Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users

OBJECTIVE

Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. We describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research.

MATERIALS AND METHODS

The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site.

RESULTS

The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development.

DISCUSSION

The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping.

CONCLUSION

CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.

Posttraumatic Stress Disorder Is Associated With Elevated Risk of Incident Stroke and Transient Ischemic Attack in Women Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) has been associated with ischemic heart disease in women veterans, but evidence for associations with other cardiovascular disorders remains limited in this population. This retrospective longitudinal cohort study evaluated the association of PTSD with incident stroke/transient ischemic attack (TIA) in women veterans.

METHODS AND RESULTS

Veterans Health Administration electronic health records were used to identify women veterans aged ≥18 years engaged with Veterans Health Administration health care from January 1, 2000 to December 31, 2019. We identified women veterans with and without PTSD without a history of stroke or TIA at start of follow-up. Propensity score matching was used to match groups on age, race or ethnicity, traditional cardiovascular risk factors, female-specific risk factors, a range of mental and physical health conditions, and number of prior health care visits. PTSD, stroke, TIA, and risk factors used in propensity score matching were based on diagnostic codes. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for associations of PTSD with an incident stroke/TIA composite. Subanalyses considered stroke and TIA separately, plus age- and race- or ethnicity-stratified analyses were carried out. The analytic sample included 208 092 women veterans (104 046 with and 104 046 without PTSD). PTSD was associated with a greater rate of developing stroke/TIA (HR, 1.33 [95% CI, 1.25-1.42], P<0.001). This elevated risk was especially pronounced in women <50 years old and in Hispanic/Latina women.

CONCLUSIONS

Findings indicate a strong association of PTSD with incident stroke/TIA in women veterans. Research is needed to determine whether addressing PTSD and its downstream consequences can offset this risk.

Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis

A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.

Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome

Background

Prior epidemiological research has linked PTSD with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records (EHR) provide an opportunity to overcome prior clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex.

Methods

PTSD was defined among biobank participants (total N=123,365) in a major healthcare system using two ICD code-based definitions: broad (1+ PTSD or acute stress codes versus 0; NCase=14,899) and narrow (2+ PTSD codes versus 0; NCase=3,026). Using a phenome-wide association (PheWAS) design, we tested associations between each PTSD definition and all prevalent disease umbrella categories, i.e., phecodes. We also conducted sex-stratified PheWAS analyses including a sex-by-diagnosis interaction term in each logistic regression.

Results

A substantial number of phecodes were significantly associated with PTSDNarrow (61%) and PTSDBroad (83%). While top associations were shared between the two definitions, PTSDBroad captured 334 additional phecodes not significantly associated with PTSDNarrow and exhibited a wider range of significantly associated phecodes across various categories, including respiratory, genitourinary, and circulatory conditions. Sex differences were observed, in that PTSDBroad was more strongly associated with osteoporosis, respiratory failure, hemorrhage, and pulmonary heart disease among male patients, and with urinary tract infection, acute pharyngitis, respiratory infections, and overweight among female patients.

Conclusions

This study provides valuable insights into a diverse range of comorbidities associated with PTSD, including both known and novel associations, while highlighting the influence of sex differences and the impact of defining PTSD using EHR.

Lifelong Association of Disorders Related to Military Trauma with Subsequent Parkinson's Disease

BACKGROUND

Trauma-related disorders such as traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are emerging as risk factors for Parkinson's disease (PD), but their association with development of PD and independence from comorbid disorders remains unknown.

OBJECTIVE

To examine TBI and PTSD related to early trauma in military veterans using a case-control study.

METHODS

PD was identified by International Classification of Diseases (ICD) code, recurrent PD-specific prescriptions, and availability of 5+ years of earlier records. Validation was performed by chart review by a movement disorder-trained neurologist. Control subjects were matched 4:1 by age, duration of preceding health care, race, ethnicity, birth year, and sex. TBI and PTSD were identified by ICD code and onset based on active duty. Association and interaction were measured for TBI and PTSD with PD going back 60 years. Interaction was measured for comorbid disorders.

RESULTS

A total of 71,933 cases and 287,732 controls were identified. TBI and PTSD increased odds of subsequent PD at all preceding 5-year intervals back to year -60 (odds ratio range: 1.5 [1.4, 1.7] to 2.1 [2.0, 2.1]). TBI and PTSD showed synergism (synergy index range: 1.14 [1.09, 1.29] to 1.28 [1.09, 1.51]) and additive association (odds ratio range: 2.2 [1.6, 2.8] to 2.7 [2.5, 2.8]). Chronic pain and migraine showed greatest synergy with PTSD and TBI. Effect sizes for trauma-related disorders were comparable with established prodromal disorders.

