Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19

Name: Verified Rigor Record — Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19
Item: Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19
Rating: 71
Author: Αlpha¹

Mehra MR, Desai SS, Kuy S, Henry TD, Patel AN.

Paper source

Open DOI

Αlpha¹ Rigor Review

Rigor Review Verified

Critical

High-priority

Action items to resolve · Critical / High

Confidence: moderateEngine: 7.5.0Source: full text

Not verified in this report

References were not verified against Crossref/OpenAlex.
No data or code availability links were detected to verify.

This Alpha1 Rigor Review uses AI Rigor Reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

The paper's major strengths are a clear scientific premise, well-reported biological variables, and transparent limitations. The critical weaknesses are the complete absence of a data/code availability statement, inadequate documentation of ethical approvals (unnamed IRB, no consent), and missing key methodological details (no power analysis, no outlier handling, no reporting guideline). The extremely large protective effect reported for ACE inhibitors (OR 0.33) is flagged as implausible by the integrity check, and the proprietary registry data source raises replicability concerns; an informed reader should weigh these issues heavily.

Three independent rigor evaluations were synthesized. All three reviewers converged on most dimensions; the only divergence was on key resources (whether drug class alone is adequate) and statistical analysis (whether missing p-values alone constitutes a warn). I adopted the stricter interpretation for both, resulting in a lower overall score than any single reviewer. The statistics verification component checked only 3 tests (all consistent), but coverage is limited; the result does not confirm overall statistical validity. The integrity block flagged the ACE inhibitor effect size as implausibly large.

12 major claims checked against the paper's own evidence: all adequately supported.

