Participant Characteristics Predicting Communication Outcomes in AAC Implementation for Individuals with ASD and IDD: A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Jay Ganz ◽  
James E Pustejovsky ◽  
Joe Reichle ◽  
Kimberly Vannest ◽  
Margaret Foster ◽  
...  
Keyword(s):  
Social Communication ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Response Ratio ◽  
Average Effect ◽  
Design Quality ◽  
Participant Characteristics ◽  
Log Response Ratio ◽  
High Degree ◽  
Degree Of Heterogeneity

This meta-analysis examined social communication outcomes in augmentative and alternative communication (AAC) interventions, or those that involved aided (e.g., speech generating devices, picture point systems) or unaided AAC (e.g., gestures, manual sign language) as a component of intervention, and the extent to which communication outcomes were predicted by participant characteristics. Variables of interest included chronological age, communication mode used prior to intervention, number of words produced and imitation skills of participants prior to intervention. Investigators identified 117 primary studies that implemented AAC interventions with school-aged individuals (up to 22 years) with autism spectrum disorder and/or intellectual disability associated with complex communication needs and assessed social-communication outcomes. All included studies involved single-case experimental designs and met basic study design quality standards. We synthesized findings across studies using two complementary effect size indices, Tau(AB) and the log response ratio, and multi-level meta-analysis with robust variance estimation. With Tau(AB), the overall average effect across 338 participants was 0.72, 95% CI [0.67, 0.76], with a high degree of heterogeneity across studies. With the log response ratio, the overall average effect corresponded to a 538% increase from baseline levels of responding, 95% CI [388%, 733%], with a high degree of heterogeneity across studies and contrasts. Moderator analyses detected few differences in effectiveness when comparing across diagnoses, ages, the number and type of communication modes the participants used prior to intervention, the number of words used by the participants prior to intervention, and imitation use prior to intervention.

scholarly journals Selecting Communicative Interventions Targets for School-Aged participants with ASD and ID: A Single-Case Experimental Design Meta-analysis

2021 ◽  
Author(s):  
Jay Ganz ◽  
James E Pustejovsky ◽  
Joe Reichle ◽  
Kimberly Vannest ◽  
Margaret Foster ◽  
...  
Keyword(s):  
Effect Size ◽  
Augmentative And Alternative Communication ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Original Research ◽  
Response Ratio ◽  
Alternative Communication ◽  
Complex Communication Needs ◽  
Log Response Ratio ◽  
Communication Needs

Objective: This meta-analysis reviews the literature on communication modes, communicative functions, and types of augmentative and alternative communication (AAC) interventions for school-age participants with autism spectrum disorders and/or intellectual disabilities who experience complex communication needs. Considering potential differences related to outcomes that were targeted for intervention could help identify the most effective means of individualizing AAC interventions. Methods: We performed a systematic literature search using Academic Search Ultimate, ERIC, PsycINFO, Web of Science, and Proquest Dissertations & Theses Global to retrieve research conducted between 1978 and the beginning of 2020. Studies included in the synthesis are (a) in English; (b) has one or more participants with an intellectual delay, developmental disability(ies); (c) reported the results of an augmentative and alternative communication (AAC) intervention to supplement or replace conventional speech for people with complex communication needs; (d) was a SCED; (e) measured social-communicative outcomes. We synthesized results across studies using multi-level meta-analyses of two case-level effect size metrics, Tau and log response ratio. We conducted moderator analyses using meta-regression with robust variance estimation.Results: Across 114 included studies with 330 participants and 767 effect size, overall Tau effects were moderate, Tau = 0.72, 95% CI [0.67, 0.77], and heterogeneous. For the subset of data series where log response ratio could be estimated, the overall average effect was LRR = 1.86, 95% CI [1.58, 2.13], and effects were highly heterogeneous. There were few statistically significant differences found between moderator categories, which included communication mode, communicative function, and type of AAC implemented.Conclusions: This meta-analysis highlights the potential differences related to outcomes that were targeted for AAC interventions for individuals with ASD and IDD. AAC intervention has been shown to improve communication outcomes in this population. However, there was a lack of sufficient data to analyze for some potential moderators such as insufficient descriptive information on participant characteristics. This is likely due to the heterogeneity of the participants and implementation factors; however, these factors were frequently underreported by original study authors which disallowed systematic analysis. That said, there is a need for more detailed participant characteristic descriptions in original research reports to support future aggregation across the literature. Sponsorship: We received funding for the review from the Institute of Education Sciences.Protocol: The review protocol was registered in the PROSPERO system (CRD42018112428).

