Society of Diabetic Nephropathy PreventionJournal of Nephropharmacology2345-42028120190101On the estimation of misclassification probabilities of
chronic kidney disease using continuous time hidden
Markov models7710.15171/npj.2019.07ENGurpritGroverAlkaSabharwalShrawanKumarArpanKumar ThakurJournal Article10.15171/npj.2019.072018070820181008Introduction: The severity of chronic kidney disease (CKD) is reflected in the form of stages
of CKD and can be decided on the basis of estimated glomerular filtration rate (eGFR). The
computation of eFGR may have computational and measurement errors which may lead to
misclassification of stages.
Objectives: Estimation of transition rates, mean sojourn times, probabilities of misclassification
of stages and odds ratios for misclassification probabilities.
Patients and Methods: The retrospective data of 117 patients suffering from CKD during the
period March 2006 to October 2016 is used. Hidden Markov model (HMM) with continuous time
has been developed to present the course of progression of CKD into various stages.
Results: Under the HMM, the estimated transition intensity corresponding to transition from
stage 1 to stage 2 is 0.0405 and reverse transition intensities are zero. The estimated mean sojourn
time corresponding to stage 1, stage 2, stage 3 and stage 4 are 15.923 years, 11.976 years, 7.936
years and 2.890 years respectively. The probability of a CKD patient with stage 1 of disease will be
misclassified as a patient of stage 2 is 0.211. The odds ratios for misclassification probabilities in the
presence of prognostic factors are computed. The probability of misclassification corresponding
to the observed stage 2 given the true stage 1 for females is approximately 3.8 times more than
that of males.
Conclusion: For CKD having progression in stages, the HMM is an appropriate model to draw
the course of progression of the disease.