Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

Klann, Jeffrey G; Weber, Griffin M; Estiri, Hossein; Moal, Bertrand; Avillach, Paul; Hong, Chuan; Castro, Victor; Maulhardt, Thomas; Tan, Amelia L M; Geva, Alon; Beaulieu-Jones, Brett K; Malovini, Alberto; South, Andrew M; Visweswaran, Shyam; Omenn, Gilbert S; Ngiam, Kee Yuan; Mandl, Kenneth D; Boeker, Martin; Olson, Karen L; Mowery, Danielle L; Morris, Michele; Follett, Robert W; Hanauer, David A; Bellazzi, Riccardo; Moore, Jason H; Loh, Ne-Hooi Will; Bell, Douglas S; Wagholikar, Kavishwar B; Chiovato, Luca; Tibollo, Valentina; Rieg, Siegbert; Li, Anthony L L J; Jouhet, Vianney; Schriver, Emily; Samayamuthu, Malarkodi J; Xia, Zongqi; Hutch, Meghan; Luo, Yuan; Kohane, Isaac S; Brat, Gabriel A; Murphy, Shawn N

INTRODUCTION: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. OBJECTIVE: We sought to develop and validate a computable phenotype for COVID-19 severity. METHODS: Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. CONCLUSION: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.