\nDevelopment of the CAT-ANX:
A Computerized Adaptive Test for Anxiety<\/a><\/div>\nby Robert D. Gibbons, David J. Weiss, Paul A. Pilkonis, Ellen Frank, Tara Moore, Jong Bae Kim, and David J. Kupfer.<\/div>\nAmerican Journal of Psychiatry<\/font><\/strong>. published on-line Aug 9, 2013<\/div>\n<\/p>\n
Objective<\/font><\/strong>: The authors developed a computerized adaptive test for anxiety that decreases patient and clinician burden and increases measurement precision.<\/div>\nMethod<\/font><\/strong>: A total of 1,614 individuals with and without generalized anxiety disorder from a psychiatric clinic and community mental health center were recruited. The focus of the present study was the development of the Computerized Adaptive Testing–Anxiety Inventory [CAT-ANX]. The Structured Clinical Interview for DSM-IV was used to obtain diagnostic classifications of generalized anxiety disorder and major depressive disorder. <\/div>\nResults<\/font><\/strong>: An average of 12 items per subject was required to achieve a 0.3 standard error in the anxiety severity estimate and maintain a correlation of 0.94 with the total 431-item test score. CAT-ANX scores were strongly related to the probability of a generalized anxiety disorder diagnosis. Using both the Computerized Adaptive Testing–- Depression Inventory and the CAT-ANX, comorbid major depressive disorder and generalized anxiety disorder can be accurately predicted. <\/div>\nConclusions<\/font><\/strong>: Traditional measurement fixes the number of items but allows measurement uncertainty to vary. Computerized adaptive testing fixes measurement uncertainty and allows the number and content of items to vary, leading to a dramatic decrease in the number of items required for a fixed level of measurement uncertainty. Potential applications for inexpensive, efficient, and accurate screening of anxiety in primary care settings, clinical trials, psychiatric epidemiology, molecular genetics, children, and other cultures are discussed.<\/div>\n<\/blockquote>\n\nThe Computerized Adaptive Diagnostic Test for Major Depressive Disorder [CAD-MDD]:
A Screening Tool for Depression<\/font><\/strong><\/div>\nby Robert D. Gibbons, Giles Hooker, Matthew D. Finkelman, David J. Weiss, Paul A. Pilkonis, Ellen Frank, Tara Moore, and David J. Kupfer.<\/div>\nJournal of Clinical Psychiatry<\/font><\/strong>. 2013 74[7]:669–674.<\/div>\n<\/p>\n
Objective<\/font><\/strong>:To develop a computerized adaptive diagnostic screening tool for depression that decreases patient and clinician burden and increases sensitivity and specificity for clinician-based DSM-IV<\/span> diagnosis of major depressive disorder [MDD].<\/div>\nMethod<\/font><\/strong>: 656 individuals with and without minor and major depression were recruited from a psychiatric clinic and a community mental health center and through public announcements [controls without depression]. The focus of the study was the development of the Computerized Adaptive Diagnostic Test for Major Depressive Disorder [CAD-MDD] diagnostic screening tool based on a decision-theoretical approach [random forests and decision trees]. The item bank consisted of 88 depression scale items drawn from 73 depression measures. Sensitivity and specificity for predicting clinician-based Structured Clinical Interview for DSM-IV <\/span>Axis I Disorders diagnoses of MDD were the primary outcomes. Diagnostic screening accuracy was then compared to that of the Patient Health Questionnaire-9 [PHQ-9].<\/div>\nResults<\/font><\/strong>: An average of 4 items per participant was required [maximum of 6 items]. Overall sensitivity and specificity were 0.95 and 0.87, respectively. For the PHQ-9, sensitivity was 0.70 and specificity was 0.91.<\/div>\nConclusions<\/font><\/strong>: High sensitivity and reasonable specificity for a clinician-based DSM-IV<\/span> diagnosis of depression can be obtained using an average of 4 adaptively administered self-report items in less than 1 minute. Relative to the currently used PHQ-9, the CAD-MDD dramatically increased sensitivity while maintaining similar specificity. As such, the CAD-MDD will identify more true positives [lower false-negative rate] than the PHQ-9 using half the number of items. Inexpensive [relative to clinical assessment], efficient, and accurate screening of depression in the settings of primary care, psychiatric epidemiology, molecular genetics, and global health are all direct applications of the current system.<\/div>\n<\/blockquote>\n\nDevelopment of a computerized adaptive test for depression.<\/a><\/div>\nby Gibbons RD, Weiss DJ, Pilkonis PA, Frank E, Moore T, Kim JB, and Kupfer DJ.<\/div>\nArchives of General Psychiatry<\/font><\/strong>. 2012 69[11]:1104-12.<\/div>\n<\/p>\n