scholarly journals Prevalence and Impact of Atrial Fibrillation in Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 10 (11) ◽  
pp. 2490
Author(s):  
Giulio Francesco Romiti ◽  
Bernadette Corica ◽  
Gregory Y. H. Lip ◽  
Marco Proietti
Keyword(s):  
Atrial Fibrillation ◽  
Systematic Review ◽  
Meta Analysis ◽  
Geographical Location ◽  
Treatment Strategies ◽  
Cardiovascular Complications ◽  
Risk Of Death ◽  
All Cause Mortality ◽  
High Degree ◽  
Degree Of Heterogeneity

Background: In patients with COVID-19, cardiovascular complications are common and associated with poor prognosis. Among these, an association between atrial fibrillation (AF) and COVID-19 has been described; however, the extent of this relationship is unclear. The aim of this study is to investigate the epidemiology of AF in COVID-19 patients and its impact on all-cause mortality. Methods: A systematic review and meta-analysis were performed and reported according to PRISMA guidelines, and a protocol for this study was registered on PROSPERO (CRD42021227950). PubMed and EMBASE were systematically searched for relevant studies. A random-effects model was used to estimate pooled odds ratios (OR) and 95% confidence intervals (CI). Results: Overall, 31 studies were included in the analysis, with a total number of 187,716 COVID-19 patients. The prevalence of AF was found to be as high as 8% of patients with COVID-19 (95% CI: 6.3–10.2%, 95% prediction intervals (PI): 2.0–27.1%), with a high degree of heterogeneity between studies; a multiple meta-regression model including geographical location, age, hypertension, and diabetes showed that these factors accounted for more than a third of the heterogeneity. AF COVID-19 patients were less likely to be female but more likely older, hypertensive, and with a critical status than those without AF. Patients with AF showed a significant increase in the risk of all-cause mortality (OR: 3.97, 95% CI: 2.76–5.71), with a high degree of heterogeneity. A sensitivity analysis focusing on new-onset AF showed the consistency of these results. Conclusions: Among COVID-19 patients, AF is found in 8% of patients. AF COVID-19 patients are older, more hypertensive, and more likely to have a critical status. In COVID-19 patients, AF is associated with a 4-fold higher risk of death. Further studies are needed to define the best treatment strategies to improve the prognosis of AF COVID-19 patients.

scholarly journals Vehicle Exposure and Spinal Musculature Fatigue in Military Warfighters: A Meta-Analysis

2016 ◽  
Vol 51 (11) ◽  
pp. 981-990 ◽  
Author(s):  
Roger O. Kollock ◽  
Kenneth E. Games ◽  
Alan E. Wilson ◽  
JoEllen M. Sefton
Keyword(s):  
Confidence Interval ◽  
Exposure Time ◽  
Sports Medicine ◽  
Occupational Safety ◽  
Data Extraction ◽  
Meta Analysis ◽  
Technical Information ◽  
Lumbar Region ◽  
Response Ratio ◽  
Log Response Ratio

Context: Spinal musculature fatigue from vehicle exposure may place warfighters at risk for spinal injuries and pain. Research on the relationship between vehicle exposure and spinal musculature fatigue is conflicting. A better understanding of the effect of military duty on musculoskeletal function is needed before sports medicine teams can develop injury-prevention programs. Objective: To determine if the literature supports a definite effect of vehicle exposure on spinal musculature fatigue. Data Sources: We searched the MEDLINE, Military & Government Collection (EBSCO), National Institute for Occupational Safety and Health Technical Information Center, PubMed, and Web of Science databases for articles published between January 1990 and September 2015. Study Selection: To be included, a study required a clear sampling method, preexposure and postexposure assessments of fatigue, a defined objective measurement of fatigue, a defined exposure time, and a study goal of exposing participants to forces related to vehicle exposure. Data Extraction: Sample size, mean preexposure and postexposure measures of fatigue, vehicle type, and exposure time. Data Synthesis: Six studies met the inclusion criteria. We used the Scottish Intercollegiate Guidelines Network algorithm to determine the appropriate tool for quality appraisal of each article. Unweighted random-effects model meta-analyses were conducted, and a natural log response ratio was used as the effect metric. The overall meta-analysis demonstrated that vehicle exposure increased fatigue of the spinal musculature (P = .03; natural log response ratio = −0.22, 95% confidence interval = −0.42, −0.02). Using the spinal region as a moderator, we observed that vehicle ride exposure significantly increased fatigue at the lumbar musculature (P = .02; natural log response ratio = −0.27, 95% confidence interval = −0.50, −0.04) but not at the cervical or thoracic region. Conclusions: Vehicle exposure increased fatigue at the lumbar region.

