Cardiorenal effects of Angiotensin-converting enzyme inhibitors and Angiotensin receptor blockers in people underrepresented in trials: analysis of routinely collected data with emulation of a reference trial (ONTARGET).

Cardiovascular disease (CVD) is a leading cause of death globally. Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB), compared in the ONTARGET trial, each prevent CVD. However, trial results may not be generalisable and their effectiveness in underrepresented groups is unclear. Using trial emulation methods within routine-care data to validate findings, we explored generalisability of ONTARGET results. For people prescribed an ACEi/ARB in the UK Clinical Practice Research Datalink GOLD from 1/1/2001-31/7/2019, we applied trial criteria and propensity-score methods to create an ONTARGET trial-eligible cohort. Comparing ARB to ACEi, we estimated hazard ratios for the primary composite trial outcome (cardiovascular death, myocardial infarction, stroke, or hospitalisation for heart failure), and secondary outcomes. As the pre-specified criteria were met confirming trial emulation, we then explored treatment heterogeneity among three trial-underrepresented subgroups: females, those aged ≥75 years and those with chronic kidney disease (CKD). In the trial-eligible population (n=137,155), results for the primary outcome demonstrated similar effects of ARB and ACEi, (HR 0.97 [95% CI: 0.93, 1.01]), meeting the pre-specified validation criteria. When extending this outcome to trial-underrepresented groups, similar treatment effects were observed by sex, age and CKD. This suggests that ONTARGET trial findings are generalisable to trial-underrepresented subgroups.

Potential interactions between medications for rate control and direct oral anticoagulants: Population-based cohort and case-crossover study.

BACKGROUND: Direct oral anticoagulants (DOACs) are commonly co-prescribed with amiodarone/diltiazem/verapamil, but whether there is a drug interaction between these drugs is unclear. OBJECTIVE: The purpose of this study was to investigate the risk of clinical outcomes associated with concomitant use of DOACs and amiodarone/diltiazem/verapamil. METHODS: We identified DOAC users in the Clinical Practice Research Datalink Aurum from January 1, 2011, to December 31, 2019. We used a cohort design to estimate hazard ratios for ischemic stroke, myocardial infarction, venous thromboembolism, intracranial bleeding, gastrointestinal bleeding, other bleeding, cardiovascular mortality, and all-cause mortality, comparing DOACs + amiodarone/diltiazem/verapamil users and DOACs + beta-blocker users. A case-crossover design comparing odds of exposure to different drug initiation patterns for all outcomes in hazard window vs referent window within an individual also was conducted. RESULTS: Of 397,459 DOAC users, we included 9075 co-prescribed amiodarone, 9612 co-prescribed diltiazem, and 2907 co-prescribed verapamil. There was no difference in risk of any outcomes between DOACs + amiodarone/diltiazem/verapamil users vs DOACs + beta-blocker users in the cohort design. However, in the case-crossover design, we observed an odds ratio (OR) of 2.09 (99% confidence interval [CI] 1.37-3.18) for all-cause mortality associated with initiation of a DOAC while taking amiodarone, which was greater than that observed for DOAC monotherapy (OR 1.30; 99% CI 1.25-1.35). Similar findings were observed for cardiovascular mortality and all-cause mortality respectively with diltiazem. CONCLUSION: Our study showed no evidence of higher bleeding or cardiovascular risk associated with co-prescribed DOACs and amiodarone, diltiazem, or verapamil. Elevated risks of cardiovascular and all-cause mortality were only observed during DOAC initiation when diltiazem/amiodarone were being taken.

Systemic Fluoroquinolone Use and Risk of Uveitis or Retinal Detachment.

