title: Modelling treatment trajectories to optimize the organization of renal replacement therapy and public health decision-making.
abstract: BACKGROUND
  Nephrologists need to better understand the impact of their decisions about long-term treatment strategies. Healthcare planning requires the anticipation of demand. Indicators from ESRD registries are especially difficult to interpret when the underlying dynamic process is not well understood. Therefore, we have developed a statistical tool to study the course of incident ESRD patient cohorts over time and to quantify, by simulations, the impact of various expected changes or new strategies.
  
  
  METHODS
  Based on the data from 67 258 ESRD adult patients, we first estimated transition rates between 10 different modalities of treatment ('compartments') with a multistate model. In a second step, we predicted the number of patients in each compartment at each time point for a cohort of 1000 patients for 180 months after the onset of renal replacement therapy (RRT). We tested two scenarios to illustrate the possibility of simulating policy changes.
  
  
  RESULTS
  Increased use of non-assisted automated peritoneal dialysis (PD) (from 7.7 to 19.2% at RRT onset) will not substantially influence the proportion of total RRT time in PD for patients aged 18-44 without diabetes. Improving access to kidney transplants from cadaveric donors for patients aged 45-69 with diabetes will increase the 15-year restricted mean lifetime by 5 months and the time spent with a functioning graft (34 versus 23%).
  
  
  CONCLUSIONS
  A model based on patients' treatment trajectories can improve the description and understanding of RRT as a dynamic phenomenon. Its use for simulation may help professionals and decision-makers to optimize renal organization and care.
authors.affiliation: REIN Registry, Agence de la Biomédecine, Saint Denis La Plaine, France.
authors.forename: Cécile
authors.initials: C
authors.lastname: Couchoud
authors.forename: Emmanuelle
authors.initials: E
authors.lastname: Dantony
authors.forename: Mad-Hélénie
authors.initials: MH
authors.lastname: Elsensohn
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Villar
authors.forename: René
authors.initials: R
authors.lastname: Ecochard
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003657:Decision Making; D005260:Female; D005500:Follow-Up Studies; D005919:Glomerular Filtration Rate; D006301:Health Services Needs and Demand; D006801:Humans; D007676:Kidney Failure, Chronic / Q000628:therapy*; D016030:Kidney Transplantation; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D011634:Public Health; D012042:Registries; D006435:Renal Dialysis; D017582:Renal Replacement Therapy; D015996:Survival Rate; D055815:Young Adult
keywords: epidemiology ESRD; model; outcome; simulation; trajectories