Georg Gutjahr

Elevated Uric Acid Increases the Risk for Kidney Disease

Recent epidemiologic studies suggest that uric acid predicts the development of new-onset kidney disease, but it is unclear whether uric acid is an independent risk factor. In this study, data from 21,475 healthy volunteers who were followed prospectively for a median of 7 yr were analyzed to examine the association between uric acid level and incident kidney disease (estimated GFR [eGFR] <60 ml/min per 1.73 m(2)). After adjustment for baseline eGFR, a slightly elevated uric acid level (7.0 to 8.9 mg/dl) was associated with a nearly doubled risk for incident kidney disease (odds ratio 1.74; 95% confidence interval 1.45 to 2.09), and an elevated uric acid (> or =9.0 mg/dl) was associated with a tripled risk (odds ratio 3.12; 95% confidence interval 2.29 to 4.25). These increases in risk remained significant even after adjustment for baseline eGFR, gender, age, antihypertensive drugs, and components of the metabolic syndrome (waist circumference, HDL cholesterol, blood glucose, triglycerides, and BP). In a fully adjusted spline model, the risk for incident kidney disease increased roughly linearly with uric acid level to a level of approximately 6 to 7 mg/dl in women and 7 to 8 mg/dl in men; above these levels, the associated risk increased rapidly. In conclusion, elevated levels of uric acid independently increase the risk for new-onset kidney disease.

Cost-effectiveness analysis of renal replacement therapy in Austria

BACKGROUND Providing renal replacement therapy (RRT) for end-stage renal disease patients is resource intensive. Despite growing financial pressure in health care systems worldwide, cost-effectiveness studies of RRT modalities are scarce. METHODS We developed a Markov model of costs, quality of life and survival to compare three different assignment strategies to chronic RRT in Europe. RESULTS Mean annual treatment costs for haemodialysis were €43,600 during the first 12 months, €40,000 between 13 and 24 months and €40,600 beyond 25 months after initiation of treatment. Mean annual treatment costs for peritoneal dialysis were €25,900 during the first 12 months, €15,300 between 13 and 24 months and €20,500 beyond 25 months. Mean annual therapy costs for a kidney transplantation during the first 12 months were €50,900 from a living donor, €51,000 from a deceased donor, €17,200 between 13 and 24 months and €12,900 beyond 25 months after engraftment. Over the next 10 years in Austria with a population of 8 million people, increased assignment to peritoneal dialysis of 20% incident patients saved €26 million with a discount rate of 3% and gained 839 quality-adjusted life years (QALYs); additionally, increasing renal transplants to 10% from live donations saved €38 million discounted and gained 2242 QALYs. CONCLUSIONS Live donor renal transplantation is cost effective and associated with increase in QALYs. Therefore, preemptive live kidney transplantation should be promoted from a fiscal as well as medical point of view.

Nocturia is an age-independent risk factor for hip-fractures in men.

Nocturia is a highly prevalent symptom in the elderly and a common reason for interrupted sleep resulting in dizziness, worse daytime functioning and higher risk of falls. The aim of this study was to determine the role of nocturia as a risk factor for hip‐fractures in men.

Is there an association between lower urinary tract symptoms and cardiovascular risk in men? A cross sectional and longitudinal analysis.

