Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile.
| Publication Type | Academic Article |
| Authors | Yang H, Hou Y, Zhang H, Chadburn A, Westblade L, Fedeli R, Steel P, Racine-Brzostek S, Velu P, Sepulveda J, Satlin M, Cushing M, Kaushal R, Zhao Z, Wang F |
| Journal | Health Data Sci |
| Volume | 2021 |
| Pagination | 7574903 |
| Date Published | 06/16/2021 |
| ISSN | 2765-8783 |
| Abstract | BACKGROUND: New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. METHODS: We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. RESULTS: A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. CONCLUSIONS: Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources. |
| DOI | 10.34133/2021/7574903 |
| PubMed ID | 36405356 |
| PubMed Central ID | PMC9629663 |