Predicting patient survival after deceased donor kidney transplantation using flexible parametric modelling

Surgical Strategies for Renal Transplantation: A Pictorial Essay

This pictorial essay aims to navigate through the complexities and challenges of renal transplantation (RT), by weaving together visual imagery with clinical insights within a comprehensive illustrative surgical guide. Herein, we provide a detailed visual exploration of the intricate anatomy and surgical processes necessary for both renal graft retrieval from the donor and also for an adequate implantation in the recipient. Regarding graft retrieval, after reviewing the relevant retroperitoneal surgical anatomy, and donor nephrectomy techniques, graft preservation and optimal backbench graft dissection principles were meticulously analyzed. Thereafter, the recipient surgical strategy for graft implantation was addressed, focusing on preoperative preparations, the site of implantation selection, exposure, operative bed dissection, graft revascularization, and urinary tract reconstruction. Careful donor and recipient selection, meticulous surgical execution, and rigorous postoperative management clearly hold a pivotal role in optimizing patient outcomes. Fostering a deeper understanding of the surgical nuances and clinical management practices that contribute to successful results post-RT, we hope to provide a useful practical tool for clinicians about to embark on the treacherous road of RT surgery. Innovative technologies and surgical practices that have already significantly improved the safety and effectiveness of RT stand testament to the importance of further scientific inquiry, conceptual developments, and clinical integration. Moving forward, it is essential that the medical community continues to refine these strategies and advocate for equitable access to transplantation, ensuring that advancements in the field translate into real-world benefits for all patients grappling with ESRD. The collaborative efforts of multidisciplinary teams are essential in addressing the complex clinical challenges associated with RT, with the ultimate goal of improving patient survival, enhancing graft longevity, and reducing healthcare disparities.

Predicting Kidney Transplantation Outcomes from Donor and Recipient Characteristics at Time Zero: Development of a Mobile Application for Nephrologists

(1) Background: We report on the development of a predictive tool that can estimate kidney transplant survival at time zero. (2) Methods: This was an observational, retrospective study including 5078 transplants. Death-censored graft and patient survivals were calculated. (3) Results: Graft loss was associated with donor age (hazard ratio [HR], 1.021, 95% confidence interval [CI] 1.018-1.024, p < 0.001), uncontrolled donation after circulatory death (DCD) (HR 1.576, 95% CI 1.241-2.047, p < 0.001) and controlled DCD (HR 1.567, 95% CI 1.372-1.812, p < 0.001), panel reactive antibody percentage (HR 1.009, 95% CI 1.007-1.011, p < 0.001), and previous transplants (HR 1.494, 95% CI 1.367-1.634, p < 0.001). Patient survival was associated with recipient age (> 60 years, HR 5.507, 95% CI 4.524-6.704, p < 0.001 vs. < 40 years), donor age (HR 1.019, 95% CI 1.016-1.023, p < 0.001), dialysis vintage (HR 1.0000263, 95% CI 1.000225-1.000301, p < 0.01), and male sex (HR 1.229, 95% CI 1.135-1.332, p < 0.001). The C-statistics for graft and patient survival were 0.666 (95% CI: 0.646, 0.686) and 0.726 (95% CI: 0.710-0.742), respectively. (4) Conclusions: We developed a mobile app to estimate survival at time zero, which can guide decisions for organ allocation.

Predicting kidney transplant survival using multiple feature representations for HLAs

Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose 4 new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.

Recommendations for living donor kidney transplantation

This Guide for Living Donor Kidney Transplantation (LDKT) has been prepared with the sponsorship of the Spanish Society of Nephrology (SEN), the Spanish Transplant Society (SET), and the Spanish National Transplant Organization (ONT). It updates evidence to offer the best chronic renal failure treatment when a potential living donor is available. The core aim of this Guide is to supply clinicians who evaluate living donors and transplant recipients with the best decision-making tools, to optimise their outcomes. Moreover, the role of living donors in the current KT context should recover the level of importance it had until recently. To this end the new forms of incompatible HLA and/or ABO donation, as well as the paired donation which is possible in several hospitals with experience in LDKT, offer additional ways to treat renal patients with an incompatible donor. Good results in terms of patient and graft survival have expanded the range of circumstances under which living renal donors are accepted. Older donors are now accepted, as are others with factors that affect the decision, such as a borderline clinical history or alterations, which when evaluated may lead to an additional number of transplantations. This Guide does not forget that LDKT may lead to risk for the donor. Pre-donation evaluation has to centre on the problems which may arise over the short or long-term, and these have to be described to the potential donor so that they are able take them into account. Experience over recent years has led to progress in risk analysis, to protect donors' health. This aspect always has to be taken into account by LDKT programmes when evaluating potential donors. Finally, this Guide has been designed to aid decision-making, with recommendations and suggestions when uncertainties arise in pre-donation studies. Its overarching aim is to ensure that informed consent is based on high quality studies and information supplied to donors and recipients, offering the strongest possible guarantees.

Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.

Prognostic analysis of m6A-related lncRNAs as potential biomarkers in intrahepatic cholangiocarcinom

Intrahepatic cholangiocarcinoma (iCCA) patients had no obvious symptoms at early stage and poor postoperative survival. Therefore, the establishment of an iCCA prognostic prediction model to carry out refined management of iCCA patients is expected to improve the survival of the iCCA patient population. In this paper, we analyzed the expression profiling data of patients from 32 iCCA tissues and eight paracancerous tissues in The Cancer Genome Atlas (TCGA) database. Perl software was used to separate M6A-related genes and lncRNAs from expression matrix files obtained from the TCGA database. The differentially expressed lncRNAs in the iCCA samples and the normal samples were screened out by differential analysis using the R package limma, and the m6A-related lncRNAs were further screened by Pearson correlation analysis. WGCNA clustering analysis constructs a random network to extract the module genes most related to iCCA, and take the intersection of differentially expressed lncRNAs related to m6A. Univariate Cox screening was performed for the intersection lncRNAs that had significant influence on the prognosis of iCCA patients, and further screening was performed by LASSO method and multivariate Cox regression analysis. Risk model was constructed and prognostic ability was evaluated according to risk score. In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA, and established a novel m6A-related lncRNA-based prognostic model, which can be used as an independent prognostic factor to predict the prognosis of clinical patients.

Mean lifetime survival estimates following solid organ transplantation in the US and UK

AIMS

Accurately estimating mean survival after solid organ transplant (SOT) is crucial for efficient healthcare resource allocation decisions. However, registry-based post-transplant recipient survival estimates vary greatly and are incomplete. Often, the methods used in lifetime survival extrapolation may not fit complex transplant data and therefore alternative methods are required. We aimed to explore the flexible cubic spline methodology as a meaningful alternative for estimating lifetime survival following SOT.

METHODS

Survival analyses were conducted in kidney, liver, heart, and lung transplant recipients. Mean survival was estimated using flexible cubic splines on the hazard scale fitted with three knots, based on where hazards changed direction, clinical advice, and best-fit curve using Akaike and Bayesian information criterion. The tail was extrapolated when data were no longer available. Extrapolation tails were compared with general population mortality, using age-matched life table hazards, and the highest hazards were taken at all times.

RESULTS

We found that mean survival post-transplant was longest for kidney transplants (US: 22.79 years; UK: 26.58 years), followed by liver (US: 20.90 years; UK: 20.38 years), heart (US: 14.82 years; UK: 15.85 years), and lung (US: 9.28 years; UK: 9.21 years). A sensitivity analysis using two knots found differences in survival ranging from -1.30 to +4.83 years across SOTs examined.

LIMITATIONS

This study does not represent individual patient survival, survival by age groups, multiple-organ transplants, or assess factors that may impact overall or organ survival.

CONCLUSIONS

Our study estimates reflect real-world survival following SOTs and demonstrate the importance of including long-term hazards in survival estimations. These lifetime survival estimates can be used by decision-makers in situations where means are preferred over medians (e.g. population projections, budgetary estimates, and cost-effectiveness models) and can thus offer a meaningful alternative to the estimates used and accepted in current practice.

