Letters to the Editor
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Rory D. de Vries, Marieke van der Heiden, Daryl Geers, Celine Imhof, Debbie van Baarle, RECOVAC-IR Collaborators
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Anthony T. Tan, Nina Le Bert, Antonio Bertoletti
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Dear Editor, We read with interest the article by Li and colleagues on the association between ACE inhibitors/angiotensin receptor blockers (ACE-I/ARB) use and in-hospital mortality among COVID-19 patients (1). The authors concluded that the use of ARB was associated with a significant reduction in in-hospital mortality among African American (AA) patients but not non-AA patients. However, we believe this conclusion is not per statistical principles and potentially misguiding readers. As noted by Altman and Bland (2), statistical analysis should be targeted to the clinical question: is the association between ARB use and in-hospital mortality different between AA and non-AA patients? To answer this question, one should directly compare the estimates (interaction test) (2), performed and reported by the authors. Here we argue that they did not accurately interpret this analysis. The authors showed an odds ratio (OR) of 0.196 (95% confidence interval [CI], 0.074 – 0.516) in the AA population and an OR of 0.687 (95% CI, 0.427 – 1.106) in the non-AA population. Accordingly, the interaction term was non-significant (95% CI, 0.185–1.292; P = 0.149).[1] As the authors stated that “Statistical significance was defined as a 2-sided P value less than 0.05”, the correct interpretation of this result would be: the association of ACEi/ARB use and in-hospital mortality was not significantly different between these two populations (2). In contrast to this interpretation, the authors concluded that the association was only present in the AA population, which is not compatible with their analysis. The potential association between ACEi/ARB use and COVID-19 in-hospital mortality is of great interest to the medical community. Further, the ability to provide reliable subgroup analyses is vital in clinical decision-making (3). Interaction analyses are essential to answer the clinically relevant question of whether a specific subgroup of patients can benefit more from an intervention. However, we believe the correct interpretation of these results does not support the author’s conclusion.
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Arthur M. Albuquerque, Carolina B. Santolia, Ashish Verma
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Dear Editor, We appreciate Albuquerque, et al.’s interest in our paper [1,2], who raised the concern that we did not accurately interpret the interaction test, noting that “one should directly compare the estimates (interaction test)” and “the authors concluded that the association was only present in the AA population, which is not compatible with their analysis.” We would like to clarify that our primary clinical question is whether ACE-I/ARB use is associated with the COVID-19 outcomes in each sub-group. We used stratified analysis to answer the question because when race/ethnicity serves as a non-specific proxy for numerous (confounding) factors, these can be (partially) controlled for through stratification [3]. Joint modeling of multiple groups is often used to gain power, but one needs to assume certain coherent distributions across different groups, which is not always true. Additionally, testing the interaction term is to assess association heterogeneity between groups, but it does not directly address whether the treatment is effective in each group. Specifically, we would like to elaborate on the following: 1) Our conclusion: “the use of ARB was associated with a significant reduction in in-hospital mortality among African American (AA) patients but not non-AA patients” was based on results from the stratified analysis. We reported that ARB in-hospital use was associated with reduced mortality in the AA stratum (OR=0.196; 95%CI:0.074-0.516; P=0.001) with statistical significance. On the other hand, the association in the non-AA stratum is not statistically significant (OR=0.687; 95%CI:0.427-1.106; P=0.122). As stated previously, our primary objective is to assess whether ACE-I/ARB use among AA patients is associated with COVID-19 mortality, rather than the difference between AA and non-AA patients. We were also aware that the estimated ORs across different stratum were not comparable as noted in [4-6]. 2) We performed the joint modeling of AA and non-AA patients as suggested by [6]. Here, ARB in-hospital use was associated with reduced mortality in entire study population (OR=0.560; 95%CI:0.371-0.846; P=0.006). The interaction term added to the model was not significant (95%CI:0.185-1.292; P=0.149). Interpreting interaction terms in logistic regression is complex and a significant interaction term in log-odds may not be significant in difference-in-differences for probability[7]. Furthermore, the assumption of the additive effects and imbalanced sample sizes could impact the inference. We believe these results and the interpretation are appropriate. We acknowledge that there are cases where comparing the interaction term in greater detail would be an important next step for understanding the association between COVID-19 mortality and race/ethnicity.
