Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
14 Mar 2024
14 Mar 2024
Historique:
received:
16
08
2023
accepted:
27
02
2024
revised:
23
02
2024
medline:
15
3
2024
pubmed:
15
3
2024
entrez:
15
3
2024
Statut:
aheadofprint
Résumé
Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.
Identifiants
pubmed: 38486050
doi: 10.1038/s41380-024-02500-0
pii: 10.1038/s41380-024-02500-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01MH121478
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Références
GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:P1204–22.
doi: 10.1016/S0140-6736(20)30925-9
Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, et al. Time for united action on depression: a lancet-world psychiatric association commission. Lancet. 2022;399:957–1022.
pubmed: 35180424
doi: 10.1016/S0140-6736(21)02141-3
Olfson M, Blanco C, Marcus SC. Treatment of adult depression in the United States. JAMA Intern Med. 2016;176:1482–91.
pubmed: 27571438
doi: 10.1001/jamainternmed.2016.5057
Cuijpers P, Miguel C, Harrer M, Plessen CY, Ciharova M, Ebert D, et al. Cognitive behavior therapy vs. control conditions, other psychotherapies, pharmacotherapies and combined treatment for depression: A comprehensive meta-analysis including 409 trials with 52,702 patients. World Psychiatry. 2023;22:105–15.
pubmed: 36640411
pmcid: 9840507
doi: 10.1002/wps.21069
Zainal NH. Is combined antidepressant medication (ADM) and psychotherapy better than either monotherapy at preventing suicide attempts and other psychiatric serious adverse events for depressed patients? A rare events meta-analysis. Psychol Med. Online ahead of print 15 November 2023. https://doi.org/10.1017/s0033291723003306 .
McHugh RK, Whitton SW, Peckham AD, Welge JA, Otto MW. Patient preference for psychological vs pharmacologic treatment of psychiatric disorders: a meta-analytic review. J Clin Psychiatry. 2013;74:595–602.
pubmed: 23842011
pmcid: 4156137
doi: 10.4088/JCP.12r07757
Goodwin RD, Dierker LC, Wu M, Galea S, Hoven CW, Weinberger AH. Trends in U.S. depression prevalence from 2015 to 2020: the widening treatment gap. Am J Prev Med. 2022;63:726–33.
pubmed: 36272761
pmcid: 9483000
doi: 10.1016/j.amepre.2022.05.014
Ross EL, Vijan S, Miller EM, Valenstein M, Zivin K. The cost-effectiveness of cognitive behavioral therapy versus second-generation antidepressants for initial treatment of major depressive disorder in the United States: a decision analytic model. Ann Intern Med. 2019;171:785–95.
pubmed: 31658472
pmcid: 7188559
doi: 10.7326/M18-1480
Cohen ZD, DeRubeis RJ. Treatment selection in depression. Annu Rev Clin Psychol. 2018;14:209–36.
pubmed: 29494258
doi: 10.1146/annurev-clinpsy-050817-084746
Driessen E, Dekker JJM, Peen J, Van HL, Maina G, Rosso G, et al. The efficacy of adding short-term psychodynamic psychotherapy to antidepressants in the treatment of depression: a systematic review and meta-analysis of individual participant data. Clin Psychol Rev. 2020;80:101886.
pubmed: 32650213
doi: 10.1016/j.cpr.2020.101886
Driessen E, Fokkema M, Dekker JJM, Peen J, Van HL, Maina G, et al. Which patients benefit from adding short-term psychodynamic psychotherapy to antidepressants in the treatment of depression? A systematic review and meta-analysis of individual participant data. Psychol Med. 2023;53:6090–101.
pubmed: 36404677
doi: 10.1017/S0033291722003270
Kessler RC, van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA, Ebert DD, et al. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci. 2017;26:22–36.
pubmed: 26810628
doi: 10.1017/S2045796016000020
Kraus C, Kadriu B, Lanzenberger R, Zarate CA Jr., Kasper S. Prognosis and improved outcomes in major depression: a review. Transl Psychiatry. 2019;9:127.
