Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

Yun-Chung Liu; Hao-Yuan Cheng; Tu-Hsuan Chang; Te-Wei Ho; Ting-Chi Liu; Ting-Yu Yen; Chia-Ching Chou; Luan-Yin Chang; Feipei Lai

doi:10.2196/28934

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

JMIR Med Inform. 2022 Jan 27;10(1):e28934. doi: 10.2196/28934.

Authors

Yun-Chung Liu^#^{1

2}, Hao-Yuan Cheng^#^{1

3}, Tu-Hsuan Chang⁴, Te-Wei Ho⁵, Ting-Chi Liu^{6

7}, Ting-Yu Yen¹, Chia-Ching Chou^#⁶, Luan-Yin Chang^#¹, Feipei Lai^{2

8

9}

Affiliations

¹ Department of Pediatrics, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei City, Taiwan.
² Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan.
³ Taiwan Centers for Disease Control, Taipei City, Taiwan.
⁴ Department of Pediatrics, Chi Mei Medical Center, Tainan City, Taiwan.
⁵ Department of Surgery, College of Medicine, National Taiwan University, Taipei City, Taiwan.
⁶ Institute of Applied Mechanics, National Taiwan University, Taipei City, Taiwan.
⁷ Department of Civil Engineering, National Taiwan University, Taipei City, Taiwan.
⁸ Department of Computer Science and Information Engineering, National Taiwan University, Taipei City, Taiwan.
⁹ Department of Electrical Engineering, National Taiwan University, Taipei City, Taiwan.

^# Contributed equally.

PMID: 35084358
PMCID: PMC8832265
DOI: 10.2196/28934

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 this study was to develop machine learning (ML) 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 with physician-rated feature importance for explainability.

Results: A total of 8464 pediatric hospitalizations due to pneumonia were recorded, and 1166 (1166/8464, 13.8%) hospitalized patients were transferred to the ICU within 24 hours. Early ICU transfer patients were younger (P<.001), had higher rates of underlying diseases (eg, cardiovascular, neuropsychological, and congenital anomaly/genetic disorders; P<.001), had abnormal laboratory data, had higher pulse rates (P<.001), had higher breath rates (P<.001), had lower oxygen saturation (P<.001), and had lower peak body temperature (P<.001) at admission than patients without ICU transfer. The random forest (RF) algorithm achieved the best performance (sensitivity 0.94, 95% CI 0.92-0.95; specificity 0.94, 95% CI 0.92-0.95; AUC 0.99, 95% CI 0.98-0.99; and average precision 0.93, 95% CI 0.90-0.96). The lowest systolic blood pressure and presence of cardiovascular and neuropsychological diseases ranked in the top 10 in both RF 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, abnormal vital signs, and laboratory data for evaluating the need for intensive care in children with pneumonia.

Keywords: child pneumonia; clinical index; decision making; intensive care; machine learning.

©Yun-Chung Liu, Hao-Yuan Cheng, Tu-Hsuan Chang, Te-Wei Ho, Ting-Chi Liu, Ting-Yu Yen, Chia-Ching Chou, Luan-Yin Chang, Feipei Lai. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.01.2022.