Predicting emergency department visits among children with asthma in two academic medical systems.

Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining.Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2-18 years (2012-2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples.Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745-0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624-0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676-0.794; p = 0.037 as compared to initial AUROC).Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site.

Asthma deaths in North Carolina: 1999-2016.

Introduction: Despite the significant decline in overall death rates in the U.S. over the past decade, many asthma deaths could have been avoided. Eastern North Carolina (N.C.) is an economically challenged region with significant health disparities and a high prevalence of asthma. Objective: The primary purpose of this project was to examine trends of asthma deaths across N.C. and identify counties in the state with the highest mortality rates over an 18-year period. Methods: CDC WONDER was used to query and evaluate age-adjusted asthma mortality rates from 1999 to 2016 among residents in N.C., greater than 1 year of age. Asthma death data were derived from death certificates using ICD-10 underlying cause-of-death codes J45 (asthma) and J46 (status asthmaticus). The Join point regression program was used to test statistical significance in age-adjusted rates for the U.S. and N.C. over the entire study period. Results: N.C. experienced a total of 2,066 decedents assigned as the underlying cause of deaths for an overall death rate of 12.5 per 1,000,000 persons. Death rates were highest among females (14.6 deaths per 1,000,000) and black or African Americans (24.7 per 1,000,000). Discussion: Overall asthma mortality rates in N.C. decreased. However, several rural and impoverished counties in eastern N.C. with a large percent of blacks or African-Americans, had the highest asthma death rates in the state. Conclusion: Healthcare providers should remain highly cognizant to provide optimal asthma management, education, and follow-up with asthma patients to help avoid unnecessary asthma related deaths.