pubmed: 30944309
pmcid: 6447556
doi: 10.1038/s41398-019-0460-3
Maj M, Stein DJ, Parker G, Zimmerman M, Fava GA, De Hert M, et al. The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry. 2020;19:269–93.
pubmed: 32931110
pmcid: 7491646
doi: 10.1002/wps.20771
Perna G, Alciati A, Daccò S, Grassi M, Caldirola D. Personalized psychiatry and depression: the role of sociodemographic and clinical variables. Psychiatry Investig. 2020;17:193–206.
pubmed: 32160691
pmcid: 7113177
doi: 10.30773/pi.2019.0289
Cohen ZD, DeRubeis RJ, Hayes R, Watkins ER, Lewis G, Byng R, et al. The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication. Clin Psychol Sci. 2023;11:59–76.
pubmed: 36698442
doi: 10.1177/21677026221076832
DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS One. 2014;9:e83875.
pubmed: 24416178
pmcid: 3885521
doi: 10.1371/journal.pone.0083875
Elkin I, Shea MT, Watkins JT, Imber SD, Sotsky SM, Collins JF, et al. National Institute of Mental Health treatment of depression collaborative research program. General effectiveness of treatments. Arch Gen Psychiatry. 1989;46:971–82.
pubmed: 2684085
doi: 10.1001/archpsyc.1989.01810110013002
Vittengl JR, Clark AL, Thase ME, Jarrett RB. Initial Steps to inform selection of continuation cognitive therapy or fluoxetine for higher risk responders to cognitive therapy for recurrent major depressive disorder. Psychiatry Res. 2017;253:174–81.
pubmed: 28388454
pmcid: 5481171
doi: 10.1016/j.psychres.2017.03.032
Wallace ML, Frank E, Kraemer HC. A novel approach for developing and interpreting treatment moderator profiles in randomized clinical trials. JAMA Psychiatry. 2013;70:1241–7.
pubmed: 24048258
pmcid: 10289205
doi: 10.1001/jamapsychiatry.2013.1960
Huibers MJ, van Breukelen G, Roelofs J, Hollon SD, Markowitz JC, van Os J, et al. Predicting response to cognitive therapy and interpersonal therapy, with or without antidepressant medication, for major depression: a pragmatic trial in routine practice. J Affect Disord. 2014;152-154:146–54.
pubmed: 24060588
doi: 10.1016/j.jad.2013.08.027
Qiu X, Wang Y. Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups. Stat Med. 2019;38:2632–51.
pubmed: 30891797
pmcid: 8548070
doi: 10.1002/sim.8105
Gunter L, Zhu J, Murphy SA. Variable selection for qualitative interactions. Stat Methodol. 2011;1:42–55.
pubmed: 21179592
pmcid: 3003934
doi: 10.1016/j.stamet.2009.05.003
Laber EB, Zhao YQ. Tree-based methods for individualized treatment regimes. Biometrika. 2015;102:501–14.
pubmed: 26893526
doi: 10.1093/biomet/asv028
Lorenzo-Luaces L, DeRubeis RJ, van Straten A, Tiemens B. A prognostic index (PI) as a moderator of outcomes in the treatment of depression: a proof of concept combining multiple variables to inform risk-stratified stepped care models. J Affect Disord. 2017;213:78–85.
pubmed: 28199892
doi: 10.1016/j.jad.2017.02.010
Lorenzo-Luaces L, Rodriguez-Quintana N, Riley TN, Weisz JR. A placebo prognostic index (PI) as a moderator of outcomes in the treatment of adolescent depression: could it inform risk-stratification in treatment with cognitive-behavioral therapy, fluoxetine, or their combination? Psychother Res. 2021;31:5–18.
pubmed: 32223373
doi: 10.1080/10503307.2020.1747657
Nemeroff CB, Heim CM, Thase ME, Klein DN, Rush AJ, Schatzberg AF, et al. Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma. Proc Natl Acad Sci USA. 2003;100:14293–6.
pubmed: 14615578
pmcid: 283585
doi: 10.1073/pnas.2336126100
Song R, Kosorok M, Zeng D, Zhao Y, Laber E, Yuan M. On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning. Stat. 2015;4:59–68.
