Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines in U.S. Veterans.

BACKGROUND: The messenger RNA (mRNA)-based vaccines BNT162b2 and mRNA-1273 are more than 90% effective against coronavirus disease 2019 (Covid-19). However, their comparative effectiveness for a range of outcomes across diverse populations is unknown. METHODS: We emulated a target trial using the electronic health records of U.S. veterans who received a first dose of the BNT162b2 or mRNA-1273 vaccine between January 4 and May 14, 2021, during a period marked by predominance of the SARS-CoV-2 B.1.1.7 (alpha) variant. We matched recipients of each vaccine in a 1:1 ratio according to their risk factors. Outcomes included documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, symptomatic Covid-19, hospitalization for Covid-19, admission to an intensive care unit (ICU) for Covid-19, and death from Covid-19. We estimated risks using the Kaplan-Meier estimator. To assess the influence of the B.1.617.2 (delta) variant, we emulated a second target trial that involved veterans vaccinated between July 1 and September 20, 2021. RESULTS: Each vaccine group included 219,842 persons. Over 24 weeks of follow-up in a period marked by alpha-variant predominance, the estimated risk of documented infection was 5.75 events per 1000 persons (95% confidence interval [CI], 5.39 to 6.23) in the BNT162b2 group and 4.52 events per 1000 persons (95% CI, 4.17 to 4.84) in the mRNA-1273 group. The excess number of events per 1000 persons for BNT162b2 as compared with mRNA-1273 was 1.23 (95% CI, 0.72 to 1.81) for documented infection, 0.44 (95% CI, 0.25 to 0.70) for symptomatic Covid-19, 0.55 (95% CI, 0.36 to 0.83) for hospitalization for Covid-19, 0.10 (95% CI, 0.00 to 0.26) for ICU admission for Covid-19, and 0.02 (95% CI, -0.06 to 0.12) for death from Covid-19. The corresponding excess risk (BNT162b2 vs. mRNA-1273) of documented infection over 12 weeks of follow-up in a period marked by delta-variant predominance was 6.54 events per 1000 persons (95% CI, -2.58 to 11.82). CONCLUSIONS: The 24-week risk of Covid-19 outcomes was low after vaccination with mRNA-1273 or BNT162b2, although risks were lower with mRNA-1273 than with BNT162b2. This pattern was consistent across periods marked by alpha- and delta-variant predominance. (Funded by the Department of Veterans Affairs and others.).

Estimating the effect of bariatric surgery on cardiovascular events using observational data?

BACKGROUND: Observational studies have estimated strongly protective effects of bariatric surgery on cardiovascular disease, but with oversimplified definitions of the intervention, eligibility criteria, and follow-up, which deviate from those in a randomized trial. We describe studying the effect of bariatric surgery on cardiovascular disease without introducing these sources of bias, which may not be entirely possible with existing observational data. METHODS: We propose target trials among persons with diabetes: (1) bariatric operation (vs. no operation) among individuals who have undergone pre-operative preparation (lifestyle modifications, screening) and (2) pre-operative preparation and bariatric surgery (vs. neither pre-operative nor operative component). RESULTS: We emulated both target trials using observational data of U.S. veterans. Comparing bariatric surgery with no surgery (target trial #1; 8,087 individuals), the 7-year cardiovascular risk was 18.0% (95% CI, 6.9 to 32.7) in the surgery group and 18.9% (95% CI, 17.7 to 20.1) in the no surgery group (risk difference -0.9, 95% CI -12.0 to 14.0). Comparing pre-operative components plus surgery vs. neither (target trial #2; 10,065 individuals), the 7-year cardiovascular risk was 17.4% (95% CI, 13.6 to 22.0) in the surgery group and 18.8% (95% CI, 17.8 to 19.9) in the no surgery group (risk difference -1.4, 95% CI -5.1 to 3.2). Body mass index and hemoglobin A1c were reduced with bariatric interventions in both emulations. CONCLUSIONS: Within limitations of available observational data, our estimates do not provide evidence that bariatric surgery reduces cardiovascular disease and support equipoise for a randomized trial of bariatric surgery for cardiovascular disease prevention.

