Validation of Machine Learning Models to Predict Adverse Outcomes in Patients with COVID-19: A Prospective Pilot Study.
Yonsei Med J
; 63(5): 422-429, 2022 May.
Article
in English
| MEDLINE | ID: covidwho-1834347
ABSTRACT
PURPOSE:
We previously developed learning models for predicting the need for intensive care and oxygen among patients with coronavirus disease (COVID-19). Here, we aimed to prospectively validate the accuracy of these models. MATERIALS ANDMETHODS:
Probabilities of the need for intensive care [intensive care unit (ICU) score] and oxygen (oxygen score) were calculated from information provided by hospitalized COVID-19 patients (n=44) via a web-based application. The performance of baseline scores to predict 30-day outcomes was assessed.RESULTS:
Among 44 patients, 5 and 15 patients needed intensive care and oxygen, respectively. The area under the curve of ICU score and oxygen score to predict 30-day outcomes were 0.774 [95% confidence interval (CI) 0.614-0.934] and 0.728 (95% CI 0.559-0.898), respectively. The ICU scores of patients needing intensive care increased daily by 0.71 points (95% CI 0.20-1.22) after hospitalization and by 0.85 points (95% CI 0.36-1.35) after symptom onset, which were significantly different from those in individuals not needing intensive care (p=0.002 and <0.001, respectively). Trends in daily oxygen scores overall were not markedly different; however, when the scores were evaluated within <7 days after symptom onset, the patients needing oxygen showed a higher daily increase in oxygen scores [1.81 (95% CI 0.48-3.14) vs. -0.28 (95% CI 1.00-0.43), p=0.007].CONCLUSION:
Our machine learning models showed good performance for predicting the outcomes of COVID-19 patients and could thus be useful for patient triage and monitoring.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Yonsei Med J
Year:
2022
Document Type:
Article
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