Predicting Survival in Thai Patients After Low Impact Hip Fracture Using Flexible Parametric Modelling: A Retrospective Cohort Study

Predictive post-hip fracture mortality models have been presented for specific time points (in-hospital, 30-days or 1-year) and most provide marginal predictions based on the patient's risk group. However, the predictive model for individual survival probability following hip fracture is not available. This study aimed to develop a flexible parametric model for predicting individual survival probability for hip fracture patients. In this retrospective study, the medical charts of 765 Thai patients admitted to hospital with a hip fracture resulting from low-impact injury from January 2014 to December 2018 were reviewed. Predictors for all-cause mortality were identified using flexible parametric survival analysis and were used to develop the predictive model. The model was calibrated using a calibration graph and discrimination performance was evaluated using the C-statistic. Internal validity was assessed using bootstrapping. The overall mortality rate of the hip fracture patients was 14%. Predictors significantly associated with survival after hip fracture were age, active malignancy, dementia or Alzheimer's disease, chronic obstructive pulmonary disorder, diabetes mellitus, hemoglobin concentration, eGFR<30 mL/min/1.73m² and operative treatments. The model-predicted survival was similar to that actually observed in the very low survival group in the first year after hip fracture. In bootstrapping, the apparent C-statistic and the test C-statistic of the reduced model were 0.79 (95% CI 0.77-0.81) and 0.79 (95% CI 0.78-0.80), respectively. The flexible survival model provides good predictive power for individual survival probability at any given time point within the first year after hip fracture and would be an easy to use tool in clinical practice.

Determining the Level of Importance of Variables in Predicting Kidney Transplant Survival Based on a Novel Ranking Method

BACKGROUND

Kidney transplantation is the best alternative treatment for end-stage renal disease. To optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival.

METHODS

Five classification models were used to classify kidney recipients in long- and short-term survival classes. Synthetic minority oversampling and random undersampling were used to overcome the imbalanced class problem. In dealing with missing values, 2 approaches were used (eliminating and imputing them). All variables were categorized into 4 levels. The ranking was evaluated using the sensitivity analysis approach.

RESULTS

Thirty-four of the 41 variables were identified as important variables, of which, 5 variables were categorized in very important level ("Recipient creatinine at discharge," "Recipient dialysis time," "Donor history of diabetes," "Donor kidney biopsy," and "Donor cause of death"), 17 variables in important level, and 12 variables in the low important level.

CONCLUSIONS

In this study, we identify new variables that have not been addressed in any of the previous studies (eg, AGE_DIF and MATCH_GEN). On the other hand, in kidney allocation systems, 2 main criteria are considered: equity and utility. One of the utility subcriteria is the graft survival. Our study findings can be used in the design of systems to predict the graft survival.

Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs

Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.

A simple score to predict early death after kidney transplantation

BACKGROUND

Few studies have focused on risk stratification for premature death after transplantation. However, stratification of individual risk is an essential step in personalized care.

MATERIAL AND METHODS

We have developed a risk score of early post-transplant death (ORLY score) in a prospective multicentre cohort including 942 patients and validated our model in a retrospective independent replication cohort including 874 patients.

RESULTS

60 patients (6.4%) from the prospective cohort died during the first three-year post-transplant. Age, male gender, diabetes, dialysis duration and chronic respiratory failure were associated with early post-transplant death. The multivariable model exhibited good discrimination ability (C-index = 0.78, 95%CI [0.75-0.81]). ORLY score highly predicted early death after transplantation (1.34; 95%CI, 1.22 to 1.48 for each increase of 1 point in score; P < .001). The predictive value of the score in the validation cohort was close to that observed in the experimental cohort (1.41; 95%CI, 1.27 to 1.56 for each increase of 1 point in score; P < .001). Merging the two cohorts, four categories of risk could be individualized: low, 0-5 (n = 522, mean risk, 1%); intermediate, 6-7 (n = 739, mean risk 4.7%); moderate, 8-10 (n = 429, mean risk 10%); and high risk 11-15 (n = 132, mean risk 19%).

CONCLUSIONS

The ORLY score discriminates patients with high risk of early death.

Equity-Efficiency Trade-offs Associated With Alternative Approaches to Deceased Donor Kidney Allocation: A Patient-level Simulation

Supplemental Digital Content is available in the text.

Background.

The number of patients waiting to receive a kidney transplant outstrips the supply of donor organs. We sought to quantify trade-offs associated with different approaches to deceased donor kidney allocation in terms of quality-adjusted life years (QALYs), costs, and access to transplantation.