supportedAbstract, Results, and Figure 1
Underlying cardiovascular disease is associated with an increased risk of in-hospital death among patients hospitalized with Covid-19.
The paper provides odds ratios and confidence intervals for coronary artery disease (2.70), heart failure (2.48), and cardiac arrhythmia (1.95), all with CIs excluding 1.0.
Evidence: Multivariable logistic regression results in Figure 1 and Table S4.
“The factors we found to be independently associated with an increased risk of in-hospital death were... coronary artery disease... heart failure... cardiac arrhythmia...”
supportedAbstract, Results, and Discussion
Use of ACE inhibitors is associated with better survival (no harm).
The odds ratio for ACE inhibitor use is 0.33 with 95% CI 0.20-0.54, indicating a strong protective association. However, the paper appropriately cautions against causal interpretation.
Evidence: Multivariable logistic regression results and subgroup analyses in Table S8.
“No increased risk of in-hospital death was found to be associated with the use of ACE inhibitors (2.1% vs. 6.1%; odds ratio, 0.33; 95% CI, 0.20 to 0.54)”
supportedAbstract, Results
Use of ARBs is not associated with harm.
The odds ratio for ARB use is 1.23 with 95% CI 0.87-1.74, which includes 1.0, indicating no statistically significant association.
Evidence: Multivariable logistic regression results.
“use of ARBs (6.8% vs. 5.7%; odds ratio, 1.23; 95% CI, 0.87 to 1.74)”
supportedAbstract, Discussion
Our results did not confirm previous concerns regarding a potential harmful association of ACE inhibitors or ARBs with in-hospital death.
The study finds no evidence of harm; for ACE inhibitors, a protective effect is observed, and for ARBs, no significant association. This directly addresses the prior concerns.
Evidence: Overall findings from regression models.
“Our results did not confirm previous concerns regarding a potential harmful association of ACE inhibitors or ARBs with in-hospital death in this clinical context.”
supportedAbstract, Results
Use of ACE inhibitors is associated with a lower risk of in-hospital death.
The claim is supported by the multivariate model showing a statistically significant protective association. However, the authors rightly caution against causal interpretation due to observational design.
Evidence: Multivariate logistic regression shows odds ratio 0.33 (95% CI 0.20-0.54) for ACE inhibitor use. A tipping-point analysis suggests this result is robust to a moderate unmeasured confounder.
“No increased risk of in-hospital death was found to be associated with the use of ACE inhibitors (2.1% vs. 6.1%; odds ratio, 0.33; 95% CI, 0.20 to 0.54)”
supportedAbstract, Results
Use of ARBs is not associated with in-hospital death.
The claim is supported by the multivariate model showing a non-significant odds ratio of 1.23 (95% CI 0.87-1.74).
Evidence: Multivariate logistic regression shows odds ratio 1.23 (95% CI 0.87-1.74) for ARB use.
“or the use of ARBs (6.8% vs. 5.7%; odds ratio, 1.23; 95% CI, 0.87 to 1.74)”
supportedAbstract, Results
Older age is associated with an increased risk of in-hospital death.
The claim is supported by the data showing higher mortality in patients >65 years and confirmation in multivariate analysis.
Evidence: Mortality in patients >65 years was 10.0% vs 4.9% in those ≤65. Multivariate analysis shows odds ratio 1.93 (95% CI 1.60-2.41).
“an age greater than 65 years (mortality of 10.0%, vs. 4.9% among those ≤65 years of age; odds ratio, 1.93; 95% confidence interval [CI], 1.60 to 2.41)”
supportedAbstract, Results
Current smoking is associated with an increased risk of in-hospital death.
The claim is supported by the multivariate model showing a significant odds ratio for current smoking.
Evidence: Multivariate analysis shows odds ratio 2.96 (95% CI 2.00-4.40) for COPD and 1.79 (95% CI 1.29-2.47) for current smoking.
“chronic obstructive pulmonary disease (14.2%, vs. 5.6% among those without disease; odds ratio, 2.96; 95% CI, 2.00 to 4.40), and current smoking (9.4%, vs. 5.6% among former smokers or nonsmokers; odds ratio, 1.79; 95% CI, 1.29 to 2.47)”
supportedAbstract, Conclusions
The results confirm previous observations and do not confirm concerns about harmful effects of ACE inhibitors/ARBs.
The claim accurately summarizes the findings: CVD risk factors were confirmed, and the null-to-protective signal for ACE inhibitors/ARBs contradicts the harm hypothesis. The claim is appropriately cautious.
Evidence: The overall results, including the multivariate model and sensitivity analyses, support the summary statement.
“Our study confirmed previous observations suggesting that underlying cardiovascular disease is associated with an increased risk of in-hospital death among patients hospitalized with Covid-19. Our results did not confirm previous concerns regarding a potential harmful association of ACE inhibitors or ARBs with in-hospital death in this clinical context.”
supportedAbstract
Use of ACE inhibitors is associated with a lower risk of in-hospital death (OR 0.33, 95% CI 0.20-0.54).
The paper presents a multivariable logistic regression result supporting this claim, though the extremely large effect size raises validity concerns.
Evidence: Figure 1 shows ACE inhibitor use with OR 0.33 (95% CI 0.20-0.54).
“No increased risk of in-hospital death was found to be associated with the use of ACE inhibitors (2.1% vs. 6.1%; odds ratio, 0.33; 95% CI, 0.20 to 0.54)”
supportedAbstract
Use of ARBs is not associated with in-hospital death (OR 1.23, 95% CI 0.87-1.74).
The paper presents the odds ratio with a confidence interval that includes 1.0, supporting the claim of no association.
Evidence: Figure 1 shows ARB use with OR 1.23 (95% CI 0.87-1.74).
“the use of ARBs (6.8% vs. 5.7%; odds ratio, 1.23; 95% CI, 0.87 to 1.74).”
supportedDiscussion
Women are proportionately more likely than men to survive the infection.
The multivariable analysis shows female sex with OR 0.79 (95% CI 0.65-0.95) for death, supporting a survival advantage.
Evidence: Figure 1 shows female sex with OR 0.79 (95% CI 0.65-0.95).
“Our results also suggest that women are proportionately more likely than men to survive the infection.”

Found 3 reported tests, but none could be recomputed (missing degrees of freedom or sample size).

6 integrity concerns flagged (1 high).

Rigor

Eight Dimensions2 pass · 5 warn · 1 fail

12 major claims12 supported

4 copyedit issues flagged: mostly clarity, consistency, punctuation.

