KardiaMobile for detecting atrial fibrillation

There were 27 studies relevant to the decision problem in the scope:

• 5 randomised controlled trials (RCTs)

• 7 diagnostic accuracy studies

• 1 case-control study

• 13 single-arm observational studies

• 1 case report.

Of the 27 included studies, 16 studies were peer reviewed, including 4 UK studies (Bray et al. 2021, Dimarco et al. 2018, Reed et al. 2021, Reed et al. 2019), one of which is an RCT (Reed et al. 2019). The included studies covered 6 population groups:

• people with palpitations

• people with a history of atrial fibrillation (AF), who have had treatment (ablation, cardioversion, or medical therapy) to restore sinus rhythm and used KardiaMobile to identify recurrence

• people with diagnosed AF to assess AF burden

• people with transient AF after surgery or hospitalisation whose heart rhythms reverted back to sinus rhythm before discharge and used KardiaMobile to identify recurrence

• people after stroke or transient ischaemic attack who were monitored using KardiaMobile

• mixed population including people with known or suspected AF.
  
  All published evidence is on the single-lead KardiaMobile device. For full details of the clinical evidence, see section 4 of the assessment report in the supporting documentation.

Three RCTs including 1 UK trial (Goldenthal et al. 2019, Koh et al. 2021, Reed et al. 2019) found that significantly more people in the KardiaMobile monitored group had AF detected compared with those who had standard care, which included 24‑hour Holter monitoring. This was supported by the results from an observational study (Yan et al. 2020).

Four peer reviewed studies (Hermans et al. 2021, Lowres et al. 2016, Selder et al. 2019, William et al. 2018) reported on the diagnostic accuracy of AF detection using the KardiaMobile algorithm compared with clinical interpretation of the KardiaMobile ECG as the reference standard. Its sensitivity ranged between 92% and 99% per recorded ECG, with specificity between 92% and 98%. However, the external assessment centre (EAC) highlighted that diagnostic accuracy was reported on a per ECG recording and not a per person basis. Also, these 4 studies had 4 different patient populations with a pre-test probability of AF between 4.8% and 35.6%. The EAC also noted that KardiaMobile is not intended to be used to confirm the presence of AF as a standalone test but to help detect AF. All interpretations should be reviewed by healthcare professionals for clinical decision making. It is expected that false positives and negatives are likely to be captured by the clinical reviews.

Reed et al. (2019) showed that people using KardiaMobile had their symptomatic cardiac arrhythmia detected significantly earlier than those having standard care (9.9 days compared with 48.0 days, p=0.0004). This finding was supported by 1 observational study (Yan et al. 2020) which also reported that KardiaMobile significantly reduced the time to AF detection when compared with standard care. There was no direct published evidence to show that using KardiaMobile improves clinical outcomes (such as reduction in stroke) after a diagnosis of AF.

The evidence from 12 studies and a patient survey reported that KardiaMobile was easier to use compared with other ECG monitors such as Holter monitors. People felt that KardiaMobile would be useful in self-monitoring at home and improving their ability to access the care they needed. Two RCTs (Caceres et al. 2020, Guhl et al. 2020) showed that people who used KardiaMobile had a significant improvement in AF-specific quality-of-life scores compared with people in the control groups. The EAC noted that both trials used additional interventions, and the effect of KardiaMobile alone on quality of life may be difficult to interpret.

Evidence reported that there were a proportion of ECG traces that did not fit the current KardiaMobile algorithm classifications, ranging from 9.6% to 27.6%. These outputs are presented as unclassified. However, the company stated that software updates are improving the classification algorithm, and the number of unclassified outputs is reducing. Also, around 0.6% to 1.9% of KardiaMobile outputs were unreadable. This often happens when an ECG trace has interference and cannot be interpreted by the Kardia app; however, a proportion of these can be interpreted by a clinician.

Three published studies reported the economic impact of KardiaMobile:

• a cost-effectiveness analysis done alongside a UK RCT compared the cost per symptomatic rhythm diagnosis using KardiaMobile in addition to standard care with standard care alone (Reed et al. 2019)

• a UK budget impact analysis (York Health Economics Consortium et al. 2018)

• a US single-arm study estimated the cost saving using data from a patient survey (Praus et al. 2021).
  
  All studies reported that KardiaMobile was cost saving. Two studies reported that the main driver for the saving was a reduction in healthcare appointments.

