Combination Therapy Use and Associated Events in Clinical Practice Following Dissemination of Trial Findings: A De-Implementation Study Using Interrupted Time Series Analysis.

PURPOSE: It takes 17 years, on average, for trial results to be implemented into practice. Using data from the Department of Veterans Affairs (VA), this study assessed the potential impact on clinical practice of the dissemination of findings from a randomized, controlled trial reporting harm with the use of combination therapy. Communication between research and VA Pharmacy Benefits Management Services (PBM)  provided the impetus for communication from the PBM about the findings of the trial in accordance with policy. METHODS: In this de-implementation study, interrupted time series analysis was used for assessing prescribing patterns and adverse clinical events before and after the dissemination of the trial findings. The de-implementation strategy was multicomponent and multilevel. Strategies were aligned with categories outlined in the Expert Recommendations for Implementing Change: train and educate stakeholders, use evaluative and iterative strategies, develop stakeholder inter-relationships, change infrastructure, provide interactive assistance, and engage consumers. VA patients with type 2 diabetes mellitus, chronic kidney disease stages 1 to 3, and a moderate or severe albuminuria who received care between July 2008 and November 2017 were included. Patients were subgrouped according to treatment with an angiotensin-converting enzyme inhibitor + angiotensin receptor blocker. The primary end point was the prevalence of combination therapy use. Secondary end points were the incidences of acute kidney injury and hyperkalemia. FINDINGS: This study followed 712,245 patients, 9297 of whom used combination therapy. Data were available from 428,535 and 283,710 patients pre- and post-intervention, respectively; among these, 8324 and 973 patients used combination therapy, the median ages were 66 and 68 years, and 96.92% and 98.82% were men. One month following communication from the PBM, the reductions in combination therapy users, acute kidney injury events, and hyperkalemia were 331.94 (95% CI, 500.27-163.32), 36.58% (95% CI, 31.90%-41.95%), and 25.49% (95% CI, 14.17%-36.07%) per 100,000 patients per month, respectively (all, P < 0.001), whereas before the communication, these changes were +14.84 (95% CI, 10.27-19.42), -3.46% (95% CI, 3.18-3.74), and -3.27% (95% CI, 2.66%-3.87%) (all, P < 0.001). IMPLICATIONS: The apparent speed and impact of the implementation of changes resulting from the dissemination of trial findings into VA clinical practice are encouraging. The speed of implementation was much faster than average for health care providers in the United States. Established communications between research and clinical practice, as well as established policy and communications between PBM and clinical practice, may be a model for other health care organizations.

Effect of vaccination on the case fatality rate for COVID-19 infections 2020-2021: multivariate modelling of data from the US Department of Veterans Affairs.

OBJECTIVES: To evaluate the benefits of vaccination on the case fatality rate (CFR) for COVID-19 infections. DESIGN, SETTING AND PARTICIPANTS: The US Department of Veterans Affairs has 130 medical centres. We created multivariate models from these data-339 772 patients with COVID-19-as of 30 September 2021. OUTCOME MEASURES: The primary outcome for all models was death within 60 days of the diagnosis. Logistic regression was used to derive adjusted ORs for vaccination and infection with Delta versus earlier variants. Models were adjusted for confounding factors, including demographics, comorbidity indices and novel parameters representing prior diagnoses, vital signs/baseline laboratory tests and outpatient treatments. Patients with a Delta infection were divided into eight cohorts based on the time from vaccination to diagnosis. A common model was used to estimate the odds of death associated with vaccination for each cohort relative to that of unvaccinated patients. RESULTS: 9.1% of subjects were vaccinated. 21.5% had the Delta variant. 18 120 patients (5.33%) died within 60 days of their diagnoses. The adjusted OR for a Delta infection was 1.87±0.05, which corresponds to a relative risk (RR) of 1.78. The overall adjusted OR for prior vaccination was 0.280±0.011 corresponding to an RR of 0.291. Raw CFR rose steadily after 10-14 weeks. The OR for vaccination remained stable for 10-34 weeks. CONCLUSIONS: Our CFR model controls for the severity of confounding factors and priority of vaccination, rather than solely using the presence of comorbidities. Our results confirm that Delta was more lethal than earlier variants and that vaccination is an effective means of preventing death. After adjusting for major selection biases, we found no evidence that the benefits of vaccination on CFR declined over 34 weeks. We suggest that this model can be used to evaluate vaccines designed for emerging variants.

