Predictive modeling of initiation and delayed mental health contact for depression.

BACKGROUND: Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment. METHODS: We obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis. RESULTS: 108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586-0.602; machine learning F-measure = 0.46). CONCLUSIONS: Having VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury.

STUDY DESIGN: A 5-year longitudinal, retrospective, cohort study. OBJECTIVES: Develop a prediction model based on electronic health record (EHR) data to identify veterans with spinal cord injury/diseases (SCI/D) at highest risk for new pressure injuries (PIs). SETTING: Structured (coded) and text EHR data, for veterans with SCI/D treated in a VHA SCI/D Center between October 1, 2008, and September 30, 2013. METHODS: A total of 4709 veterans were available for analysis after randomly selecting 175 to act as a validation (gold standard) sample. Machine learning models were created using ten-fold cross validation and three techniques: (1) two-step logistic regression; (2) regression model employing adaptive LASSO; (3) and gradient boosting. Models based on each method were compared using area under the receiver-operating curve (AUC) analysis. RESULTS: The AUC value for the gradient boosting model was 0.62 (95% CI = 0.54-0.70), for the logistic regression model it was 0.67 (95% CI = 0.59-0.75), and for the adaptive LASSO model it was 0.72 (95% CI = 0.65-80). Based on these results, the adaptive LASSO model was chosen for interpretation. The strongest predictors of new PI cases were having fewer total days in the hospital in the year before the annual exam, higher vs. lower weight and most severe vs. less severe grade of injury based on the American Spinal Cord Injury Association (ASIA) Impairment Scale. CONCLUSIONS: While the analyses resulted in a potentially useful predictive model, clinical implications were limited because modifiable risk factors were absent in the models.

Mortality and Associated Risk Factors in Community-Dwelling Persons With Early Dementia.

OBJECTIVE: Examine mortality and associations with baseline characteristics among Veterans with early dementia. METHODS: Participants included dyads of community-based Veterans with early dementia and their caregivers (N=143) enrolled in a previous longitudinal study. Department of Veterans Health Affairs' electronic records were used to retrospectively collect Veteran mortality outcomes, over a 6-year period. Measures included baseline: demographics, dementia-related factors, other comorbid conditions, functioning, and medication use. Associations with baseline characteristics and mortality were examined with bivariate analyses and a series of Cox proportional hazard models. RESULTS: Over 6 years of study follow-up, 53.1% of participants died. The mean time to death was 3.09 years, with a range of 54 days to 5.91 years. Female sex, better cognition, and higher scores on the Tinetti Gait and Balance scale were protective factors in the final multivariable model, adjusting for other characteristics. CONCLUSIONS: While newly diagnosed with early dementia, over half of our sample died in the 6-year follow-up period, with the average death occurring only 3 years after initial diagnosis. The finding of lower mortality associated with better performance on gait/balance testing indicates an important opportunity for focused interventions and early detection of gait and balance changes early during cognitive decline.

Evaluation of Complementary and Integrative Health Approaches Among US Veterans with Musculoskeletal Pain Using Propensity Score Methods.

Objectives: To examine the treatment effectiveness of complementary and integrative health approaches (CIH) on chronic pain using Propensity Score (PS) methods. Design, Settings, and Participants: A retrospective cohort of 309,277 veterans with chronic musculoskeletal pain assessed over three years after initial diagnosis. Methods: CIH exposure was defined as one or more clinical visits for massage, acupuncture, or chiropractic care. The treatment effect of CIH on self-rated pain intensity was examined using a longitudinal model. PS-matching and inverse probability of treatment weighting (IPTW) were used to account for potential selection and confounding biases. Results: At baseline, veterans with (7,621) and without (301,656) CIH exposure differed significantly in 21 out of 35 covariates. During the follow-up period, on average CIH recipients had 0.83 (95% confidence interval [CI] = 0.77 to 0.89) points higher pain intensity ratings (range = 0-10) than nonrecipients. This apparent unfavorable effect size was reduced to 0.37 (95% CI = 0.28 to 0.45) after PS matching, 0.36 (95% CI = 0.29 to 0.44) with IPTW on the treated (IPTW-T) weighting, and diminished to null when integrating IPTW-T with PS matching (0.004, 95% CI = -0.09 to 0.10). An alternative IPTW model and conventional covariate adjustment appeared least powerful in terms of potential bias reduction. Sensitivity analyses restricting the follow-up period to one year after CIH initiation derived consistent results. Conclusions: PS-based causal methods successfully eliminated baseline difference between exposure groups in all measured covariates, yet they did not detect a significant difference in the self-rated pain intensity outcome between veterans who received CIHs and those who did not during the follow-up period.