Importance: Fluoroquinolone use has been associated with increased risk of uveitis and retinal detachment in noninterventional studies, but the findings have been conflicting and causality is unclear. Objective: To estimate the association of systemic fluoroquinolone use with acute uveitis or retinal detachment, using multiple analyses and multiple databases to increase the robustness of results. Design, Setting, and Participants: This cohort study used data from the Clinical Practice Research Datalink Aurum and GOLD UK primary care records databases, which were linked to hospital admissions data. Adults prescribed a fluoroquinolone or a comparator antibiotic, cephalosporin, between April 1997 and December 2019 were included. Adults with uveitis or retinal detachment were analyzed in a separate self-controlled case series. Data analysis was performed from May 2022 to May 2023. Exposures: Systemic fluoroquinolone or comparator antibiotic. Main Outcomes and Measures: The primary outcome was a diagnosis of acute uveitis or retinal detachment. Hazard ratios (HRs) were estimated in the cohort study for the association of fluoroquinolone prescription with either uveitis or retinal detachment, using stabilized inverse probability of treatment weighted Cox regression. Rate ratios (RRs) were estimated in the self-controlled case series, using conditional Poisson regression. Estimates were pooled across databases using fixed-effects meta-analysis. Results: In total, 3 001 256 individuals in Aurum (1 893 561 women [63.1%]; median [IQR] age, 51 [35-68] years) and 434 754 in GOLD (276 259 women [63.5%]; median [IQR] age, 53 [37-70] years) were included in the cohort study. For uveitis, the pooled adjusted HRs (aHRs) for use of fluoroquinolone vs cephalosporin were 0.91 (95% CI, 0.72-1.14) at first treatment episode and 1.07 (95% CI, 0.92-1.25) over all treatment episodes. For retinal detachment, the pooled aHRs were 1.37 (95% CI, 0.80-2.36) at first treatment episode and 1.18 (95% CI, 0.84-1.65) over all treatment episodes. In the self-controlled case series, for uveitis, the pooled adjusted RRs (aRRs) for fluoroquinolone use vs nonuse were 1.13 (95% CI, 0.97-1.31) for 1 to 29 days of exposure, 1.16 (95% CI, 1.00-1.34) for 30 to 59 days, and 0.98 (95% CI, 0.74-1.31) for 60 days for longer. For retinal detachment, pooled aRRs for fluoroquinolone use vs nonuse were 1.15 (95% CI, 0.86-1.54) for 1 to 29 days of exposure, 0.94 (95% CI, 0.69-1.30) for 30 to 59 days, and 1.03 (95% CI, 0.59-1.78) for 60 days or longer. Conclusions and Relevance: These findings do not support an association of systemic fluoroquinolone use with substantively increased risk of uveitis or retinal detachment. Although an association cannot be completely ruled out, these findings indicate that any absolute increase in risk would be small and, hence, of limited clinical importance.

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Propensity score methods, such as inverse probability-of-treatment weighting (IPTW), have been increasingly used for covariate balancing in both observational studies and randomized trials, allowing the control of both systematic and chance imbalances. Approaches using IPTW are based on two steps: (i) estimation of the individual propensity scores (PS), and (ii) estimation of the treatment effect by applying PS weights. Thus, a variance estimator that accounts for both steps is crucial for correct inference. Using a variance estimator which ignores the first step leads to overestimated variance when the estimand is the average treatment effect (ATE), and to under or overestimated estimates when targeting the average treatment effect on the treated (ATT). In this article, we emphasize the importance of using an IPTW variance estimator that correctly considers the uncertainty in PS estimation. We present a comprehensive tutorial to obtain unbiased variance estimates, by proposing and applying a unifying formula for different types of PS weights (ATE, ATT, matching and overlap weights). This can be derived either via the linearization approach or M-estimation. Extensive R code is provided along with the corresponding large-sample theory. We perform simulation studies to illustrate the behavior of the estimators under different treatment and outcome prevalences and demonstrate appropriate behavior of the analytical variance estimator. We also use a reproducible analysis of observational lung cancer data as an illustrative example, estimating the effect of receiving a PET-CT scan on the receipt of surgery.

Practical considerations for sample size calculation for cluster randomized trials.

Cluster randomized trials are an essential design in public health and medical research, when individual randomization is infeasible or undesirable for scientific or logistical reasons. However, the correlation among observations within clusters leads to a decrease in statistical power compared to an individually randomised trial with the same total sample size. This correlation - often quantified using the intra-cluster correlation coefficient - must be accounted for in the sample size calculation to ensure that the trial is adequately powered. In this paper, we first describe the principles of sample size calculation for parallel-arm CRTs, and explain how these calculations can be extended to CRTs with cross-over designs, with a baseline measurement and stepped-wedge designs. We introduce tools to guide researchers with their sample size calculation and discuss methods to inform the choice of the a priori estimate of the intra-cluster correlation coefficient for the calculation. We also include additional considerations with respect to anticipated attrition, a small number of clusters, and use of covariates in the randomisation process and in the analysis.

How should a cluster randomized trial be analyzed?

In cluster randomized trials, individuals from the same cluster tend to have more similar outcomes than individuals from different clusters. This correlation must be taken into account in the analysis of every cluster trial to avoid incorrect inferences. In this paper, we describe the principles guiding the analysis of cluster trials including how to correctly account for intra-cluster correlations as well as how to analyze more advanced designs such as stepped-wedge and cluster cross-over trials. We then describe how to handle specific issues such as small sample sizes and missing data.

Use and reporting of inverse-probability-of-treatment weighting for multicategory treatments in medical research: a systematic review.

