4 Evidence

The diagnostics advisory committee (section 8) considered evidence on lead-I electrocardiogram (ECG) devices (imPulse, Kardia Mobile, MyDiagnostick and Zenicor-ECG) for detecting atrial fibrillation using single time point testing in primary care from several sources. Full details of all the evidence are in the committee papers. Evidence on the RhythmPad GP was removed from this guidance after consultation (see section 3.1). To make sure the committee papers are clear, the published diagnostics assessment report and extra relevant documents include the evidence assessed on RhythmPad GP.

Clinical effectiveness

4.1 The external assessment group (EAG) did a systematic review to identify evidence on the diagnostic accuracy and clinical effectiveness of using the lead‑I ECG devices to detect atrial fibrillation. Included studies were those that used the devices at a single time point to detect atrial fibrillation (rather than repeated use over a period of time). Because no studies were identified in the population of interest (people with signs and symptoms of atrial fibrillation and an irregular pulse on manual palpation), the EAG included studies done in a population who were asymptomatic. The EAG included in this definition people who did not present with signs and symptoms of atrial fibrillation (for example, breathlessness or palpitations) with or without a previous diagnosis of atrial fibrillation. It included people with other cardiovascular comorbidities and people who were attending a cardiovascular clinic.

4.2 The EAG divided their review into 2 parts; studies reporting diagnostic accuracy of the devices and studies reporting the clinical effectiveness of the devices.

Diagnostic accuracy

4.3 In the diagnostic test accuracy review, 9 studies were included. There is an overview of the included studies in table 1. All the studies either enrolled people with a known atrial fibrillation status (that is, people known to have atrial fibrillation and people with no history of the condition), or who were recruited from cardiology services. Only Desteghe et al. (2017) provided the reasons people were admitted to a cardiology service, with 3.4% admitted because of symptomatic atrial fibrillation.

4.4 Only 1 study was done in primary care (Vaes et al. 2014), with the rest in secondary or tertiary care. There was 1 study done in the UK (Williams et al. 2015). No published studies assessed the imPulse device.

4.5 In all studies the reference standard was a 12‑lead ECG interpreted by a trained healthcare professional (a cardiologist, electrophysiologist or GP with a special interest in cardiology). The index test (lead‑I ECG) and reference standard (12‑lead ECG) were both done within 6 hours of each other in all but 1 study (Vaes et al.). In this study the interval between tests was not reported.

Table 1 Overview of studies included in the EAG's diagnostic accuracy review

Device

Study

Population in study

Interpreter of device output

Kardia Mobile

Desteghe et al. 2017a

(Belgium)

Inpatients in a cardiology ward (35.6% had a history of atrial fibrillation)

  • Electrophysiologists

  • Algorithm

Results presented separately

Haberman et al. 2015

(USA)

Cardiology clinic patientsb

Electrophysiologist

Koltowski et al. 2017c

(Poland)

People in tertiary care

Cardiologist

Lau et al. 2013

(Australia)

People at a cardiology department (24% had a history of atrial fibrillation)

Algorithm

Williams et al. 2015

(UK)

People attending an atrial fibrillation clinic who were known to have atrial fibrillation and people with unknown atrial fibrillation status (who were attending the clinic for reasons unrelated to atrial fibrillation)

  • Cardiologist

  • GP with special interest in cardiology

Results presented separately

MyDiagnostick

Desteghe et al. 2017a

(Belgium)

Inpatients in a cardiology ward (35.6% had a history of atrial fibrillation)

  • Electrophysiologists

  • Algorithm

Results presented separately

Tieleman et al. 2014

(Netherlands)

People attending an outpatient cardiology clinic or a specialised atrial fibrillation outpatient clinic

Algorithm

Vaes et al. 2014

(Belgium)

People known to have atrial fibrillation (83.4%) and people with no history of the condition invited to take part by GPs

Algorithm

Zenicor-ECG

Doliwa et al. 2009

(Sweden)

People with atrial fibrillation, atrial flutter or sinus rhythm attending a cardiology outpatient clinic

Cardiologist

a Desteghe et al. assessed both Kardia Mobile and MyDiagnostick.

b Results from additional study participants (healthy young adults and elite athletes) were not included in the EAG's analyses.

c Koltowski et al. was only available as a conference proceeding.

