A survival analysis was applied to 1,453 patients treated between 1972 and 1978 in 33 French dialysis centers and prospectively followed up in the computerized Diaphane Dialysis Registry. 198 deaths (overall mortality = OM) were registered, of which 87 (43%) were secondary to cardiovascular complications (cardiovascular mortality = CVM). Risk factors for OM and CVM (p values < 0.05) were age, male sex, nephroangiosclerosis or diabetic nephropathy as the primary renal disease, elevated systolic and diastolic blood pressure and two weekly dialysis rather than three. In contrast with the results observed for the general population, a high body mass index and elevated cholesterol, triglycerides and uric acid were not found to be associated with significantly increased CVM or OM. On the contrary, low body mass index ( < 20 kg/m²), low cholesterol ( < 4.5 mmol/l) and low mean predialysis blood urea ( < 4.6 mmol/l) were associated with increased OM and CVM, and more especially with high stroke mortality. Results for urea but not for cholesterol remain significant after adjustment for age, sex, weekly dialysis schedule and body mass index. They suggest that, in addition to elevated blood pressure, a poor nutritional state and/or low protein intake may be important factors for explaining the high cardiovascular mortality, particularly for strokes, observed in dialyzed patients.

The number needed to treat is a meaningfil way of expressing the benefit of an active treatment over a control. It can be used either for summarising the results of a therapeutic trial or for medical decision making about an individual patient, but its use at the bedside has been impeded by the need for time consuming calculations. A nomogram has therefore been devised that will greatly simpliy the calculations. Since calculations are now easy, the number needed to treat can be used to assess the value of several interventions, although it does have its limitations. In particular it should not be used when it is not known whether the relative risk reduction associated with an intervention is constant for all levels of risk, or for periods of time longer than that studied in the original trials.

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In a meaningful CIS use situation at HEGP, this study confirms the importance of CISQ in explaining satisfaction and CI. The proposed UMISC model that can be adapted to each phase of CIS deployment could facilitate the necessary efforts of permanent CIS acceptance and continuance evaluation.

The rise of personalized medicine and the availability of high-throughput molecular analyses in the context of clinical care have increased the need for adequate tools for translational researchers to manage and explore these data. We reviewed the biomedical literature for translational platforms allowing the management and exploration of clinical and omics data, and identified seven platforms: BRISK, caTRIP, cBio Cancer Portal, G-DOC, iCOD, iDASH and tranSMART. We analyzed these platforms along seven major axes. (1) The community axis regrouped information regarding initiators and funders of the project, as well as availability status and references. (2) We regrouped under the information content axis the nature of the clinical and omics data handled by each system. (3) The privacy management environment axis encompassed functionalities allowing control over data privacy. (4) In the analysis support axis, we detailed the analytical and statistical tools provided by the platforms. We also explored (5) interoperability support and (6) system requirements. The final axis (7) platform support listed the availability of documentation and installation procedures. A large heterogeneity was observed in regard to the capability to manage phenotype information in addition to omics data, their security and interoperability features. The analytical and visualization features strongly depend on the considered platform. Similarly, the availability of the systems is variable. This review aims at providing the reader with the background to choose the platform best suited to their needs. To conclude, we discuss the desiderata for optimal translational research platforms, in terms of privacy, interoperability and technical features.

Phenome-Wide Association Studies (PheWAS) investigate whether genetic polymorphisms associated with a phenotype are also associated with other diagnoses. In this study, we have developed new methods to perform a PheWAS based on ICD-10 codes and biological test results, and to use a quantitative trait as the selection criterion. We tested our approach on thiopurine S-methyltransferase (TPMT) activity in patients treated by thiopurine drugs. We developed 2 aggregation methods for the ICD-10 codes: an ICD-10 hierarchy and a mapping to existing ICD-9-CM based PheWAS codes. Eleven biological test results were also analyzed using discretization algorithms. We applied these methods in patients having a TPMT activity assessment from the clinical data warehouse of a French academic hospital between January 2000 and July 2013. Data after initiation of thiopurine treatment were analyzed and patient groups were compared according to their TPMT activity level. A total of 442 patient records were analyzed representing 10,252 ICD-10 codes and 72,711 biological test results. The results from the ICD-9-CM based PheWAS codes and ICD-10 hierarchy codes were concordant. Cross-validation with the biological test results allowed us to validate the ICD phenotypes. Iron-deficiency anemia and diabetes mellitus were associated with a very high TPMT activity (p = 0.0004 and p = 0.0015, respectively). We describe here an original method to perform PheWAS on a quantitative trait using both ICD-10 diagnosis codes and biological test results to identify associated phenotypes. In the field of pharmacogenomics, PheWAS allow for the identification of new subgroups of patients who require personalized clinical and therapeutic management.

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