A Pragmatic Clinical Algorithm for the Prediction of Severe Neonatal Morbidity in Preterm Infants

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Background: Severe neonatal morbidity (SNM) in preterm infants is now increasingly recognised as a major public health concern due to its strong correlation with not only neonatal mortality but also longer term disability. Therefore, we aimed to investigate predictors for severe neonatal morbidity (SNM) in preterm infants and to develop a predictive model for SNM utilising a range of maternal, pregnancy, intrapartum and neonatal variables. Methods: We compared SNM across demographic, antepartum, intrapartum, gestational age and birthweight centile categories. We performed robust univariable and multivariable binomial logistic regression analyses to identify major predictors. We developed a predictive model using the multivariable regression output and evaluated its internal validity using the K-fold cross validation technique. Model performance parameters were used to evaluate the diagnostic accuracy of the final model. Results: Of the total preterm infants 74·3% (4785/6440) had SNM. The final adjusted analysis showed that antepartum haemorrhage (aOR 1·18, 95% CI: 1·02-1·36), preterm premature rupture of membranes (aOR 1·37, 95% CI: 1·21-1·55), very preterm birth (aOR 3·79, 95% CI: 3·33-4·30), moderate preterm birth (aOR 4·17, 95% CI: 3·50-4·97), instrumental delivery (aOR 1·40, 95% CI: 1·20-1·62), elective caesarean section (aOR 1·46, 95% CI: 1·29-1·66), caesarean section for non-reassuring fetal status (aOR 2·08, 95% CI: 1·75-2·47), BW <5th centile (aOR 1·79, 95% CI: 1·36-2·37) and BW 5th-<10th centile (aOR 1·52, 95% CI: 1·25-1·84) were significant predictors for SNM. The final model had a sensitivity of 71·8% and specificity of 76% with a positive predictive value of 89·6%. Conclusion and relevance: Our study demonstrates that antepartum haemorrhage, premature rupture of membrane, gestational age, birth weight, operative birth and specific indications for operative birth are significant predictors of SNM. Clinicians may use identified risks to counsel mothers and priority setting in clinical decision making process to optimise neonatal outcomes. Funding Statement: Research support from the University of Queensland and the Mater Foundation. Declaration of Interests: The authors report no conflicts of interest. Ethics Approval Statement: The study was approved by the institution’s Human Research Ethics Committee (HREC/18/MHS/46).