4 Evidence

The diagnostics advisory committee (section 9) considered evidence on the use of the BCM – Body Composition Monitor, InBody S10 and MultiScan 5000 to guide fluid management in people with chronic kidney disease having dialysis from several sources. Full details of all the evidence are in the committee papers.

Clinical effectiveness

4.1 Six randomised controlled trials (RCTs) met the inclusion criteria for the systematic review, all of which assessed use of the BCM – Body Composition Monitor (Huan-Sheng et al. 2016; Hur et al. 2013; Luo et al. 2011; Onofriescu et al. 2012; Onofriescu et al. 2014; Ponce et al. 2014). Two of these trials (Onofriescu et al. 2012 and Onofriescu et al. 2014) may have reported the same trial or outcomes from an overlapping patient population. The possible effect of this was explored by reporting the meta-analyses with and without Onofriescu et al. (2012). The Cochrane risk of bias tool was used to assess the risk of bias in the included RCTs. One of the RCTs was judged to be at low risk of overall bias (Onofriescu et al. 2012) and 1 was at high risk of bias (Luo et al. 2011). The remaining 4 RCTs did not give enough information to make a judgement on the risk of bias.

4.2 The frequency of BCM – Body Composition Monitor use varied between studies, from twice monthly to every 3 months. All of the RCTs were done outside the UK. Only 1 study included people having peritoneal dialysis (Luo et al. 2011); the remaining studies enrolled people having haemodialysis. Five trials included people aged 18 years or over and the remaining trial did not give the age-related exclusion criteria, but the mean age of participants was 52.4 years (standard deviation 13.1 years; Onofriescu et al. 2012). Other groups excluded from some of these studies were people with limb amputations, pregnant women and people with coronary stents, pacemakers or metallic implants. No RCTs were identified for the InBody S10 or the MultiScan 5000.

4.3 Eight non-randomised cohort studies, reported in 9 papers, were also included in the systematic review (Castellano et al. 2014; Hoppe et al. 2015; Kim et al. 2012; Kim et al. 2015; Oei et al. 2016; O'Lone et al. 2014; Onofriescu et al. 2015; Santhakumaran et al. 2016; Wizemann et al. 2009), all of which assessed the BCM – Body Composition Monitor device. Studies were included if they involved at least 100 participants. Two of these studies may have overlapping patient populations (1 was reported in both O'Lone et al. 2014 and Santhakumaran et al. 2016, and the other was reported in Oei et al. 2016). All participants included in the non-randomised studies had monitoring using the BCM – Body Composition Monitor.

4.4 The frequency of device use varied widely between studies, from just once in the first week of dialysis to 3 assessments per week. Two studies were done in the UK (reported in O'Lone et al. 2014 and Santhakumaran et al. 2016, and Oei et al. 2016) and none of the studies enrolled paediatric patients. Most of the studies included people having haemodialysis (6 studies) rather than peritoneal dialysis (2 studies). The risk of bias in the non-randomised studies was assessed using the Review Body for Interventional Procedures tool. None of the studies included blinded participants or study personnel, and the characteristics of participants who withdrew from the studies were not reported.

Evidence on clinical outcomes

Mortality

4.5 Three RCTs reported data on mortality (Onofriescu et al. 2014; Ponce et al. 2014; Huan-Sheng et al. 2016). Use of the BCM – Body Composition Monitor device had no significant effect on mortality rates (pooled hazard ratio 0.69; 95% confidence interval [CI] 0.23 to 2.08; p=0.51) and there was moderate statistical heterogeneity between trials.

4.6 Three non-randomised studies had results for the effects of hydration status on mortality in subgroups of participants monitored with the BCM – Body Composition Monitor device. Kim et al. (2015) reported a higher incidence of mortality in overhydrated participants (defined by relative hydration state; odds ratio 2.57; 95% CI 1.08 to 6.13; p=0.033). In O'Lone et al. (2014), absolute overhydration had a significant effect on the risk of mortality (hazard ratio 1.10; 95% CI 1.01 to 1.20; p=0.025) and Wizemann et al. (2009) reported that hydration state was an important predictor of mortality in patients having haemodialysis (adjusted hazard ratio 2.10; 90% CI 1.39 to 3.18; p=0.003).

