1 Introduction
Chronic kidney disease (CKD) is a progressive condition with heterogenous aetiology that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality [
1‐
6]. The age-standardised global prevalence of CKD stages 1–5 among adults aged ≥ 20 years in 2010 was approximately 10.4% in men and 11.8% in women and continues to rise, fuelled by ageing populations and the increasing incidence of diabetes and hypertension [
7,
8]. Currently, more than two million patients with CKD worldwide have been estimated to require renal replacement therapy (RRT) through dialysis or kidney transplantation [
9]. In addition, the humanistic and economic burdens of CKD increase significantly as renal function declines, and patients at CKD stage 3 or above are at 1–14 times higher risk for cardiovascular and overall mortality compared with the general population [
4]. Moreover, patients with CKD often experience health-related quality of life (HRQoL) impairments and increased healthcare costs [
4,
10]. Because of the need for intensive treatment, the largest costs and impacts on HRQoL are incurred when patients reach CKD stage 5 (glomerular filtration rate [GFR] < 15 mL/min/1.73 m
2) or end-stage renal disease (ESRD), which is irreversible and where patients often require RRT [
11,
12].
Cardiovascular disease (CVD) and CKD are closely interrelated, and the reduction of systolic blood pressure, blood glucose and other cardiovascular risk factors via drug treatment and/or lifestyle modifications are key components of CKD management. Renin–angiotensin–aldosterone system inhibitors, which confer both cardio- and reno-protective benefits through their anti-hypertensive effect, have been the cornerstone of CKD treatment for many years [
13,
14] and are often prescribed in addition to statins and anti-platelet therapy [
15‐
19]. While the risk of developing CKD of certain aetiologies can be reduced through medical and/or lifestyle interventions, others cannot, and the main goals of CKD management are to delay the rate of progression to ESRD to limit cardiovascular risk, preserve HRQoL and restrain healthcare costs. However, the underlying aetiology is often predictive of both the likely treatment strategy and the clinical prognosis.
As novel treatments for CKD become available and require evaluation for inclusion into drug formularies, estimates of economic (optimisation of budget usage) and clinical (maximisation of population benefits) outcomes are needed to guide payer reimbursement decisions. Given the significant costs associated with ESRD management, the rate of progression to ESRD is a significant determinant of cost effectiveness. However, the long-term outcomes of CKD, such as ESRD incidence, are not always sufficiently captured over the duration of clinical studies, and time to ESRD often varies widely between patients [
20], making extrapolation necessary yet challenging. The choice of model structure may also play a key role in the validity of these extrapolations, underlining the importance of understanding the implications of selecting a particular conceptual modelling approach. With these challenges in mind, the aim of the present review was to systematically review published economic models that simulated long-term outcomes of kidney disease to qualitatively describe the chosen model designs, rates of CKD progression and the incorporation of CVD in a population with CKD.
2 Methods
2.1 Literature Search and Data Extraction
This review was conducted according to the PRISMA-P (Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols) guidelines [
21]. A search strategy was devised using a specified set of search terms for each database (Tables 1–6 in Resource 1 of the Electronic Supplementary Material [ESM]). In brief, the search was designed to identify economic modelling publications that included adult patients with/without CKD in whom CKD progression was evaluated.
Searches were conducted in MEDLINE, Embase, the Cochrane Library and EconLit on 10 November 2017. To identify additional publications that may have modelled progression of kidney disease as part of another disease model, we conducted a supplementary search in MEDLINE to identify systematic literature reviews detailing economic models for diabetes or hypertension. We also searched the websites of the following health technology assessment agencies: UK National Institute for Health and Care Excellence, Canadian Agency for Drugs and Technologies in Health and the Australian Pharmaceutical Benefits Advisory Committee. Bibliographies of eligible articles were also searched for potential publications of interest to the review.
Titles and abstracts of the identified citations were screened by a single reviewer following specific PICOS (population, interventions, comparators, outcomes and study design) eligibility criteria (Table 7 in Resource 1 of the ESM). In brief, studies were included if they described an economic model of adult patients with/without CKD in whom CKD progression was evaluated. The model structure must have included the progression of kidney disease and any health economic outcome, including (but not restricted to) quality-adjusted life-years, incremental cost-effectiveness ratios, life-years gained or costs. Studies were limited to English language only. Abstracts/titles that did not meet the eligibility criteria were excluded; full-text publications were obtained for the remaining citations. The screening process was repeated using the PICOS criteria for full-text articles to obtain a final set of included publications that described economic models characterising CKD progression, regardless of the intervention or comparator being assessed.
