Aim In this study, we have compared 229 Wechsler Adults Intelligence Scale - Fourth Edition (WAIS-IV) cognitive profiles of different severity adults with autism spectrum disorder to verify the impact of several variables including sex, age, level of education and autism severity level in an Italian sample. Moreover, we wanted to find out the optimal cut points for the major intelligence quotients in order to discriminate autism severity levels. Methods Participants were recruited from two National Health System Center in two different Italian regions and were assessed with gold-standard instruments as a part of their clinical evaluation. According to DSM-5, cognitive domains were also measured with multi-componential tests. We used the Italian adaptation of WAIS-IV. We checked our hypotheses using linear regression models and receiver operating characteristics (ROC) curves. Results Our results showed that age and level of education have a strong impact on Verbal Comprehension (VCI) and Working Memory Indexes (WMI). Gender differences are relevant when considering the VCI and Processing Speed index (PSI) in which women obtained the best performance. These differences are still relevant when considering cut points of ROC because 69 resulted to be the optimal cut point for women, 65 for men. Conclusions Few conclusions can be assumed only examining Full Scale Intelligence Quotient (FSIQ) scores as it includes many different information about broader cognitive abilities. Looking deeper at main indexes and their subtests findings are consistent with previous research on the disorder (moderate correlations of FSIQ, Perceptual Reasoning index, WMI and PSI with the participants' age), while other results are unforeseen (no effect of sex found on FSIQ score) or novel (significant effect of education on VCI and WMI). Using an algorithm predicting optimal cut point for discriminating through autism severity levels can help clinicians to better label and quantify the required help a person may need, a test cannot replace diagnostic and clinical evaluation by experienced clinicians.

Wechsler Intelligence Scale for Adults - Fourth Edition profiles of adults with autism spectrum disorder

Attanasio, M;Mazza, M;Valenti, M;Keller, R
2022-01-01

Abstract

Aim In this study, we have compared 229 Wechsler Adults Intelligence Scale - Fourth Edition (WAIS-IV) cognitive profiles of different severity adults with autism spectrum disorder to verify the impact of several variables including sex, age, level of education and autism severity level in an Italian sample. Moreover, we wanted to find out the optimal cut points for the major intelligence quotients in order to discriminate autism severity levels. Methods Participants were recruited from two National Health System Center in two different Italian regions and were assessed with gold-standard instruments as a part of their clinical evaluation. According to DSM-5, cognitive domains were also measured with multi-componential tests. We used the Italian adaptation of WAIS-IV. We checked our hypotheses using linear regression models and receiver operating characteristics (ROC) curves. Results Our results showed that age and level of education have a strong impact on Verbal Comprehension (VCI) and Working Memory Indexes (WMI). Gender differences are relevant when considering the VCI and Processing Speed index (PSI) in which women obtained the best performance. These differences are still relevant when considering cut points of ROC because 69 resulted to be the optimal cut point for women, 65 for men. Conclusions Few conclusions can be assumed only examining Full Scale Intelligence Quotient (FSIQ) scores as it includes many different information about broader cognitive abilities. Looking deeper at main indexes and their subtests findings are consistent with previous research on the disorder (moderate correlations of FSIQ, Perceptual Reasoning index, WMI and PSI with the participants' age), while other results are unforeseen (no effect of sex found on FSIQ score) or novel (significant effect of education on VCI and WMI). Using an algorithm predicting optimal cut point for discriminating through autism severity levels can help clinicians to better label and quantify the required help a person may need, a test cannot replace diagnostic and clinical evaluation by experienced clinicians.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/206560
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