Patient-reported adverse effects associated with dialysis

4.7 Huan-Sheng et al. (2016) reported significant differences in intradialytic complications between people monitored with and without the BCM – Body Composition Monitor device. But incidences were not consistently higher in 1 group. For people monitored using BCM – Body Composition Monitor, significantly higher incidences of cramping, chest tightness and headaches were reported. However, significantly lower incidences of complications caused by hypotension during dialysis sessions and skin itching were reported. The difference in the number of patient-reported events of intradialytic fatigue in participants monitored with and without the BCM – Body Composition Monitor was not statistically significant (p=0.7).

4.8 Hur et al. (2013) reported no significant difference in the frequency of intradialytic events between groups monitored with and without the BCM – Body Composition Monitor device at 12 months (66.6 and 63.9 events per 1,000 dialysis sessions respectively; p=0.4). Similarly, Onofriescu et al. (2014) found no significant difference in the incidence of hypotension or cramps (p=0.6). Ponce et al. (2014) reported no significant difference in the incidence of hypotensive events (defined as a drop in systolic blood pressure during dialysis by at least 30 mm of mercury [Hg] or to below 90 mm Hg) at 12 months.

Incidence of cardiovascular events

4.9 One RCT (Huan-Sheng et al. 2016) reported the incidence of cardiovascular-related events, although this was in combination with the incidence of acute fluid overload events. The incidence rate in people monitored with the BCM – Body Composition Monitor device was significantly lower than the control group (incidence rate ratio 0.50 per patient-year; 95% CI 0.26 to 0.94; p=0.03).

4.10 Three non-randomised studies gave the incidence of cardiovascular events among subgroups of people monitored using the BCM – Body Composition Monitor device. Kim et al. (2015) reported no statistically significant difference in the number of cardiovascular events per year between overhydrated and non-overhydrated subgroups as determined by the level of relative overhydration (p=0.13). Onofriescu et al. (2015) also found no statistically significant difference in the incidence of coronary heart disease, peripheral vascular disease, heart failure or stroke between subgroups with lower relative fluid overload (less than 17.4%) and higher relative fluid overload (over 17.4%). Hoppe et al. (2015) reported a non-significant difference in the incidence of acute myocardial infarction and stroke between people who had been having dialysis for a shorter length of time (short dialysis vintage) and people who had been having dialysis for a longer length of time (long dialysis vintage).

Residual renal function

4.11 No RCTs gave data on residual renal function, although 2 reported urinary output which could be used as a surrogate measure. Hur et al. (2013) found a significant increase in the proportion of patients with anuria, that is when the kidneys no longer produce urine, and a significant decrease in urine output in patients without anuria in a group monitored using the BCM – Body Composition Monitor device. In the corresponding control group, there was no change in the proportion of patients with anuria and the decrease in urine output seen in patients without anuria was not significant. Luo et al. (2011) reported non-significant decreases in urine volume in groups monitored with and without the BCM – Body Composition Monitor device.

Evidence on intermediate outcomes

Blood pressure

4.12 All 6 included RCTs reported systolic blood pressure measurements. Use of the BCM – Body Composition Monitor device was associated with a significantly lower systolic blood pressure in a meta-analysis (pooled mean difference −3.48 mm Hg; 95% CI −5.96 to −1.00; p=0.006). When data from Onofriescu et al. (2012) was removed from the meta-analysis, the effect size of BCM – Body Composition Monitor-guided monitoring was reduced and was no longer significant (pooled mean difference −2.46 mm Hg; 95% CI −5.07 to 0.15; p=0.06).

4.13 The external assessment group (EAG) also did a subgroup analysis of systolic blood pressure according to the type of dialysis: peritoneal dialysis (1 RCT) or haemodialysis (5 RCTs). In the haemodialysis subgroup, use of the BCM – Body Composition Monitor device was associated with a significant decrease in systolic blood pressure (pooled mean difference −3.09 mm Hg; 95% CI −5.88 to −0.31; p=0.03). For patients having peritoneal dialysis, Luo et al. (2011) reported a mean decrease in systolic blood pressure of −6.08 mm Hg (95% CI −12.57 to 0.41) associated with use of the BCM – Body Composition Monitor device.