Mild Neurocognitive Disorder, Social Engagement, and Falls Among Older Primary Care Patients.

OBJECTIVES: (1) To estimate the association between social engagement (SE) and falls; (2) To examine the relation between mild neurocognitive disorder (MNCD) and falls by different levels of SE. DESIGN: We performed a secondary data analysis using prospective cohort study design. SETTING: Primary care. PARTICIPANTS: A total of 425 older adult primary care patients at risk for mobility decline (N=425). As previously reported, at baseline, 42% of participants exhibit MNCD. MAIN OUTCOME MEASURES: The outcome variable was the number of falls during 2 years of follow-up. Exposure variables at baseline included (1) MNCD identified using a cut-off of 1.5 SD below the age-adjusted mean on at least 2 measures within a cognitive performance battery and (2) SE, which was assessed using the social component of the Late-Life Function and Disability Instrument. High SE was defined as having a score ≥ median value (≥49 out of 100). All models were adjusted for age, sex, education, marital status, comorbidities, and pain status. RESULTS: Over 2 years of follow-up, 48% of participants fell at least once. MNCD was associated with a higher rate of falls, adjusting for the covariates (Incidence Rate Ratio=1.6, 95% confidence interval: 1.1-2.3). There was no significant association between MNCD and the rate of falls among people with high SE. In participants with low SE (having a score less than 49.5 out 100), MNCD was associated with a higher rate of falls as compared with participants with no neurocognitive disorder (No-NCD). CONCLUSIONS: Among participants with low SE, MNCD was associated with a higher rate of falls, but not among participants with high SE. The findings suggest that high SE may be protective against falls among older primary care patients with MNCD.

Frailty and cardiovascular mortality in more than 3 million US Veterans.

AIMS: Frailty is associated with an increased risk of all-cause mortality and cardiovascular (CV) events. Limited data exist from the modern era of CV prevention on the relationship between frailty and CV mortality. We hypothesized that frailty is associated with an increased risk of CV mortality. METHODS AND RESULTS: All US Veterans aged ≥65 years who were regular users of Veteran Affairs care from 2002 to 2017 were included. Frailty was defined using a 31-item previously validated frailty index, ranging from 0 to 1. The primary outcome was CV mortality with secondary analyses examining the relationship between frailty and CV events (myocardial infarction, stroke, revascularization). Survival analysis models were adjusted for age, sex, ethnicity, geographic region, smoking, hyperlipidaemia, statin use, and blood pressure medication use. There were 3 068 439 US Veterans included in the analysis. Mean age was 74.1 ± 5.8 years in 2002, 76.0 ± 8.3 years in 2014, 98% male, and 87.5% White. In 2002, the median (interquartile range) frailty score was 0.16 (0.10-0.23). This increased and stabilized to 0.19 (0.10-0.32) for 2006-14. The presence of frailty was associated with an increased risk of CV mortality at every stage of frailty. Frailty was associated with an increased risk of myocardial infarction and stroke, but not revascularization. CONCLUSION: In this population, both the presence and severity of frailty are tightly correlated with CV death, independent of underlying CV disease. This study is the largest and most contemporary evaluation of the relationship between frailty and CV mortality to date. Further work is needed to understand how this risk can be diminished. KEY QUESTION: Can an electronic frailty index identify adults aged 65 and older who are at risk of CV mortality and major CV events? KEY FINDING: Among 3 068 439 US Veterans aged 65 and older, frailty was associated with an increased risk of CV mortality at every level of frailty. Frailty was also associated with an increased risk of myocardial infarction and stroke, but not revascularization. TAKE HOME MESSAGE: Both the presence and severity of frailty are associated with CV mortality and major CV events, independent of underlying CV disease.

