Association of Sickle Cell Disease With Racial Disparities and Severe Maternal Morbidities in Black Individuals.

Importance: Little is known about the association between sickle cell disease (SCD) and severe maternal morbidity (SMM). Objective: To examine the association of SCD with racial disparities in SMM and with SMM among Black individuals. Design, Setting, and Participants: This cohort study was a retrospective population-based investigation of individuals with and without SCD in 5 states (California [2008-2018], Michigan [2008-2020], Missouri [2008-2014], Pennsylvania [2008-2014], and South Carolina [2008-2020]) delivering a fetal death or live birth. Data were analyzed between July and December 2022. Exposure: Sickle cell disease identified during the delivery admission by using International Classification of Diseases, Ninth Revision and Tenth Revision codes. Main Outcomes and Measures: The primary outcomes were SMM including and excluding blood transfusions during the delivery hospitalization. Modified Poisson regression was used to estimate risk ratios (RRs) adjusted for birth year, state, insurance type, education, maternal age, Adequacy of Prenatal Care Utilization Index, and obstetric comorbidity index. Results: From a sample of 8 693 616 patients (mean [SD] age, 28.5 [6.1] years), 956 951 were Black individuals (11.0%), of whom 3586 (0.37%) had SCD. Black individuals with SCD vs Black individuals without SCD were more likely to have Medicaid insurance (70.2% vs 64.6%), to have a cesarean delivery (44.6% vs 34.0%), and to reside in South Carolina (25.2% vs 21.5%). Sickle cell disease accounted for 8.9% and for 14.3% of the Black-White disparity in SMM and nontransfusion SMM, respectively. Among Black individuals, SCD complicated 0.37% of the pregnancies but contributed to 4.3% of the SMM cases and to 6.9% of the nontransfusion SMM cases. Among Black individuals with SCD compared with those without, the crude RRs of SMM and nontransfusion SMM during the delivery hospitalization were 11.9 (95% CI, 11.3-12.5) and 19.8 (95% CI, 18.5-21.2), respectively, while the adjusted RRs were 3.8 (95% CI, 3.3-4.5) and 6.5 (95% CI, 5.3-8.0), respectively. The SMM indicators that incurred the highest adjusted RRs included air and thrombotic embolism (4.8; 95% CI, 2.9-7.8), puerperal cerebrovascular disorders (4.7; 95% CI, 3.0-7.4), and blood transfusion (3.7; 95% CI, 3.2-4.3). Conclusions and Relevance: In this retrospective cohort study, SCD was found to be an important contributor to racial disparities in SMM and was associated with an elevated risk of SMM among Black individuals. Efforts from the research community, policy makers, and funding agencies are needed to advance care among individuals with SCD.

Assessment of an Updated Neonatal Research Network Extremely Preterm Birth Outcome Model in the Vermont Oxford Network.

Importance: The Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network (NRN) extremely preterm birth outcome model is widely used for prognostication by practitioners caring for families expecting extremely preterm birth. The model provides information on mean outcomes from 1998 to 2003 and does not account for substantial variation in outcomes among US hospitals. Objective: To update and validate the NRN extremely preterm birth outcome model for most extremely preterm infants in the United States. Design, Setting, and Participants: This prognostic study included 3 observational cohorts from January 1, 2006, to December 31, 2016, at 19 US centers in the NRN (derivation cohort) and 637 US centers in Vermont Oxford Network (VON) (validation cohorts). Actively treated infants born at 22 weeks' 0 days' to 25 weeks' 6 days' gestation and weighing 401 to 1000 g, including 4176 in the NRN for 2006 to 2012, 45 179 in VON for 2006 to 2012, and 25 969 in VON for 2013 to 2016, were studied. VON cohorts comprised more than 85% of eligible US births. Data analysis was performed from May 1, 2017, to March 31, 2019. Exposures: Predictive variables used in the original model, including infant sex, birth weight, plurality, gestational age at birth, and exposure to antenatal corticosteroids. Main Outcomes and Measures: The main outcome was death before discharge. Secondary outcomes included neurodevelopmental impairment at 18 to 26 months' corrected age and measures of hospital resource use (days of hospitalization and ventilator use). Results: Among 4176 actively treated infants in the NRN cohort (48% female; mean [SD] gestational age, 24.2 [0.8] weeks), survival was 63% vs 62% among 3702 infants in the era of the original model (47% female; mean [SD] gestational age, 24.2 [0.8] weeks). In the concurrent (2006-2012) VON cohort, survival was 66% among 45 179 actively treated infants (47% female; mean [SD] gestational age, 24.1 [0.8] weeks) and 70% among 25 969 infants from 2013 to 2016 (48% female; mean [SD] gestational age, 24.1 [0.8] weeks). Model C statistics were 0.74 in the 2006-2012 validation cohort and 0.73 in the 2013-2016 validation cohort. With the use of decision curve analysis to compare the model with a gestational age-only approach to prognostication, the updated model showed a predictive advantage. The birth hospital contributed equally as much to prediction of survival as gestational age (20%) but less than the other factors combined (60%). Conclusions and Relevance: An updated model using well-known factors to predict survival for extremely preterm infants performed moderately well when applied to large US cohorts. Because survival rates change over time, the model requires periodic updating. The hospital of birth contributed substantially to outcome prediction.

Variation in readmission rates among hospitals following admission for traumatic injury.

INTRODUCTION: Readmission following hospital discharge is both common and costly. The Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals for readmission following admission for some conditions, but this approach may not be appropriate for all conditions. We wished to determine if hospitals differed in their adjusted readmission rates following an index hospital admission for traumatic injury. PATIENTS AND METHODS: We extracted from the AHRQ National Readmission Dataset (NRD) all non-elderly adult patients hospitalized following traumatic injury in 2014. We estimated hierarchal logistic regression models to predicted readmission within 30 days. Models included either patient level predictors, hospital level predictors, or both. We quantified the extent of hospital variability in readmissions using the median odds ratio. Additionally, we computed hospital specific risk-adjusted rates of readmission and number of excess readmissions. RESULTS: Of the 177,322 patients admitted for traumatic injury 11,940 (6.7%) were readmitted within 30 days. Unadjusted hospital readmission rates for the 637 hospitals in our study varied from 0% to 20%. After controlling for sources of variability the range for hospital readmission rates was between 5.5% and 8.5%. Only 2% of hospitals had a random intercept coefficient significantly different from zero, suggesting that their readmission rates differed from the mean level of all hospitals. We also estimated that in 2014 only 11% of hospitals had more than 2 excess readmissions. Our multilevel model discriminated patients who were readmitted from those not readmitted at an acceptable level (C = 0.74). CONCLUSIONS: We found little evidence that hospitals differ in their readmission rates following an index admission for traumatic injury. There is little justification for penalizing hospitals based on readmissions after traumatic injury.