Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.
J Am Med Inform Assoc
; 28(11): 2354-2365, 2021 10 12.
Article
in English
| MEDLINE | ID: covidwho-1223363
ABSTRACT
OBJECTIVE:
To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. MATERIALS ANDMETHODS:
We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.RESULTS:
The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.DISCUSSION:
Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.CONCLUSION:
We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pandemics
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Jamia
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