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs.

In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews. We assessed inter-coder reliability using percent agreement and the kappa statistic. We undertook 15 interviews and held two workshop discussions. The main areas of requirements related to; design and functionality, resources, and information. Stakeholders also confirmed the vision of the second generation of the Ecosystem and recommendations included; adding mechanisms to better understand terms, measuring collaboration to demonstrate value, and datasets/tools to navigate spelling errors with consumer language, among others. Stakeholders also recommended capability to: communicate with developers working on the next version of the VA electronic health record (VistA Evolution), provide a mechanism to automatically monitor download of tools and to automatically provide a summary of the downloads to Ecosystem contributors and funders. After three rounds of coding and discussion, we determined the percent agreement of two coders to be 97.2% and the kappa to be 0.7851. The vision of the VA Clinical NLP Ecosystem met stakeholder needs. Interviews and discussion provided key requirements that inform the design of the VA Clinical NLP Ecosystem.

Improving identification of fall-related injuries in ambulatory care using statistical text mining.

OBJECTIVES: We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. METHODS: We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review. RESULTS: STM models based on training data from a single facility resulted in accuracy of 87.5% and 87.1%, F-measure of 87.0% and 90.9%, sensitivity of 92.1% and 94.1%, and specificity of 83.6% and 77.8% at the visit and patient levels, respectively. Results from training data from multiple facilities were almost identical. CONCLUSIONS: STM has the potential to improve identification of fall-related injuries in the VHA, providing a model for wider application in the evolving national EHR system.

Mental health treatment utilization patterns among 108,457 Afghanistan and Iraq veterans with depression.

People with depression often underutilize mental health care. This study was conceived as a first step toward a clinical decision support tool that helps identify patients who are at higher risk of underutilizing care. The primary goals were to (a) describe treatment utilization patterns, early termination, and return to care; (b) identify factors associated with early termination of treatment; and (c) evaluate the accuracy of regression models to predict early termination. These goals were evaluated in a retrospective cohort analysis of 108,457 U.S. veterans who received care from the Veterans Health Administration between 2001 and 2021. Our final sample was 16.5% female with an average age of 34.5. Veterans were included if they had a depression diagnosis, a positive depression screen, and received general health care services at least a year before and after their depression diagnosis. Using treatment quality guidelines, the threshold for treatment underutilization was defined as receiving fewer than four psychotherapy sessions or less than 84 days of antidepressants. Over one fifth of veterans (21.6%) received less than the minimally recommended care for depression. The odds of underutilizing treatment increased with lack of Veterans Administration benefits, male gender, racial/ethnic minority status, and having received mental health treatment in the past (adjusted OR > 1.1). Posttraumatic stress disorder comorbidity correlated with increased depression treatment utilization (adjusted OR < .9). Models with demographic and clinical information from medical records performed modestly in classifying patients who underutilized depression treatment (area under the curve = 0.595, 95% CI [0.588, 0.603]). Most veterans in this cohort received at least the minimum recommended treatment for depression. To improve the prediction of underutilization, patient factors associated with treatment underutilization likely need to be supplemented by additional clinical information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparing Outcomes of the Veterans Health Administration's Traumatic Brain Injury and Mental Health Screening Programs: Types and Frequency of Specialty Services Used.

