Marietta Kirchner

Cardiovascular Phenotypes in Children with CKD: The 4C Study

BACKGROUND AND OBJECTIVES Cardiovascular disease is the most important comorbidity affecting long-term survival in children with CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS The Cardiovascular Comorbidity in Children with CKD Study is a multicenter, prospective, observational study in children ages 6-17 years old with initial GFR of 10-60 ml/min per 1.73 m2. The cardiovascular status is monitored annually, and subclinical cardiovascular disease is assessed by noninvasive measurements of surrogate markers, including the left ventricular mass index, carotid intima-media thickness, and central pulse wave velocity. We here report baseline data at study entry and an explorative analysis of variables associated with surrogate markers. RESULTS A total of 737 patients were screened from October of 2009 to August of 2011 in 55 centers in 12 European countries, and baseline data were analyzed in 688 patients. Sixty-four percent had congenital anomalies of the kidney and urinary tract; 26.1% of children had uncontrolled hypertension (24-hour ambulatory BP monitoring; n=545), and the prevalence increased from 24.4% in CKD stage 3 to 47.4% in CKD stage 5. The prevalence of left ventricular hypertrophy was higher with each CKD stage, from 10.6% in CKD stage 3a to 48% in CKD stage 5. Carotid intima-media thickness was elevated in 41.6%, with only 10.8% of patients displaying measurements below the 50th percentile. Pulse wave velocity was increased in 20.1%. The office systolic BP SD score was the single independent factor significantly associated with all surrogate markers of cardiovascular disease. The intermediate end point score (derived from the number of surrogate marker measurements >95th percentile) was independently associated with a diagnosis of congenital anomalies of the kidney and urinary tract, time since diagnosis of CKD, body mass index, office systolic BP, serum phosphorus, and the hemoglobin level. CONCLUSIONS The baseline data of this large pediatric cohort show that surrogate markers for cardiovascular disease are closely associated with systolic hypertension and stage of CKD.

The Value of PSA Density in Combination with PI-RADS™ for the Accuracy of Prostate Cancer Prediction

Purpose: Multiparametric magnetic resonance imaging has an emerging role in prostate cancer diagnostics. In addition, clinical information is a reliable predictor of significant prostate cancer. We analyzed whether the negative predictive value of multiparametric magnetic resonance imaging to rule out significant prostate cancer could be improved using clinical factors, especially prostate specific antigen density. Materials and Methods: A total of 1,040 consecutive men with suspicion of prostate cancer underwent multiparametric magnetic resonance imaging first, followed by transperineal systematic and magnetic resonance imaging‐transrectal ultrasound fusion guided biopsy. Logistic regression analyses were performed to test different clinical factors as predictors of significant prostate cancer and build nomograms. To simplify these nomograms for clinical use patients were stratified into 3 prostate specific antigen density groups, including group 1—less than 0.07, group 2—0.07 to 0.15 and group 3—greater than 0.15 ng/ml/ml. After stratification we calculated the negative predictive value of a PI‐RADS (Prostate Imaging Reporting and Data System) Likert score of less than 3. Significant prostate cancer was defined as a Gleason score of 3 + 4 or greater. High grade prostate cancer was defined as a Gleason score of 4 + 3 or greater. Results: Overall 451 men were diagnosed with significant prostate cancer, including 187 with a Gleason score of 4 + 3 or greater. On ROC curve analyses the predictive power of the developed nomogram for significant prostate cancer showed a higher AUC than that of PI‐RADS alone (0.79 vs 0.75, p <0.001). The negative predictive value of harboring significant prostate cancer increased in men with unsuspicious magnetic resonance imaging from 79% up to 89% when prostate specific antigen density was 0.15 ng/ml/ml or less. In the repeat biopsy setting the negative predictive value of significant prostate cancer increased from 83% to 93%. The negative predictive value to harbor high grade prostate cancer increased from 92% up to 98% in the entire cohort. Conclusions: Using prostate specific antigen density combined with multiparametric magnetic resonance imaging improved the negative predictive value of PI‐RADS scoring. By increasing the probability of ruling out significant prostate cancer approximately 20% of unnecessary biopsies could be avoided safely.

Sample size planning for phase II trials based on success probabilities for phase III

In recent years, high failure rates in phase III trials were observed. One of the main reasons is overoptimistic assumptions for the planning of phase III resulting from limited phase II information and/or unawareness of realistic success probabilities. We present an approach for planning a phase II trial in a time-to-event setting that considers the whole phase II/III clinical development programme. We derive stopping boundaries after phase II that minimise the number of events under side conditions for the conditional probabilities of correct go/no-go decision after phase II as well as the conditional success probabilities for phase III. In addition, we give general recommendations for the choice of phase II sample size. Our simulations show that unconditional probabilities of go/no-go decision as well as the unconditional success probabilities for phase III are influenced by the number of events observed in phase II. However, choosing more than 150 events in phase II seems not necessary as the impact on these probabilities then becomes quite small. We recommend considering aspects like the number of compounds in phase II and the resources available when determining the sample size. The lower the number of compounds and the lower the resources are for phase III, the higher the investment for phase II should be.