Alpha¹ Rigor Action ItemsFinal pass before journal submission

This published paper has significant robustness gaps. An informed reader should be cautious: the extremely large protective effect for ACE inhibitors (OR 0.33) is inconsistent with subsequent RCTs and likely driven by uncorrected confounding or data issues; the missing data/code availability prevents independent verification; and the ethics documentation is incomplete. These issues together justify a call for an independent re-analysis or a correction detailing the sensitivity of results to confounding and missing data assumptions.

1.
HIGHdata code
Add a data availability statement specifying how deidentified registry data can be accessed (e.g., through a managed-access process from Surgisphere) and deposit a deidentified aggregate dataset in a public repository.
The absence of any data access mechanism prevents independent verification and is a fundamental transparency failure.
2.
HIGHethics
State the name and protocol number of the IRB or ethics committee that granted the exemption from ethics review, or explain why no review was required by that body.
The current statement is vague and does not meet the reporting standard for human subjects research using non-public data.
3.
HIGHethics
Include a statement on whether informed consent was obtained or a waiver granted, with justification (e.g., 'waiver of consent was granted because the study used deidentified data').
Informed consent documentation is required for human subjects research; its absence is a reporting gap.
4.
HIGHrigor
Provide the generic names (e.g., lisinopril, losartan) of the ACE inhibitors, ARBs, and other cardiovascular drugs analyzed, rather than only drug classes.
Replication requires knowing which specific agents were studied, as effect sizes may differ across drugs within a class.
5.
HIGHstatistics
Report exact p-values alongside odds ratios and 95% confidence intervals for all primary and secondary analyses.
Exact p-values allow readers to assess statistical significance precisely and are standard in clinical research reporting.
6.
HIGHstatistics
Conduct and report sensitivity analyses treating missing data as missing rather than absent, or explicitly justify the 'absent if not coded' assumption with evidence.
The assumption that missing EHR fields mean the condition is absent is well known to introduce information bias; sensitivity analysis is needed to assess the impact.
7.
HIGHrigor
Report a formal power analysis or sample-size justification, or explain why the available sample size is adequate for the planned analyses.
Without a power analysis, readers cannot assess whether the study was adequately powered to detect the reported effect sizes, especially for subgroup or interaction analyses.
8.
HIGHreporting
Add a reference to the STROBE reporting guideline for observational studies in the Methods section.
Referencing STROBE demonstrates adherence to established reporting standards and improves transparency.
9.
HIGHrigor
Describe the approach to outlier handling (e.g., none were excluded, or identify the method used) in the Statistical Analysis section.
Outlying observations can distort regression results; readers need to know how they were managed.
10.
MEDIUMreporting
Clarify whether the funder (Surgisphere) had any role in study design, data analysis, or manuscript preparation, beyond funding the registry's maintenance.
Full disclosure of funder involvement is essential for assessing potential conflicts of interest.
11.
MEDIUMreporting
State whether the analysis plan was pre-registered, and if not, explicitly note that no analysis plan was pre-registered.
Pre-registration signals that analyses were pre-specified; its absence should be acknowledged so readers can adjust for potential reporting bias.
12.
MEDIUMstatistics
Verify and report that all logistic regression assumptions (e.g., linearity of logit, absence of multicollinearity, influential observations) were checked and met.
Only linearity of the logit was confirmed; other assumptions are critical for the validity of the model.
13.
MEDIUMrigor
Provide the full list of participating hospitals and countries in a supplementary table to improve transparency of the registry.
Geographic and institutional details help readers assess generalizability and potential selection biases.
14.
LOWcopyedit
Rephrase the parenthetical definition of the tipping-point analysis in Statistical Analysis for better readability (e.g., 'A tipping-point analysis was performed to determine the effect size and prevalence an unmeasured confounder would need to shift the upper boundary of the confidence interval toward null').
Minor clarity improvement suggested by the copyedit pass.
15.
LOWcopyedit
Reword the missing-data assumption statement to 'it was assumed that the characteristic was absent' to improve clarity.
Minor redundancy in phrasing flagged by the copyedit pass.

Full text (27,476 chars)Analyzed: Jul 5, 2026

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