Firearm Injury Risk Prediction Among Children Transported by 9-1-1 Emergency Medical Services: A Machine Learning Analysis.

Objective: Among children transported by ambulance across the United States, we used machine learning models to develop a risk prediction tool for firearm injury using basic demographic information and home ZIP code matched to publicly available data sources.

Methods: We included children and adolescents 0-17 years transported by ambulance to acute care hospitals in 47 states from January 1, 2014 through December 31, 2022. We used 96 predictors, including basic demographic information and neighborhood measures matched to home ZIP code from 5 data sources: EMS records, American Community Survey, Child Opportunity Index, County Health Rankings, and Social Vulnerability Index.

Community Disparities in Out-of-Hospital Cardiac Arrest Prehospital Antiarrhythmic Practices.

Objectives: Antiarrhythmic administration is an important treatment for out-of-hospital cardiac arrest (OHCA) with a shockable rhythm, but little is known about disparities in such antiarrhythmic practices. We sought to investigate the association between community race/ethnicity and prehospital antiarrhythmic administration for OHCA.

Methods: We conducted a retrospective study of a national prehospital database, NEMSIS, linked to Census data.

Hospital-wide access to genomic data advanced pediatric rare disease research and clinical outcomes.

Boston Children's Hospital has established a genomic sequencing and analysis research initiative to improve clinical care for pediatric rare disease patients. Through the Children's Rare Disease Collaborative (CRDC), the hospital offers CLIA-grade exome and genome sequencing, along with other sequencing types, to patients enrolled in specialized rare disease research studies. The data, consented for broad research use, are harmonized and analyzed with CRDC-supported variant interpretation tools.