Utility‐based optimization of phase II/III programs

Phase II and phase III trials play a crucial role in drug development programs. They are costly and time consuming and, because of high failure rates in late development stages, at the same time risky investments. Commonly, sample size calculation of phase III is based on the treatment effect observed in phase II. Therefore, planning of phases II and III can be linked. The performance of the phase II/III program crucially depends on the allocation of the resources to phases II and III by appropriate choice of the sample size and the rule applied to decide whether to stop the program after phase II or to proceed. We present methods for a program-wise phase II/III planning that aim at determining optimal phase II sample sizes and go/no-go decisions in a time-to-event setting. Optimization is based on a utility function that takes into account (fixed and variable) costs of the drug development program and potential gains after successful launch. The proposed methods are illustrated by application to a variety of scenarios typically met in oncology drug development.

Association of Serum Soluble Urokinase Receptor Levels With Progression of Kidney Disease in Children

Importance Conventional methods to diagnose and monitor chronic kidney disease (CKD) in children, such as creatinine level and cystatin C–derived estimated glomerular filtration rate (eGFR) and assessment of proteinuria in spot or timed urine samples, are of limited value in identifying patients at risk of progressive kidney function loss. Serum soluble urokinase receptor (suPAR) levels strongly predict incident CKD stage 3 in adults. Objective To determine whether elevated suPAR levels are associated with renal disease progression in children with CKD. Design, Setting, and Participants Post hoc analysis of 2 prospectively followed up pediatric CKD cohorts, ie, the ESCAPE Trial (1999-2007) and the 4C Study (2010-2016), with serum suPAR level measured at enrollment and longitudinal eGFR measured prospectively. In the 2 trials, a total of 898 children were observed at 30 (ESCAPE Trial; n = 256) and 55 (4C Study; n = 642) tertiary care hospitals in 13 European countries. Renal diagnoses included congenital anomalies of the kidneys and urinary tract (n = 637 [70.9%]), tubulointerstitial nephropathies (n = 92 [10.2%]), glomerulopathies (n = 69 [7.7%]), postischemic CKD (n = 42 [4.7%]), and other CKD (n = 58 [6.5%]). Total follow-up duration was up to 7.9 years, and median follow-up was 3.1 years. Analyses were conducted from October 2016 to December 2016. Exposures Serum suPAR level was measured at enrollment, and eGFR was measured every 2 months in the ESCAPE Trial and every 6 months in the 4C Study. The primary end point of CKD progression was a composite of 50% eGFR loss, eGFR less than 10 mL/min/1.73 m2, or initiation of renal replacement therapy. Main Outcomes and Measures The primary end point in this study was renal survival, defined as a composite of 50% loss of GFR that persisted for at least 1 month, the start of renal replacement therapy, or an eGFR less than 10 mL/min/1.73 m2. Results Of the 898 included children, 560 (62.4%) were male, and the mean (SD) patient age at enrollment was 11.9 (3.5) years. The mean (SD) eGFR was 34 (16) mL/min/1.73 m2. The 5-year end point–free renal survival was 64.5% (95% CI, 57.4-71.7) in children with suPAR levels in the lowest quartile compared with 35.9% (95% CI, 28.7-43.0) in those in the highest quartile (P < .001). By multivariable analysis, the risk of attaining the end point was higher in children with glomerulopathies and increased with age, blood pressure, proteinuria, and lower eGFR at baseline. In patients with baseline eGFR greater than 40 mL/min/1.73 m2, higher log-transformed suPAR levels were associated with a higher risk of CKD progression after adjustment for traditional risk factors (hazard ratio, 5.12; 95% CI, 1.56-16.7; P = .007). Conclusions and Relevance Patients with high suPAR levels were more likely to have progression of their kidney disease. Further studies should determine whether suPAR levels can identify children at risk for future CKD.

