Comparison of transplant listing strategy in two renal dialysis centers within a regional transplant alliance

Individual and regional factors of access to the renal transplant waiting list in france in a cohort of dialyzed patients

Several studies have investigated geographical variations in access to renal transplant waiting lists, but none has assessed the impact on these variations of factors at both the patient and geographic levels. The objective of our study was to identify medical and non-medical factors at both these levels associated with these geographical variations in waiting-list placement in France. We included all incident patients aged 18-80 years in 11 French regions who started dialysis between January 1, 2006, and December 31, 2008. Both a multilevel Cox model with shared frailty and a competing risks model were used for the analyses. At the patient level, old age, comorbidities, diabetic nephropathy, non-autonomous first dialysis, and female gender were the major determinants of a lower probability of being waitlisted. At the regional level, the only factor associated with this probability was an increase in the number of patients on the waiting list from 2005 to 2009. This finding supports a slight but significant impact of a regional organ shortage on waitlisting practices. Our findings demonstrate that patients' age has a major impact on waitlisting practices, even for patients with no comorbidity or disability, whose survival would likely be improved by transplantation compared with dialysis.

Improving case-based reasoning systems by combining k-nearest neighbour algorithm with logistic regression in the prediction of patients' registration on the renal transplant waiting list

Introduction

Case-based reasoning (CBR) is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases.

Objective

We suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model, in order to improve prediction of access to the transplant waiting list.

Methods

LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation was performed under two conditions, either using predictive factors known to be related to registration, or using a combination of factors related and not related to registration.

Results and Conclusion

The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.

Medical and non-medical determinants of access to renal transplant waiting list in a French community-based network of care

BACKGROUND

Evaluation of adult candidates for kidney transplantation diverges from one centre to another. Concurrently, ethnic background, female gender, late referral to a nephrologist, distance from transplantation department and private ownership of a dialysis facility have been associated with poor access to kidney transplantation. We assessed determinants of access to a waiting list in a French community-based network of care.

METHODS

From July 1997 to June 2003, 1725 adults living in Lorraine, who started renal replacement therapy in one of the 13 facilities of the network, were included. We compared, first, the patients registered on the waiting list with those not registered and, second, the patients registered before starting dialysis with those registered after.

RESULTS

Using logistic regression, registration on the waiting list was exclusively associated with age and medical factors, except for one variable: medical follow-up in the department performing transplantation [odds ratio (OR): 1.67 (95%CI: 1.05-2.67)]. Registration before starting dialysis was not associated with medical factors but with age [OR of patients younger than 45 years vs those older than 65 years: 3.85 (95%CI: 1.05-24.92)] and medical follow-up in the department performing transplantation [OR: 3.56 (95%CI: 1.98-6.67)].

CONCLUSIONS

In a French community-based network, patients followed by the nephrology department performing transplantation are more likely to be registered on the transplant waiting list early in the course of chronic kidney disease. Age over 55 per se is a considerable barrier to access to kidney transplantation. Medical guidelines should allow a standardization of criteria for registration.