JMIR Publications

ABSTRACT

Background:

Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process.

Objective:

This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability.

Methods:

The study was conducted in pediatric and emergency outpatient settings in Japan. We developed a detection model that remotely confirms RSV infection based on patient-reported symptom information obtained via a structured electronic template incorporating the differential points of skilled pediatricians. An eXtreme Gradient Boosting-based machine learning model was developed using the data of 4174 pediatric patients aged ≤24 months. These patients visited either the pediatric or emergency department of Yokohama City Municipal Hospital between January 1, 2009, to December 31, 2015, and had RSV rapid antigen test results. The primary outcome was the diagnostic accuracy of the machine learning model for RSV infection, as determined by rapid antigen testing, measured using the area under the receiver operating characteristic curve (AUC-ROC). The clinical efficacy was evaluated by calculating the discriminative performance based on the number of days elapsed since the onset of the first symptom and exclusion rates based on thresholds of reasonable sensitivity and specificity.

Results:

Our model demonstrated an AUC-ROC of 0.811 (95% confidence interval, 0.784–0.833) with good calibration and 0.746 (95% confidence interval, 0.694-0.794) for patients within 3 days of onset. It accurately captured the temporal evolution of symptoms; based on adjusted thresholds equivalent to those of a rapid antigen test, our model predicted that 6.9% of patients (95% confidence interval, 5.4–8.5%) in the entire cohort would be positive and 68.7% (95% confidence interval, 65.4–71.9%) would be negative. Our model could eliminate the need for additional testing in approximately three-quarters of all patients.

Conclusions:

Our model may facilitate the immediate detection of RSV infection in outpatient settings and, potentially, in home environments. This approach could streamline the diagnostic process, reduce discomfort caused by invasive tests in children, and allow rapid implementation of appropriate treatments and isolation at home. The findings underscore the potential of machine learning in augmenting clinical decision-making in the early detection of RSV infection.