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A kidney offer acceptance decision tool to inform the decision to accept an offer or wait for a better kidney.

ABSTRACT: We developed a kidney offer acceptance decision tool to predict the probability of graft survival and patient survival for first-time kidney-alone candidates after an offer is accepted or declined, and we characterized the effect of restricting the donor pool with a maximum acceptable kidney donor profile index (KDPI). For accepted offers, Cox proportional hazards models estimated these probabilities using transplanted kidneys. For declined offers, these probabilities were estimated by considering the experience of similar candidates who declined offers and the probability that declining would lead to these outcomes. We randomly selected 5000 declined offers and estimated these probabilities 3 years post-offer had the offers been accepted or declined. Predicted outcomes for declined offers were well calibrated (<3% error) with good predictive accuracy (area under the curve: graft survival, 0.69; patient survival, 0.69). Had the offers been accepted, the probabilities of graft survival and patient survival were typically higher. However, these advantages attenuated or disappeared with higher KDPI, candidate priority, and local donor supply. Donor pool restrictions were associated with worse 3-year outcomes, especially for candidates with high allocation priority. The kidney offer acceptance decision tool could inform offer acceptance by characterizing the potential risk-benefit trade-off associated with accepting or declining an offer.

SUBMITTER: Wey A

PROVIDER: S-EPMC5859254 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A kidney offer acceptance decision tool to inform the decision to accept an offer or wait for a better kidney.

Wey Andrew A Salkowski Nicholas N Kremers Walter K WK Schaffhausen Cory R CR Kasiske Bertram L BL Israni Ajay K AK Snyder Jon J JJ

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 20171017 4

We developed a kidney offer acceptance decision tool to predict the probability of graft survival and patient survival for first-time kidney-alone candidates after an offer is accepted or declined, and we characterized the effect of restricting the donor pool with a maximum acceptable kidney donor profile index (KDPI). For accepted offers, Cox proportional hazards models estimated these probabilities using transplanted kidneys. For declined offers, these probabilities were estimated by consideri ...[more]

PMID: 28925596

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Project description:Background and objectivesReducing discard is important for the US transplantation system because nearly 20% of the deceased donor kidneys are discarded. One cause for the discards is the avoidance of protracted cold ischemia times. Extended cold ischemia times at transplant are associated with additional risk of graft failure and patient mortality. A preference for local (within the same donor service area) or low-Kidney Donor Risk Index organs, the endogeneity of cold ischemia time during organ allocation, and the use of provisional offers all complicate the analysis of cold ischemia times' influence on kidney acceptance decision making.Design, setting, participants, & measurementsUsing January 2018 to June 2019 Organ Procurement and Transplantation Network data, we modeled the probability of accepting an offer for a kidney after provisional acceptance. We use logistic regression that includes cold ischemia time, Kidney Donor Risk Index, and other covariates selected from literature. Endogeneity of cold ischemia time was treated by a two-stage instrumental variables approach.ResultsLogistic regression results for 3.33 million provisional acceptances from 12,369 donors and 108,313 candidates quantify trade-offs between cold ischemia time at the time of offer acceptance and donor-recipient characteristics. Overall, each additional 2 hours of cold ischemia time affected acceptance for nonlocal and local recipients (odds ratio, 0.75; 95% confidence interval, 0.73 to 0.77, odds ratio, 0.88; 95% confidence interval, 0.86 to 0.91; P<0.001). For Kidney Donor Risk Index >1.75 (Kidney Donor Profile Index >85) kidneys, an additional 2 hours of cold ischemia time for nonlocal and local recipients was associated with acceptance with odds ratio, 0.58; 95% confidence interval, 0.54 to 0.63 (nonlocal) and odds ratio, 0.65; 95% confidence interval, 0.6 to 0.7 (local); P<0.001. The effect of an additional 2 hours of cold ischemia time on acceptance of kidneys with Kidney Donor Risk Index ≤1.75 (Kidney Donor Profile Index ≤85) was less pronounced for nonlocal offers (odds ratio, 0.82; 95% confidence interval, 0.80 to 0.85; P<0.001) and not significant for local offers.ConclusionsThe acceptability of marginal organs was higher when placements were nearer to the donor and when cold ischemia time was shorter.

