ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

No abstract

Background COVID‐19 is caused by the coronavirus SARS‐CoV‐2, which uses angiotensin‐converting enzyme 2 (ACE‐2) as a receptor for cellular entry. It is theorized that ACE inhibitors (ACE‐Is) or angiotensin receptor blockers (ARBs) may increase vulnerability to SARS‐CoV‐2 by upregulating ACE‐2 expression, but ACE‐I/ARB discontinuation is associated with clinical deterioration. Objective To determine whether ACE‐I and ARB use is associated with acute kidney injury (AKI), macrovascular thrombosis and in‐hospital mortality. Methods A retrospective, single‐centre study of 558 hospital inpatients with confirmed COVID‐19 admitted from 1 March to 30 April 2020, followed up until 24 May 2020. AKI and macrovascular thrombosis were primary end‐points, and in‐hospital mortality was a secondary end‐point. Results AKI occurred in 126 (23.1%) patients, 34 (6.1%) developed macrovascular thrombi, and 200 (35.9%) died. Overlap propensity score‐weighted analysis showed no significant effect of ACE‐I/ARB use on the risk of occurrence of the specified end‐points. On exploratory analysis, severe chronic kidney disease (CKD) increases odds of macrovascular thrombi (OR: 8.237, 95% CI: 1.689–40.181, P = 0.009). The risk of AKI increased with advancing age (OR: 1.028, 95% CI: 1.011–1.044, P = 0.001) and diabetes (OR: 1.675, 95% CI: 1.065–2.633, P = 0.025). Immunosuppression was associated with lower risk of AKI (OR: 0.160, 95% CI: 0.029–0.886, P = 0.036). Advancing age, dependence on care, male gender and eGFR < 60 mL min −1 /1.73 m 2 increased odds of in‐hospital mortality. Conclusion We did not identify an association between ACE‐I/ARB use and AKI, macrovascular thrombi or mortality. This supports the recommendations of the European and American Societies of Cardiology that ACE‐Is and ARBs should not be discontinued during the COVID‐19 pandemic.

Objective To describe outcomes of acute coronavirus disease 2019 in paediatric and young adult patients with underlying cardiac disease and evaluate the association between cardiac risk factors and hospitalisation. Study design We conducted a retrospective single-institution review of patients with known cardiac disease and positive severe acute respiratory syndrome coronavirus 2 RT-PCR from 1 March, 2020 to 30 November, 2020. Extracardiac comorbidities and cardiac risk factors were compared between those admitted for coronavirus disease 2019 illness and the rest of the cohort using univariate analysis. Results Forty-two patients with a mean age of 7.7 ± 6.7 years were identified. Six were 18 years of age or more with the oldest being 22 years of age. Seventy-six percent were Hispanic. The most common cardiac diagnoses were repaired cyanotic (n = 7, 16.6%) and palliated single ventricle (n = 7, 16.6%) congenital heart disease. Fourteen patients (33.3%) had underlying syndromes or chromosomal anomalies, nine (21%) had chronic pulmonary disease and eight (19%) were immunosuppressed. Nineteen patients (47.6%) reported no symptoms. Sixteen (38.1%) reported only mild symptoms. Six patients (14.3%) were admitted to the hospital for acute coronavirus disease 2019 illness. Noncardiac comorbidities were associated with an increased risk of hospitalisation (p = 0.02), particularly chronic pulmonary disease (p = 0.01) and baseline supplemental oxygen requirement (p = 0.007). None of the single ventricle patients who tested positive required admission. Conclusions Hospitalisations for coronavirus disease 2019 were rare among children and young adults with underlying cardiac disease. Extracardiac comorbidities like pulmonary disease were associated with increased risk of hospitalisation while cardiac risk factors were not.

In the National Lung Screening Trial, both patient- and center-specific factors were associated with having a positive baseline screen. Although the model does not have sufficient accuracy to provide personalized risk estimates to guide shared decision making on an individual basis, it can nonetheless inform screening centers of the likelihood of further follow-up testing for their populations at large when allocating resources. Data collected from centers as broad-based screening is implemented can be used to improve model accuracy further.