Information on model characteristics (including structure, perspective, health states and disease setting), disease progression, event utility, costs, sensitivity analyses, drivers of cost effectiveness, validation procedures and model limitations were extracted from all included publications.
2.2 Analysis of Time to End-Stage Renal Disease (ESRD) or Death
Where reported, transition and mortality rates were extracted from publications to qualitatively describe the choice of model parameterisation on disease progression, such as time to and time with ESRD. To facilitate comparison of rates of CKD disease progression, transition rates from each eligible published model were used to estimate time to ESRD or death (ESRD/death) and time with ESRD within a simple Markov model framework. Such analyses were undertaken individually for each model and limited to decision trees and Markov models that reported sufficient data to allow such analyses. Published models were excluded from these analyses if transition rate data were not reported in sufficient detail or if regression-based simulation approaches were modelled because of their complexity. Models were also excluded if they were designed to model a horizon of < 10 years.
Health states were defined based on those reported in each individual model publication (Fig. 1 in Resource 2 of the ESM). Where possible, analyses were initiated with a standard patient profile, defined as patients aged 50 and from CKD stage 1, normo-albuminuria or earliest disease stage modelled, to provide a consistent reference point. The cycle length reported in each publication was utilised and the time to ESRD/death and time with ESRD subsequently calculated by estimating the time spent in each of the modelled health states. A maximum horizon of 50 years (up to the age of 100) was modelled across all analyses to limit the uncertainty between modelled outcomes.
4 Discussion
This review qualitatively describes how the progression of kidney disease was incorporated in economic models. Models focusing purely on CKD are typically based on (estimated or measured GFR) decline, with GFR progression predominantly based on a linear rate of decline, which could be adjusted for age, race, CKD stage, prevalence of proteinuria and clinical history (hypertension or diabetes). In comparison, diabetes models with nephropathy, where most of the economic models included nephropathy as a separate microvascular sub-model within the overall model, were most frequently based on the presence of albuminuria, where CKD progression was most commonly modelled with the use of transition probabilities calculated for progression between albuminuria health states. There were exceptions to these general rules, and the choice of modelling framework was likely driven by numerous factors, such as perspective, intervention, indication and underlying data sources used to inform disease progression, among other considerations.
The preference to model GFR over albuminuria (or vice versa) was not immediately clear and not typically justified in modelling publications; however, it may relate to the focus and structure of models in the two disease areas. For example, models focusing on CKD typically predicted health state occupancy in line with CKD stages in patients with established CKD, for which GFR provides an explicit delineator. In contrast, diabetes models with nephropathy typically aimed to predict the presence of ESRD based on a reported baseline prevalence of albuminuria, an established predictor of ESRD in patients with diabetes, often in patients with no history of CKD [
143,
144]. However, while the choice of prognostic factor may be relatively straight forward within the context of specific modelling frameworks informed by the availability of data, the clinical rationale for any preference is much less well-defined [
145]. Consensus does exist among the published literature with respect to the natural trajectory of albuminuria and GFR, with changes in albuminuria considered variable, with both negative and positive changes often observed in short timeframes, and changes in GFR usually considered to be progressive (i.e. monotonically decreasing). However, two studies predicted improvement in GFR and albuminuria health states [
23,
27], in line with the literature suggesting that renal function can improve in a subset of patients [
146‐
148]. Nevertheless, it has been suggested that the two variables be used in conjunction to predict the progression of CKD [
145,
149].
There were some limitations to our review. First, only English articles were included in the search. However, this included the vast majority of the articles published, and research suggests that exclusion of non-English articles is unlikely to result in bias [
150]. Second, only primary publications of models were included as they were deemed to contain the most relevant information regarding the model. Third, the review was limited to the information available in the study publication. Fourth, titles and abstracts of the identified citations were screened by a single reviewer. For uncertainties surrounding the relevance of a study, the corresponding full text was obtained, so a decision was made based on a thorough assessment of the study.
Across all models, a common author-identified limitation was the quality and quantity of the data sources that were used to inform the model, such as using data from clinical trials with a short duration, despite a longer follow-up study being more appropriate to model disease progression. For models of CKD without diabetes, limitations surrounded the modelling of CKD progression, including the lack of data for estimating the rate of decline in kidney function for people with neither diabetes nor hypertension [
24], lack of stratification of CKD states according to albuminuria [
25], and the assumption that GFR declines linearly [
28]. In models of CKD with diabetes, a number of authors cited the use of risk equations derived from the Framingham Heart Study as a limitation to predicting cardiovascular events in those with CKD.
