August 7, 2025Open Access

Development and internal validation of risk scores to predict survival in the pediatric population following out-of-hospital cardiac arrest.

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Authors

MMMinaz MawaniUniversity of Vermont BMBryan McNallyCross-Cutting Cardiology JKJessica KnightUniversity of Georgia

Discussion

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Implication

Logistic regression developed risk scores in pediatric patients after out-of-hospital cardiac arrest, suggesting better survival categorization improves clinical decision-making.

Key Points

Pediatric patients with out-of-hospital cardiac arrest were analyzed to create risk scores for survival and discharge.
Logistic regression and CART models achieved good classification accuracy, with AUROCC values up to 0.82 for survival prediction.
Based on predictive factors like gender and CPR, three risk scores were generated for infants, preschoolers, and adolescents.
These risk scores aid in clinical decisions, potentially improving survival outcomes for pediatric patients.

Cite This Study

Mawani et al. (2025) studied this question.

synapsesocial.com/papers/689523d29f4f1c896c42a0bb https://doi.org/10.1101/2025.08.04.25332995

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Implication

Logistic regression developed risk scores in pediatric patients after out-of-hospital cardiac arrest, suggesting better survival categorization improves clinical decision-making.

Development and internal validation of risk scores to predict survival in the pediatric population following out-of-hospital cardiac arrest.

Discussion

Member takes

Implication

Key Points

Cite This Study

Development and internal validation of risk scores to predict survival in the pediatric population following out-of-hospital cardiac arrest.

Discussion

Member takes

Implication

Key Points

Cite This Study

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