A Multi-State Markov Model for the Progression of Chronic Kidney Disease

Grover, Gurprit; Sabharwal, Alka; Kumar, Shrawan; Thakur, Arpan Kumar

doi:10.5336/biostatic.2018-62156

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…can also be efficiently handled with a multi-state model rather than a simple survival model. 9,10 However, less number of applications are found in the field of chronic kidney disease progression. The most recent study that used a multi-state model to understand the chronic kidney disease progression was by Grover et al 10 We proposed a 4 state multi-state model for the kidney disease progression of patients receiving colistin.…”

Section: Discussionmentioning

confidence: 99%

“…9,10 However, less number of applications are found in the field of chronic kidney disease progression. The most recent study that used a multi-state model to understand the chronic kidney disease progression was by Grover et al 10 We proposed a 4 state multi-state model for the kidney disease progression of patients receiving colistin. An attempt was made to provide a simple demonstration of the multi-state model J o u r n a l P r e -p r o o f targeting health professionals.…”

Section: Discussionmentioning

confidence: 99%

“…can also be efficiently handled with a multi-state model rather than a simple survival model. 9,10 In this article, we proposed a multi-state model for the kidney disease progression of the patients receiving colistin during their hospital stay. Case records of 225 patients were retrieved and their KDIGO scores at different time points were accessed for a clinical companion paper.…”

Section: Introductionmentioning

confidence: 99%

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A multi-state model for kidney disease progression

M.K.

K.M

Kamath

2022

Clinical Epidemiology and Global Health

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A multi-state model for kidney disease progression

M.K.

K.M

Kamath

2022

Clinical Epidemiology and Global Health

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“…Therefore, it is imperative to investigate the transition patterns among different levels of blood pressure for early detection and prevention. 30 Multistate Markov modelling has been widely used in healthcare research to analyse the progression of various chronic diseases, such as hepatitis, 31 diabetes, 32 chronic kidney disease 33 and Alzheimer's disease. 34 It likewise can be used to analyse the transition from a normal (nondisease) state, through a mild state to a severe and/or death state.…”

Section: Open Accessmentioning

confidence: 99%

Multistate Markov model application for blood pressure transition among the Chinese elderly population: a quantitative longitudinal study

Zheng

Xiong

Zhang³

et al. 2022

BMJ Open

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ObjectiveTo explore the transitions of different blood pressure states based on a multistate Markov model among the Chinese elderly population.SettingA community health centre in Xiamen, China.Participants1833 elderly Chinese people.MethodsA multistate Markov model was built based on 5001 blood pressure measurements from 2015 to 2020. Research was conducted to explore the process of hypertension progression, providing information on the transition probability, HR and the mean sojourn time in three blood pressure states, namely normal state, elevated state and hypertensive state.ResultsProbabilities of moving from the normal state to the hypertensive state in the first year were 16.97% (female) and 21.73% (male); they increased dramatically to 47.31% (female) and 51.70% (male) within a 3-year follow-up period. The sojourn time in the normal state was 1.5±0.08 years. Elderly women in the normal state had a 16.97%, 33.30% and 47.31% chance of progressing to hypertension within 1, 2 and 3 years, respectively. The corresponding probabilities for elderly men were 21.73%, 38.56% and 51.70%, respectively. For elderly women starting in the elevated state, the probabilities of developing hypertension were 25.07%, 43.03% and 56.32% in the next 1, 2 and 3 years, respectively; while the corresponding changes for elderly men were 20.96%, 37.65% and 50.86%. Increasing age, body mass index (BMI) and glucose were associated with the probability of developing hypertension from the normal state or elevated state.ConclusionsPreventive actions against progression to hypertension should be conducted at an early stage. More awareness should be paid to elderly women with elevated state and elderly men with normal state. Increasing age, BMI and glucose were critical risk factors for developing hypertension. The derived transition probabilities and sojourn time can serve as a significant reference for making targeted interventions for hypertension progression among the Chinese elderly population.

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“…Therefore, it would be worth investigating its natural progression for early detection and prevention of PPD. It is noted that multi-state Markov modeling is a modeling technique that has been widely used in the medical field to analyze the progression of a variety of diseases such as high blood pressure, diabetes, and chronic kidney disease [ 29 , 30 , 31 ]. Likewise, it can be used to analyze the transition from a normal (non-disease) state, through a mild state to a severe and/or death state [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

Xiong

Fang

et al. 2021

IJERPH

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Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

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A Multi-State Markov Model for the Progression of Chronic Kidney Disease

Abstract: Gurprit GROVER et al.

Cited by 7 publications

References 18 publications

A multi-state model for kidney disease progression

A multi-state model for kidney disease progression

Multistate Markov model application for blood pressure transition among the Chinese elderly population: a quantitative longitudinal study

States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

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