The Veterans Health Administration (VHA) screens veterans who deployed in support of the wars in Afghanistan and Iraq for traumatic brain injury (TBI) and mental health (MH) disorders. Chronic symptoms after mild TBI overlap with MH symptoms, for which there are already established screens within the VHA. It is unclear whether the TBI screen facilitates treatment for appropriate specialty care over and beyond the MH screens. Our primary objective was to determine whether TBI screening is associated with different types (MH, Physical Medicine & Rehabilitation [PM&R], and Neurology) and frequency of specialty services compared with the MH screens. A retrospective cohort design examined veterans receiving VHA care who were screened for both TBI and MH disorders between Fiscal Year (FY) 2007 and FY 2018 (N = 241,136). We calculated service utilization counts in MH, PM&R, and Neurology in the six months after the screens. Zero-inflated negative binomial regression models of encounters (counts) were fit separately by specialty care type and for a total count of specialty services. We found that screening positive for TBI resulted in 2.38 times more specialty service encounters than screening negative for TBI. Compared with screening positive for MH only, screening positive for both MH and TBI resulted in 1.78 times more specialty service encounters and 1.33 times more MH encounters. The TBI screen appears to increase use of MH, PM&R, and Neurology services for veterans with post-deployment health concerns, even in those also identified as having a possible MH disorder.

Complementary and Integrative Health Approaches and Pain Care Quality in the Veterans Health Administration Primary Care Setting: A Quasi-Experimental Analysis.

Background: Complementary and integrative health (CIH) approaches have been recommended in national and international clinical guidelines for chronic pain management. We set out to determine whether exposure to CIH approaches is associated with pain care quality (PCQ) in the Veterans Health Administration (VHA) primary care setting. Methods: We followed a cohort of 62,721 Veterans with newly diagnosed musculoskeletal disorders between October 2016 and September 2017 over 1-year. PCQ scores were derived from primary care progress notes using natural language processing. CIH exposure was defined as documentation of acupuncture, chiropractic or massage therapies by providers. Propensity scores (PSs) were used to match one control for each Veteran with CIH exposure. Generalized estimating equations were used to examine associations between CIH exposure and PCQ scores, accounting for potential selection and confounding bias. Results: CIH was documented for 14,114 (22.5%) Veterans over 16,015 primary care clinic visits during the follow-up period. The CIH exposure group and the 1:1 PS-matched control group achieved superior balance on all measured baseline covariates, with standardized differences ranging from 0.000 to 0.045. CIH exposure was associated with an adjusted rate ratio (aRR) of 1.147 (95% confidence interval [CI]: 1.142, 1.151) on PCQ total score (mean: 8.36). Sensitivity analyses using an alternative PCQ scoring algorithm (aRR: 1.155; 95% CI: 1.150-1.160) and redefining CIH exposure by chiropractic alone (aRR: 1.118; 95% CI: 1.110-1.126) derived consistent results. Discussion: Our data suggest that incorporating CIH approaches may reflect higher overall quality of care for patients with musculoskeletal pain seen in primary care settings, supporting VHA initiatives and the Declaration of Astana to build comprehensive, sustainable primary care capacity for pain management. Future investigation is warranted to better understand whether and to what degree the observed association may reflect the therapeutic benefits patients actually received or other factors such as empowering provider-patient education and communication about these approaches.

Mental Health Diagnoses are Not Associated With Indicators of Lower Quality Pain Care in Electronic Health Records of a National Sample of Veterans Treated in Veterans Health Administration Primary Care Settings.