Quality assessment of diagnostic accuracy studies

4.6 The QUADAS-2 tool was used to assess study quality. For patient selection, the EAG judged that all 9 studies had an unclear risk of bias and a high level of concern for applicability (because none were done in a population who had symptoms). For 1 study there was limited information available in the publication; Koltowski et al. (2017) was only available as a conference proceeding.

4.7 The included studies varied in how the devices gave a positive result for atrial fibrillation. This was either based on the lead‑I ECG device's diagnostic algorithm or on clinician interpretation of an ECG trace generated by the devices. The EAG judged that studies in which the device output was interpreted by a trained healthcare professional were more applicable (low concern) than those in which a lead‑I ECG device algorithm alone was used (high concern; Lau et al. 2013, Tieleman et al. 2014 and Vaes et al. 2014). The EAG presented results in 2 sections depending on how atrial fibrillation was identified (by a clinician or by the device's algorithm alone).

Diagnostic accuracy results: Lead-I ECG interpreted by a trained healthcare professional

4.8 Data were included from 4 studies, which assessed Kardia Mobile alone (Haberman et al. 2014; Williams et al. 2015), Kardia Mobile and MyDiagnostick (Desteghe et al. 2017) and Zenicor‑ECG alone (Doliwa et al. 2009).

4.9 Desteghe et al. reported separate accuracy estimates from lead‑I ECGs interpreted by 2 electrophysiologists; only pooled estimates using data from electrophysiologist 1 are shown in table 2 (values were similar when data from electrophysiologist 2 were used). Williams et al. reported separate accuracy estimates from lead‑I ECGs interpreted by a cardiologist or by a GP with a special interest in cardiology. Pooled accuracy estimates in table 3 used data from Williams et al. when the lead‑I ECG interpreter was a cardiologist (interpreters in other studies were cardiologists or electrophysiologists). Pooled accuracy estimates using data from Williams et al. when the interpreter was a GP with a special interest in cardiology (not shown) were similar. However, the study showed a decrease in specificity when the GP interpreted the lead‑I ECG; 76% (95% confidence interval [CI] 64% to 85%) compared with 86% (95% CI 76% to 94%) when the cardiologist interpreted them.

Table 2 Pooled diagnostic accuracy estimates for lead-I ECGs interpreted by a trained healthcare professional

Meta-analysis

Lead-I devices in included studies (number of studies)

Pooled sensitivity % (95% CI)

Pooled specificity % (95% CI)

All devicesa,c

Kardia Mobile (3b,d), Zenicor-ECG (1e)

93.9

(86.2 to 97.4)

96.5

(90.4 to 98.8)

All devicesa,c

Kardia Mobile (2), MyDiagnostick (1b,f), Zenicor-ECG (1e)

90.8

(83.8 to 95.0)

95.6

(89.4 to 98.3)

Kardia Mobilea,c

Kardia Mobile (3d)

94.0

(85.1 to 97.7)

96.8

(88.0 to 99.2)

a Data from electrophysiologist 1 from Desteghe et al. 2017.

b Data from Desteghe et al. 2017 from either Kardia Mobile or MyDiagnostick.

c Data from Williams et al. 2015 from cardiologist interpreting lead-I ECG.

d Desteghe et al. 2017; Haberman et al. 2015; Williams et al. 2015.

e Doliwa et al. 2009.

f Desteghe et al. 2017.

4.10 Only Kardia Mobile had sufficient studies to produce a device‑specific pooled estimate (see table 2). Accuracy estimates from individual studies for other devices are presented in table 3. The EAG commented that there were insufficient data to formally assess differences between the lead-I ECG devices.

Table 3 Individual study diagnostic accuracy estimates for lead-I ECGs interpreted by a trained healthcare professional

Lead-I ECG device

Study

Sensitivity % (95% CI)

Specificity % (95% CI)

MyDiagnosticka

Desteghe et al. 2017

85.0

(62.0 to 97.0)

95.0

(92.0 to 98.0)

Zenicor-ECG

Doliwa et al. 2009

92.0

(81.0 to 98.0)

96.0

(86.0 to 100.0)

a Data from electrophysiologist 1 from Desteghe et al.

Diagnostic accuracy results: ECG trace interpreted by the device's algorithm

4.11 Four studies that reported sensitivity and specificity of the lead‑I ECG device when the trace was interpreted by the device's algorithm alone were included in meta-analyses. Two studies reported data for MyDiagnostick alone (Tieleman et al. 2014; Vaes et al. 2014), 1 study for Kardia Mobile alone (Lau et al. 2013) and 1 study for both MyDiagnostick and Kardia Mobile (Desteghe et al. 2017). Pooled sensitivity and specificity estimates from meta‑analyses are presented in table 4.

