Diagnostic accuracy of the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) for detecting major depression: protocol for a systematic review and individual patient data meta-analyses. Issue 4 (13th April 2016)

• Record Type: Journal Article
• Authors: Thombs, Brett D; Benedetti, Andrea; Kloda, Lorie A; Levis, Brooke; Azar, Marleine; Riehm, Kira E; Saadat, Nazanin; Cuijpers, Pim; Gilbody, Simon; Ioannidis, John P A; McMillan, Dean; Patten, Scott B; Shrier, Ian; Steele, Russell J; Ziegelstein, Roy C; Loiselle, Carmen G; Henry, Melissa; Ismail, Zahinoor; Mitchell, Nicholas; Tonelli, Marcello
• Is Part Of: BMJ open. Volume 6:Issue 4(2016)
• Publication Date: 2016-04-13
• Subjects: Screening -- Diagnostic accuracy -- Individual Patient Data Meta-Analysis -- Major depression -- PRIMARY CARE -- Chronic illness; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2016-011913
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: The Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) has been recommended for depression screening in medically ill patients. Many existing HADS-D studies have used exploratory methods to select optimal cut-offs. Often, these studies report results from a small range of cut-off thresholds; cut-offs with more favourable accuracy results are more likely to be reported than others with worse accuracy estimates. When published data are combined in meta-analyses, selective reporting may generate biased summary estimates. Individual patient data (IPD) meta-analyses can address this problem by estimating accuracy with data from all studies for all relevant cut-off scores. In addition, a predictive algorithm can be generated to estimate the probability that a patient has depression based on a HADS-D score and clinical characteristics rather than dichotomous screening classification alone. The primary objectives of our IPD meta-analyses are to determine the diagnostic accuracy of the HADS-D to detect major depression among adults across all potentially relevant cut-off scores and to generate a predictive algorithm for individual patients. We are already aware of over 100 eligible studies, and more may be identified with our comprehensive search. Methods and analysis: Data sources will include MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO and Web of Science. Eligible studies will have datasets where patients are assessed for major depression based on a validated structured or semistructured clinical interview and complete the HADS-D within 2 weeks (before or after). Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values, and a predictive algorithm for individual patients will be generated. Ethics and dissemination: The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy.