Summary of findings
The results of the studies reported in this monograph provide support for the current policies of annual screening for the occurrence of albuminuria in people with diabetes, and subsequent treatment with ACEi or A2RB if identified. These treatments are effective in reducing progression of renal albumin excretion for those type 1 or type 2 diabetes patients identified as having albuminuria, and there is additional evidence of a benefit even for patients with type 2 diabetes and no albuminuria. The current screening tests available for detecting albuminuria, and the confirmatory testing strategy in use, lead to a substantial overdiagnosis of people at risk. However, the consequences, in terms of both the balance of harms and benefits and the costs, do not have a substantial impact. Therefore, less frequent testing intervals would save relatively small amounts of money at the expense of failing to identify small numbers of individuals at risk. For individuals with type 2 diabetes, screening to detect the presence of kidney disease (impaired eGFR) in the absence of albuminuria is also important, but the numbers affected are small.
This work provides an evidence base to underpin current policy for microalbuminuria screening in an area that has, until now, used consensus practice as a guide to action. To some extent the findings are surprising in that, of those identified as being at risk, more individuals are misclassified than actually at risk. However, although the costs and cost-effectiveness of this testing strategy are sensitive to the costs of testing, a more expensive test with similar performance to the current test, used at annual intervals, would still be cost-effective compared to use at 2-yearly intervals.
The sensitivity analyses suggest that, at current levels of annual screening costs, even if groups progressing more slowly towards renal failure were identified, the cost-effectiveness would remain within acceptable parameters.
Strengths and limitations
Our analyses are based on statistical modelling rather than trials directly comparing different monitoring strategies. The inefficiency and potential invalidity of randomised trials for diagnostic strategies have been discussed elsewhere198 and apply equally to screening and monitoring strategies, making modelling studies a frequent necessity. To overcome the issues associated with modelling, we have used external validation (such as and ) and sensitivity analyses (such as Tables 26 and 27). Compared with the original protocol, these sensitivity analyses have been limited by the nature of the available data sets, but we have established robustness of the models over time and to key sources of uncertainty as far as possible (discussed in more detail below).
For our analyses in type 1 diabetes, we benefited from an observational cohort study, the ORPS, with up to 20 years of follow-up; however, this included only children under 16 years at diagnosis of diabetes, and hence to ages no higher than 35 years during follow-up. Our analyses for type 2 diabetes benefited from a middle-aged, adult population, with a variety of durations of diabetes at recruitment, but the CARDS data extended to relatively few years of follow-up, and was collected in a clinical trial setting rather than routine care. In both cases, we addressed these limitations by comparing projections from our models with observations from other large studies with a wide range of ages and durations of type 1 and type 2 diabetes. and show our models performing well against these external data; there is generally greater variation among different studies than there is discrepancy between our models and the observed data. The interpretation of these figures is complicated by the use of different protocols for classifying microalbuminuria in different studies, which is likely to exaggerate the discrepancies between studies and the model predictions. In Chapter 5, further external validation is carried out when these models are extended to use long time horizons, to consider multiple morbidities and mortality risks, and to allow for interactions between different complications of diabetes, as recommended by the ADA Consensus Panel on computer modelling.199 As further information from trials becomes available about the extent to which these assumptions are valid, the results of this analysis will need to be revisited.
We have closely followed the ADA guidelines for diabetes modelling whenever relevant.199 In particular, we have described our model with transparency; we have carried out external validations of the models in Chapters 3 and 4 and based our cost-effectiveness modelling of type 2 diabetes on a model with previous external validation; we have used multiple simulation runs to reduce Monte Carlo uncertainty and to quantify statistical variability; we have taken into consideration the long time horizons, multiple organ systems and multiple therapies relevant to lifetime modelling of diabetes and their impact on quality and length of life; and we have stated the cost perspective of our analyses.
We have not addressed the health economics of eGFR measurement in this report. The low prevalence of impaired eGFR in our type 1 data set limits to the degree to which such analysis is possible, but, more importantly, it also shows that eGFR measurement has only a secondary role to urine albumin measurement in screening for impaired renal function in type 1 diabetes. Although we base this on a relatively young cohort with type 1 diabetes, it appears to be confirmed by follow-up of the DCCT/EDIC cohort, in which declining eGFR is seen only in those with macroalbuminuria.150 In type 2 diabetes, we also find that declining eGFR is a feature of patients who already have microalbuminuria or worse (see Table 16).
We could not extend our modelling to look at testing intervals of < 1 year. Both our data sets collected data at annual intervals, and studies that collect markers of diabetic kidney disease more frequently are likely to have shorter follow-up. Owing to the low signal-to-noise ratio and the difficulty in distinguishing change from underlying variation in the measure of both eGFR and ACR with a short interval between measurements (see Chapters 3 and 4), we deemed that evaluating intervals shorter than 1 year would not be justified. Future work might be focused on identifying individuals at greater risk of progression and draw data from such groups to re-estimate parameters for rates of progression and interindividual variation.
Interpretation
The cost-effectiveness results reported in Chapter 6 can be viewed as quantifying overall impressions from the systematic review and analyses of cohort data. The simulation modelling (see Chapters 3 and 4) found that false-positive diagnoses of microalbuminuria are frequent and false-negative classification of normoalbuminuria comparatively rare. The former would become less frequent, and the latter more frequent, if biennial or triennial screening were adopted in place of annual screening. The cost-effectiveness models of Chapter 5 consider how these two types of error – resulting, respectively, in overtreatment or in missed or delayed opportunities to treat – might translate into financial costs for providers and health costs for patients. In type 1 diabetes, the benefit of ACEi and A2RB treatment is strongly evident only in patients who have microalbuminuria (see Chapter 2). Our cost-effectiveness model for type 1 diabetes reports estimates and surrounding uncertainty for annual screening that are well within accepted thresholds of cost-effectiveness, and comparable to similar procedures.
