{"doi":"10.1136/bmjopen-2016-011913","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":"<h4>Introduction</h4>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.<h4>Methods and analysis</h4>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.<h4>Ethics and dissemination</h4>The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy.","journal":"BMJ Open","year":2016,"id":3224,"datarank":1.5756333676268022,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"self_citation_contribution":0.515098080672772,"citation_network_contribution":1.0605352869540303,"self_endowment_contribution":0.515098080672772,"citer_contribution":1.0605352869540303,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":23,"citers_with_citation_signal":21,"citers_with_endowment":21,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0402,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2016-04-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":2083,"name":"Andrea Benedetti","orcid":"0000-0002-8314-9497","position":1,"is_corresponding":false},{"id":2054,"name":"Lorie A. 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