scholarly journals Publications after conference presentations: A systematic review of published studies

2020 ◽  
pp. 01-09
Author(s):  
Sandeep Grover ◽  
Dalton N ◽  
Siddharth Sarkar
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Publication Rate ◽  
Peer Evaluation ◽  
Weighted Mean ◽  
Conference Presentations ◽  
Poster Presentations ◽  
High Degree ◽  
Oral Presentations ◽  
Degree Of Heterogeneity

Background and aims: Conferences provide an opportunity to present findings to an audience of experts in the field and get feedback for putting the research in context. Since conference proceedings provide limited space for presenting the findings, research publications are able to provide a better platform for the wider reach, scrupulous peer evaluation, and temporal consolidation of the medical scientific material. This review attempts to collate the studies which have evaluated the abstract publication ratio of the conference presentations. Methods: The systematic review and meta-analysis included peer reviewed publications which quantitatively reported the publication rate of conference presentations. Results: A total of 28 studies were included, with sample sizes ranging from 82 to 1897 abstracts (total 17,172 abstracts). The publication rate ranged from 3.8% to 78.0%, with weighted mean publication rate of 41.8% (95% confidence interval of 34.1% to 49.5%). Oral presentations had a greater chance of being published as compared to poster presentations (odds ratio of 2.693, 95% confidence intervals of 1.285 to 5.646). There was high degree of heterogeneity in the findings. Conclusions: A small proportion of the conference presentations ispublished. Efforts should be made to improve the abstract publication ratio to improve the wider dissemination of the available research.

Contingency-based delay to reinforcement following functional communication training for autistic individuals: A multilevel meta-analysis

Autism ◽  
2021 ◽  
pp. 136236132110655
Author(s):  
Reem Muharib ◽  
Art Dowdy ◽  
Adithyan Rajaraman ◽  
Joshua Jessel
Keyword(s):  
Challenging Behavior ◽  
Differential Reinforcement ◽  
Empirical Support ◽  
Functional Communication Training ◽  
Autism Spectrum ◽  
Communication Training ◽  
Functional Communication ◽  
Response Ratio ◽  
Moderating Variables ◽  
Log Response Ratio

Functional communication training, an intervention for challenging behavior rooted in principles of applied behavior analysis, has copious empirical support dating back to the mid-1980s for autistic individuals. Recently, there has been a concerted effort to thin reinforcement delivery during functional communication training using contingency-based delays that, in turn, are designed to enhance practicality and feasibility while not compromising on efficacy. In this synthesis, we meta-analyzed the literature using log response ratio effect sizes to investigate (a) combined and across type effectiveness of contingency-based delays and (b) moderating variables that might impact intervention outcomes. Findings showed that contingency-based delays were effective for autistic individuals (log response ratio = −2.17; 95% CI = (−2.76, −1.58)) and most effective when the contingency incorporated positive reinforcement (log response ratio = −2.30; 95% CI = (−2.83, −1.78)). In addition, delay procedures that included differential reinforcement of alternative behavior were overall more effective (log response ratio = −2.13; 95% CI = (−2.72, −1.55)) than those that involved differential reinforcement of other behavior (log response ratio = −1.24; 95% CI = (−3.84, 1.37)). Noteworthy moderating variables found to impact contingency-based delay efficacy included the intervention dosage and the topography of behavior. We discuss these findings and highlight directions where additional empirical research is warranted to improve our understanding about contingency-based delays for autistic individuals. Lay abstract Functional communication training, an intervention for challenging behavior rooted in principles of applied behavior analysis, has copious empirical support dating back to the mid-1980s for autistic individuals. Recently, there has been a concerted effort to thin reinforcement delivery during functional communication training using contingency-based delays that, in turn, are designed to enhance practicality and feasibility while not compromising efficacy. In this synthesis, we meta-analyzed the literature base with the goal of investigating both combined and across type effectiveness of contingency-based delays. We also aimed to investigate moderating variables that might impact intervention outcomes. Findings showed that contingency-based delays were effective for individuals with an autism spectrum disorder diagnosis and most effective when the delay incorporated some form of positive reinforcement. In addition, differential reinforcement of alternative-based delays was overall more effective when compared to differential reinforcement of other behavior-based delays. Noteworthy moderating variables found to impact contingency-based delay efficacy included the intervention dosage and the topography of behavior. We discuss these findings and highlight directions where additional empirical research is warranted to improve our understanding about contingency-based delays for individuals diagnosed with autism spectrum disorder.

