title: 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.
abstract: BACKGROUND
  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
  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.
  
  
  BACKGROUND
  The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy.
authors.affiliation: 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.
authors.forename: Brett D
authors.initials: BD
authors.lastname: Thombs
authors.affiliation: 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.
authors.forename: Andrea
authors.initials: A
authors.lastname: Benedetti
authors.affiliation: Department of Libraries, Concordia University, Montreal, Québec, Canada.
authors.forename: Lorie A
authors.initials: LA
authors.lastname: Kloda
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
authors.forename: Brooke
authors.initials: B
authors.lastname: Levis
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
authors.forename: Marleine
authors.initials: M
authors.lastname: Azar
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.
authors.forename: Kira E
authors.initials: KE
authors.lastname: Riehm
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.
authors.forename: Nazanin
authors.initials: N
authors.lastname: Saadat
authors.affiliation: Department of Clinical, Neuro and Developmental Psychology and EMGO Institute, VU University Amsterdam, Amsterdam, The Netherlands.
authors.forename: Pim
authors.initials: P
authors.lastname: Cuijpers
authors.affiliation: Department of Health Sciences, Hull York Medical School, University of York, York, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: Gilbody
authors.affiliation: 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.
authors.forename: John P A
authors.initials: JP
authors.lastname: Ioannidis
authors.affiliation: Department of Health Sciences, Hull York Medical School, University of York, York, UK.
authors.forename: Dean
authors.initials: D
authors.lastname: McMillan
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada Department of Psychiatry, University of Calgary, Calgary, Edmonton, Canada.
authors.forename: Scott B
authors.initials: SB
authors.lastname: Patten
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
authors.forename: Ian
authors.initials: I
authors.lastname: Shrier
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Mathematics and Statistics, McGill University, Montreal, Québec, Canada.
authors.forename: Russell J
authors.initials: RJ
authors.lastname: Steele
authors.affiliation: Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
authors.forename: Roy C
authors.initials: RC
authors.lastname: Ziegelstein
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Oncology, McGill University, Montreal, Québec, Canada.
authors.forename: Carmen G
authors.initials: CG
authors.lastname: Loiselle
authors.affiliation: Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Clinical Neurosciences, University of Calgary, Calgary, Edmonton, Canada.
authors.forename: Melissa
authors.initials: M
authors.lastname: Henry
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada Department of Oncology, McGill University, Montreal, Québec, Canada.
authors.forename: Zahinoor
authors.initials: Z
authors.lastname: Ismail
authors.affiliation: Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Mitchell
authors.affiliation: Department of Medicine, University of Calgary, Calgary, Edmonton, Canada.
authors.forename: Marcello
authors.initials: M
authors.lastname: Tonelli
mesh_terms: D000328:Adult; D000368:Aged; D001007:Anxiety / Q000175:diagnosis; D003863:Depression / Q000175:diagnosis*; D003865:Major Depressive Disorder / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods / Q000592:standards*; D008875:Middle Aged; D011569:Psychiatric Status Rating Scales; D012016:Reference Values; D012107:Research Design; D000078202:Systematic Reviews as Topic; D015201:Meta-Analysis as Topic
keywords: Chronic illness; Diagnostic accuracy; Individual Patient Data Meta-Analysis; Major depression; PRIMARY CARE; Screening