ABSTRACT
Background and objectives: Remdesivir, an antiviral drug routinely used in the treatment of COVID-19 has not yet received FDA approval for use in patients with advanced kidney disease defined as GFR < 30 mL/min/1.73 m2. There is concern that an excipient in Veklury (Gilead's proprietary name for remdesivir) called sulfobutylether-beta-cyclodextrin (SBECD), which is renally cleared, may accumulate and reach toxic levels in patients with advanced kidney disease. The aim of this study was to summarize characteristics and incidence of adverse events of chronic kidney disease (CKD) patients who received remdesivir during hospitalization.Design, setting, participants, and measurements.We retrospectively studied patients admitted to one of several hospitals of the Mayo Clinic Foundation with the diagnosis of COVID-19 pneumonia and CKD. Laboratory values were also measured when remdesivir was first administered and stopped. All analyses were performed in the overall patient group and three separate subgroups of patients with a GFR ≥ 15, a GFR < 15 and dialysis, and a GFR < 15 and no dialysis. Results: A total of 444 CKD patients who were admitted to the hospital with COVID-19 pneumonia between May 2020 and September 2021 were included. Information was collected on patient characteristics, hospitalization, and adverse events. In the overall cohort, median age was 72 years (Range: 21-100 years), 55.2 % of patients were male, and most (86.5 %) were Caucasian. CKD stage was 3 for 114 patients (25.7 %), 4 for 229 patients (51.6 %), and 5 for 101 patients (22.7 %). A total of 146 patients (32.9 %) were admitted to the ICU, 103 (23.2 %) died in the hospital, and 120 (27.0 %) were on dialysis. The proportion of patients with an adverse event did not differ dramatically between the GFR ≥ 15 (20.9 %), GFR < 15 and dialysis (30.2 %), and GFR < 15 and no dialysis (32.3 %) groups (P = 0.12). Conclusion: Our results suggest that the use of remdesivir in patients with very severe CKD is safe, even in those who are not on renal replacement therapy.
ABSTRACT
Background: Coronavirus 2019 (COVID-19), also known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), infection is a pandemic that causes acute respiratory injury, hospital admission and death. Older adults are at a higher risk of serious illness and death from this pandemic. Many COVID-19 patients have a pre-existing cardiovascular disease (CVD). We aim to develop a risk factor stratification tool, using Artificial Intelligence (AI) method, to predict mortality, ICU admission, and length of hospital stay, in patients with CVD during this pandemic. Methods: This is a retrospective cohort study. An IRB approval was obtained. Patients with confirmed (SARS-Cov-2) test, age more than 60 and older, who were admitted to the Sparrow hospital between March 2020 and October 2020 were included. CV risk factors including Hypertension (HTN), Chronic Ischemic Heart Disease (CHD), Heart Failure (HF), and Cardiac Arrhythmia (CA) were used. Results: Of the 426 patients with COVID-19(mean age:74.5 years), at least 1 CVD was identified in most patients. HTN being the most common (55%), followed by CHD (22%), HF (20%) and CA (3%). Multivariable logistic regression has been conducted to identify risk factors for adverse outcomes and competing risk survival analysis for mortality. Outcomes measures included hospital stay > 7 days, ICU admission, and death. Discussion: Our data suggests patients with HTN required longer hospital stay, had higher ICU admissions and death rate. Conclusion: CV risk factors are common in older adults. HTN is the commonest CVD in this population. Several CV risk factors may contribute to the severity of COVID19 and its impact on older adults. Our study suggests that CV risk factors including HTN, HF, CHD, and CA have major impact on COVID-19 infection in hospitalized geriatric populations - see graph 1. Patients with HTN, had longer hospital stay, ICU admission, and mortality. Based on this work, we suggest that a large data sample might be required to develop an AI software that can help predict outcomes and the need for certain resources for older patients.