Project description:BackgroundSignificant variability in organ acceptance thresholds have been demonstrated across the United States, but data regarding the rate and rationale for kidney donor organ decline in Canada are lacking.ObjectiveTo examine decision making regarding deceased kidney donor acceptance and non-acceptance in a population of Canadian transplant professionals.DesignA survey study of theoretical deceased donor kidney cases of increasing complexity.SettingCanadian transplant nephrologists, urologists, and surgeons making donor call decisions responding to an electronic survey between July 22 and October 4, 2022.ParticipantsInvitations to participate were distributed to 179 Canadian transplant nephrologists, surgeons, and urologists through e-mail. Participants were identified by contacting each transplant program and requesting a list of physicians who take donor call.MeasurementsSurvey respondents were asked whether they would accept or decline a given donor, assuming there was a suitable recipient. They were also asked to cite reasons for donor non-acceptance.MethodsDonor scenario-specific acceptance rates (total acceptance divided by total number of respondents for a given scenario and overall) and reasons for decline were determined and presented as a percentage of the total cases declined.ResultsIn all, 72 respondents from 7 provinces completed at least one question of the survey, with considerable variability between acceptance rates for centers; the most conservative center declined 60.9% of donor cases, whereas the most aggressive center declined only 28.1%, P-value < .001. There was an increased risk of non-acceptance with advancing age, donation after cardiac death, acute kidney injury, chronic kidney disease, and comorbidities.LimitationsAs with any survey, there is the potential for participation bias. In addition, this study examines donor characteristics in isolation, however, asks respondent to assume there is a suitable candidate available. In reality, whenever donor quality is considered, it should be considered in the context of the intended recipient.ConclusionIn a survey of increasingly medically complex deceased kidney donor cases, there was significant variability in donor decline among Canadian transplant specialists. Given relatively high rates of donor decline and apparent heterogeneity in acceptance decisions, Canadian transplant specialists may benefit from additional education regarding the benefits achieved from even medically complex kidney donors for appropriate candidates relative to remaining on dialysis on the transplant waitlist.

Project description:BackgroundThe Acute Chronic Obstructive Pulmonary Disease (COPD) Exacerbation Prediction Tool (ACCEPT) was developed for individualised prediction of COPD exacerbations. ACCEPT was well calibrated overall and had a high discriminatory power, but overestimated risk among individuals without recent exacerbations. The objectives of this study were to 1) fine-tune ACCEPT to make better predictions for individuals with a negative exacerbation history, 2) develop more parsimonious models, and 3) externally validate the models in a new dataset.MethodsWe recalibrated ACCEPT using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE, a three-year observational study, 1,803 patients, 2,117 exacerbations) study by applying non-parametric regression splines to the predicted rates. We developed three reduced versions of ACCEPT by removing symptom score and/or baseline medications as predictors. We examined the discrimination, calibration, and net benefit of ACCEPT 2·0 in the placebo arm of the Towards a Revolution in COPD Health (TORCH, a three-year randomised clinical trial of inhaled therapies in COPD, 1,091 patients, 1,064 exacerbations) study. The primary outcome for prediction was the occurrence of ≥2 moderate or ≥1 severe exacerbation in the next 12 months; the secondary outcomes were prediction of the occurrence of any moderate/severe exacerbation or any severe exacerbation.FindingsACCEPT 2·0 had an area-under-the-curve (AUC) of 0·76 for predicting the primary outcome. Exacerbation history alone (current standard of care) had an AUC of 0·68. The model was well calibrated in patients with positive or negative exacerbation histories. Changes in AUC in reduced versions were minimal for the primary outcome as well as for predicting the occurrence of any moderate/severe exacerbations (ΔAUC<0·011), but more substantial for predicting the occurrence of any severe exacerbations (ΔAUC<0·020). All versions of ACCEPT 2·0 provided positive net benefit over the use of exacerbation history alone for some range of thresholds.InterpretationACCEPT 2·0 showed good calibration regardless of exacerbation history, and predicts exacerbation risk better than current standard of care for a range of thresholds. Future studies need to investigate the utility of exacerbation prediction in various subgroups of patients.FundingThis study was funded by a team grant from the Canadian Institutes of Health Research (PHT 178432).

Project description:BackgroundResponding to the increasing demand for Deep Vein Thrombosis (DVT) treatment in the United Kingdom (UK) at times of limited budgets and resources is a great challenge for decision-makers. Therefore, there is a need to find innovative policies, which improve operational efficiency and achieve the best value for money for patients. This study aims to develop a Decision Support Tool (DST) that assesses the impact of implementing new DVT patients' management and care policies aiming at improving efficiency, reducing costs, and enhancing value for money.MethodsWith the involvement of stakeholders from a number of DVT services in the UK, we developed a DST combining discrete event simulation (DES) for DVT pathways and the Socio Technical Allocation of Resources (STAR) approach, an agile health economics technique. The model was inputted with data from the literature, local datasets from DVT services, and interviews conducted with DVT specialists. The tool was validated and verified by various stakeholders and two policies, namely shifting more patients to community services (CSs) and increasing the usage of the Novel Oral Anticoagulant (NOAC) drug were selected for testing on the model.ResultsSixteen possible scenarios were run on the model for a period of 5 years and generated treatment activity, human resources, costing, and value for money outputs. The results indicated that hospital visits can be reduced by up to 50%. Human resources' usage can be greatly lowered driven mainly by offering NOAC treatment to more patients. Also, combining both policies can lead to cost savings of up to 50%. The STAR method, which considers both service and patient perspectives, produced findings that implementing both policies provide a significantly higher value for money compared to the situation when neither is applied.ConclusionsThe combination of DES and STAR can help decision-makers determine the interventions that have the highest benefits from service providers' and patients' perspectives. This is important given the mismatch between care demand and resources and the resulting need for improving operational and economic outcomes. The DST tool has the potential to inform policymaking in DVT services in the UK to improve performance.

Dataset Information

A kidney offer acceptance decision tool to inform the decision to accept an offer or wait for a better kidney.

Publications

A kidney offer acceptance decision tool to inform the decision to accept an offer or wait for a better kidney.

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