Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Blood platelet RNA profiles do not enable for nivolumab response prediction at baseline in patients with non-small cell lung cancer.

BACKGROUND: Anti-PD-(L)1 immunotherapy has emerged as a promising treatment approach for non-small cell lung cancer (NSCLC), though the response rates remain low. Pre-treatment response prediction may improve patient allocation for immunotherapy. Blood platelets act as active immune-like cells, thereby constraining T-cell activity, propagating cancer metastasis, and adjusting their spliced mRNA content. OBJECTIVE: We investigated whether platelet RNA profiles before start of nivolumab anti-PD1 immunotherapy may predict treatment responses. METHODS: We performed RNA-sequencing of platelet RNA samples isolated from stage III-IV NSCLC patients before treatment with nivolumab. Treatment response was scored by the RECIST-criteria. Data were analyzed using a predefined thromboSeq analysis including a particle-swarm-enhanced support vector machine (PSO/SVM) classification algorithm. RESULTS: We collected and processed a 286-samples cohort, separated into a training/evaluation and validation series and subjected those to training of the PSO/SVM-classification algorithm. We observed only low classification accuracy in the 107-samples validation series (area under the curve (AUC) training series: 0.73 (95% -CI: 0.63-0.84, n = 88 samples), AUC evaluation series: 0.64 (95% -CI: 0.51-0.76, n = 91 samples), AUC validation series: 0.58 (95% -CI: 0.45-0.70, n = 107 samples)), employing a five-RNAs biomarker panel. CONCLUSIONS: We concluded that platelet RNA may have minimally discriminative capacity for anti-PD1 nivolumab response prediction, with which the current methodology is insufficient for diagnostic application.

Cerner Millennium's Care Pathways for Specialty Care Referrals: Provider and Nurse Experiences, Perceptions, and Recommendations for Improvements.

BACKGROUND: Using structured templates to guide providers in communicating key information in electronic referrals is an evidence-based practice for improving care quality. To facilitate referrals in Veterans Health Administration's (VA) Cerner Millennium electronic health record, VA and Cerner have created "Care Pathways"-templated electronic forms, capturing needed information and prompting ordering of appropriate pre-referral tests. OBJECTIVE: To inform their iterative improvement, we sought to elicit experiences, perceptions, and recommendations regarding Care Pathways from frontline clinicians and staff in the first VA site to deploy Cerner Millennium. DESIGN: Qualitative interviews, conducted 12-20 months after Cerner Millennium deployment. PARTICIPANTS: We conducted interviews with primary care providers, primary care registered nurses, and specialty providers requesting and/or receiving referrals. APPROACH: We used rapid qualitative analysis. Two researchers independently summarized interview transcripts with bullet points; summaries were merged by consensus. Constant comparison was used to sort bullet points into themes. A matrix was used to view bullet points by theme and participant. RESULTS: Some interviewees liked aspects of the Care Pathways, expressing appreciation of their premise and logic. However, interviewees commonly expressed frustration with their poor usability across multiple attributes. Care Pathways were reported as being inefficient; lacking simplicity, naturalness, consistency, and effective use of language; imposing an unacceptable cognitive load; and not employing forgiveness and feedback for errors. Specialists reported not receiving the information needed for referral triaging. CONCLUSIONS: Cerner Millennium's Care Pathways, and their associated organizational policies and processes, need substantial revision across several usability attributes. Problems with design and technical limitations are compounding challenges in using standardized templates nationally, across VA sites having diverse organizational and contextual characteristics. VA is actively working to make improvements; however, significant additional investments are needed for Care Pathways to achieve their intended purpose of optimizing specialty care referrals for Veterans.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Tumor-educated platelet blood tests for Non-Small Cell Lung Cancer detection and management.

Liquid biopsy approaches offer a promising technology for early and minimally invasive cancer detection. Tumor-educated platelets (TEPs) have emerged as a promising liquid biopsy biosource for the detection of various cancer types. In this study, we processed and analyzed the TEPs collected from 466 Non-small Cell Lung Carcinoma (NSCLC) patients and 410 asymptomatic individuals (controls) using the previously established thromboSeq protocol. We developed a novel particle-swarm optimization machine learning algorithm which enabled the selection of an 881 RNA biomarker panel (AUC 0.88). Herein we propose and validate in an independent cohort of samples (n = 558) two approaches for blood samples testing: one with high sensitivity (95% NSCLC detected) and another with high specificity (94% controls detected). Our data explain how TEP-derived spliced RNAs may serve as a biomarker for minimally-invasive clinical blood tests, complement existing imaging tests, and assist the detection and management of lung cancer patients.

