Dataset Information

Development and Internal Validation of a Prediction Model to Risk Stratify Children with Suspected Community-Acquired Pneumonia.

ABSTRACT:

Background

Although community-acquired pneumonia (CAP) is one of the most common infections in children, no tools exist to risk stratify children with suspected CAP. We developed and validated a prediction model to risk stratify and inform hospitalization decisions in children with suspected CAP.

Methods

We performed a prospective cohort study of children age 3 months to 18 years with suspected CAP in a pediatric emergency department (ED). Primary outcome was disease severity, defined as mild (discharge home or hospitalization for <24 hours with no oxygen or intravenous (IV) fluids), moderate (hospitalization <24 hours with oxygen or IV fluids, or hospitalization >24 hours), or severe (intensive care unit (ICU) stay for >24 hours, septic shock, vasoactive agents, positive-pressure ventilation, chest drainage, extracorporeal membrane oxygenation, or death). Ordinal logistic regression and bootstrapped backwards selection were used to derive and internally validate our model.

Results

Of 1128 children, 371 (32.9%) developed moderate disease and 48 (4.3%) severe disease. Severity models demonstrated excellent discrimination (optimism-corrected c-indices of 0.81) and outstanding calibration. Severity predictors in the final model included respiratory rate, systolic blood pressure, oxygenation, retractions, capillary refill, atelectasis or pneumonia on chest radiograph, and pleural effusion.

Conclusions

We derived and internally validated a score that accurately predicts disease severity in children with suspected CAP. Once externally validated, this score has potential to facilitate management decisions by providing individualized risk estimates that can be used in conjunction with clinical judgment to improve the care of children with suspected CAP.

SUBMITTER: Florin TA

PROVIDER: S-EPMC8563216 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGlobally there are over 1,400 cases of pneumonia per 100,000 children every year, where children in South Asia and Sub-Saharan Africa are disproportionately affected. Some of the cases develop poor treatment outcome (treatment failure or antibiotic change or staying longer in the hospital or death), while others develop good outcome during interventions. Although clinical decision-making is a key aspect of the interventions, there are limited tools such as risk scores to assist the clinical judgment in low-income settings. This study aimed to validate a prediction model and develop risk scores for poor outcomes of severe community-acquired pneumonia (SCAP).MethodsA cohort study was conducted among 539 under-five children hospitalized with SCAP. Data analysis was done using R version 4.0.5 software. A multivariable analysis was done. We developed a simplified risk score to facilitate clinical utility. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration plot. Bootstrapping was used to validate all accuracy measures. A decision curve analysis was used to evaluate the clinical and public health utility of our model.ResultsThe incidence of poor outcomes of pneumonia was 151(28%) (95%CI: 24.2%-31.8%). Vaccination status, fever, pallor, unable to breastfeed, impaired consciousness, CBC abnormal, entered ICU, and vomiting remained in the reduced model. The AUC of the original model was 0.927, 95% (CI (0.90, 0.96), whereas the risk score model produced prediction accuracy of an AUC of 0.89 (95%CI: 0.853-0.922. Our decision curve analysis for the model provides a higher net benefit across ranges of threshold probabilities.ConclusionsOur model has excellent discrimination and calibration performance. Similarly, the risk score model has excellent discrimination and calibration ability with an insignificant loss of accuracy from the original. The models can have the potential to improve care and treatment outcomes in the clinical settings.

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