Genome-wide association study of hospitalized patients and acute kidney injury.

Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI.

Helicobacter pylori Burden in the United States According to Individual Demographics and Geography: A Nationwide Analysis of the Veterans Healthcare System.

BACKGROUND & AIMS: There are no contemporary large-scale studies evaluating the burden of Helicobacter pylori in the United States according to detailed demographics. The primary objective was to evaluate H pylori positivity in a large national healthcare system according to individual demographics and geography. METHODS: We conducted a nationwide retrospective analysis of adults in the Veterans Health Administration who completed H pylori testing between 1999 and 2018. The primary outcome was H pylori positivity overall, as well as according to zip code-level geography, race, ethnicity, age, sex, and time period. RESULTS: Among 913,328 individuals (mean, 58.1 years; 90.2% male) included between 1999 and 2018, H pylori was diagnosed in 25.8%. Positivity was highest in non-Hispanic black (median, 40.2%; 95% confidence interval [CI], 40.0%-40.5%) and Hispanic (36.7%; 95% CI, 36.4%-37.1%) individuals and lowest in non-Hispanic white individuals (20.1%; 95% CI, 20.0%-20.2%). Although H pylori positivity declined in all racial and ethnic groups over the timeframe, the disproportionate burden of H pylori in non-Hispanic black and Hispanic compared with non-Hispanic white individuals persisted. Approximately 4.7% of the variation in H pylori positivity was explained by demographics, with race and ethnicity accounting for the vast majority. CONCLUSIONS: The burden of H pylori is substantial in the United States among veterans. These data should (1) motivate research aimed at better understanding why marked demographic differences in H pylori burden persist so that mitigating interventions may be implemented and (2) guide resource allocation to optimize H pylori testing and eradication in high-risk groups.

Comparison of COVID-19 versus influenza on the incidence, features, and recovery from acute kidney injury in hospitalized United States Veterans.

Acute kidney injury is a common complication in patients hospitalized with SARSCoV-2 (COVID-19), with prior studies implicating multiple potential mechanisms of injury. Although COVID-19 is often compared to other respiratory viral illnesses, few formal comparisons of these viruses on kidney health exist. In this retrospective cohort study, we compared the incidence, features, and outcomes of acute kidney injury among Veterans hospitalized with COVID-19 or influenza and adjusted for baseline conditions using weighted comparisons. A total of 3402 hospitalizations for COVID-19 and 3680 hospitalizations for influenza admitted between October 1, 2019 and May 31, 2020 across 127 Veterans Administration hospitals nationally were studied using the electronic medical record. Acute kidney injury occurred more frequently among those with COVID-19 compared to those with influenza (40.9% versus 29.4%, weighted analysis) and was more severe. Patients with COVID-19 were more likely to require mechanical ventilation and vasopressors and experienced higher mortality. Proteinuria and hematuria were frequent in both groups but more common in COVID-19. Recovery of kidney function was less common in patients with COVID-19 and acute kidney injury but was similar among survivors. Thus, findings from this study confirm that acute kidney injury is more common and severe among patients hospitalized with COVID-19 compared to influenza, a finding that may be driven largely by illness severity. Hence, the combined impact of these two illnesses on kidney health may be significant and have important implications for resource allocation.

Renin-angiotensin aldosterone inhibitor use at hospital discharge among patients with moderate to severe acute kidney injury and its association with recurrent acute kidney injury and mortality.

Recurrent episodes of acute kidney injury (AKI) are common among AKI survivors. Renin-angiotensin aldosterone inhibitors (RAASi) are often indicated for these patients but may increase the risk for recurrent AKI. Here, we examined whether RAASi associates with a higher risk for recurrent AKI and mortality among survivors of moderate to severe AKI in a retrospective cohort of Veterans who survived Stage II or III AKI. The primary exposure was RAASi at hospital discharge and the primary endpoint was recurrent AKI within 12 months. Cox proportional hazards models were fit on a propensity score-weighted cohort to compare time to recurrent AKI and mortality by RAASi exposure. Among 96,983 patients, 40% were on RAASi at discharge. Compared to patients who continued RAASi use, those discontinuing use experienced no difference in risk for recurrent AKI but had a significantly higher risk of mortality [hazard ratio 1.33 (95% confidence interval1.26-1.41)]. No differences in recurrent AKI risk was observed for non-users started or not on RAASi compared to prevalent users who continued RAASi. Subgroup analyses among those with diabetes, chronic kidney disease, heart failure, and malignancy were similar with exception of a modest reduction in recurrent AKI risk among RAASi discontinuers with chronic kidney disease. Thus, RAASi use among survivors of moderate to severe AKI was associated with little to no difference in risk for recurrent AKI but was associated with improved survival. Reinitiating or starting RAASi among patients with strong indications is warranted but should be balanced with individual overall risk for recurrent AKI and with adequate monitoring.