scholarly journals Bias and correction for the log response ratio in ecological meta-analysis

Ecology ◽  
2015 ◽  
Vol 96 (8) ◽  
pp. 2056-2063 ◽  
Author(s):  
Marc J. Lajeunesse
Keyword(s):  
Meta Analysis ◽  
Response Ratio ◽  
Log Response Ratio

scholarly journals Impact of antipsychotics in children and adolescents with autism spectrum disorder: a systematic review and meta-analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Gian Loreto D’Alò ◽  
◽  
Franco De Crescenzo ◽  
Laura Amato ◽  
Fabio Cruciani ◽  
...  
Keyword(s):  
Systematic Review ◽  
Adverse Events ◽  
Children And Adolescents ◽  
Social Communication ◽  
Meta Analysis ◽  
Health Benefit ◽  
Autism Spectrum ◽  
Global Functioning ◽  
Global Improvement ◽  
Adolescents With Asd

Abstract Background The net health benefit of using antipsychotics in children and adolescents with ASD is unclear. This review was performed to provide the evidence necessary to inform the Italian national guidelines for the management of ASD. Methods We performed a systematic review of randomized controlled trials (RCTs) comparing antipsychotics versus placebo for the treatment of ASD in children and adolescents. For efficacy, acceptability and safety we considered outcomes evaluated by the guideline panel critical and important for decision-making. Continuous outcomes were analyzed by using standardized mean difference (SMD), and dichotomous outcomes by calculating the risk ratio (RR), with their 95% confidence interval (95% CI). Data were analyzed using a random effects model. We used the Cochrane tool to assess risk of bias of included studies. Certainty in the evidence of effects was assessed according to the GRADE approach. Results We included 21 RCTs with 1,309 participants, comparing antipsychotics to placebo. Antipsychotics were found effective on “restricted and repetitive interests and behaviors” (SMD − 0.21, 95% CI − 0.35 to − 0.07, moderate certainty), “hyperactivity, inattention, oppositional, disruptive behavior” (SMD − 0.67, 95% CI − 0.92 to − 0.42, moderate certainty), “social communication, social interaction” (SMD − 0.38, 95% CI − 0.59 to − 0.16, moderate certainty), “emotional dysregulation/irritability” (SMD − 0.71, 95% CI − 0.98 to − 0.43, low certainty), “global functioning, global improvement” (SMD − 0.64, 95% CI − 0.96 to − 0.33, low certainty), “obsessions, compulsions” (SMD − 0.30, 95% CI − 0.55 to − 0.06, moderate certainty). Antipsychotics were not effective on “self-harm” (SMD − 0.14, 95% CI − 0.58 to 0.30, very low certainty), “anxiety” (SMD − 0.38, 95% CI − 0.82 to 0.07, very low certainty). Antipsychotics were more acceptable in terms of dropout due to any cause (RR 0.61, 95% CI 0.48 to 0.78, moderate certainty), but were less safe in terms of patients experiencing adverse events (RR 1.19, 95% CI 1.07 to 1.32, moderate certainty), and serious adverse events (RR 1.07, 95% CI 0.48 to 2.43, low certainty). Conclusions Our systematic review and meta-analysis found antipsychotics for children and adolescents with ASD more efficacious than placebo in reducing stereotypies, hyperactivity, irritability and obsessions, compulsions, and in increasing social communication and global functioning. Antipsychotics were also found to be more acceptable, but less safe than placebo.

scholarly journals Simulation study of estimating between-study variance and overall effect in meta-analyses of log-response-ratio for normal data

