Epidemiology, prognosis and management of potassium disorders in Covid-19

Abstract

Coronavirus disease (Covid-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently the largest health crisis facing most countries. Several factors have been linked with a poor prognosis for this disease, including demographic factors, pre-existing comorbidities and laboratory parameters such as white blood cell count, D-dimer, C-reactive protein, albumin, lactate dehydrogenase, creatinine and electrolytes. Electrolyte abnormalities particularly potassium disorders are common among Covid-19 patients. Based on our pooled analysis, hypokalemia and hyperkalemia occur in 24.3% and 4.15% of Covid-19 patients, respectively. Potassium level deviation from the normal range may increase the chances of unfavorable outcomes and even death. Therefore, this article reviewed the epidemiology of potassium disorders and explained how hypokalemia and hyperkalemia are capable of deteriorating cardiac outcomes and the prognosis of Covid-19 for infected patients. The article finishes by highlighting some important considerations in the management of hypokalemia and hyperkalemia in these patients.

Keywords: Covid-19; SARS-CoV-2; hypokalemia; review; serum potassium.

FIGURE 1

Hypokalemia prevalence in Covid‐19 patients. Forest plot showing the prevalence of hypokalemia in Covid‐19 patients. The squares indicate weight of each study in random‐effect model. The vertical dashed line indicates the overall pooled estimate of hypokalemia prevalence and the diamond the 95% confidence interval around that pooled estimate. The forest plot was generated using STATA 15.0 (STATA Corp, LLC, TX)

FIGURE 2

Hyperkalemia prevalence in Covid‐19 patients. Forest‐plot showing prevalence of hyperkalemia in Covid‐19 patients. The squares indicate weight of each study in random‐effect model. The vertical dashed line indicates the overall pooled estimate of hyperkalemia prevalence and the diamond the 95% confidence interval around that pooled estimate. The forest plot was generated using STATA 15.0 (STATA Corp, LLC, TX)