Discrimination Between Pre- and Postcapillary Pulmonary Hypertension Using Platelet RNA.

Background Appropriate treatment of pulmonary hypertension (PH) is critically dependent on accurate discrimination between pre- and postcapillary PH. However, clinical discrimination is challenging and frequently requires a right heart catheterization. Existing risk scores to detect postcapillary PH have suboptimal discriminatory strength. We have previously shown that platelet-derived RNA profiles may have diagnostic value for PH detection. Here, we hypothesize that platelet-derived RNAs can be employed to select unique biomarker panels for the discrimination between pre- and postcapillary PH. Methods and Results Blood platelet RNA from whole blood was isolated and sequenced from 50 patients with precapillary PH (with different PH subtypes) as well as 50 patients with postcapillary PH. RNA panels were calculated by ANOVA statistics, and classifications were performed using a support vector machine algorithm, supported by particle swarm optimization. We identified in total 4279 different RNAs in blood platelets from patients with pre- and postcapillary PH. A particle swarm optimization-selected RNA panel of 1618 distinctive RNAs with differential levels together with a trained support vector machine algorithm accurately discriminated patients with precapillary PH from patients with postcapillary PH with 100% sensitivity, 60% specificity, 80% accuracy, and 0.95 (95% CI, 0.86-1.00) area under the curve in the independent validation series (n=20). Conclusions This proof-of-concept study demonstrates that particle swarm optimization/support vector machine-enhanced classification of platelet RNA panels may be able to discriminate precapillary PH from postcapillary PH. This research provides a foundation for the development of a blood test with a high negative predictive value that would improve early diagnosis of precapillary PH and prevents unnecessary invasive testing in patients with postcapillary PH.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Veteran Perspectives on Population-Based Suicide Risk Screening in VHA Primary Care: Mixed-Methods Study.

BACKGROUND: In late 2018, VHA implemented a multi-stage suicide risk screening and evaluation initiative, Suicide Risk Identification Strategy, or "Risk ID," in primary care settings. OBJECTIVE: The main objective of this study was to characterize VHA primary care patient perspectives regarding population-based suicide risk screening through the Risk ID program. DESIGN: Mixed methods; survey and qualitative interviews. PARTICIPANTS: Veterans screened for suicide risk using Risk ID in primary care (n = 868) participated in a survey of veteran attitudes about screening (45% response rate); thirty additionally participated in follow-up qualitative interviews. MAIN MEASURES: The quantitative survey consisted of three questions on attitudes about screening for suicidal thoughts in primary care. In qualitative interviews, veterans were asked about their experiences with the Risk ID processes and recommendations for improving Risk ID. KEY RESULTS: Over 90% of veterans reported that it is appropriate for primary care providers or nurses/medical assistants to ask veterans about thoughts of suicide during primary care visits. Approximately half of veterans indicated that veterans should be asked about suicidal thoughts at every visit. Qualitative findings revealed that while most veterans were generally supportive and appreciated VHA screening for suicidal thoughts, they also expressed concern for the potential for inadvertent harm. Participants expressed conflicting preferences for how screening should be handled and delivered. CONCLUSIONS: Findings suggest that most veterans support the integration of standardized suicide risk assessment into routine primary care visits. However, findings also suggest that population-based suicide risk assessment should further consider patient experiences and preferences. Specifically, additional guidance or training for staff conducting suicide risk screening may be warranted to ensure patients feel heard (e.g., eye contact, expressing empathy) and increase patient understanding of the purpose of the screening and potential outcomes. These patient-centered approaches may improve patient experience and facilitate disclosure of suicidal thoughts.

Platelet RNA sequencing for cancer screening in patients with unprovoked venous thromboembolism: a prospective cohort study.