Impact of Coronavirus Disease 2019 (COVID-19) Severity on Long-term Events in United States Veterans Using the Veterans Affairs Severity Index for COVID-19 (VASIC).

In this retrospective cohort study of 94 595 severe acute respiratory syndrome coronavirus 2-positive cases, we developed and validated an algorithm to assess the association between coronavirus disease 2019 (COVID-19) severity and long-term complications (stroke, myocardial infarction, pulmonary embolism/deep vein thrombosis, heart failure, and mortality). COVID-19 severity was associated with a greater risk of experiencing a long-term complication 31-120 days postinfection. Most incident events occurred 31-60 days postinfection and diminished after day 91, except heart failure for severe patients and death for moderate patients, which peaked on days 91-120. Understanding the differential impact of COVID-19 severity on long-term events provides insight into possible intervention modalities and critical prevention strategies.

Scalable relevance ranking algorithm via semantic similarity assessment improves efficiency of medical chart review.

OBJECTIVE: Accurately assigning phenotype information to individual patients via computational phenotyping using Electronic Health Records (EHRs) has been seen as the first step towards enabling EHRs for precision medicine research. Chart review labels annotated by clinical experts, also known as "gold standard" labels, are essential for the development and validation of computational phenotyping algorithms. However, given the complexity of EHR systems, the process of chart review is both labor intensive and time consuming. We propose a fully automated algorithm, referred to as pGUESS, to rank EHR notes according to their relevance to a given phenotype. By identifying the most relevant notes, pGUESS can greatly improve the efficiency and accuracy of chart reviews. METHOD: pGUESS uses prior guided semantic similarity to measure the informativeness of a clinical note to a given phenotype. We first select candidate clinical concepts from a pool of comprehensive medical concepts using public knowledge sources and then derive the semantic embedding vector (SEV) for a reference article (SEVref) and each note (SEVnote). The algorithm scores the relevance of a note as the cosine similarity between SEVnote and SEVref. RESULTS: The algorithm was validated against four sets of 200 notes that were manually annotated by clinical experts to assess their informativeness to one of three disease phenotypes. pGUESS algorithm substantially outperforms existing unsupervised approaches for classifying the relevance status with respect to both accuracy and scalability across phenotypes. Averaging over the three phenotypes, the rank correlation between the algorithm ranking and gold standard label was 0.64 for pGUESS, but only 0.47 and 0.35 for the next two best performing algorithms. pGUESS is also much more computationally scalable compared to existing algorithms. CONCLUSION: pGUESS algorithm can substantially reduce the burden of chart review and holds potential in improving the efficiency and accuracy of human annotation.

Early Convalescent Plasma Therapy and Mortality Among US Veterans Hospitalized With Nonsevere COVID-19: An Observational Analysis Emulating a Target Trial.

BACKGROUND: Early convalescent plasma transfusion may reduce mortality in patients with nonsevere coronavirus disease 2019 (COVID-19). METHODS: This study emulates a (hypothetical) target trial using observational data from a cohort of US veterans admitted to a Department of Veterans Affairs (VA) facility between 1 May and 17 November 2020 with nonsevere COVID-19. The intervention was convalescent plasma initiated within 2 days of eligibility. Thirty-day mortality was compared using cumulative incidence curves, risk differences, and hazard ratios estimated from pooled logistic models with inverse probability weighting to adjust for confounding. RESULTS: Of 11 269 eligible person-trials contributed by 4755 patients, 402 trials were assigned to the convalescent plasma group. Forty and 671 deaths occurred within the plasma and nonplasma groups, respectively. The estimated 30-day mortality risk was 6.5% (95% confidence interval [CI], 4.0%-9.7%) in the plasma group and 6.2% (95% CI, 5.6%-7.0%) in the nonplasma group. The associated risk difference was 0.30% (95% CI, -2.30% to 3.60%) and the hazard ratio was 1.04 (95% CI, .64-1.62). CONCLUSIONS: Our target trial emulation estimated no meaningful differences in 30-day mortality between nonsevere COVID-19 patients treated and untreated with convalescent plasma. Clinical Trials Registration. NCT04545047.

Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans.

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ among patients hospitalized with COVID-19. We examined the effects of HCQ alone and in combination with azithromycin in a hospitalized population of US veterans with COVID-19, using a propensity score-adjusted survival analysis with imputation of missing data. According to electronic health record data from the US Department of Veterans Affairs health care system, 64,055 US Veterans were tested for the virus that causes COVID-19 between March 1, 2020 and April 30, 2020. Of the 7,193 veterans who tested positive, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and there was an increased risk of intubation when HCQ was used in combination with azithromycin (hazard ratio = 1.55; 95% confidence interval: 1.07, 2.24). In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treatment of patients hospitalized with COVID-19, using a national sample of the US veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ.

Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study.

BACKGROUND: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. METHODS: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. RESULTS: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. CONCLUSIONS: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Association of pulse rate with outcomes in heart failure with reduced ejection fraction: a retrospective cohort study.

BACKGROUND: In a real-world setting, the effect of pulse rate measured at the time of diagnosis and serially during follow-up and management, on outcomes in heart failure with reduced ejection fraction (HFrEF), has not been well-studied. Furthermore, how beta-blockade use in a real-world situation modifies this relation between pulse rate and outcomes in HFrEF is not well-known. Hence, we identified a large, national, real-world cohort of HFrEF to examine the association of pulse rate and outcomes. METHODS: Using Veterans Affairs (VA) national electronic health records we identified incident HFrEF cases between 2006 and 2012. We examined the associations of both baseline and serially measured pulse rates, with mortality and days hospitalized per year for heart failure and for any cause, using crude and multivariable Cox proportional hazards and Poisson or negative binomial models, respectively. The exposure was examined as continuous, dichotomous, and categorical. Post-hoc analyses addressed the interaction of pulse rate and beta-blocker target dose. RESULTS: We identified 51,194 incident HFrEF cases (67 ± 12 years, 98% male, 77% white. A significant positive, near linear relationship was observed for both baseline and serially measured pulse rates with all-cause mortality, all-cause hospitalization and heart failure hospitalization after adjusting for covariates including beta-blocker use. Patients who had a pulse rate ≥ 70 bpm in the past 6 months had 36% (95% CI: 31-42%), 25% (95% CI: 19-32%), and 51% (95% CI: 33-72%) increased rates of mortality, all-cause hospitalization, and heart failure hospitalization, respectively, compared to patients with pulse rates < 70 bpm. A minority of subjects (15%) were treated with guideline directed beta blockade ≥50% of recommended target dose, among whom better outcomes were seen compared to those who did not achieve target dose in patients with pulse rates both above and below 70 beats per minute. CONCLUSIONS: High pulse rate, both at the time of diagnosis and during follow-up, is strongly associated with increased risk of adverse outcomes in HFrEF patients, independent of the use of beta-blockers. In a real-world setting, the majority of HFrEF patients do not achieve target dose of beta-blockade; greater use of strategies to reduce heart rate may improve outcomes in HFrEF.

Correction: Plasma vitamin D, past chest illness, and risk of future chest illness in chronic spinal cord injury (SCI): a longitudinal observational study.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Plasma vitamin D, past chest illness, and risk of future chest illness in chronic spinal cord injury (SCI): a longitudinal observational study.