OBJECTIVE To investigate the association between lower urinary tract symptoms (LUTS) and both coronary vascular disease (CVD) and stroke in men in a cross-sectional and longitudinal setting. MATERIAL AND METHODS Men aged 30-92 years underwent a free health investigation and completed the International Prostate Symptom Score questionnaire. In the cross-sectional part, the 10-year CVD and stroke risks were estimated according to the Framingham risk score. In the follow-up period (mean 6.1 years) the CVD and stroke events were recorded. RESULTS Two-thousand ninety-two men aged 47.8 years (SD 11.5) were analyzed. No to mild LUTS were present in 1,738 men, 337 men had moderate LUTS, and 17 had severe LUTS. The 10-year risk (cross-sectional design) for CVD or stroke in men with no to mild, moderate, and severe LUTS was 8.8%, 10.6%, and 15.9% (P <.01) and 6.9%, 7.5%, and 11.7% (P <.01), respectively. Adjusted for age, diabetes, total and low-density lipoprotein cholesterol, the odds ratios for CVD and stroke risk were 0.98 (P = .58) and 0.99 (P = .72) for moderate and 1.28 (P = .03) and 1.66 (P <.01) for severe LUTS. During follow-up, 96 events (CVD or stroke) were recorded: Men with no to mild, moderate, and severe LUTS had 76 (4.4%), 15 (4.5%), and 5 (29.4%) events, respectively. The adjusted (see above) hazard ratios were 0.63 (P = .16) for moderate and 3.82 (P = .01) for severe LUTS. CONCLUSION Although moderate LUTS do not seem to be a risk factor for CVD and stroke, men with severe LUTS were at increased risk for both according to a cross-sectional and longitudinal analysis. Studies with a larger group of men with severe LUTS are warranted to further substantiate these observations.

Body mass index modifies the risk of cardiovascular death in proteinuric chronic kidney disease

BACKGROUND In subjects with end-stage renal disease, a high body mass index (BMI) is inversely related to overall mortality, which has been coined reverse epidemiology phenomenon. This study sought to investigate this paradox as well as a possible risk modification by proteinuria on the relationship of BMI with earlier stages of chronic kidney disease (CKD) concerning cardiovascular mortality. METHODS We used the Vienna Health Screening Initiative, a longitudinal cohort study from 1990 to 2006, including 49 398 volunteers (49.9% women, age 20-89 years): n = 2487 showed mild CKD (proteinuria and GFR >60 ml/min/1.73 m(2)) and n = 392 showed moderate CKD (GFR = 30-59 ml/min/1.73 m(2)). The follow-up period was 5.5 +/- 4.2 years; n = 148 cardiovascular deaths occurred. Exposure variables were BMI, glomerular filtration rate (GFR) and proteinuria. Cox regression models on cardiovascular mortality with adjustment for age, sex, log(cholesterol/HDL), uric acid, smoking, glucose, diabetes, mean blood pressure, hypertension and antihypertensive drug use were fitted. RESULTS The risk factor paradox is shown in moderate CKD (GFR = 45 ml/min/1.73 m(2)): hazard ratios (HR) of BMI contrasts decreased consistently from 1.28 (95% CI 0.33-5.82) at BMI 20 kg/m(2) versus 25 kg/m(2) to 0.76 (95% CI 0.38-1.50) at BMI 30 kg/m(2) versus 25 kg/m(2) and to 0.58 (95% CI 0.13-2.64) at BMI 35 kg/m(2) versus 25 kg/m(2), thus showing an inverse relationship compared to mild CKD/healthy participants. Examining proteinuria as an effect modifier in this context showed that in moderate CKD (contrast: proteinuria versus no proteinuria) HR decreased more profoundly from 9.43 (95% CI 2.66-27.40) at BMI 25 kg/m(2) to 3.74 (95% CI 0.93-15.70) at BMI 30 kg/m(2) and to 1.95 (95% CI 0.37-22.30) at BMI 35 kg/m(2), and conversely in non-proteinuric subjects, hazards for cardiovascular mortality increased in underweight as well as in overweight/obese subjects in a U-shaped manner. CONCLUSIONS Our results suggest that obese subjects with proteinuric CKD may not be counselled for weight reduction since a higher BMI was associated with a remarkably reduced risk of death.