Methods.

An individual patient simulation model was developed to compare 5 different approaches to kidney allocation, including the 2006 UK National Kidney Allocation Scheme (NKAS) and a QALY maximization approach designed to maximize health gains from a limited supply of donor organs. We used various sources of patient-level data to develop multivariable regression models to predict survival, health state utilities, and costs. We simulated the allocation of kidneys from 2200 deceased donors to a waiting list of 5500 patients and produced estimates of total lifetime costs and QALYs for each allocation scheme.

Results.

Among patients who received a transplant, the QALY maximization approach generated 48 045 QALYs and cost £681 million, while the 2006 NKAS generated 44 040 QALYs and cost £625 million. When also taking into consideration outcomes for patients who were not prioritized to receive a transplant, the 2006 NKAS produced higher total QALYs and costs and an incremental cost-effectiveness ratio of £110 741/QALY compared with the QALY maximization approach.

Conclusions.

Compared with the 2006 NKAS, a QALY maximization approach makes more efficient use of deceased donor kidneys but reduces access to transplantation for older patients and results in greater inequity in the distribution of health gains between patients who receive a transplant and patients who remain on the waiting list.

Projected 20- and 30-Year Outcomes for Pediatric Liver Transplant Recipients in the United States

BACKGROUND

Observed long-term outcomes no longer reflect the survival trajectory facing pediatric liver transplant (LT) recipients today. We aimed to use national registry data and parametric models to project 20- and 30-year post-transplant outcomes for recently transplanted pediatric LT recipients.

METHODS

We conducted a retrospective cohort study of 13,442 first-time pediatric (age <18) LT recipients using 1987 to 2018 Scientific Registry of Transplant Recipients data. We validated the proposed method (ie, to project long-term patient and graft survival using parametric survival models and short-term data) in 2 historic cohorts (1987-1996 and 1997-2006) and estimated long-term projections among patients transplanted between 2007 and 2018. Projections were stratified by raft type, recipient age, and indication for transplant.

RESULTS

Parsimonious parametric models with Weibull distribution can be applied to post-transplant data and used to project long-term outcomes for pediatric LT recipients beyond observed data. Projected 20-year patient survival for pediatric LT recipients transplanted in 2007 to 2018 was 84.0% (95% confidence interval 81.5-85.8), compared to observed 20-year survival of 72.8% and 63.6% among those transplanted in 1997 to 2006 and 1987 to 1996, respectively. Projected 30-year survival for pediatric LT recipients in 2007 to 2018 was 80.1% (75.2-82.7), compared to projected 30-year survival of 68.6% (66.1-70.9) in the 1997 to 2006 cohort and observed 30-year survival of 57.5% in the 1987 to 1996 cohort. Twenty- and 30-year patient and graft survival varied slightly by recipient age, graft type, and indication for transplant.

CONCLUSIONS

Projected long-term outcomes for recently transplanted pediatric LT recipients are excellent, reflective of substantial improvements in medical care, and informative for physician-patient education and decision making in the current era.

Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method

Background

Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence, the purpose of this study is to identify and weight the kidney allocation criteria considering the balance between utility and equity.

Methods

To do this, a new fuzzy hybrid approach is proposed, which consists of two steps: In the first step, Fuzzy Delphi Method (FDM) is used to identify the effective criteria in the kidney allocation algorithm. In the second step, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is employed to determine the weight of the criteria.

Results

The results showed that the highest weight belongs to “Medical emergency” criterion and the lowest weight to “5 HLA mismatches”, which is similar to Euro-transplant kidney allocation system (ETKAS). The developed method is evaluated in two steps. First, the proposed model is implemented using a real case study from the Iranian Kidney Allocation System. It was shown that the proposed model has the potential to improve allocation outcome. Second, the proposed model’s superiority to the current model is approved by the experts using the results display in the profile matrix. Finally, sensitivity analysis is performed to check the robustness of the proposed model.

Conclusions

This paper contributes to the kidney allocation literature by doing the following: (a) developing a comprehensive framework for identification and weightings of criteria for kidney allocation, (b) using, for the first time, the IF-AHP technique to consider hesitancy of decision makers and uncertainty in organ allocation, and (c) proposing an appropriate framework for the countries that intend to improve or modify their organ allocation system.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0892-y) contains supplementary material, which is available to authorized users.

