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Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder

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...

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Autores principales: Dobrosavljevic, Maja, Fazel, Seena, Du Rietz, Ebba, Li, Lin, Zhang, Le, Chang, Zheng, Jernberg, Tomas, Faraone, Stephen V, Jendle, Johan, Chen, Qi, Brikell, Isabell, Larsson, Henrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685689/
https://www.ncbi.nlm.nih.gov/pubmed/36396339
http://dx.doi.org/10.1136/ebmental-2022-300492
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author Dobrosavljevic, Maja
Fazel, Seena
Du Rietz, Ebba
Li, Lin
Zhang, Le
Chang, Zheng
Jernberg, Tomas
Faraone, Stephen V
Jendle, Johan
Chen, Qi
Brikell, Isabell
Larsson, Henrik
author_facet Dobrosavljevic, Maja
Fazel, Seena
Du Rietz, Ebba
Li, Lin
Zhang, Le
Chang, Zheng
Jernberg, Tomas
Faraone, Stephen V
Jendle, Johan
Chen, Qi
Brikell, Isabell
Larsson, Henrik
author_sort Dobrosavljevic, Maja
collection PubMed
description 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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spelling pubmed-96856892022-11-25 Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder Dobrosavljevic, Maja Fazel, Seena Du Rietz, Ebba Li, Lin Zhang, Le Chang, Zheng Jernberg, Tomas Faraone, Stephen V Jendle, Johan Chen, Qi Brikell, Isabell Larsson, Henrik Evid Based Ment Health Pharmacological Treatments 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. BMJ Publishing Group 2022-11 2022-09-05 /pmc/articles/PMC9685689/ /pubmed/36396339 http://dx.doi.org/10.1136/ebmental-2022-300492 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Pharmacological Treatments
Dobrosavljevic, Maja
Fazel, Seena
Du Rietz, Ebba
Li, Lin
Zhang, Le
Chang, Zheng
Jernberg, Tomas
Faraone, Stephen V
Jendle, Johan
Chen, Qi
Brikell, Isabell
Larsson, Henrik
Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
title Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
title_full Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
title_fullStr Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
title_full_unstemmed Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
title_short Risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
title_sort risk prediction model for cardiovascular diseases in adults initiating pharmacological treatment for attention-deficit/hyperactivity disorder
topic Pharmacological Treatments
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685689/
https://www.ncbi.nlm.nih.gov/pubmed/36396339
http://dx.doi.org/10.1136/ebmental-2022-300492
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