Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19

Alaleh Azhir; Jonas Hügel; Jiazi Tian; Jingya Cheng; Ingrid V Bassett; Douglas S Bell; Elmer V Bernstam; Maha R Farhat; Darren W Henderson; Emily S Lau; Michele Morris; Yevgeniy R Semenov; Virginia A Triant; Shyam Visweswaran; Zachary H Strasser; Jeffrey G Klann; Shawn N Murphy; Hossein Estiri

doi:10.1016/j.medj.2024.10.009

Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19

Med. 2024 Nov 2:S2666-6340(24)00407-0. doi: 10.1016/j.medj.2024.10.009. Online ahead of print.

Authors

Alaleh Azhir¹, Jonas Hügel², Jiazi Tian³, Jingya Cheng³, Ingrid V Bassett⁴, Douglas S Bell⁵, Elmer V Bernstam⁶, Maha R Farhat⁷, Darren W Henderson⁸, Emily S Lau⁴, Michele Morris⁹, Yevgeniy R Semenov¹⁰, Virginia A Triant⁴, Shyam Visweswaran⁹, Zachary H Strasser⁴, Jeffrey G Klann⁴, Shawn N Murphy¹¹, Hossein Estiri¹²

Affiliations

¹ Clinical Augmented Intelligence Group, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
² Clinical Augmented Intelligence Group, Massachusetts General Hospital, Boston, MA, USA; Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
³ Clinical Augmented Intelligence Group, Massachusetts General Hospital, Boston, MA, USA.
⁴ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁵ Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁸ Center for Clinical and Translational Science, University of Kentucky, Lexington, KY, USA.
⁹ Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁰ Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA.
¹¹ Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
¹² Clinical Augmented Intelligence Group, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. Electronic address: hestiri@mgh.harvard.edu.

PMID: 39520983
DOI: 10.1016/j.medj.2024.10.009

Abstract

Background: Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC.

Methods: In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews to tune and validate the algorithm.

Findings: The PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying PASC cohorts compared to the ICD-10-CM code U09.9. The algorithm identified a cohort of over 24,000 patients with 79.9% precision. Our estimated prevalence of PASC was 22.8%, which is close to the national estimates for the region. We also provide in-depth analyses, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC.

Conclusions: PASC precision phenotyping boasts superior precision and prevalence estimation while exhibiting less bias in identifying patients with PASC. The cohort derived from this algorithm will serve as a springboard for delving into the genetic, metabolomic, and clinical intricacies of PASC, surmounting the constraints of prior PASC cohort studies.

Funding: This research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID).

Keywords: COVID-19; PASC; SARS-COV-2; Translation to population health; attention mechanism; long Covid; machine learning; phenotyping; post-acute sequalae.