Rates of ICD-10 Code U09.9 Documentation and Clinical Characteristics of VA Patients With Post-COVID-19 Condition.

Importance: A significant proportion of SARS-CoV-2 infected individuals experience post-COVID-19 condition months after initial infection. Objective: To determine the rates, clinical setting, risk factors, and symptoms associated with the documentation of International Statistical Classification of Diseases Tenth Revision (ICD-10), code U09.9 for post-COVID-19 condition after acute infection. Design, Setting, and Participants: This retrospective cohort study was performed within the US Department of Veterans Affairs (VA) health care system. Veterans with a positive SARS-CoV-2 test result between October 1, 2021, the date ICD-10 code U09.9 was introduced, and January 31, 2023 (n = 388 980), and a randomly selected subsample of patients with the U09.9 code (n = 350) whose symptom prevalence was assessed by systematic medical record review, were included in the analysis. Exposure: Positive SARS-CoV-2 test result. Main Outcomes and Measures: Rates, clinical setting, risk factors, and symptoms associated with ICD-10 code U09.9 in the medical record. Results: Among the 388 980 persons with a positive SARS-CoV-2 test, the mean (SD) age was 61.4 (16.1) years; 87.3% were men. In terms of race and ethnicity, 0.8% were American Indian or Alaska Native, 1.4% were Asian, 20.7% were Black, 9.3% were Hispanic or Latino, 1.0% were Native Hawaiian or Other Pacific Islander; and 67.8% were White. Cumulative incidence of U09.9 documentation was 4.79% (95% CI, 4.73%-4.87%) at 6 months and 5.28% (95% CI, 5.21%-5.36%) at 12 months after infection. Factors independently associated with U09.9 documentation included older age, female sex, Hispanic or Latino ethnicity, comorbidity burden, and severe acute infection manifesting by symptoms, hospitalization, or ventilation. Primary vaccination (adjusted hazard ratio [AHR], 0.80 [95% CI, 0.78-0.83]) and booster vaccination (AHR, 0.66 [95% CI, 0.64-0.69]) were associated with a lower likelihood of U09.9 documentation. Marked differences by geographic region and facility in U09.9 code documentation may reflect local screening and care practices. Among the 350 patients undergoing systematic medical record review, the most common symptoms documented in the medical records among patients with the U09.9 code were shortness of breath (130 [37.1%]), fatigue or exhaustion (78 [22.3%]), cough (63 [18.0%]), reduced cognitive function or brain fog (22 [6.3%]), and change in smell and/or taste (20 [5.7%]). Conclusions and Relevance: In this cohort study of 388 980 veterans, documentation of ICD-10 code U09.9 had marked regional and facility-level variability. Strong risk factors for U09.9 documentation were identified, while vaccination appeared to be protective. Accurate and consistent documentation of U09.9 is needed to maximize its utility in tracking patients for clinical care and research. Future studies should examine the long-term trajectory of individuals with U09.9 documentation.

Fried food consumption and risk of coronary artery disease: The Million Veteran Program.

INTRODUCTION: Previous studies of the relationship between fried food consumption and coronary artery disease (CAD) have yielded conflicting results. We tested the hypothesis that frequent fried food consumption is associated with a higher risk of incident CAD events in Million Veteran Program (MVP) participants. METHODS: Veterans Health Administration electronic health record data were linked to questionnaires completed at MVP enrollment. Self-reported fried food consumption at baseline was categorized: (<1, 1-3, 4-6 times per week or daily). The outcome of interest was non-fatal myocardial infarction or CAD events. We fitted a Cox regression model adjusting for age, sex, race, education, exercise, smoking and alcohol consumption. RESULTS: Of 154,663 MVP enrollees with survey data, mean age was 64 years and 90% were men. During a mean follow-up of approximately 3 years, there were 6,725 CAD events. There was a positive linear relationship between frequency of fried food consumption and risk of CAD (p for trend 0.0015). Multivariable adjusted hazard ratios (95% CI) were 1.0 (ref), 1.07 (1.01-1.13), 1.08 (1.01-1.16), and 1.14 (1.03-1.27) across consecutive increasing categories of fried food intake. CONCLUSIONS: In a large national cohort of U.S. Veterans, fried food consumption has a positive, dose-dependent association with CAD.

Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans.

