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Key patterns and predictors of response to treatment for military veterans with post-traumatic stress disorder: a growth mixture modelling approach

Published online by Cambridge University Press:  15 November 2017

A. J. Phelps
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
Z. Steel
Affiliation:
St John of God Richmond Hospital and School of Psychiatry, University of New South Wales, Sydney, Australia
O. Metcalf
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
N. Alkemade
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
K. Kerr
Affiliation:
Toowong Private Hospital, 496 Milton Road, Toowong, Queensland, Australia
M. O'Donnell
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
J. Nursey
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
J. Cooper
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
A. Howard
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
R. Armstrong
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
D. Forbes
Affiliation:
Department of Psychiatry, Phoenix Australia – Centre for Posttraumatic Mental Health, University of Melbourne, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, Australia
Corresponding
E-mail address:

Abstract

Background

To determine the patterns and predictors of treatment response trajectories for veterans with post-traumatic stress disorder (PTSD).

Methods

Conditional latent growth mixture modelling was used to identify classes and predictors of class membership. In total, 2686 veterans treated for PTSD between 2002 and 2015 across 14 hospitals in Australia completed the PTSD Checklist at intake, discharge, and 3 and 9 months follow-up. Predictor variables included co-morbid mental health problems, relationship functioning, employment and compensation status.

Results

Five distinct classes were found: those with the most severe PTSD at intake separated into a relatively large class (32.5%) with small change, and a small class (3%) with a large change. Those with slightly less severe PTSD separated into one class comprising 49.9% of the total sample with large change effects, and a second class comprising 7.9% with extremely large treatment effects. The final class (6.7%) with least severe PTSD at intake also showed a large treatment effect. Of the multiple predictor variables, depression and guilt were the only two found to predict differences in response trajectories.

Conclusions

These findings highlight the importance of assessing guilt and depression prior to treatment for PTSD, and for severe cases with co-morbid guilt and depression, considering an approach to trauma-focused therapy that specifically targets guilt and depression-related cognitions.

Type
Original Articles
Copyright
Copyright © Commonwealth of Australia and Cambridge University Press 2017