CONTEXT<\/font><\/strong> Unlike other areas of medicine, psychiatry is almost entirely dependent on patient report to assess the presence and severity of disease; therefore, it is particularly crucial that we find both more accurate and efficient means of obtaining that report. <\/div>\nOBJECTIVE<\/strong><\/font> To develop a computerized adaptive test [CAT] for depression, called the Computerized Adaptive Test-Depression Inventory [CAT-DI], that decreases patient and clinician burden and increases measurement precision.<\/div>\nMAIN OUTCOME MEASURES<\/font><\/strong> The focus of this study was the development of the CAT-DI. The 24-item Hamilton Rating Scale for Depression, Patient Health Questionnaire 9, and the Center for Epidemiologic Studies Depression Scale were used to study the convergent validity of the new measure, and the Structured Clinical Interview for DSM-IV was used to obtain diagnostic classifications of minor and major depressive disorder. <\/div>\nRESULTS<\/font><\/strong> A mean of 12 items per study participant was required to achieve a 0.3 SE in the depression severity estimate and maintain a correlation of r = 0.95 with the total 389-item test score. Using empirically derived thresholds based on a mixture of normal distributions, we found a sensitivity of 0.92 and a specificity of 0.88 for the classification of major depressive disorder in a sample consisting of depressed patients and healthy controls. Correlations on the order of r = 0.8 were found with the other clinician and self-rating scale scores. The CAT-DI provided excellent discrimination throughout the entire depressive severity continuum [minor and major depression], whereas the traditional scales did so primarily at the extremes [eg, major depression]. <\/div>\nCONCLUSIONS<\/font><\/strong> Traditional measurement fixes the number of items administered and allows measurement uncertainty to vary. In contrast, a CAT fixes measurement uncertainty and allows the number of items to vary. The result is a significant reduction in the number of items needed to measure depression and increased precision of measurement.<\/div>\n<\/blockquote>\nEver since Dr. Gibbons published his two-part meta-analysis opposing the black box warning on antidepressants, I’ve followed his publications with skepticism. As the title of my series [an anatomy of a deceit 1…<\/a>] says, I thought those publications were deliberately obfuscated and questionable. Similarly, I had plenty of complaints about his next paper in November last year on a computerized psychometric for depression [really?…<\/a>]. Since then, two more have appeared. One complaint is that this is obviously a commercial product [see their company’s web site, Adaptive Testing Technologies<\/a>]. So I’m not keen on the fact that it was developed funded by the NIMH or that these high impact journals are publishing what are essentially advertisements for their new product. Nor do I particularly like the idea of using such an instrument in doctor’s waiting rooms [or anywhere else] as a tool to troll for patients to medicate. But my further complaints are other than the ones mentioned so far.<\/div>\nPeople in physicians’ waiting rooms, particularly new patients, have reason to be either depressed or anxious. They’ve noticed something wrong, a symptom that’s frightened them enough to send them to a doctor. I worry that using some instrument to identify anxiety or depression prior to their visit is a vulnerability. It identifies them as "mental" and may lead a busy physician to jump to conclusions and assume the questionnaire\/computer data points to mental disorder rather than the expected emotional turmoil from a frightening symptom. We’ve all seen mental patients’ physical illnesses misdiagnosed repeatedly, and this kind of screening could create just such a problem. If a physician can’t identify clinically significant anxiety or depression without such an instrument, I think the proper course of action is for that doctor to take a refresher course in patient evaluation. <\/p>\n