Table 4 Pooled diagnostic accuracy estimates for lead-I ECG traces interpreted by device algorithm alone

Meta-analysis

Lead-I devices in included studies (number of studies)

Pooled sensitivity % (95% CI)

Pooled specificity % (95% CI)

All devicesa

Kardia Mobile (1b), MyDiagnostick (3c)

96.2

(86.0 to 99.0)

95.2

(92.9 to 96.8)

All devicesa

Kardia Mobile (2d), MyDiagnostick (2e)

95.3

(70.4 to 99.4)

96.2

(94.2 to 97.6)

MyDiagnostick

MyDiagnostick (3c)

95.2

(79.0 to 99.1)

94.4

(91.9 to 96.2)

Kardia Mobile

Kardia Mobile (2d)

88.0

(32.3 to 99.1)

97.2

(95.1 to 98.5)

a Data from Desteghe et al. 2017 from either Kardia Mobile or MyDiagnostick.

b Lau et al. 2013.

c Desteghe et al. 2017; Tieleman et al. 2014; Vaes et al. 2014.

d Desteghe et al. 2017; Lau et al. 2013.

e Tieleman et al. 2014; Vaes et al. 2014.

4.12 The EAG noted that the companies who make the lead‑I ECG devices stated that atrial fibrillation should not be diagnosed using the algorithm alone; ECG traces produced by the devices should be reviewed by a qualified healthcare professional.

Comparisons between lead-I ECG devices

4.13 The EAG commented that the available data were not sufficient to formally assess differences between the different lead-I ECG devices. Desteghe et al. (2017) assessed the concordance between Kardia Mobile and MyDiagnostick. There was no statistically significant difference in agreement between the devices (based on kappa values) when assessing all patients (p=0.677) or after excluding those with an implanted device (for example, a pacemaker or implantable cardiac defibrillator; p=0.411).

4.14 The EAG commented that the pooled sensitivity and specificity values were similar across all the meta-analyses done, irrespective of how the lead‑I ECG trace was interpreted (algorithm or healthcare professional) or which lead‑I ECG devices were used (pooled estimates produced by the EAG used Kardia Mobile, MyDiagnostick and Zenicor-ECG).

Diagnostic accuracy results: further studies excluded from the EAG's main report

4.15 The EAG identified further studies that reported sensitivity and specificity estimates of the lead‑I ECG devices. However, it did not include them in its main report because they did not meet 1 of the eligibility criteria for inclusion, that is, that the reference standard in the studies was not a 12‑lead ECG interpreted by a trained healthcare professional. Results were presented in appendix 6 of the diagnostics assessment report. They included 1 unpublished study which assessed imPulse (no other studies were identified for this device). Ranges were reported for sensitivity (67% to 100%) and specificity (83% to 100%). These data were used in the economic model.

Evidence on clinical effectiveness of the lead-I ECG devices

4.16 The EAG included 19 studies in its clinical effectiveness review. Of these, 7 studies were done in primary care (Orchard et al. 2014; Chan et al. 2016; Chan et al. 2017; Gibson et al. 2017; Hussain and Thakrar, 2016; Kaasenbrood et al. 2016; Orchard et al. 2016). There were 2 studies done in the UK (Gibson et al. 2017; Hussain and Thakrar, 2016). Of the studies, 13 included data for Kardia Mobile, 5 for MyDiagnostick, 1 for Zenicor-ECG and 1 for imPulse. No studies were identified that assessed the clinical effectiveness of lead‑I ECG devices when used for people with signs and symptoms of atrial fibrillation presenting in primary care.

Diagnostic yield

4.17 There were 13 studies that reported diagnostic yield of atrial fibrillation detection by lead‑I ECG devices (various devices), which ranged from 0.38% to 5.84%. However, the location of testing varied between studies; primary care (6 studies), secondary care (2 studies), tertiary care (1 study) and in the community (4 studies). In the primary care studies, the range was 0.49% to 5.84%. None of the studies assessed people with signs and symptoms of atrial fibrillation. The enrolled populations varied from the general population or people who were attending primary care for a reason unrelated to atrial fibrillation (for example, for flu vaccination) to people admitted to a cardiology ward and people with known atrial fibrillation. The prevalence of atrial fibrillation in these populations is likely to vary and may not be applicable to the population that is the focus of this assessment. No data were found on any benefit of lead‑I ECGs in identifying people with paroxysmal atrial fibrillation, compared with later ECG testing.

