Modelling treatment trajectories to optimize the organization of renal replacement therapy and public health decision-making | Zendy

Cécile Couchoud | Zendy; Emmanuelle Dantony | Zendy; MadHélénie Elsensohn | Zendy; Emmanuel Villar | Zendy; René Écochard | Zendy

Open Access

Modelling treatment trajectories to optimize the organization of renal replacement therapy and public health decision-making

Author(s) -

Cécile Couchoud,

Emmanuelle Dantony,

MadHélénie Elsensohn,

Emmanuel Villar,

René Écochard

Publication year - 2013

Publication title -

nephrology dialysis transplantation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.654

H-Index - 168

eISSN - 1460-2385

pISSN - 0931-0509

DOI - 10.1093/ndt/gft204

Subject(s) - medicine , anticipation (artificial intelligence) , intensive care medicine , renal replacement therapy , process (computing) , public health , risk analysis (engineering) , management science , nursing , computer science , artificial intelligence , operating system , economics

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.

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