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Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile
He S Yang1,2, Yu Hou3, Hao Zhang3
1Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Health Data Science
|November 21, 2022
Summary
A distinct laboratory profile (COVID-19-HRP) identified severe COVID-19 cases. This profile decreased over time, mirroring lower viral loads and improved outcomes in SARS-CoV-2 patients during the pandemic
Area of Science:
- Infectious Diseases
- Medical Informatics
- Clinical Chemistry
Background:
- New York City (NYC) observed fluctuating SARS-CoV-2 (COVID-19) cases in 2020.
- Changes in COVID-19 patient laboratory results and their correlation with outcomes remain under-reported.
Purpose of the Study:
- To identify patterns in routine laboratory test results for COVID-19 patients.
- To correlate these patterns with disease severity and temporal trends during the pandemic.
- To assess the utility of laboratory profiles in patient triaging and resource optimization.
Main Methods:
- Retrospective analysis of 5,785 patients from a NYC emergency department (March-June).
- Utilized machine learning to analyze routine blood tests and SARS-CoV-2 RT-PCR results.
- Identified a high-risk laboratory profile (COVID-19-HRP) comprising 21 blood tests.
Main Results:
- A distinct COVID-19-HRP was identified in approximately half of SARS-CoV-2 positive patients.
- Patients with COVID-19-HRP exhibited higher viral loads (lower Ct values) and poorer outcomes.
- The prevalence of COVID-19-HRP decreased from March/April to May/June, coinciding with reduced viral loads and less distinct lab profiles.
Conclusions:
- Longitudinal analysis reveals temporal shifts in COVID-19 patient laboratory profiles, reflecting disease evolution in NYC.
- The identified laboratory profile can aid in tracking COVID-19 severity and predicting patient outcomes.
- This approach may enhance patient triaging and resource allocation during public health crises.
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