Using the M-CHAT-R/F to Identify Developmental Concerns in a High-Risk 18-Month-Old Sibling Sample

The Developmental Autism Early Screening (DAES): A Novel Test for Screening Autism Spectrum Disorder

This study was undertaken to set a novel developmental screening test for autism spectrum disorder (ASD) using the Griffiths Scales of Child Development (Griffith III) (Green et al., 2016; Stroud et al., 2016), in order to intercept the early atypical developmental patterns indicating ASD risk in the first 3 years of age. An observational and interactive ASD screener, the Developmental Autism Early Screening (DAES), was developed by detecting Griffiths III items differentiating toddlers with ASD risk from those with global developmental delay (DD) or neurotypical development. The DAES was validated with ASD-specific diagnostic instruments (ADOS-2) and the cut-off score based on sensitivity, specificity and positive predictive value that best differentiates between ASD and non-ASD children was identified. We enrolled a total sample of 297 subjects, including children at risk for ASD or DD and neurotypical children. At a cut-off score of 12.5, the DAES had a sensitivity of 93%, specificity of 98.4%, positive predictive value of 96.3% and negative predictive value of 96.9% for identifying children at risk for ASD from non-ASD participants (DD/neurotypical children). The DAES total score correlated significantly with the ADOS-2 calibrated severity scores (CSS) (R = 0.53, p < 0.001). Three ASD risk ranges were identified according to DAES total and ADOS-2 CSS: Little-to-no risk (CSS: 1-3, DAES: 1-7); Mild-to-moderate risk (CSS: 4-5, DAES: 8-14); Moderate-to-severe risk (CSS: 6-10, DAES ≥ 15). The DAES provides a direct approach based on developmental profiles to stratify risk for ASD in early childhood ensuring at risk children the most appropriate diagnostic procedures and targeted intervention.

Brief Report: Single and Repeat Screening with the Modified Checklist for Autism in Toddlers-Revised in Young Children at Higher Likelihood for Autism Spectrum Disorder

PURPOSE

To compare the utility of single versus repeated autism screening in a sample at higher likelihood (HL) for ASD, following both screen positives and all screen negatives to diagnostic outcome.

METHODS

Using a prospective infant sibling design, the current study followed 135 toddlers at HL for ASD and conducted diagnostic evaluations on the full sample at 18, 24, and 36 months. The psychometric properties of the M-CHAT-R using both concurrent and predictive diagnostic evaluations were compared in a group screened once (at 18 months only, n = 60) or twice (at both 18 and 24 months, n = 75). The study also examined consistency in reporting of ASD symptoms across the M-CHAT-R and a developmental concerns interview, comparing the HL group to a group with lower likelihood (LL) for ASD (n = 88).

RESULTS

Sensitivity and specificity of the M-CHAT-R were high (75 - 95%), consistent with previous research. Positive predictive value (43 - 76%) was higher in this HL group than in previous community samples. Repeat screening improved sensitivity with little cost to specificity. At both 18 and 24 months, HL parents were more consistent in their reporting on the M-CHAT-R and a concerns interview than LL parents.

CONCLUSION

The M-CHAT-R has strong psychometric properties when used with groups at HL for ASD, suggesting that scores over the screening cutoff of 3 should lead to prompt diagnostic evaluation referrals in children with older siblings on the spectrum.

Meta-analysis of the Modified Checklist for Autism in Toddlers, Revised/Follow-up for Screening

CONTEXT

The Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) is used worldwide to screen for autism spectrum disorder (ASD).

OBJECTIVE

To calculate psychometric properties of the M-CHAT-R/F for subsequent diagnosis of ASD.

DATA SOURCES

Systematic searches of Medline, Embase, SCOPUS, and Trip Pro databases from January 2014 to November 2021.

STUDY SELECTION

Studies were included if they (1) used the M-CHAT-R/F (2) applied standard scoring protocol, (3) used a diagnostic assessment for ASD, and (4) reported at least 1 psychometric property of the M-CHAT-R/F.

DATA EXTRACTION

Two independent reviewers completed screening, full-text review, data extraction, and quality assessment, following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A random-effects model was used to derive pooled estimates and assess for between-study heterogeneity.