Using machine learning and an ensemble of methods to predict kidney transplant survival

We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the kidney allocation system in the U.S., and other models published recently in the literature. The model has a five-year concordance index of 0.724 (in comparison, the concordance index is 0.697 for the Estimated Post Transplant Survival model, the state of the art currently in use). It combines predictions from random survival forests with a Cox proportional hazards model. The rankings of importance for the model’s variables differ by transplant recipient age. Better survival predictions could eventually lead to more efficient allocation of kidneys and improve patient outcomes.

Receiving Hypertensive Donor Grafts Is Associated with Inferior Prognosis in Simultaneous Liver-Kidney Transplantation Recipients

Background

The impact of hypertensive (HTN) donor grafts on the prognosis of simultaneous liver-kidney transplantation (SLKT) patient is not known, and an applicable risk scoring system for SLKT patient survival is lacking. This study aimed to evaluate the impact of donor HTN on patient survival of SLKT recipients and to identify independent risk factors.

Material/Methods

Data from 3844 adult SLKT recipients receiving deceased donor grafts from March 2002 to December 2014 in the Scientific Registry of Transplant Recipients (SRTR) database were retrospectively analyzed. Kaplan-Meier analysis was used to compare patient and graft survival. Multivariate Cox proportional hazard models were built to identify independent risk factors associated with patient and graft survival.

Results

SLKT patients receiving HTN donor grafts had significantly shorter 5-year patient survival and kidney graft survival rates than did those receiving non-HTN donor grafts (50.1% vs. 63.2%, p<0.0001 and 45.4% vs. 67.8%, p<0.0001, respectively). Multivariate analysis identified HTN donor, donor age, donation after cardiac death, cold ischemia time, recipient age, recipient condition at transplant, recipient hepatitis C infection, need for life support, and recipient pre-transplant albumin level as independent risk factors associated with inferior patient survival in SLKT recipients. A risk scoring model that predicted excellent stratification of prognostic subgroups was established (AUC, 0.762; 95% CI, 0.739–0.785).

Conclusions

An SLKT patient receiving a graft from an HTN donor has an inferior prognosis. A risk scoring system applicable to patient survival in SLKT recipients was developed.

The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review

BACKGROUND

Prognostic models incorporating survival analysis predict the risk (i.e., probability) of experiencing a future event over a specific time period. In 2002, Royston and Parmar described a type of flexible parametric survival model called the Royston-Parmar model in Statistics in Medicine, a model which fits a restricted cubic spline to flexibly model the baseline log cumulative hazard on the proportional hazards scale. This feature permits absolute measures of effect (e.g., hazard rates) to be estimated at all time points, an important feature when using the model. The Royston-Parmar model can also incorporate time-dependent effects and be used on different scales (e.g., proportional odds, probit). These features make the Royston-Parmar model attractive for prediction, yet their current uptake for prognostic modeling is unknown. Thus, the objectives were to conduct a scoping review of how the Royston-Parmar model has been applied to prognostic models in health research, to raise awareness of the model, to identify gaps in current reporting, and to offer model building considerations and reporting suggestions for other researchers.

METHODS

Five electronic databases and gray literature indexed in web sources from 2001 to 2016 were searched to identify articles for inclusion in the scoping review. Two reviewers independently screened 1429 articles, and after applying exclusion criteria through a two-step screening process, data from 12 studies were abstracted.

RESULTS

Since 2001, only 12 studies were identified that used the Royston-Parmar model in some capacity for prognostic modeling, 10 of which used the model as the basis for their prognostic model. The restricted cubic spline varied across studies in the number of interior knots (range 1 to 6), and only three studies reported knot placement. Three studies provided details about the baseline function, with two studies using a figure and the third providing coefficients. However, no studies provided adequate information on their restricted cubic spline to permit others to validate or completely use the model.

CONCLUSIONS

Despite the advantages of the Royston-Parmar model for prognostic models, they are not widely used in health research. Better reporting of details about the restricted cubic spline is needed, so the prognostic model can be used and validated by others.

REGISTRATION

The protocol was registered with Open Science Framework (https://osf.io/r3232/).