Geographic Optimization of Liver Allocation in Transplantation

Transcription

1 1 1 Geographic Optimization of Liver Allocation in Transplantation Esri Health GIS Conference Florian Bayer, Dr. Christian Jacquelinet, Benoit Audry, Dr. Corinne Antoine

2 2 2 a - Introduction Study Objectives Overall context : How can spatial analysis and geography improve organ allocation? How can liver allocation processes be improved? What compromise between the recipient s condition and organ transport requirements

3 3 3 a - Introduction Contents 1. Context: French liver transplant organ allocation rules 2. Methodology 3. Results 4. Conclusion and future work

4 Context

5 Context L Agence de la biomédecine Created by the bioethics law of August 6, 2004 from the Etablissement Français des Greffes created in 1994 French public body Responsible for: - Operational coordination and administration of organ transplantation and tissue harvesting - Coordination and administration of hematopoietic stem cell grafting - Administration of assisted reproductive technology, human embryology and human genetics

6 1 - Context L Agence de la biomédecine It is responsible for Organ allocation rules and the national waiting list To ensure the impartial distribution of organs to the most appropriate recipient To make an optimal compromise between efficacy, equity and feasibility In liver transplant, this compromise must take into account : Specific recipient condition (i.e. emergency life threatening conditions) The quality/safety of the transplant and organ The distance between donor and transplant centres (cold ischemia time, transplantation team s security ) 6 6

7 Context Liver Score The Liver Score was introduced in March 2007 Ł To attribute livers to the best recipient, by "just-in-time" optimisation Ł Without excluding potential recipients

8 Context Liver Score Set of functions based on : 1. The recipient s health using the MELD (Model for End-Stage Liver Disease) for cirrhosis, and waiting time for hepatocellular carcinoma. estimating the risk of death on the waiting list. 2. The distance between donor and transplant centres To reduce team s travel To reduce cold ischemia To take into account the team s location whilst removing the frontier effect 3. Time spent on the waiting list Accelerate access to transplantation while patients are still in the optimal period 4. Documented recourse to Experts to deal with exceptional circumstances

9 1 - Context Liver Score V1,V2,V3 V4 Disparity of local networks in hepatic transplantation in 2010 Urgency Urgency Local Liver Score Liver Score Exceptional Allocation Exceptional Allocation 9 9

10 Context Percentage of local network transplants by team

11 Context The distance in the Liver Score Double geographic model : Local : the distance doesn t matter National : the distance matters Sicker patients further from the organ or outside the network could benefit from the local organ, but don t. The coverage of just-in-time principle of the Liver Score was only partial Problem of Efficacy and equity

12 Context Objectives 1. Optimising the distance function in the current Liver Score by linking the key decision factors of liver transplant : The distance between donor and transplant team The recipient s health (The organ quality) 2. To show that an alternative to a local network is conceivable Without reducing organ procurement and transplant activity Making a compromise between seriousness of the condition and distance

13 Methodology

14 Methodology Methodology 1/ Review of existing studies u essentially about finding the best static area for transplantation 2/ Finding the best geographical theory and tools for this study u The principles of spatial interaction 3/ Integration of these principles in the Liver Score a. Optimization of the new model with Arcgis b. Tests and simulations with Arcgis (day to day) and with our simulation platform (years)

15 Methodology The principles of spatial interaction 1 st : size effect At equal distance, the importance of relations between two places is equal to their ability to transmit and receive 2 nd : distance effect At equal weight, the importance of relations between two places is inversely proportional to their distance 3 rd : frontier effect Size (population) High Low Attractivity Source : Claude Grasland

16 Methodology Some principles of spatial interaction u Strong analogy with the Newton's law of universal gravitation. u Application to geography (Pareto / Stewart / Reilly s law) : «The force of attraction exerted by a place j on place i is proportional to its size and inversely proportional to the square of the distance from i to j» Aij = Mj X Mi / Dij 2 Aij attractivity of j on i i the location of the transplant team j the localisation of the patient on the waiting list Mj the j mass Mi the i mass Dij the distance between i and j

17 Methodology Some principles of spatial interaction High Liver Score without distance Low Liver Score without distance Graft Attraction Assess this attraction with the liver score without distance and the distance to the organ. This compromise depends on the distance decay (entropy principle).

