DIAGNOSTICS ASSESSMENT PROGRAMME

A pairwise analysis approach was taken to estimating cost effectiveness. This was because of the lack of data comparing algorithms and because the comparator for each algorithm is a brand-specific CIED that is not using the heart failure algorithm. The EAG explained that the comparator costs differ for each pairwise analysis because different data sources were used to derive model inputs. For HeartLogic and TriageHF, evidence on hospitalisation rates was available, so different rates were used for these 2 interventions and their comparators. Because of the lack of evidence for CorVue and HeartInsight, no difference in hospitalisation was assumed between these heart failure algorithms and their comparators. The hospitalisation rate for these 2 algorithms and their comparators was assumed to be an average of the rates used for HeartLogic and TriageHF.

The EAG used comparative hospitalisation data to model the impact of heart failure algorithms rather than using prognostic accuracy data and a linked evidence approach. False-positive alerts were indirectly captured in the model for HeartLogic and TriageHF because study results on the number of unscheduled visits would be impacted by false-positive alerts. For CorVue and HeartInsight, no published data was available on unscheduled visits, so it was assumed that there was no difference in the number of unscheduled visits between these heart failure algorithms and their comparators. This may underestimate the impact that false-positive alerts have on the cost-effectiveness estimates for CorVue and HeartInsight.

The EAG's economic model showed that HeartLogic and TriageHF were more effective and less costly than standard care. Probabilistic sensitivity analysis showed that the probability of HeartLogic being cost effective was 81% at a threshold of £20,000 per quality-adjusted life year (QALY) gained and 73% at a threshold of £30,000 per QALY gained. The probability of TriageHF being cost effective was 85% at a threshold of £20,000 per QALY gained and 76% at a threshold of £30,000 per QALY gained. The base-case probabilistic sensitivity analysis used probability distributions around mortality and intervention costs. The committee recalled that harms (such as death; see section 3.9) when using heart failure algorithms are expected to be low and intervention costs would not be higher than the list price. An additional analysis with uncertainty around intervention costs and mortality excluded showed that both HeartLogic and TriageHF have a 100% probability of being cost effective at thresholds of £20,000 and £30,000 per QALY gained.

There was no comparative evidence on hospitalisations for CorVue or HeartInsight that was considered suitable for inclusion in the EAG's economic model. A conservative base-case model assumption was made of no difference in hospitalisations between CorVue and HeartInsight and their comparators. In the base-case model, CorVue and HeartInsight were more costly than standard care and were equally as effective. Threshold analysis showed that only a small reduction in hospitalisations would make these heart failure algorithms cost effective.

The EAG's model used published hospitalisation rates for HeartLogic and TriageHF. For HeartLogic, the hospitalisation incidence rate ratio of 0.28 was calculated from Treskes et al. (2021), which indicates a 72% lower rate of hospitalisations in the intervention group. For TriageHF, the hospitalisation incidence rate ratio of 0.42 was taken from Ahmed et al. (2024), which indicates a 58% lower rate of hospitalisations in the intervention group. These inputs resulted in the technologies being dominant (less costly and more effective than standard care) in the model base case. The committee noted that post-hoc or subgroup analyses from these publications show that reductions in hospitalisations were smaller in some subgroups than in the overall study populations. For example, an analysis by Ahmed et al. (2024) that was limited to data collected before the onset of the COVID-19 in the UK, gave a hospitalisation incidence rate ratio of 0.69, indicating a 31% lower rate of hospitalisations in the intervention group. The committee also recalled the concerns regarding the quality of the comparative evidence for HeartLogic (see section 3.13) and TriageHF (see section 3.14) and noted that the risk of confounding may impact the reliability of the results from these studies. But, only small reductions in hospitalisations are needed for HeartLogic and TriageHF to be cost effective in the EAG's model. So the committee concluded that HeartLogic and TriageHF are likely to be cost-effective uses of NHS resources.

There was a lack of evidence for the impact of heart failure algorithms on heart-failure-related mortality rates and health-related quality of life. In the EAG's base-case model, conservative assumptions were made that there is no difference in heart-failure-related mortality rates between heart failure algorithms and their comparators. The committee noted that if there was an improvement in mortality or if health-related quality of life is greater when using heart failure algorithms, then there would be gains in QALYs. These potential benefits could not be captured in the model because of the lack of evidence.

The base case modelled 2 scheduled visits per year, in both the intervention and comparator arm. In clinical practice, people who have stable heart failure would likely only have 1 scheduled visit per year. Other people may also only have 1 visit because of capacity issues. The EAG modelled 2 additional different scenarios for the intervention arms: 0 scheduled follow-up visits per year and 1 scheduled follow-up visit per year. These scenario analyses did not impact the direction of the model results.

In the EAG's base-case model it was assumed that all alerts are reviewed and followed by an unscheduled, in-person follow-up visit. In practice, alerts may be followed by an initial remote interaction (such as a phone call) to determine whether an in-person visit is necessary. Scenarios were modelled in which it was assumed that 25% and 50% of alerts initially require non-face-to-face follow up. The committee agreed that the base-case scenario and scenario analyses were reasonable. Scenarios were also been done in which the base-case number of interactions in the intervention arm was doubled and quadrupled. The committee agreed that this assumption was reasonable. This was because the overall number of interactions is likely to be increased in the heart failure algorithm arm because of healthcare professionals reviewing alerts and following up with people remotely or in-person. Unscheduled emergency visits to an emergency department or primary care settings were not modelled. The committee noted that the number of emergency visits is expected to be lower for people with heart failure algorithms because alerts are intended to be triggered before the person experiences symptoms.

There was very limited evidence in people who have a CIED and do not have a diagnosis of heart failure but are at high risk of new onset acute heart failure. One study for TriageHF reported that a proportion of people in the cohort did not have a prior diagnosis of heart failure. But results were not reported separately for each population and so could not be used by the EAG to model the population of people at risk of heart failure. The committee agreed that data is needed on the prognostic accuracy and clinical impact of using heart failure algorithms in this population.

The committee noted that the evidence available for HeartLogic and TriageHF does suggest that these algorithms can accurately detect the signs of worsening heart failure that could lead to hospitalisation or an unscheduled clinic event. The evidence also suggests that HeartLogic and TriageHF can reduce the number of heart failure events compared with conventional remote monitoring. However, there are some concerns about the quality of the evidence for these algorithms and the size of the effects that could be seen. Therefore, the committee recommended collection of registry data with use of HeartLogic and TriageHF to confirm the extent of the benefit seen in the studies.