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. Author manuscript; available in PMC: 2022 Mar 1.
Published in final edited form as: Contemp Clin Trials. 2021 Jan 8;102:106269. doi: 10.1016/j.cct.2021.106269

The Effect of Reducing Posttraumatic Stress Disorder Symptoms on Cardiovascular Risk: Design and Methodology of a Randomized Clinical Trial

Stefanie T LoSavio 1, Jean C Beckham 1,2, Stephanie Y Wells 1,2,3, Patricia A Resick 1, Andrew Sherwood 1, Cynthia J Coffman 3,4, Angela C Kirby 1,2,3, Tiffany A Beaver 1,2, Michelle F Dennis 1,2, Lana L Watkins 1,*
PMCID: PMC8009821  NIHMSID: NIHMS1665722  PMID: 33429088

Abstract

Posttraumatic stress disorder (PTSD) has been associated with accelerated progression of coronary heart disease (CHD). However, the underlying pathophysiological pathway has remained elusive and it is unclear whether there is a direct link between PTSD and CHD risk. This paper describes the methods of a randomized controlled trial developed to examine how changes in PTSD symptoms affect CHD disease pathways. One hundred twenty participants with current PTSD and who are free of known CHD will be randomized to receive either an evidence-based treatment for PTSD (Cognitive Processing Therapy; CPT) or a waitlist control (WL). Before and after CPT/WL, participants undergo assessment of CHD risk biomarkers reflecting autonomic nervous system dysregulation, systemic inflammation, and vascular endothelial dysfunction. The primary hypothesis is that individuals who show improvement in PTSD symptoms will show improvement in CHD risk biomarkers, whereas individuals who fail to improve or show worsening PTSD symptoms will have no change or worsening in CHD biomarkers. This study is expected to provide knowledge of the role of both the direct impact of PTSD symptoms on CHD risk pathways and the role of these systems as candidate mechanisms underlying the relationship between PTSD and CHD risk. Further, results will provide guidance on the utility of cognitive therapy as a tool to mitigate the accelerated progression of CHD in PTSD.

Clinical Trials Registration: https://clinicaltrials.gov/ct2/show/NCT02736929; Unique identifier: NCT02736929

Keywords: coronary heart disease, posttraumatic stress disorder, biomarkers, mechanisms, stress response system

Introduction

Coronary heart disease (CHD) is the leading cause of morbidity and mortality in the U.S. (Benjamin et al., 2019). One factor conferring 2–3-fold increased risk of CHD is posttraumatic stress disorder (PTSD; Beristianos et al., 2016; Boscarino, 2008; Kubzanksy et al., 2009; Vaccarino et al., 2013). However, whether PTSD symptoms impact CHD risk directly, and the pathophysiological mechanisms involved, remain unknown.

A likely pathway between PTSD and CHD is chronic activation of stress response systems (McEwen, 2000; Baker et al., 2012). PTSD is characterized by autonomic nervous system (ANS) dysregulation, with heightened sympathetic nervous system (SNS) activity (Yehuda et al., 1992; McFall et al., 1990) and greater parasympathetic nervous system (PNS) withdrawal (Shah et al., 2013), as well as chronic systemic inflammation (O’Donovan et al., 2017; Plantinga et al., 2013; Rosen et al., 2017). However, other health factors may contribute to the association between PTSD and CHD, including smoking, substance abuse, sedentary activity, and poor medical compliance (Beckham et al., 1995; Fu et al., 2007; Kubzansky et al., 2014; McFarlane, 1998). These factors, and fluctuations of PTSD symptoms during observational periods in prospective studies of CHD risk, have made it difficult to interpret the relationship between PTSD and CHD risk (Boscarino, 2008; Vaccarino et al., 2013). The objective of this study is to modify PTSD symptoms with treatment to determine whether PTSD symptom trajectory tracks with CHD risk biomarker change. If so, it would suggest PTSD is directly linked to CHD pathways and PTSD treatment can reduce CHD risk (See Figure 1). Few studies have examined the impact of PTSD treatment on CHD biomarkers; however, data from small samples suggest heart rate variability (HRV), inflammatory markers, and SNS-mediated heart rate responses may be normalized following treatment (Blanchard et al., 2002; Hinton et al., 2009; Nishith et al., 2003; Rabe et al., 2006; Tucker et al., 2004).

