Dataset Information

Ischemic strokes in COVID-19: risk factors, obesity paradox, and distinction between trigger and causal association.

ABSTRACT:

Background and purpose

Stroke has been described as a COVID-19 complication. However, its occurrence rate, risk factors, and causal relationships are still not well established.

Methods

We describe the characteristics of confirmed COVID-19-related strokes among all cases of COVID-19 hospitalized in our health network, from November 1, 2020 to April 30, 2021. Risk factor analysis has been conducted for ischemic stroke (IS), which represents 92% of all confirmed cases of Covid-19-related strokes, and a "causal attribution to infection" classification is provided.

Results

In all, 62/4105 hospitalized COVID-19 patients had an acute stroke (1.51%). Severe COVID-19 (OR 2.27-CI 1.06-4.77; p = 0.032), atrial fibrillation (OR 3.65-CI 1.63-7.98; p = 0.001), and ischemic heart disease (OR 4.590-CI 1.714-12.137; p = 0.002) proved to be independent risk factors for IS, while obesity was a protective factor (OR 0.90-CI 0.82-0.97; p = 0.012). COVID-19 had a causal role in 32.1% of IS cases, was a relevant cofactor in 28.6% of cases of IS, and was a possible trigger in 39.3% of events.

Conclusion

Our stroke occurrence rate is consistent with other population-based reports (range 0.34-2.7%). Prespecified peculiar clinical and radiological features allow the distinction between "IS caused by COVID-19" and "IS triggered by COVID-19." Clinical history of vascular diseases and risk factors is crucial in determining the risk of IS in patients with COVID-19. However, the protective effect of a BMI > 30 kg/m² seems to suggest an obesity paradox.

SUBMITTER: Janes F

PROVIDER: S-EPMC10428626 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Ischemic strokes in COVID-19: risk factors, obesity paradox, and distinction between trigger and causal association.

Janes Francesco F Sozio Emanuela E Gigli Gian Luigi GL Ripoli Andrea A Sbrana Francesco F Kuris Fedra F Nesi Lorenzo L Semenzin Tosca T Bertolino Giacomo G Deana Cristian C Bagatto Daniele D Ciardi Chiara C Fabris Martina M Merlino Giovanni G Bax Francesco F Nilo Annacarmen A Pez Sara S Valente Mariarosaria M Tascini Carlo C

Frontiers in neurology 20230801

<h4>Background and purpose</h4>Stroke has been described as a COVID-19 complication. However, its occurrence rate, risk factors, and causal relationships are still not well established.<h4>Methods</h4>We describe the characteristics of confirmed COVID-19-related strokes among all cases of COVID-19 hospitalized in our health network, from November 1, 2020 to April 30, 2021. Risk factor analysis has been conducted for ischemic stroke (IS), which represents 92% of all confirmed cases of Covid-19-re ...[more]

PMID: 37592943

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Project description:BackgroundWhile obesity confers an increased risk of death in the general population, numerous studies have reported an association between obesity and improved survival among critically ill patients. This contrary finding has been referred to as the obesity paradox. In this retrospective study, two causal inference approaches were used to address whether the survival of non-obese critically ill patients would have been improved if they had been obese.MethodsThe study cohort comprised 6557 adult critically ill patients hospitalized at the Intensive Care Unit of the Ghent University Hospital between 2015 and 2017. Obesity was defined as a body mass index of ≥ 30 kg/m2. Two causal inference approaches were used to estimate the average effect of obesity in the non-obese (AON): a traditional approach that used regression adjustment for confounding and that assumed missingness completely at random and a robust approach that used machine learning within the targeted maximum likelihood estimation framework along with multiple imputation of missing values under the assumption of missingness at random. 1754 (26.8%) patients were discarded in the traditional approach because of at least one missing value for obesity status or confounders.ResultsObesity was present in 18.9% of patients. The in-hospital mortality was 14.6% in non-obese patients and 13.5% in obese patients. The raw marginal risk difference for in-hospital mortality between obese and non-obese patients was - 1.06% (95% confidence interval (CI) - 3.23 to 1.11%, P = 0.337). The traditional approach resulted in an AON of - 2.48% (95% CI - 4.80 to - 0.15%, P = 0.037), whereas the robust approach yielded an AON of - 0.59% (95% CI - 2.77 to 1.60%, P = 0.599).ConclusionsA causal inference approach that is robust to residual confounding bias due to model misspecification and selection bias due to missing (at random) data mitigates the obesity paradox observed in critically ill patients, whereas a traditional approach results in even more paradoxical findings. The robust approach does not provide evidence that the survival of non-obese critically ill patients would have been improved if they had been obese.

Dataset Information

Ischemic strokes in COVID-19: risk factors, obesity paradox, and distinction between trigger and causal association.

Background and purpose

Methods

Results

Conclusion

Publications

Ischemic strokes in COVID-19: risk factors, obesity paradox, and distinction between trigger and causal association.

Similar Datasets

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