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References

Akaike, H (1973). Information Theory and an Extension of the Maximum Likelihood Principle. Akademiai Kiado: Budapest, Hungary.Google Scholar
Australian Centre for Posttraumatic Mental Health (2007). Australian Guidelines for the Treatment of Adults with Acute Stress Disorder and Posttraumatic Stress Disorder. ACPMH: Melbourne, Victoria.Google Scholar
Bjelland, I, Dahl, AA, Haug, TT, Neckelmann, D (2002). The validity of the hospital anxiety and depression scale: an updated literature review. Journal of Psychosomatic Research 52, 6977.CrossRefGoogle Scholar
Blake, D, Weathers, F, Nagy, L, Kaloupek, D, Charney, D, Keane, T (1995). Clinician-Administered PTSD Scale for DSM-IV (CAPS-DX). National Center for Posttraumatic Stress Disorder, Behavioral Science Division. Medical Center: Boston, VA, Boston, MA.Google Scholar
Blanchard, EB, Jones-Alexander, J, Buckley, TC, Forneris, CA (1996). Psychometric properties of the PTSD Checklist (PCL). Behaviour Research and Therapy 34, 669673.CrossRefGoogle Scholar
Bryant, RA, O’Donnell, ML, Creamer, M, McFarlane, AC, Clark, CR, Silove, D (2010). The psychiatric sequelae of traumatic injury. American Journal of Psychiatry 167, 312320.CrossRefGoogle ScholarPubMed
Creamer, M, Forbes, D, Biddle, D, Elliott, P (2002). Inpatient v. day hospital treatment for chronic, combat-related posttraumatic stress disorder: a naturalistic comparison. Journal of Nervous Mental Disease 190, 183189.CrossRefGoogle Scholar
Currier, JM, Holland, JM, Drescher, KD (2014). Residential treatment for combat-related posttraumatic stress disorder: identifying trajectories of change and predictors of treatment response. PLoS ONE 9. doi: 10.1371/journal.pone.0101741.CrossRefGoogle ScholarPubMed
Elliott, P, Biddle, D, Hawthorne, G, Forbes, D, Creamer, M (2005). Patterns of treatment response in chronic posttraumatic stress disorder: an application of latent growth mixture modeling. Journal of Traumatic Stress 18, 303311.CrossRefGoogle ScholarPubMed
Evans, L, Cowlishaw, S, Hopwood, M (2009). Family functioning predicts outcomes for veterans in treatment for chronic posttraumatic stress disorder. Journal of Family Psychology 23, 531539.CrossRefGoogle ScholarPubMed
Felmingham, KL, Dobson-Stone, C, Schofield, PR, Quirk, GJ, Bryant, R (2013). The brain-derived neurotrophic factor val66met polymorphism predicts response to exposure therapy in posttraumatic stress disorder. Biological Psychiatry 73, 10591063.CrossRefGoogle ScholarPubMed
Feng, ZD, McCulloch, CE (1996). Using bootstrap likelihood ratios in finite mixture models. Journal of the Royal Statistical Society. Series B (Methodological) 58, 609617.Google Scholar
Fontana, A, Rosenheck, R (1998). Effects of compensation-seeking on treatment outcomes among veterans with posttraumatic stress disorder. The Journal of Nervous and Mental Disease 186, 223230.CrossRefGoogle ScholarPubMed
Forbes, D, Hawthorne, G, Elliott, P, McHugh, T, Biddle, D, Creamer, M, Novaco, RW (2004). A concise measure of anger in combat-related posttraumatic stress disorder. Journal of Traumatic Stress 17, 249256.CrossRefGoogle ScholarPubMed
Frueh, BC, Grubaugh, AL, Elhai, JD, Buckley, TC (2007). US department of veterans affairs disability policies for posttraumatic stress disorder: administrative trends and implications for treatment, rehabilitation, and research. American Journal of Public Health 97, 21432145.CrossRefGoogle Scholar
Hunsley, J, Best, M, Lefebvre, M, Vito, D (2001). The seven-item short form of the dyadic adjustment scale: further evidence for construct validity. American Journal of Family Therapy 29, 325335.CrossRefGoogle Scholar
Jung, T, Wickrama, KAS (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychological Compass 2, 302317.CrossRefGoogle Scholar
Litz, BT, Lebowitz, L, Gray, MJ, Nash, WP (2015). Adaptive Disclosure: A New Treatment for Military Trauma, Loss and Moral Injury. Guilford Press: New York, NY.Google Scholar
Lo, Y, Mendell, N, Rubin, DB (2001). Testing the number of components in a normal mixture. Biometrika 88, 767778.CrossRefGoogle Scholar
Morris, SB, Deshon, RP (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological Methods 7, 105125.CrossRefGoogle ScholarPubMed
Muthén, LK, Muthén, BO (2004). Mplus. Verson 3.1 ed. Muthén & Muthén: Los Angeles, CA.Google Scholar
Otis, JD, Keane, TM, Kerns, RD, Monson, C, Scioli, E (2009). The development of an integrated treatment for veterans with comorbid chronic pain and posttraumatic stress disorder. Pain Medicine 10, 13001311.CrossRefGoogle ScholarPubMed
Pe, ML, Raes, F, Kuppens, P (2013). The cognitive building blocks of emotion regulation: ability to update working memory moderates the efficacy of rumination and reappraisal on emotion. PLoS ONE 8, 112.CrossRefGoogle ScholarPubMed
Ram, N, Grimm, KJ (2009). Methods and measures: growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development 33, 565576.CrossRefGoogle Scholar
Ramaswamy, V, Desarbo, WS, Reibstein, DJ, Robinson, WT (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science 12, 103124.CrossRefGoogle Scholar
Rizvi, SL, Vogt, DS, Resick, PA (2009). Cognitive and affective predictors of treatment outcome in cognitive processing therapy and prolonged exposure for posttraumatic stress disorder. Behaviour Research and Therapy 47, 737743.CrossRefGoogle ScholarPubMed
Saunders, JB, Aasland, OG, Babor, TF, De La Fuente, JR, Grant, M (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption. Addiction 88, 791804.CrossRefGoogle ScholarPubMed
Schumm, JA, Walter, KH, Chard, KM (2013). Latent class differences explain variability in PTSD symptom changes during cognitive processing therapy for veterans. Psychological Trauma: Theory, Research, Practice, and Policy 5, 536544.CrossRefGoogle Scholar
Schwarz, G (1978). Estimating the dimensions of a model. Annals of Statistics 6, 461464.CrossRefGoogle Scholar
Smith, ER, Duaxa, JM, Rauch, AM (2013). Perceived perpetration during traumatic events: clinical suggestions from experts in prolonged exposure therapy. Cognitive and Behavioral Practice 20, 461470.CrossRefGoogle Scholar
Stapleton, JA, Taylor, S, Asmundson, GJ (2006). Effects of three PTSD treatments on anger and guilt: exposure therapy, eye movement desensitization and reprocessing, and relaxation training. Journal of Traumatic Stress 19, 1928.CrossRefGoogle ScholarPubMed
Steenkamp, MM, Dickstein, BD, Salters-Pedneault, K, Hofmann, SG, Litz, BT (2012). Trajectories of PTSD symptoms following sexual assault: is resilience the modal outcome? Journal of Traumatic Stress 25, 469474.CrossRefGoogle ScholarPubMed
Steenkamp, MM, Litz, BT, Hoge, CW, Marmar, CR (2015). Psychotherapy for military-related PTSD: a review of randomized clinical trials. JAMA 314, 489500.CrossRefGoogle ScholarPubMed
Steenkamp, MM, Nash, WP, Lebowitz, L, Litz, BT (2013). How best to treat deployment-related guilt and shame: commentary on Smith, Duax, and Rauch (2013). Cognitive and Behavioral Practice 20, 471475.CrossRefGoogle Scholar
Stein, NR, Mills, MA, Arditte, K, Mendoza, C, Borah, AM, Resick, PA, Litz, BT (2012). A scheme for categorizing traumatic military events. Behavior Modification 36, 787807.CrossRefGoogle ScholarPubMed
Yehuda, R, Daskalakis, NP, Desarnaud, F, Makotkine, L, Lehrner, AL, Koch, E, Flory, JD, Buxbaum, JD, Meaney, MJ, Bierer, LM (2013). Epigenetic biomarkers as predictors and correlates of symptom improvement following psychotherapy in combat veterans with PTSD. Frontiers in Psychiatry 4, 118.CrossRefGoogle ScholarPubMed
Yehuda, R, Hoge, CW (2016). The meaning of evidence-based treatments for veterans with posttraumatic stress disorder. JAMA Psychiatry 73, 433434.CrossRefGoogle ScholarPubMed
Zigmond, AS, Snaith, RP (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica 67, 361370.CrossRefGoogle ScholarPubMed

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