Search

Objective: In longitudinal studies, devices used to measure exposures, like pulse wave velocity (PWV), can change from visit to visit. Calibration studies, where a subset of participants receive measurements from both devices at follow-up, are often used to assess differences in the device measurements. Regression calibration and multiple imputation are common statistical methods to correct for those differences, but no study yet exists to compare the two when the quantity of interest is change in the exposure over time. We compared both methods in a hypothetical study of change in PWV and its association with total brain volume. Methods: We simulated true values of PWV at baseline and follow up, as well as imperfect measurements of PWV using an “old” device and “new” device. Two statistical methods were compared: regression calibration , which calibrates the new device measurements at follow up to the old device using linear regression in a calibration study; and multiple imputation , which imputes the (mostly) missing old device measurements at follow up.We varied the bias and measurement error of each device and for each scenario simulated 1,000 datasets of size n=2,500. Two percent of participants in each iteration were chosen to participate in the calibration study, and thus had measurements on the old and new devices at follow up. We used 200 bootstrap replicates to calculate the standard errors for the regression calibration method and 50 imputed datasets for the multiple imputation method. To compare the methods we used bias of the estimated association and how well the standard errors approximated the empirical standard errors. Results: Regression calibration was virtually unbiased for the association between change in PWV and total brain volume when the old device had larger measurement error than the new device. The maximum bias for regression calibration across all scenarios was still small (6%). When the old device had more measurement error or the two devices had equal measurement error, multiple imputation underestimated the association by more than 10%. This underestimation was reduced to approximately 2% when the new device had a larger measurement error than the old device. In all scenarios, regression calibration underestimated the empirical standard error by approximately 35%, while multiple imputation underestimated it by only 2-5%. Conclusions: In analyses of change in PWV and total brain volume, when unbiased estimation is the main objective, regression calibration is favorable to multiple imputation. When null hypothesis significance testing is the main objective, multiple imputation may be favorable in order to not underestimate the standard errors. We expect these conclusions to apply to other change in exposure and outcome relationships with similar ratios between the association’s magnitude and the amount of measurement error.

Objective: Hypertensive disorders of pregnancy (HDP) are a spectrum of disorders with increasing severity: gestational hypertension, preeclampsia, preeclampsia superimposed on chronic hypertension, and severe preeclampsia/eclampsia. Although factors associated with developing an HDP have been established, it is unclear which factors are associated with more severe HDP. This study aimed to estimate the associations between clinical, demographic, and social factors and severity of HDP. Methods: We conducted a retrospective, cross-sectional study of electronic medical records of women who delivered between 2014 and 2017 at two academic medical centers and were diagnosed with an HDP during pregnancy or up to 6 weeks postpartum. Patients were categorized by highest severity of HDP. We used ordinal logistic regression models to estimate odds ratios (ORs) for the severity of HDP by covariates including demographic data and clinical characteristics. Results: Among 5,607 women diagnosed with an HDP, 2,609 (47%) had gestational hypertension, 1,748 (31%) had preeclampsia, 608 (11%) had preeclampsia superimposed on chronic hypertension, and 642 (11%) had severe preeclampsia/eclampsia. Median (Q1, Q3) maternal age was 30 (25, 34) years; 46% (2,588 of 5,607) were white race, 32% (1,811 of 5,607) were black race, and 22% were other race (1,208 of 5,607); 15% (822 of 5,607) were Hispanic/Latina; 13% (735 of 5,607) had gestational diabetes; 42% (2,351 of 5,607) received care at institution A, 44% (2,445 of 5,607) at institution B, and 14% (811 of 5,607) at both; median (Q1, Q3) maternal body mass index near delivery was 35(30, 41) kg/m^2; and median (Q1, Q3) household income of ZIP code of residence was $50,734 ($43,592, $69,380). Factors independently associated with more severe HDP (OR, 95% CI) included: older maternal age (1.15 per 10-year increase, 1.05 - 1.27), Black race (1.48, 1.3 - 1.68) versus white, Hispanic/Latina ethnicity (1.51, 1.22 - 1.88), gestational diabetes (1.35, 1.16 - 1.65), and receiving care at institution A vs B (1.23, 1.1 - 1.38) or both institutions vs institution B (1.32, 1.13 - 1.54). Maternal body mass index near delivery (0.98 per 10 kg/m^2 increase, 0.92 - 1.05) was not meaningfully associated with more severe HDP. Conclusions: Several factors were associated with more severe HDP. Importantly, these factors may help identify women who are at a higher risk for developing a more severe form of HDP. Further study is needed to confirm these findings and determine interventions that can mitigate risk to improve women’s cardiovascular health.

