A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample.

AIM: Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD). METHODS: Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC - UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old). RESULTS: The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79-0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71-0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower -0.57 (95% CI 0.54-0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73-0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/. CONCLUSIONS: The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.

Measuring quality of care for rheumatic diseases using an electronic medical record.

OBJECTIVES: The objective of this study was twofold: (1) to determine how best to measure adherence with time-dependent quality indicators (QIs) related to laboratory monitoring, and (2) to assess the accuracy and efficiency of gathering QI adherence information from an electronic medical record (EMR). METHODS: A random sample of 100 patients were selected who had at least three visits with the diagnosis of rheumatoid arthritis (RA) at Brigham and Women's Hospital Arthritis Center in 2005. Using the EMR, it was determined whether patients had been prescribed a disease-modifying antirheumatic drug (DMARD) (QI #1) and if patients starting therapy received appropriate baseline laboratory testing (QI #2). For patients consistently prescribed a DMARD, adherence with follow-up testing (QI #3) was calculated using three different methods, the Calendar, Interval and Rolling Interval METHOD: . RESULTS: It was found that 97% of patients were prescribed a DMARD (QI #1) and baseline tests were completed in 50% of patients (QI #2). For follow-up testing (QI #3), mean adherence was 60% for the Calendar Method, 35% for the Interval Method, and 48% for the Rolling Interval Method. Using the Rolling Interval Method, adherence rates were similar across drug and laboratory testing type. CONCLUSIONS: Results for adherence with laboratory testing QIs for DMARD use differed depending on how the QIs were measured, suggesting that care must be taken in clearly defining methods. While EMRs will provide important opportunities for measuring adherence with QIs, they also present challenges that must be examined before widespread adoption of these data collection methods.