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Assessing Unique Risk Factors for COVID-19 Complications Among Cancer Patients: A Multi-ethnic Cohort Study.

Hala T BornoMi-Ok KimIrina TolstykhAmy LinJulian C HongSasha YousefiSylvia ZhangRana R McKayOlivier HarismendyPedram RazaviPelin CinarHope RugoVadim S KoshkinMaya RabowChristine WangAdina BaileyEric J Small
Published in: Journal of immigrant and minority health (2022)
A myriad of organ-specific complications have been observed with COVID-19. While racial/ethnic minorities have been disproportionately burdened by this disease, our understanding of the unique risk factors for complications among a diverse population of cancer patients remains limited. This is a multi-institutional, multi-ethnic cohort study evaluating COVID-19 complications among cancer patients. Patients with an invasive cancer diagnosis and confirmed SARS-CoV-2 infection were identified from March to November 2020. Demographic and clinical data were obtained and a multivariate logistic regression was employed to evaluate the impact of demographic and clinical factors on COVID-19 complications. The study endpoints were evaluated independently and included any complication, sepsis, pulmonary complications and cardiac complications. A total of 303 patients were evaluated, of whom 48% were male, 79% had solid tumors, and 42% were Hispanic/Latinx (Hispanic). Malignant hematologic cancers were associated with a higher risk of sepsis (OR 3.93 (95% CI 1.58-9.81)). Male patients had a higher risk of sepsis (OR 4.42 (95% CI 1.63-11.96)) and cardiac complications (OR 2.02 (95% CI 1.05-3.89)). Hispanic patients had a higher odds of any complication (OR 2.31 (95% CI 1.18-4.51)) and other race was associated with a higher odds of cardiac complications (OR 2.41 (95% CI 1.01-5.73)). Clinically, fever, cough, and ≥2 co-morbidities were independently significantly associated with any complication. This analysis evaluated covariates that can significantly predict a myriad of complications among a multi-ethnic cohort of cancer patients. The conclusions drawn from this analysis elucidate a mechanistic understanding of differential illness severity from COVID-19.
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