| 4 | Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile显示文摘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. | He S.Yang Yu Hou Hao Zhang Amy Chadburn Lars F.Westblade Richard Fedeli Peter A.DSteel Sabrina E.Racine-Brzostek Priya Velu Jorge L.Sepulveda Michael J.Satlin Melissa M.Cushing Rainu Kaushal Zhen Zhao Fei Wang | 2021 | Health Data Science2021，，1： | 0 |
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