Timing of Recovery From Moderate to Severe AKI and the Risk for Future Loss of Kidney Function.

RATIONALE & OBJECTIVE: The extent of recovery of kidney function following acute kidney injury (AKI) is known to be associated with future chronic kidney disease. Less is known about how the timing of recovery affects the rate of future loss of kidney function. STUDY DESIGN: We performed a retrospective cohort study examining the independent association between the timing of recovery from moderate to severe AKI and future loss of kidney function. SETTING & PARTICIPANTS: 47,903 adult US veterans with stage 2 or 3 AKI who recovered to within 120% of baseline creatinine level within 90 days of peak injury. EXPOSURE: The timing of recovery of kidney function from peak inpatient serum creatinine level grouped into 1 to 4, 5 to 10, 11 to 30, and 31 to 90 days. OUTCOME: A sustained 40% decline in estimated glomerular filtration rate below that calculated from the last serum creatinine level available during the 90-day recovery period or kidney failure (2 outpatient estimated glomerular filtration rates<15mL/min/1.73m2, dialysis procedures > 90 days apart, kidney transplantation, or registry within the US Renal Data System). ANALYTICAL APPROACH: Time to the primary outcome was examined using multivariable Cox proportional hazards regression. RESULTS: Among 47,903 patients, 29,316 (61%), 10,360 (22%), 4,520 (9%), and 3,707 (8%) recovered within 1 to 4, 5 to 10, 11 to 30, and 31 to 90 days, respectively. With a median follow-up of 42 months, unadjusted incidence rates for the kidney outcome were 2.01, 3.55, 3.86, and 3.68 events/100 person-years, respectively. Compared with 1 to 4 days, recovery within 5 to 10, 11 to 30, and 31 to 90 days was associated with increased rates of the primary outcome: adjusted HRs were 1.33 (95% CI, 1.24-1.43), 1.41 (95% CI, 1.28-1.54), and 1.58 (95% CI, 1.43-1.75), respectively. LIMITATIONS: Predominately male population, residual confounding, and inability to make causal inferences because of the retrospective observational study design. CONCLUSIONS: The timing of recovery provides an added dimension to AKI phenotyping and prognostic information regarding the future occurrence of loss of kidney function. Studies to identify effective interventions on the timing of recovery from AKI are warranted.

Combining the ASA Physical Classification System and Continuous Intraoperative Surgical Apgar Score Measurement in Predicting Postoperative Risk.

The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. Logistic regression methods were used to construct a series of predictive models in order to continuously estimate the risk of major postoperative complications, and to alert care providers during surgery should the risk exceed a given threshold. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminative ability of a model utilizing a continuously measured surgical Apgar score relative to models that use only preoperative clinical factors or continuously monitored individual constituents of the surgical Apgar score (i.e. heart rate, blood pressure, and blood loss). AUROC estimates were validated internally using a bootstrap method. 4,728 patients were included. Combining the ASA PS classification with continuously measured surgical Apgar score demonstrated improved discriminative ability (AUROC 0.80) in the pooled cohort compared to ASA (0.73) and the surgical Apgar score alone (0.74). To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.

Associations Between Gastrointestinal Symptoms and COVID-19 Severity Outcomes Based on a Propensity Score-Weighted Analysis of a Nationwide Cohort.

Background and Aims: Gastrointestinal (GI) symptoms are well-recognized manifestations of coronavirus disease 2019 (COVID-19). Our primary objective was to evaluate the association between GI symptoms and COVID-19 severity. Methods: In this nationwide cohort of US veterans, we evaluated GI symptoms (nausea/vomiting/diarrhea) reported 30 days before and including the date of positive SARS-CoV-2 testing (March 1, 2020, to February 20, 2021). All patients had ≥1 year of prior baseline data and ≥60 days follow-up relative to the test date. We used propensity score (PS)-weighting to balance covariates in patients with vs without GI symptoms. The primary composite outcome was severe COVID-19, defined as hospital admission, intensive care unit admission, mechanical ventilation, or death within 60 days of positive testing. Results: Of 218,045 SARS-CoV-2 positive patients, 29,257 (13.4%) had GI symptoms. After PS weighting, all covariates were balanced. In the PS-weighted cohort, patients with vs without GI symptoms had severe COVID-19 more often (29.0% vs 17.1%; P < .001). When restricted to hospitalized patients (14.9%; n=32,430), patients with GI symptoms had similar frequencies of intensive care unit admission and mechanical ventilation compared with patients without symptoms. There was a significant age interaction; among hospitalized patients aged ≥70 years, lower COVID-19-associated mortality was observed in patients with vs without GI symptoms, even after accounting for COVID-19-specific medical treatments. Conclusion: In the largest integrated US health care system, SARS-CoV-2-positive patients with GI symptoms experienced severe COVID-19 outcomes more often than those without symptoms. Additional research on COVID-19-associated GI symptoms may inform preventive efforts and interventions to reduce severe COVID-19.

