ABSTRACT
OBJECTIVENeurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations. METHODSUsing electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity. RESULTSAmong the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%-7.8%, pFDR<.001) and unspecified disorders of the brain (8.1%, 95%CI: 5.7%-10.5%, pFDR<.001), compared to pre-admission prevalence. During hospitalization, patients who experienced severe COVID-19 status had 22% (95%CI: 19%-25%) increase in the relative risk (RR) of disorders of consciousness, 24% (95%CI: 13%-35%) increase in other cerebrovascular diseases, 34% (95%CI: 20%-50%) increase in nontraumatic intracranial hemorrhage, 37% (95%CI: 17%-60%) increase in encephalitis and/or myelitis, and 72% (95%CI: 67%-77%) increase in myopathy compared to those who never experienced severe disease. INTERPRETATIONUsing an international network and common EHR data elements, we highlight an increase in the prevalence of central and peripheral neurological phenotypes in patients hospitalized with SARS-CoV-2 infection, particularly among those with severe disease.
ABSTRACT
IntroductionScientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented, COVID-19-related symptoms, findings, and disorders from clinical text sources in the electronic health record. Word embeddings can identify terms related to these clinical concepts from both the biomedical and non-biomedical domains and are being shared with the open-source community at large. However, its unclear how useful openly-available word embeddings are for developing lexicons for COVID-19-related concepts. ObjectiveGiven an initial lexicon of COVID-19-related terms, characterize the returned terms by similarity across various, open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to word embedding source. Materials and MethodsWe compared 7 openly-available word embedding sources. Using a series of COVID-19-related terms for associated symptoms, findings, and disorders, we conducted an inter-annotator agreement study to determine how accurately the most semantically similar returned terms could be classified according to semantic types by three annotators. We conducted a qualitative study of COVID-19 queried terms and their returned terms to identify useful patterns for constructing lexicons. We demonstrated the utility of applying such terms to discharge summaries by reporting the proportion of patients identified by concept for pneumonia, acute respiratory distress syndrome, and COVID-19 cohorts. ResultsWe observed high, pairwise inter-annotator agreement (Cohens Kappa) for symptoms (0.86 to 0.99), findings (0.93 to 0.99), and disorders (0.93 to 0.99). Word embedding sources generated based on characters tend to return more lexical variants and synonyms; in contrast, embeddings based on tokens more often return a variety of semantic types. Word embedding sources queried using an adjective phrase compared to a single term (e.g., dry cough vs. cough; muscle pain vs. pain) are more likely to return qualifiers of the same semantic type (e.g., "dry" returns consistency qualifiers like "wet", "runny"). Terms for fever, cough, shortness of breath, and hypoxia retrieved a higher proportion of patients than other clinical features. Terms for dry cough returned a higher proportion of COVID-19 patients than pneumonia and ARDS populations. DiscussionWord embeddings are a valuable technology for learning terms, including synonyms. When leveraging openly-available word embedding sources, choices made for the construction of the word embeddings can significantly influence the phrases returned.
