Hunting for protective drugs at the break of a pandemic: Causal inference from hospital data.

At the break of a pandemic, the protective efficacy of therapeutic interventions needs rapid evaluation. An experimental approach to the problem will not always be appropriate. An alternative route are observational studies, whether based on regional health service data or hospital records. In this paper, we discuss the use of methods of causal inference for the analysis of such data, with special reference to causal questions that may arise in a pandemic. We apply the methods by using the aid of a directed acyclic graph (DAG) representation of the problem, to encode our causal assumptions and to logically connect the scientific questions. We illustrate the usefulness of DAGs in the context of a controversy over the effects of renin aldosterone system inhibitors (RASIs) in hypertensive individuals at risk of (or affected by) severe acute respiratory syndrome coronavirus 2 disease. We consider questions concerning the existence and the directions of those effects, their underlying mechanisms, and the possible dependence of the effects on context variables. This paper describes the cognitive steps that led to a DAG representation of the problem, based on background knowledge and evidence from past studies, and the use of the DAG to analyze our hospital data and assess the interpretive limits of the results. Our study contributed to subverting early opinions about RASIs, by suggesting that these drugs may indeed protect the older hypertensive Covid-19 patients from the consequences of the disease. Mechanistic interaction methods revealed that the benefit may be greater (in a sense to be made clear) in the older stratum of the population.

Antecedent use of renin-angiotensin system inhibitors is associated with reduced mortality in elderly hypertensive Covid-19 patients.

OBJECTIVES: The effect of renin-angiotensin system inhibitors (RASIs) on mortality in patients with coronavirus disease (Covid-19) is debated. From a cohort of 1352 consecutive patients admitted with Covid-19 to Papa Giovanni XXIII Hospital in Bergamo, Italy, between February and April 2020, we selected and studied hypertensive patients to assess whether antecedent (prior to hospitalization) use of RASIs might affect mortality from Covid-19 according to age. METHODS AND RESULTS: Arterial hypertension was present in 688 patients. Overall mortality (in-hospital or shortly after discharge) was 35% (N = 240). After adjusting for 26 medical history variables via propensity score matching, antecedent use of RASIs (N = 459, 67%) was associated with a lower mortality in older hypertensive patients (age above the median of 68 years in the whole series), whereas no evidence of a significant effect was found in the younger group of the same population (P interaction = 0.001). In an analysis of the subgroup of 432 hypertensive patients older than 68 years, we considered two RASI drug subclasses, angiotensin-converting enzyme inhibitors (ACEIs, N = 156) and angiotensin receptor blockers (ARBs, N = 140), and assessed their respective effects by taking no-antecedent-use of RASIs as reference. This analysis showed that both antecedent use of ACEIs and antecedent use of ARBs were associated with a lower Covid-19 mortality (odds ratioACEI = 0.57, 95% confidence interval 0.36--0.91, P = 0.018) (odds ratioARB = 0.49, 95% confidence interval 0.29--0.82, P = 0.006). CONCLUSION: In the population of over-68 hypertensive Covid-19 patients, antecedent use of ACEIs or ARBs was associated with a lower all-cause mortality, whether in-hospital or shortly after discharge, compared with no-antecedent-use of RASIs.

Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.

OBJECTIVES: Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19. DESIGN: The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework. SETTING: 3 secondary and tertiary level centres in Greater Manchester, the UK. PARTICIPANTS: 392 hospitalised patients with a diagnosis of COVID-19. RESULTS: 392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome. CONCLUSIONS: This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.