Probabilistic Foundation of Confirmatory Adaptive Designs

Adaptive designs allow the investigator of a confirmatory trial to react to unforeseen developments by changing the design. This broad flexibility comes at the price of a complex statistical model where important components, such as the adaptation rule, remain unspecified. It has thus been doubted whether Type I error control can be guaranteed in general adaptive designs. This criticism is fully justified as long as the probabilistic framework on which an adaptive design is based remains vague and implicit. Therefore, an indispensable step lies in the clarification of the probabilistic fundamentals of adaptive testing. We demonstrate that the two main principles of adaptive designs, namely the conditional Type I error rate and the conditional invariance principle, will provide Type I error rate control, if the conditional distribution of the second-stage data, given the first-stage data, can be described in terms of a regression model. A similar assumption is required for regression analysis where the distribution of the covariates is a nuisance parameter and the model needs to be identifiable independently from the covariate distribution. We further show that under the assumption of a regression model, the events of an arbitrary adaptive design can be embedded into a formal probability space without the need of posing any restrictions on the adaptation rule. As a consequence of our results, artificial constraints that had to be imposed on the investigator only for mathematical tractability of the model are no longer necessary.

Adaptive seamless designs with interim treatment selection: a case study in oncology

The planning of an oncology clinical trial with a seamless phase II/III adaptive design is discussed. Two regimens of an experimental treatment are compared to a control at an interim analysis, and the most-promising regimen is selected to continue, together with control, until the end of the study. Because the primary endpoint is expected to be immature at the interim regimen selection analysis, designs that incorporate primary as well as surrogate endpoints in the regimen selection process are considered. The final testing of efficacy at the end of the study comparing the selected regimen to the control with respect to the primary endpoint uses all relevant data collected both before and after the regimen selection analysis. Several approaches for testing the primary hypothesis are assessed with regard to power and type I error rate. Because the operating characteristics of these designs depend on the specific regimen selection rules considered, benchmark scenarios are proposed in which a perfect surrogate and no surrogate is used at the regimen selection analysis. The operating characteristics of these benchmark scenarios provide a range where those of the actual study design are expected to lie. A discussion on family-wise error rate control for testing primary and key secondary endpoints as well as an assessment of bias in the final treatment effect estimate for the selected regimen are also presented.

An Approach to the Conditional Error Rate Principle with Nuisance Parameters

In the presence of nuisance parameters, the conditional error rate principle is difficult to apply because of the dependency of the conditional error function of the preplanned test on nuisance parameters. To use the conditional error rate principle with nuisance parameters, we propose to search among tests that guarantee overall error control for the test that maximizes a weighted combination of the conditional error rates over possible values of the nuisance parameters. We show that the optimization problem that defines such a test can be solved efficiently by existing algorithms.

Likelihood ratio tests for a dose‐response effect using multiple nonlinear regression models

We consider the problem of testing for a dose-related effect based on a candidate set of (typically nonlinear) dose-response models using likelihood-ratio tests. For the considered models this reduces to assessing whether the slope parameter in these nonlinear regression models is zero or not. A technical problem is that the null distribution (when the slope is zero) depends on non-identifiable parameters, so that standard asymptotic results on the distribution of the likelihood-ratio test no longer apply. Asymptotic solutions for this problem have been extensively discussed in the literature. The resulting approximations however are not of simple form and require simulation to calculate the asymptotic distribution. In addition, their appropriateness might be doubtful for the case of a small sample size. Direct simulation to approximate the null distribution is numerically unstable due to the non identifiability of some parameters. In this article, we derive a numerical algorithm to approximate the exact distribution of the likelihood-ratio test under multiple models for normally distributed data. The algorithm uses methods from differential geometry and can be used to evaluate the distribution under the null hypothesis, but also allows for power and sample size calculations. We compare the proposed testing approach to the MCP-Mod methodology and alternative methods for testing for a dose-related trend in a dose-finding example data set and simulations.

Familywise error control in multi-armed response-adaptive two-stage designs.

For comparing multiple treatments against a single control with normally distributed observations, we consider two-stage designs of the following form: During the first stage, control and treatments are allocated by response-adaptive randomization; after completion of the first stage, some treatments are selected to proceed to the second stage; during the second stage, control and selected treatments are allocated by block randomization. Tests for such designs that use the data from both stages have been based on simulation under the global null hypothesis. We present an approach that does not rely on simulation and protects the familywise error rate in the strong sense. The main idea is to view the trial as a data-dependent modification of a simpler design, for which we know the distributions of its test statistics. To account for the data-dependent modification, we use the conditional invariance principle (Brannath et al., 2007).