Etienne Dantan

Each additional hour of cold ischemia time significantly increases the risk of graft failure and mortality following renal transplantation

Although cold ischemia time has been widely studied in renal transplantation area, there is no consensus on its precise relationship with the transplantation outcomes. To study this, we sampled data from 3839 adult recipients of a first heart-beating deceased donor kidney transplanted between 2000 and 2011 within the French observational multicentric prospective DIVAT cohort. A Cox model was used to assess the relationship between cold ischemia time and death-censored graft survival or patient survival by using piecewise log-linear function. There was a significant proportional increase in the risk of graft failure for each additional hour of cold ischemia time (hazard ratio, 1.013). As an example, a patient who received a kidney with a cold ischemia time of 30 h presented a risk of graft failure near 40% higher than a patient with a cold ischemia time of 6 h. Moreover, we found that the risk of death also proportionally increased for each additional hour of cold ischemia time (hazard ratio, 1.018). Thus, every additional hour of cold ischemia time must be taken into account in order to increase graft and patient survival. These findings are of practical clinical interest, as cold ischemia time is among one of the main modifiable pre-transplantation risk factors that can be minimized by improved management of the peri-transplantation period.

Comparison of survival outcomes between Expanded Criteria Donor and Standard Criteria Donor kidney transplant recipients: a systematic review and meta-analysis.

In 2002, the United Network for Organ Sharing proposed increasing the pool of donor kidneys to include Expanded Criteria Donor (ECD). Outside the USA, the ECD definition remains the one used without questioning whether such a graft allocation criterion is valid worldwide. We performed a meta‐analysis to quantify the differences between ECD and Standard Criteria Donor (SCD) transplants. We paid particular attention to select studies in which the methodology was appropriate and we took into consideration the geographical area. Thirty‐two publications were included. Only five studies, all from the USA, reported confounder‐adjusted hazard ratios comparing the survival outcomes between ECD and SCD kidney transplant recipients. These five studies confirmed that ECD recipients seemed to have poorer prognosis. From 29 studies reporting appropriate survival curves, we estimated the 5‐year pooled nonadjusted survivals for ECD and SCD recipients. The relative differences between the two groups were lower in Europe than in North America, particularly for death‐censored graft failure. It is of primary importance to propose appropriate studies for external validation of the ECD criteria in non‐US kidney transplant recipients.

The Spectrum of Renal Involvement in Patients With Inflammatory Myopathies

AbstractData regarding the incidence and outcome of renal involvement in patients with inflammatory myopathies (IM) remain scarce. We assessed the incidence and causes of acute kidney injury (AKI) and chronic kidney disease (CKD) in 150 patients with dermatomyositis, polymyositis, and antisynthetase syndrome followed in 3 French referral centers. Renal involvement occurred in 35 (23.3%) patients: AKI in 16 (10.7%), and CKD in 31 (20.7%) patients. The main cause of AKI was drug or myoglobinuria-induced acute tubular necrosis. Male sex, cardiovascular risk factors, cardiac involvement, and initial proteinuria >0.3 g/d were associated with the occurrence of AKI. The outcome of patients with AKI was poor: 13 (81%) progressed to CKD and 2 (12.5%) reached end-stage renal disease. In multivariate survival analysis, age at IM onset, male sex, a history of cardiovascular events, and a previous episode of AKI were associated with the risk of CKD. We also identified 14 IM patients who underwent a kidney biopsy in 10 nephrology centers. Renal pathology disclosed a wide range of renal disorders, mainly immune-complex glomerulonephritis. We identified in 5 patients a peculiar pattern of severe acute renal vascular damage consisting mainly of edematous thickening of the intima of arterioles.We found that AKI and CKD are frequent in patients with IM. Prevention of AKI is crucial in these patients, as AKI is a major contributor to their relatively high risk of CKD. A peculiar pattern of acute vascular damage is part of the spectrum of renal diseases associated with IM.