The Association of Health Care System Resources With Lung Cancer Screening Implementation: A Cohort Study.

BACKGROUND: The Veterans Health Administration issued policy for lung cancer screening resources at eight Veterans Affairs Medical Centers (VAMCs) in a demonstration project (DP) from 2013 through 2015. RESEARCH QUESTION: Do policies that provide resources increase lung cancer screening rates? STUDY DESIGN AND METHODS: Data from eight DP VAMCs (DP group) and 20 comparable VAMCs (comparison group) were divided into before DP (January 2011-June 2013), DP (July 2013-June 2015), and after DP (July 2015-December 2018) periods. Coprimary outcomes were unique veterans screened per 1,000 eligible per month and those with 1-year (9-15 months) follow-up screening. Eligible veterans were estimated using yearly counts and the percentage of those with eligible smoking histories. Controlled interrupted time series and difference-in-differences analyses were performed. RESULTS: Of 27,746 veterans screened, the median age was 66.5 years and most were White (77.7%), male (95.6%), and urban dwelling (67.3%). During the DP, the average rate of unique veterans screened at DP VAMCs was 17.7 per 1,000 eligible per month, compared with 0.3 at comparison VAMCs. Adjusted analyses found a higher rate increase at DP VAMCs by 0.93 screening per 1,000 eligible per month (95% CI, 0.25-1.61) during this time, with an average facility-level difference of 17.4 screenings per 1,000 eligible per month (95% CI, 12.6-22.3). Veterans with 1-year follow-up screening also increased more rapidly at DP VAMCs during the DP, by 0.39 screening per 1,000 eligible per month (95% CI, 0.18-0.60), for an average facility-level difference of 7.2 more screenings per 1,000 eligible per month (95% CI, 5.2-9.2). Gains were not maintained after the DP. INTERPRETATION: In this cohort, provision of resources for lung cancer screening implementation was associated with an increase in veterans screened and those with 1-year follow-up screening. Screening gains associated with the DP were not maintained.

Association of Rurality With Annual Repeat Lung Cancer Screening in the Veterans Health Administration.

PURPOSE: Lung cancer causes the largest number of cancer-related deaths in the United States. Lung cancer incidence rates, mortality rates, and rates of advanced stage disease are higher among those who live in rural areas. Known disparities in lung cancer outcomes between rural and nonrural populations may be in part because of barriers faced by rural populations. The authors tested the hypothesis that among Veterans who receive initial lung cancer screening, rural Veterans would be less likely to complete annual repeat screening than nonrural Veterans. METHODS: A retrospective cohort study was conducted of 10 Veterans Affairs medical centers from 2015 to 2019. Rural and nonrural Veterans undergoing lung cancer screening were identified. Rural status was defined using the rural-urban commuting area codes. The primary outcome was annual repeat lung cancer screening in the 9- to 15-month window (primary analysis) and 31-day to 18-month window (sensitivity analysis) after the first documented lung cancer screening. To examine rurality as a predictor of annual repeat lung cancer screening, multivariable logistic regression models were used. RESULTS: In the final analytic sample of 11,402 Veterans, annual repeat lung cancer screening occurred in 27.7% of rural Veterans (641 of 2,316) and 31.8% of nonrural Veterans (2,891 of 9,086) (adjusted odds ratio: 0.86; 95% confidence interval: 0.73-1.03). Similar results were seen in the sensitivity analysis, with 41.6% of rural Veterans (963 of 2,316) versus 45.2% of nonrural Veterans (4,110 of 9,086) (adjusted odds ratio: 0.88; 95% confidence interval: 0.73-1.04) having annual repeat screening in the expanded 31-day to 18-month window. CONCLUSIONS: Among a national cohort of Veterans, rural residence was associated with numerically lower odds of annual repeat lung cancer screening than nonrural residence. Continued, intentional outreach efforts to increase annual repeat lung cancer screening among rural Veterans may offer an opportunity to decrease deaths from lung cancer.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models. Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model. Design, Setting, and Participants: This was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020. Exposures: Acute myocardial infarction that required hospital admission. Main Outcomes and Measures: The main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts. Results: The final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models. Conclusions and Relevance: In this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

Prevalence and Risk Factors for Opioid-Induced Constipation in an Older National Veteran Cohort.

Objectives: This research describes the prevalence and covariates associated with opioid-induced constipation (OIC) in an observational cohort study utilizing a national veteran cohort and integrated data from the Center for Medicare and Medicaid Services (CMS). Methods: A cohort of 152,904 veterans with encounters between 1 January 2008 and 30 November 2010, an exposure to opioids of 30 days or more, and no exposure in the prior year was developed to establish existing conditions and medications at the start of the opioid exposure and determining outcomes through the end of exposure. OIC was identified through additions/changes in laxative prescriptions, all-cause constipation identification through diagnosis, or constipation related procedures in the presence of opioid exposure. The association of time to constipation with opioid use was analyzed using Cox proportional hazard regression adjusted for patient characteristics, concomitant medications, laboratory tests, and comorbidities. Results: The prevalence of OIC was 12.6%. Twelve positively associated covariates were identified with the largest associations for prior constipation and prevalent laxative (any laxative that continued into the first day of opioid exposure). Among the 17 negatively associated covariates, the largest associations were for erythromycins, androgens/anabolics, and unknown race. Conclusions: There were several novel covariates found that are seen in the all-cause chronic constipation literature but have not been reported for opioid-induced constipation. Some are modifiable covariates, particularly medication coadministration, which may assist clinicians and researchers in risk stratification efforts when initiating opioid medications. The integration of CMS data supports the robustness of the analysis and may be of interest in the elderly population warranting future examination.

National Lung Cancer Screening Utilization Trends in the Veterans Health Administration.

BACKGROUND: Many Veterans are high risk for lung cancer. Low-dose computed tomography (LDCT) is an effective strategy for lung cancer early detection in a high-risk population. Our objective was to describe and compare annual and geographic utilization trends for LDCT screening in the Veteran's Health Administration (VHA). METHODS: A national retrospective cohort of screened Veterans from January 1, 2011 to May 31, 2018 was used to calculate annual and regional rates of initial LDCT utilization per 1000 eligible Veterans. We identified Veterans with a first LDCT exam using common procedure terminology codes G0297 or 71250 and described as "lung cancer screening," "screening," or "LCS." The number of screen-eligible Veterans per year was calculated as unique Veterans aged 55 to 80 years seen at a Veterans Affairs medical center (VAMC) in that year, multiplied by 32% (estimated proportion with eligible smoking history). We present 95% confidence intervals (CI) for rates. RESULTS: Screened Veterans had a mean age of 66.1 years (standard deviation [SD] = 5.6); 95.5% male; 77.4% Caucasian. There were 119 300 LDCT exams, of which 80 819 (67.7%) were initial. Nationally, initial screens increased from 0 (95% CI = 0.00 to 0.00) in 2011 to 29.6 (95% CI = 29.26 to 29.88) scans per 1000 eligible Veterans in 2018 (Ptrend < .001). Initial screens increased over time within all geographic regions, most prominently in northeastern and Florida VAMCs. CONCLUSION: VHA LDCT utilization increased from 2011 to 2018. However, overall utilization remained low. Future interventions are needed to increase lung cancer screening utilization among eligible Veterans.

Uptake of Video-Assisted Thoracoscopic Lung Resections Within the Veterans Affairs for Known or Suspected Lung Cancer.

