A Multistate Model for the Analyzing of Chronic Kidney Disease Progression with Detecting Risk Factors Effect
The evolution of kidney disease can be well modeled using a multistate model. In health economics, where the specification of health policies may be based on projections of long-term costs, it can play a pivotal role in medical decision making for kidney disease, where the prediction of outcomes is necessary under different treatment strategies and the multi-state model is an adequate tool to model the effects of covariates that influence the onset, progression of kidney function.
The primary purpose of this research is to create a stochastic model for the progression of Chronic Kidney Disease (CKD) into various stages.
The kidney dysfunction progression data from 153 patients was analyzed using a continuous time homogeneous multistate model based on Markov processes. Kidney disease was classified into four phases using the Kidney Disease Improving Global Outcome (KDIGO) scale. The model incorporates four transitional states and a renal failure in an absorbing condition. Each patient's gender, age, BMI, diabetes, hypertension, Corona virus status, urea, serum creatinine, albumin, and disease duration were noted as predictive markers.
The average time spent in states 1, 2, 3, and 4 prior to kidney failure was 7.23, 4.24, 4.59, and 1.68 years respectively. Covid-19 has a fivefold increased risk of progressing from stage 3 and 4 to absorbing condition. Also Age, hypertension, diabetes, urea, and serum creatinine all play a significant role in the progression of chronic kidney disease (CKD) into later stages
The findings of the study will be helpful to public health officials, who can utilize them to establish treatment programs and policies that will increase patients' chances of surviving their conditions. In addition, modeling the evolution of the disease assists in gaining an idea of the anticipated severity of the condition.