(841) The association between maternal comorbidity burden and severe neonatal morbidity

Objective: To determine the association between maternal comorbidity burden and severe neonatal morbidity (SNM), and to evaluate whether racial and ethnic group differences help to explain any disparities in outcomes.

Study Design: This was a retrospective cohort study of all deliveries at ≥23 weeks gestational age at two tertiary hospitals in New York from 2019-2022. Exclusion criteria were fetal demise and multiple gestation. The primary exposure was obstetric comorbidity index (OB-CMI) score, calculated at admission for delivery. This score is based on 24 weighted comorbidity indicators identified by ICD-10 codes and clinical documentation. Covariate factors assessed included race and ethnicity, health insurance, marital status, parity, and preferred language. The primary outcome was SNM, a composite neonatal adverse outcome indicator which includes pre-determined diagnoses and procedures. Multivariable logistic regression was used to model the probability of SNM as a function of OB-CMI score group, while adjusting for covariate factors.

Results:
A total of 54,248 patients were included. Non-Hispanic White patients constituted the largest race and ethnicity group (42.8%), followed by Asian or Pacific Islander (18.7%), Non-Hispanic Black (14.5%), and Hispanic (12.2%). The overall SNM rate was 7.6% (n=4,127). SNM increased from 5% among patients with an OB-CMI score of zero to greater than 30% when OB-CMI scores were 9 or more (Figure 1). Results of regression modeling are presented in Figure 2. Each successive OB-CMI group had an increased odds of SNM, and patients with OB-CMI ≥4 had more than 4 times greater odds of SNM compared to patients with an OB-CMI = 0. Non-Hispanic Black patients and Hispanic patients were at increased risk for SNM compared to non-Hispanic White Patients.


Conclusion: OB-CMI score is positively correlated with SNM. Racial and ethnic disparities in SNM were observed. Future studies should assess if these differences can be better explained by social determinants of health, environmental exposures, genetics, and nutrition.