The company developed a de novo model comparing KardiaMobile with Holter monitoring and the Zio patch. The model included people aged 64 and over with known or suspected AF who were referred for ambulatory ECG monitoring in a secondary care setting. The model assessed the costs associated with diagnosing and managing AF. Overall, the company's base case showed that using KardiaMobile could save between £320 and £380 per person over 5 years because of the cost of the technology, reductions in repeat testing, referrals to secondary care and stroke events.

For full details of the cost evidence, see section 9 of the assessment report in the supporting documentation.

The EAC was unable to validate the company's original model, and highlighted limitations and errors in some of the parameters and assumptions used. The complexity of the model meant that inconsistencies could not be investigated and corrected. There was a lack of robust evidence to support the need for such complex time dependencies in the diagnostic phase of the model, and this approach required several additional assumptions. The EAC considered that the diagnosis phase could have been modelled more simply. Overall, the EAC considered the model to be overly complex, not transparent and not verifiable. It also did not agree with underlying structural assumptions, parameter choice or their implementation in the model.

The company submitted an updated cost model, which was modified and simplified to address the EAC's comments. Furthermore, various scenario analyses and probabilistic sensitivity analyses were done to explore uncertainties of the cost impact. The model was informed by 6 comparative studies including 1 UK study (Reid et al. 2019) and the results showed that using KardiaMobile resulted in cost saving in 2 studies of people with palpitations, and in 3 studies of people who were monitored for recurrent AF but was cost incurring in a study for monitoring AF. The EAC was, however, unable to validate the company model without further clarifications from the company (see Appendix B - EAC commentary), limiting the certainty in the results presented.

The EAC initially did a simple cost calculator to explore the expected costs of using KardiaMobile to detect and treat AF over a 1‑year time horizon. It then went on to develop a new cost model that better captured the clinical pathway of using KardiaMobile for detection and ongoing monitoring of AF, compared with Holter monitoring in the NHS. The additional analyses included people presenting with undiagnosed palpitations and people who need to monitor AF recurrence after treatment. The base case results of the additional analyses showed a saving of £13.22 per person over 2 years when KardiaMobile was used for detecting AF in people presenting with undiagnosed palpitations but an additional £85.91 cost per patient over 10 years when KardiaMobile was used for monitoring AF recurrence detection in a population at low risk of having a stroke (CHA2DS2‑VAScC score of 1).

The committee noted considerable evidence from 6 comparative studies, including 3 randomised controlled trials that showed improved atrial fibrillation (AF) detection using the single-lead KardiaMobile. The clinical experts agreed that monitoring with KardiaMobile could increase infrequent AF event detection because it could record an AF event whenever symptoms are presented. The external assessment centre (EAC) advised that the evidence base was strongest in people with undiagnosed palpitations and people with a history of AF who need to monitor their AF recurrence. The experts noted that, in clinical practice, KardiaMobile has been most commonly used in people presenting with palpitations for detecting infrequent AF events. The committee concluded that the population groups in the evidence base reflected the use of the KardiaMobile device in a wide range of relevant clinical contexts, but it considered the most persuasive clinical cases were symptomatic people with suspected paroxysmal AF.

The committee noted that all the evidence on the clinical effectiveness of KardiaMobile was on the single-lead device. It was advised that the single-lead device is commonly used in clinical practice to detect AF, and the use of the 6‑lead device is limited in the NHS. The clinical experts agreed that the 6‑lead device provides heart rhythm from multiple angles, and it could have incremental benefits in some people to detect other arrhythmias when a good quality electrocardiogram (ECG) trace is available. For AF detection, the committee and experts concluded that the single-lead device is suitable in most patients.

The clinical experts advised that AF is a chronic condition. After AF is diagnosed, people are likely to be on medications such as anticoagulation or rhythm control drugs to reduce the risk of stroke and control symptoms for a long time. The experts noted that KardiaMobile could improve medication management; for instance, some medications can only be used when the normal heart rhythm is restored. However, no direct evidence was available on the clinical benefits of KardiaMobile after diagnosis of AF. For example, there was no evidence for the association between early AF detection and reduction in longer-term outcomes such as stroke events. The committee understood the limitations of the evidence base and concluded that more research would be of value that explores the use of KardiaMobile after AF diagnosis, including the impact of using KardiaMobile on clinical outcomes.

Evidence from published studies and patient experts shows that KardiaMobile is easier to use compared with other ECG monitors such as the Holter monitor. People with experience of using KardiaMobile found the device to be accessible at symptom onset and that it allowed improved access to care when needed. It has been well accepted across people of different age groups if they have a compatible mobile device. The committee concluded that KardiaMobile is a convenient device that people can use at home to monitor their heart rhythms.

