Impact of left ventricular hypertrophy late after aortic valve replacement for aortic stenosis on cardiovascular morbidity and mortality

AIMS: The goal of this study was to assess the prevalence of left ventricular (LV) hypertrophy in patients with aortic stenosis late (>6 months) after aortic valve replacement and its impact on cardiac-related morbidity and mortality. METHODS AND RESULTS: In a single tertiary centre, echocardiographic data of LV muscle mass were collected. Detailed information of medical history and angiographic data were gathered. Ninety-nine of 213 patients (46%) had LV hypertrophy late (mean 5.8 +/- 5.4 years) after aortic valve replacement. LV hypertrophy was associated with impaired exercise capacity, higher New York Heart Association dyspnoea class, a tendency for more frequent chest pain expressed as higher Canadian Cardiovascular Society class, and more rehospitalizations. 24% of patients with normal LV mass vs. 39% of patients with LV hypertrophy reported cardiac-related morbidity (p = 0.04). In a multivariate logistic regression model, LV hypertrophy was an independent predictor of cardiac-related morbidity (odds ratio 2.31, 95% CI 1.08 to 5.41), after correction for gender, baseline ejection fraction, and coronary artery disease and its risk factors. Thirty seven deaths occurred during a total of 1959 patient years of follow-up (mean follow-up 9.6 years). Age at aortic valve replacement (hazard ratio 1.85, 95% CI 1.39 to 2.47, for every 5 years increase in age), coexisting coronary artery disease at the time of surgery (hazard ratio 3.36, 95% CI 1.31 to 8.62), and smoking (hazard ratio 4.82, 95% CI 1.72 to 13.45) were independent predictors of overall mortality late after surgery, but not LV hypertrophy. CONCLUSIONS: In patients with aortic valve replacement for isolated aortic stenosis, LV hypertrophy late after surgery is associated with increased morbidity.

QTc interval and resting heart rate as long-term predictors of mortality in type 1 and type 2 diabetes mellitus: a 23-year follow-up

We evaluated the association of QT interval corrected for heart rate (QT(c)) and resting heart rate (rHR) with mortality (all-causes, cardiovascular, cardiac, and ischaemic heart disease) in subjects with type 1 and type 2 diabetes.

Apolipoprotein B as a long-term predictor of mortality in type 1 diabetes mellitus: a 15-year follow up

To evaluate the association of apolipoprotein B (apo B) with mortality due to all causes, to cardiac disease and to ischaemic heart disease (IHD) in subjects with type 1 diabetes mellitus.

Doxycycline reduces mortality and injury to the brain and cochlea in experimental pneumococcal meningitis

Bacterial meningitis is characterized by an inflammatory reaction to the invading pathogens that can ultimately lead to sensorineural hearing loss, permanent brain injury, or death. The matrix metalloproteinases (MMPs) and tumor necrosis factor alpha-converting enzyme (TACE) are key mediators that promote inflammation, blood-brain barrier disruption, and brain injury in bacterial meningitis. Doxycycline is a clinically used antibiotic with anti-inflammatory effects that lead to reduced cytokine release and the inhibition of MMPs. Here, doxycycline inhibited TACE with a 50% inhibitory dose of 74 microM in vitro and reduced the amount of tumor necrosis factor alpha released into the cerebrospinal fluid by 90% in vivo. In an infant rat model of pneumococcal meningitis, a single dose of doxycycline (30 mg/kg) given as adjuvant therapy in addition to ceftriaxone 18 h after infection significantly reduced the mortality, the blood-brain barrier disruption, and the extent of cortical brain injury. Adjuvant doxycycline (30 mg/kg given subcutaneously once daily for 4 days) also attenuated hearing loss, as assessed by auditory brainstem response audiometry, and neuronal death in the cochlear spiral ganglion at 3 weeks after infection. Thus, doxycycline, probably as a result of its anti-inflammatory properties, had broad beneficial effects in the brain and the cochlea and improved survival in this model of pneumococcal meningitis in infant rats.

