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Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.
Yang, He S; Hou, Yu; Vasovic, Ljiljana V; Steel, Peter A D; Chadburn, Amy; Racine-Brzostek, Sabrina E; Velu, Priya; Cushing, Melissa M; Loda, Massimo; Kaushal, Rainu; Zhao, Zhen; Wang, Fei.
  • Yang HS; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Hou Y; New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY.
  • Vasovic LV; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY.
  • Steel PAD; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Chadburn A; New York-Presbyterian Hospital, Lower Manhattan Hospital, New York, NY.
  • Racine-Brzostek SE; New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY.
  • Velu P; Department of Emergency Medicine, Weill Cornell Medicine, New York, NY.
  • Cushing MM; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Loda M; New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY.
  • Kaushal R; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
  • Zhao Z; New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY.
  • Wang F; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY.
Clin Chem ; 66(11): 1396-1404, 2020 11 01.
Article in English | MEDLINE | ID: covidwho-727045
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ABSTRACT

BACKGROUND:

Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.

METHOD:

We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital.

RESULTS:

The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days.

CONCLUSION:

This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Machine Learning / Hematologic Tests Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: Clin Chem Journal subject: Chemistry, Clinical Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Machine Learning / Hematologic Tests Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: Clin Chem Journal subject: Chemistry, Clinical Year: 2020 Document Type: Article