4.14 Four non-randomised studies gave data on blood pressure among subgroups of people monitored using the BCM – Body Composition Monitor device. No statistically significant differences in blood pressure were seen in the following subgroup comparisons: patients in whom the average overhydration decreased within 6 months compared with those in whom it did not decrease (Castellano et al. 2014), patients who had been having dialysis for a short length of time compared with those who had been having it for longer (Hoppe et al. 2015), and patients with a high relative fluid overload (more than 17.4%) compared with those in whom it was low (less than 17.4%; Onofriescu et al. 2015). Kim et al. (2012) reported that systolic blood pressure was higher in hyperhydrated patients when compared with dehydrated patients (significance not stated).

Arterial stiffness

4.15 Three RCTs gave data on carotid-femoral pulse wave velocity as a surrogate for arterial stiffness (Hur et al. 2013; Onofriescu et al. 2012; Onofriescu et al. 2014) and were included in a meta-analysis. Arterial stiffness is thought to be associated with an increased risk of cardiovascular events in the longer term. Pulse wave velocity was significantly reduced in patients who were monitored using the BCM – Body Composition Monitor device and standard clinical assessment compared with standard clinical assessment alone (mean difference −1.53 meters per second [m/s]; 95% CI −3.00 to −0.07; p=0.04). There was high statistical heterogeneity between the studies. If data from Onofriescu et al. (2012) were removed from the meta-analysis, the pooled effect was no longer significant (mean difference −1.18 m/s; 95% CI −3.14 to 0.78; p=0.24).

Absolute overhydration

4.16 Five RCTs (Huan-Sheng et al. 2016; Hur et al. 2013; Luo et al. 2011; Onofriescu et al. 2012; Ponce et al. 2014) assessed absolute overhydration; that is, the volume of fluid by which the participants were above their target volume (as assessed by the BCM – Body Composition Monitor device). No data on underhydration were available. A meta-analysis of the mean difference in absolute overhydration volumes showed that absolute overhydration was significantly lower in groups monitored with the BCM – Body Composition Monitor device (mean difference = −0.39 litres, 95% CI −0.62 to −0.15, p=0.001).

4.17 The EAG did a subgroup analysis for absolute overhydration, as assessed by the BCM – Body Composition Monitor device, according to type of dialysis. They compared the pooled effect of using the BCM – Body Composition Monitor device on absolute overhydration in the overall group (all 5 studies) and a subgroup of studies on people having haemodialysis (4 of these studies). A difference in effect between the overall and haemodialysis subgroup was seen, but the EAG stated that this was not large enough to suggest a significant dialysis effect.

Relative overhydration

4.18 Four RCTs had results for relative overhydration (Huan-Sheng et al. 2016; Onofriescu et al. 2012; Onofriescu et al. 2014; Ponce et al. 2014); that is, a person's absolute overhydration volume normalised against their total extracellular body water volume (both volumes assessed by the BCM – Body Composition Monitor device). A meta-analysis of the reported mean differences in the relative overhydration between groups monitored with and without the BCM – Body Composition Monitor device showed that relative overhydration was significantly lower when the BCM – Body Composition Monitor device was used (mean difference = −1.54; 95%CI −3.01 to −0.07; p=0.04).

Hospitalisation

4.19 Three RCTs gave data on hospitalisations. Huan-Sheng et al. (2016) reported that the difference in all-cause hospitalisation in patient groups monitored with and without the BCM – Body Composition Monitor device was not significant (hazard ratio 1.19; 95% CI 0.79 to 1.80). Hur et al. (2013) found that the difference in rates of hospitalisation caused by new cardiovascular events in the control and BCM – Body Composition Monitor monitored groups was not statistically significant. In Ponce et al. (2014), 39.6% of participants in the BCM – Body Composition Monitor monitored group and 31.8% of the standard clinical assessment group were hospitalised at least once.

