Hepatic retransplantation in cholestatic liver disease: Impact of the interval to retransplantation on survival and resource utilization

The aim of our study was to quantitatively assess the impact of hepatic retransplantation on patient and graft survival and resource utilization. We studied patients undergoing hepatic retransplantation among 447 transplant recipients with primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC) at 3 transplantation centers. Cox proportional hazards regression analysis was used for survival analysis. Measures of resource utilization included the duration of hospitalization, length of stay in the intensive care unit, and the duration of transplantation surgery. Forty-six (10.3%) patients received 2 or more grafts during the follow-up period (median, 2.8 years). Patients who underwent retransplantation had a 3.8-fold increase in the risk of death compared with those without retransplantation (P < .01). Retransplantation after an interval of greater than 30 days from the primary graft was associated with a 6.7-fold increase in the risk of death (P < .01). The survival following retransplantations performed 30 days or earlier was similar to primary transplantations. Resource utilization was higher in patients who underwent multiple consecutive transplantations, even after adjustment for the number of grafts during the hospitalization. Among cholestatic liver disease patients, poor survival following hepatic retransplantation is attributed to late retransplantations, namely those performed more than 30 days after the initial transplantation. While efforts must be made to improve the outcome following retransplantation, a more critical evaluation may be warranted for late retransplantation candidates

Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically

A prognostic model for the outcome of liver transplantation in patients with cholestatic liver disease

We studied the outcome of 436 patients with primary biliary cirrhosis (PBC) or primary sclerosing cholangitis (PSC) who underwent orthotopic liver transplant (OLT) at three major liver transplant centers. Univariate predictors of outcome included age, Karnofsky score, Child's class, Mayo risk score, United Network for Organ Sharing (UNOS) status, nutritional status, serum albumin, serum bilirubin, international normalized ratio, and the presence of ascites, encephalopathy, renal failure (serum creatinine > 2 mg/dL), and edema refractory to diuretics. Using these predictors, we developed a four variable mathematical prognostic model to help the liver transplant physician predict the following: 1) the amount of intraoperative blood loss; 2) the number of days in the intensive care unit (ICU); and 3) severe complications after surgery. The model uses age, renal failure, Child's class, and United Network for Organ Sharing status. This study is the first to model the outcome of liver transplant in patients with a specific etiology of chronic liver disease (PBC or PSC). The model may be used to help select patients for OLT and to plan the timing of their transplantation

Variables associated with odds of finishing and finish time in a 161-km ultramarathon

We sought to determine the degree to which age, sex, calendar year, previous event experience and ambient race day temperature were associated with finishing a 100-mile (161-km) trail running race and with finish time in that race. We computed separate generalized linear mixed-effects regression models for (1) odds of finishing and (2) finish times of finishers. Every starter from 1986 to 2007 was used in computing the models for odds of finishing (8,282 starts by 3,956 individuals) and every finisher in the same period was included in the models for finish time (5,276 finishes). Factors associated with improved odds of finishing included being a first-time starter and advancing calendar year. Factors associated with reduced odds of finishing included advancing age above 38 years and warmer weather. Beyond 38 years of age, women had worse odds of finishing than men. Warmer weather had a similar effect on finish rates for men and women. Finish times were slower with advancing age, slower for women than men, and less affected by warm weather for women than for men. Calendar year was not associated with finish time after adjustment for other variables

Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys

<p>Abstract</p> <p>Background</p> <p>The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).</p> <p>Methods</p> <p>We performed simulations to validate the MI approach for estimating hazard ratios and the predictive ability of a model or of an additional variable in case-cohort surveys. As an illustration, we analyzed a case-cohort survey from the Three-City study to estimate the predictive ability of D-dimer plasma concentration on coronary heart disease (CHD) and on vascular dementia (VaD) risks.</p> <p>Results</p> <p>When the imputation model of the phase-2 variable was correctly specified, MI estimates of hazard ratios and predictive abilities were similar to those obtained with full data. When the imputation model was misspecified, MI could provide biased estimates of hazard ratios and predictive abilities. In the Three-City case-cohort study, elevated D-dimer levels increased the risk of VaD (hazard ratio for two consecutive tertiles = 1.69, 95%CI: 1.63-1.74). However, D-dimer levels did not improve the predictive ability of the model.</p> <p>Conclusions</p> <p>MI is a simple approach for analyzing case-cohort data and provides an easy evaluation of the predictive ability of a model or of an additional variable.</p

The Influence of Meteorology on the Spread of Influenza: Survival Analysis of an Equine Influenza (A/H3N8) Outbreak

The influences of relative humidity and ambient temperature on the transmission of influenza A viruses have recently been established under controlled laboratory conditions. The interplay of meteorological factors during an actual influenza epidemic is less clear, and research into the contribution of wind to epidemic spread is scarce. By applying geostatistics and survival analysis to data from a large outbreak of equine influenza (A/H3N8), we quantified the association between hazard of infection and air temperature, relative humidity, rainfall, and wind velocity, whilst controlling for premises-level covariates. The pattern of disease spread in space and time was described using extraction mapping and instantaneous hazard curves. Meteorological conditions at each premises location were estimated by kriging daily meteorological data and analysed as time-lagged time-varying predictors using generalised Cox regression. Meteorological covariates time-lagged by three days were strongly associated with hazard of influenza infection, corresponding closely with the incubation period of equine influenza. Hazard of equine influenza infection was higher when relative humidity was <60% and lowest on days when daily maximum air temperature was 20–25°C. Wind speeds >30 km hour−1 from the direction of nearby infected premises were associated with increased hazard of infection. Through combining detailed influenza outbreak and meteorological data, we provide empirical evidence for the underlying environmental mechanisms that influenced the local spread of an outbreak of influenza A. Our analysis supports, and extends, the findings of studies into influenza A transmission conducted under laboratory conditions. The relationships described are of direct importance for managing disease risk during influenza outbreaks in horses, and more generally, advance our understanding of the transmission of influenza A viruses under field conditions