scholarly journals Imputing the Number of Responders from the Mean and Standard Deviation of CGI-Improvement in Clinical Trials Investigating Medications for Autism Spectrum Disorder

2021 ◽  
Vol 11 (7) ◽  
pp. 908
Author(s):  
Spyridon Siafis ◽  
Alessandro Rodolico ◽  
Oğulcan Çıray ◽  
Declan G. Murphy ◽  
Mara Parellada ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Standard Deviation ◽  
Linear Regression ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Imputation Method ◽  
P Value ◽  
Subgroup Differences ◽  
The Mean

Introduction: Response to treatment, according to Clinical Global Impression-Improvement (CGI-I) scale, is an easily interpretable outcome in clinical trials of autism spectrum disorder (ASD). Yet, the CGI-I rating is sometimes reported as a continuous outcome, and converting it to dichotomous would allow meta-analysis to incorporate more evidence. Methods: Clinical trials investigating medications for ASD and presenting both dichotomous and continuous CGI-I data were included. The number of patients with at least much improvement (CGI-I ≤ 2) were imputed from the CGI-I scale, assuming an underlying normal distribution of a latent continuous score using a primary threshold θ = 2.5 instead of θ = 2, which is the original cut-off in the CGI-I scale. The original and imputed values were used to calculate responder rates and odds ratios. The performance of the imputation method was investigated with a concordance correlation coefficient (CCC), linear regression, Bland–Altman plots, and subgroup differences of summary estimates obtained from random-effects meta-analysis. Results: Data from 27 studies, 58 arms, and 1428 participants were used. The imputation method using the primary threshold (θ = 2.5) had good performance for the responder rates (CCC = 0.93 95% confidence intervals [0.86, 0.96]; β of linear regression = 1.04 [0.95, 1.13]; bias and limits of agreements = 4.32% [−8.1%, 16.74%]; no subgroup differences χ2 = 1.24, p-value = 0.266) and odds ratios (CCC = 0.91 [0.86, 0.96]; β = 0.96 [0.78, 1.14]; bias = 0.09 [−0.87, 1.04]; χ2 = 0.02, p-value = 0.894). The imputation method had poorer performance when the secondary threshold (θ = 2) was used. Discussion: Assuming a normal distribution of the CGI-I scale, the number of responders could be imputed from the mean and standard deviation and used in meta-analysis. Due to the wide limits of agreement of the imputation method, sensitivity analysis excluding studies with imputed values should be performed.

scholarly journals Assessment of knowledge about childhood autism spectrum disorder among healthcare workers in Makkah-Saudi Arabia

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Aalia Akhtar Hayat ◽  
Areej Habib Meny ◽  
Nabila Salahuddin ◽  
Faisal M. Alnemary ◽  
Kumar-Ricky Ahuja ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Saudi Arabia ◽  
Healthcare Workers ◽  
Healthcare Professionals ◽  
Health Workers ◽  
Autism Spectrum ◽  
Childhood Autism ◽  
Spectrum Disorder ◽  
P Value ◽  
The Mean

