Assessment of KDIGO definitions in patients with histopathologic evidence of acute renal disease.

BACKGROUND AND OBJECTIVES: AKI is a clinical syndrome with various causes involving glomerular, interstitial, tubular, and vascular compartments of the kidney. Acute kidney disease (AKD) is a new concept that includes both AKI and the conditions associated with subacute decreases in GFR (AKD/non-AKI). This study aimed to investigate the correlation between AKI/AKD defined by clinical presentation and diffuse histologic criteria for acute abnormalities based on renal biopsy. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: All 303 patients who were histologically diagnosed as having acute tubular necrosis (ATN), acute tubulointerstitial nephritis, cellular crescentic GN, acute thrombotic microangiopathy, or complex lesions on renal biopsy from January 2009 to December 2011 were enrolled in the study. The 2012 Kidney Disease Improving Global Outcomes AKD/AKI definitions were applied to classify patients as follows: AKI, AKD/non-AKI, non-AKD, or unclassified. RESULTS: A total of 273 patients (90.1%) met the AKD criteria; 198 patients (65.3%) were classified as having AKI according to serum creatinine (SCr) and urine output criteria. The urine output criteria added 4.3% to the SCr criteria and reclassified 6.7% of the AKI cases into higher stages. Of patients with ATN on pathology, 79.2% met AKI criteria; this was a higher percentage than for those who had other individual pathologic lesions (50%-64%). The major cause of not being defined as having AKI was a slower SCr increase than that required by the definition of AKI (98, 93.3%). Patients with AKI had more severe clinical conditions and worse short-term renal outcome than those in the non-AKI group. CONCLUSIONS: Diffuse, acute abnormality defined by renal biopsy and AKI defined by clinical presentation are two different entities. Most patients who have diffuse acute histologic findings met the criteria for AKD, whereas only two thirds met the definition of AKI.

Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a simulation study.

BACKGROUND: Chance imbalance in baseline prognosis of a randomized controlled trial can lead to over or underestimation of treatment effects, particularly in trials with small sample sizes. Our study aimed to (1) evaluate the probability of imbalance in a binary prognostic factor (PF) between two treatment arms, (2) investigate the impact of prognostic imbalance on the estimation of a treatment effect, and (3) examine the effect of sample size (n) in relation to the first two objectives. METHODS: We simulated data from parallel-group trials evaluating a binary outcome by varying the risk of the outcome, effect of the treatment, power and prevalence of the PF, and n. Logistic regression models with and without adjustment for the PF were compared in terms of bias, standard error, coverage of confidence interval and statistical power. RESULTS: For a PF with a prevalence of 0.5, the probability of a difference in the frequency of the PF≥5% reaches 0.42 with 125/arm. Ignoring a strong PF (relative risk = 5) leads to underestimating the strength of a moderate treatment effect, and the underestimate is independent of n when n is >50/arm. Adjusting for such PF increases statistical power. If the PF is weak (RR = 2), adjustment makes little difference in statistical inference. Conditional on a 5% imbalance of a powerful PF, adjustment reduces the likelihood of large bias. If an absolute measure of imbalance ≥5% is deemed important, including 1000 patients/arm provides sufficient protection against such an imbalance. Two thousand patients/arm may provide an adequate control against large random deviations in treatment effect estimation in the presence of a powerful PF. CONCLUSIONS: The probability of prognostic imbalance in small trials can be substantial. Covariate adjustment improves estimation accuracy and statistical power, and hence should be performed when strong PFs are observed.

The aging Canadian population and hospitalizations for acute myocardial infarction: projection to 2020.

BACKGROUND: The risk of experiencing an acute myocardial infarction (AMI) increases with age and Canada's population is aging. The objective of this analysis was to examine trends in the AMI hospitalization rate in Canada between 2002 and 2009 and to estimate the potential increase in the number of AMI hospitalizations over the next decade. METHODS: Aggregated data on annual AMI hospitalizations were obtained from the Canadian Institute for Health Information for all provinces and territories, except Quebec, for 2002/03 and 2009/10. Using these data in a Poisson regression model to control for age, gender and year, the rate of AMI hospitalizations was extrapolated between 2010 and 2020. The extrapolated rate and Statistics Canada population projections were used to estimate the number of AMI hospitalizations in 2020. RESULTS: The rates of AMI hospitalizations by gender and age group showed a decrease between 2002 and 2009 in patients aged ≥ 65 years and relatively stable rates in those aged < 64 years in both males and females. However, the total number of AMI hospitalizations in Canada (excluding Quebec) is projected to increase by 4667 from 51847 in 2009 to 56514 in 2020, a 9.0% increase. Inflating this number to account for the unavailable Quebec data results in an increase of approximately 6200 for the whole of Canada. This would amount to an additional cost of between $46 and $54 million and sensitivity analyses indicate that it could be between $36 and $65 million. CONCLUSIONS: Despite projected decreasing or stable rates of AMI hospitalization, the number of hospitalizations is expected to increase substantially as a result of the aging of the Canadian population. The cost of these hospitalizations will be substantial. An increase of this extent in the number of AMI hospitalizations and the ensuing costs would significantly impact the already over-stretched Canadian healthcare system.

Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: a simulation study.

BACKGROUND: Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs. METHODS: Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure. RESULTS: While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis. CONCLUSIONS: All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.

Bayesian adjustment for exposure misclassification in case-control studies.

Poor measurement of explanatory variables occurs frequently in observational studies. Error-prone observations may lead to biased estimation and loss of power in detecting the impact of explanatory variables on the response. We consider misclassified binary exposure in the context of case-control studies, assuming the availability of validation data to inform the magnitude of the misclassification. A Bayesian adjustment to correct the misclassification is investigated. Simulation studies show that the Bayesian method can have advantages over non-Bayesian counterparts, particularly in the face of a rare exposure, small validation sample sizes, and uncertainty about whether exposure misclassification is differential or non-differential. The method is illustrated via application to several real studies.