STUDY DESIGN: Observational study. OBJECTIVE: Assess associations between vitamin D levels and other risk factors on future chest illness in a chronic spinal cord injury (SCI) cohort. SETTING: Veterans Affairs Boston and the Boston, MA community. METHODS: Between August 2009 and August 2017, 253 participants with chronic SCI were followed over a median of 3.2 years (up to 7.4 years) with two to four visits a median of 1.7 years apart. At each visit, plasma 25-hydroxyvitamin D level was obtained, spirometry performed, and a respiratory questionnaire assessing chest illnesses since last visit was completed. Repeated measures negative binomial regression was used to assess chest illness risk longitudinally. RESULTS: At entry, 25% had deficient vitamin D levels (<20 nanograms/milliliter (ng/ml)), 52% were insufficient (20 to <30 ng/ml), and 23% were sufficient (≥30 ng/ml). Over 545 study visits, chest illnesses (n = 106) were reported by 60 participants. In multivariable models (including previous chest illness history), deficient vitamin D levels (compared with those with sufficient levels) were associated with future chest illness though with wide confidence limits (relative risk (RR) = 1.36, 95% confidence intervals (CI) = 0.74, 2.47). The strongest association with chest illness during the follow-up period was in persons who reported pneumonia/bronchitis after injury and a chest illness in the three years before study entry (RR = 7.62; 95% CI = 3.70, 15.71). CONCLUSION: Assessed prospectively in chronic SCI, there was a suggestive association between deficient vitamin D levels and future chest illness. Past chest illness history was also strongly associated with future chest illness.

Association of Statin Use With All-Cause and Cardiovascular Mortality in US Veterans 75 Years and Older.

Importance: Data are limited regarding statin therapy for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in adults 75 years and older. Objective: To evaluate the role of statin use for mortality and primary prevention of ASCVD in veterans 75 years and older. Design, Setting, and Participants: Retrospective cohort study that used Veterans Health Administration (VHA) data on adults 75 years and older, free of ASCVD, and with a clinical visit in 2002-2012. Follow-up continued through December 31, 2016. All data were linked to Medicare and Medicaid claims and pharmaceutical data. A new-user design was used, excluding those with any prior statin use. Cox proportional hazards models were fit to evaluate the association of statin use with outcomes. Analyses were conducted using propensity score overlap weighting to balance baseline characteristics. Exposures: Any new statin prescription. Main Outcomes and Measures: The primary outcomes were all-cause and cardiovascular mortality. Secondary outcomes included a composite of ASCVD events (myocardial infarction, ischemic stroke, and revascularization with coronary artery bypass graft surgery or percutaneous coronary intervention). Results: Of 326 981 eligible veterans (mean [SD] age, 81.1 [4.1] years; 97% men; 91% white), 57 178 (17.5%) newly initiated statins during the study period. During a mean follow-up of 6.8 (SD, 3.9) years, a total 206 902 deaths occurred including 53 296 cardiovascular deaths, with 78.7 and 98.2 total deaths/1000 person-years among statin users and nonusers, respectively (weighted incidence rate difference [IRD]/1000 person-years, -19.5 [95% CI, -20.4 to -18.5]). There were 22.6 and 25.7 cardiovascular deaths per 1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -3.1 [95 CI, -3.6 to -2.6]). For the composite ASCVD outcome there were 123 379 events, with 66.3 and 70.4 events/1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -4.1 [95% CI, -5.1 to -3.0]). After propensity score overlap weighting was applied, the hazard ratio was 0.75 (95% CI, 0.74-0.76) for all-cause mortality, 0.80 (95% CI, 0.78-0.81) for cardiovascular mortality, and 0.92 (95% CI, 0.91-0.94) for a composite of ASCVD events when comparing statin users with nonusers. Conclusions and Relevance: Among US veterans 75 years and older and free of ASCVD at baseline, new statin use was significantly associated with a lower risk of all-cause and cardiovascular mortality. Further research, including from randomized clinical trials, is needed to more definitively determine the role of statin therapy in older adults for primary prevention of ASCVD.

The Association Between Social Engagement, Mild Cognitive Impairment, and Falls Among Older Primary Care Patients.

