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- Cortese, Samuele, et al.
(författare)
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The management of sleep disturbances in children with attention-deficit/hyperactivity disorder (ADHD) : an update of the literature
- 2024
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Ingår i: Expert Review of Neurotherapeutics. - : Expert Reviews Ltd.. - 1473-7175 .- 1744-8360. ; 24:6, s. 585-596
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Forskningsöversikt (refereegranskat)abstract
- INTRODUCTION: Sleep disorders represent an important comorbidity in individuals with ADHD. While the links between ADHD and sleep disturbances have been extensively investigated, research on the management of sleep disorders in individuals with ADHD is relatively limited, albeit expanding.AREAS COVERED: The authors searched PubMed, Medline, PsycInfo, Embase+Embase Classic, Web of Sciences databases, and clinicaltrials.gov up to 4 January 2024, for randomized controlled trials (RCTs) of any intervention for sleep disorders associated with ADHD. They retained 16 RCTs (eight on pharmacological and eight on non-pharmacological interventions), supporting behavioral intervention and melatonin, and nine ongoing RCTs registered on clinicaltrials.gov. EXPERT OPINION: The pool of RCTs testing interventions for sleep disorders in individuals with ADHD is expanding. However, to inform clinical guidelines, there is a need for additional research in several areas, including 1) RCTs based on a precise phenotyping of sleep disorders; 2) pragmatic RCTs recruiting neurodevelopmental populations representative of those seen in clinical services; 3) trials testing alternative interventions (e.g. suvorexant or light therapy) or ways to deliver them (e.g. online); 4) sequential and longer-term RCTs; 5) studies testing the impact of sleep interventions on outcomes other than sleep; 6) and implementation of advanced evidence synthesis and precision medicine approaches.
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| 2. |
- Salazar de Pablo, Gonzalo, et al.
(författare)
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Individualized prediction models in ADHD : a systematic review and meta-regression
- 2024
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Ingår i: Molecular Psychiatry. - : Springer. - 1359-4184 .- 1476-5578.
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Forskningsöversikt (refereegranskat)abstract
- 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.
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