Figure 1. Pathophysiological Pathways to CHD in PTSD.

Figure 1

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Note. The hypothesized pathways (solid lines) by which PTSD symptoms directly increase CHD risk and comorbid psychiatric conditions and adverse health behaviors increase CHD risk (dotted lines). PTSD = Posttraumatic stress disorder, ANS = Autonomic nervous system, APNS = Peripheral nervous system, SNS = Sympathetic nervous system, CRP = C-reactive protein, CHD = Coronary heart disease.

Moderating Factors: Sex and Depression

Evidence suggests pathways underlying CHD risk may differ by sex (e.g., Boscarino, 2008; Kemp et al., 1995; Kubzansky et al., 2009; Holmstrup et al., 2020). For example, a recent review concluded that men are more vulnerable to the effects of stress on sleep, sedentary behavior, body adiposity, and blood pressure, whereas women are more vulnerable to impacts on diet, glucose regulation, and dyslipidemia (Taylor et al., 2018). Additionally, presence of co-morbid depression may moderate relationships because depression and PTSD have opposing effects on aspects of the stress response systems, including SNS activity, inflammation, and cortisol release (Heath et al., 2013; Kim et al., 2013; Kosten et al., 1987; Lemieux & Coe, 1995; Yehuda et al., 1992; Yehuda, et al., 1998; Yehuda, et al., 1996; Young & Breslau, 2004). Therefore, the potential moderating roles of these characteristics will be explored.

Study Aims and Hypotheses

The purpose of this mechanistic clinical trial is to contribute to the understanding of the PTSD-CHD risk relationship by: determining the efficacy of PTSD treatment for improving CHD risk biomarkers (Aim 1), examining mechanisms of change in CHD risk biomarkers (Aim 2), and exploring potential moderators of the effects of PTSD on CHD biomarkers, including sex and depression. Hypotheses are, compared to waitlist, PTSD treatment will result in improved 24-hour HRV, 24-hour urinary catecholamines, inflammation, and vascular endothelial function (Hypothesis 1) and improvements in CHD risk biomarkers will be mediated by reductions in PTSD symptoms (Hypothesis 2).

Material and Methods

Overall Study Design

The current study is a randomized clinical trial that uses an evidence-based intervention for PTSD, Cognitive Processing Therapy (CPT; Resick, Monson, et al., 2017), to reduce PTSD symptom severity. Participants are recruited, screened, and randomized to either CPT or a minimal attention waitlist control condition (WL). CHD biomarkers are evaluated at baseline and again after completion of CPT or WL (see Figure 2). Target outcomes are the putative pathways leading to CHD risk, including ANS control measured by 24-hour HRV, 24-hour urinary catecholamine excretion, and inflammatory activity estimated by high sensitivity C-reactive protein (hs-CRP). In addition, we assess vascular endothelial function by measuring brachial artery flow-mediated dilation (FMD). Twenty-four hour HRV was selected to provide an index of PNS control because it is a strong independent predictor of incident CHD and cardiac death in both community samples (Dekker et al., 2000; Dekker et al., 1997; Liao et al., 1997; Rodrigues et al., 2010; Tsuji et al., 1996) and CHD patients (Algra et al., 1993; Hikuri et al., 1999) and is significantly reduced in patients with PTSD (Shah et al., 2013; Dennis et al., 2014). Twenty-four-hour urinary catecholamine excretion has been found to be elevated in PTSD and is predictive of increased risk of mortality (Reuben et al., 2000). Peripheral inflammation and vascular endothelial dysfunction were selected due to their correlation with current and future cardiovascular morbidity and mortality (Ridker, 2007; Wassel et al., 2010) and because chronic PTSD has been associated both with substantial elevations in hs-CRP and endothelial dysfunction (Gill et al., 2013; Heath et al., 2013; Spitzer et al., 2010). We will examine whether changes in PTSD symptoms explain changes in CHD biomarkers. In so doing, this study is designed to achieve the next critical step in advancing our understanding of PTSD by establishing whether PTSD symptoms convey CHD risk directly and independently of maladaptive health behaviors and psychiatric comorbidity.

Figure 2.

