The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis
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The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis. / 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; Cuijpers, 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; Härter, Martin; Hegerl, Ulrich; Hides, Leanne; Hobfoll, Stevan E; Hudson, Marie; Hyphantis, Thomas N; Inagaki, Masatoshi; Ismail, Khalida; Jetté, Nathalie; Khamseh, Mohammad E; Kiely, Kim M; Kwan, Yunxin; Lamers, Femke; Liu, Shen-Ing; Lotrakul, Manote; Loureiro, Sonia R; Löwe, Bernd; Marsh, Laura; McGuire, Anthony; Mohd-Sidik, Sherina; Munhoz, Tiago N; Muramatsu, Kumiko; Osório, Flávia L; Patel, Vikram; Pence, Brian W; Persoons, Philippe; Picardi, Angelo; Reuter, Katrin; Rooney, Alasdair G; da Silva Dos Santos, Iná 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.
in: PSYCHOTHER PSYCHOSOM, Jahrgang 89, Nr. 1, 2020, S. 25-37.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis
AU - He, Chen
AU - Levis, Brooke
AU - Riehm, Kira E
AU - Saadat, Nazanin
AU - Levis, Alexander W
AU - Azar, Marleine
AU - Rice, Danielle B
AU - Krishnan, Ankur
AU - Wu, Yin
AU - Sun, Ying
AU - Imran, Mahrukh
AU - Boruff, Jill
AU - Cuijpers, Pim
AU - Gilbody, Simon
AU - Ioannidis, John P A
AU - Kloda, Lorie A
AU - McMillan, Dean
AU - Patten, Scott B
AU - Shrier, Ian
AU - Ziegelstein, Roy C
AU - Akena, Dickens H
AU - Arroll, Bruce
AU - Ayalon, Liat
AU - Baradaran, Hamid R
AU - Baron, Murray
AU - Beraldi, Anna
AU - Bombardier, Charles H
AU - Butterworth, Peter
AU - Carter, Gregory
AU - Chagas, Marcos Hortes Nisihara
AU - Chan, Juliana C N
AU - Cholera, Rushina
AU - Clover, Kerrie
AU - Conwell, Yeates
AU - de Man-van Ginkel, Janneke M
AU - Fann, Jesse R
AU - Fischer, Felix H
AU - Fung, Daniel
AU - Gelaye, Bizu
AU - Goodyear-Smith, Felicity
AU - Greeno, Catherine G
AU - Hall, Brian J
AU - Harrison, Patricia A
AU - Härter, Martin
AU - Hegerl, Ulrich
AU - Hides, Leanne
AU - Hobfoll, Stevan E
AU - Hudson, Marie
AU - Hyphantis, Thomas N
AU - Inagaki, Masatoshi
AU - Ismail, Khalida
AU - Jetté, Nathalie
AU - Khamseh, Mohammad E
AU - Kiely, Kim M
AU - Kwan, Yunxin
AU - Lamers, Femke
AU - Liu, Shen-Ing
AU - Lotrakul, Manote
AU - Loureiro, Sonia R
AU - Löwe, Bernd
AU - Marsh, Laura
AU - McGuire, Anthony
AU - Mohd-Sidik, Sherina
AU - Munhoz, Tiago N
AU - Muramatsu, Kumiko
AU - Osório, Flávia L
AU - Patel, Vikram
AU - Pence, Brian W
AU - Persoons, Philippe
AU - Picardi, Angelo
AU - Reuter, Katrin
AU - Rooney, Alasdair G
AU - da Silva Dos Santos, Iná S
AU - Shaaban, Juwita
AU - Sidebottom, Abbey
AU - Simning, Adam
AU - Stafford, Lesley
AU - Sung, Sharon
AU - Tan, Pei Lin Lynnette
AU - Turner, Alyna
AU - van Weert, Henk C P M
AU - White, Jennifer
AU - Whooley, Mary A
AU - Winkley, Kirsty
AU - Yamada, Mitsuhiko
AU - Thombs, Brett D
AU - Benedetti, Andrea
N1 - © 2019 S. Karger AG, Basel.
PY - 2020
Y1 - 2020
N2 - 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 ≥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 ≥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.
AB - 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 ≥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 ≥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.
U2 - 10.1159/000502294
DO - 10.1159/000502294
M3 - SCORING: Journal article
C2 - 31593971
VL - 89
SP - 25
EP - 37
JO - PSYCHOTHER PSYCHOSOM
JF - PSYCHOTHER PSYCHOSOM
SN - 0033-3190
IS - 1
ER -