Test failure rate

4.18 Test failure rate (which included both the device failing to produce a result and producing a poor-quality ECG trace) varied between 0.1% and 9% (various devices). Reasons suggested for uninterpretable lead‑I ECGs were sinus tachycardia or bradycardia, that patients had a tremor or that hospitalised patients were unable to hold the devices firmly enough.

Time to diagnosis of atrial fibrillation

4.19 A study done in Australia (Lowres et al. 2014) reported a time to diagnosis of atrial fibrillation of 16.6 days (standard deviation of 14.3 days) from detection by an initial lead‑I ECG diagnostic test at a pharmacy to confirmed diagnosis with a 12‑lead ECG.

Ease of use of devices

4.20 Tieleman et al. (2014) reported that people were able to use MyDiagnostick with minimal instructions. Chan et al. (2017) reported that Kardia Mobile was easy to use. Orchard et al. (2016) commented that it may be difficult for older people to hold the Kardia Mobile device still enough to take a reading. In Desteghe et al. (2017), 7% of people were excluded from the study because they could not hold the devices as intended (the study used both MyDiagnostick and Kardia Mobile).

Effect on clinical decision making

4.21 In Hussain and Thakrar (2016), 5 out of 6 people had a change in the clinical management of their condition after atrial fibrillation was detected by Kardia Mobile (1 person died as an inpatient after referral to hospital). In Lowres et al. (2014), oral anticoagulants were prescribed for 6 out of 10 new patients with atrial fibrillation detected by a lead‑I ECG followed by a 12‑lead ECG interpreted by a cardiologist.

Evidence on patient- and healthcare professional-reported outcomes

4.22 In Orchard et al. (2016), which used Kardia Mobile, patients and GPs commented that they liked using the device. Chan et al. (2017) reported that all patients asked were willing to have further testing with Kardia Mobile at future GP visits, and 86% of GPs surveyed considered that the device was useful for atrial fibrillation screening and they would use it in their daily practice. Gibson et al. (2017) reported generally positive responses to using MyDiagnostick, although some issues with implementing use of the device were raised. A further study reported that Kardia Mobile was easily administered and that no one declined testing with the device (Hussain and Thakrar 2016). In Chan et al. (2017), interviewed patients commented that having access to the lead-I ECG device in the surgery was more convenient than having to attend another healthcare facility for a 12‑lead ECG.

'Real world' data

4.23 The EAG also looked at unpublished evidence from a quality control audit on the use of Kardia Mobile across Eastbourne, Hailsham and Seaford clinical commissioning group and Hastings and Rother clinical commissioning group. This was provided by a specialist committee member as an example of an ongoing audit. Over a 2-year period the device was used in primary care or for home visits if people had an irregular pulse or signs of atrial fibrillation. There were 183 ECG traces reported, identifying 128 cases of atrial fibrillation from the lead‑I ECG trace alone. The proportion of people newly diagnosed with atrial fibrillation (69.9%) was considerably higher than the diagnostic yield in studies identified by the EAG (0.38% to 5.84%), although the audit was designed for quality control, and not to assess atrial fibrillation yield.

Cost effectiveness

Systematic review of cost-effectiveness evidence

4.24 The EAG did a systematic review to identify published full economic evaluations of lead‑I ECG devices for detecting atrial fibrillation. Studies were excluded if they assessed the devices for repeated ECG measurements (rather than at a single time point) or if they assessed the devices for screening a population or for an asymptomatic 'silent atrial fibrillation' population. The EAG did not identify any published studies that met their inclusion criteria. However, the EAG highlighted 2 recently published economic evaluations (Welton et al. 2017 and Jacobs et al. 2018) that suggested that lead-I ECG devices may represent a cost-effective use of resources for systematic, opportunistic screening of people aged 65 years and over during a routine GP appointment.

Modelling approach

4.25 The EAG developed a de novo economic model designed to evaluate the cost effectiveness of using the lead‑I ECG devices for single time point testing of people presenting in primary care with signs and symptoms of atrial fibrillation and who have an irregular pulse.

Model structure

4.26 The model compared the effect of using a lead‑I ECG device in primary care for people with signs and symptoms of atrial fibrillation who have an irregular pulse (detected by manual pulse palpation) with standard diagnostic testing (that is, without the use of a lead‑I ECG device). The model was in 2 phases: a diagnostic phase followed by a post-diagnostic phase.

Diagnostic phase

4.27 This phase covered the initial assessment of people presenting in primary care with signs and symptoms of atrial fibrillation, and who have had manual pulse palpation that shows an irregular pulse. The model compared 2 strategies: referral for a subsequent 12‑lead ECG to check for atrial fibrillation (standard diagnostic pathway) or having a lead‑I ECG in primary care at the same primary care appointment to check for atrial fibrillation (lead‑I ECG pathway) followed by a 12‑lead ECG if the clinician thought this was appropriate.

4.28 The diagnostic phase model covered the first 3 months after the initial primary care appointment. By the end of the diagnostic phase, people have either been diagnosed as having atrial fibrillation, or no atrial fibrillation has been detected (either correctly or incorrectly). People diagnosed with atrial fibrillation can have anticoagulants and rate control treatment (beta blockers).

4.29 People can have up to 2 cerebrovascular events (transient ischaemic attack, ischaemic or haemorrhagic stroke), a non-major bleeding event, or die. This was modelled using a Markov model. The probability of having a cerebrovascular event for people with atrial fibrillation is reduced if they are taking anticoagulants. However, anyone taking anticoagulants has an associated higher risk of having a bleeding event.

Post-diagnostic phase

4.30 After the 3-month diagnostic phase model, people entered a second Markov model. This had the same structure as the Markov model in the diagnostic phase after a diagnosis has been made, but ran over a 30‑year timespan (with 3‑month cycles). People entered based on their history of cerebrovascular events (none, 1 or 2) and they could have further cerebrovascular events, non‑major bleeding events, or die.

Model inputs

4.31 The starting age of the modelled cohort was 70 years, and the model was run over 30 years. The cohort consisted of people with signs and symptoms of atrial fibrillation including an irregular pulse. This included people with atrial fibrillation (assumed to be 20% based on clinical advice) and people without the condition (assumed to have either atrial or ventricular ectopy).

Diagnostic accuracy of lead-I ECG devices

4.32 Estimates of the diagnostic accuracy of the 4 lead‑I ECG devices were obtained from the EAG's systematic review and meta-analyses. The EAG used estimates of accuracy based on healthcare professionals interpreting the ECG traces, because it assumed that atrial fibrillation would not be diagnosed based on a device's algorithm alone.

Table 5 Sensitivity and specificity values of lead-I ECG devices used in the economic model

Lead-I ECG

Interpreter of ECG

Data source

Sensitivity %

Specificity %

imPulse

Healthcare professional

Reeves (unpublished)

83.5a

91.5a

Kardia Mobileb

Healthcare professional

Pooled analysisc

94.0

96.8

MyDiagnostick

Healthcare professional

Desteghe et al. (2017)d

85.0

95.0

Zenicor-ECG

Healthcare professional

Doliwa et al. (2009)

92.0

96.0

a EAG used the midpoint from the range reported in the Reeves report.

b Alternative accuracy estimates based on a pooled estimate in which data from electrophysiologist 2 from Desteghe et al. were used in a scenario analysis; sensitivity 91.3%, specificity 97.4%.

c Pooled estimate from 3 studies; see table 2.

d Desteghe et al. reported accuracy estimates from 2 electrophysiologists. Estimates used in the base case were from electrophysiologist 1 (see table 3); values from electrophysiologist 2 were used in a scenario analysis (sensitivity of 80.0%, specificity of 98.0%).

Treatment effects: mortality and cerebrovascular events

4.33 For people with atrial fibrillation, the rate of mortality and cerebrovascular events (transient ischaemic attack, ischaemic or haemorrhagic stroke) in people who did not have anticoagulants was taken from Sterne et al. (2017). The effect of anticoagulants on the incidence of these events in people with atrial fibrillation was also taken from this study. For people without atrial fibrillation the rate of mortality and cerebrovascular events was taken from various sources (for example, Public Health England report, Office for National Statistics report, Rothwell et al. 2005). The risk of cerebrovascular events and mortality for people with untreated atrial fibrillation does not vary by type of atrial fibrillation. That is, risk is the same for paroxysmal, permanent and persistent atrial fibrillation. After people have a cerebrovascular event, their risk of mortality increases. The EAG assumed that this risk was 2.6 times greater based on a study of stroke survivors in Norway (Mathisen et al. 2016). The risk of having a further cerebrovascular event was based on a meta-analysis of stroke survivors (Mohan et al. 2011) with increased risk in the first year, then a lower risk from year 2 onwards.