Rasch-family models are more valuable than score-based approaches for analysing longitudinal patient-reported outcomes with missing data

The objective was to compare classical test theory and Rasch-family models derived from item response theory for the analysis of longitudinal patient-reported outcomes data with possibly informative intermittent missing items. A simulation study was performed in order to assess and compare the performance of classical test theory and Rasch model in terms of bias, control of the type I error and power of the test of time effect. The type I error was controlled for classical test theory and Rasch model whether data were complete or some items were missing. Both methods were unbiased and displayed similar power with complete data. When items were missing, Rasch model remained unbiased and displayed higher power than classical test theory. Rasch model performed better than the classical test theory approach regarding the analysis of longitudinal patient-reported outcomes with possibly informative intermittent missing items mainly for power. This study highlights the interest of Rasch-based models in clinical research and epidemiology for the analysis of incomplete patient-reported outcomes data.

A joint model for longitudinal and time-to-event data to better assess the specific role of donor and recipient factors on long-term kidney transplantation outcomes

In renal transplantation, serum creatinine (SCr) is the main biomarker routinely measured to assess patient’s health, with chronic increases being strongly associated with long-term graft failure risk (death with a functioning graft or return to dialysis). Joint modeling may be useful to identify the specific role of risk factors on chronic evolution of kidney transplant recipients: some can be related to the SCr evolution, finally leading to graft failure, whereas others can be associated with graft failure without any modification of SCr. Sample data for 2749 patients transplanted between 2000 and 2013 with a functioning kidney at 1-year post-transplantation were obtained from the DIVAT cohort. A shared random effect joint model for longitudinal SCr values and time to graft failure was performed. We show that graft failure risk depended on both the current value and slope of the SCr. Deceased donor graft patient seemed to have a higher SCr increase, similar to patient with diabetes history, while no significant association of these two features with graft failure risk was found. Patient with a second graft was at higher risk of graft failure, independent of changes in SCr values. Anti-HLA immunization was associated with both processes simultaneously. Joint models for repeated and time-to-event data bring new opportunities to improve the epidemiological knowledge of chronic diseases. For instance in renal transplantation, several features should receive additional attention as we demonstrated their correlation with graft failure risk was independent of the SCr evolution.

A mini-review of quality of life as an outcome in prostate cancer trials: patient-centered approaches are needed to propose appropriate treatments on behalf of patients

BackgroundPatients with prostate cancer (PC) may be ready to make trade-offs between their quantity and their quality of life. For instance, elderly patients may prefer the absence of treatment if it is associated with a low-risk of disease progression, compared to treatments aiming at preventing disease progression but with a substantial deterioration of their Health-Related Quality of Life (HRQoL). Therefore, it seems relevant to compare the treatments by considering both survival and HRQoL. In this mini-review, the aim was to question whether the potential trade-offs between survival and HRQoL are considered in high impact factor journals.MethodsThe study was conducted from the PubMed database for recent papers published between May 01, 2013, and May 01, 2015. We also restricted our search to nine medical journals with 2013 impact factor > 15.ResultsAmong the 30 selected studies, only six collected individual HRQoL as a secondary endpoint by using the Functional Assessment of Cancer Therapy-Prostate (FACT-P) questionnaire. In four studies, the time to HRQoL change was analyzed, but its definitions varied. In two studies, the mean changes in HRQoL between the baseline and the 12- or 16-week follow-up were analyzed. None of the six studies reported in a single endpoint both the quantity and the quality of life.ConclusionsOur mini-review, which only focused on recent publications in journals with high-impact, suggests moving PC clinical research towards patient-centered outcomes-based studies. This may help physicians to propose the most appropriate treatment on behalf of patients. We recommend the use of indicators such as Quality-Adjusted Life-Years (QALYs) as principal endpoint in future clinical trials.

A multistate additive relative survival semi-Markov model

Medical researchers are often interested to investigate the relationship between explicative variables and times-to-events such as disease progression or death. Such multiple times-to-events can be studied using multistate models. For chronic diseases, it may be relevant to consider semi-Markov multistate models because the transition intensities between two clinical states more likely depend on the time already spent in the current state than on the chronological time. When the cause of death for a patient is unavailable or not totally attributable to the disease, it is not possible to specifically study the associations with the excess mortality related to the disease. Relative survival analysis allows an estimate of the net survival in the hypothetical situation where the disease would be the only possible cause of death. In this paper, we propose a semi-Markov additive relative survival (SMRS) model that combines the multistate and the relative survival approaches. The usefulness of the SMRS model is illustrated by two applications with data from a French cohort of kidney transplant recipients. Using simulated data, we also highlight the effectiveness of the SMRS model: the results tend to those obtained if the different causes of death are known.

