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Assessment of performance characteristics of COVID-19 ICD-10-CM diagnosis code U07.1 using SARS-CoV-2 nucleic acid amplification test results.
Moll, Keran; Hobbi, Shayan; Zhou, Cindy Ke; Fingar, Kathryn; Burrell, Timothy; Hernandez-Medina, Veronica; Obidi, Joyce; Alawar, Nader; Anderson, Steven A; Wong, Hui-Lee; Shoaibi, Azadeh.
  • Moll K; IBM Consulting, Bethesda, Maryland, United States of America.
  • Hobbi S; IBM Consulting, Bethesda, Maryland, United States of America.
  • Zhou CK; U.S. Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, United States of America.
  • Fingar K; IBM Consulting, Bethesda, Maryland, United States of America.
  • Burrell T; IBM Consulting, Bethesda, Maryland, United States of America.
  • Hernandez-Medina V; IBM Consulting, Bethesda, Maryland, United States of America.
  • Obidi J; U.S. Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, United States of America.
  • Alawar N; IBM Consulting, Bethesda, Maryland, United States of America.
  • Anderson SA; U.S. Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, United States of America.
  • Wong HL; U.S. Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, United States of America.
  • Shoaibi A; U.S. Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, United States of America.
PLoS One ; 17(8): e0273196, 2022.
Article in English | MEDLINE | ID: covidwho-1993518
ABSTRACT
The Food and Drug Administration's Biologics Effectiveness and Safety Initiative conducts active surveillance to protect public health during the coronavirus disease 2019 (COVID-19) pandemic. This study evaluated performance of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 in identifying COVID-19 cases in claims compared with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification test results in linked electronic health records (EHRs). Care episodes in three populations were defined using COVID-19-related diagnoses (population 1), SARS-CoV-2 nucleic acid amplification test procedures (population 2), and all-cause hospitalizations (population 3) in two linked claims-EHR databases IBM® MarketScan® Explorys® Claims-EMR Data Set (commercial) and OneFlorida Data Trust linked Medicaid-EHR. Positive and negative predictive values were calculated. Respectively, populations 1, 2, and 3 included 26,686, 26,095, and 2,564 episodes (commercial) and 29,117, 23,412, and 9,629 episodes (Florida Medicaid). The positive predictive value was >80% and the negative predictive value was >95% in each population, with the highest positive predictive value in population 3 (commercial 91.9%; Medicaid 93.1%). Findings did not vary substantially by patient age. Positive predictive values in populations 1 and 2 fluctuated during April-June 2020. They then stabilized in the commercial but not the Medicaid population. Negative predictive values were consistent over time in all populations and databases. Our findings indicate that U07.1 has high performance in identifying COVID-19 cases and noncases in claims databases. Performance may vary across populations and periods.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0273196

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0273196