-C testing were evaluated using a Poisson model fit to pre-MI rates and extrapolated to the post-MI period. We predicted LDL-C testing rates if no MI had occurred (ie, based on pre-MI trends) and estimated rate differences and ratios (contrasting observed vs predicted rates). Results: Overall, 389,367 patients were hospitalized for their first MI during the study period. In the month following discharge, 9% received LDL-C testing, increasing to 27% at 3 months and 52% at 12 months. Mean rates (tests per 1000 patients per month) in the pre- and post-MI periods were 51.9 (95% CI: 51.7, 52.1) and 84.4 (95% CI: 84.1, 84.6), respectively. Over 12 months post-MI, observed rates were higher than predicted rates; the maximum rate difference was 66 tests per 1000 patients in month 2 (rate ratio 2.2), stabilizing at a difference of 15-20 (ratio 1.2-1.3) for months 6-12. Conclusion: Although LDL-C testing increased following MI hospitalization, rates remained lower than recommended by clinical guidelines. This highlights a potential gap in care, where increased LDL-C testing after MI may provide opportunities for LLT modification and decrease risk of subsequent cardiovascular events.

Medicaid Coverage Disruptions Among Children Enrolled in North Carolina Medicaid From 2016 to 2018.

Importance: Brief disruptions in insurance coverage among eligible participants are associated with poorer health outcomes for children. Objective: To describe factors associated with coverage disruptions among children enrolled in North Carolina Medicaid from 2016 to 2018 and estimate the outcome of preventing such disruptions on medical expenditures. Design Setting and Participants: This was a retrospective cohort study using North Carolina Medicaid claims data. All enrolled individuals were aged 1 to 20 years on January 1, 2016, and with 30 days of prior continuous enrollment. Children were observed from January 1, 2016, until December 31, 2018. Analyses were conducted from June 2020 through December 2020. Main Outcomes and Measures: Risk of Medicaid coverage disruptions of 1 to less than 12 months was assessed. Among children who disenrolled from Medicaid for 30 or more days, the risk of reenrollment within 1 to 6 months and 7 to 11 months was assessed. An inverse probability of censoring weights method was then used to estimate the outcome of an intervention to reduce coverage disruptions through preventing disenrollment on per member per month (PMPM) cost. Results: The study population included 831 173 Medicaid beneficiaries aged 1 to 5 years (23%), 6 to 17 years (68%), and 18 to 20 years (9%); 35% were Black, 44% were White, and 14% were Hispanic/Latinx. Among those with a first disenrollment (n = 214 401, 26%), the risk of reenrollment within 6 months and 7 to 11 months was 19% and 7%, respectively. Risk of coverage disruption was higher for Black children (hazard ratio [HR], 1.21; 95% CI, 1.18-1.24), children of other races (Asian, American Indian, Hawaiian or Pacific Islander, multiple races, or unreported; HR, 1.37; 95% CI, 1.33-1.40), and Latinx children (HR, 1.65; 95% CI, 1.60-1.70) compared with White children. Risk of coverage disruption was also higher for children with higher medical complexity (HR, 1.15; 95% CI, 1.12-1.19). The risk of coverage disruption was lower for children living in counties with the highest unemployment rates (HR, 0.89; 95% CI, 0.85-0.94), and comparisons between county-level measures of child poverty and graduation rates showed little or no difference. The estimated PMPM cost for the full population under a scenario in which all medical costs were included was $125.73. Estimated PMPM cost for the full cohort in a counterfactual scenario in which disenrollment was prevented was slightly lower ($122.14). Across all subgroups, estimated PMPM costs were modestly lower ($2-$8) in the scenario in which disenrollment was prevented. Conclusions and Relevance: In this cohort study, the risk of Medicaid coverage disruption was high, with many eligible children in historically marginalized communities continuing to experience unstable enrollment. In addition to improving health outcomes, preventing coverage gaps through policies that decrease disenrollment may also reduce Medicaid costs.

Statistical considerations in a delayed-start design to demonstrate disease modification effect in neurodegenerative disorders.

There has been a paradigm shift in diagnostic conceptualization of Alzheimer's disease (AD) based on the current evidence suggesting that structure and biology changes start to occur before clinical symptoms emerge. Consequently, therapeutic drug development is also shifting to treat early AD patients using biomarkers for enrichment in clinical trials. A similar paradigm shift is occurring for Parkinson disease. In the absence of acceptable biomarkers that could be combined with a clinical endpoint to demonstrate a disease modification (DM) effect in neurodegenerative disorders, a delayed-start design can be applied to demonstrate a lasting effect on the disease course. The delayed-start design includes two treatment periods, where in period 1, patients are randomized to receive an active treatment or placebo, and in period 2, placebo patients are switched to the active treatment while patients in the active treatment arm will continue the same treatment. The hypothesis is that patients who start the active treatment later will fail to catch up to the treatment benefit achieved by patients who receive the active treatment in both periods. A usual analytical approach has sought to demonstrate the divergence of slope during period 1 and the parallelism of slopes during period 2 as the DM effect. However, due to heterogeneity in timing and the magnitude of maximal effect among patients, nonlinear response over time could be observed within the two treatment arms in both periods. We propose an approach to evaluate the DM effect with the linearity assumption for treatment differences, but not for each arm separately.