A multi-state model for kidney disease progression

M.K Lintu,K.M Shreyas,Asha Kamath

doi:10.1016/j.cegh.2021.100946

M.K Lintu, K.M Shreyas + Show 1 more

Open Access

https://doi.org/10.1016/j.cegh.2021.100946

Copy DOI

Abstract

BackgroundUnderstanding 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. ObjectiveThe goal of the present study is to propose a stochastic model for kidney disease progression and to demonstrate the application of the same. MethodologyWe 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. ResultsWe 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. ConclusionThe 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.

Highlights

Kidney disease is an important public health problem
We proposed a multi-state model for the kidney disease progression of the patients receiving colistin during their hospital stay
Modelling the disease progression helps in understanding the expected burden of the disease which can be further useful for the national public health policymakers

Summary

Introduction

Kidney disease is an important public health problem. early intervention can avoid kidney problems permanently. As kidney diseases often end up with hospitalization, modelling the length of stay, survival, and progression of kidney disease is of great interest among clinicians. Standard survival approaches such as the Kaplan-Meier method or Cox proportional hazards model are sufficient to handle the simple survival settings with no intermediate events. 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 reemerging antibiotic) in a tertiary care hospital in coastal Karnataka. 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. Modelling the disease progression helps in understanding the expected burden of the disease

Objectives

Methods

Conclusion