Predicting Cognitive High Risk in Children with Attention-deficit/hyperactivity Disorder Using the SNAP-IV scale and IVA-CPT: A Cross-sectional Study

Predicting Cognitive High Risk in Children with Attention-deficit/hyperactivity Disorder Using the SNAP-IV scale and IVA-CPT: A Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Cognitive High Risk in Children with Attention-deficit/hyperactivity Disorder Using the SNAP-IV scale and IVA-CPT: A Cross-sectional Study Zehong Lin, Nan Peng, Yike Zhu, Bo Zhou, Lin Liu, Run Liu, Jinting Niu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8626275/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To investigate the value of the SNAP-IV scale combined with IVA-CPT objective attention/executive function indicators in identifying the risk of cognitive ability in children with ADHD, and to evaluate its incremental predictive ability over traditional subjective scales, providing a basis for clinical screening and stratified interventions. Methods A total of 248 children with ADHD (191 boys, 57 girls), with a mean age of 8.98 ± 1.68 years, were enrolled from the ADHD outpatient clinic at the Children's Health Center of Capital center for Children's Health, Capital Medical University. All participants completed the SNAP-IV scale, WISC and IVA-CPT Attention Test. Cognitive high-risk was defined by a Wechsler Full Scale IQ < 85. A logistic regression model (Model 1) was constructed including only the SNAP-IV total score, age, sex, parental ADHD status, and whether the child was an only child. Model 2 was constructed by adding IVA-CPT composite scores for attention quotient, response control quotient, and auditory/visual attention and control quotients. The area under the curve (AUC) of the ROC curve and DCA curve was compared between the two models. Results The AUC of Model 1 was 0.612, with a sensitivity of 0.804, and specificity of 0.426, indicating that its predictive ability for cognitive high-risk was close to random. After incorporating IVA indicators, the AUC of Model 2 improved to 0.707, with a sensitivity of 0.587, and specificity of 0.787, which was significantly superior to Model 1 (p = 0.034). DCA analysis further demonstrated that Model 2 offered more favorable guidance for decision-making at moderate-risk thresholds.Additionally, no significant correlation was found between the SNAP-IV score and any IVA dimension score, indicating that subjective symptom scales and objective attention function indicators assess different aspects of cognitive performance. Conclusion The SNAP-IV scale alone is insufficient for effectively identifying the cognitive high-risk individuals among children with ADHD. However, the addition of IVA-CPT attention and response control indicators significantly improves predictive performance. This study supports the use of a combined screening strategy of "subjective behavioral scales + objective cognitive tests" in ADHD assessments, which can help identify cognitive high-risk children early and facilitate early intervention. Trial registration Not applicable. Attention Deficit/Hyperactivity Disorder (ADHD) SNAP-IV IVA-CPT Low Cognitive Ability Predictive Model Figures Figure 1 Figure 2 Figure 3 Background Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that typically manifests in early childhood, characterized by age-inappropriate inattention and/or hyperactivity and impulsivity. It affects approximately 8% of children and 6% of adolescents worldwide[1], with the prevalence increasing annually. In recent years, with advancements in neuropsychology and educational research, ADHD has come to be recognized not as a singular symptom disorder but as a highly heterogeneous spectrum, exhibiting significant internal differences in cognitive function, comorbidity patterns, and other aspects. Among these, some children with ADHD also present with low intellectual functioning or cognitive deficits, which often result in learning difficulties, an increased risk of school dropout, and poor social adaptation. These children typically have a worse prognosis than those with purely symptomatic forms of ADHD[2-4]. A meta-analysis has shown that ADHD patients score, on average, 9 points lower than non-ADHD patients on intelligence tests[5]. Therefore, accurately identifying "cognitively at-risk" children within the ADHD population in the early stages holds significant clinical and public health implications Currently, clinical evaluation of ADHD primarily relies on subjective behavioral scales, such as the SNAP-IV and Conners scales. The SNAP-IV, one of the most widely used assessment tools internationally, is effective in reflecting the severity of symptoms related to inattention, hyperactivity/impulsivity, and oppositional defiance in children[6]. However, subjective scales are susceptible to evaluator bias, cultural background, and contextual factors, making it difficult to directly reflect the true level of children's neurocognitive function[7]. On the other hand, objective tests, such as the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), can quantify children's attention and executive function through multidimensional measures, including reaction time, error rate, attention index, and response control index. These tests are widely used as supplementary assessments for ADHD[8]. However, research on how to integrate subjective scales and objective tests in daily clinical practice, and use them to identify children with ADHD who also have cognitive impairments, is still scarce. Moreover, from the perspective of precision medicine and stratified diagnosis and treatment, a diagnosis of "ADHD" alone is no longer sufficient to meet the clinical needs. Doctors and parents are more concerned with identifying which children diagnosed with ADHD are more likely to have underlying cognitive impairments or lower intellectual functioning and therefore require more intensive educational support and rehabilitation interventions. Therefore, the establishment of predictive models capable of identifying cognitively at-risk children within the ADHD cohort holds considerable clinical value. Materials and Methods (I) Study Subjects This study adopted a cross-sectional design and included 248 children diagnosed with ADHD who attended the ADHD outpatient clinic at the Children's Health Center of Capital center for Children's Health, Capital Medical University. Among them, 191 were male and 57 were female. All participants were clinically diagnosed by two experienced developmental and behavioral pediatricians according to the relevant diagnostic criteria. Children with clear brain organic diseases, severe sensory impairments, or other neurological disorders that could significantly affect cognitive assessments were excluded. The inclusion criteria were as follows: ① age between 6 and 18 years; ② completion of the standardized Wechsler Intelligence Scale for Children (WISC) test, with a reliable Full Scale IQ score between 70 and 120; ③ completion of the SNAP-IV scale; ④ completion of the IVA-CPT test, with available data on the comprehensive Attention Quotient (FSAQ), Response Control Quotient (FRCQ), and other relevant indicators. (II) Research Tools SNAP-IV Scale The SNAP-IV scale includes three dimensions: inattention, hyperactivity/impulsivity, and oppositional defiant behavior. It uses a 0-3 rating scale, with higher scores indicating more severe symptoms. In this study, the total scores for inattention, hyperactivity/impulsivity, and oppositional defiant behavior were extracted and summed to form the total SNAP-IV score, which reflects the overall severity of ADHD symptoms. Wechsler Intelligence Scale The Wechsler Intelligence Scale for Children was used to assess the intelligence of all participants, yielding the Full Scale IQ. In this study, "cognitive high-risk" was defined as a state where children/adolescents may have significant cognitive impairment but have not yet reached the threshold for intellectual disability. This was operationally defined by a Full Scale IQ (FSIQ) < 85 as a screening criterion to distinguish between cognitive high-risk and non-high-risk children. This criterion was based on authoritative sources in cognitive neuroscience and clinical neurology, including the Handbook of Clinical Neurology, which points out that neurodevelopmental disorders (including ADHD) often involve impairments in various cognitive dimensions such as attention, executive function, processing speed, and working memory. These impairments may indicate learning difficulties, poor social adaptation, and functional risks, even if the IQ does not meet the traditional threshold for intellectual disability (usually IQ≤ 70)[6]. IVA-CPT Attention Test The IVA-CPT is a continuous performance test that integrates visual and auditory stimuli to simultaneously assess children's attention and response control abilities across two channels. The test outputs include the Full Scale Attention Quotient (FSAQ), Full Scale Response Control Quotient (FRCQ), and their respective auditory/visual sub-scores (AQ (auditory/visual) and RCQ (auditory/visual)). In this study, FSAQ, FRCQ, AQ (auditory/visual), and RCQ (auditory/visual) were selected as objective cognitive indicators. (III) Variable Definitions and Data Processing After data entry, data cleaning and quality control were performed using R statistical software. The normality of continuous variables was tested. Gender, whether parents had ADHD, and whether the child was an only child were coded as 0/1, where 0 represents "No" and 1 represents "Yes". The cognitive high-risk variable was calculated based on the Wechsler Full Scale IQ (0 = IQ ≥ 85, 1 = IQ < 85) for subsequent logistic regression analysis. The total SNAP-IV score was obtained by summing the scores for inattention, hyperactivity/impulsivity, and oppositional defiant behavior. (IV) Statistical Methods Descriptive Analysis Continuous variables were described by mean±standard deviation(sd), while categorical variables were described by frequency and percentage. The baseline distribution of the Wechsler Full Scale IQ was plotted as a histogram and tested for normality to provide an overall understanding of the sample's intellectual distribution. Correlation Analysis Pearson correlation analysis was used to explore the linear relationship between the total SNAP-IV score, sub-scores for inattention, hyperactivity/impulsivity, and oppositional defiant behavior, and key indicators from the IVA-CPT (FSAQ, FRCQ, AQ (auditory/visual), RCQ (auditory/visual)) to determine the association between subjective symptom scales and objective attention function tests. Prediction Model Construction A logistic regression model (Model 1) was first constructed using cognitive high-risk (0/1) as the dependent variable, with the SNAP-IV total score and demographic characteristics (age at diagnosis, gender, parental ADHD status, and whether the child is an only child) as predictors. This model simulates a routine clinical scenario where risk assessment is based on subjective symptom scales and basic demographic information. A second model (Model 2) was constructed by adding the IVA-CPT FSAQ, FRCQ, AQ (auditory/visual), and RCQ (auditory/visual) indicators. The area under the Receiver Operating Characteristic (ROC) curve (AUC) and Decision Curve Analysis (DCA ) decision performance for both models were evaluated and compared to assess their predictive performance. Results Results (I) General Demographics and Baseline IQ Distribution A total of 248 children with ADHD were included in this study, of which 191 (77%) were male and 57 (23%) were female. The average age at first visit was 8.98 ± 1.68 years, and the average Wechsler Full Scale IQ was 95.91 ± 11.82. No significant gender differences were observed (see Table 1). Table1 Table of General Demographics and Baseline Item Total (Mean ± SD) Girls (Mean ± SD) Boys (Mean ± SD) p-value N = 248 N = 57 N = 191 Age at first visit 8.98 ±1.68 8.89 ±1.26 9.01 ±1.79 0.585 Father with ADHD 24 (9.68%) 5 (8.77%) 19 (9.95%) Mother with ADHD 12 (4.84%) 1 (1.75%) 11 (5.76%) Only child 92 (37.10%) 21 (36.84%) 71 (37.17%) Wechsler IQ 95.91 ±11.82 94.44 ±11.76 96.35 ±11.83 0.286 Inattention score 15.37 ±5.90 14.30 ±5.56 15.69 ±5.97 0.107 Hyperactivity/impulsivity score 9.33 ±6.12 8.19 ±5.25 9.68 ±6.33 0.078 Oppositional defiant score 8.10 ±5.26 8.96 ±4.95 7.84 ±5.34 0.144 SNAP-IV total score 32.71 ±14.41 31.49 ±13.16 33.07 ±14.78 0.442 FRCQ 83.48 ±16.97 87.79 ±16.65 82.19 ±16.90 0.029 RCQ（Auditory） 88.34 ±67.60 87.65 ±16.29 88.54 ±76.56 0.880 RCQ（Visual） 87.28 ±17.05 91.09 ±18.02 86.14 ±16.63 0.068 FSAQ 77.44 ±22.52 86.25 ±24.42 74.82 ±21.30 0.002 AQ（Auditory） 81.85 ±19.78 88.49 ±17.14 79.87 ±20.12 0.002 AQ（Visual） 74.92 ±22.47 82.47 ±21.92 72.66 ±22.19 0.004 According to the IQ < 85 criterion, 46 cases were classified into the cognitive high-risk group, accounting for approximately 18.5% of all cases. The distribution of Wechsler Full Scale IQ scores followed a normal distribution (D = 0.045, p > 0.05), with the majority of children falling within the 90–100 IQ range (Figure 1). (II) Correlation Analysis Between SNAP-IV Total Score and IVA Attention Indices Correlation analysis between the SNAP-IV scores (total score, inattention score, hyperactivity/impulsivity score, and oppositional defiant score) and the various indices of the IVA-CPT was performed using a heatmap (Figure 2). The results indicated no significant correlation between the SNAP-IV scale and the IVA-CPT indices, suggesting that the severity of subjective symptoms does not fully align with the attention function levels measured under laboratory conditions. This finding reflects, to some extent, the context-dependent nature of ADHD symptoms and the discrepancy between parents' subjective perceptions and objective test results. (III) Logistic Regression Model for Predicting Cognitively High-risk In Model 1, SNAP-IV total score, age at first visit, gender, parental ADHD status, and whether the child is an only child were included as independent variables in the logistic regression (Table 2). The results showed that Model 1 had an area under the curve (AUC) of 0.612, a best threshold of 0.138, sensitivity of 0.804, and specificity of 0.426 (Figure 3a), indicating that the model's discriminative power was limited and close to random chance. Based on this, Model 2 was constructed by adding the IVA-CPT FSAQ, FRCQ, AQ (auditory/visual), and RCQ (auditory/visual) indices (Table 2). The results showed that Model 2's AUC increased to 0.707, with a best threshold of 0.228, sensitivity of 0.587, and specificity of 0.787 (Figure 3b), demonstrating moderate discriminative ability, which was significantly better than Model 1 (p=0.034) (Figure 3c). DCA analysis indicated that Model 2 provided more favorable guidance for decision-making at moderate-risk thresholds (Figure 3d). Additionally, being an only child was found to be a protective factor (OR ≈ 0.45, 95% CI ≈ 0.20–0.93, P ≈ 0.04), while age at visit showed a slight positive trend as a risk factor (OR ≈ 1.07), though the statistical significance was insufficient (p = 0.491) (Table 2). Overall, Model 2 demonstrated better discriminatory performance than Model 1 in distinguishing cognitively high-risk individuals, suggesting that the inclusion of IVA-CPT objective indices improved both the sensitivity and specificity of the model. Table 2. Logistic regression coefficients Variable Model 1 Model 2 OR Odds ratios with 95% confidence p-value OR Odds ratios with 95% confidence p-value Gender 0.95 0.45 -2.11 0.89 0.76 0.34 -1.75 0.50 Age at first visit 1.05 0.87 -1.27 0.57 1.07 0.87 -1.31 0.49 Father with ADHD 0.91 0.25-2.66 0.88 0.90 0.24 -2.71 0.86 Mother with ADHD 0.39 0.02 -2.15 0.37 0.47 0.02 -2.77 0.49 Only child 0.47 0.22-0.96 0.05 0.45 0.20 -0.93 0.04 SNAP-IV total score 1.00 0.98-1.03 0.83 1.00 0.98 -1.02 0.96 FRCQ - - - 1.03 0.97 -1.10 0.46 RCQ（Auditory） - - - 0.99 0.95 -1.00 0.41 RCQ（Visual） - - - 1.00 0.95 -1.04 0.84 FSAQ - - - 0.95 0.89 -1.01 0.13 AQ（Auditory） - - - 1.02 0.98 -1.06 0.44 AQ（Visual） - - - 1.01 0.98 -1.05 0.59 Discussion Using real-world data from 248 clinically diagnosed children with ADHD, this study systematically compared the performance of the SNAP-IV scale alone versus its combination with IVA-CPT indices in predicting cognitively high-risk status. The results indicate that the subjective symptom rating scales widely used in routine clinical practice have clear limitations in identifying cognitively high-risk children. When objective indices of attention and response control derived from IVA-CPT are added, the performance of the risk prediction model is substantially