Figure 2

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Study Design Overview

Participants

A total of 120 male and female adults (approximately 50% each) will be randomly assigned to receive either CPT or WL. Inclusion criteria are: 1) between the ages of 40 and 65 years; 2) current PTSD lasting at least 3 months based on the Clinician Administered PTSD Scale for DSM-5 (CAPS-5; Weathers, Blake, et al., 2013; Weathers et al., 2018; meeting diagnostic criteria plus a total score of 25 or greater); and 3) stable on psychiatric medications for 4 weeks. Exclusion criteria are: 1) participation in other evidence-based trauma-focused therapy for PTSD (current or past 6 months); 2) current dementia or other memory loss condition, as indicated by self-report or scores less than 20 on the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005); 3) current psychotic spectrum disorder or bipolar disorder based on the Structured Clinical Interview for DSM-5 (SCID-5; First et al., 2015); 4) current uncontrolled substance use disorder that would interfere with performing study procedures, indicated by the SCID-5; 5) urine drug screen positive for cocaine and/or methamphetamine and reported regular use of that substance; 6) severely impaired hearing or speech; 7) current pregnancy; 8) established heart disease, abnormal heart rhythm, cancer, or epilepsy; 9) HIV positive status with unstable disease status and/or unstable medication use; 10) current exposure to ongoing trauma (e.g., physically abusive relationship); 11) prominent suicidal or homicidal ideation (as assessed through a clinical interview); 12) serious/terminal illness or other health problem that would prohibit participation in the study; 13) current acute inflammatory condition such as recent infection or fever, 1-month history of accident or surgery, or chronic condition with a primary inflammatory component (e.g., rheumatoid arthritis, lupus; 14) unwillingness to accept randomization; or 15) cannot agree to attend therapy sessions at least once per week.

Study Procedures

Recruitment

Participants are recruited from the community and from outpatient clinics at Duke University Medical Center and the Durham Veterans Affairs Health Care System (VA). Study staff use several methods to recruit potential participants including referrals from clinic providers; brochures and flyers posted in mental health clinics throughout the medical campuses and community areas (e.g., laundromats, bus lines, substance use treatment centers, rape crisis centers, battered women shelter/programs, restaurants, grocery stores); and advertisements in local newspapers, Duke University’s clinical research website, and online classified advertising websites (e.g., Craigslist). Additionally, we identify potentially eligible participants using searches from the VA’s Corporate Data Warehouse (which pulls diagnosis and contact information from the computerized patient medical record system), as well as Duke’s medical record system. Potentially eligible participants identified via these data pulls are sent a letter asking them to consider participation in the study and are provided contact information for the study coordinator. We also use social media to reach potentially eligible participants, including a Facebook page with study flyers and information. Finally, we use a recruitment method referred to as respondent-driven sampling, or “seed recruitment.” Seed recruitment is suitable for sampling “hidden populations” of participants who are best known by their own peers (Heckathorn, 1997). It includes providing incentives to participants for referral of other eligible participants. In our model, each participant, or seed, receives six coupons to recruit other people in his/her social networks. The recruitment coupons provide a brief description of the survey and a phone number for contacting the study coordinator.

Any participant who contacts the study coordinator or other study staff regarding the study is provided more information and is interviewed using a telephone screen to assess eligibility. If a participant is deemed potentially eligible, they are scheduled to attend a formal screening visit (see Assessment Procedures below).

Randomization

Following enrollment and completion of a baseline assessment, participants are randomized in a 2:1 ratio to receive either CPT or WL. Randomization is stratified by variables known to be related to both PTSD and to CHD outcomes: sex and comorbid major depressive disorder.

Interventions

CPT.

CPT is a brief, evidence-based, trauma-focused cognitive behavioral treatment for PTSD (Resick et al., 2017). It is one of the most-well studied and effective PTSD treatments; in one meta-analysis, CPT had the largest effect sizes of treatments for PTSD (Watts et al., 2013). In the current study, CPT consists of two 1-hour therapy sessions each week for 6 weeks. Any participant who cannot attend two sessions per week may participate in one session per week for a 12-week period. Study therapists are mental health providers trained in CPT (i.e., completed 3-day didactic training covering session-by-session procedures and core components of CPT). The treatment developer provides consultation as part of the study. Therapy sessions are recorded, and two independent raters trained in CPT rate a random selection of 20% of sessions for fidelity. Using the Cognitive Processing Therapy: Therapist Adherence and Competence Protocol Individual Version-Revised (Macdonald, Wiltsey-Stirman, Wachen, & Resick, unpublished), ratings are on the presence/absence of CPT elements, competence on these elements, as well as proscribed elements.

