JMIR Publications

ABSTRACT

Background:

Timely decision making regarding intensive care unit (ICU) admission for children with pneumonia is crucial for a better prognosis. Despite attempts to establish a guideline or triage system for evaluating ICU care needs, no clinically applicable paradigm is available.

Objective:

The aim of the present study is to develop machine learning algorithms to predict ICU care needs for pediatric pneumonia patients within 24 hours of admission, evaluate their performance, and identify clinical indices for making decisions for pediatric pneumonia patients.

Methods:

Pneumonia patients admitted to National Taiwan University Hospital from January 2010 to December 2019 aged under 18 years were enrolled. Their underlying diseases, clinical manifestations, and laboratory data at admission were collected. The outcome of interest was ICU transfer within 24 hours of hospitalization. We compared clinically relevant features between early ICU transfer patients and patients without ICU care. ML algorithms were developed to predict ICU admission. The performance of the algorithms was evaluated using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and average precision. The relative feature importance of the best-performing algorithm was compared to physician-rated feature importance for explainability.

Results:

A total of 8,464 pediatric hospitalizations due to pneumonia were recorded, and 1,166 (13.8%) hospitalized patients were transferred to the ICU within 24 hours. Early ICU transfer patients were younger (p < 0.001), had higher rates of underlying diseases (e.g., cardiovascular, neuropsychological and congenital anomaly/genetic disorder, p < 0.001), had abnormal laboratory data, had higher pulse rate (p < 0.001), had higher breath rate (p < 0.001), had lower oxygen saturation (p < 0.001) and had lower peak body temperature (p < 0.001) at admission. The extreme gradient boosted trees (XGB) algorithm achieved the best performance [sensitivity 0.94 (95% 0.92–0.96), specificity 0.94 (95% CI 0.92–0.95), AUC 0.99 (95% CI 0.98–0.99), and average precision 0.94 (95% CI 0.92–0.96)]. The lowest breath rate, lowest oxygen saturation, presence of cardiovascular disease and neuropsychological disease ranked in the top ten in both XGB relative feature importance and clinician judgment.

Conclusions:

The ML approach could provide a clinically applicable triage algorithm and identify important clinical indices, such as age, underlying diseases, and abnormal vital signs, and laboratory data for evaluating the need for intensive care in children with pneumonia.