ArticleDiseases (Basel, Switzerland)2024
Comparative Analysis of qSOFA, PRIEST, PAINT, and ISARIC4C Scores in Predicting Severe COVID-19 Outcomes Among Patients Aged over 75 Years.
Article in Diseases (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundElderly patients, particularly those over 75 years old, have been disproportionately affected by COVID-19, exhibiting higher rates of severe outcomes, such as ICU admissions and mortality. This study aimed to evaluate and compare the effectiveness of various clinical scoring systems-qSOFA, PRIEST, PAINT, and ISARIC4C-in predicting ICU admission, the need for mechanical ventilation, and mortality among elderly COVID-19 patients.
methodsIn this retrospective cohort study conducted at two tertiary care hospitals, 131 elderly patients (aged ≥ 75) and 226 younger controls (aged < 65) with confirmed COVID-19 were included. Clinical scores were computed at admission and five days after symptom onset. Kaplan-Meier survival analysis and Receiver Operating Characteristic (ROC) curve analysis were performed to assess the predictive performance of the scores regarding severe outcomes.
resultsKaplan-Meier analysis indicated significantly lower survival probabilities for elderly patients with high scores at admission. Those with an ISARIC4C score above 11.8 had a survival probability of 25% compared to 74% for those below this threshold (
conclusionsThe ISARIC4C, qSOFA, PRIEST, and PAINT scores are robust predictors of severe outcomes in elderly COVID-19 patients over 75 years old, as confirmed by Kaplan-Meier and ROC analyses. These tools can be crucial for early identification of patients at high risk of adverse outcomes, guiding clinical decision making, and optimizing resource allocation. The use of these scoring systems should be encouraged in clinical settings to enhance the management of elderly COVID-19 patients. Further research is necessary to validate these findings across different populations and settings.
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