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The diagnostics advisory committee looked at evidence on algorithm-based remote monitoring of heart failure data in people with cardiac implantable electronic devices (CIEDs). Evidence was considered from several sources, including an external assessment report and an overview of that report. Full details are in the project documents for this guidance.
Wider availability of algorithm-based remote monitoring may allow greater access to care for people who are less able to attend in-person appointments (because of costs associated with travel, poor public transport, time taken from work, physical impairments, or anxiety). An alert may be followed by an initial telephone call to determine whether in-person follow up is needed. This could reduce the number of unnecessary in-person clinic visits.
The patient expert explained that CIEDs provide a sense of security to people because they know that their condition is being managed. With conventional remote monitoring, an unscheduled review would only be triggered if the person reports worsening symptoms. Algorithm-based remote monitoring provides reassurance to people because they know that alerts are transmitted automatically and reviewed by a healthcare professional, potentially before they experience symptoms. This could prevent people being admitted to hospital and improve their quality of life. But, the committee acknowledged that for some people, false-positive alerts (when an alert is triggered but there are no signs of decompensation) may cause unnecessary anxiety. The committee recommended more research on people's experiences with having heart failure algorithms activated on their CIEDs.
The committee noted that the heart failure algorithms needed to be considered independently of each other because they are each unique, leading to different alert rates and different levels of accuracy. Each of the heart failure algorithms collects different data types to monitor decompensation or predict a person's risk status. The committee noted that CorVue collects only intrathoracic impedance data, while the other algorithms monitor additional factors (see sections 2.5 to 2.11). The committee noted that the prognostic accuracy of CorVue may be affected by the collection of only 1 data type.
For the heart failure event endpoint defined by the Framingham Heart Study, CorVue had sensitivity of 68%. For endpoints related to hospitalisation, clinic visits and changes to treatment, sensitivity ranged from 20% to 61.9%. This suggests that people who are experiencing decompensation may not have an alert triggered using CorVue. Clinical experts noted that heart failure algorithms should have a high sensitivity. This is to ensure that people with early signs of a heart failure event can be identified, assessed and treated if necessary, and so people are not missed. False positives were also considered to be high in all studies reporting this outcome. All studies were also assessed by the external assessment group (EAG) as being at a high risk of bias, with a key concern being the analysis methods. The committee concluded that CorVue cannot accurately predict heart failure events.
Evidence from a single published study suggested that, at the nominal threshold of 4.5, HeartInsight had 65.5% sensitivity and 86.7% specificity for the endpoint of first post-implant heart failure hospitalisation. For the endpoint of heart failure hospitalisation, outpatient intravenous intervention or death, HeartInsight had 54.8% sensitivity and 86.5% specificity. The positive predictive value was reported as 7.7%, indicating that there is a high probability that an alert is a false positive. This study was also assessed by the EAG as being at a high risk of bias, with a key concern being the analysis methods. The committee concluded that it is uncertain whether HeartInsight can accurately predict heart failure events.
Using the study endpoint of worsening heart failure, HeartLogic showed sensitivity ranging from 70% to 100%, and specificity ranging from 61% to 93%. False positives and unexplained alert rates were generally low in 6 studies. Statistically significant associations were observed between being in alert and hospitalisations, length of hospital stay, rate of heart failure events and rate of emergency care visits. All studies reporting prognostic accuracy data for HeartLogic were assessed by the EAG as being at a high risk of bias with a key concern being the analysis methods. The committee also considered data from a study that was published after the EAG's review was complete. Singh et al. (2024) evaluated HeartLogic in 1,458 people. For the endpoint of detecting heart failure events, HeartLogic demonstrated sensitivity of 74.5% and false-positive rate of 1.48 alerts per patient-year. The committee concluded that while there are some concerns regarding the quality of the prognostic accuracy data, it is likely that HeartLogic can predict heart failure events.
