TY - JOUR TI - A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea AU - Brintz, Ben J AU - Haaland, Benjamin AU - Howard, Joel AU - Chao, Dennis L AU - Proctor, Joshua L AU - Khan, Ashraful I AU - Ahmed, Sharia M AU - Keegan, Lindsay T AU - Greene, Tom AU - Keita, Adama Mamby AU - Kotloff, Karen L AU - Platts-Mills, James A AU - Nelson, Eric J AU - Levine, Adam C AU - Pavia, Andrew T AU - Leung, Daniel T A2 - Schiffer, Joshua T A2 - Franco, Eduardo A2 - Brown, Joe VL - 10 PY - 2021 DA - 2021/02/02 SP - e63009 C1 - eLife 2021;10:e63009 DO - 10.7554/eLife.63009 UR - https://doi.org/10.7554/eLife.63009 AB - Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test’ epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics. KW - clinical prediction rule KW - diarrhea KW - enteric infection KW - antibiotic stewardship KW - clinical decision support JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -