Exploring the Effects of Pharmacological, Psychosocial, and Alternative/Complementary Interventions in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Meta-Regression Approach.

BACKGROUND: There have been various therapies for attention-deficit/hyperactivity disorder (ADHD), but the previous meta-analysis of ADHD efficacy remains unclear. This study aims to systemically meta-regress the effect sizes (ES) of psychostimulant pharmacotherapy (methylphenidate and lisdexamfetamine), non-stimulant pharmacotherapy (atomoxetine and alpha-2 agonists), psychosocial therapy (parental behavioral therapy [PBT]), combination therapy (psychostimulant plus PBT), and alternative/complementary interventions to determine the right treatment for ADHD. METHODS: We searched various ADHD interventions from the MEDLINE and PubMed databases (National Center for Biotechnology Information) between January 1, 1980, and July 30, 2018. Following the meta-analysis of random effects, the meta-regression analyses were used to explore factors potentially influencing treatment efficacy. The confounding variables included type of treatment, type of study, age, type of symptom scale used, and year of publication. RESULTS: A total of 107 trials (n = 9883 participants) were included. After adjustment, compared with the psychostimulant therapy (28 trial, 2134 participants), non-stimulant pharmacotherapy (28 trials, 4991 participants) and alternative/complement intervention (25 trials, 1195 participants) were less effective by the ES of -0.384 (P = .004) and -0.419 (P = .028), respectively. However, compared with psychostimulant, PBT (19 trials, 1122 participants; ES = -0.308, P = .095) and the combination of psychostimulant and PBT (7 trials, 441participants; ES = -0.196, P = .209) did not differ significantly. CONCLUSIONS: Psychostimulant therapy surpassed non-stimulant pharmacotherapy and alternative/complement intervention. Psychostimulant therapy, PBT, and the combination of psychostimulant therapy and PBT appear to be similar in efficacy according to this meta-regression.

Optimizing early prediction for antipsychotic response in schizophrenia.

OBJECTIVE: Researchers, by studying first-generation antipsychotics, have established an early prediction model, which had a favorable specificity but a low sensitivity. This study aims to optimize early prediction of treatment response for schizophrenia using a novel statistic method that can be done even under the Microsoft Excel system of a personal computer. METHODS: One hundred twenty-three inpatients with acutely exacerbated schizophrenia were given optimal therapy of risperidone, a commonly used second-generation antipsychotic agent. Response was defined as a reduction of 20% or more in the Positive and Negative Syndrome Scale total score. We applied the generalized estimating equation method's logistic regression to establish an early prediction model based on the treatment results of the first and the second weeks. RESULTS: The proposed method correctly predicted nonresponse at 4 and 6 weeks in 80.8% and 81.8% of the patients, respectively. The method also identified responder at 4 and 6 weeks in 80.0% and 82.8%, respectively. The predictive powers (or correct prediction rates) at 4 and 6 weeks were 80.3% and 82.4%, respectively. In addition, the results based on the responses in Positive and Negative Syndrome Scale scores were slightly better than those in Brief Psychiatric Rating Scale scores. CONCLUSIONS: Using the first 2 weeks' treatment results to predict the fourth or sixth week's treatment response is acceptable in terms of specificity, sensitivity, and predictive power. Further studies are needed. Moreover, whether this model could be applied to establish a prediction system for other psychotropics, such as antidepressants, also deserves research.