improved. First, the correlation analysis showed no obvious associations between the SNAP-IV scores and the various IVA-CPT indices, suggesting that these two types of measures capture at least partly distinct psychological processes. SNAP-IV primarily reflects the frequency and severity of symptoms as observed by parents in everyday contexts, whereas IVA-CPT quantitatively assesses sustained attention, response inhibition, and alertness in a standardized experimental setting. The behavior of children with ADHD at home or in the classroom is additionally influenced by multiple factors, such as motivation, contextual support, emotional state, and parenting style [7, 8]. As a result, discrepancies or even “mismatches” may arise between subjective rating scales and objective performance-based tests. Integrating these two sources of information is therefore necessary to obtain a more comprehensive picture of the functional profile of children with ADHD. Second, from the perspective of predictive modeling, reliance on SNAP-IV total scores and demographic variables alone yielded an AUC close to chance level for identifying cognitively high-risk cases, which may lead to under-recognition of a subset of vulnerable children. In frontline clinical practice in China, due to constraints in time and resources, many children undergo only subjective rating scale assessments and do not receive further objective cognitive testing. This limitation may hinder early identification and intervention for children with comorbid learning difficulties or cognitive impairment [9, 10]. Our findings demonstrate that, when IVA-CPT indices of composite attention and response control are incorporated into the SNAP-based model, the AUC increases from approximately 0.612 to about 0.707, indicating that IVA-CPT provides additional and clinically meaningful information for the specific task of “identifying cognitively high-risk individuals within an ADHD cohort.”However, it is noteworthy that despite the improvement in AUC, the sensitivity of Model 2 decreased from 0.804 to 0.587, while specificity increased from 0.426 to 0.787. This change may reflect the previous sensitivity of subjective scales to "negative behaviors" — when parents are anxious or score subjectively high, many normal children may be misclassified as high-risk. The inclusion of IVA-CPT helps mitigate this subjective bias, thereby improving the accuracy of low-risk identification, but at the cost of reduced sensitivity to high-risk individuals. Therefore, future research should consider optimizing the combination of variables, adjusting thresholds, or weight distributions to maximize sensitivity while maintaining high specificity, thus achieving more reliable screening for cognitively high-risk children. Third, the present findings offer useful insights for the development of future multimodal assessment frameworks and digital screening systems. With the rapid advancement of artificial intelligence and wearable technologies, an increasing number of studies are attempting to integrate behavioral rating scales, cognitive task performance, physiological signals, and even neuroimaging data into unified models to achieve more accurate individualized prediction [11, 12]. SNAP-IV and IVA-CPT represent questionnaire-based and experimental-task–based assessments, respectively, and this study constitutes an important step toward combining these two layers of information. In the future, it would be feasible to migrate such cognitive tasks to tablet-based or gamified environments to reduce the burden on children and caregivers, improve engagement and adherence, and facilitate the broader implementation of a “scale plus objective task” assessment pathway in community and school settings [13]. This study has several limitations. First, the cross-sectional design allows us to examine only the associations between SNAP-IV/IVA indices and IQ at a single time point, and precludes inferences about causal relationships between changes in cognitive function and IQ development. Second, the sample was drawn from a single specialty outpatient clinic, which may introduce selection bias; for example, children presenting for care may have more severe symptoms or parents with stronger treatment-seeking motivation. The results therefore may not fully generalize to children with ADHD in community or regular school settings. Third, we did not include a control group, and thus were unable to evaluate the diagnostic validity of the combined SNAP–IVA indicators for distinguishing children with ADHD from those without ADHD; our analyses were limited to risk stratification within the ADHD population. Future studies should conduct external validation in multicenter, large-sample cohorts and seek to incorporate additional objective biomarkers, such as electroencephalography, functional neuroimaging, and eye-tracking measures, in order to further enhance the robustness and interpretability of the models. Conclusion In summary, this study shows that, among children with ADHD, reliance on the SNAP-IV scale alone is insufficient for adequately identifying those at high risk of cognitive impairment. When IVA-CPT–derived indices are added, both the AUC and the explanatory power of the prediction model increase markedly. Subjective behavioral rating scales and objective cognitive performance tests provide complementary information, and their combined use contributes to a more accurate and refined risk assessment framework. Future research should further validate this model in larger, multicenter samples and explore its application within digital screening platforms and in the development of individualized intervention strategies. Abbreviations ADHD: Attention-deficit/hyperactivity disorder AUC: area under the curve DCA: Decision curve analysis FSAQ: Full Scale Attention Quotient FRCQ: Full Scale Response Control Quotient IVA-CPT: Integrated Visual and Auditory Continuous Performance Test IQ: Intelligence quotient RCQ: Response Control Quotient ROC: Receiver operating characteristic SNAP-IV: Swanson, Nolan and Pelham Rating Scale, version IV WISC: Wechsler Intelligence Scale for Children Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Capital Institute of Pediatrics (approval number: SHERLL2024039). Written informed consent was obtained from the parents or legal guardians of all participating children, and assent was obtained from children where appropriate. Clinical trial number: not applicable. Consent for publication This manuscript does not contain any individual person’s identifiable data in any form (including images or videos). Consent for publication was therefore not required. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Beijing Natural Science Foundation Haidian Original Innovation Joint Fund (grant number:L232121), Capital's Funds for Health Improvement and Research (grant number:2024-1-1131), New Quality Foundation of Capital Institute of Pediatrics (grant number:XZYB-2025-02) , High-level Talent Development Program Project of Haidian District Health and Wellness System(grant number:2025HDLJ005), Youth Talent Fund of Capital Institute of Pediatrics ( grant number: YCYJ-2025-01) . The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions ZL and LW conceived and designed the study. JN,RL,YZ,CY,QA,QX,XW and YZcollected the data. NP, ZL,LZ, JW and BZ performed the statistical analyses and interpreted the results. ZL,NP and LW drafted the manuscript. All authors critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work. Acknowledgements The authors would like to thank the participating children and their families, as well as the clinical and research staff at the Children’s Health Center of Capital Medical University for their support. Authors’ information 1 Department of Child Health Care, Capital Center for Children's Health, Capital Medical University, Beijing,100020, China; 2 Capital Institute of Pediatrics, Beijing, 100020, China; 3 Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China; 4 Capital Institute of Pediatrics, Peking University Teaching Hospital, Beijing 100020,China; * Lin Zehong and Nan Peng contributed equally to this work. References Salari N, Ghasemi H, Abdoli N, Rahmani A, Shiri MH, Hashemian AH, et al. The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis. Ital J Pediatr. 