A novel method for handling pre-existing conditions in multivariate prediction model development for COVID-19 death in the Department of Veterans Affairs.

Many mathematical models have been proposed to predict death following the Coronavirus Disease 2019 (COVID-19); all started with comorbidity subsets for this still-little understood disease. Thus, we derived a novel predicted probability of death model (PDeathDx) upon all diagnostic codes documented in the Department of Veterans Affairs. We present the conceptual underpinnings and analytic approach in estimating the independent contribution of pre-existing conditions. This is the largest study to-date following patients with COVID-19 to predict mortality. Cases were identified with at least one positive nucleic acid amplification test. Starting in 1997, we use diagnoses from the first time a patient sought care until 14 days before a positive nucleic acid amplification test. We demonstrate the clear advantage of using an unrestricted set of pre-existing conditions to model COVID-19 mortality, as models using conventional comorbidity indices often assign little weight or usually do not include some of the highest risk conditions; the same is true of conditions associated with COVID-19 severity. Our findings suggest that it is risky to pick comorbidities for analysis without a systematic review of all those experienced by the cohort. Unlike conventional approaches, our comprehensive methodology provides the flexibility that has been advocated for comorbidity indices since 1993; such an approach can be readily adapted for other diseases and outcomes. With our comorbidity risk adjustment approach outperforming conventional indices for predicting COVID-19 mortality, it shows promise for predicting outcomes for other conditions of interest.

Chronic use of non-steroidal anti-inflammatory drugs (NSAIDs) or acetaminophen and relationship with mortality among United States Veterans after testing positive for COVID-19.

Non-steroidal anti-inflammatory drugs (NSAIDs) and acetaminophen are among the most-frequently used medications. Although these medications have different mechanisms of action, they have similar indications and treatment duration has been positively correlated with cardiovascular risk although the degree of risk varies by medication. Our objective was to study treatment effects of chronic use of individual NSAID medications and acetaminophen on all-cause mortality among patients who tested positive for COVID-19 while accounting for adherence. We used the VA national datasets in this retrospective cohort study to differentiate between sporadic and chronic medication use: sporadic users filled an NSAID within the last year, but not recently or regularly. Using established and possible risk factors for severe COVID-19, we used propensity scores analysis to adjust for differences in baseline characteristics between treatment groups. Then, we used multivariate logistic regression incorporating inverse propensity score weighting to assess mortality. The cohort consisted of 28,856 patients. Chronic use of aspirin, ibuprofen, naproxen, meloxicam, celecoxib, diclofenac or acetaminophen was not associated with significant differences in mortality at 30 days (OR = 0.98, 95% CI: 0.95-1.00; OR = 0.99, 95% CI: 0.98-1.00; OR = 1.00, 95% CI: 0.98-1.01; OR = 0.99, 95% CI: 0.98-1.00; OR = 1.00, 95% CI: 0.98-1.01; OR = 0.99, 95% CI: 0.97-1.01; and OR = 1.00, 95% CI: 0.99-1.02, respectively) nor at 60 days (OR = 0.97, 95% CI: 0.95-1.00; OR = 1.00, 95% CI: 0.99-1.01; OR = 0.99, 95% CI: 0.98-1.01; OR = 0.99, 95% CI: 0.97-1.00; OR = 0.99, 95% CI: 0.97-1.01; OR = 0.99, 95% CI: 0.97-1.01; and OR = 1.01, 95% CI: 0.99-1.02, respectively). Although the study design cannot determine causality, the study should assure patients as it finds no association between mortality and chronic use of these medications compared with sporadic NSAID use among those infected with COVID-19.