Objective: To measure the knowledge of healthcare professionals about increasingly prevalent Autism Spectrum Disorder (ASD) along with perceptions around its management and prognosis and comparison across various specialties. Methods: This Cross sectional survey based comparative analysis took place at Maternity and Children Hospital and King Faisal Hospital Makkah from December 2017 to May 2018. The validated self-administered “Knowledge about childhood autism among health workers” questionnaire was used along with additional questions regarding perceptions about ASD. The mean and mean percent scores were calculated. Chi squared test and ANOVA were applied to find the association between quantitative and qualitative variables respectively. Results: Out of 162 participants, 153 returned the questionnaire and 147 were included in final analysis. Physicians constituted 81.6% (120) of participants. The mean score for participants was 9.80(S.E.M ±0.32) where non-physicians yielded higher mean score (11.2±4.41) as compared to physicians (9.6±3.28) (p=0.113). Psychiatrists had highest score of 16/19 while general physicians had lowest (6/19). Participants with more years of experience had higher mean scores (p-value = 0.01). About 72.10% (106) of participants opted for medication as a treatment option. Nearly 38.1% (56) of participants were skeptical about improvement of ASD with early interventions. Conclusion: There is a lack of knowledge about ASD amongst healthcare professionals in Saudi Arabia. Experienced professionals working with ASD children can be utilized to deliver targeted trainings nationwide. doi: https://doi.org/10.12669/pjms.35.4.605 How to cite this:Hayat AA, Meny AH, Salahuddin N, Alnemary FM, Ahuja KR, Azeem MW. Assessment of knowledge about childhood autism spectrum disorder among healthcare workers in Makkah- Saudi Arabia. Pak J Med Sci. 2019;35(4):---------. doi: https://doi.org/10.12669/pjms.35.4.605 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Endogenous Oxytocin Levels in Autism—A Meta-Analysis

2021 ◽  
Vol 11 (11) ◽  
pp. 1545
Author(s):  
Matthijs Moerkerke ◽  
Mathieu Peeters ◽  
Lyssa de Vries ◽  
Nicky Daniels ◽  
Jean Steyaert ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Diagnostic Marker ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Developmental Changes ◽  
Group Differences ◽  
Subgroup Differences ◽  
Level Measurement ◽  
Children With Asd ◽  
Atypical Development

Oxytocin (OT) circuitry plays a major role in the mediation of prosocial behavior. Individuals with autism spectrum disorder (ASD) are characterized by impairments in social interaction and communication and have been suggested to display deficiencies in central OT mechanisms. The current preregistered meta-analysis evaluated potential group differences in endogenous OT levels between individuals with ASD and neurotypical (NT) controls. We included 18 studies comprising a total of 1422 participants. We found that endogenous OT levels are lower in children with ASD as compared to NT controls (n = 1123; g = −0.60; p = 0.006), but this effect seems to disappear in adolescent (n = 152; g = −0.20; p = 0.53) and adult populations (n = 147; g = 0.27; p = 0.45). Secondly, while no significant subgroup differences were found in regard to sex, the group difference in OT levels of individuals with versus without ASD seems to be only present in the studies with male participants (n = 814; g = −0.44; p = 0.08) and not female participants (n = 192; g = 0.11; p = 0.47). More research that employs more homogeneous methods is necessary to investigate potential developmental changes in endogenous OT levels, both in typical and atypical development, and to explore the possible use of OT level measurement as a diagnostic marker of ASD.

scholarly journals Broader autism phenotype and couple interactions in parents of children with autism

Autism ◽  
2019 ◽  
Vol 23 (8) ◽  
pp. 2068-2079 ◽  
Author(s):  
Sigan L Hartley ◽  
Emily J Hickey ◽  
Leann DaWalt ◽  
Geovanna Rodriguez
Keyword(s):  
Autism Spectrum Disorder ◽  
Standard Deviation ◽  
Problem Solving ◽  
Broader Autism Phenotype ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Couple Problem ◽  
Electrodermal Reactivity ◽  
The Mean ◽  
Autism Phenotype

The broader autism phenotype refers to sub-clinical autism spectrum disorder characteristics involving socially and emotionally aloof and rigid personality traits and social communication difficulties. Relatives of individuals with autism spectrum disorder, including parents, evidence an increased rate of broader autism phenotype. The goal of this study was to evaluate the association between actor (one’s own) and partner (their partner’s) broader autism phenotype and the self-reported, observed, and physiological (i.e. electrodermal reactivity) markers of the quality of videotaped couple problem-solving interactions in 158 couples, who had a child with autism spectrum disorder (aged 5–12 years). The mean age of mothers was 39.79 (standard deviation = 5.06) years and the mean age of fathers was 41.77 (standard deviation = 6.02) years for fathers, and 36.6% of parents did not have a college degree. Actor–partner interdependence models, using structural equation modeling in analysis of moment structures, were conducted. Results indicated that parent broader autism phenotype was positively related to adverse couple problem-solving interactions across all measurement methods (observed codes, self-reported affect, and electrodermal reactivity). These effects were independent of child-related challenges. The effect of parent broader autism phenotype occurred through both actor and partner pathways and was strongest for father broader autism phenotype.