2020 ◽  
Author(s):  
Ilyas Bakbergenuly ◽  
David C. Hoaglin ◽  
Elena Kulinskaya
Keyword(s):  
Profile Likelihood ◽  
Meta Analysis ◽  
Response Ratio ◽  
Normal Distributions ◽  
Lognormal Distributions ◽  
Effect Measure ◽  
Log Response Ratio ◽  
Interval Estimators ◽  
Point Estimators ◽  
Meta Analyses

Methods for random-effects meta-analysis require an estimate of the between-study variance, $\tau^2$. The performance of estimators of $\tau^2$ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect. For the effect measure log-response-ratio (LRR, also known as the logarithm of the ratio of means, RoM), we review four point estimators of $\tau^2$ (the popular methods of DerSimonian-Laird (DL), restricted maximum likelihood, and Mandel and Paule (MP), and the less-familiar method of Jackson), four interval estimators for $\tau^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, and Jackson), five point estimators of the overall effect (the four related to the point estimators of $\tau^2$ and an estimator whose weights use only study-level sample sizes), and seven interval estimators for the overall effect (four based on the point estimators for $\tau^2$, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of HKSJ that uses the MP estimator of $\tau^2$ instead of the DL estimator, and an interval based on the sample-size-weighted estimator). We obtain empirical evidence from extensive simulations of data from normal distributions. Simulations from lognormal distributions are in a separate report Bakbergenuly et al. 2019b.

Predictive Models of Intensive Care Unit Mortality - Severity of Illness Scores or Artificial Intelligence instruments? - Literature Review and Metanalysis (Preprint)

2021 ◽  
Author(s):  
Cristina Barboi ◽  
Andreas Tzavelis ◽  
Lutfiyya NaQiyba Muhammad
Keyword(s):  
Artificial Intelligence ◽  
Prediction Models ◽  
Meta Analysis ◽  
Severity Of Illness ◽  
Mortality Prediction ◽  
Risk Of Bias ◽  
Icu Mortality ◽  
Mortality Prediction Models ◽  
High Degree ◽  
Degree Of Heterogeneity

BACKGROUND The Severity of Illness Scores (SIS)- Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), and Sequential Organ Failure Assessment (SOFA) - are current risk stratification and mortality prediction tools used in Intensive Care Units (ICU) across the globe, and rely on scores that assess disease severity on admission. Developers of Artificial Intelligence (AI) or Machine Learning (ML) models predictive of ICU mortality use the SIS performance as a reference point when reporting the performance of these computational constructs. OBJECTIVE Using systematic review and meta-analysis, we evaluated studies that compare ML-based mortality prediction models to SIS-based models. The review should inform clinicians regarding the prognostic value of ML-based ICU mortality prediction models compared with SIS models and their validity in supporting clinical decision-making. METHODS We performed a systematic search using PubMed, Scopus, Embase, and IEEE databases. Studies that report the performance of newly developed ML models predictive of ICU mortality and compare it with the performance of SIS models on the same datasets were eligible for inclusion. ML and the SIS models with a reported Area Under the Receiver Operating Characteristic (AUROC) curve were included in the meta-analysis to identify the group with superior performance. Data were extracted with guidance from the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist[1] and was appraised for risk of bias and applicability using PROBAST (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies ) [2]. RESULTS After screening the literature, we identified and included 20 papers containing 47 ML models based on seven types of algorithms that were compared with three types of SIS models. The AUROC for predicting ICU mortality ranged between 0.828-0.875 for ML-based models and between 0.707-0.760 for SI-based models. We noted substantial heterogeneity among the models reported, and considerable variation among the AUROC estimates for both ML and SIS model types. Due to the high degree of heterogeneity, we performed a limited random-effect meta-analysis of externally validated subgroups of ML models and the subgroups of SIS used for comparison. CONCLUSIONS ML-based models can accurately predict ICU mortality as an alternative to traditional scoring models. The high degree of heterogeneity observed within and between studies limit the assessment of pooled results. The differences in development strategies, validation, statistical, and computational methods that these models rely on impede a head-to-head comparison, and we cannot declare the superiority of one model over the other. Consequently, we make no recommendation regarding the ML-based ICU mortality prediction models’ performance in clinical practice. To bridge the knowledge gap from design to practice, ML model developers must provide explainer models and make those knowledge objects reproducible, interoperable, and transparent[3]. CLINICALTRIAL the review was registered and approved by the international prospective register of systematic reviews, PROSPERO (reference number CRD42021203871).

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