Prior research has demonstrated disparities in general medical care for patients with mental health conditions, but little is known about disparities in pain care. The objective of this retrospective cohort study was to determine whether mental health conditions are associated with indicators of pain care quality (PCQ) as documented by primary care clinicians in the Veterans Health Administration (VHA). We used natural language processing to analyze electronic health record data from a national sample of Veterans with moderate to severe musculoskeletal pain during primary care visits in the Fiscal Year 2017. Twelve PCQ indicators were annotated from clinician progress notes as present or absent; PCQ score was defined as the sum of these indicators. Generalized estimating equation Poisson models examined associations among mental health diagnosis categories and PCQ scores. The overall mean PCQ score across 135,408 person-visits was 8.4 (SD = 2.3). In the final adjusted model, post-traumatic stress disorder was associated with higher PCQ scores (RR = 1.006, 95%CI 1.002-1.010, P = .007). Depression, alcohol use disorder, other substance use disorder, schizophrenia, and bipolar disorder diagnoses were not associated with PCQ scores. Overall, results suggest that in this patient population, presence of a mental health condition is not associated with lower quality pain care. PERSPECTIVE: This study used a natural language processing approach to analyze medical records to determine whether mental health conditions are associated with indicators of pain care quality as documented by primary care clinicians. Findings suggest that presence of a diagnosed mental health condition is not associated with lower quality pain care.

A match made in heaven? Trying to combine ACS-NSQIP and NCDB databases.

BACKGROUND: As part of a larger study evaluating breast cancer care, we attempted to validate our matching strategies between the National Cancer Data Base (NCDB) and ACS National Surgical Quality Improvement Program (ACS-NSQIP). METHODS: Using 2002-2006 data, we attempted to match cases by a three-tiered approach. Three groups resulted: (1) successfully matched, (2) NCDB case with no corresponding match in ACS-NSQIP, and (3) ACS-NSQIP case with no match in NCDB. Single institution (University of Utah) data were used for a nested validation study of the unmatched groups. RESULTS: The initial match yielded a 23.4% net match rate (rate of 8.6% at the University of Utah). In subset review of unmatched University of Utah cancer registry cases (NCDB, n = 153), 56% (n = 86) of cases had their index surgery at the University of Utah, with 15 potential matches in the unmatched ACS-NSQIP data using age and date of surgery and no potential match for 41 cases. Twenty-five remaining cases had a potential surgery date match if age was varied by 1 y with 18 confirmed matches. Review of unmatched ACS-NSQIP cases (n = 107) yielded 15 potential matches in the University of Utah cancer registry, with no potential match for 63 cases. Twenty-nine cases had a potential surgery date match if age was varied, with 26 confirmed matches. Review of ACS-NSQIP cases from 2006 for cancer status and stage revealed two cancer patients who were not in the cancer registry. CONCLUSIONS: Linking ACS-NSQIP and NCDB without a captive patient population results in low overall match rates due, in part, to specific inclusion criteria and different variable definitions for each database.

Health outcomes associated with military deployment: mild traumatic brain injury, blast, trauma, and combat associations in the Florida National Guard.

OBJECTIVES: To determine the association between specific military deployment experiences and immediate and longer-term physical and mental health effects, as well as examine the effects of multiple deployment-related traumatic brain injuries (TBIs) on health outcomes. DESIGN: Online survey of cross-sectional cohort. Odds ratios were calculated to assess the association between deployment-related factors (ie, physical injuries, exposure to potentially traumatic deployment experiences, combat, blast exposure, and mild TBI) and current health status, controlling for potential confounders, demographics, and predeployment experiences. SETTING: Nonclinical. PARTICIPANTS: Members (N=3098) of the Florida National Guard (1443 deployed, 1655 not deployed). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Presence of current psychiatric diagnoses and health outcomes, including postconcussive and non-postconcussive symptoms. RESULTS: Surveys were completed an average of 31.8 months (SD=24.4, range=0-95) after deployment. Strong, statistically significant associations were found between self-reported military deployment-related factors and current adverse health status. Deployment-related mild TBI was associated with depression, anxiety, posttraumatic stress disorder (PTSD), and postconcussive symptoms collectively and individually. Statistically significant increases in the frequency of depression, anxiety, PTSD, and a postconcussive symptom complex were seen comparing single to multiple TBIs. However, a predeployment TBI did not increase the likelihood of sustaining another TBI in a blast exposure. Associations between blast exposure and abdominal pain, pain on deep breathing, shortness of breath, hearing loss, and tinnitus suggested residual barotrauma. Combat exposures with and without physical injury were each associated not only with PTSD but also with numerous postconcussive and non-postconcussive symptoms. The experience of seeing others wounded or killed or experiencing the death of a buddy or leader was associated with indigestion and headaches but not with depression, anxiety, or PTSD. CONCLUSIONS: Complex relationships exist between multiple deployment-related factors and numerous overlapping and co-occurring current adverse physical and psychological health outcomes. Various deployment-related experiences increased the risk for postdeployment adverse mental and physical health outcomes, individually and in combination. These findings suggest that an integrated physical and mental health care approach would be beneficial to postdeployment care.

