The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis.
Depression
Diagnostic accuracy
Meta-analysis
Patient Health Questionnaire-9
Screening
Journal
Psychotherapy and psychosomatics
ISSN: 1423-0348
Titre abrégé: Psychother Psychosom
Pays: Switzerland
ID NLM: 0024046
Informations de publication
Date de publication:
2020
2020
Historique:
received:
02
05
2019
accepted:
19
07
2019
pubmed:
9
10
2019
medline:
18
11
2020
entrez:
9
10
2019
Statut:
ppublish
Résumé
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. 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. 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. 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). The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.
Sections du résumé
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.
Identifiants
pubmed: 31593971
pii: 000502294
doi: 10.1159/000502294
pmc: PMC6960351
mid: NIHMS1059138
doi:
Types de publication
Journal Article
Meta-Analysis
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
25-37Subventions
Organisme : AHRQ HHS
ID : R36 HS018246
Pays : United States
Organisme : NIMH NIH HHS
ID : R34 MH072925
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH069666
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH073687
Pays : United States
Organisme : NIMH NIH HHS
ID : R34 MH084673
Pays : United States
Organisme : NCRR NIH HHS
ID : TL1 RR024135
Pays : United States
Organisme : Medical Research Council
ID : MR/J000914/1
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : F30 MH096664
Pays : United States
Organisme : MRF
ID : MRF_C0396
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL079235
Pays : United States
Organisme : NCIPC CDC HHS
ID : R49 CE002093
Pays : United States
Organisme : NIMHD NIH HHS
ID : T37 MD001449
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007356
Pays : United States
Organisme : NIMH NIH HHS
ID : K02 MH065919
Pays : United States
Organisme : NIMH NIH HHS
ID : R24 MH071604
Pays : United States
Informations de copyright
© 2019 S. Karger AG, Basel.
Références
BMJ. 2019 Apr 9;365:l1476
pubmed: 30967483
Psychother Psychosom. 2018;87(6):321-326
pubmed: 30269137
JAMA. 1999 Nov 10;282(18):1737-44
pubmed: 10568646
Ann Intern Med. 2011 Oct 18;155(8):529-36
pubmed: 22007046
Can J Psychiatry. 2005 Nov;50(13):851-6
pubmed: 16483120
Psychother Psychosom. 2013;82(3):187-8
pubmed: 23548877
Psychother Psychosom. 2012;81(6):333-43
pubmed: 22964522
Psychol Med. 1992 May;22(2):465-86
pubmed: 1615114
BMC Med. 2014 Jan 28;12:13
pubmed: 24472580
Psychol Med. 2001 Aug;31(6):1001-13
pubmed: 11513368
PLoS One. 2016 Feb 26;11(2):e0150067
pubmed: 26919313
BMJ. 2010 Feb 05;340:c221
pubmed: 20139215
Int J Clin Pract. 2012 Jan;66(1):11-5
pubmed: 22171900
J Gen Intern Med. 2001 Sep;16(9):606-13
pubmed: 11556941
Med Care. 2003 Nov;41(11):1284-92
pubmed: 14583691
Clin Epidemiol. 2017 Jul 06;9:355-365
pubmed: 28740432
Am Fam Physician. 2018 Oct 15;98(8):508-515
pubmed: 30277728
Stat Med. 2002 Jun 15;21(11):1539-58
pubmed: 12111919
Gen Hosp Psychiatry. 2015 Jan-Feb;37(1):67-75
pubmed: 25439733
J Psychosom Res. 2017 Jun;97:18-22
pubmed: 28606494
BMJ. 2011 Aug 18;343:d4825
pubmed: 21852353
JAMA. 2018 Jan 23;319(4):388-396
pubmed: 29362800
Br J Psychiatry. 2018 Jun;212(6):377-385
pubmed: 29717691
CMAJ. 2012 Mar 6;184(4):413-8
pubmed: 21930744
Stat Med. 2008 Dec 20;27(29):6111-36
pubmed: 18816508
Psychol Med. 1999 Sep;29(5):1013-20
pubmed: 10576294
CMAJ. 2013 Jun 11;185(9):775-82
pubmed: 23670157
BMC Psychiatry. 2015 Aug 05;15:190
pubmed: 26242577
Syst Rev. 2014 Oct 27;3:124
pubmed: 25348422
Scand J Prim Health Care. 2011 Jun;29(2):80-4
pubmed: 21542671
JAMA. 2016 Jan 26;315(4):380-7
pubmed: 26813211
BMJ. 2014 Feb 04;348:g1253
pubmed: 24496211
JAMA. 2015 Apr 28;313(16):1657-65
pubmed: 25919529
J Natl Compr Canc Netw. 2015 Oct;13(10):1203-11
pubmed: 26483060
Gen Hosp Psychiatry. 2017 Sep;48:25-31
pubmed: 28917391
Gen Hosp Psychiatry. 2015 Nov-Dec;37(6):567-76
pubmed: 26195347
Mayo Clin Proc. 2007 Nov;82(11):1395-402
pubmed: 17976360
Arch Gen Psychiatry. 1988 Dec;45(12):1069-77
pubmed: 2848472
Psychosom Med. 2002 Nov-Dec;64(6):897-905
pubmed: 12461195
Circulation. 2008 Oct 21;118(17):1768-75
pubmed: 18824640
Arch Gen Psychiatry. 1981 Apr;38(4):381-9
pubmed: 6260053
J Affect Disord. 2001 Oct;66(2-3):159-64
pubmed: 11578668