RESULTS

Of 667 studies identified, 15 with 18 distinct samples from 10 countries (49 841 children) were used in the meta-analysis. Pooled positive predictive value (PPV), was 57.7% (95% confidence interval [CI] 48.6-66.8, τ2 = 0.031). PPV was higher among high-risk (75.6% [95% CI 66.0-85.2]) than low-risk samples (51.2% [95% CI 43.0-59.5]). Pooled negative predictive value was 72.5% (95% CI 62.5-82.4 τ2 = 0.031), sensitivity was 82.6% (95% CI 76.2-88.9) and specificity 45.7% (95% CI 25.0-66.4).

LIMITATIONS

Negative predictive value, sensitivity, and specificity were calculated based on small sample sizes because of limited or no evaluation of screen-negative children.

CONCLUSIONS

These results support use of the M-CHAT-R/F as a screening tool for ASD. Caregiver counseling regarding likelihood of an ASD diagnosis after positive screen should acknowledge the moderate PPV.

Sensitivity and Specificity of the Modified Checklist for Autism in Toddlers (Original and Revised): A Systematic Review and Meta-analysis

Importance

The Modified Checklist for Autism in Toddlers (M-CHAT) and the M-CHAT, Revised With Follow-up (M-CHAT-R/F)-henceforth referred to as M-CHAT(-R/F)-are the most commonly used toddler screeners for autism spectrum disorder (ASD). Their use often differs from that in the original validation studies, resulting in a range of estimates of sensitivity and specificity. Also, given the variability in reports of the clinical utility of the M-CHAT(-R/F), researchers and practitioners lack guidance to inform autism screening protocols.

Objective

To synthesize variability in sensitivity and specificity of M-CHAT(-R/F) across multiple factors, including procedures for identifying missed cases, likelihood level, screening age, and single compared with repeated screenings.

Data Sources

A literature search was conducted with PubMed, Web of Science, and Scopus to identify studies published between January 1, 2001, and August 31, 2022.

Study Selection

Articles were included if the studies used the M-CHAT(-R/F) (ie, original or revised version) to identify new ASD cases, were published in English-language peer-reviewed journals, included at least 10 ASD cases, reported procedures for false-negative case identification, screened children by 48 months, and included information (or had information provided by authors when contacted) needed to conduct the meta-analysis.

Data Extraction and Synthesis

The systematic review and meta-analysis was conducted within the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. The Quality Assessment of Diagnostic Accuracy Studies-2 tool evaluated bias in sample selection. Data extraction and quality assessment were performed by 2 authors independently. The overall diagnostic accuracy of the M-CHAT(-R/F) was assessed with the hierarchic summary receiver operating characteristic (HSROC) model.

Main Outcomes and Measures

Sensitivity, specificity, diagnostic odds ratios, and HSROC curves of M-CHAT(-R/F).

Results

The review included 50 studies with 51 samples. The pooled sensitivity of M-CHAT(-R/F) was 0.83 (95% CI, 0.77-0.88), and the pooled specificity was 0.94 (95% CI, 0.89-0.97). Heterogeneity analyses revealed greater diagnostic accuracy for low- vs high-likelihood samples, a concurrent vs prospective case confirmation strategy, a large vs small sample size, use of M-CHAT(-R/F) Follow-up, and non-English vs English only.

Conclusions and Relevance

Overall, results of this study suggest the utility of the M-CHAT(-R/F) as an ASD screener. The wide variability in psychometric properties of M-CHAT(-R/F) highlights differences in screener use that should be considered in research and practice.

Nutritional status and eating behavior of children with autism spectrum disorders in Vietnam: A case-control study

Background: Atypical eating behaviors and feeding issues are common in children with autism spectrum disorder (ASD). Aim: This study aims to evaluate the nutritional status and eating behavior of the ASD and typically developing groups. Methods: A case-control study was conducted from January to April 2019 in Nghe An, Vietnam. A total of 93 children in each group participated in the study with their main caregivers. We applied the Children's Eating Behavior Inventory (CEBI) to evaluate the eating behaviors of children. Results: Overweight and obesity in ASD children accounted for 41.9%. The CEBI score and %CEBI of the ASD group was higher than that of the control group (104.0 ± 14.2 and 91.3 ± 8.3; 24.1 ± 21.4 and 5.0 ± 11.8, respectively). Most of the adverse mealtime behaviors of ASD children focused on excessive duration (52.7%), distraction (40.9%), and food refusal (39.8%). In total 88.2% of caregivers had to comfort their ASD children at every meal, followed by threatening, scolding, or punishing children if they refused to eat (57.0%). Conclusion: Being overweight/obese and having eating behavior difficulties were prevalent among ASD children in Vietnam. Safer alternatives, such as lifestyle measures and seeking help from a nutritional therapist, can help ASD children reduce weight and modify their erroneous feeding habits.