We have not, because data remain unclear, included any estimates of treatment at even earlier stages of renal impairment, for example with hyperfiltration or with levels of albumin excretion in the upper tertiles of normoalbuminuria. However, in type 2 diabetes, treatment with ACEi and A2RB appears to have renal benefit even in patients who do not yet have microalbuminuria and, therefore, the overtreatment resulting from false-positive diagnoses in annual screening results in benefits as well as costs. Screening for microalbuminuria in type 2 diabetes appears, therefore, highly cost-effective.
Similarly, the findings from the statistical modelling in Chapters 3 and 4 are consistent with the overall findings of the research. For example, the short-term variability of ACR and eGFR measurements is high compared with the average annual change (see Tables 9 and 14). As a result, many diagnoses of microalbuminuria or decreased eGFR are a result of measurement error rather than true change (see Tables 10, 15 and 17). This is consistent with the previous findings, for other chronic conditions, that annual screening leads to high rates of false-positive diagnoses.134,135 Further, because of the extreme variability of ACR measurements (despite log transformation; illustrated by the 95th centiles in and ), even the practice of confirming microalbuminuria across multiple tests does not fully prevent high rates of false-positive diagnoses. Our models predict that the prevalence of microalbuminuria does not increase indefinitely with age until almost the whole population has diabetic kidney disease, but ‘levels off’ with about 50% affected (see and ).
Future research
This research has identified a number of issues into which further research is needed. These include areas in which we have been unable to identify research that addresses our research questions, new issues arising from our findings and issues relating to the translation of our findings into clinical practice.
The simulation models used in this work have been developed to enable further data to be added as they become available. The type 2 model, drawing on data from UKPDS, is already in the process of revision by the UKPDS group to take account of the data from long-term follow-up of the cohort. When the UKPDS model becomes available, it will allow our findings to be reviewed and, if necessary, revised.
The type 1 diabetes simulation model is constructed from a number of sources, and as further data become available from larger type 1 studies it can also be expanded, with new equations incorporated into a revised model. It therefore offers a new tool for a wide range of cost–benefit studies which have previously been carried out with models that draw heavily on data from studies of patients with type 2 diabetes.
The uncertainty around the estimates for cost-effectiveness are largely driven by the rate at which new microalbuminuria occurs. There are a number of potential ways in which these findings might be taken forward. Our estimates of cost-effectiveness may be improved by establishing cohorts of individuals at increased risk of developing microalbuminuria and diabetic kidney disease. For example, with increasing capacity to personalise protocols for individual screening, those identified at increased or low risk for progression could be screened more, or less, frequently. Another approach might be to establish whether proactive treatment of those identified at risk might lead to longer-term benefits. However, recent evidence suggests that rates of progression of diabetic kidney disease are falling.200 In establishing cohorts, estimates of sample size may need to be adjusted to take account of these trends.
An alternative approach to screening for microalbuminuria may be to look for other markers that can be used to identify risk of diabetic kidney disease or to monitor the impact of treatment intended to reduce risk. Among the candidates for new markers is cystatin C, following studies that have shown accurate identification of individuals with diabetes who are undergoing decline in renal function.201 Cystatin C has been suggested as a more proximal marker of renal damage than microalbuminuria.202 Initial studies have suggested that cystatin C may identify deterioration in renal function more accurately than eGFR in patients with type 1 diabetes,203 but cost-effectiveness studies are needed to establish whether the improved detection is of sufficient clinical importance to change treatment and, in particular, whether it adds to management, based on occurrence of microalbuminuria. Other strategies that might facilitate an assessment of risk include the measurement of uric acid, tumour necrosis factor receptors, certain advanced glycation end products and chemokines. However, further research to establish the utility of these measures and potentially further trials would be required to change clinical practice; the horizon for such developments is some way into the future.
Attempts have been made to characterise groups at increased risk of developing microalbuminuria. For example, data from a large cohort study have been used to develop a prediction rule for progression to microalbuminuria in type 1 diabetes.33 However, it is not clear from our data whether more frequent measurement of albuminuria would be helpful. Further research is required to establish whether some of those who have false-positive screening test results are unlikely to develop true microalbuminuria, or whether the increased variability that leads to false-positive test results is a precursor of established microalbuminuria and, therefore, treatment may be justified. This strategy is currently being tested in a randomised trial.18
Another strategy that might be explored for managing cardiovascular risk in association with screening for microalbuminuria is to investigate whether the occurrence of microalbuminuria, or increasing risk of it developing, might justify other treatments in addition to ACEi/A2RB. Chapter 1 lists a number of possibilities. One further possibility is whether aspirin might have an effect. Aspirin was previously recommended for individuals with diabetes, as the condition was viewed as a cardiovascular risk equivalent. More recent research has thrown doubt on this assumption, and a large-scale trial is currently under way to identify benefit from aspirin in diabetes.204 Subgroup analysis of this trial based on the occurrence of microalbuminuria, where data are available, might provide information to inform assumptions around the benefits of screening included in our models. It is possible, for example, that, for many individuals with diabetes, aspirin treatment may not be beneficial, but, among those at high risk and identified with microalbuminuria, there might be a benefit from adding insulin to treatment regimens.
Qualitative work with subsequent surveys, and, if appropriate, trials, may also be helpful in establishing whether patient knowledge of renal albumin status provides an additional motivational factor sufficient to increase adherence to medication, without causing an adverse impact on well-being.
