Gulf War Illness: A Randomized Controlled Trial Combining Mindfulness Meditation and Auricular Acupuncture.

Background: Many Gulf War (GW) Veterans report chronic symptoms including pain, fatigue, and cognitive impairment, commonly defined as Gulf War Illness (GWI). Complementary and integrative health (CIH) therapies may potentially improve multiple symptoms of GWI. Objective: To examine the effectiveness of combining 2 commonly available CIH therapies, mindfulness meditation and auricular acupuncture, in improving health-related functioning and multiple symptom domains of GWI (e.g., pain, fatigue). Methods: This study was a randomized controlled trial in which Veterans with GWI were randomly assigned to either the intervention group (n = 75), wherein they received 2 distinct CIH therapies - mindfulness meditation and auricular acupuncture, or the active control group, wherein they received a GW Health Education (GWHE) program (n = 74), each lasting 8 weeks. Self-report health measures were assessed at baseline, endpoint, and 3 month follow-up. Results: In the intention-to-treat analyses, there were significant between-group differences for mental-health related functioning, fatigue, depression symptoms, and Kansas total severity scores for symptoms in which the CIH group had improved scores for these outcomes at endpoint compared to the GWHE group (all P ≤ .05). The CIH group also had significant reductions in pain interference at endpoint and follow-up compared to baseline (estimated marginal mean difference: -2.52 and -2.22, respectively; all P = .01), whereas no significant changes were observed in the GWHE group. For pain characteristics, the GWHE group had a worsening of pain at endpoint compared to baseline (estimated marginal mean difference: +2.83; P = .01), while no change was observed in the CIH group. Conclusion: Findings suggest a possible beneficial effect of combining 2 CIH therapies, mindfulness meditation and auricular acupuncture, in reducing overall symptom severity and individual symptom domains of fatigue, musculoskeletal, and mood/cognition in Veterans with GWI. Trial Registration: Clinical Trials identifier NCT02180243.

Trends and opportunities in computable clinical phenotyping: A scoping review.

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.

The impact of immunosuppressive agents on immune checkpoint inhibitor efficacy in patients with advanced melanoma: A real-world, multicenter, retrospective study.

BACKGROUND: Immune-related adverse events (irAEs) associated with immune checkpoint inhibitors (ICIs) are often managed via immunosuppressive agents (ISAs); however, their impact on ICI efficacy is not well studied. The impact of the use of ISAs on ICI efficacy in patients with advanced melanoma was therefore investigated. METHODS: This is a real-world, multicenter, retrospective cohort study of patients with advanced melanoma who received ICIs (n = 370). Overall survival (OS) and time to treatment failure (TTF) from the time of ICI initiation were compared among patients in subgroups of interest by unadjusted and 12-week landmark sensitivity-adjusted analyses. The association of irAEs and their management with OS and TTF were evaluated using univariate and multivariable Cox proportional hazards regression models. RESULTS: Overall, irAEs of any grade and of grade ≥3 occurred in 57% and 23% of patients, respectively. Thirty-seven percent of patients received steroids, and 3% received other ISAs. Median OS was longest among patients receiving both (not reached [NR]), shorter among those receiving only systemic steroids (SSs) (84.2 months; 95% CI, 40.2 months to NR), and shortest among those who did not experience irAEs (10.3 months; 95% CI, 6-20.1 months) (p < .001). Longer OS was significantly associated with the occurrence of irAEs and the use of SSs with or without ISAs upon multivariable-adjusted analysis (p < .001). Similar results were noted with anti-programmed death 1 (PD-1) monotherapy and combination anti-PD-1 plus anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) therapy, and with 12-week landmark sensitivity analysis (p = .01). CONCLUSIONS: These findings in patients with melanoma who were treated with ICIs suggest that the use of SSs or ISAs for the management of irAEs is not associated with inferior disease outcomes, which supports the use of these agents when necessary.

Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.

