Med Decis Making - Identification of a multistate continuous-time nonhomogeneous Markov chain model for patients with decreased renal function.

Tópicos

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Resumo

JECTIVES: Markov chain models are frequently used to study the clinical course of chronic diseases. The aim of this article is to adopt statistical methods to describe the time dynamics of chronically ill patients when 2 kinds of data sets--fully and partially observable data are available.MODEL: We propose a 6-state continuous-time Markov chain model for the progression of chronic kidney disease (CKD), where little is known about the transitions between the disease stages. States 1 to 3 of the model correspond to stages III to V of chronic kidney disease in the Kidney Disease Outcomes Quality Initiative (KDOQI) CKD classification. States 4 and 5 relate to dialysis and transplantation (renal replacement therapy), respectively. Death is the (absorbing) state 6.METHODS AND DATA: The model can be investigated and identified using Kolmogorov's forward equations and the methods of survival analysis. Age dependency, covariates in the form of the Cox regression, and unobservable risks of transition (frailties) can be included in the model. We applied our model to a data set consisting of all 2097 patients from all renal centers in a region in North Rhine-Westphalia (Germany) in 2005-2010.RESULTS: We compared transitions and relative risks to the few data published and found them to be reasonable. For example, patients with diabetes had a significantly higher risk for disease progression compared with patients without diabetes.CONCLUSIONS: In summary, modeling may help to quantify disease progression and its predictors when only partially observable prospective data are available.

Resumo Limpo

jectiv markov chain model frequent use studi clinic cours chronic diseas aim articl adopt statist method describ time dynam chronic ill patient kind data sets partial observ data availablemodel propos state continuoustim markov chain model progress chronic kidney diseas ckd littl known transit diseas stage state model correspond stage iii v chronic kidney diseas kidney diseas outcom qualiti initi kdoqi ckd classif state relat dialysi transplant renal replac therapi respect death absorb state method data model can investig identifi use kolmogorov forward equat method surviv analysi age depend covari form cox regress unobserv risk transit frailti can includ model appli model data set consist patient renal center region north rhinewestphalia germani result compar transit relat risk data publish found reason exampl patient diabet signific higher risk diseas progress compar patient without diabetesconclus summari model may help quantifi diseas progress predictor partial observ prospect data avail

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