pubmed: 25883393
pmcid: 4394905
doi: 10.1002/sta4.78
Zhao Y, Zeng D, Rush AJ, Kosorok MR. Estimating individualized treatment rules using outcome weighted learning. J Am Stat Assoc. 2012;107:1106–18.
pubmed: 23630406
pmcid: 3636816
doi: 10.1080/01621459.2012.695674
Luedtke A, Sadikova E, Kessler RC. Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clin Psychol Sci. 2019;7:445–61.
doi: 10.1177/2167702618815466
VanderWeele TJ, Luedtke AR, van der Laan MJ, Kessler RC. Selecting optimal subgroups for treatment using many covariates. J Epidemiol. 2019;30:334–41.
doi: 10.1097/EDE.0000000000000991
Ashrafioun L, Pigeon WR, Conner KR, Leong SH, Oslin DW. Prevalence and correlates of suicidal ideation and suicide attempts among veterans in primary care referred for a mental health evaluation. J Affect Disord. 2016;189:344–50.
pubmed: 26474375
doi: 10.1016/j.jad.2015.09.014
Trivedi RB, Post EP, Sun H, Pomerantz A, Saxon AJ, Piette JD, et al. Prevalence, comorbidity, and prognosis of mental health among US Veterans. Am J Public Health. 2015;105:2564–9.
pubmed: 26474009
pmcid: 4638236
doi: 10.2105/AJPH.2015.302836
Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023;7:719–42.
pubmed: 37380750
pmcid: 10632090
doi: 10.1038/s41551-023-01056-8
Kessler RC, Luedtke A. Pragmatic precision psychiatry: a new direction for optimizing treatment selection. JAMA Psychiatry. 2021;78:1384–90.
pubmed: 34550327
doi: 10.1001/jamapsychiatry.2021.2500
U.S. Department of Veterans Affairs. Corporate Data Warehouse (CDW): U.S. Department of Veterans Affairs; 2023 [updated 2023 Jan 11; cited 2023 Jun 26]. Available from: https://www.hsrd.research.va.gov/for_researchers/cdw.cfm .
Hoffmire C, Stephens B, Morley S, Thompson C, Kemp J, Bossarte RM. VA suicide prevention applications network: a national healthcare system-based suicide event tracking system. Public Health Rep. 2016;131:816–21.
pubmed: 28123228
pmcid: 5230828
doi: 10.1177/0033354916670133
U.S. Centers for Disease Control and Prevention. National Death Index: U.S. Centers for Disease Control and Prevention; 2022 [updated 2022 Jan 10; cited 2023 Jun 26]. Available from: https://www.cdc.gov/nchs/ndi/index.htm .
Du S, Yao J, Shen GC, Lin B, Udo T, Hastings J, et al. Social drivers of mental health: A U.S. study using machine learning. Am J Prev Med. 2023;65:827–34.
pubmed: 37286016
doi: 10.1016/j.amepre.2023.05.022
Kent DM. Overall average treatment effects from clinical trials, one-variable-at-a-time subgroup analyses and predictive approaches to heterogeneous treatment effects: toward a more patient-centered evidence-based medicine. Clin Trials 2023;20:328–37.
pubmed: 37148125
doi: 10.1177/17407745231171897
van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6:Article25.
pubmed: 17910531
Polley E, LeDell E, Kennedy C, Lendle S, van der Laan M. SuperLearner: Super Learner Prediction: The Comprehensive R Archive Network; 2021 [updated 2021 May 10; cited 2023 Jun 26]. Available from: https://cran.r-project.org/web/packages/SuperLearner/index.html .
Funk MJ, Westreich D, Wiesen C, Stürmer T, Brookhart MA, Davidian M. Doubly robust estimation of causal effects. Am J Epidemiol. 2011;173:761–7.
pubmed: 21385832
pmcid: 3070495
doi: 10.1093/aje/kwq439
Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ. 2019;367:l5657.
pubmed: 31645336
doi: 10.1136/bmj.l5657
Breiman L. Random forests. Mach Learn. 2001;45:5–32.
doi: 10.1023/A:1010933404324
van der Laan M, Gruber S. Working Paper 290: Targeted minimum loss based estimation of an intervention specific mean outcome: U.C. Berkeley Division of biostatistics working paper series; 2011 [updated 2011 Aug; cited 2023 Jun 26]. Available from: https://biostats.bepress.com/ucbbiostat/paper290/ .