Autism screening at 18 months of age: a comparison of the Q-CHAT-10 and M-CHAT screeners

Background

Autism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers—Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months.

Methods

This is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old.

Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule—Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines.

Results

While the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10.

Limitations

Many parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages.

Conclusions

Limitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00480-4.

Referral, Assessment and Use of Screening Measures Related to Autism Spectrum Disorder at a Tertiary Hospital Setting

Children with developmental concerns in Australia continue to experience inequitable healthcare and service-related delays, even when diagnostic risk is identified. This study sought to explore service and demographic pathway factors leading up to autism spectrum disorder (ASD) assessment, including value of screening measures applied at triage. Following a trial of centralised intake for referred young children with suspected ASD, observational, retrospective pathway data was explored using bivariate and regression analyses. The mean age of 159 children referred with autism symptoms was 3.6 years, and 64% were diagnosed with ASD. Service allocation was associated with diagnosis, whilst screening tool results were not. Improved pathways are needed to limit wasted waiting times and direct each child to needs-based services.

Screening for Autism Spectrum Disorder in High-Risk Younger Siblings

OBJECTIVE

Most autism spectrum disorder (ASD) screening measures have been developed for use with low-risk (LR) children; however, measures may perform differently in high-risk (HR) younger sibling populations. The current study sought to investigate the performance of an ASD screening measure, the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F), in a sample of HR younger siblings and directly compared its performance with that in an LR sample.

METHODS

High-risk younger siblings (n = 187) and LR children (n = 15,848) were screened using the M-CHAT-R/F. Screen-positive children completed comprehensive evaluations. The M-CHAT-R/F psychometric properties and clinical characteristics were compared across the samples.

RESULTS

The M-CHAT-R/F demonstrated a significantly higher screen-positive rate and ASD detection rate in the HR sample compared with the LR sample. Children with ASD in the HR sample had stronger verbal, nonverbal, and overall cognitive abilities compared with children with ASD in the LR sample despite comparable ASD severity and adaptive functioning. High positive predictive value of the M-CHAT-R at initial screen, with only incremental change after Follow-Up, suggests that Follow-Up is less critical in HR than LR samples. A significantly lower number of changed responses during Follow-Up further supports improved reporting accuracy of parents with ASD experience compared with parents less familiar with ASD.

CONCLUSION

The findings suggest that the M-CHAT-R/F can distinguish between ASD and non-ASD at 18 to 24 months in an HR sibling sample, with performance comparable with or better than its performance in the general population.

Primary care of children with autism spectrum disorder: Applying evidence-based interventions

Pediatric autism spectrum disorder (ASD) consists of multisystem components that primary care providers (PCPs) must address. PCPs improve health outcomes associated with ASD when they administer developmental screening tools and thoroughly assess identified concerns. Pursuing specialty health services early in childhood combined with managing comorbid conditions curtails symptom escalation and disease progression.

Impact of a digital Modified Checklist for Autism in Toddlers-Revised on likelihood and age of autism diagnosis and referral for developmental evaluation

LAY ABSTRACT

This was a project in primary care for young children (1-2 years old). We tested a parent questionnaire on a tablet. This tablet questionnaire asked questions to see whether the child may have autism. We compared the paper and pencil version of the questionnaire to the tablet questionnaire. We read the medical charts for the children until they were 4 years old to see whether they ended up having autism. We found that doctors were more likely to recommend an autism evaluation when a parent used the tablet questionnaire. We think that the tablet's automatic scoring feature helped the doctors. We also think that the doctors benefited from the advice the tablet gave them.

Autism spectrum disorder risk prediction: A systematic review of behavioral and neural investigations

A reliable diagnosis of autism spectrum disorder (ASD) is difficult to make until after toddlerhood. Detection in an earlier age enables early intervention, which is typically more effective. Recent studies of the development of brain and behavior in infants and toddlers have provided important insights in the diagnosis of autism. This extensive review focuses on published studies of predicting the diagnosis of autism during infancy and toddlerhood younger than 3 years using behavioral and neuroimaging approaches. After screening a total of 782 papers, 17 neuroimaging and 43 behavioral studies were reviewed. The features for prediction consist of behavioral measures using screening tools, observational and experimental methods, brain volumetric measures, and neural functional activation and connectivity patterns. The classification approaches include logistic regression, linear discriminant function, decision trees, support vector machine, and deep learning based methods. Prediction performance has large variance across different studies. For behavioral studies, the sensitivity varies from 20% to 100%, and specificity ranges from 48% to 100%. The accuracy rates range from 61% to 94% in neuroimaging studies. Possible factors contributing to this inconsistency may be partially due to the heterogeneity of ASD, different targeted populations (i.e., high-risk group for ASD and general population), age when the features were collected, and validation procedures. The translation to clinical practice requires extensive further research including external validation with large sample size and optimized feature selection. The use of multi-modal features, e.g., combination of neuroimaging and behavior, is worth further investigation to improve the prediction accuracy.

