Validation of Real-World Case Definitions for COVID-19 Diagnosis and Severe COVID-19 Illness Among Patients Infected with SARS-CoV-2: Translation of Clinical Trial Definitions to Real-World Settings
Authors: Duh Mei Sheng , Nguyen Catherine , Rubino Heather , Herrick Christopher , Chang Rose , DerSarkissian Maral , Hsieh Yichuan Grace , Banatwala Azeem , Yu Louise H. , Belsky Gregory , Murphy Marykate E. , Boyle-Kelly Janet , Cagan Andrew , Stangle Bruce E. , Cremieux Pierre Y. , Kolitsopoulos Francesca , Murphy Shawn N. 
Published: September 2023

Purpose

This study assessed the performance of International Classification of Diseases 10th Revision, Clinical Modification (ICD-10-CM) coronavirus disease 2019 (COVID-19) diagnostic code U07.1 against polymerase chain reaction (PCR) test results (Objective 1), and electronic medical record (EMR)-based codified algorithm for severe COVID-19 illness based on endpoints used in the Pfizer-BioNTech COVID-19 vaccine trial against chart review (Objective 2).

Methods

This retrospective, longitudinal cohort study used EMR data from the Mass General Brigham COVID-19 Data Mart (3/1/2020–11/19/2020) for adult patients with ≥1 PCR test, antigen test, or code U07.1 (Objective 1) and adult patients with a positive PCR test hospitalized with COVID-19 (Objective 2).

Results

Among 354,124 patients in Objective 1, 96% had ≥1 PCR test (including 6% with ≥1 positive PCR test; 11% with ≥1 code U07.1). Code U07.1 had low sensitivity (54%) and positive predictive value (PPV; 63%) but high specificity (97%) against the PCR test. Among 300 patients hospitalized for COVID-19 randomly sampled for chart review in Objective 2, the EMR-based case definition for severe COVID-19 illness had high PPV (>95%), showing better performance than severe/critical COVID-19 endpoints defined by the World Health Organization (PPV: 79%).

Conclusions

COVID-19 diagnosis based on ICD-10-CM code U07.1 had inadequate sensitivity and requires confirmation by PCR testing. The EMR-based case definition showed high PPV and can be used to identify cases of severe COVID-19 illness in real-world datasets. These findings highlight the importance of validating outcomes in real-world data, and can guide researchers analyzing COVID-19 data when PCR tests are not readily available.

KEY POINTS

This study evaluated the performance of International Classification of Diseases 10th Revision, Clinical Modification (ICD-10-CM) codes and an electronic medical record (EMR)-based algorithm for identifying coronavirus disease 2019 (COVID-19) diagnosis and severe COVID-19 illness in real-world data.

ICD-10-CM code U07.1 for COVID-19 had low sensitivity and positive predictive value (PPV) against PCR tests.

The EMR-based algorithm for severe COVID-19 illness developed from the Pfizer– BioNTech COVID-19 vaccine trial had high PPV against chart review, and may be used to identify severe cases in real-world data.

These results highlight the importance of validating outcomes when conducting analyses of real-world datasets.

PLAIN LANGUAGE SUMMARY

As polymerase chain reaction (PCR) tests for coronavirus disease 2019 (COVID-19) diagnosis are becoming less frequently used and there is no standard definition of severe COVID-19 illness, it is important to have a way of correctly identifying COVID-19 diagnosis or severe COVID-19 illness in real-world data (e.g., electronic medical records [EMRs]). This study examined: 1) how a diagnosis code for COVID-19 used in EMRs (i.e., U07.1) compares to PCR test results in terms of accurately identifying patients with COVID-19; and 2) whether a definition for severe COVID-19 illness developed based on the Pfizer–BioNTech COVID-19 vaccine trial and a definition used by the World Health Organization [WHO]) can be used to accurately identify patients with severe COVID-19 illness in EMRs. The results showed that code U07.1 was not very accurate in identifying patients with COVID-19. On the other hand, the developed definition for severe COVID-19 illness was more accurate than the WHO definition and was able to identify most patients with severe COVID-19 illness in real-world data.