Abstract Background and Aims To assess the performances of the current eGFR equations, including the race-free CKD-EPI-2021, in the kidney transplant population, and compare these performances to a race-free kidney-recipient-specific (KRS) GFR equation. Method We included adult kidney recipients transplanted between 01/01/2000 and 01/01/2021 in 17 academic cohorts in Europe, the USA and Australia comprising 14 transplant centres and three clinical trials. Measured GFRs (mGFR) were assessed using 51Cr-EDTA, 99Tc-DTPA, inulin, iothalamate or iohexol clearance, according to the local practice. A KRS GFR equation was developed using additive and multiplicative stepwise linear regressions and its performance was compared to those of the current GFR equations. The performances were assessed with the P30 and the correct classification of chronic kidney disease (CKD) stage metrics. Results The study included 15 489 patients, having 50 464 GFR values both measured and estimated by creatinine-based equations. Among the current GFR equations, race-free CKD-EPI-2021 equation showed the lowest performance compared with MDRD and CKD-EPI-2009 equations. We then built a race-free KRS GFR equation based on an additive model including creatinine, age, and sex. We showed that using race did not increase the performance of the equation. We found that the race-free KRS GFR equation showed significantly improved performance compared with the race-free CKD-EPI-2021 equation and performed well in the external validation cohorts (P30 ranging from 73.0% to 91.3%). Finally, we showed that the race-free KRS GFR equation performed well in a series of kidney transplant recipient subpopulations stratified by race, sex, age, body mass index, donor type, therapeutics, creatinine and GFR measurement methods and timing. Based on these results we developed an online application that estimates GFR based on recipient age, sex and creatinine: https://transplant-prediction-system.shinyapps.io/eGFR_equation_KTX/ Conclusion Using multiple, international cohorts of kidney recipients, we developed and validated a new race-free KRS GFR equation that demonstrated high accuracy and outperformed the race-free CKD-EPI-2021 equation developed in individuals with native kidneys.
Machine learning techniques are becoming increasingly popular in radiomics studies. They can handle high dimensional sets of radiomics features with higher robustness than usual statistical analyses, by capturing complex interactions between features themselves and between feature combinations and clinical endpoints under investigation in order to build efficient prognostic/predictive models. However, there is no "one fits all" solution and deciding which algorithm is the most accurate for a given application is not always straightforward. In this paper, to keep a realistic perspective on various emerging clinical applications based on radiomics, we performed an evaluation of the popular random forest classifier for predicting local failure in cervix cancer exploiting identical data, but relying on different methodologies to select and combine features of interest. The main objective was to demonstrate various challenges of model building and tuning for radiomics applications. The results obtained in the present work could provide general guidelines to assist in the practical development of radiomics-based models.
Export Activation of complement through the alternative pathway has a key role in the pathogenesis of IgA nephropathy (IgAN). Large, international, genome-wide association studies have shown that deletion of complement factor H–related genes 1 and 3 (CFHR3,1Δ) is associated with a reduced risk of developing IgAN, although the prognostic value of these deletions in IgAN remains unknown. Here, we compared the renal outcomes of patients with IgAN according to their CFHR3,1Δ genotype. This retrospective, monocentric cohort study included 639 white patients with biopsy-proven IgAN since 1979 (mean age at diagnosis, 40.1 years; median follow-up, 132 months). We determined the number of CFHR3 and CFHR1 gene copies by quantitative PCR and collected clinical and biologic data by reviewing the patients' medical records. In all, 30.5% of the patients were heterozygous and 4% were homozygous for CFHR3,1Δ. We did not detect an association between CFHR3,1Δ and age, eGFR, urinary protein excretion rate, or the presence of hypertension or hematuria at the time of diagnosis. The mean intensities of immune IgA, IgG, and C3 deposits were lower in the group with heterozygous or homozygous gene deletions than in those with no deletion. However, CFHR3,1Δ did not associate with progression to stage 3 CKD or renal death. In conclusion, the CFHR3,1Δ genotype did not associate with progression toward CKD stages 3 and 5 in our white population of patients with IgAN, although it did associate with a reduced level of glomerular immune deposits.
Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.
The prognosis of IgA nephropathy (IgAN) is very heterogeneous. Predicting the nature and the rate of the disease progression is crucial for refining patient treatment. The aim of this study was to evaluate the prognostic impact of an Oxford classification-based repeat kidney tissue evaluation to predict end-stage renal disease (ESRD).Patients with biopsy-proven primary IgAN who underwent two renal biopsies at our centre were analyzed retrospectively. Renal biopsies were scored by two pathologists blinded to the clinical data and according to the updated Oxford classification. Cox models were generated to evaluate the prognostic impact considering the Oxford classification elementary lesions from the first (Model 1) or the second (Model 2) biopsy, adjusted on clinical data at time of reevaluation. The prognostic impacts of the dynamic evolution of each elementary lesion between biopsies were also assessed through univariate and multivariate evaluation.A total of 168 adult patients were included, with a median follow-up duration of 18 (range 11-24) years. The second biopsy was performed either systematically (n = 112) of for-cause (n = 56), after a median time of 5.4 years. The prognostic performances of Model 2 (second biopsy) were significantly better than Model 1 (first biopsy, analysis of deviance P < 0.0001). The dynamic changes of C and T lesions were significantly associated with the progression toward ESRD after adjustment on variables from Model 2.Both static and dynamic Oxford-based histological evaluation offered by a repeat biopsy improves the prediction of ESRD in patients with IgAN.
acceptable with no statistically differences between two groups.The treatment was well tolerated without statistically differences between two groups.Randomized trials with higher number of pts and longer follow-up are needed to confirm our results
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