Academic Journal
Individualized prediction models in ADHD: a systematic review and meta-regression
| Title: | Individualized prediction models in ADHD: a systematic review and meta-regression |
|---|---|
| Authors: | Gonzalo Salazar de Pablo, Raquel Iniesta, Alessio Bellato, Arthur Caye, Maja Dobrosavljevic, Valeria Parlatini, Miguel Garcia‐Argibay, Lin Li, Anna Cabras, Mian Haider Ali, Lucinda Archer, Alan J. Meehan, Halima Suleiman, Marco Solmi, Paolo Fusar‐Poli, Zheng Chang, Stephen V. Faraone, Henrik Larsson, Samuele Cortese |
| Contributors: | Salazar de Pablo, Gonzalo, Iniesta, Raquel, Bellato, Alessio, Caye, Arthur, Dobrosavljevic, Maja, Parlatini, Valeria, Garcia-Argibay, Miguel, Lin, Li, Cabras, Anna, Haider Ali, Mian, Archer, Lucinda, Meehan, Alan J., Suleiman, Halima, Solmi, Marco, Fusar-Poli, Paolo, Chang, Zheng, Faraone, Stephen V., Larsson, Henrik, Cortese, Samuele, Li, Lin, Meehan, Alan J, Faraone, Stephen V |
| Source: | Mol Psychiatry Salazar de Pablo, G, Iniesta, R, Bellato, A, Caye, A, Dobrosavljevic, M, Parlatini, V, Garcia-Argibay, M, Li, L, Cabras, A, Haider Ali, M, Archer, L, J Meehan, A, Suleiman, H, Solmi, M, Fusar-Poli, P, Chang, Z, Faraone, S V, Larsson, H & Cortese, S 2024, ' Individualized prediction models in ADHD: A systematic review and meta-regression ', Molecular Psychiatry, vol. 29, no. 12, pp. 3865-3873 . https://doi.org/10.1038/s41380-024-02606-5 |
| Publisher Information: | Springer Science and Business Media LLC, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Cognitive Neuroscience, MEDLINE, FOS: Political science, Systematic Review, 692/53, 692/699/476/1311, Humans [MeSH], Prognosis [MeSH], systematic-review, Precision Medicine/methods [MeSH], Attention Deficit Disorder with Hyperactivity [MeSH], FOS: Law, Analysis of Brain Functional Connectivity Networks, Psykiatri, models, Health Sciences, Machine learning, adhd, Humans, ADHD, Psychology, Precision Medicine, Attention-Deficit/Hyperactivity Disorder, Internal medicine, Political science, Psychiatry, Predictive modelling, Life Sciences, Neural Mechanisms of Cognitive Control and Decision Making, prediction, Prognosis, Computer science, 3. Good health, FOS: Psychology, Psychiatry and Mental health, Meta-analysis, Attention Deficit Disorder with Hyperactivity, Systematic review, Medicine, Regression analysis, Law, Neuroscience, Meta-Analysis |
| Description: | There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research. |
| Document Type: | Article Other literature type |
| File Description: | application/pdf; text |
| Language: | English |
| ISSN: | 1476-5578 1359-4184 |
| DOI: | 10.1038/s41380-024-02606-5 |
| DOI: | 10.60692/7mkzc-n3a71 |
| DOI: | 10.60692/w9tr7-s4g19 |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/38783054 https://kclpure.kcl.ac.uk/ws/files/294696669/s41380-024-02606-5.pdf https://repository.publisso.de/resource/frl:6509758 http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-113817 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....b18c0c1b91b225df89770cf394936744 |
| Database: | OpenAIRE |
| ISSN: | 14765578 13594184 |
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| DOI: | 10.1038/s41380-024-02606-5 |