RESUMEN
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 (ß = 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.
RESUMEN
Adolescent depression is associated with unhelpful emotional mental imagery. Here, we investigated whether vividness of negative and positive prospective mental imagery predict negative affect and anhedonia in adolescents. 111 people from Israel completed measures of prospective mental imagery, negative affect, and anhedonia at two time-points approximately three months apart. Using three cross-lagged panel models, we showed once 'concurrent' (across-variable, within-time) and 'stability' paths (across-time, within-variable) were estimated, there were no significant cross-lag paths between: i) T1 prospective negative mental imagery and T8 negative affect (i.e. increased vividness of negative future imagery at Time 1 did not predict increased negative affect at Time 8); ii) T1 prospective positive mental imagery and T8 negative affect (i.e. reduced vividness of positive future imagery at Time 1 did not predict increased negative affect at Time 8); and iii) T1 prospective positive mental imagery and T8 anhedonia (i.e. reduced vividness of positive future imagery at Time 1 did not predict increased anhedonia at Time 8). Given high levels of attrition, future research should aim to explore these associations in a larger, more diverse population, as such data could inform on whether modifying earlier prospective mental imagery may influence later time/context-specific effects of prospective mental imagery on negative affect and anhedonia.