CONCLUSIONS

TBI and PTSD are associated with later PD and are synergistic with chronic pain and migraine. These findings provide evidence for TBI and PTSD as risk factors preceding PD by decades and could aid in prognostic calculation and earlier intervention. © 2023 International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Alzheimer's disease and related dementias among aging veterans: Examining gene-by-environment interactions with post-traumatic stress disorder and traumatic brain injury

INTRODUCTION

Post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) confer risk for Alzheimer's disease and related dementias (ADRD).

METHODS

This study from the Million Veteran Program (MVP) evaluated the impact of apolipoprotein E (APOE) ε4, PTSD, and TBI on ADRD prevalence in veteran cohorts of European ancestry (EA; n = 11,112 ADRD cases, 170,361 controls) and African ancestry (AA; n = 1443 ADRD cases, 16,191 controls). Additive-scale interactions were estimated using the relative excess risk due to interaction (RERI) statistic.

RESULTS

PTSD, TBI, and APOE ε4 showed strong main-effect associations with ADRD. RERI analysis revealed significant additive APOE ε4 interactions with PTSD and TBI in the EA cohort and TBI in the AA cohort. These additive interactions indicate that ADRD prevalence associated with PTSD and TBI increased with the number of inherited APOE ε4 alleles.

DISCUSSION

PTSD and TBI history will be an important part of interpreting the results of ADRD genetic testing and doing accurate ADRD risk assessment.

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

BACKGROUND

The availability of electronic medical record (EMR) free-text data for research varies. However, access to short diagnostic text fields is more widely available.

OBJECTIVE

This study assesses agreement between free-text and short diagnostic text data from primary care EMR for identification of posttraumatic stress disorder (PTSD).

METHODS

This retrospective cross-sectional study used EMR data from a pan-Canadian repository representing 1574 primary care providers at 265 clinics using 11 EMR vendors. Medical record review using free text and short diagnostic text fields of the EMR produced reference standards for PTSD. Agreement was assessed with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

RESULTS

Our reference set contained 327 patients with free text and short diagnostic text. Among these patients, agreement between free text and short diagnostic text had an accuracy of 93.6% (CI 90.4%-96.0%). In a single Canadian province, case definitions 1 and 4 had a sensitivity of 82.6% (CI 74.4%-89.0%) and specificity of 99.5% (CI 97.4%-100%). However, when the reference set was expanded to a pan-Canada reference (n=12,104 patients), case definition 4 had the strongest agreement (sensitivity: 91.1%, CI 90.1%-91.9%; specificity: 99.1%, CI 98.9%-99.3%).

CONCLUSIONS

Inclusion of free-text encounter notes during medical record review did not lead to improved capture of PTSD cases, nor did it lead to significant changes in case definition agreement. Within this pan-Canadian database, jurisdictional differences in diagnostic codes and EMR structure suggested the need to supplement diagnostic codes with natural language processing to capture PTSD. When unavailable, short diagnostic text can supplement free-text data for reference set creation and case validation. Application of the PTSD case definition can inform PTSD prevalence and characteristics.

Characterizing primary care patients with posttraumatic stress disorder using electronic medical records: a retrospective cross-sectional study

BACKGROUND

Posttraumatic stress disorder (PTSD) has significant morbidity and economic costs. This study describes the prevalence and characteristics of patients with PTSD using primary care electronic medical record (EMR) data.

METHODS

This retrospective cross-sectional study used EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). This study included 1,574 primary care providers located in 7 Canadian provinces. There were 689,301 patients that visited a CPCSSN provider between 1 January 2017 and 31 December 2019. We describe associations between PTSD and patient characteristics using descriptive statistics, chi-square, and multiple logistic regression models.

RESULTS

Among the 689,301 patients included, 8,817 (1.3%, 95% CI 1.2-1.3) had a diagnosis of PTSD. On multiple logistic regression analysis, patients with depression (OR 4.4, 95% CI 4.2-4.7, P < 0.001), alcohol abuse/dependence (OR 1.7, 95% CI 1.6-1.9, P < 0.001), and/or drug abuse/dependence (OR 2.6, 95% CI 2.5-2.8, P < 0.001) had significantly higher odds of PTSD compared with patients without those conditions. Patients residing in community areas considered the most material deprived (OR 2.1, 95% CI 1.5-2.1, P < 0.001) or the most socially deprived (OR 2.8, 95% CI 2.7-5.3, P < 0.001) had higher odds of being diagnosed with PTSD compared with patients in the least deprived areas.

