Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study.

Background: At the time a kidney offer is made by an organ donation organization (ODO), transplant physicians must inform candidates on the pros and cons of accepting or declining the offer. Although physicians have a general idea of expected wait time to kidney transplantation by blood group in their ODO, there are no tools that provide quantitative estimates based on the allocation score used and donor/candidate characteristics. This limits the shared decision-making process at the time of kidney offer as (1) the consequences of declining an offer in terms of wait-time prolongation cannot be provided and (2) the quality of the current offer cannot be compared with that of offers that could be made to the specific candidate in the future. This is especially relevant to older transplant candidates as many ODOs use some form of utility matching in their allocation score. Objective: We aimed to develop a novel method to provide personalized estimates of wait time to next offer and quality of future offers for kidney transplant candidates if they refused a current deceased donor offer from an ODO. Design: A retrospective cohort study. Setting: Administrative data from Transplant Quebec. Patients: All patients who were actively registered on the kidney transplant wait list at any point between March 29, 2012 and December 13, 2017. Measurements: The time to next offer was defined as the number of days between the time of the current offer and the next offer if the current one were declined. The quality of the offers was measured with the 10-variable Kidney Donor Risk Index (KDRI) equation. Methods: Candidate-specific kidney offer arrival was modeled with a marked Poisson process. To derive the lambda parameter for the marked Poisson process for each candidate, the arrival of donors was examined in the 2 years prior to the time of the current offer. The Transplant Quebec allocation score was calculated for each ABO-compatible offer with the characteristics that the candidate presented at the time of the current offer. Offers where the candidate's score was lower than the scores of actual recipients of the second kidneys transplanted were filtered out from the candidate-specific kidney offer arrival. The KDRIs of offers that remained were averaged to provide an estimate of the quality of future offers, to be compared with that of the current offer. Results: During the study period, there were 848 unique donors and 1696 transplant candidates actively registered. The models provide the following information: average time to next offer, time to which there is a 95% probability of receiving a next offer, average KDRI of future offers. The C-index of the model was 0.72. When compared with providing average group estimates of wait time and KDRI of future offers, the model reduced the root-mean-square error in the predicted time to next offer from 137 to 84 days and that of predicted KDRI of future offers from 0.64 to 0.55. The precision of the model's predictions was higher when observed times to next offer were 5 months or less. Limitations: The models assume that patients declining an offer remain wait-listed until the next one. The model only updates wait time every year after the time of an offer and not in a continuous fashion. Conclusion: By providing personalized quantitative estimates of time to and quality of future offers, our new approach can inform the shared decision-making process between transplant candidates and physicians when a kidney offer from a deceased donor is made by an ODO.

Contexte: Lorsqu'un organisme de don d'organes (ODO) propose un rein pour la transplantation, les médecins transplantologues se doivent d'informer les candidats des avantages et inconvénients d'accepter ou de refuser cette offre. Bien que les médecins aient une idée générale du temps d'attente à prévoir dans leur ODO pour une transplantation rénale selon le groupe sanguin, il n'existe aucun outil fournissant des estimations quantitatives fondées sur la cote d'attribution utilisée et les caractéristiques du donneur/candidat. Cela limite le processus partagé de prise de décision au moment d'une offre, car 1) les conséquences du refus relativement à la prolongation du temps d'attente ne peuvent être fournies; et 2) parce que la qualité de l'offre en cours ne peut être comparée à celle des offres qui pourraient être faites ultérieurement au même candidat. Ceci est particulièrement pertinent pour les candidats à une transplantation qui sont plus âgés, car de nombreux ODO utilisent une certaine forme de correspondance d'utilité dans leur cote d'attribution. Objectif: Nous souhaitions développer une nouvelle méthode pour fournir des estimations personnalisées du temps d'attente jusqu'à l'offre suivante et de la qualité des offres ultérieures pour les candidats à la transplantation rénale ayant refusé l'offre d'un ODO pour le rein d'un donneur décédé. Type d'étude: Étude de cohorte rétrospective. Cadre: Données administratives de Transplant Québec. Sujets: Tous les patients qui étaient activement inscrits sur la liste d'attente pour une greffe rénale à un moment donné entre le 29 mars 2012 et le 13 décembre 2017. Mesures: Le temps jusqu'à l'offre suivante a été défini comme le nombre de jours entre le moment de l'offre en cours et celui de la suivante, si la première est refusée. L'équation KDRI (Kidney Donor Risk Index) à 10 variables a servi à mesurer la qualité des offres. Méthodologie: L'arrivée d'une offre de rein spécifique à un candidat a été modélisée par un processus de Poisson marqué. L'arrivée des donneurs a été examinée pour les 2 ans précédant le moment de l'offre en cours afin de dériver le paramètre lambda du processus de Poisson marqué pour chaque candidat. La cote d'attribution de Transplant Québec a été calculée pour chaque offre compatible ABO avec les caractéristiques que le candidat présentait au moment de l'offre en cours. Les offres pour lesquelles la cote du candidat était inférieure aux cotes des receveurs réels des deuxièmes reins transplantés ont été retirées de l'arrivée des offres spécifiques à un candidat. La moyenne des valeurs KDRI des offres restantes a été calculée pour fournir une estimation de la qualité des offres futures, à comparer à celle de l'offre en cours. Résultats: Au cours de la période étudiée, 848 donneurs uniques et 1 696 candidats à la transplantation étaient inscrits activement. Les modèles fournissent les informations suivantes: le temps moyen jusqu'à l'offre suivante, délai au bout duquel il y a une probabilité de 95 % de recevoir la prochaine offre, la moyenne des valeurs KDRI des offres futures. L'indice C du modèle était de 0,72. Par rapport aux estimations moyennes du groupe en ce qui concerne le temps d'attente et la valeur KDRI des offres futures, le modèle a permis de réduire l'erreur quadratique moyenne de 137 à 84 jours pour le temps jusqu'à la prochaine offre, et de 0,64 à 0,55 pour la valeur KDRI prévue des offres futures. La précision des prédictions offertes par le modèle était plus élevée lorsque le temps jusqu'à l'offre suivante était de cinq mois ou moins. Limites: Le modèle suppose que les patients qui refusent une offre demeurent sur la liste d'attente jusqu'à l'offre suivante. Le modèle ne met à jour le temps d'attente que chaque année après la date de l'offre, et non de façon continue. Conclusion: En fournissant des estimations quantitatives personnalisées du temps jusqu'à l'offre suivante et de la qualité des offres futures, notre nouvelle approche peut éclairer le processus décisionnel partagé des candidats à la transplantation et des médecins lorsqu'une offre de rein provenant d'un donneur décédé est faite par le biais d'un ODO.