4.20 Two non-randomised studies gave data on hospitalisation. Kim et al. (2015) found no significant differences in the number of hospital days per event between overhydrated and non-overhydrated groups (as determined by the BCM – Body Composition Monitor device). Onofriescu et al. (2015) found a significantly higher all-cause hospitalisation rate for patients classified as overhydrated when a relative overhydration value of 17.4% was used as a cut-off to define people as overhydrated, but not when a value of 15% was used.

Left ventricular hypertrophy and left ventricular mass index

4.21 Measures of left ventricular hypertrophy, and surrogates of this, such as left ventricular mass index, may be associated with longer-term cardiac morbidity. Hur et al. (2013) reported that left ventricular hypertrophy was present at 12 months in 44% of participants monitored using the BCM – Body Composition Monitor device and in 50% of participants monitored using standard clinical assessment alone. This was a non-significant reduction from baseline in both groups (67% and 53% respectively). But there was a statistically significant reduction in left ventricular mass index from baseline in the group monitored using the BCM – Body Composition Monitor device (p<0.001), although not in the group monitored using standard clinical assessment (p=0.9).

Use of antihypertensive medication

4.22 Two non-randomised studies gave data on the use of antihypertensive medication in subgroups of people monitored using the BCM – Body Composition Monitor device. In Castellano et al. (2014), consumption of antihypertensive medication was significantly higher in a subgroup of patients who did not have reduced relative overhydration after 6 months of monitoring. Kim et al. (2012) found no significant difference in medication use between people who were dehydrated or hyperhydrated.

People under 18 years

4.23 Three non-randomised studies that enrolled people under 18 years were identified by the EAG (all of which assessed the BCM – Body Composition Monitor). One of these studies (reported in Zaloszyc et al. [2013] and Zaloszyc et al. [2016]) investigated the association between relative hydration status (measured using the BCM – Body Composition Monitor device) and blood pressure in children having dialysis. The study authors concluded that hypertension was not always related to overhydration; and that using bioimpedance spectroscopy could prevent incorrect reduction of a child's target weight to try and reduce hypertension, when it is not caused by excess fluid.

Ongoing trials

4.24 Four ongoing trials that will report outcomes which may be relevant to this assessment were identified. One of these trials, the BioImpedance Spectroscopy to maintain Renal Output (the BISTRO trial), will be UK based. This multi-centre study, funded by the National Institute for Health Research, has a primary outcome of time to anuria (loss of urine output). The study will involve random allocation of participants (adults starting haemodialysis because of chronic kidney disease stage 5) for either regular assessment with a bioimpedance device plus standard treatment or standard treatment alone. Secondary outcomes will include the rate of kidney function reduction, vascular access failure, cardiovascular events, hospital admissions, death and patient-reported outcomes, such as quality of life, dialysis symptoms and functional status (measured at baseline, then every 3 months for up to 24 months). The trial is scheduled to start recruiting in March 2017, with a planned publication date of February 2020.

Cost effectiveness

Review of economic evidence

4.25 The EAG did a systematic review to identify existing studies on the cost effectiveness of using multiple frequency bioimpedance devices to monitor the fluid status of people with chronic kidney disease who are on dialysis. No studies reporting full economic evaluations relevant to the scope of this assessment were identified.

Modelling approach

4.26 The EAG developed a de novo economic model to assess the cost effectiveness of using multiple frequency bioimpedance testing to help guide fluid management decisions in people having dialysis for chronic kidney disease. The model took the perspective of NHS and personal social services.

Model structure

4.27 A Markov model was developed to simulate the effects of monitoring the fluid status of cohorts of people on dialysis, using a multiple frequency bioimpedance device with standard assessment or by standard assessment alone. The model was run as a cohort simulation over a 30‑year time horizon for a 66 year old mixed dialysis population in the base-case analyses. All future costs and benefits included in modelling were discounted at a rate of 3.5% per annum.

