1 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Psychiatry, McGill University, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada Department of Medicine, McGill University, Montreal, Québec, Canada Department of Educational and Counselling Psychology, McGill University, Montreal, Québec, Canada Department of Psychology, McGill University, Montreal, Québec, Canada.
2 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada Department of Medicine, McGill University, Montreal, Québec, Canada Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Québec, Canada.
3 Department of Libraries, Concordia University, Montreal, Québec, Canada.
4 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
5 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.
6 Department of Clinical, Neuro and Developmental Psychology and EMGO Institute, VU University Amsterdam, Amsterdam, The Netherlands.
7 Department of Health Sciences, Hull York Medical School, University of York, York, UK.
8 Department of Medicine, Health Research and Policy, Stanford Prevention Research Center, Stanford School of Medicine, Stanford, California, USA Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA.
9 Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada Department of Psychiatry, University of Calgary, Calgary, Edmonton, Canada.
10 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Mathematics and Statistics, McGill University, Montreal, Québec, Canada.
11 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
12 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Oncology, McGill University, Montreal, Québec, Canada.
13 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Clinical Neurosciences, University of Calgary, Calgary, Edmonton, Canada.
14 Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada Department of Oncology, McGill University, Montreal, Québec, Canada.
15 Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
16 Department of Medicine, University of Calgary, Calgary, Edmonton, Canada.

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.

BMJ Open. 2016 Apr 13;6(4):e011913

Authors: Thombs BD, Benedetti A, Kloda LA, Levis B, Azar M, Riehm KE, Saadat N, Cuijpers P, Gilbody S, Ioannidis JP, McMillan D, Patten SB, Shrier I, Steele RJ, Ziegelstein RC, Loiselle CG, Henry M, Ismail Z, Mitchell N, Tonelli M

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.

PMID: 27075844 [PubMed - indexed for MEDLINE]