ArticleOpen forum infectious diseases2022
An Elastic Net Regression Model for Identifying Long COVID Patients Using Health Administrative Data: A Population-Based Study.
Article in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Immunological Mechanisms and Machine Learning Applications in Post-COVID-19 Syndrome: A Narrative Review.Microorganisms · 2026Article
- Self-reported Health Service Utilization and Barriers to Care Among US Adults with a History of Post COVID-19 Condition.Journal of general internal medicine · 2025Article
- Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative.Journal of medical Internet research · 2025Article
- Article
- Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.PloS one · 2025Observational
- Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data.Journal of medical Internet research · 2024Article
- Psychological factors associated with Long COVID: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Cohort profile: the British Columbia COVID-19 Cohort (BCC19C)-a dynamic, linked population-based cohort.Frontiers in public health · 2024Article
- Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm.Journal of clinical medicine · 2023Article
- A survey on the role of artificial intelligence in managing Long COVID.Frontiers in artificial intelligence · 2023Review
- The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.Frontiers in public health · 2023Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Long coronavirus disease (COVID) patients experience persistent symptoms after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Healthcare utilization data could provide critical information on the disease burden of long COVID for service planning; however, not all patients are diagnosed or assigned long COVID diagnostic codes. We developed an algorithm to identify individuals with long COVID using population-level health administrative data from British Columbia (BC), Canada. Methods: An elastic net penalized logistic regression model was developed to identify long COVID patients based on demographic characteristics, pre-existing conditions, COVID-19-related data, and all symptoms/conditions recorded >28-183 days after the COVID-19 symptom onset/reported (index) date of known long COVID patients (n = 2430) and a control group (n = 24 300), selected from all adult COVID-19 cases in BC with an index date on/before October 31, 2021 (n = 168 111). Known long COVID cases were diagnosed in a clinic and/or had the International Classification of Diseases, Tenth Revision, Canada (ICD-10-CA) code for "post COVID-19 condition" in their records. Results: The algorithm retained known symptoms/conditions associated with long COVID, demonstrating high sensitivity (86%), specificity (86%), and area under the receiver operator curve (93%). It identified 25 220 (18%) long COVID patients among the remaining 141 381 adult COVID-19 cases, >10 times the number of known cases. Known and predicted long COVID patients had comparable demographic and health-related characteristics. Conclusions: Our algorithm identified long COVID patients with a high level of accuracy. This large cohort of long COVID patients will serve as a platform for robust assessments on the clinical course of long COVID, and provide much needed concrete information for decision-making.
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