Management of complex wrist fractures with volar and dorsal locked (double-locked) K-lock.

This article is a technical note to outline a novel technique of fixation in complex, comminuted distal radius fractures using a double-locked K-wire construct using a new implant called K-lock. In these (AO) C-type fractures, with significant dorsal comminution, it is often difficult to attain stable and secure fixation of the dorsal rim fragments, especially the dorsal lunate fossa fragment. This often results in patients being treated by temporary spanning devices or asking to have a restricted use of the hand during a given period to avoid loss of position. If dorsal plating is necessary, because of the severity of the comminution, a double-locked K-wire (locked in both the dorsal and volar plates) offers a fixation option and may create a significantly stronger construct and allow confident early mobilization. The K-lock was recently launched by Newclip Technics as an adjunct to the Xpert Wrist 2.4 set as a fragment-specific fixation option. The wire has less chance of displacing or fracturing the fragment and has a smooth surface compared with a screw; this wire would be safer close to the joint in severe distal intra-articular comminution. Of the 9 cases performed so far (as is our usual practice), despite the complexity of the fractures, none were immobilized postoperatively and all started hand therapy in the first week. Most were driving by 2 weeks and returned to light work at 4 weeks and heavy work or sports at 6 to 8 weeks. This principle of fixation may also be extended to other fractures where dual plating is used.

External Validation of the DynPG for Kidney Transplant Recipients.

BACKGROUND: In kidney transplantation, dynamic prediction of patient and kidney graft survival (DynPG) may help to promote therapeutic alliance by delivering personalized evidence-based information about long-term graft survival for kidney transplant recipients. The objective of the current study is to externally validate the DynPG. METHODS: Based on 6 baseline variables, the DynPG can be updated with any new serum creatinine measure available during the follow-up. From an external validation sample of 1637 kidney recipients with a functioning graft at 1-year posttransplantation from 2 European transplantation centers, we assessed the prognostic performance of the DynPG. RESULTS: As one can expect from an external validation sample, differences in several recipient, donor, and transplantation characteristics compared with the learning sample were observed. Patients were mainly transplanted from deceased donors (91.6% versus 84.8%; P < 0.01), were less immunized against HLA class I (18.4% versus 32.7%; P < 0.01) and presented less comorbidities (62.2% for hypertension versus 82.7%, P < 0.01; 25.1% for cardiovascular disease versus 33.9%, P < 0.01). Despite these noteworthy differences, the area under the ROC curve varied from 0.70 (95% confidence interval [CI], 0.64-0.76) to 0.76 (95% CI, 0.64-0.88) for prediction times at 1 and 6 years posttransplantation respectively, and calibration plots revealed reasonably accurate predictions. CONCLUSIONS: We validated the prognostic capacities of the DynPG in terms of both discrimination and calibration. Our study showed the robustness of the DynPG for informing both the patient and the physician, and its transportability for a cohort presenting different features than the one used for the DynPG development.

Dynamic predictions of kidney graft survival in the presence of longitudinal outliers.

In kidney transplantation, dynamic predictions of graft survival may be obtained from joint modelling of longitudinal and survival data for which a common assumption is that random-effects and error terms in the longitudinal sub-model are Gaussian. However, this assumption may be too restrictive, e.g. in the presence of outliers, and more flexible distributions would be required. In this study, we relax the Gaussian assumption by defining a robust joint modelling framework with t-distributed random-effects and error terms to obtain dynamic predictions of graft survival for kidney transplant patients. We take a Bayesian paradigm for inference and dynamic predictions and sample from the joint posterior densities. While previous research reported improved performances of robust joint models compared to the Gaussian version in terms of parameter estimation, dynamic prediction accuracy obtained from such approach has not been yet evaluated. Our results based on a training sample from the French DIVAT kidney transplantation cohort illustrate that estimates for the slope parameters in the longitudinal and survival sub-models are sensitive to the distributional assumptions. From both an internal validation sample from the DIVAT cohort and an external validation sample from the Lille (France) and Leuven (Belgium) transplantation centers, calibration and discrimination performances appeared to be better under the robust joint models compared to the Gaussian version, illustrating the need to accommodate outliers in the dynamic prediction context. Simulation results support the findings of the validation studies.

Dynamic prediction models for graft failure in paediatric kidney transplantation.