WL Control.

To control for the effects of attention and some of the non-specifics of therapy such as speaking to a non-judgmental, supportive person, participants assigned to WL receive minimal attention in the form of weekly telephone calls. These waitlist calls are used to assess current emotional state and to provide supportive, nondirective, brief counseling if participants report experiencing a crisis. Any participant assigned to the WL group is given the opportunity to receive CPT after the post-intervention assessment.

Assessment Procedures

Participants first complete a screening assessment, at which they provide informed consent and complete a full assessment of inclusion and exclusion criteria, including medical history taking, psychiatric diagnostic interviews, and drug and pregnancy screens. Once enrolled in the study, participants come to the lab fasting for ultrasound-guided endothelial function assessment and for blood draw, which are conducted between 0800–1100 hours (see detailed procedures below). Baseline assessments also include a 24-hour urine collection and an ambulatory electrocardiogram measurement. Following the completion of CPT or WL, the CHD biomarker assessments are repeated, as is the diagnostic assessment of PTSD. Assessors blinded to participant condition complete the PTSD and CHD biomarkers assessments. During CPT and WL, self-reported PTSD and depression symptoms are collected weekly. See Table 1 for the assessment schedule.

Table 1.

Assessment Schedule

Assessment Screening Baseline Intervention (weekly administration) Post-Intervention
PTSD
CAPS-5 (clinician-rated) X X
PCL-5 (self-report) X X X
Depression
SCID-5 (clinician-rated) X
BDI-II (self-report) X X X
CHD Biomarkers
24-hour HRV with ECG via Holter X X
24-hour urinary catecholamine excretion X X
Vascular endothelial function assessed by FMD X X
Inflammatory markers via hs-CRP X X
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Measures

Primary Outcomes: CHD Risk Biomarkers.

24-hour HRV.

Continuous ECG data is recorded for one full day and one full night using a DigiTrak XT Holter recorder (Philips Healthcare, Andover, MA, USA). A checklist is used to ensure that the skin is prepared properly and electrodes and leads are intact, and the signal quality is confirmed by visual inspection of each of the three ECG channels. The recorder is placed in a carrying pouch, which fits onto a shoulder strap or belt apparatus. Following instrumentation, patients are reminded to engage in their normal pattern of activity and instructed to remove the Holter device and electrodes at the specified time on the day following instrumentation. The Holter recording is prepared for HRV analysis; the ECG recording is downloaded, scanned and edited using Philips Holter 2010 Plus software (Philips Heathcare, Andover, MA, USA), with verification of labeled beats performed by an experienced ECG analyst. The labeled beat-to-beat file is then processed using customized HRV analysis software.

24-Hour Urinary Catecholamine Excretion.

Participants are asked to collect urine into sterilized plastic bottles (containing a small amount of preservative) over a 24-hour period and to store the bottles in a small portable cooler that is provided. Samples are assayed for norepinephrine, epinephrine, cortisol and creatinine. To normalize the catecholamine values for body size and urine volume, catecholamine and cortisol excretion is provided in units of μg of catecholamine (i.e., norepinephrine or epinephrine) per mg creatinine for each sample (White et al., 1995). In prior studies, urinary catecholamine data have proven informative, with low subject burden and excellent compliance (Sherwood et al., 2002). In the present study, 24-hour urinary catecholamines are measured to estimate SNS activity. Coded urine samples are sent to Lab Corporation of America for liquid chromatography/tandem mass spectrometry.

Vascular Endothelial Function assessed by FMD.

