A multi-state model for kidney disease progression

BackgroundUnderstanding the progression of kidney disease is great interest among clinicians. The multi-state model an adequate tool to effects covariates that influence onset, progression, and regression function.ObjectiveThe goal present study propose a stochastic for demonstrate application same.MethodologyWe proposed semi-parametric continuous time homogeneous Markov data obtained from retrospective 225 patients prescribed with colistin (a re-emerging antibiotic) in tertiary care hospital coastal Karnataka. Different stages were defined based on Kidney Disease Improving Global Outcome (KDIGO) score. consists three transient states, absorbing state death. Covariate bidirectional transition rates estimated using model.ResultsWe used see their progression. All under therapy. median length stay was 21 days. A total 83 (36.89%) died hospital. prognostic factors such as gender, hypertension, sepsis, surgery are significant affecting different stages.ConclusionThe findings will be useful public health policymakers implement policies treatment plans improve survival patients. Moreover, modelling helps understanding expected burden disease.