Development and Validation of a Questionnaire to Assess Parent Reported Quality of Life Pre and Post Vision Therapy in a Population with Autism Spectrum Disorder

2019 ◽  
pp. 195-207
Keyword(s):  
Quality Of Life ◽  
Autism Spectrum Disorder ◽  
Social Behavior ◽  
Psychometric Properties ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Visual Behavior ◽  
The Mean ◽  
Vision Therapy

Background: Autism spectrum disorder is characterized in part by atypical behavior in the communication, social, and visual domains. Success in vision therapy is judged not only by changes in optometric findings, but through improvement in quality of life involving communication, social behavior and visual behavior. It would therefore be beneficial to have a validated questionnaire to assess parent reported quality of life pre and post vision therapy specific to patients with autism spectrum disorder. To our knowledge, a questionnaire of this nature has not been previously published in the literature. Methods: Questionnaire items were generated through surveying medical literature based on symptoms in three different categories: visual behavior, social behavior and communication. A pool of 34 questions was developed initially and then with thorough discussion with other experts, a 20-point questionnaire was developed with each item reflected in the construct concept. A draft of 20 questions was then sent to 10 subject experts with clinical experience in the field for more than 20 years, to review the pooled items. Validity and reliability was established prior to assessing the psychometric properties of the ASD/QOL-VT. Prospective observational study was conducted for a duration of 18 months. The study included individuals undergoing vision therapy in the age range of 3 to 15 years who had been diagnosed with ASD. The questionnaire was administered to parents of these children prior to the start of vision therapy. All subjects completed a minimum of 60 vision therapy sessions. The questionnaire was readministered after completing 60 sessions of vision therapy. Results: Cronbach’s alpha value for this questionnaire was 0.93, which reflected very good internal consistency. Factorial analysis yielded four factors with an Eigen value exceeding 1.0 which accounted for 68% variation in the model. The Cronbach alpha value for subscales identified by factorial analysis is 0.97 indicating excellent internal reliability. The mean pre vision therapy social behavior, communication and visual behavior score was 12.0±3.21, 17.07±4.57 and 26.97±6.41 respectively. The mean post vision therapy scores for social behavior, communication and visual behavior was 8.27±4.16, 11.33±5.27 and 17.93±6.52 respectively. On paired t test, the mean difference in score was statistically significant with P<0.001 in all three subcategories. Conclusions: Our study presents the development of a valid and reliable parent questionnaire, the ASD/QOL-VT, that judges communication, social behavior, and visual behavior in autism. Results of the study conducted indicate that vision therapy can result in significant improvements in the quality of life of patients with ASD as judged by their parents. This is evidenced by statistically significant changes in psychometric properties of the ASD/QOL-VT in social behavior, communication and visual behavior.

New Strategies for Clinical Trials in Autism Spectrum Disorder

2020 ◽  
Vol 15 ◽  
Author(s):  
Rini Pauly ◽  
Catherine A. Ziats ◽  
Ludovico Abenavoli ◽  
Charles E. Schwartz ◽  
Luigi Boccuto
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genetic Profiling ◽  
Omics Technologies ◽  
Molecular Abnormalities ◽  
Sequencing Technologies ◽  
Linear Relationships ◽  
Personalized Approach

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed. Methods: The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD. Results: In order to analyze the big data collected by assays such as whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and therefore establish a functional informative network among the different data sources. Conclusion: The potential advantage provided by these new integrated omics-based strategies is to better characterize the genetic background of ASD cohorts, identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

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