학술논문
The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis
Document Type
Report
Author
He, Chen; Levis, Brooke; Riehm, Kira E.; Saadat, Nazanin; Levis, Alexander W.; Azar, Marleine; Rice, Danielle B.; Krishnan, Ankur; Wu, Yin; Sun, Ying; Imran, Mahrukh; Boruff, Jill; Cujipers, Pim; Gilbody, Simon; Ioannidis, John P.A.; Kloda, Lorie A.; McMillan, Dean; Patten, Scott B.; Shrier, Ian; Ziegelstein, Roy C.; Akena, Dickens H.; Arroll, Bruce; Ayalon, Liat; Baradaran, Hamid R.; Baron, Murray; Beraldi, Anna; Bombardier, Charles H.; Butterworth, Peter; Carter, Gregory; Chagas, Marcos Hortes Nisihara; Chan, Juliana C.N.; Cholera, Rushina; Clover, Kerrie; Conwell, Yeates; de Man-van Ginkel, Janneke M.; Fann, Jesse R.; Fischer, Felix H.; Fung, Daniel; Gelaye, Bizu; Goodyear-Smith, Felicity; Greeno, Catherine G.; Hall, Brian J.; Harrison, Patricia A.; Harter, Martin; Hegerl, Ulrich; Hides, Leanne; Hobfoll, Stevan E.; Hudson, Marie; Hyphantis, Thomas N.; Inagaki, Masatoshi; Ismail, Khalida; Jette, Nathalie; Khamseh, Mohammed E.; Kiely, Kim M.; Kwan, Yunxin; Lamers, Femke; Liu, Shen-Ing; Lotrakul, Manote; Loureiro, Sonia R.; Lowe, Bernd; Marsh, Laura; McGuire, Anthony; Mohd-Sidik, Sherina; Munhoz, Tiago N.; Muramatsu, Kumiko; Osorio, Flavia L.; Patel, Vikram; Pence, Brian W.; Persoons, Philippe; Picardi, Angelo; Reuter, Katrin; Rooney, Alasdair; da Silva dos Santos, Ina S.; Shaaban, Juwita; Sidebottom, Abbey; Simning, Adam; Stafford, Lesley; Sung, Sharon; Tan, Pei Lin Lynnette; Turner, Alyna; van Weert, Henk C.P.M.; White, Jennifer; Whooley, Mary A.; Winkley, Kirsty; Yamada, Mitsuhiko; Thombs, Brett D.; Benedetti, Andrea
Source
Psychotherapy and Psychosomatics. January 1, 2020, Vol. 89 Issue 1, p25, 13 p.
Subject
Language
English
ISSN
0033-3190
Abstract
Author(s): Chen He [a,b]; Brooke Levis [a,b]; Kira E. Riehm [a]; Nazanin Saadat [a]; Alexander W. Levis [a,b]; Marleine Azar [a,b]; Danielle B. Rice [a,c]; Ankur Krishnan [a]; Yin Wu [...]
Background: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results. Objective: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of [GreaterEqual]10. Methods: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview. Results: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of [GreaterEqual]10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88). Conclusions: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression. Keywords: Depression, Diagnostic accuracy, Meta-analysis, Patient Health Questionnaire-9, Screening
Background: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results. Objective: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of [GreaterEqual]10. Methods: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview. Results: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of [GreaterEqual]10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88). Conclusions: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression. Keywords: Depression, Diagnostic accuracy, Meta-analysis, Patient Health Questionnaire-9, Screening