Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19.
BMJ Support Palliat Care
; 2020 Sep 22.
Article
in English
| MEDLINE | ID: covidwho-788172
ABSTRACT
OBJECTIVES:
To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.METHODS:
A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results.RESULTS:
Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI 78.2% to 94.3%), specificity of 60.6% (95% CI 55.2% to 65.8%), accuracy of 65.5% (95% CI 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI 53.5% to 75.3%).CONCLUSIONS:
Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Language:
English
Year:
2020
Document Type:
Article
Affiliation country:
Bmjspcare-2020-002602
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