GLUT1-DS Italian registry: past, present, and future: a useful tool for rare disorders.

BACKGROUND: GLUT1 deficiency syndrome is a rare, genetically determined neurological disorder for which Ketogenic Dietary Treatment represents the gold standard and lifelong treatment. Patient registries are powerful tools providing insights and real-world data on rare diseases. OBJECTIVE: To describe the implementation of a national web-based registry for GLUT1-DS. METHODS: This is a retrospective and prospective, multicenter, observational registry developed in collaboration with the Italian GLUT1-DS association and based on an innovative, flexible and configurable cloud computing technology platform, structured according to the most rigorous requirements for the management of patient's sensitive data. The Glut1 Registry collects baseline and follow-up data on the patient's demographics, history, symptoms, genotype, clinical, and instrumental evaluations and therapies. RESULTS: Five Centers in Italy joined the registry, and two more Centers are currently joining. In the first two years of running, data from 67 patients (40 females and 27 males) have been collected. Age at symptom onset was within the first year of life in most (40, 60%) patients. The diagnosis was formulated in infancy in almost half of the cases (34, 51%). Symptoms at onset were mainly paroxysmal (mostly epileptic seizure and paroxysmal ocular movement disorder) or mixed paroxysmal and fixed symptoms (mostly psychomotor delay). Most patients (53, 79%) are currently under Ketogenic dietary treatments. CONCLUSIONS: We describe the principles behind the design, development, and deployment of the web-based nationwide GLUT1-DS registry. It represents a stepping stone towards a more comprehensive understanding of the disease from onset to adulthood. It also represents a virtuous model from a technical, legal, and organizational point of view, thus representing a possible paradigmatic example for other rare disease registry implementation.

The promise of machine learning applications in solid organ transplantation.

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor-recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

Forecasting future needs and optimal allocation of medical residency positions: the Emilia-Romagna Region case study.

OBJECTIVES: Italian regional health authorities annually negotiate the number of residency grants to be financed by the National government and the number and mix of supplementary grants to be funded by the regional budget. This study provides regional decision-makers with a requirement model to forecast the future demand of specialists at the regional level. METHODS: We have developed a system dynamics (SD) model that projects the evolution of the supply of medical specialists and three demand scenarios across the planning horizon (2030). Demand scenarios account for different drivers: demography, service utilization rates (ambulatory care and hospital discharges) and hospital beds. Based on the SD outputs (occupational and training gaps), a mixed integer programming (MIP) model computes potentially effective assignments of medical specialization grants for each year of the projection. RESULTS: To simulate the allocation of grants, we have compared how regional and national grants can be managed in order to reduce future gaps with respect to current training patterns. The allocation of 25 supplementary grants per year does not appear as effective in reducing expected occupational gaps as the re-modulation of all regional training vacancies.

Design of an irreversible electroporation system for clinical use.

Irreversible electroporation is an ablation modality in which microseconds, high-voltage electrical pulses are applied to induce cell necrosis in a target tissue. To perform irreversible electroporation it is necessary to use a medical device specifically designed for this use. The design of an irreversible electroporation system is a complex task in which the effective delivery of high energy pulses and the safety of the patient and operator are equally important. Pulses of up to 3000 V of amplitude and 50 A of current need to be generated to irreversibly electroporate a target volume of approximately 50 to 70 cm3 with as many as six separate electrodes; therefore, a traditional approach based on high voltage amplifiers becomes hard to implement. In this paper, we present the process that led to the first irreversible electroporator capable of such performances approved for clinical use. The main design choices and its architecture are outlined. Safety issues are also explained along with the solutions adopted.