A multi-state model for kidney disease progression - M.K. Lintu; K.M. Shreyas; Asha Kamath (Elsevier, 2022)
Background: Understanding the progression of kidney disease is of great interest among clinicians. The multi-state model is an adequate tool to model the effects of covariates that influence the onset, progression, and regression of kidney function. Objective: The goal of the present study is to propose a stochastic model for kidney disease progression and to demonstrate the application of the same. Methodology: We proposed a semi-parametric continuous time homogeneous multi-state Markov model for the kidney disease progression data obtained from a retrospective study of 225 patients prescribed with colistin (a re-emerging antibiotic) in a tertiary care hospital in coastal Karnataka. Different stages of kidney disease were defined based on the Kidney Disease Improving Global Outcome (KDIGO) score. The model consists of three transient states, and an absorbing state death. Covariate effects on the bidirectional transition rates were estimated using the multi-state model. Results: We used the data of 225 patients to see their kidney disease progression. All the patients were under colistin therapy. The median length of hospital stay was 21 days. A total of 83 (36.89%) patients died in the hospital. The prognostic factors such as gender, hypertension, sepsis, and surgery are significant factors affecting kidney disease in different stages. Conclusion: The findings of the study will be useful for public health policymakers to implement the policies and treatment plans to improve the survival of the patients. Moreover, modelling the disease progression helps in understanding the expected burden of the disease.