We developed an algorithm for identifying U.S. veterans with a history of posttraumatic stress disorder (PTSD), using the Department of Veterans Affairs (VA) electronic medical record (EMR) system. This work was motivated by the need to create a valid EMR-based phenotype to identify thousands of cases and controls for a genome-wide association study of PTSD in veterans. We used manual chart review (n = 500) as the gold standard. For both the algorithm and chart review, three classifications were possible: likely PTSD, possible PTSD, and likely not PTSD. We used Lasso regression with cross-validation to select statistically significant predictors of PTSD from the EMR and then generate a predicted probability score of being a PTSD case for every participant in the study population (range: 0-1.00). Comparing the performance of our probabilistic approach (Lasso algorithm) to a rule-based approach (International Classification of Diseases [ICD] algorithm), the Lasso algorithm showed modestly higher overall percent agreement with chart review than the ICD algorithm (80% vs. 75%), higher sensitivity (0.95 vs. 0.84), and higher accuracy (AUC = 0.95 vs. 0.90). We applied a 0.7 probability cut-point to the Lasso results to determine final PTSD case-control status for the VA population. The final algorithm had a 0.99 sensitivity, 0.99 specificity, 0.95 positive predictive value, and 1.00 negative predictive value for PTSD classification (grouping possible PTSD and likely not PTSD) as determined by chart review. This algorithm may be useful for other research and quality improvement endeavors within the VA.

Spanish Abstracts by Asociación Chilena de Estrés Traumático (ACET) Validación de un algoritmo basado en registros médicos electrónicos para identificar el trastorno por estrés postraumático en veteranos de los EE. UU. VALIDACIÓN DE ALGORITOMO DE TEPT Desarrollamos un algoritmo para identificar a los veteranos de EE. UU. con historial de trastorno de estrés postraumático (TEPT), utilizando el sistema de registro médico electrónico (RME) del Departamento de Asuntos de Veteranos (AS). Este trabajo fue motivado por la necesidad de crear un fenotipo válido, basado en RME para identificar miles de casos y controles para un estudio de asociación del genoma del TEPT en los veteranos. Utilizamos la revisión manual de tablas (n = 500) como gold estándar. Tanto para el algoritmo como para la revisión de la tabla, fueron posibles tres clasificaciones: PTSD probable, PTSD posible y probablemente no PTSD. Usamos la regresión Lasso con validación cruzada para seleccionar los factores de pronóstico estadísticamente significativos del TEPT a partir de la RME y luego generar una puntuación de probabilidad pronosticada de ser un caso de TEPT para cada participante en la población del estudio (rango: 0-1.00). Comparando el rendimiento de nuestro enfoque probabilístico (algoritmo Lasso) con un enfoque basado en reglas (algoritmo de Clasificación Internacional de Enfermedades [CIE]), el algoritmo Lasso mostró un porcentaje de acuerdo global modestamente más alto con la revisión de tablas que el algoritmo CIE (80% vs. 75). %), mayor sensibilidad (0.95 frente a 0.84) y mayor precisión (AUC = 0.95 frente a 0.90). Aplicamos un punto de corte de probabilidad de 0.7 a los resultados de Lasso para determinar el estado final de control de caso de TEPT para la población de AV. El algoritmo final tuvo una sensibilidad de 0.99, una especificidad de 0.99, un valor predictivo positivo de 0.95 y un valor predictivo negativo de 1.00 para la clasificación de TEPT (agrupación de TEPT posible y probablemente no TEPT) según lo determinado por la revisión de la tabla. Este algoritmo puede ser útil para otros esfuerzos de investigación y mejora de la calidad dentro del AV.

Effects of Genetic Variants Associated with Familial Hypercholesterolemia on Low-Density Lipoprotein-Cholesterol Levels and Cardiovascular Outcomes in the Million Veteran Program.

Background: Familial hypercholesterolemia (FH) is characterized by inherited high levels of low-density lipoprotein cholesterol (LDL-C) and premature coronary heart disease (CHD). Over a thousand low-frequency variants in LDLR, APOB and PCSK9 have been implicated in FH but few have been examined at the population level. We aim to estimate the phenotypic effects of a subset of FH variants on LDL-C and clinical outcomes among 331,107 multi-ethnic participants. Methods: We examined the individual and collective association between putatively pathogenic FH variants included on the MVP biobank array and the maximum LDL-C level over an interval of 15 years (maxLDL). We assessed the collective effect on clinical outcomes by leveraging data from 61.7 million clinical encounters. Results: We found 8 out of 16 putatively pathogenic FH variants with ≥30 observed carriers to be significantly associated with elevated maxLDL (9.4-80.2 mg/dL). Phenotypic effects were similar for European and African Americans despite substantial differences in carrier frequencies. Based on observed effects on maxLDL, we identified a total of 748 carriers (1:443) who had elevated maxLDL (36.5±1.4 mg/dL, p=1.2×10-152), and higher prevalence of clinical diagnoses related to hypercholesterolemia and CHD in a phenome-wide scan. Adjusted for maxLDL, FH variants collectively associated with higher prevalence of CHD (odds ratio, 1.59 [95% CI 1.36-1.86], p=1.1×10-8) but not peripheral artery disease. Conclusions: The distribution and phenotypic effects of putatively pathogenic FH variants were heterogeneous within and across variants. More robust evidence of genotype-phenotype associations of FH variants in multi-ethnic populations is needed to accurately infer at-risk individuals from genetic screening.