Coyle J tmle3: The Extensible TMLE framework: tlverse; 2021 [cited 2023 Jun 26]. Available from: https://tlverse.org/tmle3/ .
Athey S, Tibshirani R, Wager S. Generalized random forests. Ann Stat. 2019;47:1179–203.
doi: 10.1214/18-AOS1709
Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2018;113:1228–42.
doi: 10.1080/01621459.2017.1319839
Tibshirani J, Athey S, Friedberg R, Hadad V, Hirshberg D, Miner L, et al. Package ‘grf’: Generalized Random Forests 2022 [updated 2022 Dec 15; cited 2023 Jun 26]. Available from: https://cran.r-project.org/web/packages/grf/grf.pdf .
Lundberg S, Lee SI. A unified approach to interpreting model predictions. 31st International Conference on Neural Information Processing Systems; Long Beach, California, USA; December 4 - 9, 2017.
Greenwell B. fastshap: Fast Approximate Shapley Values version 0.0.7 2021 [updated 2021 Dec 6; cited 2023 Jun 26]. Available from: https://cran.r-project.org/web/packages/fastshap/index.html .
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD statement. Ann Intern Med. 2015;162:55–63.
pubmed: 25560714
doi: 10.7326/M14-0697
Penfold RB, Johnson E, Shortreed SM, Ziebell RA, Lynch FL, Clarke GN, et al. Predicting suicide attempts and suicide deaths among adolescents following outpatient visits. J Affect Disord. 2021;294:39–47.
pubmed: 34265670
pmcid: 8820270
doi: 10.1016/j.jad.2021.06.057
Kessler RC, Stein MB, Petukhova MV, Bliese P, Bossarte RM, Bromet EJ, et al. Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Mol Psychiatry. 2017;22:544–51.
pubmed: 27431294
doi: 10.1038/mp.2016.110
Malhi GS, Bell E, Boyce P, Bassett D, Berk M, Bryant R, et al. The 2020 Royal Australian and New Zealand College of psychiatrists clinical practice guidelines for mood disorders: bipolar disorder summary. Bipolar Disord. 2020;22:805–21.
pubmed: 33296123
doi: 10.1111/bdi.13036
McIntyre RS, Rosenblat JD, Nemeroff CB, Sanacora G, Murrough JW, Berk M, et al. Synthesizing the evidence for ketamine and esketamine in treatment-resistant depression: an international expert opinion on the available evidence and implementation. Am J Psychiatry. 2021;178:383–99.
pubmed: 33726522
pmcid: 9635017
doi: 10.1176/appi.ajp.2020.20081251
McIntyre RS, Suppes T, Tandon R, Ostacher M. Florida best practice psychotherapeutic medication guidelines for adults with major depressive disorder. J Clin Psychiatry. 2017;78:703–13.
pubmed: 28682531
doi: 10.4088/JCP.16cs10885
Ross EL, Zivin K, Maixner DF. Cost-effectiveness of electroconvulsive therapy vs. pharmacotherapy/psychotherapy for treatment-resistant depression in the United States. JAMA Psychiatry. 2018;75:713–22.
pubmed: 29800956
pmcid: 6145669
doi: 10.1001/jamapsychiatry.2018.0768
Reti IM. A rational insurance coverage policy for repetitive transcranial magnetic stimulation for major depression. J Ect. 2013;29:e27–e28.
pubmed: 23446702
doi: 10.1097/YCT.0b013e3182801cd7
Delgadillo J, Ali S, Fleck K, Agnew C, Southgate A, Parkhouse L, et al. Stratified care vs. stepped care for depression: a cluster randomized clinical trial. JAMA Psychiatry. 2022;79:101–8.
