In this study, we developed an MLA to rapidly and systematically predict a positive RSV NAAT test among hospitalized pediatric patients. This algorithm used inputs that are routinely collected and reported in patients’ EHRs within two hours of admission to predict a positive NAAT for RSV later in the same admission. Our work demonstrates the utility of leveraging machine learning techniques to rapidly predict previously unidentified infections among hospitalized patients.
There are two major innovations in our study that substantially contribute to the field. First, our study focuses specifically on identifying likely RSV infections rapidly upon presentation to a hospital emergency room. This differs from previously developed MLAs focused on pediatric RSV infections, which have focused either on predicting future RSV diagnosis, hospitalization, or severe progression of disease in the months to years following data collection, or on identifying RSV infections among pediatric patients that were already hospitalized with known symptoms of respiratory viral infection.15–17 Several of these previous algorithms also were developed using data only from preterm infants,16,17 thereby limiting their generalizability as compared to our MLA. As a preventive tool, Heaton et al. developed an MLA to predict seasonal RSV outbreaks to allow for timely immunoprophylaxis injections for children predisposed to poor infection outcomes.18 Other studies using MLAs that predict suitable treatment courses19 or patient outcomes15,20 for bronchiolitis patients, a disease commonly caused by RSV, require a proper diagnosis prior to running the algorithm. These RSV preventive and treatment studies do not address the need for broad screening of incoming pediatric patients and rapid identification of RSV infected patients. Our study therefore provides unprecedented utility among RSV-focused MLAs for hospital healthcare providers to improve the efficiency and accuracy of their initial care for pediatric patients. Second, our MLA is designed to predict RSV positive tests without requiring detailed patient data that require surplus time and effort over standard-of-care protocols performed early in hospitalization. This differs from previously developed risk scores or MLAs that required inputs of ICD diagnosis codes, transcriptome data, and/or documentation of specific symptoms that take additional time to collect and log in patients’ EHRs.16,17,21−23 The relative simplicity of our MLA indicates that integration into hospital settings would be more efficient and immediately useful to clinicians who care for pediatric inpatients.
If successfully implemented as a rapid, preliminary RSV screening system in a hospital setting, our algorithm could provide several primary services to healthcare providers caring for pediatric patients. First, it could be used as a tool for identifying patients to be enrolled or not enrolled in cohort studies or clinical trials that involve active RSV infection - either to include or exclude patients who are actively infected.24 This would save clinical researchers time and effort by substantially narrowing their scope of viral testing. Second, it could help hospital infection prevention personnel to more quickly identify infected patients who may need to be placed on additional precautions to prevent healthcare-associated transmission of RSV. Outbreaks of RSV in pediatric hospital settings are well documented and have been shown to contribute to increased patient morbidity, mortality, and complexity of care.6,25,26 Third, our algorithm could better inform delivery of care for infected patients by identifying them more rapidly and with greater efficiency of viral testing. Taken together, these advantages could be leveraged particularly well in tertiary care research and teaching hospitals that would benefit from an efficient alternative to established risk scores or systematic viral testing to identify infected patients.
There are several limitations to this study. First, the use of NAAT testing for RSV as a “gold standard” likely excluded many diagnoses of infection by rapid antigen detection, which may have skewed the RSV prevalence and predictive power of the MLA. Second, we did not include data on the presence or absence of respiratory symptoms that are known to be strong predictors of RSV infection,2,8,27 because these data were often missing from EHRs of the patients included in this study. Future directions of this research could potentially be improved by considering RSV diagnoses made by rapid antigen testing. Additionally, future studies should include the presence or absence of known symptoms of acute respiratory disease to identify patients with RSV.