2023;49(1):48. doi: 10.1186/s13052-023-01456-1. Crosbie J, Schachar R. Deficient inhibition as a marker for familial ADHD. Am J Psychiatry. 2001;158(11):1884-90. doi: 10.1176/appi.ajp.158.11.1884. Sibley MH, Graziano PA, Ortiz M, Rodriguez L, Coxe S. 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Andrikopoulos D, Vassiliou G, Fatouros P, Tsirmpas C, Pehlivanidis A, Papageorgiou C. Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study. BMC Psychiatry. 2024;24(1):547. doi: 10.1186/s12888-024-05987-7. Lumsden J, Edwards EA, Lawrence NS, Coyle D, Munafò MR. Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy. JMIR Serious Games. 2016;4(2):e11. doi: 10.2196/games.5888. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8626275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582412627,"identity":"22292473-4991-4678-b93a-16c951347ff4","order_by":0,"name":"Zehong Lin","email":"","orcid":"","institution":"Capital Center for Children's Health, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zehong","middleName":"","lastName":"Lin","suffix":""},{"id":582412628,"identity":"34a4e344-e592-40a5-bdd4-33c317ec5cd6","order_by":1,"name":"Nan Peng","email":"","orcid":"","institution":"Capital Institute of Pediatrics, 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Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""},{"id":582412638,"identity":"69415d74-93d7-4f5b-a153-ec58b948a31e","order_by":5,"name":"Run Liu","email":"","orcid":"","institution":"Capital Institute of Pediatrics, Peking University Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Run","middleName":"","lastName":"Liu","suffix":""},{"id":582412639,"identity":"d572a00a-e986-4c4b-afb1-043f1f4eb341","order_by":6,"name":"Jinting Niu","email":"","orcid":"","institution":"Capital Institute of Pediatrics, Peking University Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinting","middleName":"","lastName":"Niu","suffix":""},{"id":582412643,"identity":"7012f81d-10cb-4187-8954-ac665afced17","order_by":7,"name":"Yuyuan Zeng","email":"","orcid":"","institution":"Capital Institute of Pediatrics, Chinese Academy of Medical Sciences \u0026 Peking Union Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Wang","suffix":""},{"id":582412651,"identity":"ad85789d-1bd4-45d9-be3e-4fef833a75ff","order_by":11,"name":"Qi Xu","email":"","orcid":"","institution":"Capital Center for Children's Health, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Xu","suffix":""},{"id":582412652,"identity":"21b3f14a-e2e9-4aa8-a3c2-b3c18c3f57f8","order_by":12,"name":"Lili Zhang","email":"","orcid":"","institution":"Capital Center for Children's Health, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhang","suffix":""},{"id":582412653,"identity":"d39affaf-e570-4c93-b57e-c7fef324b6d0","order_by":13,"name":"Jianhong Wang","email":"","orcid":"","institution":"Capital Center for Children's Health, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianhong","middleName":"","lastName":"Wang","suffix":""},{"id":582412654,"identity":"7a029021-f779-447c-897e-745e8dd20325","order_by":14,"name":"Lin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLCCBwZQxscGNjAtQVBLAlQL40zitUBpZt4GBsJa+NvPPnyQUHCHweD42cOvbXfwRRscYD54m4fBLg+XFokz6cYGCQbPGAzO5KVZ555hy91wgC3ZmochuRiXFgOGNDaJBIPDDGYHcsyMc9tAWnjMpHkYDiQ24NLC/wyq5fwbM2NLsBb+b/i1SMBsuZFj/JgRYgsbXi0SN54xG4C02N94Y8bYC/TLzMNsxpZzDJJxauHvT2N88OHPYQbJ/hzjDz93HMvtO9788MabCjucWmCgHqiADRgdx4CxAwkWogDzBwaGGuKUjoJRMApGwYgCALOiVrSYuVc8AAAAAElFTkSuQmCC","orcid":"","institution":"Capital Center for Children's Health, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-17 13:23:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8626275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8626275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101785231,"identity":"ccc4c583-8a59-4ab4-8528-c89f2e65ae88","added_by":"auto","created_at":"2026-02-03 15:34:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55107,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Wechsler Full Scale IQ and Normality Test in Children with ADHD. Panel a. shows the distribution of Wechsler Full Scale IQ in children with ADHD in this study, with the majority of children falling within the 90–100 IQ range. Panel b. presents the results of the Lilliefors test for normality on the Wechsler Full Scale IQ values, which yielded D = 0.045, p \u0026gt; 0.05, indicating that the variable follows a normal distribution.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626275/v1/f35f446dfc470c75909ed0d4.jpeg"},{"id":102745216,"identity":"fc488d08-57ab-415e-830e-adbc69a88aa5","added_by":"auto","created_at":"2026-02-16 08:44:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129669,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Heatmap Between SNAP-IV Scores and IVA Full Scale Attention Quotient. Panels a, b, c, and d display the correlation between the SNAP-IV total score, inattention score, hyperactivity/impulsivity score, and oppositional defiant score with the various indices of the IVA-CPT. The results show no significant correlation between these indicators, further confirming that the dimensions reflected by the two sets of measures do not fully overlap.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626275/v1/b34b8a0628f7cebaae2e881e.jpeg"},{"id":101881098,"identity":"9eac0ecb-d529-482a-9e02-0c20a49497f6","added_by":"auto","created_at":"2026-02-04 15:09:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116392,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC and DCA Curves for Model 1 and Model 2 in Predicting High Cognitive Risk. \u0026nbsp;Panel a shows that Model 1 has an AUC of 0.612, a best threshold of 0.138, sensitivity of 0.804, and specificity of 0.426; Panel b shows that Model 2 has an AUC of 0.707, a best threshold of 0.228, sensitivity of 0.587, and specificity of 0.787. Figure c demonstrates that the difference in AUC between Model 2 and Model 1 is statistically significant (p=0.034). Figure d displays the difference in the DCA curves between Model 1 and Model 2.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626275/v1/3ea7613bac710d1c5a32e4a4.jpeg"},{"id":107902632,"identity":"fcb75e1e-5b23-4e4d-867a-fd6f56827697","added_by":"auto","created_at":"2026-04-27 11:58:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":578835,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8626275/v1/0bd6463b-b7aa-4643-b517-503f424c579b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Cognitive High Risk in Children with Attention-deficit/hyperactivity Disorder Using the SNAP-IV scale and IVA-CPT: A Cross-sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that typically manifests in early childhood, characterized by age-inappropriate inattention and/or hyperactivity and impulsivity. It affects approximately 8% of children and 6% of adolescents worldwide[1], with the prevalence increasing annually. In recent years, with advancements in neuropsychology and educational research, ADHD has come to be recognized not as a singular symptom disorder but as a highly heterogeneous spectrum, exhibiting significant internal differences in cognitive function, comorbidity patterns, and other aspects. Among these, some children with ADHD also present with low intellectual functioning or cognitive deficits, which often result in learning difficulties, an increased risk of school dropout, and poor social adaptation. These children typically have a worse prognosis than those with purely symptomatic forms of ADHD[2-4]. A meta-analysis has shown that ADHD patients score, on average, 9 points lower than non-ADHD patients on intelligence tests[5]. Therefore, accurately identifying \u0026quot;cognitively at-risk\u0026quot; children within the ADHD population in the early stages holds significant clinical and public health implications\u003c/p\u003e\n\u003cp\u003eCurrently, clinical evaluation of ADHD primarily relies on subjective behavioral scales, such as the SNAP-IV and Conners scales. The SNAP-IV, one of the most widely used assessment tools internationally, is effective in reflecting the severity of symptoms related to inattention, hyperactivity/impulsivity, and oppositional defiance in children[6]. However, subjective scales are susceptible to evaluator bias, cultural background, and contextual factors, making it difficult to directly reflect the true level of children\u0026apos;s neurocognitive function[7]. On the other hand, objective tests, such as the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), can quantify children\u0026apos;s attention and executive function through multidimensional measures, including reaction time, error rate, attention index, and response control index. These tests are widely used as supplementary assessments for ADHD[8]. However, research on how to integrate subjective scales and objective tests in daily clinical practice, and use them to identify children with ADHD who also have cognitive impairments, is still scarce.