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  • 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

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Mental health
Protocol
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
  1. Brett D Thombs1,2,3,4,5,6,
  2. Andrea Benedetti3,4,7,
  3. Lorie A Kloda8,
  4. Brooke Levis1,3,
  5. Marleine Azar1,3,
  6. Kira E Riehm1,
  7. Nazanin Saadat1,
  8. Pim Cuijpers9,
  9. Simon Gilbody10,
  10. John P A Ioannidis11,12,
  11. Dean McMillan10,
  12. Scott B Patten13,14,
  13. Ian Shrier1,3,
  14. Russell J Steele1,15,
  15. Roy C Ziegelstein16,
  16. Carmen G Loiselle1,17,
  17. Melissa Henry1,18,
  18. Zahinoor Ismail13,17,
  19. Nicholas Mitchell19,
  20. Marcello Tonelli20
  1. 1Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada
  2. 2Department of Psychiatry, McGill University, Montreal, Québec, Canada
  3. 3Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada
  4. 4Department of Medicine, McGill University, Montreal, Québec, Canada
  5. 5Department of Educational and Counselling Psychology, McGill University, Montreal, Québec, Canada
  6. 6Department of Psychology, McGill University, Montreal, Québec, Canada
  7. 7Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Québec, Canada
  8. 8Department of Libraries, Concordia University, Montreal, Québec, Canada
  9. 9Department of Clinical, Neuro and Developmental Psychology and EMGO Institute, VU University Amsterdam, Amsterdam, The Netherlands
  10. 10Department of Health Sciences, Hull York Medical School, University of York, York, UK
  11. 11Department of Medicine, Health Research and Policy, Stanford Prevention Research Center, Stanford School of Medicine, Stanford, California, USA
  12. 12Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA
  13. 13Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada
  14. 14Department of Psychiatry, University of Calgary, Calgary, Edmonton, Canada
  15. 15Department of Mathematics and Statistics, McGill University, Montreal, Québec, Canada
  16. 16Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  17. 17Department of Oncology, McGill University, Montreal, Québec, Canada
  18. 18Department of Clinical Neurosciences, University of Calgary, Calgary, Edmonton, Canada
  19. 19Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
  20. 20Department of Medicine, University of Calgary, Calgary, Edmonton, Canada
  1. Correspondence to Dr Brett D Thombs; brett.thombs{at}mcgill.ca

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.

  • Screening
  • Diagnostic accuracy
  • Individual Patient Data Meta-Analysis
  • Major depression
  • PRIMARY CARE
  • Chronic illness

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

http://dx.doi.org/10.1136/bmjopen-2016-011913

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