OBJECTIVES: To examine associations between mild cognitive impairment (MCI) and falls among primary care patients, and to investigate whether social engagement (SE) modifies these associations. DESIGN: Cross sectional analysis using baseline data from an observational cohort study. SETTING: Primary care. PARTICIPANTS: Community-dwelling older adults (N=430) at risk of mobility decline with a mean age of 76.6 years (range 65-96y). MAIN OUTCOME MEASURES: The number of falls in the past year was reported at the baseline interview. MCI was identified using a cutoff of 1.5 SD below the age-adjusted mean on at least 2 of the standardized cognitive performance tests. SE (eg, keeping in touch with friends and family, volunteering, participating social activities…) was assessed with the Late Life Function and Disability Instrument, and required a score above the median value 49.5 out of 100. RESULTS: MCI was present among 42% of participants and 42% reported at least 1 fall in the preceding year. Using generalized estimating equations, MCI was associated with a 77% greater rate of falls (P<.05). There was a statistically significant interaction between SE and MCI on the rate of falls (P<.01), such that at a high level of SE, MCI was not statistically associated with falls (P=.83). In participants with lower levels of SE, MCI is associated with 1.3 times greater rate of falls (P<.01). CONCLUSIONS: While MCI is associated with a greater risk for falls, higher levels of SE may play a protective role.

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.

Yield and bias in defining a cohort study baseline from electronic health record data.

AIMS: Despite growing interest in using electronic health records (EHR) to create longitudinal cohort studies, the distribution and missingness of EHR data might introduce selection bias and information bias to such analyses. We aimed to examine the yield and potential for these healthcare process biases in defining a study baseline using EHR data, using the example of cholesterol and blood pressure (BP) measurements. METHODS: We created a virtual cohort study of cardiovascular disease (CVD) from patients with eligible cholesterol profiles in the New England (NE) and Southeast (SE) networks of the Veterans Health Administration in the United States. Using clinical data from the EHR, we plotted the yield of patients with BP measurements within an expanding timeframe around an index date of cholesterol testing. We compared three groups: (1) patients with BP from the exact index date; (2) patients with BP not on the index date but within the network-specific 90th percentile around the index date; and (3) patients with no BP within the network-specific 90th percentile. RESULTS: Among 589,361 total patients in the two networks, 146,636 (61.0%) of 240,479 patients from NE and 289,906 (83.1%) of 348,882 patients from SE had BP measurements on the index date. Ninety percent had BP measured within 11 days of the index date in NE and within 5 days of the index date in SE. Group 3 in both networks had fewer available race data, fewer comorbidities and CVD medications, and fewer health system encounters. CONCLUSIONS: Requiring same-day risk factor measurement in the creation of a virtual CVD cohort study from EHR data might exclude 40% of eligible patients, but including patients with infrequent visits might introduce bias. Data visualization can inform study-specific strategies to address these challenges for the research use of EHR data.

Development and validation of a heart failure with preserved ejection fraction cohort using electronic medical records.

BACKGROUND: Heart failure (HF) with preserved ejection fraction (HFpEF) comprises nearly half of prevalent HF, yet is challenging to curate in a large database of electronic medical records (EMR) since it requires both accurate HF diagnosis and left ventricular ejection fraction (EF) values to be consistently ≥50%. METHODS: We used the national Veterans Affairs EMR to curate a cohort of HFpEF patients from 2002 to 2014. EF values were extracted from clinical documents utilizing natural language processing and an iterative approach was used to refine the algorithm for verification of clinical HFpEF. The final algorithm utilized the following inclusion criteria: any International Classification of Diseases-9 (ICD-9) code of HF (428.xx); all recorded EF ≥50%; and either B-type natriuretic peptide (BNP) or aminoterminal pro-BNP (NT-proBNP) values recorded OR diuretic use within one month of diagnosis of HF. Validation of the algorithm was performed by 3 independent reviewers doing manual chart review of 100 HFpEF cases and 100 controls. RESULTS: We established a HFpEF cohort of 80,248 patients (out of a total 1,155,376 patients with the ICD-9 diagnosis of HF). Mean age was 72 years; 96% were males and 12% were African-Americans. Validation analysis of the HFpEF algorithm had a sensitivity of 88%, specificity of 96%, positive predictive value of 96%, and a negative predictive value of 87% to identify HFpEF cases. CONCLUSION: We developed a sensitive, highly specific algorithm for detecting HFpEF in a large national database. This approach may be applicable to other large EMR databases to identify HFpEF patients.