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Shilong Li, Pei Wang, Li Li
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Manami Tsunoi, Sunao Iyoda, Tadayuki Iwase
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Inès Ambite, Ulrich Dobrindt, Catharina Svanborg
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To the Editor: Povysil G. et al. report that “rare loss-of-function (LOF) variants in type I interferon (IFN) immunity genes are not associated with severe COVID-19” (1). We disagree with the authors’ interpretation of our data and their own (2), for six reasons: 1) Only predicted LOF (pLOF) variants are relevant for comparison between the two studies, because, unlike us, these authors did not test variants experimentally. The relevant proportion in our data is therefore not 24/659=3.5%, but 9/659= 1.36%, whereas theirs is 1/713=0.14%. 2) Our definitions of ‘severe/critical’ patients are different: we defined critical disease as severity grades 6-10 of the WHO scale (3), whereas they restricted their recruitment to grades 7-10 (i.e., excluding patients on high-flow oxygen, considered in our study). Their cohort of ‘mild’ cases may therefore include ‘severe’ COVID-19 cases (grade 6), such as perhaps their ‘mild’ TLR3 pLOF carrier. 3) Their ‘controls’ are subjects from the general population, without depletion of COVID-19 genetic risk factors, whereas we used pauci-/asymptomatic infected subjects (grades 1-3) as ‘controls’. Consequently their power computation in Figure 1 is based on an incorrect hypothesis about the odds ratio, which would be expected to be lower when using general population controls (as they did), than when using pauci- and asymptomatic infected individuals (as we did). 4) The ethnic origin of the patients differs between the two studies: 58% of our 659 patients (and 8 of our 9 pLOF carriers) were European, versus only 10% of their 713 patients with severe disease (and their pLOF carrier is East Asian). 5) Age is a key factor neglected in their comparison: our sample was much younger (mean age: 51.8 years) than theirs (mean: 65.9 years), and seven of our nine pLOF carriers were < 60 years old. We performed a comparison stratified by age (<60/≥60 years), and no significant difference in pLOF proportion was found between the two studies, even ignoring the only patient carrying a pLOF they found (of unknown age): 7/458 in our sample vs. 0/192 in their sample (p=0.11, Fisher’s exact test) for patients <60 years old, and 2/201 vs. 0/521 (p=0.07) for patients ≥60 years old. 6) Finally, and crucially, the authors did not exclude patients with autoantibodies against type I IFN, which account for at least 10% of critical cases and are much more frequent in patients > 60 years of age, particularly men (4).
Authors
Qian Zhang, Aurélie Cobat, Paul Bastard, Luigi D. Notarangelo, Helen C. Su, Laurent Abel, Jean-Laurent Casanova
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The authors reply: We appreciate the interest of Dr. Zhang and colleagues in our manuscript. The main difference between our publication and that of Zhang et al. (1), was that we assessed all rare predicted loss-of-function variants (pLOFs) meeting the same criteria in cases and controls, which is a well-established paradigm in the field (2). On the other hand, Zhang et al. included specific variants which were experimentally confirmed only in cases, but not controls, precluding a valid case-control comparison. We matched patients as closely as possible to the previous study, and the inclusion of more severe cases (WHO grades 7-10) should only strengthen the signal against population controls. The use of population controls is standard in such settings and has minimal impact on power, because only a small proportion of individuals exposed to SARS-Cov-2 develop severe disease (3). Additionally, for the pLOF model we report adequate power even for an odds ratio of 5.5, which is considerably lower than the one reported by Zhang et al. We tested the same dominant model as Zhang et al., even though LOF variants in these genes have only been reported to cause disease under recessive inheritance (4). We have serious concerns about confounding by ancestry in the analysis by Zhang et al. in which the pLOF carriers were mostly European, but functionally validated missense variants were found in various nationalities from Asia, Europe, Latin America, and the Middle East. Because the rates of pLOFs vary considerably across populations, adjusting for only 3 principal components of ancestry in rare-variant association tests of multi-ethnic cohorts does not provide adequate control for population structure. While we noted that age differences may contribute to the discrepancies between the two studies, Zhang et al. do not discuss the role of age in the interpretation of their results stating: “Inborn errors of TLR3- and IRF7-dependent type I IFN immunity at eight loci were found in as many as 23 patients (3.5%) of various ages (17 to 77 years) and ancestries (various nationalities from Asia, Europe, Latin America, and the Middle East) and in patients of both sexes.” We also note that the patients with autoantibodies were not excluded from the primary analysis by Zhang et al., but this was done only in the post-hoc analysis. Most importantly, our negative findings are in full agreement with the recently published independent study of 586,157 individuals, including 20,952 cases of COVID-19 (4,928 hospitalized and 1,304 requiring ventilation or resulting in death) (5). There were no significant associations with any of the 13 candidate genes examined either individually or in aggregate, or when comparisons included all hospitalized cases or only the most severe cases. Indeed, none of the associations displayed even marginal significance. Therefore, consistent with our study, these findings do not support substantial contributions of inborn errors in type I IFN immunity to COVID-19 severity. These negative results underscore the importance of proper study design, selection of appropriate genetic models, adequate control for genetic ancestry, and adherence to unbiased methods for genetic discovery rather than focusing only on a candidate biological pathway.
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Gundula Povysil, Guillaume Butler-Laporte, Ali G. Gharavi, J. Brent Richards, David B. Goldstein, Krzysztof Kiryluk
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Marzia Pennati, Gennaro Colella, Marco Folini, Lorenzo Citti, Maria Grazia Daidone, Nadia Zaffaroni
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Mihai G. Netea, Cees J. Tack, Paetrick M. Netten, Jos A. Lutterman, Jos W.M. Van der Meer
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