Using the study endpoint of worsening heart failure in people with a 'high-risk' status, TriageHF demonstrated sensitivity ranging from 87.9% to 98.6% and specificity ranging from 59.4% to 63.4%. The false-positive rate was reported in 1 study and was considered to be low. Most studies reporting prognostic accuracy data were assessed by the EAG as being at a high or unclear risk of bias. A key concern was the analysis methods, and some of the studies were only available as abstracts that contained limited information. The committee concluded that while there are some concerns regarding the quality of the prognostic accuracy data, it is likely that TriageHF can predict heart failure events.
The committee noted that heart failure algorithms are intended to be used to support review of heart failure data by healthcare professionals, and should not be used in isolation to make treatment decisions. This is because events other than heart failure decompensation can sometimes trigger an alert. For example, viral respiratory illnesses can increase a person's intrathoracic impedance, which could cause an alert to be triggered even if the person has no decompensation. In a study this would be classed as a false-positive alert. The committee noted that these alerts and the follow up with a person would still be valuable because a non-heart failure clinical event may still require intervention.
Some studies reported high rates of false-positive alerts. But the committee noted that all alerts would be reviewed alongside other clinical information and discussed with the person before a treatment decision is made. So the committee considered that unnecessary treatment arising from false-positive alerts is unlikely and so harms from over treatment when using heart failure algorithms are expected to be low.
The committee discussed the impact that the number of false positives could have on service burden. Specialist committee members indicated that this burden is low in their experience, because initial interaction following an alert is usually via phone call rather than in-person. The committee concluded that more research should be done on the rate of false-positive alerts.
Shapiro et al. (2017) showed a statistically significant reduction in hospitalisations for people using the CorVue algorithm. But, this study was assessed by the EAG as being at a substantial risk of confounding because the comparator was people with no implanted device having home care. The reduction in hospitalisations reported in the study could therefore be due to the CIED rather than the CorVue algorithm. The committee concluded that it is uncertain whether use of CorVue can reduce hospitalisations.
No evidence was identified that compared the HeartInsight algorithm with no algorithm use.
Evidence suggests that using the HeartLogic algorithm instead of conventional remote monitoring, provides statistically significant reductions in:
hospitalisations
rate of heart failure events
length of hospital stay and
emergency or urgent care visits
The 2 key comparative studies that were used in cost-effectiveness modelling both had small sample sizes; Treskes et al. (2021) included 68 people and Feijen et al. (2023) included 161 people. This raised concerns regarding the statistical power of these studies to detect the effects of heart failure algorithms. These studies were also assessed by the EAG as being at serious risk of bias. The committee concluded that while there are concerns regarding the quality of the comparative evidence for HeartLogic, it is likely that HeartLogic can reduce heart failure events compared with no algorithm use.
For TriageHF, comparative evidence was limited to 1 study, Ahmed et al. (2024). This is a real-world, UK study of 758 people. This study reported a statistically significant reduction in hospitalisation with TriageHF compared with no algorithm use. Ahmed et al. (2024) was assessed by the EAG as being at a critical risk of bias because of risks of confounding and selection bias. The committee concluded that while there are concerns regarding the quality of the comparative evidence from Ahmed et al., it is likely that TriageHF can reduce heart failure events compared with no algorithm use.
CIEDs may fail to transmit data if there are technical problems or connectivity issues, including internet problems, or if they are out of range of the transmission device. The committee noted that failure rates in the studies appear to be high, but it is difficult to separate transmission failure due to technical issues from transmission failure due to connectivity issues. If data transmission is missed, each algorithm has in-built retry mechanisms that will attempt transmission again. Healthcare professionals will be notified if a person's data transmission is missed for a number of weeks. The companies commented that they pay stringent attention to device failures and always follow up on these. High failure rates reported in Debski et al. (2020) have been addressed by ensuring that devices are correctly programmed and that local protocols are in place. The committee concluded that they have no concerns regarding transmission failure, because systems are in place to manage and resolve this.
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