Insights into the association of ACEIs/ARBs use and COVID-19 prognosis: a multistate modelling study of nationwide hospital surveillance data from Belgium

Abstract

ObjectivesThe widespread use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) by patients with chronic conditions raised early concerns on the potential exacerbation of COVID-19 severity and fatality. Previous studies addressing this question have used standard methods that may lead to biased estimates when analysing hospital data because of the presence of competing events and event-related dependency. We investigated the association of ACEIs/ARBs’ use with COVID-19 disease outcomes using time-to-event data in a multistate setting to account for competing events and minimise bias.SettingNationwide surveillance data from 119 Belgian hospitals.ParticipantsMedical records of 10 866 patients hospitalised from 14 March 2020to 14 June 2020 with a confirmed SARS-CoV-19 infection and information about ACEIs/ARBs’ use.Primary outcome measureMultistate, multivariate Cox-Markov models were used to estimate the hazards of patients transitioning through health states from admission to discharge or death, along with transition probabilities calculated by combining the baseline cumulative hazard and regression coefficients.ResultsAfter accounting for potential confounders, there was no discernable association between ACEIs/ARBs’ use and transfer to intensive care unit (ICU). Contrastingly, for patients without ICU transfer, ACEIs/ARBs’ use was associated with a modest increase in recovery (HR 1.07, 95% CI 1.01 to 1.13, p=0.027) and reduction in fatality (HR 0.83, 95% CI 0.75 to 0.93, p=0.001) transitions. For patients transferred to ICU admission, no evidence of an association between ACEIs/ARBs’ use and recovery (HR 1.16, 95% CI 0.97 to 1.38, p=0.098) or in-hospital death (HR 0.91, 95% CI 0.73 to 1.12, p=0.381) was observed. Male gender and older age were significantly associated with higher risk of ICU admission or death. Chronic cardiometabolic comorbidities were also associated with less recovery.ConclusionsFor the first time, a multistate model was used to address magnitude and direction of the association of ACEIs/ARBs’ use on COVID-19 progression. By minimising bias, this study provided a robust indication of a protective, although modest, association with recovery and survival.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model

“…Patient’s characteristics were similar to those reported earlier in analyses using the Belgian COVID-19 surveillance data ( 17 , 32 – 35 ) ( Table 1 ). Approximately 85% of the hospitalised COVID-19 patients were aged ≥ 50 years and more than half of them were men.…”

“…A multi-state model in the hospital setting describes the course of hospital stay from admission to discharge, including intermediate events of disease progression among the hospitalised patients. The present analysis is built upon the same set-up of the multi-state model as devised in our previous work ( 17 ), but we have extended the multi-state methodology for the deployment of a risk prediction model.…”

“…More recent COVID-19 risk prediction models, however, accounted for some of the data complexity by considering time-to-event outcomes, hereby incorporating the time elapsing before an event is experienced or the observation is censored, either using Cox proportional hazard models (3)(4)(5)(6)(7)(8)(9) or in a competing risk framework (10)(11)(12). However, to the best of our knowledge, only a very few prediction scores have been built using time-to-event data in a multi-state setting that should fit better hospital progression, as documented by an increasing number of studies using a multi-state approach in the COVID-19 context (13)(14)(15)(16)(17).…”

“…When it is of interest to model non-fatal intermediate events, the competing risk model can be easily extended to a multi-state model for the analysis of successive events ( 18 ). When analysing hospital data, especially in the context of COVID-19, it is important to acknowledge the presence of competing risks and intermediate events using adequate statistical models to provide robust findings ( 17 ).…”

COVID-19 and the Use of Angiotensin II Receptor Blockers in Older Chronic Hypertensive Patients: Systematic Review and Meta-Analysis

“… N1 (ARAII) = 131 N2 (ACEI) = 171 N3 (no ACEI/ARAII) = 477 Mortality N1 OR 0.58 IC 0.35–1.07 p = 0.0796 Mortality N2 OR 0.55 IC 0.3–0.98 p = 0.0436 Odds Ratio Prevalence Mortality In patients over 50 years of age hospitalized for COVID-19, the use of ACEIs significantly reduces the risk of death. / NO 2a/B Peñalvo et al, 2021 (Belgium) [ 41 ] Cohorts n = 10,866 hospitalized patients from 119 Belgian hospitals Age = 67.82 Heart Disease (CVD) = 3984 (37%); DMT2 = 2522 (23%); Kidney Disease = 1513 (14%); Obesity = 782 (7%); Chronic obstructive Lung Disease = 1731 (16%); HBP = 4593 (42%); Cognitive issues = 1320 (12%); ICU care: (1) 425–(2) 990; Length of hospital stay: (1) 13.9–(2) 12.1 They compare ACEI/ARAII versus not taking them in hospitalized patients ACEI/ARAII in non-ICU patients are associated with a slight increase in recovery HR 1.07 IC 1.01–1.13 ( p = 0.027) and mortality reduction HR 0.83 IC 0.75–0.93 ( p = 0.001) not so in ICU patients in recovery HR 1.16 IC 0.97–1.38 ( p = 0.098) nor in reduction of ICU mortality HR 0.91 IC 0.73–1.12 ( p = 0.381) Hazard Ratio Prevalence Recovery Mortality The use of ACEI/ARAII in hospitalized patients, according to the results observed, protects the patient not admitted to the ICU, being associated with a discreet reduction in mortality. / YES 2a/B Sha et al, 2020 (USA) [ 42 ] Cohorts retrospetives n = 531 Afro-american patients hospitalized for COVID-19 Age = 60.01 +/− 15 Congestive Heart Disease = 79 (14%); DMT2 = 228 (42%); Kidney disease = 77 (14%); Chronic Obstructive Lung Disease = 36 (6%); BMI = 35 +/− 8.1; HBP = 425 (80%) Comparison of ACEI/ARAII versus not taking them in hospitalized Afro-Americans N1 (ACEI/ARAII) = 207 N2 (no ACEI/ARAII) =324 Hospital Mortality 18,4% N1 vs. 14,8% N2 ( p = 0.28) Mechanical ventilation 22.2% N1 vs. 16% N2 ( p = 0.07) Hospital length of stay 10 days N1 vs. 8.8 days N2 ( p = 0.14) Relative risk The use of ACEI/ARAII in hospit...…”