CONCLUSIONS

The prevalence of PTSD in Canadian primary care is 1.3% (95% CI 1.25-1.31). Using EMR records we confirmed the co-occurrence of PTSD with other mental health conditions within primary care settings suggesting benefit for improved screening and evidence-based resources to manage PTSD.

Joint longitudinal trajectories of pain intensity and opioid prescription in veterans with back pain

PURPOSE

We describe pain intensity and opioid prescription jointly over time in Veterans with back pain to better understand their relationship.

METHODS

We performed a retrospective cohort study on electronic health record data from 117 126 Veterans (mean age 49.2 years) diagnosed with back pain in 2015. We used latent class growth analysis to jointly model pain intensity (0-10 scores) and opioid prescriptions over 2 years to identify classes of individuals similar in their trajectory of pain and opioid over time. Multivariable multinomial logit models assessed sociodemographic and clinical predictors of class membership.

RESULTS

We identified six trajectory classes: a "no pain/no opioid" class (22.2%), a "mild pain/no opioid" class (45.0%), a "moderate pain/no opioid" class (24.6%), a "moderate, decreasing pain/decreasing opioid" class (3.3%), a "moderate pain/high opioid" class (2.6%), and a "moderate, increasing pain/increasing opioid" class (2.3%). Among those in moderate pain classes, being white (vs. non-white) and older were associated with higher odds of being prescribed opioids. Veterans with mental health diagnoses had increased odds of being in the painful classes versus "no pain/no opioid" class.

CONCLUSION

We found distinct patterns in the long-term joint course of pain and opioid prescription in Veterans with back pain. Understanding these patterns and associated predictors may help with development of targeted interventions for patients with back pain.

Development and Evaluation of a Rules-based Algorithm for Primary Open-Angle Glaucoma in the VA Million Veteran Program

The availability of electronic health record (EHR)-linked biobank data for research presents opportunities to better understand complex ocular diseases. Developing accurate computable phenotypes for ocular diseases for which gold standard diagnosis includes imaging remains inaccessible in most biobank-linked EHRs. The objective of this study was to develop and validate a computable phenotype to identify primary open-angle glaucoma (POAG) through accessing the Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) and Million Veteran Program (MVP) biobank. Accessing CPRS clinical ophthalmology data from VA Medical Center Eye Clinic (VAMCEC) patients, we developed and iteratively refined POAG case and control algorithms based on clinical, prescription, and structured diagnosis data (ICD-CM codes). Refinement was performed via detailed chart review, initially at a single VAMCEC (n = 200) and validated at two additional VAMCECs (n = 100 each). Positive and negative predictive values (PPV, NPV) were computed as the proportion of CPRS patients correctly classified with POAG or without POAG, respectively, by the algorithms, validated by ophthalmologists and optometrists with access to gold-standard clinical diagnosis data. The final algorithms performed better than previously reported approaches in assuring the accuracy and reproducibility of POAG classification (PPV >83% and NPV >97%) with consistent performance in Black or African American and in White Veterans. Applied to the MVP to identify cases and controls, genetic analysis of a known POAG-associated locus further validated the algorithms. We conclude that ours is a viable approach to use combined EHR-genetic data to study patients with complex diseases that require imaging confirmation.

Understanding the comorbidity between posttraumatic stress severity and coronary artery disease using genome-wide information and electronic health records