\u003c/p\u003e\n\u003cp\u003eMoreover, from the perspective of precision medicine and stratified diagnosis and treatment, a diagnosis of \u0026quot;ADHD\u0026quot; alone is no longer sufficient to meet the clinical needs. Doctors and parents are more concerned with identifying which children diagnosed with ADHD are more likely to have underlying cognitive impairments or lower intellectual functioning and therefore require more intensive educational support and rehabilitation interventions. Therefore, the establishment of predictive models capable of identifying cognitively at-risk children within the ADHD cohort holds considerable clinical value.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e(I) Study Subjects\u003c/p\u003e\n\u003cp\u003eThis study adopted a cross-sectional design and included 248 children diagnosed with ADHD who attended the ADHD outpatient clinic at the Children\u0026apos;s Health Center of Capital center for Children\u0026apos;s Health, Capital Medical University. Among them, 191 were male and 57 were female. All participants were clinically diagnosed by two experienced developmental and behavioral pediatricians according to the relevant diagnostic criteria. Children with clear brain organic diseases, severe sensory impairments, or other neurological disorders that could significantly affect cognitive assessments were excluded. The inclusion criteria were as follows: ① age between 6 and 18 years; ② completion of the standardized Wechsler Intelligence Scale for Children (WISC) test, with a reliable Full Scale IQ score between 70 and 120; ③ completion of the SNAP-IV scale; ④ completion of the IVA-CPT test, with available data on the comprehensive Attention Quotient (FSAQ), Response Control Quotient (FRCQ), and other relevant indicators.\u003c/p\u003e\n\u003cp\u003e(II) Research Tools\u003c/p\u003e\n\u003cp\u003eSNAP-IV Scale\u003c/p\u003e\n\u003cp\u003eThe SNAP-IV scale includes three dimensions: inattention, hyperactivity/impulsivity, and oppositional defiant behavior. It uses a 0-3 rating scale, with higher scores indicating more severe symptoms. In this study, the total scores for inattention, hyperactivity/impulsivity, and oppositional defiant behavior were extracted and summed to form the total SNAP-IV score, which reflects the overall severity of ADHD symptoms.\u003c/p\u003e\n\u003cp\u003eWechsler Intelligence Scale\u003c/p\u003e\n\u003cp\u003eThe Wechsler Intelligence Scale for Children was used to assess the intelligence of all participants, yielding the Full Scale IQ. In this study, \u0026quot;cognitive high-risk\u0026quot; was defined as a state where children/adolescents may have significant cognitive impairment but have not yet reached the threshold for intellectual disability. This was operationally defined by a Full Scale IQ (FSIQ) \u0026lt; 85 as a screening criterion to distinguish between cognitive high-risk and non-high-risk children. This criterion was based on authoritative sources in cognitive neuroscience and clinical neurology, including the Handbook of Clinical Neurology, which points out that neurodevelopmental disorders (including ADHD) often involve impairments in various cognitive dimensions such as attention, executive function, processing speed, and working memory. These impairments may indicate learning difficulties, poor social adaptation, and functional risks, even if the IQ does not meet the traditional threshold for intellectual disability (usually IQ\u0026le; 70)[6].\u003c/p\u003e\n\u003cp\u003eIVA-CPT Attention Test\u003c/p\u003e\n\u003cp\u003eThe IVA-CPT is a continuous performance test that integrates visual and auditory stimuli to simultaneously assess children\u0026apos;s attention and response control abilities across two channels. The test outputs include the Full Scale Attention Quotient (FSAQ), Full Scale Response Control Quotient (FRCQ), and their respective auditory/visual sub-scores (AQ (auditory/visual) and RCQ (auditory/visual)). In this study, FSAQ, FRCQ, AQ (auditory/visual), and RCQ (auditory/visual) were selected as objective cognitive indicators.\u003c/p\u003e\n\u003cp\u003e(III) Variable Definitions and Data Processing\u003c/p\u003e\n\u003cp\u003eAfter data entry, data cleaning and quality control were performed using R statistical software. The normality of continuous variables was tested. Gender, whether parents had ADHD, and whether the child was an only child were coded as 0/1, where 0 represents \u0026quot;No\u0026quot; and 1 represents \u0026quot;Yes\u0026quot;. The cognitive high-risk variable was calculated based on the Wechsler Full Scale IQ (0 = IQ \u0026ge; 85, 1 = IQ \u0026lt; 85) for subsequent logistic regression analysis. The total SNAP-IV score was obtained by summing the scores for inattention, hyperactivity/impulsivity, and oppositional defiant behavior.\u003c/p\u003e\n\u003cp\u003e(IV) Statistical Methods\u003c/p\u003e\n\u003cp\u003eDescriptive Analysis\u003c/p\u003e\n\u003cp\u003eContinuous variables were described by mean\u0026plusmn;standard deviation(sd), while categorical variables were described by frequency and percentage. The baseline distribution of the Wechsler Full Scale IQ was plotted as a histogram and tested for normality to provide an overall understanding of the sample\u0026apos;s intellectual distribution.\u003c/p\u003e\n\u003cp\u003eCorrelation Analysis\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was used to explore the linear relationship between the total SNAP-IV score, sub-scores for inattention, hyperactivity/impulsivity, and oppositional defiant behavior, and key indicators from the IVA-CPT (FSAQ, FRCQ, AQ (auditory/visual), RCQ (auditory/visual)) to determine the association between subjective symptom scales and objective attention function tests.\u003c/p\u003e\n\u003cp\u003ePrediction Model Construction\u003c/p\u003e\n\u003cp\u003eA logistic regression model (Model 1) was first constructed using cognitive high-risk (0/1) as the dependent variable, with the SNAP-IV total score and demographic characteristics (age at diagnosis, gender, parental ADHD status, and whether the child is an only child) as predictors. This model simulates a routine clinical scenario where risk assessment is based on subjective symptom scales and basic demographic information. A second model (Model 2) was constructed by adding the IVA-CPT FSAQ, FRCQ, AQ (auditory/visual), and RCQ (auditory/visual) indicators. The area under the Receiver Operating Characteristic (ROC) curve (AUC) and Decision Curve Analysis (DCA ) decision performance for both models were evaluated and compared to assess their predictive performance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(I) General Demographics and Baseline IQ Distribution\u003c/p\u003e\n\u003cp\u003eA total of 248 children with ADHD were included in this study, of which 191 (77%) were male and 57 (23%) were female. The average age at first visit was 8.98 \u0026plusmn; 1.68 years, and the average Wechsler Full Scale IQ was 95.91 \u0026plusmn; 11.82. No significant gender differences were observed (see Table 1).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 646px;\"\u003e\n \u003cp\u003eTable1 Table of General Demographics and Baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 143px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eGirls\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eBoys\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eN = 248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eN = 57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eN = 191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eAge at first visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e8.98 \u0026plusmn;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e8.89 \u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e9.01 \u0026plusmn;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFather with ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e24 (9.