Importance: Minimally invasive lobectomy for early-stage lung cancer has become more prevalent. Video-assisted thoracoscopic surgery has lower rates of morbidity, better long-term survival, and equivalent oncologic outcomes compared with thoracotomy. However, little has been published on the use and outcomes of video-assisted thoracoscopic surgery within Veterans Affairs. There is a public assumption that the the Veterans Affairs is slow to adopt new procedures and technologies. Objective: To determine the uptake of video-assisted thoracoscopic surgery within the Veterans Affairs for patients with known or suspected lung cancer. Design, Setting, and Participants: In this retrospective cohort study of national Veterans Affairs Corporate Data Warehouse data from January 2002 to December 2015, a total of 11 004 veterans underwent lung resection for known or suspected lung cancer. Data were analyzed from March to November 2018. Exposures: Open or video-assisted thoracoscopic lobectomy or wedge resection. Main Outcomes and Measures: Patient demographic characteristics and procedure and diagnosis International Classification of Diseases, Ninth Revision codes were abstracted from Corporate Data Warehouse data. Results: Of the 11 004 included veterans, 10 587 (96.2%) were male, and the median (interquartile range) age was 66.0 (61.0-72.0) years. Of 11 004 included procedures, 8526 (77.5%) were lobectomies and 2478 (22.5%) were wedge resections. The proportion of video-assisted thoracoscopic lung resections increased steadily from 15.6% in 2002 to 50.6% in 2015. Video-assisted thoracoscopic surgery use by Veterans Integrated Service Networks ranged from 0% to 81.7%, and higher Veterans Integrated Service Network volume was correlated with higher video-assisted thoracoscopic surgery use (Pearson r = 0.35; 95% CI, 0.15-0.52; P < .001). Video-assisted thoracoscopic surgery use and rate of uptake varied widely across Veteran Affairs regions (P < .001 by Wilcoxon signed rank test). Conclusions and Relevance: Paralleling academic hospitals, most lung resections are now performed in the Veterans Affairs using video-assisted thoracoscopic surgery. More research is needed to identify reasons behind the heterogeneous uptake of video-assisted thoracoscopic surgery across Veterans Affairs regions.

Construct Validity of the Societal Outreach Scale (SOS).

Background: The American Physical Therapy Association (APTA) has been working toward a vision of increasing professional focus on societal-level health. However, performance of social responsibility and related behaviors by physical therapists remain relatively poorly integrated into practice. Promoting a focus on societal outreach is necessary for all health care professionals to impact the health of their communities. Objective: The objective was to document the validity of the 14-item Societal Outreach Scale (SOS) for use with practicing physical therapists. Design: This study used a cross-sectional survey. Methods: The SOS was transmitted via email to all therapists who were licensed and practicing in 10 states in the United States that were purposefully selected to assure a broad representation. A sample of 2612 usable responses was received. Factor analysis was applied to assess construct validity of the instrument. Results: Of alternate models, a 3-factor model best demonstrated goodness of fit with the sample data according to conventional indices (standardized root mean squared residual = .03, comparative fit index .96, root mean square error of approximation = .06). The 3 factors measured by the SOS were labeled Societal-Level Health Advocacy, Community Engagement/Social Integration, and Political Engagement. Internal consistency reliability was 0.7 for all factors. The 3-factor SOS demonstrated acceptable validity and reliability. Limitations: Though the sample included a broad representation of physical therapists, this was a single cross-sectional study. Additional confirmatory factor analysis, reliability testing, and word refinement of the tool are warranted. Conclusions: Given the construct validity and reliability of the 3-factor SOS, it is recommended for use as a validated instrument to measure physical therapists' performance of social responsibility and related behaviors.

Clinician Recommendations and Perceptions of Factors Associated With Ankle Brace Use.

BACKGROUND: Little information is available regarding the ankle braces orthopaedic sports medicine clinicians recommend or clinicians' concerns that may influence their decisions to recommend use of an ankle brace. HYPOTHESES: (1) Clinicians most frequently recommend lace-up braces with straps. (2) Clinicians who are concerned about potential adverse side effects from ankle brace use are less likely to recommend an ankle brace to prevent ankle sprain injuries. STUDY DESIGN: Descriptive survey study. LEVEL OF EVIDENCE: Level 3. METHODS: Surveys were sent via e-mail to 1000 randomly selected members of the Orthopaedic Section of the American Physical Therapy Association (APTA) and 1000 randomly selected members of the National Athletic Trainers' Association (NATA). A total of 377 individuals responded to the survey. RESULTS: Lace-up braces, specifically lace-up braces with straps, were the most frequently recommended type of ankle brace. Regression analyses indicated that the only perceived adverse side effect significantly related to frequency of ankle brace recommendation was a potential negative influence on ankle strength. CONCLUSION: Based on our sample, clinicians recommend lace-up ankle braces with straps most frequently to prevent ankle sprain injuries. Clinicians who are concerned about weakness of ankle musculature may be less likely to recommend use of an ankle brace. CLINICAL RELEVANCE: Clinicians may effectively reduce the number of ankle sprain injuries by recommending an ankle brace use after an initial ankle sprain injury.