4.28 In the model, people started in a stable state on either haemodialysis or peritoneal dialysis and over time could either stay in that state or move to others when events (such as a kidney transplant, cardiovascular event or death) happened. These events could occur in each cycle of the model, which was set as 3 months. The characteristics of the cohort of patients modelled (for example, their age, the proportion of people on haemodialysis or peritoneal dialysis, gender, and incidence of comorbidities) were based on the UK Renal Registry Report (2015). Mortality rates and hospitalisation rates were informed by a combination of European (ERA-EDTA Registry Annual Report 2013) and UK Renal Registry data, and other published sources.

4.29 The model also had an option to allow people in the 'stable' and 'post-CV event' dialysis states to be further classified as either severely overhydrated or normohydrated (based on their relative overhydration). This allowed scenarios to be run in the model in which mortality and hospitalisation rates were increased for dialysis patients who were overhydrated. No 'underhydrated' state was included because of a lack of evidence on the prevalence of underhydration in UK dialysis cohorts, the effect of underhydration on the risk of adverse events and quality of life, and the effectiveness of the BCM – Body Composition Monitor device in reducing the incidence of underhydration.

Model inputs

4.30 Parameter values used in the model were taken from focused reviews of the literature to identify baseline risks for mortality and hospitalisation, and also sources for cost and utility data, and the clinical-effectiveness review. Several possible outcomes that may be affected by using the BCM – Body Composition Monitor device were not included in base-case modelling because of a lack of evidence. These included changes in quality of life (independent of effects of hospitalisation and cardiovascular events), maintenance of residual renal function and effects on dialysis requirements (number and duration of sessions).

4.31 The clinical-effectiveness review only found data on using the BCM – Body Composition Monitor, therefore only this device has been assessed in base-case analyses. Several scenarios were used to model the effect of BCM – Body Composition Monitor-guided fluid management on baseline model parameters. Direct evidence was only available for the effect of BCM – Body Composition Monitor-guided monitoring on all-cause mortality. Several identified trials also reported the effects of BCM – Body Composition Monitor-guided monitoring on surrogate endpoints, such as pulse wave velocity as a measure of arterial stiffness. The EAG did a further literature search to identify evidence that could be used to link changes in these surrogate endpoints to final health outcomes. Using this linked approach, estimated effects of BCM – Body Composition Monitor-guided monitoring on mortality and non-fatal cardiovascular events were calculated. The EAG also modelled an effect of assuming that BCM – Body Composition Monitor-guided monitoring reduced the proportion of people who were seriously overhydrated (with relative overhydration over 15%). This was applied by classifying people in dialysis states in the model as either severely overhydrated or normohydrated, which allowed mortality and hospitalisation rates to be adjusted upwards for proportions of people in the dialysis cohorts who were estimated to be severely overhydrated. Table 1 gives a summary of the relative effects applied to different parameters in the base-case scenario analyses.

Table 1 Summary of effect estimates for BCM – Body Composition Monitor-guided monitoring used in base-case scenario analyses

Scenario

Relative effect on all-cause mortality (HR; 95% CI)

Relative effect on hospitalisation for non-fatal CV (HR; 95% CI)

Effect on blood pressure medication costs (£ mean reduction)

Proportional reduction in severe overhydration

Scenario 1

0.69

(0.23 to 2.08)

1.00

0.00

Scenario 2

0.69

(0.23 to 2.08)

0.91

(0.82 to 1.01)

0.00

Scenario 3

0.91

(0.82 to 1.01)

0.91

(0.82 to 1.01)

0.00

Scenario 4

0.91

(0.82 to 1.01)

0.91

(0.82 to 1.01)

−12.98

Scenario 5

0.28

Scenario 6

0.38

Abbreviations: CI, confidence interval; CV, cardiovascular; HR, hazard ratio.

Costs

4.32 The model incorporates health service costs associated with maintenance dialysis, blood pressure medication, erythropoietin stimulating agents, all-cause inpatient hospitalisation, renal transplantation (including work-up, surgery and follow-up), post-transplantation immunosuppression and outpatient visits. Dialysis costs, per session (haemodialysis) or per day (peritoneal dialysis), were taken from NHS reference costs (2014 to 2015). For haemodialysis, the average cost of £154 per haemodialysis session was calculated based on the cost per type of session, at home or at a unit, weighted by relative incidence. For peritoneal dialysis, the average cost per day of £69 was taken from the NHS reference costs.