BACKGROUND: Opioids play a role in pain management after surgery, but prolonged use contributes to developing opioid use disorder. Identifying patients at risk of prolonged use is critical for deploying interventions that reduce or avoid opioids; however, available predictive models do not incorporate patient-reported data (PRD), and it remains unclear whether PRD can predict postoperative use behavior. The authors used a machine learning approach leveraging preoperative PRD and electronic health record data to predict persistent opioid use after upper extremity surgery. METHODS: Included patients underwent upper extremity surgery, completed preoperative PRD questionnaires, and were prescribed opioids after surgery. The authors trained models using a 2018 cohort and tested in a 2019 cohort. Opioid use was determined by patient report and filled prescriptions up to 6 months after surgery. The authors assessed model performance using area under the receiver operating characteristic, sensitivity, specificity, and Brier score. RESULTS: Among 1656 patients, 19% still used opioids at 6 weeks, 11% at 3 months, and 9% at 6 months. The XGBoost model trained on PRD plus electronic health record data achieved area under the receiver operating characteristic 0.73 at 6 months. Factors predictive of prolonged opioid use included income; education; tobacco, drug, or alcohol abuse; cancer; depression; and race. Protective factors included preoperative Patient-Reported Outcomes Measurement Information System Global Physical Health and Upper Extremity scores. CONCLUSIONS: This opioid use prediction model using preintervention data had good discriminative performance. PRD variables augmented electronic health record-based machine learning algorithms in predicting postsurgical use behaviors and were some of the strongest predictors. PRD should be used in future efforts to guide proper opioid stewardship. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

Correction: Zhang et al. Association between Body Mass Index and Immune-Related Adverse Events (irAEs) among Advanced-Stage Cancer Patients Receiving Immune Checkpoint Inhibitors: A Pan-Cancer Analysis. Cancers 2021, 13, 6109.

Subha Madhavan (S.M.) and Anas Belouali (A.B.) were not included as authors in the published article [...].

Assessing the Racial and Socioeconomic Disparities in Postpartum Depression Using Population-Level Hospital Discharge Data: Longitudinal Retrospective Study.

BACKGROUND: In the United States, >3.6 million deliveries occur annually. Among them, up to 20% (approximately 700,000) of women experience postpartum depression (PPD) according to the Centers for Disease Control and Prevention. Absence of accurate reporting and diagnosis has made phenotyping of patients with PPD difficult. Existing literature has shown that factors such as race, socioeconomic status, and history of substance abuse are associated with the differential risks of PPD. However, limited research has considered differential temporal associations with the outcome. OBJECTIVE: This study aimed to estimate the disparities in the risk of PPD and time to diagnosis for patients of different racial and socioeconomic backgrounds. METHODS: This is a longitudinal retrospective study using the statewide hospital discharge data from Maryland. We identified 160,066 individuals who had a hospital delivery from 2017 to 2019. We applied logistic regression and Cox regression to study the risk of PPD across racial and socioeconomic strata. Multinomial regression was used to estimate the risk of PPD at different postpartum stages. RESULTS: The cumulative incidence of PPD diagnosis was highest for White patients (8779/65,028, 13.5%) and lowest for Asian and Pacific Islander patients (248/10,760, 2.3%). Compared with White patients, PPD diagnosis was less likely to occur for Black patients (odds ratio [OR] 0.31, 95% CI 0.30-0.33), Asian or Pacific Islander patients (OR 0.17, 95% CI 0.15-0.19), and Hispanic patients (OR 0.21, 95% CI 0.19-0.22). Similar findings were observed from the Cox regression analysis. Multinomial regression showed that compared with White patients, Black patients (relative risk 2.12, 95% CI 1.73-2.60) and Asian and Pacific Islander patients (relative risk 2.48, 95% CI 1.46-4.21) were more likely to be diagnosed with PPD after 8 weeks of delivery. CONCLUSIONS: Compared with White patients, PPD diagnosis is less likely to occur in individuals of other races. We found disparate timing in PPD diagnosis across different racial groups and socioeconomic backgrounds. Our findings serve to enhance intervention strategies and policies for phenotyping patients at the highest risk of PPD and to highlight needs in data quality to support future work on racial disparities in PPD.

Real-World Studies Link Nonsteroidal Anti-inflammatory Drug Use to Improved Overall Lung Cancer Survival.

