Risk factors for periventricular-intraventricular haemorrhage severity in preterm infants: a propensity score-matched analysis

Insurance Disparities in Patient Outcomes and Healthcare Resource Utilization Following Neonatal Intraventricular Hemorrhage

BACKGROUND

Within the field of pediatric neurosurgery, insurance status has been shown to be associated with surgical delay, longer time to referral, and longer hospitalization in epilepsy treatment, myelomeningocele repair, and spasticity surgery.1,2 The aim of this study was to investigate the association of insurance status with inpatient adverse events (AEs), length of stay (LOS), and costs for newborns diagnosed with intraventricular hemorrhage (IVH).

METHODS

A retrospective cohort study was performed using the 2016-2019 National Inpatient Sample database. Patients with a primary diagnosis of intraventricular hemorrhage were identified using ICD-10-CM diagnostic and procedural codes. Patients were categorized based on insurance status: Medicaid or Private Insurance (PI). Multivariate logistic regression analyses were used to identify the impact of insurance status on extended LOS (defined as >75th percentile of LOS) and exorbitant cost (defined as >75th percentile of cost).

RESULTS

Demographics differed significantly between groups, with the majority of newborns in the PI cohort being White (Medicaid: 35.8% vs. PI: 60.3%, P < 0.001) and the majority of Medicaid patients being in the 0-25th quartile of household income (Medicaid: 40.9% vs. PI: 12.9%, P < 0.001). While insurance status was not independently associated with increased odds of extended LOS or exorbitant cost, Medicaid patients had a greater mean LOS and total cost of admission than PI patients.

CONCLUSIONS

Demographic characteristics, mean LOS, and mean total cost differed significantly between Medicaid and PI patients, indicating potential disparities based on insurance status. However, insurance status was not independently associated with increased healthcare utilization, necessitating further research in this area of study.

Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database

OBJECTIVE

Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH.

METHOD

This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated.

RESULTS

A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788.

CONCLUSIONS

This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.