Our technique for assessing FMD follows procedures first described by Celermajer and colleagues (1992) and conforms to current consensus guidelines (Thijssen et al., 2011). Longitudinal B-mode ultrasound images of the brachial artery, 4–6 cm proximal to the antecubital crease, are obtained at end-diastole. Peak hyperemic flow and shear stress is derived by standard formulae based upon Doppler velocity measurements during the first 10 seconds following deflation of the occlusion cuff (Pyke et al., 2008; Mitchell et al., 2004). Peak FMD response is assessed from 10–120 seconds post-deflation of the cuff, with peak arterial diameter quantified using polynomial curve fitting, and FMD is defined as the maximum percent change in arterial diameter relative to pre-inflation baseline.

Inflammatory Marker Assessment.

Participants are determined to be free of acute inflammatory conditions or infections based on a health survey and temperature check before blood draw. In the case of acute infections/fever, assessment is delayed until the condition resolves, and all participants are required to have completed any antibiotic use for a minimum of two weeks prior to blood draw. A blood sample is also collected at this time to determine interleukin-6, fasting glucose, and insulin in order to provide an additional measure of inflammation and to estimate glycemic control.

PTSD Symptom Assessment.

Clinician-Assessed PTSD Diagnosis and Severity.

The Clinician-Administered PTSD Scale for DSM-5, monthly version (CAPS-5; Weathers, Blake et al., 2013) is a gold-standard assessment used to assess PTSD diagnostic status and symptom severity at baseline, and the weekly version of the CAPS-5 is used at post-intervention. The CAPS-5 is a 30-item structured assessment with 20 items that represent the DSM-5 symptoms ranging in severity from 0 (absent) to 4 (extreme); total symptom severity is determined by summing together these 20 items and scores range from 0–80 with greater scores indicating greater severity. Participants are included in the study if they meet criteria for PTSD and have a CAPS-5 total score of 25 or greater at baseline. Diagnostic raters are trained using CAPS-5 standardized training (i.e., manual, videotapes, and co-rating with a trained rater). Interrater reliability for diagnoses based on videotapes of participant interviews across our previous studies has been high, Fleiss’ kappa = .96.

Self-reported PTSD Symptoms.

PTSD symptom severity is also measured via self-report by the PTSD Checklist (PCL-5; Weathers, Litz et al., 2013). The PCL-5 is administered at screening, during intervention, and at the post-intervention follow-up. The PCL-5 includes 20 items ranging from 0 (not at all) to 4 (extremely) with total severity scores ranging from 0–80 and greater scores indicate greater severity.

Moderating Variable of Depression.

Clinician-Assessed Major Depressive Disorder Diagnosis.

The SCID-5 (First et al., 2015) is used to assess for major depressive disorder diagnosis. Diagnostic raters are trained using SCID-5 standardized training (i.e., manual, videotapes, and co-rating with a trained rater). Interrater reliability for diagnoses by our raters based on videotapes of participant interviews has been high, Fleiss’ kappa = .94.

Self-reported Depression Symptoms.

Depression symptoms are also measured via self-report by the Beck Depression Inventory-II (BDI-II; Beck et al., 1996). The BDI-II is administered at screening, during intervention, and at the post-intervention follow-up. The BDI-II has 21 items ranging from 0 to 3 for severity with greater scores indicating greater severity; total scores can range from 0 to 63.

Data Analytic Strategy

The main goal for this study is to understand how changes in PTSD symptom severity impact changes in CHD markers; therefore, we will conduct both an intent-to-treat analysis in which participants will be analyzed as part of the group to which they are randomized, regardless of intervention adherence, and a per-protocol or as-treated analysis to strengthen the evidence that the mechanism for the effects of CPT vs. WL on CHD markers is through PTSD symptom severity changes. To test the primary hypothesis of a CPT effect post-intervention (Aim 1), we will use a general linear model that accounts for the correlation between participants’ repeated outcome measurements over time with an unstructured covariance matrix (Verbeke & Molenberghs, 2000; Fitzmaurice et al., 2004). The predictors in the model will include time and the treatment arm by time interaction. We will estimate the model parameters using the SAS procedure MIXED (SAS Version 9, Cary, NC), and contrasts will be written in the context of this model to test the difference of mean change in CHD markers between the two treatment arms at post-intervention. For improvement in precision, the model will also be adjusted for stratification variables (Committee for Proprietary Medicinal Products, 2004). A sensitivity analysis will also include potentially important baseline covariates (e.g., functional comorbidity, smoking status, substance abuse, and habitual physical activity).