Advanced Parental Age and Risk of Positive Autism Spectrum Disorders Screening

Background:

Autism Spectrum Disorder (ASD) is a life -long neurodevelopmental disorder and significantly influences the quality of life in children. The screening of ASD in children aged between 16-30 months to early detection and early intervention for better prognosis.

Methods:

This cross-sectional study was conducted in the southwest of Iran (Yasuj) with dominant Lore ethnicity in 2017. A total of 1504 mother- child pairs with children aged between 16-30 months were selected through simple random sampling from the integrated national health system as the framework. ASD screening was implemented using the Modified checklist for autism in toddlers- revised, with follow-up interview (M-CHAT-R/F). Demographic data such as sex of children, and parental age at their time of pregnancy were collected for all children.

Results:

Risk of ASD was low, moderate and high in 1447 (96.2%), 54 (3.6%) and 3 (0.2%) in screening, respectively. The estimated rate of ASD prevalence was 80 per 10000 (12 out of 1504) or 1 in 125. Mother's age ≥35 (P value = 0.002, OR = 11.65, CI_95%: 2.49-54.35) and father's age ≥40 (P value = 0.0001, OR = 19.64, CI_95%: 4.89-78.82) were predicting factors of ASD in toddlers aged 16-30 months.

Conclusions:

Given that, increasing the age of marriage in Iran and recent trend towards delayed childbearing; children born to older parents are at a higher risk for having ASD. So, increasing the public awareness is necessary.

Ongoing Pediatric Health Care for the Child Who Has Been Maltreated

Pediatricians provide continuous medical care and anticipatory guidance for children who have been reported to state child protection agencies, including tribal child protection agencies, because of suspected child maltreatment. Because families may continue their relationships with their pediatricians after these reports, these primary care providers are in a unique position to recognize and manage the physical, developmental, academic, and emotional consequences of maltreatment and exposure to childhood adversity. Substantial information is available to optimize follow-up medical care of maltreated children. This new clinical report will provide guidance to pediatricians about how they can best oversee and foster the optimal physical health, growth, and development of children who have been maltreated and remain in the care of their biological family or are returned to their care by Child Protective Services agencies. The report describes the pediatrician's role in helping to strengthen families' and caregivers' capabilities and competencies and in promoting and maximizing high-quality services for their families in their community. Pediatricians should refer to other reports and policies from the American Academy of Pediatrics for more information about the emotional and behavioral consequences of child maltreatment and the treatment of these consequences.

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach

Background

Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned parents.

Objective

The objective of this study was to test the feasibility of assessing autism spectrum disorder risk in parental concerns from Web-based sources, using automated text analysis tools and minimal standard questioning.

Methods

Participants were 115 parents with concerns regarding their child’s social-communication development. Children were 16- to 30-months old, and 57.4% (66/115) had a family history of autism spectrum disorder. Parents reported their concerns online, and completed an autism spectrum disorder-specific screener, the Modified Checklist for Autism in Toddlers-Revised, with Follow-up (M-CHAT-R/F), and a broad developmental screener, the Ages and Stages Questionnaire (ASQ). An algorithm predicted autism spectrum disorder risk using a combination of the parent's text and a single screening question, selected by the algorithm to enhance prediction accuracy.

Results

Screening measures identified 58% (67/115) to 88% (101/115) of children at risk for autism spectrum disorder. Children with a family history of autism spectrum disorder were 3 times more likely to show autism spectrum disorder risk on screening measures. The prediction of a child’s risk on the ASQ or M-CHAT-R was significantly more accurate when predicted from text combined with an M-CHAT-R question selected (automatically) than from the text alone. The frequently automatically selected M-CHAT-R questions that predicted risk were: following a point, make-believe play, and concern about deafness.

Conclusions

The internet can be harnessed to prescreen for autism spectrum disorder using parental concerns by administering a few standardized screening questions to augment this process.