Interestingly, most models that captured the risk of CVD predicted an increase in cardiovascular risk as CKD progressed; conversely, only two models predicted an increased risk of CKD progression as a function of CVD incidence [
23,
92]. However, it should be noted that the modelling of these components was often relatively simplistic and failed to provide clinical justification or rationale for the choice of modelled relationship. Across all models, cardiovascular-related risk was most frequently derived from the Framingham Heart Study, with estimates modified to reflect differences in specific geographical populations or patient characteristics. The Framingham risk equations are an extremely well-validated set of equations derived from a large cohort of patients (over multiple generations) followed-up over several decades and have helped identify major cardiovascular risk factors and their impact on the evolving risk of CVD [
151‐
156]. However, the generalisability of the Framingham equations to a CKD population is uncertain, given the equations were derived using data from a population-based cohort with a low prevalence of CKD [
157]. Various studies have demonstrated that patients with CKD have a higher prevalence of CVD than can reasonably be explained based on traditional cardiovascular risk factors, suggesting that CKD itself, or other unknown factors, are additionally impacting on patients’ cardiovascular risk [
3,
158,
159]. As a result, the Framingham risk equations demonstrate poor overall accuracy in predicting cardiovascular events in individuals with CKD, emphasising the need for the development of cardiovascular risk equations in patients with CKD specifically to support future cost-effectiveness studies [
156].
Across models, CKD was generally described and modelled as a single disease, thus ignoring (perhaps relevant) differences in aetiologies. However, the interventions under evaluation were typically indicated across all CKD aetiologies and targeted to other diseases rather than to CKD itself, with decision makers most interested in an evaluation of cost effectiveness across the entire CKD population (cohort level as opposed to individual patient level). Further, while specific CKD aetiologies were not often defined or cited, modelled populations were usually defined based on an underlying clinical trial design, which typically limited patient heterogeneity through specific inclusion/exclusion criteria. Therefore, within this context, a simplification or generalisation of CKD aetiologies may be considered appropriate.
The clinical course of CKD progression (CKD stage 1–4) to ESRD is known to vary substantially across patients and may be influenced by various risk factors, including CKD aetiology, sex, presence of comorbidities (e.g. diabetes and CVD), anaemia, hypertension and proteinuria [
160,
161]. Such heterogeneity in underlying disease poses challenges in the accurate modelling of CKD progression, and the models identified often did not consider these factors. Overall, the body of evidence on prognostic factors related to disease outcomes is relatively large in the field of diabetes, with a greater volume of prospective studies from which disease progression estimates can be derived or multivariable risk equations utilised. However, the tendency across all identified models was to capture input data at the mean rather than the observed patient-level variation, which often precluded the ability to capture true patient heterogeneity. More commonplace was the independent sampling of input data, through probabilistic sensitivity analysis to approximate heterogeneity, although this approach is associated with inherent limitations and does not actually capture how relevant differences in patients’ clinical profiles affect cost-effectiveness outcomes.
The time it takes a patient to reach ESRD/death, and the time for which a patient resides in the ESRD state, is of significance, particularly from the health economics perspective, since ESRD is associated with a significant resource burden and, consequently, may influence cost-effectiveness decisions. While there was general agreement amongst model type with respect to mean time to ESRD/death, mean time with ESRD was typically shorter among CKD models than among diabetes models with nephropathy. Such a difference may possibly be attributed to the excess morbidity and mortality associated with diabetes. In addition, a difference in the age distribution of patients may be a contributing factor. Overall, given the relatively large variations in time to and time with ESRD and their potential influence on health economics studies, we suggest that further research may be undertaken to fully quantify the differences across models’ structures and better understand the heterogeneity between modelled populations and model designs, and the likely causes of such differences.
The heterogeneity with respect to both patient characteristics and disease outcome prediction, combined with a reasonable body of evidence, does seem to contribute to a wide use of microsimulation models (compared with Markov-based cohort models) in the field of diabetes. However, the rationale for model selection was often not cited in the various model publications identified in this review, and estimates of CKD progression in diabetes models with CKD components still rely heavily on a limited number of older datasets that may not fully capture contemporary patient care. Even though (at least) a similar level of patient and disease heterogeneity exists in the field of CKD, a Markov design was relatively common for simulating CKD progression. This may be driven by the limited data available to characterise the true variation in CKD progression patterns, although several risk equations are currently available [
162]. It could be hypothesised that the economic modelling of CKD is less well-researched than that of diabetes because of the significant advancements in the treatment of diabetes over recent years and paralleled advancements in modelling methodologies. With the expected development of new CKD treatment options, we anticipate that our understanding of the most appropriate modelling methods for CKD will improve, with this review providing a basis from which further discussion may be considered.