Variations in length of stay among survived very preterm infants admitted to Chinese neonatal intensive care units

Background This study aimed to describe length of stay (LOS) to discharge and site variations among very preterm infants (VPIs) admitted to 57 Chinese neonatal intensive care units (NICUs) and to investigate factors associated with LOS for VPIs. Methods This retrospective multicenter cohort study enrolled all infants < 32 weeks’ gestation and admitted to 57 NICUs which had participated in the Chinese Neonatal Network, within 7 days after birth in 2019. Exclusion criteria included major congenital anomalies, NICU deaths, discharge against medical advice, transfer to non-participating hospitals, and missing discharge date. Two multivariable linear models were used to estimate the association of infant characteristics and LOS. Results A total of 6580 infants were included in our study. The overall median LOS was 46 days [interquartile range (IQR): 35–60], and the median corrected gestational age at discharge was 36 weeks (IQR: 35–38). LOS and corrected gestational age at discharge increased with decreasing gestational age. The median corrected gestational age at discharge for infants at 24 weeks, 25 weeks, 26 weeks, 27–28 weeks, and 29–31 weeks were 41 weeks, 39 weeks, 38 weeks, 37 weeks and 36 weeks, respectively. Significant site variation of LOS was identified with observed median LOS from 33 to 71 days in different hospitals. Conclusions The study provided concurrent estimates of LOS for VPIs which survived in Chinese NICUs that could be used as references for medical staff and parents. Large variation of LOS independent of infant characteristics existed, indicating variation of care practices requiring further investigation and quality improvement.

Prediction modelling in the early detection of neonatal sepsis

Background Prediction modelling can greatly assist the health-care professionals in the management of diseases, thus sparking interest in neonatal sepsis diagnosis. The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis. Methods PubMed, Scopus, CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended. Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Extricate data consisted of objective, study design, patient characteristics, type of statistical model, predictors, outcome, sample size and location. Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles. Results An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model, while the remaining two had applied artificial intelligence. Potential predictors like neonatal fever, birth weight, foetal morbidity and gender, cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis. Moreover, birth weight, endotracheal intubation, thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis; while gestational age, intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection. Conclusion Prediction modelling approaches were able to recognise promising maternal, neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus, can be considered as a novel way for clinician decision-making towards the disease diagnosis if not used alone, in the years to come.