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. 2025 Mar 28;17(3):e81360.
doi: 10.7759/cureus.81360. eCollection 2025 Mar.

Clinical Prediction Models for Pneumonia in Children Presenting to an Emergency Department in a Resource-Limited Setting Using Lung Ultrasound Diagnosis as the Gold Standard

Darlene R House  1 , Yogendra Amatya  2 , Benjamin K Nti  3 , Frances M Russell  3
Affiliations

Clinical Prediction Models for Pneumonia in Children Presenting to an Emergency Department in a Resource-Limited Setting Using Lung Ultrasound Diagnosis as the Gold Standard

Darlene R House et al. Cureus. .

Abstract

Introduction: Clinical prediction rules for pediatric pneumonia often rely on radiographic pneumonia for diagnosis; however, lung ultrasound has higher diagnostic accuracy. Our objective was to derive a clinical prediction model for pneumonia in children under five using lung ultrasound as the criterion standard.

Methods: This was a prospective study of children under five presenting to an emergency department (ED) with respiratory complaints in a resource-limited setting. Clinical findings, chest X-ray, and lung ultrasound results were recorded for each patient. Classification tree models were used to predict pneumonia using lung ultrasound as the criterion standard. Separate models were used without and with inclusion of chest X-ray results.

Results: Of 386 patients enrolled, 125 patients (32.4%) had pneumonia on lung ultrasound. The mean age was 20.8 (SD 15.5) months. Using recursive feature selection, three variables provided the best prediction for pneumonia, namely, crepitations, retractions, and difficulty breathing, demonstrating a sensitivity of 74.2% and specificity of 38.5%. The algorithm including chest X-ray provided a sensitivity of 51.6% and specificity of 87.7%.

Conclusions: Using lung ultrasound as the gold standard, no single clinical finding or combination of clinical findings provided enough accuracy to reliably diagnose pneumonia in children under five years.

Keywords: developing countries; diagnosis; lung ultrasound; pediatrics; pneumonia.

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Conflict of interest statement

Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Patan Institutional Review Committee issued approval drs1805271178. The study was performed as part of our registered study with clinicaltrials.gov, registration number NCT03630380. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: Darlene R. House declare(s) personal fees from Caption Health. She provided consultations for lung ultrasound AI development. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Prediction model of pneumonia based on clinical variables.
The blue endpoint predicts no pneumonia and the green endpoints predict pneumonia with varying certainty. The top number for each endpoint is the predicted probability of having pneumonia, while percentages represent the percentage of patients that fit into that category.
Figure 2
Figure 2. Prediction model of pneumonia adding chest X-ray with clinical characteristics.
Blue endpoints predict no pneumonia and green endpoints predict pneumonia. The top number for each endpoint is the predicted probability of having pneumonia while percentages represent the percentage of patients that fit into that category.
See this image and copyright information in PMC

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