A simple presurgical necrotizing enterocolitis-mortality scoring system

OBJECTIVE: To assess the relationship between early laboratory parameters, disease severity, type of management (surgical or conservative) and outcome in necrotizing enterocolitis (NEC). STUDY DESIGN: Retrospective collection and analysis of data from infants treated in a single tertiary care center (1980 to 2002). Data were collected on disease severity (Bell stage), birth weight (BW), gestational age (GA) and pre-intervention laboratory parameters (leukocyte and platelet counts, hemoglobin, lactate, C-reactive protein). RESULTS: Data from 128 infants were sufficient for analysis. Factors significantly associated with survival were Bell stage (P<0.05), lactate (P<0.05), BW and GA (P<0.01, P<0.001, respectively). From receiver operating characteristics curves, the highest predictive value resulted from a score with 0 to 8 points combining BW, Bell stage, lactate and platelet count (P<0.001). At a cutoff level of 4.5 sensitivity and specificity for predicting survival were 0.71 and 0.72, respectively. CONCLUSION: Some single parameters were associated with poor outcome in NEC. Optimal risk stratification was achieved by combining several parameters in a score.

Mortality of HIV-1-infected patients in the first year of antiretroviral therapy: comparison between low-income and high-income countries

BACKGROUND: Highly active antiretroviral therapy (HAART) is being scaled up in developing countries. We compared baseline characteristics and outcomes during the first year of HAART between HIV-1-infected patients in low-income and high-income settings. METHODS: 18 HAART programmes in Africa, Asia, and South America (low-income settings) and 12 HIV cohort studies from Europe and North America (high-income settings) provided data for 4810 and 22,217, respectively, treatment-naive adult patients starting HAART. All patients from high-income settings and 2725 (57%) patients from low-income settings were actively followed-up and included in survival analyses. FINDINGS: Compared with high-income countries, patients starting HAART in low-income settings had lower CD4 cell counts (median 108 cells per muL vs 234 cells per muL), were more likely to be female (51%vs 25%), and more likely to start treatment with a non-nucleoside reverse transcriptase inhibitor (NNRTI) (70%vs 23%). At 6 months, the median number of CD4 cells gained (106 cells per muL vs 103 cells per muL) and the percentage of patients reaching HIV-1 RNA levels lower than 500 copies/mL (76%vs 77%) were similar. Mortality was higher in low-income settings (124 deaths during 2236 person-years of follow-up) than in high-income settings (414 deaths during 20,532 person-years). The adjusted hazard ratio (HR) of mortality comparing low-income with high-income settings fell from 4.3 (95% CI 1.6-11.8) during the first month to 1.5 (0.7-3.0) during months 7-12. The provision of treatment free of charge in low-income settings was associated with lower mortality (adjusted HR 0.23; 95% CI 0.08-0.61). INTERPRETATION: Patients starting HAART in resource-poor settings have increased mortality rates in the first months on therapy, compared with those in developed countries. Timely diagnosis and assessment of treatment eligibility, coupled with free provision of HAART, might reduce this excess mortality.

Risks of non-accidental mortality by baseline CD4+ T-cell strata in hepatitis-C-positive and -negative individuals initiating highly active antiretroviral therapy

BACKGROUND: Patients coinfected with hepatitis C virus (HCV) and HIV experience higher mortality rates than patients infected with HIV alone. We designed a study to determine whether risks for later mortality are similar for HCV-positive and HCV-negative individuals when subjects are stratified on the basis of baseline CD4+ T-cell counts. METHODS: Antiretroviral-naive individuals, who initiated highly active antiretroviral therapy (HAART) between 1996 and 2002 were included in the study. HCV-positive and HCV-negative individuals were stratified separately by baseline CD4+ T-cell counts of 50 cell/microl increments. Cox-proportional hazards regression was used to model the effect of these strata with other variables on survival. RESULTS: CD4+ T-cell strata below 200 cells/microl, but not above, imparted an increased relative hazard (RH) of mortality for both HCV-positive and HCV-negative individuals. Among HCV-positive individuals, after adjustment for baseline age, HIV RNA levels, history of injection drug use and adherence to therapy, only CD4+ T-cell strata of <50 cells/microl (RH=4.60; 95% confidence interval [CI] 2.72-7.76) and 50-199 cells/microl (RH=2.49; 95% CI 1.63-3.81) were significantly associated with increased mortality when compared with those initiating therapy at cell counts >500 cells/microl. The same baseline CD4+ T-cell strata were found for HCV-negative individuals. CONCLUSION: In a within-groups analysis, the baseline CD4+ T-cell strata that are associated with increased RHs for mortality are the same for HCV-positive and HCV-negative individuals initiating HAART. However, a between-groups analysis reveals a higher absolute mortality risk for HCV-positive individuals.