The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records.

BACKGROUND: Affective characteristics are associated with depression severity, course, and prognosis. Patients' affect captured by clinicians during sessions may provide a rich source of information that more naturally aligns with the depression course and patient-desired depression outcomes. OBJECTIVE: In this paper, we propose an information extraction vocabulary used to pilot the feasibility and reliability of identifying clinician-recorded patient affective states in clinical notes from electronic health records. METHODS: Affect and mood were annotated in 147 clinical notes of 109 patients by 2 independent coders across 3 pilots. Intercoder discrepancies were settled by a third coder. This reference annotation set was used to test a proof-of-concept natural language processing (NLP) system using a named entity recognition approach. RESULTS: Concepts were frequently addressed in templated format and free text in clinical notes. Annotated data demonstrated that affective characteristics were identified in 87.8% (129/147) of the notes, while mood was identified in 97.3% (143/147) of the notes. The intercoder reliability was consistently good across the pilots (interannotator agreement [IAA] >70%). The final NLP system showed good reliability with the final reference annotation set (mood IAA=85.8%; affect IAA=80.9%). CONCLUSIONS: Affect and mood can be reliably identified in clinician reports and are good targets for NLP. We discuss several next steps to expand on this proof of concept and the value of this research for depression clinical research.

Measuring pain care quality in the Veterans Health Administration primary care setting.

ABSTRACT: The lack of a reliable approach to assess quality of pain care hinders quality improvement initiatives. Rule-based natural language processing algorithms were used to extract pain care quality (PCQ) indicators from documents of Veterans Health Administration primary care providers for veterans diagnosed within the past year with musculoskeletal disorders with moderate-to-severe pain intensity across 2 time periods 2013 to 2014 (fiscal year [FY] 2013) and 2017 to 2018 (FY 2017). Patterns of documentation of PCQ indicators for 64,444 veterans and 124,408 unique visits (FY 2013) and 63,427 veterans and 146,507 visits (FY 2017) are described. The most commonly documented PCQ indicators in each cohort were presence of pain, etiology or source, and site of pain (greater than 90% of progress notes), while least commonly documented were sensation, what makes pain better or worse, and pain's impact on function (documented in fewer than 50%). A PCQ indicator score (maximum = 12) was calculated for each visit in FY 2013 (mean = 7.8, SD = 1.9) and FY 2017 (mean = 8.3, SD = 2.3) by adding one point for every indicator documented. Standardized Cronbach alpha for total PCQ scores was 0.74 in the most recent data (FY 2017). The mean PCQ indicator scores across patient characteristics and types of healthcare facilities were highly stable. Estimates of the frequency of documentation of PCQ indicators have face validity and encourage further evaluation of the reliability, validity, and utility of the measure. A reliable measure of PCQ fills an important scientific knowledge and practice gap.

Optimizing the Impact of Pragmatic Clinical Trials for Veteran and Military Populations: Lessons From the Pain Management Collaboratory.

