Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.
Comput Biol Med
; 124: 103949, 2020 09.
Artículo
en Inglés
| MEDLINE | ID: covidwho-695377
ABSTRACT
BACKGROUND:
Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.METHODS:
In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020.RESULTS:
197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05).CONCLUSIONS:
In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Neumonía Viral
/
Insuficiencia Respiratoria
/
Infecciones por Coronavirus
/
Técnicas de Laboratorio Clínico
/
Aprendizaje Automático
/
Betacoronavirus
Tipo de estudio:
Estudio de cohorte
/
Estudios diagnósticos
/
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Adulto
/
Anciano
/
Femenino
/
Humanos
/
Masculino
/
Middle aged
País/Región como asunto:
America del Norte
Idioma:
Inglés
Revista:
Comput Biol Med
Año:
2020
Tipo del documento:
Artículo
País de afiliación:
J.compbiomed.2020.103949
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