The National Morbidity, Mortality, and Air Pollution Study Database in R

The NMMAPS data package contains daily mortality, air pollution, and weather data originally assembled as part of the National Morbidity,Mortality, and Air Pollution Study (NMMAPS). The data have recently been updated and are available for 108 United States cities for the years 1987--2000. The package provides tools for building versions of the full database in a structured and reproducible manner. These database derivatives may be more suitable for particular analyses. We describe how to use the package to implement a multi-city time series analysis of mortality and PM(10). In addition we demonstrate how to reproduce recent findings based on the NMMAPS data.

Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

TRENDS IN PARTICULATE MATTER AND MORTALITY: AN APPROACH TO THE ASSESSMENT OF UNMEASURED CONFOUNDING

We propose a method for diagnosing confounding bias under a model which links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 U.S. counties from 2000-2002. We decompose the association between PM2.5 and mortality into two components: 1) the association between “national trends” in PM2.5 and mortality; and 2) the association between “local trends,” defined as county-specificdeviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these two spatio-temporalscales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.

Seasonal Analyses of Air Pollution and Mortality in 100 U.S. Cities

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors develop Bayesian semi-parametric hierarchical models for estimating time-varying effects of pollution on mortality in multi-site time series studies. The methods are applied to the updated National Morbidity and Mortality Air Pollution Study database for the period 1987--2000, which includes data for 100 U.S. cities. At the national level, a 10 micro-gram/m3 increase in PM(10) at lag 1 is associated with a 0.15 (95% posterior interval: -0.08, 0.39),0.14 (-0.14, 0.42), 0.36 (0.11, 0.61), and 0.14 (-0.06, 0.34) percent increase in mortality for winter, spring, summer, and fall, respectively. An analysis by geographical regions finds a strong seasonal pattern in the northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

Mortality from neurodegenerative disease and exposure to extremely low-frequency magnetic fields: 31 years of observations on Swiss railway employees

AIMS: The objective of the present study was to investigate the relationship between extremely low-frequency magnetic field (ELF-MF) exposure and mortality from several neurodegenerative conditions in Swiss railway employees. METHODS: We studied a cohort of 20,141 Swiss railway employees with 464,129 person-years of follow-up between 1972 and 2002. For each individual, cumulative exposure was calculated from on-site measurements and modelling of past exposure. We compared cause-specific mortality in highly exposed train drivers (mean exposure: 21 microT) with less exposed occupational groups (for example station masters: 1 microT). RESULTS: The hazard ratio for train drivers compared to station masters was 1.96 [95% confidence interval (CI) = 0.98-3.92] for senile dementia and 3.15 (95% CI = 0.90-11.04) for Alzheimer's disease. For every 10 microT years of cumulative exposure senile dementia mortality increased by 5.7% (95% CI = 1.3-10.4), Alzheimer's disease by 9.4% (95% CI = 2.7-16.4) and amyotrophic lateral sclerosis by 2.1% (95% CI = -6.8 to 11.7). There was no evidence for an increase in mortality from Parkinson's disease and multiple sclerosis. CONCLUSIONS: This study suggests a link between exposure to ELF-MF and Alzheimer's disease and indicates that ELF-MF might act in later stages of the disease process.