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e5 (8.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e19 (9.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eMother with ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (4.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e1 (1.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e11 (5.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eOnly child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e92 (37.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e21 (36.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e71 (37.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eWechsler IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e95.91 \u0026plusmn;11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e94.44 \u0026plusmn;11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e96.35 \u0026plusmn;11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eInattention score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e15.37 \u0026plusmn;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e14.30 \u0026plusmn;5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e15.69 \u0026plusmn;5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eHyperactivity/impulsivity score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e9.33 \u0026plusmn;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e8.19 \u0026plusmn;5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e9.68 \u0026plusmn;6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eOppositional defiant score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e8.10 \u0026plusmn;5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e8.96 \u0026plusmn;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e7.84 \u0026plusmn;5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eSNAP-IV total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e32.71 \u0026plusmn;14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e31.49 \u0026plusmn;13.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e33.07 \u0026plusmn;14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFRCQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e83.48 \u0026plusmn;16.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e87.79 \u0026plusmn;16.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e82.19 \u0026plusmn;16.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eRCQ（Auditory）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e88.34 \u0026plusmn;67.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e87.65 \u0026plusmn;16.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e88.54 \u0026plusmn;76.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eRCQ（Visual）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e87.28 \u0026plusmn;17.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e91.09 \u0026plusmn;18.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e86.14 \u0026plusmn;16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFSAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e77.44 \u0026plusmn;22.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e86.25 \u0026plusmn;24.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e74.82 \u0026plusmn;21.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eAQ（Auditory）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e81.85 \u0026plusmn;19.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e88.49 \u0026plusmn;17.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e79.87 \u0026plusmn;20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eAQ（Visual）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e74.92 \u0026plusmn;22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e82.47 \u0026plusmn;21.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e72.66 \u0026plusmn;22.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to the IQ \u0026lt; 85 criterion, 46 cases were classified into the cognitive high-risk group, accounting for approximately 18.5% of all cases. The distribution of Wechsler Full Scale IQ scores followed a normal distribution (D = 0.045, p \u0026gt; 0.05), with the majority of children falling within the 90\u0026ndash;100 IQ range (Figure 1).\u003c/p\u003e\n\u003cp\u003e(II) Correlation Analysis Between SNAP-IV Total Score and IVA Attention Indices\u003c/p\u003e\n\u003cp\u003eCorrelation analysis between the SNAP-IV scores (total score, inattention score, hyperactivity/impulsivity score, and oppositional defiant score) and the various indices of the IVA-CPT was performed using a heatmap (Figure 2). The results indicated no significant correlation between the SNAP-IV scale and the IVA-CPT indices, suggesting that the severity of subjective symptoms does not fully align with the attention function levels measured under laboratory conditions. This finding reflects, to some extent, the context-dependent nature of ADHD symptoms and the discrepancy between parents\u0026apos; subjective perceptions and objective test results.\u003c/p\u003e\n\u003cp\u003e(III) Logistic Regression Model for Predicting Cognitively High-risk\u003c/p\u003e\n\u003cp\u003eIn Model 1, SNAP-IV total score, age at first visit, gender, parental ADHD status, and whether the child is an only child were included as independent variables in the logistic regression (Table 2). The results showed that Model 1 had an area under the curve (AUC) of 0.612, a best threshold of 0.138, sensitivity of 0.804, and specificity of 0.426 (Figure 3a), indicating that the model\u0026apos;s discriminative power was limited and close to random chance. Based on this, Model 2 was constructed by adding the IVA-CPT FSAQ, FRCQ, AQ (auditory/visual), and RCQ (auditory/visual) indices (Table 2). The results showed that Model 2\u0026apos;s AUC increased to 0.707, with a best threshold of 0.228, sensitivity of 0.587, and specificity of 0.787 (Figure 3b), demonstrating moderate discriminative ability, which was significantly better than Model 1 (p=0.034) (Figure 3c). DCA analysis indicated that Model 2 provided more favorable guidance for decision-making at moderate-risk thresholds (Figure 3d). Additionally, being an only child was found to be a protective factor (OR \u0026asymp; 0.45, 95% CI \u0026asymp; 0.20\u0026ndash;0.93, P \u0026asymp; 0.04), while age at visit showed a slight positive trend as a risk factor (OR \u0026asymp; 1.07), though the statistical significance was insufficient (p = 0.491) (Table 2).\u003c/p\u003e\n\u003cp\u003eOverall, Model 2 demonstrated better discriminatory performance than Model 1 in distinguishing cognitively high-risk individuals, suggesting that the inclusion of IVA-CPT objective indices improved both the sensitivity and specificity of the model.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"626\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 626px;\"\u003e\n \u003cp\u003eTable 2. Logistic regression coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 261px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 256px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eOdds ratios with 95% confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eOdds ratios with 95% confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.45 -2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.34 -1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eAge at first visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.87 -1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.87 -1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eFather with ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.25-2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.24 -2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eMother with ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.02 -2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.02 -2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eOnly child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.22-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.20 -0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eSNAP-IV total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.98 -1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eFRCQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.97 -1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eRCQ（Auditory）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.95 -1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eRCQ（Visual）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.95 -1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eFSAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.89 -1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eAQ（Auditory）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.98 -1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eAQ（Visual）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.98 -1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing real-world data from 248 clinically diagnosed children with ADHD, this study systematically compared the performance of the SNAP-IV scale alone versus its combination with IVA-CPT indices in predicting cognitively high-risk status. The results indicate that the subjective symptom rating scales widely used in routine clinical practice have clear limitations in identifying cognitively high-risk children. When objective indices of attention and response control derived from IVA-CPT are added, the performance of the risk prediction model is substantially improved.\u003c/p\u003e\n\u003cp\u003eFirst, the correlation analysis showed no obvious associations between the SNAP-IV scores and the various IVA-CPT indices, suggesting that these two types of measures capture at least partly distinct psychological processes. SNAP-IV primarily reflects the frequency and severity of symptoms as observed by parents in everyday contexts, whereas IVA-CPT quantitatively assesses sustained attention, response inhibition, and alertness in a standardized experimental setting. The behavior of children with ADHD at home or in the classroom is additionally influenced by multiple factors, such as motivation, contextual support, emotional state, and parenting style [7, 8]. As a result, discrepancies or even \u0026ldquo;mismatches\u0026rdquo; may arise between subjective rating scales and objective performance-based tests. Integrating these two sources of information is therefore necessary to obtain a more comprehensive picture of the functional profile of children with ADHD.\u003c/p\u003e\n\u003cp\u003eSecond, from the perspective of predictive modeling, reliance on SNAP-IV total scores and demographic variables alone yielded an AUC close to chance level for identifying cognitively high-risk cases, which may lead to under-recognition of a subset of vulnerable children. In frontline clinical practice in China, due to constraints in time and resources, many children undergo only subjective rating scale assessments and do not receive further objective cognitive testing. This limitation may hinder early identification and intervention for children with comorbid learning difficulties or cognitive impairment [9, 10]. Our findings demonstrate that, when IVA-CPT indices of composite attention and response control are incorporated into the SNAP-based model, the AUC increases from approximately 0.612 to about 0.707, indicating that IVA-CPT provides additional and clinically meaningful information for the specific task of \u0026ldquo;identifying cognitively high-risk individuals within an ADHD cohort.\u0026rdquo;However, it is noteworthy that despite the improvement in AUC, the sensitivity of Model 2 decreased from 0.804 to 0.587, while specificity increased from 0.426 to 0.787. This change may reflect the previous sensitivity of subjective scales to \u0026quot;negative behaviors\u0026quot; \u0026mdash; when parents are anxious or score subjectively high, many normal children may be misclassified as high-risk. The inclusion of IVA-CPT helps mitigate this subjective bias, thereby improving the accuracy of low-risk identification, but at the cost of reduced sensitivity to high-risk individuals. Therefore, future research should consider optimizing the combination of variables, adjusting thresholds, or weight distributions to maximize sensitivity while maintaining high specificity, thus achieving more reliable screening for cognitively high-risk children.\u003c/p\u003e\n\u003cp\u003eThird, the present findings offer useful insights for the development of future multimodal assessment frameworks and digital screening systems. With the rapid advancement of artificial intelligence and wearable technologies, an increasing number of studies are attempting to integrate behavioral rating scales, cognitive task performance, physiological signals, and even neuroimaging data into unified models to achieve more accurate individualized prediction [11, 12]. SNAP-IV and IVA-CPT represent questionnaire-based and experimental-task\u0026ndash;based assessments, respectively, and this study constitutes an important step toward combining these two layers of information. In the future, it would be feasible to migrate such cognitive tasks to tablet-based or gamified environments to reduce the burden on children and caregivers, improve engagement and adherence, and facilitate the broader implementation of a \u0026ldquo;scale plus objective task\u0026rdquo; assessment pathway in community and school settings [13].\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the cross-sectional design allows us to examine only the associations between SNAP-IV/IVA indices and IQ at a single time point, and precludes inferences about causal relationships between changes in cognitive function and IQ development. Second, the sample was drawn from a single specialty outpatient clinic, which may introduce selection bias; for example, children presenting for care may have more severe symptoms or parents with stronger treatment-seeking motivation. The results therefore may not fully generalize to children with ADHD in community or regular school settings. Third, we did not include a control group, and thus were unable to evaluate the diagnostic validity of the combined SNAP\u0026ndash;IVA indicators for distinguishing children with ADHD from those without ADHD; our analyses were limited to risk stratification within the ADHD population. Future studies should conduct external validation in multicenter, large-sample cohorts and seek to incorporate additional objective biomarkers, such as electroencephalography, functional neuroimaging, and eye-tracking measures, in order to further enhance the robustness and interpretability of the models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study shows that, among children with ADHD, reliance on the SNAP-IV scale alone is insufficient for adequately identifying those at high risk of cognitive impairment. When IVA-CPT\u0026ndash;derived indices are added, both the AUC and the explanatory power of the prediction model increase markedly. Subjective behavioral rating scales and objective cognitive performance tests provide complementary information, and their combined use contributes to a more accurate and refined risk assessment framework. Future research should further validate this model in larger, multicenter samples and explore its application within digital screening platforms and in the development of individualized intervention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD: Attention-deficit/hyperactivity disorder\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDCA: Decision curve analysis\u003c/p\u003e\n\u003cp\u003eFSAQ: Full Scale Attention Quotient\u003c/p\u003e\n\u003cp\u003eFRCQ: Full Scale Response Control Quotient\u003c/p\u003e\n\u003cp\u003eIVA-CPT: Integrated Visual and Auditory Continuous Performance Test\u003c/p\u003e\n\u003cp\u003eIQ: Intelligence quotient\u003c/p\u003e\n\u003cp\u003eRCQ: Response Control Quotient\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSNAP-IV: Swanson, Nolan and Pelham Rating Scale, version IV\u003c/p\u003e\n\u003cp\u003eWISC: Wechsler Intelligence Scale for Children\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Capital Institute of Pediatrics (approval number: SHERLL2024039). Written informed consent was obtained from the parents or legal guardians of all participating children, and assent was obtained from children where appropriate.