4.33 Costs of bioimpedance monitoring included in modelling were purchase costs for devices (annuitised over 5 years), maintenance costs, staff costs related to using the device, training costs and device consumable costs (such as electrodes). The costs of the bioimpedance devices are shown in table 2.

Table 2 Costs of the multiple frequency bioimpedance devices

Bioimpedance device

Cost

Expected service life

Maintenance cost

Estimated annual cost of device consumables

Estimated annual cost per patient per year e

BCM – Body Composition Monitor

£5,750

5 years

£250

£13.26 c

£96.50 f

InBody S10

£8,100

5 years

a

£2.08 d

£93.03

MultiScan 5000

£7,600

5 years

£70b

£4.40

£91.22

a No maintenance costs provided.

b Assumes a replacement set of leads annually.

c Assumes use of patient cards

d Assumes use of reusable electrodes and cost of results sheets.

e Assumes testing every 3 months

f Including maintenance contract without parts and labour.

Health-related quality of life and quality-adjusted life year decrements

4.34 Health state utility values for people on dialysis and post-renal transplant were identified through a focused search of the literature. Two systematic reviews were found that published EQ‑5D data for UK patients (Liem et al. 2008; Wyld et al. 2012). Further searches did not identify any other studies reporting EQ‑5D data for UK patients after 2010 (the end date for searches in the most recent systematic review). Short and longer-term utility multipliers associated with cardiovascular events were calculated based on data from the Health Survey for England (2003 and 2006). Decreases in health state utilities resulting from hospitalisations were taken from the NICE guideline on peritoneal dialysis.

Base-case results

4.35 The following main assumptions were applied in the base-case analysis:

  • Hydration status was assessed with a bioimpedance device every 3 months and, if needed, people had their target weight modified in line with the results.

  • Any effect of BCM – Body Composition Monitor-guided monitoring on the length and frequency of dialysis sessions was assumed to be cost neutral.

  • In the starting cohort of modelled patients, 87% were having haemodialysis and 13% were having peritoneal dialysis.

  • The starting age of the cohort was 66 years.

  • Survival on haemodialysis and peritoneal dialysis was assumed to be equivalent, and patients did not switch between dialysis modes.

  • Fixed proportions of the cohort were on a waiting list for transplant, and waited a median of about 3 years, depending on survival. No transplants were done in patients over 75 years.

  • It was assumed that 17.6% of all inpatient hospitalisations were because of cardiovascular events.

  • Health state utility decrements were applied in the acute period for all hospitalisation events, and ongoing health state utility decrements were also applied after hospitalisation for a cardiovascular event.

  • Effects of bioimpedance monitoring on all-cause mortality were applied for 10 years in the model.

  • Effects of bioimpedance monitoring on cardiovascular-related or all-cause hospitalisation were applied over the lifetime of the cohort.

4.36 Six base-case scenarios were modelled, each differing in the assumed effects of BCM – Body Composition Monitor-guided monitoring, as described above in table 1. Incremental cost-effectiveness ratios (ICERs) were calculated both with and without dialysis costs (table 3), because including BCM – Body Composition Monitor-guided monitoring in the model prolonged life expectancy, so dialysis was needed over a longer period which increased dialysis costs.

Table 3 Deterministic cost-effectiveness scenarios for BCM – Body Composition Monitor-guided fluid management compared with standard practice (with and without dialysis costs)

Intervention

Including dialysis costs

Without dialysis costs

ICER (cost per QALY gained)

Net monetary benefit*

ICER (cost per QALY gained)

Net monetary benefit*

Scenario 1

Standard assessment

−£104,097

£7,793

BCM

£62,532

−£128,366

£16,378

£9,859

Scenario 2

Standard assessment

−£104,097

£7,793

BCM

£60,855

−£127,786

£15,435

£10,440

Scenario 3

Standard assessment

−£104,097

£7,793

BCM

£59,144

−£109,983

£15,636

£8,449

Scenario 4

Standard assessment

−£104,097

£7,793

BCM

£58,721

−£109,919

£15,212

£8,513

Scenario 5

Standard assessment

−£106,708

£8,285

BCM

£66,013

−£109,858

£21,206

£8,203

Scenario 6

Standard assessment

−£106,708

£8,285

BCM

£64,157

−£110,810

£19,350

£8,346

Abbreviations: BCM, BCM – Body Composition Monitor; ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life year.