Introduction: Heart failure (HF) exacerbations are often precipitated by dietary noncompliance, which is common around holidays. We examined whether HF hospitalizations would increase after major national holidays. Methods: The ARIC Study performed surveillance of HF admissions among patients age ≥55 years in 4 US communities from 2005-2014. HF admissions were identified by ICD-9 codes and validated by physician review. For each holiday (H), 4 days before the holiday (H-4 to H-1), and 4 days after the holiday (H+1 to H+4), we compared average daily HF admission rates, hospital length of stay (LOS), 28-day mortality, and 1-year mortality to the remaining days in each holiday’s month using Poisson regression using a complex sampling design. Results: ARIC identified 92,354 HF admissions (weighted). Patients admitted in holiday months were 46% male and 71% white, with average age 75 years. Compared to the holiday months, patients admitted on H were older (78 years, p Conclusions: HF admission rates, hospital LOS, and mortality were similar in the 4 days following a holiday compared to the remainder of each holiday’s month. However, the day before and/or day of a holiday were associated with lower HF admission rates, shorter hospital LOS, and lower 28-day mortality.

Ambient particulate matter has been shown to be associated with declining human health, although the association between fine particulate matter (PMWe utilized satellite-derived measures of PMAmong 30,239 participants in the REGARDS study, 746 incident events were observed: 72 hemorrhagic, 617 ischemic, and 57 of unknown type. Participants exposed to higher levels of PMIn this large cohort of African-Americans and whites, no association was observed between PM

Background: Length of stay (LOS) during heart failure (HF) hospitalizations has decreased, accompanied by recent increases in post-discharge mortality and 30-day readmission rates among these patients. Methods: We conducted a retrospective cohort study of 19,477 Medicare beneficiaries in order to determine whether patients with the longest LOS also had the highest risk of death or readmission. We included patients hospitalized for HF and discharged alive between 2007 and 2011 with documented left ventricular (LV) systolic, diastolic, or combined dysfunction. We estimated the associations of LOS, risk for 30-day mortality, and risk for 30-day readmission with: type of LV dysfunction, HF severity, comorbidity burden, discharge disposition, demographics, nursing home residence, and calendar year. Gamma regression, Poisson regression, and Cox proportional hazards were used to model LOS, 30-day mortality, and 30-day readmission, respectively. Each model included all covariates. Results: The mean (IQR) LOS was 5 (4 - 8) days, 7% of the patients died within 30 days, and 23% of the patients were readmitted within 30 days. Patients with isolated diastolic dysfunction (DD) had 2% shorter LOS than patients with any systolic dysfunction (SD), but had 10% lower 30-day mortality. Comorbidities were associated with a 3% to 10% longer LOS, with the exception of chronic kidney disease (CKD) (16% longer) and malnutrition (30% longer). Only CKD, malnutrition, and chronic obstructive pulmonary disease (COPD) were associated with 30-day mortality (22%, 51%, and 14% higher risk, respectively). Most comorbidities were associated with 10% - 20% higher risk of 30-day readmission. Regarding discharge disposition, patients discharged home had 40% shorter LOS, 78% lower 30-day mortality, and 11% lower risk for 30-day readmission than patients discharged to a skilled nursing facility (SNF). Conclusions: We found no mismatch between LOS and prognosis among Medicare beneficiaries hospitalized for HF.

Introduction: Recent work has suggested that there is some association between acute exposures to fine particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) and ischemic stroke; however, the evidence is conflicting. Thus, we assessed whether PM2.5 was associated with ischemic stroke in participants in the Reasons for Geographic And Racial Differences in Stroke (REGARDS) cohort. Methods: We used a time-stratified case-crossover design to determine if exposure to PM2.5 was associated with an increased risk of ischemic stroke. We fit conditional logistic regression models to determine the odds ratio of ischemic stroke for those exposed to moderate (PM2.5 15-40 μg/m3) relative to good (PM2.5 ≤ 15 μg/m3) levels of PM2.5. We adjusted for temperature at the time of exposure, and assessed whether the association differed by region of residence (stroke belt vs. non-belt regions). Results: Among 442 participants who experienced an incident ischemic stroke in REGARDS, we found that there was no association with PM2.5 exposure (OR: 0.89, 95% CI: 0.69-1.15), and that there was no impact of region of residence on these results (p for interaction=0.14). Conclusions: We did not confirm earlier research indicating that there is an acute association between PM2.5 and ischemic stroke. More research is needed to understand these conflicting results, and to assess the impact of longer term exposures of PM2.5 on stroke incidence.