BACKGROUND: Platelet RNA sequencing has been shown to accurately detect cancer in previous studies. OBJECTIVES: To compare the diagnostic accuracy of platelet RNA sequencing with standard-of-care limited cancer screening in patients with unprovoked venous thromboembolism (VTE). METHODS: Patients aged ≥40 years with unprovoked VTE were recruited at 13 centers and followed for 12 months for cancer. Participants underwent standard-of-care limited cancer screening, and platelet RNA sequencing analysis was performed centrally at study end for cases and selected controls. Sensitivity and specificity were calculated, using the predefined primary positivity threshold of 0.54 for platelet RNA sequencing aiming at 86% test sensitivity, and an additional predefined threshold of 0.89 aiming at 99% test specificity. RESULTS: A total of 476 participants were enrolled, of whom 25 (5.3%) were diagnosed with cancer during 12-month follow-up. For each cancer patient, 3 cancer-free patients were randomly selected for the analysis. The sensitivity of limited screening was 72% (95% CI, 52-86) at a specificity of 91% (95% CI, 82-95). The area under the receiver operator characteristic for platelet RNA sequencing was 0.54 (95% CI, 0.41-0.66). At the primary positivity threshold, all patients had a positive test, for a sensitivity estimated at 100% (95% CI, 87-99) and a specificity of 8% (95% CI, 3.7-16.4). At the secondary threshold, sensitivity was 68% (95% CI, 48-83; p value compared with limited screening 0.71) at a specificity of 36% (95% CI, 26-47). CONCLUSION: Platelet RNA sequencing had poor diagnostic accuracy for detecting occult cancer in patients with unprovoked VTE with the current algorithm.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Primary Care and Mental Health Staff Perspectives on Universal Suicide Risk Screening and Care Coordination.

Little is known about staff or patient perspectives on suicide risk screening programs. The objectives of this study were to characterize Veterans Health Administration (VHA) primary care and mental health staff perspectives regarding the VHA Suicide Risk Identification Strategy screening and assessment program and to describe coordination of suicide prevention-related care following positive screening results. Qualitative interviews were conducted with 40 primary care and mental health staff at 12 VHA facilities. An inductive-deductive hybrid approach was used to conduct a thematic analysis. Several key themes were identified. Primary care and mental health staff participants accepted having a structured process for screening for suicidal ideation and conducting risk assessments, but both groups noted limitations and challenges with initial assessment and care coordination following screening. Mental health staff reported more concerns than primary care staff about negative impacts of the screening and assessment process on treatment. Both groups felt that better training was needed for primary care staff to effectively discuss and evaluate suicide risk. The results suggested that additional modifications of the screening and assessment process are needed for patients already known to have elevated risk of suicide or chronic suicidal ideation.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Does screening for PTSD lead to VA mental health care? Identifying the spectrum of initial VA screening actions.

Despite the active posttraumatic stress disorder (PTSD) screening program in Department of Veterans Affairs (VA) primary care clinics and the availability of empirically supported treatments for PTSD at VA, many veterans for whom screening suggests treatment may be indicated do not gain access to VA-based mental health care. To determine where we may be losing veterans to follow-up, we need to begin by identifying the initial action taken in response to a positive PTSD screen in primary care. Using VA administrative data and chart review, we identified the spectrum of initial actions taken after veterans screened positive for PTSD in VA primary care clinics nationwide between October 2017 and September 2018 (N = 41,570). We collapsed actions into those that could lead to VA-based mental health care (e.g., consult placed to a VA mental health clinic) versus not (e.g., veteran declined care), and then examined the association between these categories of actions and contextual- and individual-level variables. More than 61% of veterans with positive PTSD screens had evidence that an initial action toward VA-based mental health care was taken. Urban-dwelling and female veterans were significantly more likely to have evidence of these initial actions, whereas White and Vietnam-era veterans were significantly less likely to have this evidence. Our findings suggest that most veterans screening positive for PTSD in VA primary care clinics have evidence of initial actions taken toward VA-based mental health care; however, a substantial minority do not, making them unlikely to receive follow-up care. Findings highlight the potential benefit of targeted primary care-based access interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA.

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

Blood platelets as an RNA biomarker platform for neuro-oncological diseases.

Blood-based liquid biopsies are an upcoming approach for earlier cancer detection, diagnostics, prognostics, therapy-response prediction, and therapy monitoring, including in patients with tumors of the central nervous system. Among these, liquid biopsies are plasma-derived markers such as cell-free DNA, RNA and proteins, extracellular vesicles, circulating glioma cells, immune cells, and blood platelets. Blood platelets are involved in the local and systemic response to the presence of cancer, thereby sequestering and splicing RNAs, which may be clinically useful as blood-based biomarkers. In this review, we discuss the available literature regarding the role of blood platelets in gliomas and provide suggestions for future research efforts.