Pragmatic clinical trials (PCTs) are well-suited to address unmet healthcare needs, such as those arising from the dual public health crises of chronic pain and opioid misuse, recently exacerbated by the COVID-19 pandemic. These overlapping epidemics have complex, multifactorial etiologies, and PCTs can be used to investigate the effectiveness of integrated therapies that are currently available but underused. Yet individual pragmatic studies can be limited in their reach because of existing structural and cultural barriers to dissemination and implementation. The National Institutes of Health, Department of Defense, and Department of Veterans Affairs formed an interagency research partnership, the Pain Management Collaboratory. The partnership combines pragmatic trial design with collaborative tools and relationship building within a large network to advance the science and impact of nonpharmacological approaches and integrated models of care for the management of pain and common co-occurring conditions. The Pain Management Collaboratory team supports 11 large-scale, multisite PCTs in veteran and military health systems with a focus on team science with the shared aim that the "whole is greater than the sum of the parts." Herein, we describe this integrated approach and lessons learned, including incentivizing all parties; proactively offering frequent opportunities for problem-solving; engaging stakeholders during all stages of research; and navigating competing research priorities. We also articulate several specific strategies and their practical implications for advancing pain management in active clinical, "real-world," settings.

Examining the Relationship Between Environmental Factors and Inpatient Hospital Falls: Protocol for a Mixed Methods Study.

BACKGROUND: Patient falls are the most common adverse events reported in hospitals. Although it is well understood that the physical hospital environment contributes to nearly 40% of severe or fatal hospital falls, there are significant gaps in the knowledge about the relationship between inpatient unit design and fall rates. The few studies that have examined unit design have been conducted in a single hospital (non-Veterans Health Administration [VHA]) or a small number of inpatient units, limiting generalizability. The goal of this study is to identify unit design factors contributing to inpatient falls in the VHA. OBJECTIVE: The first aim of the study is to investigate frontline and management perceptions of and experiences with veteran falls as they pertain to inpatient environmental factors. An iterative rapid assessment process will be used to analyze the data. Interview findings will directly inform the development of an environmental assessment survey to be conducted as part of aim 2 and to contribute to interpretation of aim 2. The second aim of this study is to quantify unit design factors and compare spatial and environmental factors of units with higher- versus lower-than-expected fall rates. METHODS: We will first conduct walk-through interviews with facility personnel in 10 medical/surgical units at 3 VHA medical centers to identify environmental fall risk factors. Data will be used to finalize an environmental assessment survey for nurse managers and facilities managers. We will then use fall data from the VA Inpatient Evaluation Center and patient data from additional sources to identify 50 medical/surgical nursing units with higher- and lower-than-expected fall rates. We will measure spatial factors by analyzing computer-aided design files of unit floorplans and environmental factors from the environmental assessment survey. Statistical tests will be performed to identify design factors that distinguish high and low outliers. RESULTS: The VA Health Services Research and Development Service approved funding for the study. The research protocol was approved by institutional review boards and VA research committees at both sites. Data collection started in February 2018. Results of the data analysis are expected by February 2022. Data collection and analysis was completed for aim 1 with a manuscript of results in progress. For aim 2, the medical/surgical units were categorized into higher- and lower-than-expected fall categories, the environmental assessment surveys were distributed to facility managers and nurse managers. Data to measure spatial characteristics are being compiled. CONCLUSIONS: To our knowledge, this study is the first to objectively identify spatial risks for falls in hospitals within in a large multihospital system. Findings can contribute to evidence-based design guidelines for hospitals such as those of the Facility Guidelines Institute and the Department of Veterans Affairs. The metrics for characterizing spatial features are quantitative indices that could be incorporated in larger scale contextual studies examining contributors to falls, which to date often exclude physical environmental factors at the unit level. Space syntax measures could be used as physical environmental factors in future research examining a range of contextual factors-social, personal, organizational, and environmental-that contribute to patient falls. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24974.

Assessing the impact of the COVID-19 pandemic on pragmatic clinical trial participants.