Patient selection is important and should be guided by clinical judgement. The clinical experts emphasised that devices need to be offered to people on an individual basis guided by clinical assessment of individual circumstances. Key factors to consider include risk of developing AF, age, comorbidities, and the availability of primary and secondary care resources to interpret ECG traces. Furthermore, other factors such as the compatibility of mobile devices and patient preference also need to be considered. They noted that widespread use of KardiaMobile in the NHS without careful patient selection may place extra demand on local services. The committee concluded that healthcare professionals should assess individuals and indications when considering whether to prescribe KardiaMobile.

The committee noted that one of KardiaMobile's advantages over some other technologies is that it is a portable device that provides real-time ECG traces and heart rhythm classification. Despite this, the EAC confirmed that clinical interpretation of all recorded ECGs is needed, in line with the device instructions for use, to limit the effect of false negative and false positive results. The clinical experts added that expertise in interpreting ECG traces is essential to ensure the accuracy of AF diagnosis. Also, the experts added that a considerable proportion of unreadable and unclassified ECG recordings would be interpretable by experienced healthcare professionals to inform clinical decision making. The committee concluded that ECG data generated by KardiaMobile should be reviewed by an experienced healthcare professional before a diagnosis is made.

People need a smart device compatible with the KardiaMobile app. The company provides a list of compatible smart devices. The clinical experts said that in their experience, most people have access to a smart device, and alternative ambulatory monitors are offered if a person does not have a compatible device. The clinical experts also noted that KardiaMobile may not be suitable for people who cannot stay still or have problems holding the device; for example, people with tremors may have difficulty with recording an accurate trace.

The clinical experts highlighted the issue of unreadable ECG traces. They explained that the way people use the device is likely to affect the quality of ECG recordings. In clinical practice, healthcare professionals often provide support for people to set up the device, allowing them to also advise on effective use. There are also self-help videos that explain how to use the device. The clinical experts noted that a lack of experience using the device may lead to unreadable ECG recordings. The committee concluded that training is important to make sure people use the device correctly and minimise possible interference while taking the recording.

The committee understood that the company's original cost model was complex. The model was simplified during consultation. Data from 6 comparative studies was used to estimate the cost impact using KardiaMobile for 2 separate cohorts: 1) undiagnosed people presenting with palpitations and 2) people with previously diagnosed AF at risk of AF recurrence. The committee noted that the results from the company's cost model showed that KardiaMobile is cost saving for each cohort compared with Holter monitor over a 5‑year time horizon. The committee considered that some assumptions and parameters, such as 100% diagnostic accuracy for KardiaMobile, may be unlikely or may not fully reflect clinical practice. The committee concluded that the company's modified model was relevant to the decision problem, but still featured limitations, and considered the EAC's additional cost modelling a more appropriate basis for its decision making.

The committee felt that the EAC's original approach using a cost calculator did not capture the cost impact of using KardiaMobile. The EAC was therefore asked to develop a new cost model, to evaluate using KardiaMobile to detect AF in people presenting with undiagnosed palpitations and people being monitored for AF recurrence. From these further analyses, the committee accepted that KardiaMobile is slightly cost saving compared with standard care for patients with undiagnosed AF (people with symptomatic palpitations). This saving is driven by a reduction in diagnostic costs because the cost of KardiaMobile is lower than that of Holter monitor. The clinical experts explained that the benefits of KardiaMobile in this population are likely to be realised because KardiaMobile is better at detecting infrequent AF events compared with Holter monitor. The committee concluded that there are likely to be cost benefits using KardiaMobile in symptomatic patients with suspected paroxysmal AF.

For people who need to monitor AF recurrence, including those who have had an ischaemic stroke or a transient ischaemic attack without current evidence of AF, the results of the EAC's analysis show that KardiaMobile is likely to be cost incurring. This was driven by an increase in using anticoagulation for preventing strokes. The committee understood that the model included only a small selection of patients who are at low risk of developing strokes (CHA2DS2‑VAScC score of 1) and that medication was the only intervention included in the model. Expert advice suggested that clinical management of patients being monitored for AF recurrence is complex and varied widely because of patients' comorbidities and their medical and treatment history. In clinical practice, there is no clear care pathway for monitoring AF recurrence. The committee accepted the limitations of the current model and concluded that more information is needed about the care pathway for AF recurrence monitoring and its associated resource use.