Detection and localization of early- and late-stage cancers using platelet RNA.

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

Distinct Platelet Ribonucleic Acid Signatures in Patients with Pulmonary Hypertension.

Rationale: Pulmonary hypertension encompasses progressive disorders leading to right ventricular dysfunction and early death. Late detection is an important cause of poor clinical outcomes. However, biomarkers that accurately predict the presence of pulmonary hypertension are currently lacking. Objectives: In this study, we provide evidence that blood platelets contain a distinctive ribonucleic acid (RNA) profile that may be exploited for the detection of pulmonary hypertension. Methods: Blood platelet RNA was isolated prospectively from 177 prevalent patients with different subtypes of pulmonary hypertension as well as 195 control subjects clinically not suspected of pulmonary hypertension. Sequencing libraries were created using SMARTer (Switching Mechanism at 5' end of RNA Template) copy desoxyribonucleic acid amplification and sequenced on the Illumina High Throughput Sequencing platform. RNA-sequencing reads were mapped to the human reference genome, and intron-spanning spliced RNA reads were selected. Differential spliced RNA panels were calculated by analysis of variance statistics. A particle swarm optimization-enhanced classification algorithm was built employing a development (n = 213 samples) and independent validation series (n = 159 samples). Results: We detected a total of 4,014 different RNAs in blood platelets from patients with pulmonary hypertension (n = 177) and asymptomatic control subjects (n = 195). Gene ontology analysis revealed enhanced RNA concentrations for genes related to RNA processing, translation, and mitochondrial function. A particle swarm optimization-selected RNA panel of 408 distinctive differentially spliced RNAs mediated detection of pulmonary hypertension with 93% sensitivity, 62% specificity, 77% accuracy, 0.89 (95% confidence interval, 0.83-0.93) area under the curve, and a negative predictive value of 91% in the independent validation series. The prediction score was independent of age, sex, smoking, pulmonary hypertension subtype, and the use of pulmonary hypertension-specific medication or anticoagulants. Conclusions: A platelet RNA panel may accurately discriminate patients with pulmonary hypertension from asymptomatic control subjects. In the light of current diagnostic delays, this study is the starting point for further development and evaluation of a platelet RNA-based blood test to ultimately improve early diagnosis and clinical outcomes in patients with pulmonary hypertension.

Electronic Population-Based Depression Detection and Management Through Universal Screening in the Veterans Health Administration.

Importance: In 2016, the US Preventive Services Task Force newly recommended universal screening for depression, with the expectation that screening would be associated with appropriate treatment. Few studies have been able to assess the population-based trajectory from screening to receipt of follow-up and treatment for individuals with depression. Objective: To examine adherence to guidelines for follow-up and treatment among primary care patients who newly screened positive for depression in the Veterans Health Administration (VA). Design, Setting, and Participants: This retrospective cohort study used VA electronic data to identify patients who newly screened positive for depression on the 2-item Patient Health Questionnaire at 82 primary care VA clinics in California, Arizona, and New Mexico between October 1, 2015, and September 30, 2019. Data analysis was performed from December 2020 to August 2021. Main Outcomes and Measures: Receipt of guideline-concordant care for screen-positive patients who were determined by clinicians as having depression was assessed. Timely follow-up (within 84 days of screening) was defined as receiving 3 or more mental health specialty visits, 3 or more psychotherapy visits, or 3 or more primary care visits with a depression diagnosis according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Completing at least minimal treatment (within 12 months) was defined as having 60 days or more of antidepressant prescriptions filled, 4 or more mental health specialty visits, or 3 or more psychotherapy visits. Results: The final cohort included 607 730 veterans (mean [SD] age, 59.4 [18.2] years; 546 516 men [89.9%]; 339 811 non-Hispanic White [55.9%]); 8%, or 82 998 of 997 185 person-years, newly screened positive for depression. Clinicians identified fewer than half with depression (15 155 patients), of whom 32% (5034 of 15 650 person-years) met treatment guidelines for timely follow-up and 77% (12 026 of 15 650 person-years) completed at least minimal treatment. Younger age (odds ratio, 0.990; 95% CI, 0.986-0.993; P < .001), Black race (odds ratio, 1.19; 95% CI, CI 1.05-1.34; P = .01), and having comorbid psychiatric diagnoses were significantly associated with timely follow-up. Individual quality metric components (eg, medication or psychotherapy) were associated differently with overall quality results among patient groups, except for age. Conclusions and Relevance: In this cohort study, most patients met the guidelines for completing at least minimal treatment, but only a minority received timely follow-up after screening positive and being identified as having depression. More research is needed to understand whether the discrepancy between patients who screened positive and patients identified as having depression reflects a gap in recognition of needed care.

