1. Practitioner Review: Clinical utility of the QbTest for the assessment and diagnosis of attention‐deficit/hyperactivity disorder – a systematic review and meta‐analysis.
- Author
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Bellato, Alessio, Hall, Charlotte L., Groom, Madeleine J., Simonoff, Emily, Thapar, Anita, Hollis, Chris, and Cortese, Samuele
- Subjects
MEDICAL information storage & retrieval systems ,ATTENTION-deficit hyperactivity disorder ,RECEIVER operating characteristic curves ,RESEARCH funding ,RESEARCH methodology evaluation ,DECISION making in clinical medicine ,META-analysis ,CLASSIFICATION of mental disorders ,DESCRIPTIVE statistics ,SYSTEMATIC reviews ,MEDLINE ,NEUROPSYCHOLOGICAL tests ,COMPUTER-aided diagnosis ,ONLINE information services ,QUALITY assurance ,CONFIDENCE intervals ,SENSITIVITY & specificity (Statistics) ,PSYCHOLOGY information storage & retrieval systems ,MEDICAL triage ,EVALUATION - Abstract
Background: Several computerised cognitive tests (e.g. continuous performance test) have been developed to support the clinical assessment of attention‐deficit/hyperactivity disorder (ADHD). Here, we appraised the evidence‐base underpinning the use of one of these tests – the QbTest – in clinical practice, by conducting a systematic review and meta‐analysis investigating its accuracy and clinical utility. Methods: Based on a preregistered protocol (CRD42022377671), we searched PubMed, Medline, Ovid Embase, APA PsycINFO and Web of Science on 15th August 2022, with no language/type of document restrictions. We included studies reporting accuracy measures (e.g. sensitivity, specificity, or Area under the Receiver Operating Characteristics Curve, AUC) for QbTest in discriminating between people with and without DSM/ICD ADHD diagnosis. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS‐2). A generic inverse variance meta‐analysis was conducted on AUC scores. Pooled sensitivity and specificity were calculated using a random‐effects bivariate model in R. Results: We included 15 studies (2,058 participants; 48.6% with ADHD). QbTest Total scores showed acceptable, rather than good, sensitivity (0.78 [95% confidence interval: 0.69; 0.85]) and specificity (0.70 [0.57; 0.81]), while subscales showed low‐to‐moderate sensitivity (ranging from 0.48 [0.35; 0.61] to 0.65 [0.52; 0.75]) and moderate‐to‐good specificity (from 0.65 [0.48; 0.78] to 0.83 [0.60; 0.94]). Pooled AUC scores suggested moderate‐to‐acceptable discriminative ability (Q‐Total: 0.72 [0.57; 0.87]; Q‐Activity: 0.67 [0.58; 0.77); Q‐Inattention: 0.66 [0.59; 0.72]; Q‐Impulsivity: 0.59 [0.53; 0.64]). Conclusions: When used on their own, QbTest scores available to clinicians are not sufficiently accurate in discriminating between ADHD and non‐ADHD clinical cases. Therefore, the QbTest should not be used as stand‐alone screening or diagnostic tool, or as a triage system for accepting individuals on the waiting‐list for clinical services. However, when used as an adjunct to support a full clinical assessment, QbTest can produce efficiencies in the assessment pathway and reduce the time to diagnosis. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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