Propensity score-based comparison of the graft failure risk between kidney transplant recipients of standard and expanded criteria donor grafts: Toward increasing the pool of marginal donors

From a prospective and multicentric French cohort, we proposed an external validation study for the expanded criteria donor (ECD), based on 4833 kidney recipients transplanted for the first time between 2000 and 2014. We estimated the subject‐specific effect from a multivariable Cox model. We confirmed a 1.75‐fold (95% confidence interval [CI] 1.53‐2.00, P < .0001) increase in graft failure risk if a given patient received an ECD graft compared to a graft from a donor with standard criteria (standard criteria donor [SCD]). Complementarily, we estimated the population‐average effect using propensity scores. We estimated a 1.34‐fold (95% CI 1.09‐1.64, P = .0049) increase in graft failure risk among ECD patients receiving an ECD graft compared to receiving a SCD graft. With a 10‐year follow‐up, it corresponded to a decrease of 8 months of the mean time to graft failure due to ECD transplantation (95% CI 2‐14 months). The population‐average relative risk due to ECD transplantation and the corresponding absolute effect seem finally not so high. Regarding the increase of quality of life in transplantation, our study constitutes an argument to extend the definition of marginality by considering more grafts at high risk and thereby enlarging the pool of kidney grafts.

The 1-year Renal Biopsy Index: a scoring system to drive biopsy indication at 1-year post-kidney transplantation

Surveillance biopsies after renal transplantation remain debatable. To drive the decision of such intervention, we propose a predictive score of abnormal histology at 1‐year post‐transplantation, named 1‐year Renal Biopsy Index (1‐RBI). We studied 466 kidney recipients from the DIVAT cohort alive with a functioning graft and a surveillance biopsy at 1‐year post‐transplantation. Patients displaying abnormal histology (49%) (borderline, acute rejection, interstitial fibrosis and tubular atrophy [IFTA] grade 2 or 3, glomerulonephritis) were compared to the normal or subnormal (IFTA grade 1) histology group. Obtained from a lasso penalized logistic regression, the 1‐RBI was composed of recipient gender, serum creatinine at 3, 6, and 12 month post‐transplantation and anticlass II immunization at transplantation (internal validation: AUC = 0.71, 95% CI [0.53–0.83]; external validation: AUC = 0.62, 95% CI [0.58–0.66]). While we could not determinate a threshold able to identify patients at high chance of normal or subnormal histology, we estimated and validated a discriminating threshold capable of identifying a subgroup of 15% of the patients with a risk of abnormal histology higher than 80%. The 1‐RBI is computable online at www.divat.fr. The 1‐RBI could be a useful tool to standardize 1‐year biopsy proposal and may for instance help to indicate one in case of high risk of abnormal histology.

An R2-curve for evaluating the accuracy of dynamic predictions: An R2-curve for evaluating dynamic predictions

In the context of chronic diseases, patients health evolution is often evaluated through the study of longitudinal markers and major clinical events such as relapses or death. Dynamic predictions of such types of events may be useful to improve patients management all along their follow-up. Dynamic predictions consist of predictions that are based on information repeatedly collected over time, such as measurements of a biomarker, and that can be updated as soon as new information becomes available. Several techniques to derive dynamic predictions have already been suggested, and computation of dynamic predictions is becoming increasingly popular. In this work, we focus on assessing predictive accuracy of dynamic predictions and suggest that using an R2 -curve may help. It facilitates the evaluation of the predictive accuracy gain obtained when accumulating information on a patients health profile over time. A nonparametric inverse probability of censoring weighted estimator is suggested to deal with censoring. Large sample results are provided, and methods to compute confidence intervals and bands are derived. A simulation study assesses the finite sample size behavior of the inference procedures and illustrates the shape of some R2 -curves which can be expected in common settings. A detailed application to kidney transplant data is also presented.