pubmed: 34878526
doi: 10.1001/jamapsychiatry.2021.3539
Browne G, Steiner M, Roberts J, Gafni A, Byrne C, Dunn E, et al. Sertraline and/or interpersonal psychotherapy for patients with dysthymic disorder in primary care: 6-month comparison with longitudinal 2-year follow-up of effectiveness and costs. J Affect Disord. 2002;68:317–30.
pubmed: 12063159
doi: 10.1016/S0165-0327(01)00343-3
Schramm E, van Calker D, Dykierek P, Lieb K, Kech S, Zobel I, et al. An intensive treatment program of interpersonal psychotherapy plus pharmacotherapy for depressed inpatients: acute and long-term results. Am J Psychiatry. 2007;164:768–77.
pubmed: 17475736
doi: 10.1176/ajp.2007.164.5.768
Riggs PD, Mikulich-Gilbertson SK, Davies RD, Lohman M, Klein C, Stover SK. A randomized controlled trial of fluoxetine and cognitive behavioral therapy in adolescents with major depression, behavior problems, and substance use disorders. Arch Pediatr Adolesc Med. 2007;161:1026–34.
pubmed: 17984403
doi: 10.1001/archpedi.161.11.1026
Hollon SD, DeRubeis RJ, Evans MD, Wiemer MJ, Garvey MJ, Grove WM, et al. Cognitive therapy and pharmacotherapy for depression. Singly and in combination. Arch Gen Psychiatry. 1992;49:774–81.
pubmed: 1417429
doi: 10.1001/archpsyc.1992.01820100018004
Vitiello B, Silva SG, Rohde P, Kratochvil CJ, Kennard BD, Reinecke MA, et al. Suicidal events in the treatment for adolescents with depression study (TADS). J Clin Psychiatry. 2009;70:741–7.
pubmed: 19552869
pmcid: 2702701
doi: 10.4088/JCP.08m04607
Molnar C. Interpretable machine learning: a guide for making black box models explainable. Christoph Mulnar: Munich, Germany, 2022.
Meehan AJ, Lewis SJ, Fazel S, Fusar-Poli P, Steyerberg EW, Stahl D, et al. Clinical prediction models in psychiatry: A systematic review of two decades of progress and challenges. Mol Psychiatry. 2022;27:2700–8.
pubmed: 35365801
pmcid: 9156409
doi: 10.1038/s41380-022-01528-4
Furman JL, Trivedi MH. Chapter 29 - Biomarker-based treatment selection: A precision medicine approach for depression. In: Quevedo J, Carvalho AF, Zarate CA, editors. Neurobiology of Depression. Academic Press: Cambridge, MA, 2019, pp 331–40.
Glasgow RE, Kwan BM, Matlock DD. Realizing the full potential of precision health: the need to include patient-reported health behavior, mental health, social determinants, and patient preferences data. J Clin Transl Sci. 2018;2:183–5.
pubmed: 30370072
pmcid: 6202010
doi: 10.1017/cts.2018.31
Leung LB, Ziobrowski HN, Puac-Polanco V, Bossarte RM, Bryant C, Keusch J, et al. Are veterans getting their preferred depression treatment? A national observational study in the Veterans Health Administration. J Gen Intern Med. 2022;37:3235–41.
pubmed: 34613577
doi: 10.1007/s11606-021-07136-2
Delevry D, Le QA. Effect of treatment preference in randomized controlled trials: Systematic review of the literature and meta-analysis. Patient. 2019;12:593–609.
pubmed: 31372909
doi: 10.1007/s40271-019-00379-6
Windle E, Tee H, Sabitova A, Jovanovic N, Priebe S, Carr C. Association of patient treatment preference with dropout and clinical outcomes in adult psychosocial mental health interventions: a systematic review and meta-analysis. JAMA Psychiatry. 2020;77:294–302.
pubmed: 31799994
doi: 10.1001/jamapsychiatry.2019.3750
Maslej MM, Furukawa TA, Cipriani A, Andrews PW, Sanches M, Tomlinson A, et al. Individual differences in response to antidepressants: a meta-analysis of placebo-controlled randomized clinical trials. JAMA Psychiatry. 2021;78:490–7.
