Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for
Maja Dobrosavljevic1, Seena Fazel2, Ebba Du Rietz3
1School of Medical Sciences, Örebro University, Örebro, Sweden maja.dobrosavljevic@oru.se.
Insights
Cardiovascular disease (CVD) prediction models need improvement for attention-deficit/hyperactivity disorder (ADHD) patients. Novel risk factors enhance CVD prediction in adults treated for ADHD.
Area of Science:
- Cardiology
- Psychiatry
- Pharmacology
- Epidemiology
Background:
- Existing cardiovascular disease (CVD) prediction models may lack accuracy for individuals starting attention-deficit/hyperactivity disorder (ADHD) pharmacotherapy.
- There is a need to enhance CVD risk prediction in this specific population.
Purpose of the Study:
- To improve the predictive accuracy of traditional CVD risk factors for adults initiating ADHD pharmacological treatment.
- To incorporate novel CVD risk factors associated with ADHD, including comorbid psychiatric disorders, sociodemographic factors, and psychotropic medications.
Main Methods:
- A cohort of 24,186 adults in Sweden without prior CVDs, treated for ADHD between 2008-2011, was followed for up to 2 years.
- CVDs were identified using International Classification of Diseases diagnoses and dispensed medication data from national registers.
- Cox proportional hazards regression was used to develop the prediction model.
Main Results:
- The final model incorporated eight traditional and four novel CVD risk factors.
- The model demonstrated acceptable discrimination (C-index=0.72) and calibration (Brier score=0.008).
- Adding novel risk factors significantly improved prediction (Integrated Discrimination Improvement=0.003, p<0.001).
Conclusions:
- Inclusion of novel CVD risk factors may enhance CVD prediction in ADHD patients compared to traditional factors alone.
- Further external validation and clinical impact studies are recommended.
- Individuals with ADHD initiating treatment and identified at higher CVD risk warrant closer monitoring.
Background:
Available prediction models of cardiovascular diseases (CVDs) may not accurately predict outcomes among individuals initiating pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD).
Objective:
To improve the predictive accuracy of traditional CVD risk factors for adults initiating pharmacological treatment of ADHD, by considering novel CVD risk factors associated with ADHD (comorbid psychiatric disorders, sociodemographic factors and psychotropic medication).
Methods:
The cohort composed of 24 186 adults residing in Sweden without previous CVDs, born between 1932 and 1990, who started pharmacological treatment of ADHD between 2008 and 2011, and were followed for up to 2 years. CVDs were identified using diagnoses according to the International Classification of Diseases, and dispended medication prescriptions from Swedish national registers. Cox proportional hazards regression was employed to derive the prediction model.
Findings:
The developed model included eight traditional and four novel CVD risk factors. The model showed acceptable overall discrimination (C index=0.72, 95% CI 0.70 to 0.74) and calibration (Brier score=0.008). The Integrated Discrimination Improvement index showed a significant improvement after adding novel risk factors (0.003 (95% CI 0.001 to 0.007), p<0.001).
Conclusions:
The inclusion of the novel CVD risk factors may provide a better prediction of CVDs in this population compared with traditional CVD predictors only, when the model is used with a continuous risk score. External validation studies and studies assessing clinical impact of the model are warranted.
Clinical Implications:
Individuals initiating pharmacological treatment of ADHD at higher risk of developing CVDs should be more closely monitored.
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