Incidence and risk of post-COVID-19 thromboembolic disease and the impact of aspirin prescription; nationwide observational cohort at the US Department of Veteran Affairs.

INTRODUCTION: COVID-19 triggers prothrombotic and proinflammatory changes, with thrombotic disease prevalent in up to 30% SARS-CoV-2 infected patients. Early work suggests that aspirin could prevent COVID-19 related thromboembolic disorders in some studies but not others. This study leverages data from the largest integrated healthcare system in the United States to better understand this association. Our objective was to evaluate the incidence and risk of COVID-19 associated acute thromboembolic disorders and the potential impact of aspirin. METHODS: This retrospective, observational study utilized national electronic health record data from the Veterans Health Administration. 334,374 Veterans who tested positive for COVID-19 from March 2, 2020, to June 13, 2022, were included, 81,830 of whom had preexisting aspirin prescription prior to their COVID-19 diagnosis. Patients with and without aspirin prescriptions were matched and the odds of post-COVID acute thromboembolic disorders were assessed. RESULTS: 10.1% of Veterans had a documented thromboembolic disorder within 12 months following their COVID-19 diagnosis. Those with specific comorbidities were at greatest risk. Preexisting aspirin prescription was associated with a significant decrease risk of post-COVID-19 thromboembolic disorders, including pulmonary embolism (OR [95% CI]: 0.69 [0.65, 0.74]) and deep vein thrombosis (OR [95% CI]: 0.76 [0.69, 0.83], but an increased risk of acute arterial diseases, including ischemic stroke (OR [95% CI]: 1.54 [1.46, 1.60]) and acute ischemic heart disease (1.33 [1.26, 1.39]). CONCLUSIONS: Findings demonstrated that preexisting aspirin prescription prior to COVID-19 diagnosis was associated with significantly decreased risk of venous thromboembolism and pulmonary embolism but increased risk of acute arterial disease. The risk of arterial disease may be associated with increased COVID-19 prothrombotic effects superimposed on preexisting chronic cardiovascular disease for which aspirin was already prescribed. Prospective clinical trials may help to further assess the efficacy of aspirin use prior to COVID-19 diagnosis for the prevention of post-COVID-19 thromboembolic disorders.

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives.

Postsurgical falls have significant patient and societal implications but remain challenging to identify and track. Detecting postsurgical falls is crucial to improve patient care for older adults and reduce healthcare costs. Large language models (LLMs) offer a promising solution for reliable and automated fall detection using unstructured data in clinical notes. We tested several LLM prompting approaches to postsurgical fall detection in two different healthcare systems with three open-source LLMs. The Mixtral-8×7B zero-shot had the best performance at Stanford Health Care (PPV = 0.81, recall = 0.67) and the Veterans Health Administration (PPV = 0.93, recall = 0.94). These results demonstrate that LLMs can detect falls with little to no guidance and lay groundwork for applications of LLMs in fall prediction and prevention across many different settings.

NSI and PCL-5 Normative Tables for Active Duty Service Members Affected by Traumatic Brain Injury.

INTRODUCTION: Many service members (SMs) have been diagnosed with traumatic brain injury. Currently, military treatment facilities do not have access to established normative tables which can assist clinicians in gauging and comparing patient-reported symptoms. The aim of this study is to provide average scores for both the Neurobehavioral Symptom Inventory (NSI) and Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) for active duty SMs based upon varying demographic groups. METHODS: Average scores were calculated for both the NSI and PCL-5 surveys from SMs who attended a military outpatient traumatic brain injury clinic. For this analysis, only the initial surveys for each SM were considered. The identifying demographics included age group, gender, grade, and race. RESULTS: Four normative tables were created to show the average scores of both the NSI and PCL-5 surveys grouped by demographics. The tables are grouped by Age Group/Gender/Race and Grade/Gender/Race. CONCLUSION: Clinicians and healthcare administrators can use the scores reported in this study to determine where SM NSI or PCL-5 scores fall within the average for their demographic group.

Correlations Between the Neurobehavioral Symptom Inventory and Other Commonly Used Questionnaires for Traumatic Brain Injury.

OBJECTIVE: To evaluate the correlations between the Neurobehavioral Symptom Inventory (NSI) and other questionnaires commonly administered within military traumatic brain injury clinics. SETTING: Military outpatient traumatic brain injury clinics. PARTICIPANTS: In total, 15,428 active duty service members who completed 24,162 NSI questionnaires between March 2009 and May 2020. DESIGN: Observational retrospective analysis of questionnaires collected as part of standard clinical care. MAIN MEASURES: NSI, Post-Traumatic Stress Disorder Checklist for DSM-5 and Military Version, Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder, Headache Impact Test (HIT-6), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Activities-Specific Balance Confidence Scale (ABC), Dizziness Handicap Inventory (DHI), Alcohol Use Disorders Identification Test (AUDIT), and the World Health Organization Quality of Life Instrument-Abbreviated Version. Only questionnaires completed on the same date as the NSI were examined. RESULTS: The total NSI score was moderately to strongly correlated with all questionnaires except for the AUDIT. The strongest correlation was between the NSI Affective Score and the PHQ9 (r = 0.86). The NSI Vestibular Score was moderately correlated with the ABC (r = -0.55) and strongly correlated with the DHI (r = 0.77). At the item level, the HIT-6 showed strong correlation with NSI headache (r = 0.80), the ISI was strongly correlated with NSI difficulty sleeping (r = 0.63), and the ESS was moderately correlated with NSI fatigue (r = 0.39). CONCLUSION: Clinicians and healthcare administrators can use the correlations reported in this study to determine if questionnaires add incremental value for their clinic as well as to make more informed decisions regarding which questionnaires to administer.