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any individual person\u0026rsquo;s identifiable data in any form (including images or videos). Consent for publication was therefore not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Beijing Natural Science Foundation Haidian Original Innovation Joint Fund (grant number:L232121), Capital\u0026apos;s Funds for Health Improvement and Research (grant number:2024-1-1131), New Quality Foundation of Capital Institute of Pediatrics (grant number:XZYB-2025-02) , High-level Talent Development Program Project of Haidian District Health and Wellness System(grant number:2025HDLJ005), Youth Talent Fund of Capital Institute of Pediatrics\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003egrant number:\u003cstrong\u003eYCYJ-2025-01)\u003c/strong\u003e. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZL and LW conceived and designed the study. JN,RL,YZ,CY,QA,QX,XW and YZcollected the data. NP, ZL,LZ, JW and BZ performed the statistical analyses and interpreted the results. ZL,NP and LW drafted the manuscript. All authors critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participating children and their families, as well as the clinical and research staff at the Children\u0026rsquo;s Health Center of Capital Medical University for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;Department of Child Health Care, Capital Center for Children\u0026apos;s Health, Capital Medical University, Beijing,100020, China; \u003csup\u003e2\u003c/sup\u003e Capital Institute of Pediatrics, Beijing, 100020, China;\u003csup\u003e3\u003c/sup\u003e Capital Institute of Pediatrics, Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College, Beijing, 100020, China; \u003csup\u003e4\u003c/sup\u003eCapital Institute of Pediatrics, Peking University Teaching Hospital, Beijing 100020,China; \u003csup\u003e*\u0026nbsp;\u003c/sup\u003eLin Zehong and Nan Peng contributed equally to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSalari N, Ghasemi H, Abdoli N, Rahmani A, Shiri MH, Hashemian AH, et al. The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis. Ital J Pediatr. 2023;49(1):48. doi: 10.1186/s13052-023-01456-1.\u003c/li\u003e\n\u003cli\u003eCrosbie J, Schachar R. Deficient inhibition as a marker for familial ADHD. Am J Psychiatry. 2001;158(11):1884-90. doi: 10.1176/appi.ajp.158.11.1884.\u003c/li\u003e\n\u003cli\u003eSibley MH, Graziano PA, Ortiz M, Rodriguez L, Coxe S. Academic impairment among high school students with ADHD: The role of motivation and goal-directed executive functions. J Sch Psychol. 2019;77:67-76. doi: 10.1016/j.jsp.2019.10.005.\u003c/li\u003e\n\u003cli\u003eFrench B, Nalbant G, Wright H, Sayal K, Daley D, Groom MJ, et al. The impacts associated with having ADHD: an umbrella review. Front Psychiatry. 2024;15:1343314. doi: 10.3389/fpsyt.2024.1343314.\u003c/li\u003e\n\u003cli\u003eFrazier TW, Demaree HA, Youngstrom EA. Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology. 2004;18(3):543-55. doi: 10.1037/0894-4105.18.3.543.\u003c/li\u003e\n\u003cli\u003eFernell E, Gillberg C. Borderline intellectual functioning. Handbook of clinical neurology. Elsevier; 2020. p. 77-81.\u003c/li\u003e\n\u003cli\u003eMoore DA, Russell AE, Matthews J, Ford TJ, Rogers M, Ukoumunne OC, et al. School‐based interventions for attention‐deficit/hyperactivity disorder: A systematic review with multiple synthesis methods. Review of Education. 2018;6(3):209-63. \u003c/li\u003e\n\u003cli\u003eLiu Q, Feng Y, Chen W, Zhu Y, Preece DA, Gao Y, et al. Emotion regulation strategy and its relationship with emotional dysregulation in children with attention-deficit/hyperactivity disorder: behavioral and brain findings. Eur Child Adolesc Psychiatry. 2025;34(7):2241-52. doi: 10.1007/s00787-025-02643-7.\u003c/li\u003e\n\u003cli\u003eJin YC, Li J, Tao FB, Sun Y, Kolbe L. National challenges and national actions to improve child and adolescent mental health in China. Asian J Psychiatr. 2025;110:104584. doi: 10.1016/j.ajp.2025.104584.\u003c/li\u003e\n\u003cli\u003eWolraich ML, Hagan JF, Jr., Allan C, Chan E, Davison D, Earls M, et al. Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Pediatrics. 2019;144(4). doi: 10.1542/peds.2019-2528.\u003c/li\u003e\n\u003cli\u003eCao M, Martin E, Li X. Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Transl Psychiatry. 2023;13(1):236. doi: 10.1038/s41398-023-02536-w.\u003c/li\u003e\n\u003cli\u003eAndrikopoulos D, Vassiliou G, Fatouros P, Tsirmpas C, Pehlivanidis A, Papageorgiou C. Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study. BMC Psychiatry. 2024;24(1):547. doi: 10.1186/s12888-024-05987-7.\u003c/li\u003e\n\u003cli\u003eLumsden J, Edwards EA, Lawrence NS, Coyle D, Munaf\u0026ograve; MR. Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy. JMIR Serious Games. 2016;4(2):e11. doi: 10.2196/games.5888. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Attention Deficit/Hyperactivity Disorder (ADHD), SNAP-IV, IVA-CPT, Low Cognitive Ability, Predictive Model","lastPublishedDoi":"10.21203/rs.3.rs-8626275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8626275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo investigate the value of the SNAP-IV scale combined with IVA-CPT objective attention/executive function indicators in identifying the risk of cognitive ability in children with ADHD, and to evaluate its incremental predictive ability over traditional subjective scales, providing a basis for clinical screening and stratified interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 248 children with ADHD (191 boys, 57 girls), with a mean age of 8.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68 years, were enrolled from the ADHD outpatient clinic at the Children's Health Center of Capital center for Children's Health, Capital Medical University. All participants completed the SNAP-IV scale, WISC and IVA-CPT Attention Test. Cognitive high-risk was defined by a Wechsler Full Scale IQ\u0026thinsp;\u0026lt;\u0026thinsp;85. A logistic regression model (Model 1) was constructed including only the SNAP-IV total score, age, sex, parental ADHD status, and whether the child was an only child. Model 2 was constructed by adding IVA-CPT composite scores for attention quotient, response control quotient, and auditory/visual attention and control quotients. The area under the curve (AUC) of the ROC curve and DCA curve was compared between the two models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe AUC of Model 1 was 0.612, with a sensitivity of 0.804, and specificity of 0.426, indicating that its predictive ability for cognitive high-risk was close to random. After incorporating IVA indicators, the AUC of Model 2 improved to 0.707, with a sensitivity of 0.587, and specificity of 0.787, which was significantly superior to Model 1 (p\u0026thinsp;=\u0026thinsp;0.034). DCA analysis further demonstrated that Model 2 offered more favorable guidance for decision-making at moderate-risk thresholds.Additionally, no significant correlation was found between the SNAP-IV score and any IVA dimension score, indicating that subjective symptom scales and objective attention function indicators assess different aspects of cognitive performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe SNAP-IV scale alone is insufficient for effectively identifying the cognitive high-risk individuals among children with ADHD. However, the addition of IVA-CPT attention and response control indicators significantly improves predictive performance. This study supports the use of a combined screening strategy of \"subjective behavioral scales\u0026thinsp;+\u0026thinsp;objective cognitive tests\" in ADHD assessments, which can help identify cognitive high-risk children early and facilitate early intervention.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Predicting Cognitive High Risk in Children with Attention-deficit/hyperactivity Disorder Using the SNAP-IV scale and IVA-CPT: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:34:25","doi":"10.21203/rs.3.rs-8626275/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df613093-ce07-4eef-944a-cb98afeb84a4","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T11:57:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:34:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8626275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8626275","identity":"rs-8626275","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}