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Objectives: Use of Shewhart control charts in quality improvement (QI) initiatives is increasing. These charts are typically used in one or more phases of the Plan Do Study Act (PDSA) cycle to monitor summaries of process and outcome data, abstracted from clinical information systems, over time. We summarize methodological criteria of Shewhart control charts and investigate adherence of published QI studies to these criteria.Methods: We searched Medline, Embase and CINAHL for studies using Shewhart control charts in QI processes in direct patient care. We extracted methodological criteria for Shewhart control charts, and for the use of these charts in PDSA cycles, from textbooks and methodological literature.Results: We included 34 studies, presenting 64 control charts of which 40 control charts plotted two phases of the PDSA cycle. The criterion to use 10-35 data points in a control chart was least adhered to (48.4% non-adherence). Other criteria were: transformation of the data in case of a skewed distribution (43.7% non adherence), when comparing data from two phases of the PDSA cycle the Plan phase (the first phase) needs to be stable (40.0% non-adherence), using a maximum of four different rules to detect special cause variation (14.1% non-adherence), and setting control limits at three standard deviations from the mean (all control charts adhered).Conclusion: There is room for improvement with regard to the methodological construction of Shewhart control charts used in QI processes. Higher adherence to all methodological criteria will decrease the risk of incorrect conclusions about the process being monitored. [ABSTRACT FROM AUTHOR]

What is this patient's prognosis regarding graft rejection? Do patients using a particular drug live longer than those not using it? How does this co-morbidity affect access to transplantation? To answer this type of questions one needs to perform survival analysis. This paper focuses on the Kaplan-Meier method, the most popular method used for survival analysis. It makes it possible to calculate the incidence rate of events like recovery of renal function, myocardial infarction or death by using information from all subjects at risk for these events. It explains how the method works, how survival probabilities are calculated, survival data can be summarized and survival in groups can be compared using the logrank test for hypothesis testing. In addition, it provides some guidance regarding the presentation of survival plots. Finally, it discusses the limitations of the Kaplan-Meier method and refers to other methods that better serve additional purposes. [ABSTRACT FROM AUTHOR]

Background. So far, little attention has been paid to the value of dialysis adequacy for patients' quality of life (QL). Therefore we studied the impact of demographic, Clinical, and dialysis characteristics on physical symptoms and perceived QL. [ABSTRACT FROM PUBLISHER]

Epidemiological studies aim at assessing the relationship between exposures and outcomes. Clinicians are interested in knowing not only whether a link between a given exposure (e.g. smoking) and a certain outcome (e.g. myocardial infarction) is statistically significant, but also the magnitude of this relationship. The 'measures of effect' are indexes that summarize the strength of the link between exposures and outcomes and can help the clinician in taking decisions in every day clinical practice. In epidemiological studies, the effect of exposure can be measured both in relative and absolute terms. The risk ratio, the incidence rate ratio, and the odds ratio are relative measures of effect. Risk difference is an absolute measure of effect and it is calculated by subtracting the risk of the outcome in exposed individuals from that of unexposed. [ABSTRACT FROM AUTHOR]

In Europe there is considerable variation in mortality on renal replacement therapy (RRT). The causes of this variation are still poorly understood. We hypothesized that differences in mortality in the general population contribute to differences in mortality on RRT. To evaluate this relationship, we studied general population statistics obtained from Eurostat and the individual data of 67,692 patients on RRT from 15 national and regional renal registries. These 15 registries were divided into two geographical regions: North and South Europe. Cox regression was used to assess the relative risk of death (RR) for each region with adjustment for age, gender, diabetes, and additionally general population mortality. In patients on RRT the age, gender and diabetes adjusted RR of death was 0.65 (95% CI (0.64-0.66)) for South compared to North, while in the general population the age and gender standardized RR of death was 0.91. After adjustment for general population mortality in addition to age, gender, and diabetes, the RR of death for patients on RRT in the South changed from 0.65 to 0.74 (95% CI (0.72-0.75)), which indicates that general population mortality accounted for 26% of the region-related mortality difference on RRT. In conclusion, within Europe there exist considerable international differences in the mortality of patients on RRT. Twenty-six percent of the European north-south mortality difference in RRT could be attributed to differences in general population mortality. Our data support the hypothesis that general population mortality is an important factor to take into account when making RRT mortality comparisons. [ABSTRACT FROM AUTHOR]