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. 2021 May:117:103748.
doi: 10.1016/j.jbi.2021.103748. Epub 2021 Mar 25.

ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes

Juan Zhao  1 , Monika E Grabowska  2 , Vern Eric Kerchberger  3 , Joshua C Smith  1 , H Nur Eken  4 , QiPing Feng  5 , Josh F Peterson  1 , S Trent Rosenbloom  1 , Kevin B Johnson  6 , Wei-Qi Wei  7
Affiliations

ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes

Juan Zhao et al. J Biomed Inform. 2021 May.

Abstract

Objective: Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) - that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.

Methods: We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms.

Results: We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including "anosmia" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "cough with fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC).

Conclusion: ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.

Keywords: COVID-19; EHR; Natural language processing.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Volcano plot of a ConceptWAS scan for 19, 692 patients that included COVID-19-positive group (cases) and negative group (controls). The points are colored by the semantic type of the concepts. Selected associations related to signs, symptoms, or diseases/syndromes are labeled. The volcano plot indicates -log10 (p-value) for association (y-axis) plotted against their respective log2 (fold change) (x-axis). The dashed line represents significance level using a Bonferroni correction.
Fig. 2
Fig. 2
Forest plot comparing individual concepts between COVID-19-positive (case) and COVID-19-negative (control) patients. Selected associations include the significant signals related to semantic types of symptoms that met Bonferroni-corrected significance (p-value < 2.55E-06). The odds ratio has been adjusted for age, gender, and race. The concepts are ordered by p-value.
Fig. 3
Fig. 3
Temporal ConceptWAS using every 2-week cumulative data. For significant signals (related to signs, symptoms) using all data (labeled in Fig. 2), the plot indicates their -log 10 (p-value) for association (y-axis) against using the cumulative data started between March 8, 2020 to n weeks (x-axis). The dashed line indicates a significant association using a Bonferroni correction.
Fig. A1
Fig. A1
Flowchart of study design for ConceptWAS between COVID-19-positive (case) and COVID-19 negatives (control).
Fig. B1
Fig. B1
Proportion of cases/controls with clinical notes on the days around COVID-19 test date. The x-axis indicates the note day relative to the COVID-19 test date. > 86% patients who have a PCR test had a clinical note within 24 h before the test date.
Fig. C1
Fig. C1
Schematic framework of the ConceptWAS and NLP pipeline.
Fig. D1
Fig. D1
The cumulative number of COVID-19-positive(cases) along weeks.
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