The association between coronary artery disease (CAD) and posttraumatic stress disorder (PTSD) contributes to the high morbidity and mortality observed for these conditions. To understand the dynamics underlying PTSD-CAD comorbidity, we investigated large-scale genome-wide association (GWA) statistics from the Million Veteran Program (MVP), the UK Biobank (UKB), the Psychiatric Genomics Consortium, and the CARDIoGRAMplusC4D Consortium. We observed a genetic correlation of CAD with PTSD case-control and quantitative outcomes, ranging from 0.18 to 0.32. To investigate possible cause-effect relationships underlying these genetic correlations, we performed a two-sample Mendelian randomization (MR) analysis, observing a significant bidirectional relationship between CAD and PTSD symptom severity. Genetically-determined PCL-17 (PTSD 17-item Checklist) total score was associated with increased CAD risk (odds ratio = 1.04; 95% confidence interval, 95% CI = 1.01-1.06). Conversely, CAD genetic liability was associated with reduced PCL-17 total score (beta = -0.42; 95% CI = -0.04 to -0.81). Because of these opposite-direction associations, we conducted a pleiotropic meta-analysis to investigate loci with concordant vs. discordant effects on PCL-17 and CAD, observing that concordant-effect loci were enriched for molecular pathways related to platelet amyloid precursor protein (beta = 1.53, p = 2.97 × 10-7) and astrocyte activation regulation (beta = 1.51, p = 2.48 × 10-6) while discordant-effect loci were enriched for biological processes related to lipid metabolism (e.g., triglyceride-rich lipoprotein particle clearance, beta = 2.32, p = 1.61 × 10-10). To follow up these results, we leveraged MVP and UKB electronic health records (EHR) to assess longitudinal changes in the association between CAD and posttraumatic stress severity. This EHR-based analysis highlighted that earlier CAD diagnosis is associated with increased PCL-total score later in life, while lower PCL total score was associated with increased risk of a later CAD diagnosis (Mann-Kendall trend test: MVP tau = 0.932, p < 2 × 10-16; UKB tau = 0.376, p = 0.005). In conclusion, both our genetically-informed analyses and our EHR-based follow-up investigation highlighted a bidirectional relationship between PTSD and CAD where multiple pleiotropic mechanisms are likely to be involved.

Using electronic medical record diagnostic codes to identify veterans with posttraumatic stress disorder

Researchers studying posttraumatic stress disorder (PTSD) often use diagnostic codes within electronic medical records (EMRs) to identify individuals with the disorder. This study evaluated the performance of algorithms for defining PTSD based on International Classification of Diseases (ICD) code use within EMR data. We used data from a registry of U.S. veterans for whom both structured interview data and Veterans Health Administration EMR data were available. Using interview-diagnosed PTSD as the reference criterion, we calculated diagnostic accuracy statistics for algorithms that required the presence of at least one and up to seven encounters in which a PTSD diagnosis was present in EMR data within any clinical source, mental health clinic, or specialty PTSD clinic. We evaluated algorithm accuracy in the total sample (N = 1,343; 64.1% with PTSD), within a subsample constrained to lower PTSD prevalence (n = 712; 32.3% with PTSD), and as a function of demographic characteristics. Algorithm accuracy was influenced by PTSD prevalence. Results indicated that higher thresholds for the operationalization of PTSD may be justified among samples in which PTSD prevalence is lower. Requiring three PTSD diagnoses from a mental health clinic or four diagnoses from any clinical source may be a suitable minimum standard for identifying individuals with PTSD in EMRs; however, accuracy may be optimized by requiring additional diagnoses. The performance of many algorithms differed as a function of educational attainment and age, suggesting that samples of individuals with PTSD developed based on EMR ICD codes may skew toward including older, less-educated veterans.

Feasibility of Extracting Meaningful Patient Centered Outcomes From the Electronic Health Record Following Critical Illness in the Elderly

Background

Meaningful patient centered outcomes of critical illness such as functional status, cognition and mental health are studied using validated measurement tools that may often be impractical outside the research setting. The Electronic health record (EHR) contains a plethora of information pertaining to these domains. We sought to determine how feasible and reliable it is to assess meaningful patient centered outcomes from the EHR.

Methods

Two independent investigators reviewed EHR of a random sample of ICU patients looking at documented assessments of trajectory of functional status, cognition, and mental health. Cohen's kappa was used to measure agreement between 2 reviewers. Post ICU health in these domains 12 month after admission was compared to pre- ICU health in the 12 months prior to assess qualitatively whether a patient's condition was “better,” “unchanged” or “worse.” Days alive and out of hospital/health care facility was a secondary outcome.

Results

Thirty six of the 41 randomly selected patients (88%) survived critical illness. EHR contained sufficient information to determine the difference in health status before and after critical illness in most survivors (86%). Decline in functional status (36%), cognition (11%), and mental health (11%) following ICU admission was observed compared to premorbid baseline. Agreement between reviewers was excellent (kappa ranging from 0.966 to 1). Eighteen patients (44%) remained home after discharge from hospital and rehabilitation during the 12- month follow up.

Conclusion

We demonstrated the feasibility and reliability of assessing the trajectory of changes in functional status, cognition, and selected mental health outcomes from EHR of critically ill patients. If validated in a larger, representative sample, these outcomes could be used alongside survival in quality improvement studies and pragmatic clinical trials.

Posttraumatic Stress Disorder Does Not Compromise Behavioral Pain Treatment: Secondary Analysis of a Randomized Clinical Trial Among Veterans

Background

Individuals with posttraumatic stress disorder (PTSD) and chronic pain evince different presentations, coping strategies, and treatment utilization patterns than individuals with chronic pain alone. Theorists have suggested that comorbid PTSD may complicate chronic pain treatment, and that integrated pain and PTSD treatment may be preferable to pain treatment alone.

Objective

Assess whether comorbid PTSD moderates Veterans’ response to yoga and/or cognitive behavioral therapy (CBT) for pain.

Methods

Veterans with Gulf War illness (n = 75) were assessed using the Brief Pain Inventory at baseline and posttreatment as part of a randomized clinical trial. PTSD status was abstracted from participants’ medical records.

Results

PTSD+ participants (n = 41) reported more pain at baseline than PTSD− participants (n = 34; d = .66, p < .01). PTSD+ participants experienced more improvement in pain from baseline to posttreatment than PTSD− participants by a small to moderate, marginally statistically significant amount (d = .39, p = .07). The relationship between PTSD and treatment outcome was not moderated by treatment type (yoga vs CBT; p = .99). Observation of treatment responses across PTSD status (+ vs −) and treatment (yoga vs CBT) revealed that PTSD+ participants responded well to yoga.

Conclusion

PTSD is not associated with reduced effectiveness of behavioral chronic pain treatment among Veterans with Gulf War illness. Therefore behavioral pain treatment should be made readily available to Veterans with pain and PTSD. Yoga deserves further consideration as a treatment for pain among individuals with PTSD.

Identifying Posttraumatic Stress Disorder and Disparity Among Transgender Veterans Using Nationwide Veterans Health Administration Electronic Health Record Data

Purpose: The prevalence of posttraumatic stress disorder (PTSD) and other psychiatric disorders is high among military veterans and even higher among transgender veterans. Prior prevalence estimates have become outdated, and novel methods of estimation have since been developed but not used to estimate PTSD prevalence among transgender veterans. This study provides updated estimates of PTSD prevalence among transgender and cisgender veterans. Methods: We examined Veterans Health Administration (VHA) medical record data from October 1, 1999 to April 1, 2021 for 9995 transgender veterans and 29,985 cisgender veteran comparisons (1:3). We matched on age group at first VHA health care visit, sex assigned at birth, and year of first VHA visit. We employed both probabilistic and rule-based algorithms to estimate the prevalence of PTSD for transgender and cisgender veterans. Results: The prevalence of PTSD was 1.5-1.8 times higher among transgender veterans. Descriptive data suggest that the prevalence of depression, schizophrenia, bipolar disorder, alcohol and non-alcohol substance use disorders, current/former smoking status, and military sexual trauma was also elevated among transgender veterans. Conclusion: The PTSD and overall psychiatric burden observed among transgender veterans was significantly higher than that of their cisgender peers, especially among recent users of VHA care. These PTSD findings are consistent with prior literature and minority stress theory, and they were robust across probabilistic and two rule-based methods employed in this study. As such, enhanced and careful screening, outreach, and evidence-based practices are recommended to help reduce this disparity among transgender veterans.

Self-Reported Autonomic Dysregulation in Gulf War Illness

INTRODUCTION

Autonomic nervous system dysregulation is commonly observed in Gulf War illness (GWI). Using a new sample, we sought to replicate and extend findings from a previous study that found autonomic symptoms predicted physical functioning in Veterans with GWI.

MATERIALS AND METHODS

A linear regression model was used to predict physical functioning (36-item Short Form Health Survey (SF-36); n = 73, 75% male). First, we examined the predictive value of independent variables individually in the model including: the 31-item Composite Autonomic Symptom Score (COMPASS-31) total score, body mass index (BMI), mental health burden (i.e., post-traumatic stress disorder [PTSD] and/or depression), and COMPASS-31 subscales: orthostatic intolerance, vasomotor, secretomotor, gastrointestinal, bladder, and pupillomotor. Next, we estimated linear regression models containing the three variables (autonomic symptoms, BMI, and mental health burden) identified as predictors of physical functioning from the prior study.

RESULTS

These linear regression models significantly predicted physical functioning and accounted for 15% of the variance with COMPASS-31, 36.6% of variance with COMPASS-31 and BMI, and 38.2% of variance with COMPASS-31, BMI, and mental health burden. Then, forward step-wise linear regressions were applied to explore new models including COMPASS-31 subscales. Two new models accounted for more of the variance in physical functioning: 39.3% with added gastrointestinal symptoms (β = -2.206, P = .001) and 43.4% of variance with both gastrointestinal (β = -1.592, P = .008) and secretomotor subscales (β = -1.533, P = .049). Unlike the previous study we intended to replicate, mental health burden was not a significant predictor in any of our models.

CONCLUSIONS

Treatments that address autonomic dysregulation should be prioritized for research and clinical recommendations for Veterans with GWI who experience chronic pain.

Diagnosing post-traumatic stress disorder using electronic medical record data

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model's ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

Background

There have been few studies describing how production EMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. The aim of this study is to provide a generalisable methodology for constructing clinically-defined EMR-derived patient cohorts using structured and unstructured data in EMRs.

Methods

Patients with possible acute coronary syndrome (ACS) were used as an exemplar. Cardiologists defined clinical criteria for patients presenting with possible ACS. These were mapped to data tables within the production EMR system creating seven inclusion criteria comprised of structured data fields (orders and investigations, procedures, scanned electrocardiogram (ECG) images, and diagnostic codes) and unstructured clinical documentation. Data were extracted from two local health districts (LHD) in Sydney, Australia. Outcome measures included examination of the relative contribution of individual inclusion criteria to the identification of eligible encounters, comparisons between inclusion criterion and evaluation of consistency of data extracts across years and LHDs.

Results

Among 802,742 encounters in a 5 year dataset (1/1/13–30/12/17), the presence of an ECG image (54.8% of encounters) and symptoms and keywords in clinical documentation (41.4–64.0%) were used most often to identify presentations of possible ACS. Orders and investigations (27.3%) and procedures (1.4%), were less often present for identified presentations. Relevant ICD-10/SNOMED CT codes were present for 3.7% of identified encounters. Similar trends were seen when the two LHDs were examined separately, and across years.

Conclusions

Clinically-defined EMR-derived cohorts combining structured and unstructured data during cohort identification is a necessary prerequisite for critical validation work required for development of real-time clinical decision support and learning health systems.

Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program

We conducted genome-wide association analyses of over 250,000 participants of European (EUR) and African (AFR) ancestry from the Million Veteran Program using electronic health record-validated post-traumatic stress disorder (PTSD) diagnosis and quantitative symptom phenotypes. Applying genome-wide multiple testing correction, we identified three significant loci in European case-control analyses and 15 loci in quantitative symptom analyses. Genomic structural equation modeling indicated tight coherence of a PTSD symptom factor that shares genetic variance with a distinct internalizing (mood-anxiety-neuroticism) factor. Partitioned heritability indicated enrichment in several cortical and subcortical regions, and imputed genetically regulated gene expression in these regions was used to identify potential drug repositioning candidates. These results validate the biological coherence of the PTSD syndrome, inform its relationship to comorbid anxiety and depressive disorders and provide new considerations for treatment.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

The use of machine learning techniques in trauma-related disorders: a systematic review

Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, evaluating methodological and technical features on machine learning studies. We concluded that etiologic and clinical heterogeneity of ASD/PTSD patients is suitable to machine learning techniques and a major challenge for the future is to use it in clinical practice for the benefit of patients in an individual level.

Genomics of posttraumatic stress disorder in veterans: Methods and rationale for Veterans Affairs Cooperative Study #575B

OBJECTIVES

Heritability in the risk for developing posttraumatic stress disorder (PTSD) has been established, but most genome-wide association studies (GWASs) of PTSD involve relatively small sample sizes and limited identification of associated genetic loci. This report describes the methodology of a Veterans Affairs (VA) Cooperative Studies Program GWAS of PTSD among combat-exposed U.S. veterans.

METHODS

Probable cases (with PTSD) and probable controls (without PTSD) were identified from among veterans enrolled in the VA Million Veteran Program (MVP) with an algorithm developed using questionnaire responses and electronic health record information. This algorithm, based on a statistical model, relied on medical chart reviews as a reference standard and was refined using telephone interviews. Subsequently, to evaluate the impact of probabilistic phenotyping on statistical power, the threshold probability for case-control selection was varied in simulations.

RESULTS

As of September 2018, >695,000 veterans have enrolled in MVP. For current analyses, genotyping data were available for >353,000 participants, including >83,000 combat-exposed veterans. A threshold probability of 0.7 for case and control designation yielded an interim >16,000 cases and >33,000 controls.

CONCLUSIONS

A formal methodological approach was used to identify cases and controls for subsequent GWAS analyses to identify genetic risk loci for PTSD.