Heart failure algorithms for remote monitoring in people with cardiac implantable electronic devices

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 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 2.8). The committee noted that the prognostic accuracy of CorVue may be affected by the collection of only 1 data type.

Prognostic accuracy studies for CorVue reported low to adequate sensitivity (range = 20% to 68%). 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 so 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 and all studies were assessed as having a high risk of bias because of small numbers of people included. 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 assessed as having a high risk of bias because of concerns about missing data and confounding. The committee concluded that it is uncertain whether HeartInsight can accurately predict heart failure events.

The studies reporting prognostic accuracy data for HeartLogic show adequate to high sensitivity (range = 66% to 100%) and specificity (range = 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 as having a high risk of bias because of a lack of robust analysis, and small number of people in the studies. The committee concluded that while there is a stronger evidence base for the prognostic accuracy of HeartLogic than some of the other heart failure algorithms, it is uncertain whether HeartLogic can accurately predict heart failure events.

For TriageHF, sensitivity (range = 37.4% to 87.9%) and specificity (range = 44.4% to 90.2%) showed considerable variability. The committee noted that some of this variability was due to differences in the timeframes of the reporting and different outcome measures used to determine the prognostic accuracy. The false-positive rate was reported in 1 study and was considered to be low. All studies reporting prognostic accuracy data have a high risk of bias, for reasons including missing information and a lack of controlling for confounding factors in the statistical analysis. Specifically, age, sex, New York Heart Association Functional classification, smoking status and other comorbidities. The committee concluded that while there is a stronger evidence base for the prognostic accuracy of TriageHF than some of the other heart failure algorithms, it is uncertain whether TriageHF can accurately 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. Some studies reported high rates of false-positive alert. 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. But, false positive alerts may cause people unnecessary anxiety (see section 3.1). The committee concluded that more research should be done on the rate of false positive alerts.

Some studies compared CIEDs that use heart failure algorithms with CIEDs that do not, to assess whether heart failure algorithms reduce heart failure events.

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, as systems are in place to manage and resolve this.