Probability of success for phase III after exploratory biomarker analysis in phase II

The probability of success or average power describes the potential of a future trial by weighting the power with a probability distribution of the treatment effect. The treatment effect estimate from a previous trial can be used to define such a distribution. During the development of targeted therapies, it is common practice to look for predictive biomarkers. The consequence is that the trial population for phase III is often selected on the basis of the most extreme result from phase II biomarker subgroup analyses. In such a case, there is a tendency to overestimate the treatment effect. We investigate whether the overestimation of the treatment effect estimate from phase II is transformed into a positive bias for the probability of success for phase III. We simulate a phase II/III development program for targeted therapies. This simulation allows to investigate selection probabilities and allows to compare the estimated with the true probability of success. We consider the estimated probability of success with and without subgroup selection. Depending on the true treatment effects, there is a negative bias without selection because of the weighting by the phase II distribution. In comparison, selection increases the estimated probability of success. Thus, selection does not lead to a bias in probability of success if underestimation due to the phase II distribution and overestimation due to selection cancel each other out. We recommend to perform similar simulations in practice to get the necessary information about the risk and chances associated with such subgroup selection designs.

Grundlagen für den Umgang mit Daten

In diesem Kapitel soll anhand der Daten, die fur die nachfolgenden Auswertungen herangezogen werden, das Vorgehen beim Importieren von Exceldaten in SPSS erlautert und auf die Bearbeitung bzw. Berechnung neuer Variablen eingegangen werden.

Statistische Tests und Grafiken

Der primare Endpunkt in einer klinischen Studie ist jenes Zielkriterium bzw. Outcome, anhand dessen der Erfolg der Studie (z.B. Nachweis der Wirksamkeit einer neuen Therapie) beurteilt wird. Zur Analyse des primaren Endpunkts stehen zum einen deskriptive, zum anderen konfirmatorische Methoden zur Verfugung. Mittels konfirmatorischer Methoden sollen Ruckschlusse auf die interessierende Grundgesamtheit gezogen und die Studienergebnisse somit verallgemeinert werden.

Prevalence of Hypertension in Children with Early-Stage ADPKD

BACKGROUND AND OBJECTIVES Autosomal dominant polycystic kidney disease is the most common inheritable kidney disease, frequently thought to become symptomatic in adulthood. However, patients with autosomal dominant polycystic kidney disease may develop signs or symptoms during childhood, in particular hypertension. Although ambulatory BP monitoring is the preferred method to diagnose hypertension in pediatrics, data in children with autosomal dominant polycystic kidney disease are limited. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Our retrospective multicenter study was conducted to collect ambulatory BP monitoring recordings from patients with autosomal dominant polycystic kidney disease age <18 years old. Basic anthropometric parameters as well as data on kidney function, BP treatment, and kidney ultrasound were also collected. RESULTS Data from 310 children with autosomal dominant polycystic kidney disease with a mean age of 11.5±4.1 years old were collected at 22 European centers. At the time when ambulatory BP monitoring was performed, 95% of children had normal kidney function. Reference data for ambulatory BP monitoring were available for 292 patients. The prevalence rates of children with hypertension and/or those who were treated with antihypertensive drugs were 31%, 42%, and 35% during daytime, nighttime, or the entire 24-hour cycle, respectively. In addition, 52% of participants lacked a physiologic nocturnal BP dipping, and 18% had isolated nocturnal hypertension. Logistic regression analysis showed a significant association between a categorical cyst score that was calculated on the basis of the number of cysts >1 cm per kidney and daytime hypertension (odds ratio, 1.70; 95% confidence interval, 1.21 to 2.4; P=0.002), nighttime hypertension (odds ratio, 1.31; 95% confidence interval, 1.05 to 1.63; P=0.02), or 24-hour hypertension (odds ratio, 1.39; 95% confidence interval, 1.08 to 1.81; P=0.01). Kidney length, expressed as SD score, was also significantly associated with nighttime hypertension (odds ratio, 1.23; 95% confidence interval, 1.06 to 1.42; P=0.10). CONCLUSIONS These data indicate high prevalence of hypertension in children with autosomal dominant polycystic kidney disease starting at young ages.

Optimal sample size allocation and go/no-go decision rules for phase II/III programs where several phase III trials are performed

The conduct of phase II and III programs is costly, time-consuming and, due to high failure rates in late development stages, risky. There is a strong connection between phase II and III trials as the go/no-go decision and the sample size chosen for phase III are based on the results observed in phase II. An integrated planning of phase II and III is therefore reasonable. The success of phase II/III programs crucially depends on the allocation of the resources to phase II and III in terms of sample size and the rule applied to decide whether to stop or to proceed with phase III. Recently, a utility-based approach was proposed, where optimal planning of phase II/III programs is achieved by taking fixed and variable costs of the drug development program and potential gains after a successful launch into account. However, this method is restricted to programs with a single phase III trial, while regulatory authorities usually require statistical significance in two or more phase III trials. We present a generalization of this procedure to programs where two or more phase III trials are performed. Optimal phase II sample sizes and go/no-go decision rules are provided for time-to-event outcomes and cases, where at least one, two, or three phase III trials need to be successful. Different drug development program strategies (e.g. one large vs. two phase III trials) are compared within these different cases. Application to practical examples typically met in oncology trials illustrates the proposed method.