For Aim 2, we will conduct mediation analyses (Kraemer et al., 2008) using change in PTSD symptom severity as a potential mediator of treatment group effect on post-intervention change in CHD risk biomarkers. We will also examine the role of trajectories of change in PTSD symptom severity as a potential mediator by deriving an individual-level summary measure of PTSD symptom change using the successive PTSD symptoms assessed during intervention delivery. To assess mediation, we will first fit a model to examine the relationship between the mediator (i.e., change in PTSD symptoms during treatment) and treatment group and then fit a model examining the relationship between the mediator and post-intervention change in CHD biomarkers. We will use an extension of the Sobel first-order test (Fritz & MacKinnon, 2007) to determine whether the effects of CPT on CHD risk biomarkers are attributable to CPT’s effects on PTSD symptom improvement. The model will also be adjusted for stratification variables, and, as with Aim 1, we will evaluate and include other potentially important covariates.

We will also examine whether the effect of PTSD symptom severity change on change in CHD risk biomarkers is moderated by sex or depression. We will fit linear models including the predictors: PTSD symptom severity change and the PTSD symptom severity change by moderator variable interaction (sex or depression status), with the addition of covariates for baseline demographic and clinical characteristics. We will estimate 95% confidence intervals from these models for the difference in changes in CHD risk markers for PTSD symptom severity level changes by sex or depression status.

Missing Data

Because the main predictors of interest, intervention arm and patient characteristics, are collected at baseline, we do not anticipate much missing data in these variables. There may be missing values in the follow-up outcome measures (e.g., due to dropout or item non-response). Our main analysis technique, general linear mixed models via maximum likelihood estimation, implicitly accommodates missingness when missingness is due either to treatment, to prior outcome, or to other baseline covariates included in the model, defined as missing at random (Hedeker & Gibbons, 2006; Schafer & 1997). Depending on the type and scope of missing data (O’Kelly & Ratitch, 2014; Schafer & Yucel, 2002), we will also explore multiple imputation as a sensitivity analysis conducted via the SAS procedure PROC MI or the SAS macro IVEware (http://www.isr.umich.edu/src/smp/ive/).

Power Analysis

The sample size of n = 120 subjects (80 in the CPT arm and 40 in WL control arm) is based on a 2:1 randomization and comparison of HRV improvement between CPT and WL. For sample size calculations we used methods appropriate for ANCOVA type analyses (Borm et al., 2007), which are equivalent in terms of efficiency to our linear model for randomized trials (Fitzmaurice et al., 2011). This method is based on performing a two-sample t-test sample size calculation for the between group difference and adjusting based on an assumed correlation between baseline and follow-up time point outcome measures. Using the most conservative assumptions, with 80% power, alpha = 0.05, SD = 50, rho = 0.3, and 20% attrition rate by post-intervention, we can detect an effect size difference of 0.60 (Cohen, 1988), corresponding to a difference of 30 units in mean HRV levels at post-intervention between CPT and WL. For the Aim 2 mediation analysis, we determined detectable effect sizes for mediation based on empirical power estimates for the product-of-coefficient test given by Fritz and MacKinnon (2007) and will have 80% power to detect medium to large effect size differences for each of the parameters individually that are part of the product test.

COVID-19 Addendum

Due to the occurrence of the COVID-19 pandemic during the study, some study procedures have been altered to adhere to physical distancing guidelines. Consenting has occurred remotely via e-consent procedures. Additionally, self-report and clinician-administered measures (e.g., PCL-5, BDI-II, CAPS-5, SCID-5) have been completed remotely (e.g., via phone, videoconferencing). Previous research indicates that clinician-administered assessments completed face-to-face versus remotely are strongly correlated (e.g., Aziz & Kenford, 2004: Litwack et al., 2014; Porcari et al., 2008; Simon et al., 1993). The delivery modality of CPT was also changed from office-based, in-person therapy to home-based clinical videoconferencing (CVT). Home-based CVT allows patients to be located in their homes or another private area while synchronously meeting with a provider located elsewhere (e.g., their office or home). In previous research, CPT delivered via CVT has been shown to be non-inferior to traditional office-based care (e.g., Morland et al., 2014; 2015; see also Moring et al. 2020). Finally, when the COVID-19 pandemic social distancing guidelines initially took effect, for individuals who were enrolled and receiving CPT or minimal attention in the WL group, the time between the end of CPT or WL and the post-intervention assessment was extended by approximately 4 months until in-person visits were allowed to resume. PTSD symptoms were monitored monthly in these participants.