Association of Integrated Mental Health Services with Physical Health Quality Among VA Primary Care Patients.

BACKGROUND: Integrated care for comorbid depression and chronic medical disease improved physical and mental health outcomes in randomized controlled trials. The Veterans Health Administration (VA) implemented Primary Care-Mental Health Integration (PC-MHI) across all primary care clinics nationally to increase access to mental/behavioral health treatment, alongside physical health management. OBJECTIVE: To examine whether widespread, pragmatic PC-MHI implementation was associated with improved care quality for chronic medical diseases. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included 828,050 primary care patients with at least one quality metric among 396 VA clinics providing PC-MHI services between October 2013 and September 2016. MAIN MEASURE(S): For outcome measures, chart abstractors rated whether diabetes and cardiovascular quality metrics were met for patients at each clinic as part of VA's established quality reporting program. The explanatory variable was the proportion of primary care patients seen by integrated mental health specialists in each clinic annually. Multilevel logistic regression models examined associations between clinic PC-MHI proportion and patient-level quality metrics, adjusting for regional, patient, and time-level effects and clinic and patient characteristics. KEY RESULTS: Median proportion of patients seen in PC-MHI per clinic was 6.4% (IQR=4.7-8.7%). Nineteen percent of patients with diabetes had poor glycemic control (hemoglobin A1c >9%). Five percent had severely elevated blood pressure (>160/100 mmHg). Each two-fold increase in clinic PC-MHI proportion was associated with 2% lower adjusted odds of poor glycemic control (95% CI=0.96-0.99; p=0.046) in diabetes. While there was no association with quality for patients diagnosed with hypertension, patients without diagnosed hypertension had 5% (CI=0.92-0.99; p=0.046) lower adjusted odds of having elevated blood pressures. CONCLUSIONS AND RELEVANCE: Primary care clinics where integrated mental health care reached a greater proportion of patients achieved modest albeit statistically significant gains in key chronic care quality metrics, providing optimism about the expected effects of large-scale PC-MHI implementation on physical health.

An evidence-based, structured, expert approach to selecting essential indicators of primary care quality.

BACKGROUND: The purpose of this article is to illustrate the application of an evidence-based, structured performance measurement methodology to identify, prioritize, and (when appropriate) generate new measures of health care quality, using primary care as a case example. Primary health care is central to the health care system and health of the American public; thus, ensuring high quality is essential. Due to its complexity, ensuring high-quality primary care requires measurement frameworks that can assess the quality of the infrastructure, workforce configurations, and processes available. This paper describes the use of the Productivity Measurement and Enhancement System (ProMES) to compile a targeted set of such measures, prioritized according to their contribution and value to primary care. METHODS: We adapted ProMES to select and rank existing primary care measures according to value to the primary care clinic. Nine subject matter experts (SMEs) consisting of clinicians, hospital leaders and national policymakers participated in facilitated expert elicitation sessions to identify objectives of performance, corresponding measures, and priority rankings. RESULTS: The SMEs identified three fundamental objectives: access, patient-health care team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted, currently existing measures from three different data sources for primary care. One indicator, Team 2-Day Post Discharge Contact Ratio, was selected as an indicator of both team partnerships and technical quality. Indicators were prioritized according to value using the contingency functions developed by the SMEs. CONCLUSION: Our article provides an actionable guide to applying ProMES, which can be adapted to the needs of various industries, including measure selection and modification from existing data sources, and proposing new measures. Future work should address both logistical considerations (e.g., data capture, common data/programming language) and lingering measurement challenges, such as operationalizating measures to be meaningful and interpretable across health care settings.