Inflammation is a cancer hallmark. Nonsteroidal anti-inflammatory drugs (NSAIDs) improve overall survival (OS) in certain cancers. Real-world studies explored here if NSAIDs improve non-small cell lung cancer (NSCLC) OS. Analyses independently interrogated clinical databases from The University of Texas MD Anderson Cancer Center (MDACC cohort, 1987 to 2015; 33,162 NSCLCs and 3,033 NSAID users) and Georgetown-MedStar health system (Georgetown cohort, 2000 to 2019; 4,497 NSCLCs and 1,993 NSAID users). Structured and unstructured clinical data were extracted from electronic health records (EHRs) using natural language processing (NLP). Associations were made between NSAID use and NSCLC prognostic features (tobacco use, gender, race, and body mass index, BMI). NSAIDs were statistically-significantly (P < 0.0001) associated with increased NSCLC survival (5-year OS 29.7% for NSAID users versus 13.1% for non-users) in the MDACC cohort. NSAID users gained 11.6 months over nonusers in 5-year restricted mean survival time. Stratified analysis by stage, histopathology and multicovariable assessment substantiated benefits. NSAID users were pooled independent of NSAID type and by NSAID type. Landmark analysis excluded immortal time bias. Survival improvements (P < 0.0001) were confirmed in the Georgetown cohort. Thus, real-world NSAID usage was independently associated with increased NSCLC survival in the MDACC and Georgetown cohorts. Findings were confirmed by landmark analyses and NSAID type. The OS benefits persisted despite tobacco use and did not depend on gender, race, or BMI (MDACC cohort, P < 0.0001). These real-world findings could guide future NSAID lung cancer randomized trials.

Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features.

Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ ).

Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction.

Objective: Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance. Methods: This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance. Results: Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695-0.7]; ref) to AUC = 0.708 (95% CI [0.705-0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4). Discussion: We demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction. Conclusion: These findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.

Automated Identification of Patients With Immune-Related Adverse Events From Clinical Notes Using Word Embedding and Machine Learning.

PURPOSE: Although immune checkpoint inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed immune-related adverse events (irAEs). To ensure treatment safety, research efforts are needed to comprehensively detect and understand irAEs. Retrospective analysis of data from electronic health records can provide knowledge to characterize these toxicities. However, such information is not captured in a structured format within the electronic health record and requires manual chart review. MATERIALS AND METHODS: In this work, we propose a natural language processing pipeline that can automatically annotate clinical notes and determine whether there is evidence that a patient developed an irAE. Seven hundred eighty-one cases were manually reviewed by clinicians and annotated for irAEs at the patient level. A dictionary of irAEs keywords was used to perform text reduction on clinical notes belonging to each patient; only sentences with relevant expressions were kept. Word embeddings were then used to generate vector representations over the reduced text, which served as input for the machine learning classifiers. The output of the models was presence or absence of any irAEs. Additional models were built to classify skin-related toxicities, endocrine toxicities, and colitis. RESULTS: The model for any irAE achieved an average F1-score = 0.75 and area under the receiver operating characteristic curve = 0.85. This outperformed a basic keyword filtering approach. Although the classifier of any irAEs achieved good accuracy, individual irAE classification still has room for improvement. CONCLUSION: We demonstrate that patient-level annotations combined with a machine learning approach using keywords filtering and word embeddings can achieve promising accuracy in classifying irAEs in clinical notes. This model may facilitate annotation and analysis of large irAEs data sets.

Acoustic and language analysis of speech for suicidal ideation among US veterans.

BACKGROUND: Screening for suicidal ideation in high-risk groups such as U.S. veterans is crucial for early detection and suicide prevention. Currently, screening is based on clinical interviews or self-report measures. Both approaches rely on subjects to disclose their suicidal thoughts. Innovative approaches are necessary to develop objective and clinically applicable assessments. Speech has been investigated as an objective marker to understand various mental states including suicidal ideation. In this work, we developed a machine learning and natural language processing classifier based on speech markers to screen for suicidal ideation in US veterans. METHODOLOGY: Veterans submitted 588 narrative audio recordings via a mobile app in a real-life setting. In addition, participants completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. RESULTS: A combined set of 15 acoustic and linguistic features of speech were identified by the ensemble feature selection. Random Forest classifier, using the selected set of features, correctly identified suicidal ideation in veterans with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. CONCLUSIONS: Speech analysis of audios collected from veterans in everyday life settings using smartphones offers a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.

