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Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.
Yu, Limin; Halalau, Alexandra; Dalal, Bhavinkumar; Abbas, Amr E; Ivascu, Felicia; Amin, Mitual; Nair, Girish B.
  • Yu L; Department of Pathology, Beaumont Health System, Royal Oak, MI, United States of America.
  • Halalau A; Department of Internal Medicine, Beaumont Health System, Royal Oak, MI, United States of America.
  • Dalal B; Division of Pulmonary and Critical Care Medicine, Beaumont Health System, Royal Oak, MI, United States of America.
  • Abbas AE; Department of Cardiovascular Medicine, Beaumont Health System, Royal Oak, MI, United States of America.
  • Ivascu F; Department of General Surgery, Beaumont Health System, Royal Oak, MI, United States of America.
  • Amin M; Department of Pathology, Beaumont Health System, Royal Oak, MI, United States of America.
  • Nair GB; Division of Pulmonary and Critical Care Medicine, Beaumont Health System, Royal Oak, MI, United States of America.
PLoS One ; 16(4): e0249285, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1167111
ABSTRACT

BACKGROUND:

The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.

OBJECTIVES:

To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted.

METHODS:

Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort.

RESULTS:

Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%.

CONCLUSION:

Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.
Asunto(s)

Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Respiración Artificial / Ventiladores Mecánicos / Pandemias / Aprendizaje Automático / COVID-19 Tipo de estudio: Estudio de cohorte / Estudio observacional / Estudio pronóstico Límite: Anciano / Femenino / Humanos / Masculino / Middle aged Idioma: Inglés Revista: PLoS One Asunto de la revista: Ciencia / Medicina Año: 2021 Tipo del documento: Artículo País de afiliación: Journal.pone.0249285

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Respiración Artificial / Ventiladores Mecánicos / Pandemias / Aprendizaje Automático / COVID-19 Tipo de estudio: Estudio de cohorte / Estudio observacional / Estudio pronóstico Límite: Anciano / Femenino / Humanos / Masculino / Middle aged Idioma: Inglés Revista: PLoS One Asunto de la revista: Ciencia / Medicina Año: 2021 Tipo del documento: Artículo País de afiliación: Journal.pone.0249285