Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

Abstract

Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January-September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7-7.8%, p_FDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7-10.5%, p_FDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19-25%), cerebrovascular diseases (24%, 13-35%), nontraumatic intracranial hemorrhage (34%, 20-50%), encephalitis and/or myelitis (37%, 17-60%) and myopathy (72%, 67-77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.

Figure 1

Schematic diagram of the cohort and data generation workflow for each healthcare system. The figure was created with Biorender (Biorender.com).

Figure 2

Characteristics of the study population across healthcare systems and countries. (A) Total number of male (left) and female (right) patients grouped by country shown in square-root scale. (B) Proportion of ever-severe cases by median age estimate at each healthcare system, grouped by country. Node size corresponds to the total number of patients per system. (C) Distribution of self-identified race among patients at healthcare systems in Singapore and the United States. The Other/Unknown category includes patients who did not identify with any of the predefined race categories and/or whose data were not reported. Most European healthcare systems did not report race. (D) Average proportion of patients in each age group within each country. FR, France; DE, Germany; ES, Spain; IT, Italy; SG, Singapore; US(A), United States of America.

Figure 3

Prevalence of neurological phenotypes among all patients. (A) Difference in prevalence of each neurological ICD-10 code by healthcare system and country, calculated as after admission—before admission date (eEq. 2). Pink color on the heat map indicates increased prevalence, while green color indicates decreased prevalence. Please see eFig. 1 for the absolute values of prevalence. (B) Total counts of patients with a given neurological ICD-10 code (left) and the mean proportion of patients (right) before and after admission date across all healthcare systems. The mean proportion estimates are shown as circles and the 95% confidence intervals are shown as bars.

Figure 4

Analysis of enrichment or depletion of neurological conditions after admission in patients with severe disease. For each neurological ICD-10 code, we show the log₂ enrichment (LOE) and its 95% confidence interval (left), and the absolute difference between the observed (filled triangle) and expected (⋅) number of patients experiencing severe COVID-19 in square-root scale (right). A purple positive LOE value for an ICD-10 code indicates a statistically significantly higher proportion of severe cases having a given neurological ICD-10 code when compared to the never-severe cases. Conversely, a green negative LOE value indicates a statistically significantly lower proportion of severe cases having a given neurological ICD-10 code when compared to the never-severe cases. Neurological ICD-10 codes are ordered by the expected number of severe cases after admission.