18 Methodology The «basic» model Aij = Mj X Mi / Dij α Mj the mass of the patient on the waiting list Liver Score without distance component Mi the mass of the graft status At this time, no consensus for its evaluation. Mi = 1 Distant Dij between i et j Travel time by road The last parameter is α, the distance decay (entropy). It was calibrated with a linear regression based on real teams flow. A VBA Arcgis tool has been developped for adjustements

19 Methodology Methodology Data : liver transplant ( ) Geocoding Arcmap linear regression Sas VBA Arcgis Geographic data Day to day simulation Setting parameters Annual simulation Arcmap Arcmap Simulation platform

20 Methodology Expectations Better compromise between distance and recipient s health Potentially an organ can now be distributed : 1. All over the territory for the sickest patients : gravity > distance 2. Near the liver procurement team for the less urgent : distance > gravity This reducing teams transport (time, costs and risks) February 2011 : implementation of the gravity model Score = Score HD / exp [ 0,4 x [ (DLPG) / (seuil)] 0,15 ]

21 Results

22 3 - Results Results : day to day Fictive patients waiting list for a liver transplantation Patient Rank (score without distance) Liver score without distance Score V3 Score 1 (900) Bordeaux Bordeaux Bordeaux V4 2 (870) Paris 1 Paris 1 Paris 1 3 (845) Paris 2 Paris 2 Paris 2 4 (610) Marseille Rennes Rennes 5 (610) Grenoble Caen Marseille 6 (588) Lyon Lille Grenoble 7 (573) Toulouse Besançon Caen 8 (553) Besançon Marseille Lyon 9 (540) Rennes Toulouse Toulouse 10 (529) Caen Strasbourg Besançon 11 (520) Lille Lyon Lille 12 (493) Strasbourg Grenoble Strasbourg Donor liver 13 (478) Montpellier Montpellier Montpellier 14 (470) Nice Nice Nice 22

23 Results Area of certitude to be the first on the waiting list, for each patient according to their severity score and the model used (same list as before) Liver Score V3 Liver Score V4 more local competition p2 p10 p3 p2 p11 p6 p9 p5 p1 p1 p4 Patient area of certitude to get the graft (2 areas) Patient area of certitude to get the graft (14 areas) Source : Agence de la biomédecine 2009

24 Results Area of certitude to be the first on the waiting list, for each patient according to their severity score and the model used (same list as before) Liver Score V4 Day 1 Liver Score V4 Day 2 (Paris Lyon Score 720 Bordeaux 500 ) Patient area of certitude to get the graft (14 areas) Patient area of certitude to get the graft (14 areas)

25 Results V1/V2 V3 V4

26 Results Cumulative incidence rate Score V3 : death and delisting (aggravated condition) stratified by inscription MELD (cirrhosis) Score V4 : death and delisting (aggravated condition) stratified by inscription MELD (cirrhosis) Cumulative incidence rate Months Benefice for every MELD class, except [26-29] High competition with hepatocellular carcinoma Months

27 Results Meld class distribution by km from 02/22/2010 to 02/21/2011 for Cirrhosis (without HCC) local and national score V3 (n=335) 70 De 0 à 150 km (n=169) De 150 à 300 km (n=41) Plus de 300 km (n=125) [06-10] (n=8) [11-14] (n=21) [15-19] (n=36) [20-25] (n=77) [26-29] (n=54) [30-34] (n=50) [35-40] (n=89)

28 Results Meld class distribution by km from 02/22/2011 to 02/21/2012 for Cirrhosis (without HCC) local and national score V4 (n=329) 70 De 0 à 150 km (n=175) De 150 à 300 km (n=48) plus de 300 km (n=106) [06-10] (n=0) [11-14] (n=2) [15-19] (n=30) [20-25] (n=90) [26-29] (n=47) [30-34] (n=65) [35-40] (n=95)

29 Results Montpellier team s travel for liver score transplantation between february 2010 and february 2011 Montpellier team s travel for liver score transplantation between february 2011 and february km less for all transplantation teams between V3 and V4, with the same activity

30 Results Deciles distribution of cold ischemia time Global reduction of cold ischemia time : 491 (v3) to 470 (v4) minutes on average

31 Conclusion

32 Conclusion Limits The main inconvenience of such an approach is the understandability of the model for clinicians, patients and citizens. Advantages Applying the gravity model allows slightly improving access to transplantation for the most urgent patients Team transport and cold ischemia time decrease Better compromise between distance and urgency Significant decrease in death and delisting due to worsening health (-15%), together with a reduction of disparities according to MELD, indications and centers

33 Conclusion Futur works Taking into account health care professionals feedback for model adjustments Evaluation of post-transplant survival Professionals still working on a better recipient priority score for hepatocellular carcinoma Working on the same approach for the Kidney matching Score

34 34 Thank you for your attention Benoît Rajau pour l Agence de la biomédecine Contributeurs de OpenStreetMap. Tiles courtesy of Humanitarian OpenStreetMap Team