The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections.
Yunlei LiChantal B van HoutenStefan A BoersRuud JansenAsi CohenDan EngelhardRobert KraaijSaskia D HiltemannJie JuDavid FernándezCristian MankocEva GonzálezWouter J de WaalKarin M de Winter-de GrootTom F W WolfsPieter MeijersBart LuijkJan Jelrik OosterheertSanjay U C SankatsingAik W J BossinkMichal SteinAdi KleinJalal AshkarEllen BambergerIsaac SrugoMajed OdehYaniv DotanOlga BoicoLiat EtshteinMeital PazRoy NavonTom FriedmanEinav SimonTanya M GottliebEster Pri-OrGali KronenfeldKfir OvedEran EdenAndrew P StubbsLouis J BontJohn P HaysPublished in: PloS one (2022)
We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.