Safety and efficacy of immune checkpoint inhibitors (ICIs) in cancer patients with HIV, hepatitis B, or hepatitis C viral infection.

BACKGROUND: Patients with chronic viral infections including human immunodeficiency virus (HIV), hepatitis B (HBV) and hepatitis C (HCV) are at increased risk of developing malignancies. The safety and efficacy of ICI therapy in patients with both cancer and chronic viral infections is not well established as most clinical trials of ICIs excluded these patient populations. METHODS: We performed a retrospective analysis of patients with advanced-stage cancers and HIV, HBV, or HCV infection treated with ICI therapy at 5 MedStar Health hospitals from January 2011 to April 2018. RESULTS: We identified 50 patients including 16 HIV, 29 HBV/HCV, and 5 with concurrent HIV and either HBV or HCV. In the HIV cohort (n = 21), any grade immune-related adverse events (irAEs) were 24% with grade ≥ 3 irAEs 14%. Among 5 patients with matched pre/post-treatment results, no significant changes in HIV viral load and CD4+ T-cell counts were observed. RECIST confirmed (n = 18) overall response rate (ORR) was 28% with 2 complete responses (CR) and 3 partial responses (PR). Responders included 2 patients with low baseline CD4+ T-cell counts (40 and 77 cells/ul, respectively). In the HBV/HCV cohort (n = 34), any grade irAEs were 44% with grade ≥ 3 irAEs 29%. RECIST confirmed ORR was 21% (6 PR). Among the 6 patients with known pre/post-treatment viral titers (2 HCV and 4 HBV), there was no evidence of viral reactivation. CONCLUSIONS: Our retrospective series is one of the largest case series to report clinical outcomes among HIV, HBV and HCV patients treated with ICI therapy. Toxicity and efficacy rates were similar to those observed in patients without chronic viral infections. Viral reactivation was not observed. Tumor responses occurred in HIV patients with low CD4 T-cell counts. While prospective studies are needed to validate above findings, these data support not excluding such patients from ICI-based clinical trials or treatment.

An Internet-based writing intervention for PTSD in veterans: A feasibility and pilot effectiveness trial.

[Correction Notice: An Erratum for this article was reported in Vol 9(4) of Psychological Trauma: Theory, Research, Practice, and Policy (see record 2016-54154-001). In the article, the names of authors Adil Alaoui and Anas Belouali were misspelled as Adil Aloui and Anas Beloui respectively. All versions of this article have been corrected.] Objective: Veterans suffering from posttraumatic stress disorder (PTSD) may avoid or fail to follow through with a full course of face-to-face mental health treatment for a variety of reasons. We conducted a pilot effectiveness trial of an online intervention for veterans with current PTSD to determine the feasibility, safety, and preliminary effectiveness of an online writing intervention (i.e., Warriors Internet Recovery & EDucation [WIRED]) as an adjunct to face-to-face psychotherapy. Method: Veterans (N = 34) who had served in Iraq or Afghanistan with current PTSD subsequent to deployment-related trauma were randomized to Veterans Affairs (VA) mental health treatment as usual (TAU) or to treatment as usual plus the online intervention (TAU + WIRED). All research participants were recruited from the Trauma Services Program, VA Medical Center, Washington, DC. They completed baseline assessments as well as assessments 12 weeks and 24 weeks after the baseline assessment. The online intervention consisted of therapist-guided writing, using principles of prolonged exposure and cognitive therapy. The intervention was adapted from an evidence-based treatment used in The Netherlands and Germany for individuals who had been exposed to nonmilitary traumas. Results: In addition to showing that the online intervention was both feasible to develop and implement, as well as being safe, the results showed preliminary evidence of the effectiveness of the TAU + WIRED intervention in this patient population, with particular evidence in reducing PTSD symptoms of hyperarousal. Conclusion: With minor modifications to enhance the therapeutic alliance, this intervention should be tested in a larger clinical trial to determine whether this method of online intervention might provide another alternative to face-to-face treatment for veterans with PTSD. (PsycINFO Database Record

G-DOC Plus - an integrative bioinformatics platform for precision medicine.

BACKGROUND: G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. RESULTS: G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability. CONCLUSION: G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu .