Faculty Bibliography

Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

The quality of a cluster analysis of unlabeled units depends on the quality of the between units dissimilarity measures. Data dependent dissimilarity is more objective than data independent geometric measures such as Euclidean distance. As suggested by Breiman, many data driven approaches are based on decision tree ensembles, such as a random forest (RF), that produce a proximity matrix that can easily be transformed into a dissimilarity matrix. A RF can be obtained using labels that distinguish units with real data from units with synthetic data. The resulting dissimilarity matrix is input to a clustering program and units are assigned labels corresponding to cluster membership. We introduce a General Iterative Cluster (GIC) algorithm that improves the proximity matrix and clusters of the base RF. The cluster labels are used to grow a new RF yielding an updated proximity matrix which is entered into the clustering program. The process is repeated until convergence. The same procedure can be used with many base procedures such as the Extremely Randomized Tree ensemble. We evaluate the performance of the GIC algorithm using benchmark and simulated data sets. The properties measured by the Silhouette Score are substantially superior to the base clustering algorithm. The GIC package has been released in R: https://cran.r-project.org/web/packages/GIC/index.html.

Background/Significance: During the COVID-19 pandemic, people with substance use disorders have experienced increased rates of overdose, decreased access to substance use disorder treatment, and increased risk for adverse COVID outcomes (NIDA, 2020). Throughout the pandemic, NYU Langone Health has continued using the Tobacco, Alcohol, and Prescription Substance (TAPS) screening tool for all inpatient admissions in order to identify and provide proactive consultation to hospitalized patients at risk for substance use disorders.
Method(s): We conducted a retrospective review of adult inpatient medical and surgical admissions to NYU Langone Health, using data collected from a pre-defined Epic report based on TAPS documentation. We compared groups pre-COVID-19 pandemic (defined as 9/2018-9/2019) and during COVID-19 pandemic (defined as 3/2020-3/2021) for the following outcomes: (1) nursing compliance rate with TAPS administration, (2) prevalence of patients with substance use disorders as measured by positive TAPS screen, and (3) severity of alcohol use disorder among patients with TAPS positive for alcohol.
Result(s): During the pre-COVID-19 period, 24,057 patients were screened with a compliance rate of 90% and a positivity rate of 6% (N=1673). ICU compliance was 84%. Prevalence of patients at risk for various substance use disorders was as follows: 4.3% (N=1027) alcohol, 1.5% (N=357) cannabis, 0.32% (N=78) heroin, 0.24% (N=57) opiates, 0.15% (N=35) sedatives, 0.48% (N=116) stimulants, and 0.01% (N=3) prescription stimulants. Of positive alcohol screens, 26.7% (274/1027) represented the highest severity of use (Alcohol Score 4). During the COVID-19 period, 17,931 patients were screened with a compliance rate of 82% and positivity rate of 6% (N=1374). ICU compliance was 74%. Prevalence of patients at risk for various substance use disorders was as follows: 4.3% (N=772) alcohol, 1.5% (N=272) cannabis, 0.60% (N=108) heroin, 0.26% (N=46) opiates, 0.20% (N=35) sedatives, 0.69% (N=124) stimulants, and 0.04% (N=7) prescription stimulants. Of positive alcohol screens, 41.2% (318/772) were highest severity. We were unable to meaningfully test for significant given limitations of Epic datasets and variability in unit composition and staffing throughout COVID-19 period.
Discussion(s): There was decreased compliance with TAPS administration during COVID-19 as compared to pre-COVID-19, as well as overall low compliance in ICUs during both time periods. There were similar rates of positive screens for all substance use disorders pre-COVID-19 and during COVID-19, with an increase in positive heroin and other opiate screens during COVID-19. Among patients with positive alcohol screens, there was increased severity of alcohol scores during COVID-19 relative to pre-COVID-19. Conclusion/Implications: These results suggest a change in patterns of substance use during the COVID-19 pandemic, consistent with findings from prior studies of increased opioid overdoses (Slavova 2020, Georgia Department of Public Health 2020) and severity of substance use (NIDA 2020). Poor ICU compliance suggests increased barriers to TAPS administration in patients with critical illness and/or altered mental status, which may lead to decreased identification and treatment of patients at increased risk for substance use disorders. These results may inform clinical practice and future studies regarding utilization of TAPS screen and proactive addiction psychiatry consultation service in acute care settings. References: 1. NIDA. 2020, September 14. Addressing the Unique Challenges of COVID-19 for People in Recovery. Retrieved from https://www.drugabuse.gov/about-nida/noras-blog/2020/09/addressing-u nique-challenges-covid-19-people-in-recovery on 2021, March 15 2. Slavova, S., Rock, P., Bush, H. M., Quesinberry, D., & Walsh, S. L. (2020). Signal of increased opioid overdose during COVID-19 from emergency medical services data. Drug and alcohol dependence, 214, 108176. 3. Georgia Department of Public Health. 2020, June 19. Suspected Drug Overdose Increases in Georgia Amid COVID-19. Retrieved from https://www.drugabuse.gov/sites/default/files/suspected_drug_overdos e_increases_in_georgia_amid_covid-19_1.pdf
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Background: CL psychiatrists are uniquely positioned to combat structural racism in medicine Currently, there are no published papers examining racial bias in the management of psychiatric emergencies in the general medical hospital. Given the potential for restrictive clinical interventions that directly challenge a patient's autonomy (including intramuscular injections and restraints), our group embarked on a long-term, quality improvement project to detect and address racial bias affecting the clinical management of these psychiatric emergencies.
Method(s): Our institution has a multidisciplinary behavioral code team known as the Behavioral Emergency Response Team (BERT) that responds to behavioral emergencies throughout the medical hospital. Secondary BERT event data occurring from 2017 to 2020 was combined with demographic data from the electronic medical record. Race and ethnic data were collapsed into unique, phenotypic categories. BERT events were coded based on the most restrictive intervention utilized. Descriptive statistics were used to describe the sample and examine whether race / ethnicity correlated with BERT intervention utilized, diagnostic impression, reason for BERT activation, or recurrent BERTs.
Result(s): Our sample included 1532 BERT events representing N = 902 unique patients. The main interaction of BERT intervention by Race / Ethnic category reached statistical significance (p=0.04). Though most BERTs only required verbal de-escalation (n=419, 46.45%), 3% of BERTs (n = 29) escalated to 4-pt restraints (most restrictive intervention). Though reaching level 5 was rare, Black patients had a statistically significant higher likelihood of receiving this intervention compared with White patients (6% v 2%, p=0.027) and compared with all other non-Black patients (6% v 2%, p=0.040). Although the overall comparison for Race/Ethnicity and the diagnostic impression "Psychosis" did not reach significance (p=0.086), targeted analysis showed that Black patients were significantly more likely to have "Psychosis" listed as a contributing factor compared with White patients (p=0.009) and all other non-Black patients (p=0.016). Several other comparisons with Race / Ethnic category reached statistical significance: Age (p=0.048), and need for interpreter yes/no (p<0.001). Closer examination of the interaction of Race/Ethnicity x Need for Interpreter revealed that half of events involving Asian patients (n=22, 53.66%) and a third of events involving Hispanic patients (n=29, 30.53%) required interpreter services.
Discussion(s): This study demonstrates the feasibility of investigating racial bias in behavioral emergency management. The results of this preliminary analysis suggest multiple areas for enhanced education, self-awareness development, and programmatic improvement to target systemic racism, decrease racial bias, and improve patient care. These areas include bias in restraints use, the role of language in behavioral emergencies, and the influence of race on perception of underlying diagnosis.
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OBJECTIVE:To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T. METHODS:Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder. RESULTS:A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R. CONCLUSION/CONCLUSIONS:The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Background and Objective/UNASSIGNED:Posttraumatic stress disorder (PTSD) is a serious and frequently debilitating psychiatric condition that can occur in people who have experienced traumatic stessors, such as war, violence, sexual assault and other life-threatening events. Treatment of PTSD and traumatic brain injury (TBI) in veterans is challenged by diagnostic complexity, partially due to PTSD and TBI symptom overlap and to the fact that subjective self-report assessments may be influenced by a patient's willingness to share their traumatic experiences and resulting symptoms. Corticotropin-releasing factor (CRF) is one of the main mediators of hypothalamic pituitary adrenal (HPA)-axis responses in stress and anxiety. Methods and Results/UNASSIGNED:We analyzed serum CRF levels in 230 participants including heathy controls (64), and individuals with PTSD (53), TBI (70) or PTSD+TBI (43) by enzyme immunoassay (EIA). Significantly lower CRF levels were found in both the PTSD and PTSD+TBI groups compared to healthy control (PTSD vs Controls: P=0.0014, PTSD + TBI vs Controls: P=0.0011) and chronic TBI participants (PTSD vs TBI: P<0.0001PTSD + TBI vs TBI: P<0.0001) , suggesting a PTSD-related mechanism independent from TBI and associated with CRF reduction. CRF levels negatively correlated with PTSD severity on the CAPS-5 scale in the whole study group. Conclusions/UNASSIGNED:Hyperactivation of the HPA axis has been classically identified in acute stress. However, the recognized enhanced feedback inhibition of the HPA axis in chronic stress supports our findings of lower CRF in PTSD patients. This study suggests that reduced serum CRF in PTSD should be further investigated. Future validation studies will establish if CRF is a possible blood biomarker for PTSD and/or for differentiating PTSD and chronic TBI symptomatology.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.