Characterizing the impacts of disruption attributable to the COVID-19 pandemic on clinical research is important, especially in pain research where psychological, social, and economic stressors attributable to the COVID-19 pandemic may greatly impact treatment effects. The National Institutes of Health - Department of Defense - Department of Veterans Affairs Pain Management Collaboratory (PMC) is a collective effort supporting 11 pragmatic clinical trials studying nonpharmacological approaches and innovative integrated care models for pain management in veteran and military health systems. The PMC rapidly developed a brief pandemic impacts measure for use across its pragmatic trials studying pain while remaining broadly applicable to other areas of clinical research. Through open discussion and consensus building by the PMC's Phenotypes and Outcomes Work Group, the PMC Coronavirus Pandemic (COVID-19) Measure was iteratively developed. The measure assesses the following domains (one item/domain): access to healthcare, social support, finances, ability to meet basic needs, and mental or emotional health. Two additional items assess infection status (personal and household) and hospitalization. The measure uses structured responses with a three-point scale for COVID-19 infection status and four-point ordinal rank response for all other domains. We recommend individualized adaptation as appropriate by clinical research teams using this measure to survey the effects of the COVID-19 pandemic on study participants. This can also help maintain utility of the measure beyond the COVID-19 pandemic to characterize impacts during future public health emergencies that may require mitigation strategies such as periods of quarantine and isolation.

Serious Falls in Middle-Aged Veterans: Development and Validation of a Predictive Risk Model.

BACKGROUND/OBJECTIVES: Due to high rates of multimorbidity, polypharmacy, and hazardous alcohol and opioid use, middle-aged Veterans are at risk for serious falls (those prompting a visit with a healthcare provider), posing significant risk to their forthcoming geriatric health and quality of life. We developed and validated a predictive model of the 6-month risk of serious falls among middle-aged Veterans. DESIGN: Cohort study. SETTING: Veterans Health Administration (VA). PARTICIPANTS: Veterans, aged 45 to 65 years, who presented for care within the VA between 2012 and 2015 (N = 275,940). EXPOSURES: The exposures of primary interest were substance use (including alcohol and prescription opioid use), multimorbidity, and polypharmacy. Hazardous alcohol use was defined as an Alcohol Use Disorders Identification Test - Consumption (AUDIT-C) score of 3 or greater for women and 4 or greater for men. We used International Classification of Diseases, Ninth Revision (ICD-9), codes to identify alcohol and illicit substance use disorders and identified prescription opioid use from pharmacy fill-refill data. We included counts of chronic medications and of physical and mental health comorbidities. MEASUREMENTS: We identified serious falls using external cause of injury codes and a machine-learning algorithm that identified serious falls in radiology reports. We used multivariable logistic regression with general estimating equations to calculate risk. We used an integrated predictiveness curve to identify intervention thresholds. RESULTS: Most of our sample (54%) was aged 60 years or younger. Duration of follow-up was up to 4 years. Veterans who fell were more likely to be female (11% vs 7%) and White (72% vs 68%). They experienced 43,641 serious falls during follow-up. We identified 16 key predictors of serious falls and five interaction terms. Model performance was enhanced by addition of opioid use, as evidenced by overall category-free net reclassification improvement of 0.32 (P < .001). Discrimination (C-statistic = 0.76) and calibration were excellent for both development and validation data sets. CONCLUSION: We developed and internally validated a model to predict 6-month risk of serious falls among middle-aged Veterans with excellent discrimination and calibration.

Using patient safety indicators to estimate the impact of potential adverse events on outcomes.

The authors estimated the impact of potentially preventable patient safety events, identified by Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs), on patient outcomes: mortality, length of stay (LOS), and cost. The PSIs were applied to all acute inpatient hospitalizations at Veterans Health Administration (VA) facilities in fiscal 2001. Two methods-regression analysis and multivariable case matching- were used independently to control for patient and facility characteristics while predicting the effect of the PSI on each outcome. The authors found statistically significant (p < .0001) excess mortality, LOS, and cost in all groups with PSIs. The magnitude of the excess varied considerably across the PSIs. These VA findings are similar to those from a previously published study of nonfederal hospitals, despite differences between VA and non-VA systems. This study contributes to the literature measuring outcomes of medical errors and provides evidence that AHRQ PSIs may be useful indicators for comparison across delivery systems.