BACKGROUND: Several models have been proposed to predict kidney graft failure in adult recipients but none in younger recipients. Our objective was to propose a dynamic prediction model for graft failure in young kidney transplant recipients. METHODS: We included 793 kidney transplant recipients waitlisted before the age of 18 years who received a first kidney transplantation before the age of 21 years in France in 2002-13 and survived >90 days with a functioning graft. We used a Cox model including baseline predictors only (sex, age at transplant, primary kidney disease, dialysis duration, donor type and age, human leucocyte antigen matching, cytomegalovirus serostatus, cold ischaemia time and delayed graft function) and two joint models also accounting for post-transplant estimated glomerular filtration rate (eGFR) trajectory. Predictive performances were evaluated using a cross-validated area under the curve (AUC) and R2 curves. RESULTS: When predicting the risk of graft failure from any time within the first 7 years after paediatric kidney transplantation, the predictions for the following 3 or 5 years were accurate and much better with the joint models than with the Cox model (AUC ranged from 0.83 to 0.91 for the joint models versus 0.56 to 0.64 for the Cox model). CONCLUSION: Accounting for post-transplant eGFR trajectory strongly increased the accuracy of graft failure prediction in young kidney transplant recipients.

Comparison of machine perfusion versus cold storage in kidney transplant recipients from expanded criteria donors: a cohort-based study.

BACKGROUND: Most studies comparing the efficacy of hypothermic machine perfusion (HMP) versus static cold storage (SCS) are based on short-term outcomes. We aimed to better evaluate the mid-term impact of HMP in patients receiving expanded criteria donor (ECD) kidneys. METHODS: The analyses were based on the French Données Informatisées et VAlidées en Transplantation (DIVAT) observational cohort. Patients aged ≥45 years transplanted for the first or second times from an ECD donor since 2010 were studied. Our study reported the graft and/or patient survivals and the incidence of acute rejection episode. The Cox models and the Kaplan-Meier estimators, weighted on the propensity score, were used to study the times-to-events. RESULTS: Among the 2019 included patients, 1073 were in the SCS group versus 946 in the HMP group. The mean life expectancy with functioning graft was 5.7 years [95% confidence interval (CI) 5.4-6.1] for the HMP cohort followed-up for 8 years post-transplantation versus 6.0 years (95% CI 5.7-6.2) for the SCS group. These mid-term results were comparable in the patients receiving grafts from donors aged ≥70 years and in the transplantations with cold ischaemia time ≥18 h. CONCLUSIONS: Our study challenges the utility of using HMP to improve mid-term patient and graft survival. Nevertheless, the improvement of the short-term outcomes is indisputable. It is necessary to continue technological innovations to obtain long-term results.

Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care.

BACKGROUND: Informing kidney transplant recipients of their prognosis and disease progression is of primary importance in a patient-centred vision of care. By participating in decisions from the outset, transplant recipients may be more adherent to complex medical regimens due to their enhanced understanding. METHODS: We proposed to include repeated measurements of serum creatinine (SCr), in addition to baseline characteristics, in order to obtain dynamic predictions of the graft failure risk that could be updated continuously during patient follow-up. Adult recipients from the French Données Informatisées et VAlidées en Transplantation (DIVAT) cohort transplanted for the first or second time from a heart-beating or living donor and alive with a functioning graft at 1 year post-transplantation were included. RESULTS: The model was composed of six baseline parameters, in addition to the SCr evolution. We validated the dynamic predictions by evaluating both discrimination and calibration accuracy. The area under the receiver operating characteristic curve varied from 0.72 to 0.76 for prediction times at 1 and 6 years post-transplantation, respectively, while calibration plots showed correct accuracy. We also provided an online application tool (https://shiny.idbc.fr/DynPG). CONCLUSION: We have created a tool that, for the first time in kidney transplantation, predicts graft failure risk both at an individual patient level and dynamically. We believe that this tool would encourage willing patients into participative medicine.

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

In the context of chronic diseases, patient's 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 patient's 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.

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.

Vitamin D deficiency is an independent risk factor for PTDM after kidney transplantation.

An association between 25 hydroxyvitamin D [25(OH)D] deficiency and type 2 diabetes was observed in the general population. Such association was not investigated in kidney transplant recipients. We prospectively evaluated 444 patients following primary kidney transplantation between 2000 and 2010. The 25(OH)D level at transplantation was classified into three grades: deficiency (< 10 ng/ml), insufficiency (≥ 10 and < 30 ng/ml), and normal range (≥ 30 ng/ml). Time to Post-Transplant Diabetes Mellitus (PTDM) was defined according to the day of first prescription of hypoglycemic treatment. The 25(OH)D level at transplantation was deficient in 88 patients, insufficient in 264 patients, and normal in 92 patients. At 1 year post-transplantation, cumulative incidence of PTDM was 13.2%. Cox multivariate analysis indicated that 25(OH)D deficiency (≤ 10 ng/ml) at the time of transplantation was an independent risk factor for PTDM within the first year post-transplantation (HR = 2.41, 95% CI 1.01-5.75, P = 0.048), whereas insufficiency tended to increase this risk, although not significantly. 25(OH)D deficiency is a new independent risk factor for PTDM within the first year after kidney transplantation. Our study suggests that 25(OH)D may be a marker of general health in kidney transplant recipients and could alert clinicians for PTDM risk.