* Calculated at a willingness to pay threshold of £20,000 per QALY gained.

4.37 Cumulative costs per patient monitored with and without the BCM – Body Composition Monitor device in scenario 3 were calculated. Costs were higher for BCM – Body Composition Monitor-guided monitoring because people on average lived for longer, with dialysis costs making up most (74%) of the increase in cost.

Analysis of alternative scenarios

4.38 Several further scenario analyses, based on varying parameters in the base-case scenario 3 model, were done. Results were generally reported without considering the costs of dialysis (unless otherwise stated) and in relation to the ICER produced in base-case scenario 3 when dialysis costs were excluded (£15,636 per quality-adjusted life year [QALY] gained). The results were as follows:

  • Increasing the frequency of BCM – Body Composition Monitor monitoring to every month (from every 3 months) increased the ICER to £19,818 per QALY gained.

  • Applying the estimated costs associated with monitoring in paediatric centres (which have a lower throughput of patients and so higher estimated costs of bioimpedance monitoring) to the modelled adult population increased the ICER to £20,329 per QALY gained (assuming testing every 3 months). This was increased to £23,647 per QALY gained if testing was assumed to be done every month.

  • Assuming that BCM – Body Composition Monitor-guided fluid management resulted in a 2% improvement in health state utility over a patient's lifetime reduced the ICER to £11,758 per QALY gained (£44,477 if dialysis costs were included). If this improvement was increased to 5%, the ICER reduced further to £8,570 per QALY gained (£32,418 if dialysis costs were included).

  • If BCM – Body Composition Monitor-guided monitoring was assumed to result in a 10% reduction in lifetime dialysis costs, BCM– Body Composition Monitor-guided care dominated standard care (that is, costs less but produces more QALYs). If a 5% reduction in lifetime dialysis costs was assumed, the ICER for BCM – Body Composition Monitor-guided care (including dialysis costs) was £19,759 per QALY gained (compared with £59,144 per QALY gained in the base-case analysis when including dialysis costs).

  • If BCM – Body Composition Monitor-guided monitoring was assumed to have no effect on mortality (that is, the effects were only because of changes in the incidence of non-fatal cardiovascular events), the ICER including the cost of dialysis was £21,327 per QALY gained (compared with £59,144 per QALY gained in base-case analysis).

  • If BCM – Body Composition Monitor-guided monitoring was assumed to have no effect after 3 years, the ICER for BCM – Body Composition Monitor-guided monitoring increased to £18,324 per QALY gained.

4.39 Further scenario analyses produced little change in the base-case scenario ICERs, with ICER values (not including dialysis costs) of between £9,000 and £19,000 per QALY gained.

InBody S10 and MultiScan 5000

4.40 No clinical-effectiveness data were found for the InBody S10 or MultiScan 5000. These devices were therefore not included in base-case cost-effectiveness modelling. But they were included in scenario analyses which assumed that these devices reduced mortality and non-fatal cardiovascular events to the same extent as the BCM – Body Composition Monitor device in scenario 3 (but with different costs). The ICERs produced for these devices were very similar, being between £15,000 and £16,000 per QALY gained.

Subgroup analyses

4.41 Subgroup analysis were also done with the dialysis population grouped by comorbidity status (none or at least 1), dialysis modality (haemodialysis or peritoneal dialysis), starting age of the cohort, whether a person was on a transplant list or not, and whether or not they were chronically overhydrated. No large differences in cost effectiveness by subgroup were identified. ICERs for all subgroups stayed below £16,500 per QALY gained (when dialysis costs were not included), except for people listed for a transplant who had an ICER of £20,297 per QALY gained.