Long-term outcomes in patients with acute coronary syndromes related to prolonging dual antiplatelet therapy more than 12 months after coronary stenting.

OBJECTIVES: To assess the impact of stent type on the risk of death or myocardial infarction (MI) related to dual antiplatelet therapy (DAPT) more than 12 months (prolonged DAPT) versus 12 or less months after PCI for an acute coronary syndrome (ACS). BACKGROUND: The recent DAPT study reported lower recurrent ischemic events from prolonged DAPT in patients treated with PCI for an ACS, but was underpowered to determine the impact of stent type. METHODS: We determined clinical outcomes after PCI for an ACS (median follow-up: DES = 26 months, BMS = 46 months) in 18,484 patients in the Veterans Affairs system treated with first generation drug-eluting stents (DES) or bare-metal stents (BMS). We used landmark analyses starting 1 year after the index PCI to assess the risk of prolonged DAPT on the primary endpoint of death or MI. Multivariable and propensity models adjusted for confounding. RESULTS: There was a significant interaction between stent type and prolonged DAPT for death and MI (P = 0.0036), death (P = 0.054), and major bleeding (P = 0.0013). Patients treated with prolonged DAPT had lower risks of death or MI (HR = 0.71, 95% CI = 0.61, 0.82) and death (HR = 0.74, 95%CI = 0.62, 0.89) with DES, but not BMS, and higher risks of major bleeding, particularly with BMS (HR = 1.67, P < 0.001) than DES (HR = 1.24, p = 0.01). CONCLUSIONS: Prolonging DAPT more than 12 months after PCI for ACS only associated with a lower risk of ischemic events in the 1-4 years after PCI in those receiving first generation DES. Stent type may influence the benefit of prolonged DAPT. © 2016 Wiley Periodicals, Inc.

A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study.

BACKGROUND: Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. METHODS: In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. RESULTS: In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. CONCLUSIONS: We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

Weight gain on antipsychotic medication is associated with sustained use among veterans with schizophrenia.

OBJECTIVE: Continuous antipsychotic treatment is important in schizophrenia, and studies have shown that rates of discontinuation are high. Some studies suggest that weight gain may lead schizophrenic patients to discontinue treatment, whereas other studies show smaller effects of weight gain on medication discontinuation, and some find weight gain associated with symptom improvement. Our retrospective cohort study investigated the effect of weight change on the continued use for 1 year (persistence) of all antipsychotics, then among users of first-generation antipsychotics and second-generation antipsychotics (SGAs), and lastly subgroups of SGAs. METHODS: We identified 2130 patients with schizophrenia starting an antipsychotic that had not used 1 in the prior year. Using multivariable logistic regression adjusted for demographic and clinical variables, we determined the odds of remaining persistent on medication among patients who either gained weight or did not gain weight in the following year. RESULTS: For all antipsychotics combined, weight change was not associated with persistence. Among SGAs, weight gain was associated with a 23% increase in the adjusted odds ratio (OR) for persistence (OR, 1.23; 95% confidence interval [CI], 1.00-1.51), whereas there was a nonsignificant decrease in the adjusted odds of persistence among first-generation antipsychotic users (OR, 0.74; 95% CI, 0.43-1.28). When SGAs were divided into subgroups (clozapine/olanzapine, risperidone/quetiapine), both had increases in the likelihood of persistence, but only the association for clozapine/olanzapine was significant at a trend level (adjusted OR, 1.46; 95% CI, 0.99-2.16). CONCLUSIONS: These findings are supportive of other research that shows weight gain does not invariably lead to medication discontinuation and may be associated with clinical improvement.