A multi-state model for kidney disease progression

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

doi:10.1016/j.cegh.2021.100946

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…Once a patient transit to state 2, it will take him or her more time to attain to state 1. This outcome is similar to a study in multi-state model for kidney disease progression [24] .…”

Section: Discussion Of Resultssupporting

confidence: 83%

“…Also, patients with severe stroke are more likely (0.0942) to die than patients with moderate (0.0000004) or mild stroke (0.03108). This result do not contradict literature [24] . Estimates from (Table 4) shows that a patient spent an average of (5 visits points) 10 months (5.0946, SE=1.001) at state 2 before transiting to state 1, average of 1.396 visits (about 3 months) at state 3 before transiting to state 2 (1.396, SE = 0.8189) and an average of 2 months (0.9049, 𝑆𝐸 = 0.19051) at state 4 and then transiting to state 3.…”

Section: Discussion Of Resultssupporting

confidence: 82%

“…Female patients ( 23) also outweigh male patients (14). A patient was either a Christian (13) or Islamic religion (24). Only one (1) patient was single, three (3) were widowed, and the majority (33) were married.…”

Section: Data Visualization and Analysismentioning

confidence: 99%

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Multi-State analysis of secondary prevention of stroke in Northern Ghana

Howard¹,

Adams²

2022

Int. J. Stat. Appl. Math.

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We obtained secondary data from the medical unit of the Tamale Teaching Hospital in which stroke patients were on rehabilitation. The purpose of the study is to develop an illness-to-death model that will enable us to observe the transition rates of patients during rehabilitation at some discrete points in time. A review of literature on stroke in many research works revealed that none of the articles estimated the probabilities of transition at different level of the disease. We employed Continuous Time Markov Model (CTMC) in Multi-state Models (MSM) to observe the transition rates of the patients at two equal monthly intervals for two years. Results from the transition analysis indicated that patients with mild stroke will remain in this state for about 10 months before recovery and will never become severe if the patient adheres to treatment. While old and older age groups have some chance of transiting to a less severe state and similar probabilities of transiting from mild to a more severe state, the study also suggests that the recovery rate is fast in higher states than in lower states. It must be noticed that patients spent more time at state 2, followed by state three (3), and less time at state 4. This suggests that patients with mild stroke longer at mild state than other states and hence they need to continue to adhere to treatment to gain total recovery. We therefore recommend that; patients who transit to mild state should be advised by medical officers to continue to adhere to treatment to speed up total recovery

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“…Once a patient transit to state 2, it will take him or her more time to attain to state 1. This outcome is similar to a study in multi-state model for kidney disease progression [24] .…”

Section: Discussion Of Resultssupporting

confidence: 83%

Section: Discussion Of Resultssupporting

confidence: 82%

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Multi-State analysis of secondary prevention of stroke in Northern Ghana

Howard¹,

Adams²

2022

Int. J. Stat. Appl. Math.

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“…In that study, Lyons et al presented the estimated probabilities of being in a specified clinical status where the AKI-related states rely on the worst AKI stage within 12-h blocks. Another recent multistate modeling application for investigating kidney disease progression was given for a cohort of 225 patients who were prescribed colistin 38 . In addition to AKI, a retrospective study for modeling transitions between CKD stages via multistate methods was given for a cohort of 117 hospitalized and non-hospitalized patients 39 .…”

Section: Discussionmentioning

confidence: 99%

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Adiyeke,

Ren,

Guan

et al. 2023

Sci Rep

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Persistence of acute kidney injury (AKI) or insufficient recovery of renal function was associated with reduced long-term survival and life quality. We quantified AKI trajectories and describe transitions through progression and recovery among hospitalized patients. 245,663 encounters from 128,271 patients admitted to UF Health between 2012 and 2019 were retrospectively categorized according to the worst AKI stage experienced within 24-h periods. Multistate models were fit for describing characteristics influencing transitions towards progressed or regressed AKI, discharge, and death. Effects of age, sex, race, admission comorbidities, and prolonged intensive care unit stay (ICU) on transition rates were examined via Cox proportional hazards models. About 20% of encounters had AKI; where 66% of those with AKI had Stage 1 as their worst AKI severity during hospitalization, 18% had Stage 2, and 16% had Stage 3 AKI (12% with kidney replacement therapy (KRT) and 4% without KRT). At 3 days following Stage 1 AKI, 71.1% (70.5–71.6%) were either resolved to No AKI or discharged, while recovery proportion was 38% (37.4–38.6%) and discharge proportion was 7.1% (6.9–7.3%) following AKI Stage 2. At 14 days following Stage 1 AKI, patients with additional frail conditions stay had lower transition proportion towards No AKI or discharge states. Multistate modeling framework is a facilitating mechanism for understanding AKI clinical course and examining characteristics influencing disease process and transition rates.

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“…Multistate models are used in a variety of clinical settings. [14][15][16][17][18] Evaluation of the performance of a prediction model, through a validation study, is essential before it can be used in practice. While the target cohorts and specific approaches may change depending on the target of the validation, [19][20][21] model validation should routinely involve assessment of calibration and discrimination, [22][23][24] as well as other relevant techniques such as decision curve analysis, 25,26 measures of explained variation (R 2 statistics) and the Brier score.…”

Section: Introductionmentioning

confidence: 99%

Calibration plots for multistate risk prediction models

Pate,

Sperrin,

Riley

et al. 2024

Statistics in Medicine

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IntroductionThere is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation.MethodsWe studied pseudo‐values based on the Aalen‐Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR‐IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR‐IPCW). The MLR‐IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records.ResultsThe pseudo‐value, BLR‐IPCW, and MLR‐IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low‐density regions of predicted transition probability.ConclusionsWe recommend implementing either the pseudo‐value or BLR‐IPCW approaches to produce a calibration curve, combined with the MLR‐IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the “calibmsm” R package available on CRAN.

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A multi-state model for kidney disease progression

Cited by 7 publications

References 16 publications

Multi-State analysis of secondary prevention of stroke in Northern Ghana

Multi-State analysis of secondary prevention of stroke in Northern Ghana

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Calibration plots for multistate risk prediction models

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