OBJECTIVES: Causal inference methods for observational data represent an alternative to randomised controlled trials when they are not feasible or when real-world evidence is sought. Inverse-probability-of-treatment weighting (IPTW) is one of the most popular approaches to account for confounding in observational studies. In medical research, IPTW is mainly applied to estimate the causal effect of a binary treatment, even when the treatment has in fact multiple categories, despite the availability of IPTW estimators for multiple treatment categories. This raises questions about the appropriateness of the use of IPTW in this context. Therefore, we conducted a systematic review of medical publications reporting the use of IPTW in the presence of a multi-category treatment. Our objectives were to investigate the frequency of use and the implementation of these methods in practice, and to assess the quality of their reporting. STUDY DESIGN AND SETTING: Using Pubmed, Embase and Web of Science, we screened 5660 articles and retained 106 articles in the final analysis that were from 17 different medical areas. This systematic review is registered on PROSPERO (CRD42022352669). RESULTS: The number of treatment groups varied between 3 and 9, with a large majority of articles (90 [84.9%]) including 3 or 4 groups. The most commonly used method for estimating the weights was multinomial regression (51 [48.1%]) and generalized boosted models (48 [45.3%]). The covariates of the weight model were reported in 91 articles (85.9 %). Twenty-six articles (24.5 %) did not discuss the balance of covariates after weighting, and only 16 articles (15.1 %) referred to the assumptions needed to obtain correct inferences. CONCLUSION: The results of this systematic review illustrate that medical publications scarcely use IPTW methods for more than two treatment categories. Among the publications that did, the quality of reporting was suboptimal, in particular in regard to the assumptions and model building. IPTW for multi-category treatments could be applied more broadly in medical research, and the application of the proposed guidelines in this context will help researchers to report their results and to ensure reproducibility of their research.

Risk of death following chikungunya virus disease in the 100 Million Brazilian Cohort, 2015-18: a matched cohort study and self-controlled case series.

BACKGROUND: Chikungunya virus outbreaks have been associated with excess deaths at the ecological level. Previous studies have assessed the risk factors for severe versus mild chikungunya virus disease. However, the risk of death following chikungunya virus disease compared with the risk of death in individuals without the disease remains unexplored. We aimed to investigate the risk of death in the 2 years following chikungunya virus disease. METHODS: We used a population-based cohort study and a self-controlled case series to estimate mortality risks associated with chikungunya virus disease between Jan 1, 2015, and Dec 31, 2018, in Brazil. The dataset was created by linking national databases for social programmes, notifiable diseases, and mortality. For the matched cohort design, individuals with chikungunya virus disease recorded between Jan 1, 2015, and Dec 31, 2018, were considered as exposed and those who were arbovirus disease-free and alive during the study period were considered as unexposed. For the self-controlled case series, we included all deaths from individuals with a chikungunya virus disease record, and each individual acted as their own control according to different study periods relative to the date of disease. The primary outcome was all-cause natural mortality up to 728 days after onset of chikungunya virus disease symptoms, and secondary outcomes were cause-specific deaths, including ischaemic heart diseases, diabetes, and cerebrovascular diseases. FINDINGS: In the matched cohort study, we included 143 787 individuals with chikungunya virus disease who were matched, at the day of symptom onset, to unexposed individuals using sociodemographic factors. The incidence rate ratio (IRR) of death within 7 days of chikungunya symptom onset was 8·40 (95% CI 4·83-20·09) as compared with the unexposed group and decreased to 2·26 (1·50-3·77) at 57-84 days and 1·05 (0·82-1·35) at 85-168 days, with IRR close to 1 and wide CI in the subsequent periods. For the secondary outcomes, the IRR of deaths within 28 days after disease onset were: 1·80 (0·58-7·00) for cerebrovascular diseases, 3·75 (1·33-17·00) for diabetes, and 3·67 (1·25-14·00) for ischaemic heart disease, and there was no evidence of increased risk in the subsequent periods. For the self-controlled case series study, 1933 individuals died after having had chikungunya virus disease and were included in the analysis. The IRR of all-cause natural death within 7 days of symptom onset of chikungunya virus disease was 8·75 (7·18-10·66) and decreased to 1·59 (1·26-2·00) at 57-84 days and 1·09 (0·92-1·29) at 85-168 days. For the secondary outcomes, the IRRs of deaths within 28 days after disease onset were: 2·73 (1·50-4·96) for cerebrovascular diseases, 8·43 (5·00-14·21) for diabetes, and 2·38 (1·33-4·26) for ischaemic heart disease, and there was no evidence of increased risk at 85-168 days. INTERPRETATION: Chikungunya virus disease is associated with an increased risk of death for up to 84 days after symptom onset, including deaths from cerebrovascular diseases, ischaemic heart diseases, and diabetes. This study highlights the need for equitable access to approved vaccines and effective anti-chikungunya virus therapeutics and reinforces the importance of robust vector-control efforts to reduce viral transmission. FUNDING: Brazilian National Research Council (CNPq), Fundação de Amparo à Pesquisa do Estado da Bahia, Wellcome Trust, and UK Medical Research Council. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.