Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation

Abstract

Background: A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reason for admission, indication for delivery, optimal steroid use, gestational age, and other potential prognostic factors. Using stepwise multivariable analysis, the only two variables independently associated with serious neonatal morbidity were gestational age and the presence of suspected intrauterine growth restriction as a reason for admission. This finding was surprising given the beneficial effects of antenatal steroids and hazards associated with some causes of preterm birth. Multivariable logistic regression techniques have limitations. Without testing for multiple interactions, linear regression will identify only individual factors with the strongest independent relationship to the outcome for the entire study group. There may not be a single “best set” of risk factors or one set that applies equally well to all subgroups. In contrast, machine learning techniques find the most predictive groupings of factors based on their frequency and strength of association, with no attempt to identify independence and no assumptions about linear relationships.

Objective: To determine if machine learning techniques would identify specific clusters of conditions with different probability estimates for severe neonatal morbidity and to compare these findings to those based on the original multivariable analysis.

Materials and methods: This was a secondary analysis of data collected in a multicenter, prospective study on all admissions to the neonatal intensive care unit between 2013 and 2015 in 10 hospitals. We included all patients with a singleton, stillborn, or live newborns, with a gestational age between 23 0/7 and 31 6/7 week. The composite endpoint, severe neonatal morbidity, defined by the presence of any of five outcomes: death, grade 3 or 4 intraventricular hemorrhage (IVH), and ≥28 days on ventilator, periventricular leukomalacia (PVL), or stage III necrotizing enterocolitis (NEC), was present in 238 of the 1039 study patients. We studied five explanatory variables: maternal age, parity, gestational age, admission reason, and status with respect to antenatal steroid administration. We concentrated on Classification and Regression Trees because the resulting structure defines clusters of risk factors that often bear resemblance to clinical reasoning. Model performance was measured using area under the receiver–operator characteristic curves (AUC) based on 10 repetitions of 10-fold cross-validation.

Results: A hybrid technique using a combination of logistic regression and Classification and Regression Trees had a mean cross-validated AUC of 0.853. A selected point on its receiver–operator characteristic (ROC) curve corresponding to a sensitivity of 81% was associated with a specificity of 76%. Rather than a single curve representing the general relationship between gestational age and severe morbidity, this technique found seven clusters with distinct curves. Abnormal fetal testing as a reason for admission with or without growth restriction and incomplete steroid administration would place a 20-year-old patient on the highest risk curve.

Conclusions: Using a relatively small database and a few simple factors known before birth it is possible to produce a more tailored estimate of the risk for severe neonatal morbidity on which clinicians can superimpose their medical judgment, experience, and intuition.

Keywords:

• Artificial intelligence
• machine learning
• neonatal death
• prediction

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Drs Hamilton and Warrick are employed by PeriGen who provided the financial support for this project.