Cystatin C as a predictor of mortality in elderly patients with chronic kidney disease

Background: The prevalence of chronic kidney disease (CKD) in the elderly is high. Serum cystatin C is an accurate marker of kidney function and it also has prognostic utility in CKD patients. The aim of our study was to determine the prediction of serum cystatin C and other markers of kidney function on long-term survival in elderly CKD patients.

Methods: Fifty eight adult Caucasian patients, older than 65 years, without known malignancy, thyroid disease and/or not on steroid therapy were enrolled in the study. In each patient, ⁵¹CrEDTA clearance, serum creatinine, serum cystatin C, and estimated glomerular filtration rate using different equations were determined on the same day and patients were then followed for 11 years or until their death.

Results: The means are as follows: ⁵¹CrEDTA clearance 53.3?±?17.4?ml/min/1.73?m², serum creatinine 1.62?±?0.5?mg/dl, serum cystatin C 1.79?±?0.5?mg/l, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation 40.1?±?14?ml/min/1.73?m², Berlin Initiative Study 2 (BIS2) equation 38.9?±?10.7?ml/min/1.73?m², full age spectrum (FAS) creatinine equation 43.8?±?13.8?ml/min/1.73?m², FAS cystatin C equation 40.1?±?11.7?ml/min/1.73?m². In the follow up period, 47 (81%) patients died. Cox regression analysis showed different hazard ratios (HRs) for death: for ⁵¹CrEDTA clearance HR 1.022 (95% CI 1.004–1.042; p?=?.015), serum creatinine HR 1.013 (95% CI 1.006–1.019; p?=?.001), serum cystatin C HR 2.028 (95% CI 1.267–3.241; p?=?.003), CKD-EPI creatinine equation HR 1.048 (95% CI 1.019–1.076; p?=?.001), BIS2 equation HR 1.055 (95% CI 1.021–1.088; p?=?.001), FAS creatinine equation HR 1.046 (95% CI 1.017–1.074; p?=?.001), FAS cystatin C equation HR 1.039 (95% CI 1.010–1.071; p?=?.009).

Conclusions: Our results showed the highest HR for serum cystatin C among kidney function markers for prediction of outcome in elderly CKD patients.     

Group-based Criminal Trajectory Analysis Using Cross-validation Criteria

In this article, we discuss the challenge of determining the number of classes in a family of finite mixture models with the intent of improving the specification of latent class models for criminal trajectories. We argue that the traditional method of using either the Proc Traj or Mplus package to compute and maximize the Bayesian Information Criterion (BIC) is problematic: Proc Traj and Mplus do not always compute the MLE (and hence the BIC) accurately, and furthermore, BIC on its own does not always indicate a reasonable-seeming number of groups even when computed correctly. As an alternative, we propose the new freely available software package, crimCV, written in the R-programming language, and the methodology of cross-validation error (CVE) to determine the number of classes in a fair and reasonable way. In this article, we apply the new methodology to two samples of N = 378 and N = 386 male juvenile offenders whose criminal behavior was tracked from late childhood/early adolescence into adulthood. We show how using CVE, as implemented with crimCV, can provide valuable insight for determining the number of latent classes in these cases. These results suggest that cross-validation may represent a promising alternative to AIC or BIC for determining an optimal number of classes in finite mixture models, and in particular for setting, the number of latent classes in group-based trajectory analysis.