Discussion

CHD and PTSD are debilitating conditions associated with increased mortality, morbidity, and health care costs. Previous research has demonstrated a link between PTSD and CHD; however, controlled designs are needed to better understand the pathways that confer CHD risk. The present study was designed to determine whether there is a direct link between PTSD and CHD risk and the role of candidate pathophysiological mechanisms. By defining how PTSD is a risk factor for CHD, and identifying the disease pathways involved, the study will inform strategies to ameliorate CHD risk for individuals with PTSD.

If the study hypotheses are supported, they will be consistent with the view that PTSD symptom severity, over and above the effects of other health risk behaviors, conveys heightened CHD risk among those with PTSD. The results of this study will inform patients, providers, and policymakers on the relevance and appropriateness of trauma-focused, cognitive behavioral psychotherapy for this population. Focusing CHD prevention efforts within a behavioral health framework is consistent with a movement in the field to better integrate mental health with medical prevention and treatment (e.g., Bierman, 2019).

Strengths and Potential Challenges

The present study was designed to build upon previous research with several key strengths. First, the sample will include participants who meet full diagnostic criteria for PTSD. Participants will include veterans and non-veterans and represent a range of trauma types, enhancing generalizability. We are manipulating PTSD symptoms by randomly assigning participants to either an evidence-based PTSD treatment or a control condition, allowing examination for a direct effect of PTSD on CHD risk. Finally, the sample size will be sufficiently large to detect main effects and to evaluate mediators and moderators.

While the present study was designed to maximize confidence in inferences as well as generalizability, some limitations and potential challenges should be noted. One potential challenge is attrition. Dropout from PTSD treatment is common, with dropout rates around 20–25% in randomized clinical trials of CPT (e.g., Resick et al., 2002; Chard, 2005). However, dropout is not expected to compromise the analytic approach. First, participants are encouraged to remain in the study to complete follow-up assessments even if they no longer wish to remain in treatment. As such, the analytic strategy includes plans to analyze data from the full intent-to-treat sample. Additionally, even if participants do not remain in treatment long enough to fully reduce their PTSD symptoms their data will contribute to a range of PTSD symptom change scores that will be useful to examine the relationship between PTSD symptom change and CHD risk.

Another question concerns the timeframe in which we plan to assess for change in the CHD biomarkers. Although the intervention period is relatively short, research has demonstrated significant PTSD symptom change with 12 sessions of CPT delivered in as few as 6 weeks (e.g., Resick et al., 2015; Resick, Wachen, et al., 2017). In prior randomized controlled trials, the biomarkers under study have also shown improvements following short-term interventions (Blumenthal et al., 2005; Blumenthal et al., 2016; Sherwood et al., 2016).

As noted earlier, an unanticipated challenge has been COVID-19. We have adjusted our study procedures to conduct as many aspects of the study remotely as is feasible and appropriate. To assess for any impact of these changes on the study, we can compare data from individuals participating in the study during the COVID-19 timeframe to those who participated before the onset of COVID-19. However, the altered study procedures are not expected to affect outcomes given that self-report and clinician-administered measures are often delivered remotely in research, and CPT outcomes have been shown to be non-inferior when delivered via CVT.

Conclusions

Given the high mortality rates due to CHD, innovative approaches are needed to target factors conferring CHD risk. This study will be an important step in understanding the direct and indirect pathways between PTSD and CHD, highlighting promising points of intervention.

Acknowledgments

This work was supported by the National Heart, Lung, and Blood Institute (R01HL130322; Watkins/Beckham; https://clinicaltrials.gov/ct2/show/NCT02736929) and a Senior Research Career Scientist Award through VA Clinical Sciences and Research and Development (IK6BX003777). The authors are solely responsible for the design and conduct of this study, the drafting and editing of the manuscript, and its final contents. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of NIH, the VA, the United States government, or any of the institutions with which the authors are affiliated.

Footnotes

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