Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

OBJECTIVE: To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR). BACKGROUND: Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers. METHODS: We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was developed from 150 clinic notes from institution #1 (development set), then tested on a separate set of 219 notes from institution #1 (internal test set) with 248 unique seizure frequency elements. The algorithm was separately applied to 100 notes from institution #2 (external test set) with 124 unique seizure frequency elements. Algorithm performance was measured by recall (sensitivity), precision (positive predictive value), and F1 score (geometric mean of precision and recall). RESULTS: In the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lower with 22% recall (27/124), 73% precision (27/37), and 0.40 F1 score. CONCLUSIONS: These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.

Subclinical Atherosclerosis in Young, Socioeconomically Vulnerable Hispanic and Non-Hispanic Black Adults.

BACKGROUND: Non-Hispanic Black persons are at greater risk of cardiovascular (CV) events than other racial/ethnic groups; however, their differential vulnerability to early subclinical atherosclerosis is poorly understood. OBJECTIVES: This work aims to study the impact of race/ethnicity on early subclinical atherosclerosis in young socioeconomically disadvantaged adults. METHODS: Bilateral carotid and femoral 3-dimensional vascular ultrasound examinations were performed on 436 adults (parents/caregivers and staff) with a mean age of 38.0 ± 11.1 years, 82.3% female, 66% self-reported as Hispanic, 34% self-reported as non-Hispanic Black, and no history of CV disease recruited in the FAMILIA (Family-Based Approach in a Minority Community Integrating Systems-Biology for Promotion of Health) trial from 15 Head Start preschools in Harlem (neighborhood in New York, New York, USA). The 10-year Framingham CV risk score was calculated, and the relationship between race/ethnicity and the presence and extent of subclinical atherosclerosis was analyzed with multivariable logistic and linear regression models. RESULTS: The mean 10-year Framingham CV risk was 4.0%, with no differences by racial/ethnic category. The overall prevalence of subclinical atherosclerosis was significantly higher in the non-Hispanic Black (12.9%) than in the Hispanic subpopulation (6.6%). After adjusting for 10-year Framingham CV risk score, body mass index, fruit and vegetable consumption, physical activity, and employment status, non-Hispanic Black individuals were more likely than Hispanic individuals to have subclinical atherosclerosis (OR: 3.45; 95% CI: 1.44-8.29; P = 0.006) and multiterritorial disease (P = 0.026). CONCLUSIONS: After adjustment for classic CV risk, lifestyle, and socioeconomic factors, non-Hispanic Black younger adults seem more vulnerable to early subclinical atherosclerosis than their Hispanic peers, suggesting that the existence of emerging or undiscovered CV factors underlying the residual excess risk (Family-Based Approach in a Minority Community Integrating Systems-Biology for Promotion of Health [FAMILIA (Project 2)]; NCT02481401).

Lessons Learned From 10 Years of Preschool Intervention for Health Promotion: JACC State-of-the-Art Review.

Implementing a health promotion program for children is a complex endeavor. In this review, we outline the key lessons learned over 10 years of experience in implementing the SI! Program (Salud Integral-Comprehensive Health) for cardiovascular health promotion in preschool settings in 3 countries: Colombia (Bogotá), Spain (Madrid), and the United States (Harlem, New York). By matching rigorous efficacy studies with implementation science, we can help bridge the divide between science and educational practice. Achieving sustained lifestyle changes in preschool children through health promotion programs is likely to require the integration of several factors: 1) multidisciplinary teams; 2) multidimensional educational programs; 3) multilevel interventions; 4) local program coordination and community engagement; and 5) scientific evaluation through randomized controlled trials. Implementation of effective health promotion interventions early in life may induce long-lasting healthy behaviors that could help to curb the cardiovascular disease epidemic.