Sensitivity analyses

Deterministic sensitivity analyses

4.42 One-way sensitivity analyses were done on model parameters for base-case scenario 3 (both with and without dialysis costs). When dialysis costs were included, adjusting the hazard ratio for all-cause mortality to 1.00 resulted in the most favourable ICER for BCM – Body Composition Monitor-guided monitoring. This was because these people have the same survival as those having standard monitoring, and therefore do not have higher dialysis costs, but do have the benefit of a reduced cardiovascular hospitalisation rate. When dialysis costs are included, ICERs produced by varying model parameters within their specified ranges generally stayed above £30,000 per QALY gained.

4.43 When dialysis costs were not included, the ICERs stayed sensitive to varying all-cause mortality. But, in this case, the least favourable ICER occurs when the hazard ratio is equal to 1.00.

Probabilistic sensitivity analyses

4.44 The EAG did probabilistic sensitivity analyses for base-case scenarios 1, 3 and 4 (both with and without dialysis costs included). Results are shown in table 4. The probabilistic ICERs produced for all 3 base-case scenarios were similar to the deterministic ICERs (shown in table 3 above). If dialysis costs were included, the probability of BCM – Body Composition Monitor-guided monitoring being cost effective at a maximum acceptable ICER of £20,000 per QALY gained was 26% in scenario 1 and less than 6% in scenarios 3 and 4. If dialysis costs were excluded, BCM – Body Composition Monitor-guided monitoring was 67% to 75% likely to be cost effective at this maximum acceptable ICER in the 3 scenarios. The EAG warned that the uncertainty in the parameters produced by linking the effects of monitoring with the BCM – Body Composition Monitor device on arterial stiffness to mortality and non-fatal cardiovascular events (as in base-case scenarios 3 and 4) may not be fully captured in the probabilistic modelling.

Table 4 Probabilistic cost-effectiveness scenarios for BCM – Body Composition Monitor-guided fluid management compared with standard assessment (both with and without dialysis costs included)

Intervention

With dialysis costs

Without dialysis costs

ICER (cost per QALY gained)

Probability of cost effectiveness at £20,000 per QALY gained

ICER (cost per QALY gained)

Probability of cost effectiveness at £20,000 per QALY gained

Scenario 1

Standard assessment

0.737

0.328

BCM

£63,983

0.263

£16,269

0.672

Scenario 3

Standard assessment

0.941

0.306

BCM

£58,396

0.059

£15,579

0.694

Scenario 4

Standard assessment

0.952

0.255

BCM

£58,011

0.048

£15,015

0.745

Abbreviations: BCM, BCM – Body Composition Monitor; ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life year.

4.45 As noted in the clinical-effectiveness section, removing the Onofriescu et al. (2012) data from meta-analysis reduced the estimated effect of BCM – Body Composition Monitor-guided monitoring on reducing arterial stiffness. Because the pooled estimate of arterial stiffness was used to estimate the relative treatment effects of the BCM – Body Composition Monitor in modelling (in base-case scenarios 2, 3 and 4), a revised cost-effectiveness analyses was done with BCM – Body Composition Monitor-guided modelling assumed to have a smaller and more uncertain effect on hospitalisation for cardiovascular events and mortality. Similar ICERs were produced for revised base-case scenarios 2, 3 and 4 and also for most of the further revised sensitivity, subgroup and scenario analyses. But there was greater uncertainty about the cost-effectiveness results in the revised probabilistic analyses. When dialysis costs were included, the probability of BCM – Body Composition Monitor being cost effective increased from less than 6% to about 13% for scenarios 3 and 4. When dialysis costs were excluded, the probability of BCM – Body Composition Monitor being cost effective decreased for revised scenarios 3 and 4 (from about 72% to about 62%). This reflected the greater uncertainty in the effect of BCM – Body Composition Monitor-guided monitoring on reducing arterial stiffness, and so the linked effect on all-cause mortality and hospitalisation for cardiovascular events.

  • National Institute for Health and Care Excellence (NICE)