pubmed: 33595620
doi: 10.1001/jamapsychiatry.2020.4564
Kamenov K, Twomey C, Cabello M, Prina AM, Ayuso-Mateos JL. The efficacy of psychotherapy, pharmacotherapy and their combination on functioning and quality of life in depression: a meta-analysis. Psychol Med. 2017;47:414–25.
pubmed: 27780478
doi: 10.1017/S0033291716002774
Guo Z, Cheng J, Lorch SA, Small DS. Using an instrumental variable to test for unmeasured confounding. Stat Med. 2014;33:3528–46.
pubmed: 24930696
pmcid: 4145076
doi: 10.1002/sim.6227
Vertosick EA, Assel M, Vickers AJ. A systematic review of instrumental variable analyses using geographic region as an instrument. Cancer Epidemiol. 2017;51:49–55.
pubmed: 29035744
pmcid: 5700852
doi: 10.1016/j.canep.2017.10.005
U.S. Department of Veterans Affairs. Primary Care-Mental Health Integration (PC-MHI) 2022 [updated 2022 Sep 19. Available from: https://www.patientcare.va.gov/primarycare/PCMHI.asp .
Brookhart MA, Schneeweiss S. Preference-based instrumental variable methods for the estimation of treatment effects: assessing validity and interpreting results. Int J Biostat. 2007;3:Article 14.
pubmed: 19655038
doi: 10.2202/1557-4679.1072
Davies NM, Gunnell D, Thomas KH, Metcalfe C, Windmeijer F, Martin RM. Physicians’ prescribing preferences were a potential instrument for patients’ actual prescriptions of antidepressants. J Clin Epidemiol. 2013;66:1386–96.
pubmed: 24075596
pmcid: 3824069
doi: 10.1016/j.jclinepi.2013.06.008
Ertefaie A, Small DS, Flory JH, Hennessy S. A tutorial on the use of instrumental variables in pharmacoepidemiology. Pharmacoepidemiol Drug Saf. 2017;26:357–67.
pubmed: 28239929
doi: 10.1002/pds.4158
Swanson SA, Miller M, Robins JM, Hernán MA. Definition and evaluation of the monotonicity condition for preference-based instruments. J Epidemiol. 2015;26:414–20.
doi: 10.1097/EDE.0000000000000279
Qiu H, Carone M, Sadikova E, Petukhova M, Kessler RC, Luedtke A. Optimal individualized decision rules using instrumental variable methods. J Am Stat Assoc. 2021;116:174–91.
pubmed: 33731969
doi: 10.1080/01621459.2020.1745814
Adekkanattu P, Sholle ET, DeFerio J, Pathak J, Johnson SB, Campion TR Jr. Ascertaining depression severity by extracting Patient Health Questionnaire-9 (PHQ-9) scores from clinical notes. AMIA Annu Symp Proc. 2018;2018:147–56.
pubmed: 30815052
pmcid: 6371338
Xu Z, Vekaria V, Wang F, Cukor J, Su C, Adekkanattu P, et al. Using machine learning to predict antidepressant treatment outcome from electronic health records. Psychiatr Res Clin Pract. 2023;5:118–25.
pubmed: 38077277
pmcid: 10698704
doi: 10.1176/appi.prcp.20220015
Han S, Zhang RF, Shi L, Richie R, Liu H, Tseng A, et al. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing. J Biomed Inform. 2022;127:103984.
pubmed: 35007754
doi: 10.1016/j.jbi.2021.103984
Wang G, Yang G, Du Z, Fan L, Li X ClinicalGPT: Large language models finetuned with diverse medical data and comprehensive evaluation. arXiv. 2023; e-pub ahead of print 16 June 2023; https://doi.org/10.48550/arXiv.2306.0996 .
U.S. Department of Veterans Affairs. VA Informatics and Computing Infrastructure (VINCI): U.S. Department of Veterans Affairs; 2022 [updated 2022 March 16; cited 2023 Jun 27]. Available from: https://www.research.va.gov/programs/vinci/default.cfm .