Abstract

Background: We aimed to establish and validate diagnostic models for distinguishing bacterial/viral infections among sepsis neonates and also a model for prognostic evaluation. Methods: Training data sets (cohorts) of neonatal sepsis patients were derived retrospectively from 2017 to 2019, and the verifying sets were followed up from 2019 to 2021. The backward elimination method of logistic regression was used in identifying the optimum feature combination by adding all potential factors to the regression equation. Results: The current study established 3 models. For distinguishing bacterial sepsis patients and bacterial culture-negative patients, we found Y=1.930+0.105X1+0.891X2-1.389X3-0.774X4 (Y symbolizes the status of bacterial infectious sepsis, X1 is age increase, X2 is intra-amniotic infection (mother), X3 is vomiting sign, and X4 is cough sign). Similarly, for distinguishing bacterial infectious sepsis patients and bacterial/viral double-positive patients, we found Y=2.918+1.568X1+1.882X2-0.113X3-2.214X4-2.255X5-2.312X6 (Y means the bacterial/viral double-positive status, X1 is IL-6 increase, X2 means CRP increase, X3 means age increase, X4 means high fever sign, X5 is cyanotic sign, and X6 is HGB increase). For predicting hospital days as one of the prognoses, we found Y=-1.993+0.073X1+1.963X2+0.466X3-0.791X4-0.633X5 (Y means worse prognosis, which is hospital days longer than 7 days, X1 means age increase, X2 means intra-amniotic infection (mother), X3 is IL-6 increase, X4 is convulsion with unconsciousness, and X5 is cough sign). Then, the ROC curves of the models from the verifying cohort indicated that all of the 3 models had good performance among sepsis children. Conclusions: Two diagnostic models and one prognostic model were established for clinical reference from the current first-step analysis with excellent model performance, which could be suggested as new useful diagnostic tools and a therapeutic strategy guiding marker for neonatal sepsis in the future.

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