Treatment effect: clinically relevant bleeding

4.34 The risk of clinically relevant bleeding is increased for people who have anticoagulants, based on Sterne et al. (2017). This is the case for people with or without atrial fibrillation.

Costs

Lead-I ECG device costs

4.35 Annual costs of the devices used in the base-case model are shown in table 6. Because the lead‑I ECG could be used outside the scope of this assessment, the EAG also did a scenario analysis that excluded the costs of the devices. No extra cost was included for administering and interpreting the lead‑I ECG because it was assumed that this could be done during a standard GP consultation.

Table 6 Estimated annual costs of lead-I ECG devices

Lead-I ECG

Item

Unit cost (£) c

Expected lifespan (years)

Annual cost (£)

Unit cost per test b (£)

imPulse

Device

175

2

87.50

1.62

Kardia Mobile

Device

82.50

5

16.50a

0.31

MyDiagnostick

Device

450

5

90

1.67

Zenicor-ECG

Device and 36-month licence

1,980

10

613.27

11.40

Extra 36-month licence

1,780

3

a Costs of any additional tablet or device needed not included (the effect of this additional cost is assessed in scenario analysis F).

b Assumes 54 people tested per year.

c Excluding VAT.

Costs of 12-lead ECGs and Holter monitoring

4.36 The EAG devised base cases that differed depending on where 12‑lead ECGs were done. If a 12‑lead ECG was done in primary care, the cost of administering it was assumed to be £12.34. This was based on the costs of the device, disposables and staff time to do and interpret the ECG. The cost of administering a 12‑lead ECG in secondary care was assumed to be £52 (from NHS reference costs). The cost of Holter monitoring (for 7 days) was assumed to be £120.23.

Treatment and event costs

4.37 Costs for anticoagulant (apixaban) and rate control (beta blockers) treatment were obtained from the British national formulary and NHS drug tariff. Costs of bleeding events and transient ischaemic attack were taken from NHS reference costs. Age and sex-adjusted 1- and 5‑year costs for strokes were from the Sentinel Stroke National Audit Programme's cost and cost-effectiveness report (2016).

Health-related quality of life and QALY decrements

4.38 Berg et al. (2010) was used to provide utility values for people with atrial fibrillation (see table 7). Beta blockers were assumed to improve symptoms for people with atrial fibrillation.

Table 7 Utility values used in base-case economic model (at age 70; age- and sex-adjusted)

Atrial fibrillation status (95% CI)

Atrial fibrillation

No atrial fibrillation

Untreated

0.665 (0.537 to 0.881)

0.744 (0.480 to 0.942)

Treated

0.744 (0.480 to 0.942)

0.744 (0.480 to 0.942)

4.39 People without atrial fibrillation were assumed to be having a short symptomatic episode caused by atrial or ventricular ectopy that resolved quickly. For people who had an ischaemic or haemorrhagic stroke, a lifetime utility decrement was applied at the time of the first stroke (no further decrements were applied for subsequent strokes). The size of the decrement was −0.272 (95% CI −0.345 to −0.198) for both types of stroke. Transient ischaemic attacks and bleeding events were assumed to have no long-term effect on health-related quality of life, and no utility decrement was applied for these events.

Base-case assumptions

4.40 The following assumptions were applied in the base-case analyses:

  • Of the people presenting in primary care with signs and symptoms of atrial fibrillation, and who have an irregular pulse, 20% have atrial fibrillation.

  • Of the people with atrial fibrillation, 50% have paroxysmal atrial fibrillation. The EAG commented that there is a lack of evidence on the prevalence of paroxysmal atrial fibrillation in people with symptoms, and noted that a recent study (Welton et al. 2017) had reported wide variation in prevalence (although not necessarily in a symptomatic population). The effect of varying this prevalence was investigated in sensitivity analysis.

  • Additional interpretation by a cardiologist is needed for 10% of lead‑I ECG tests.

  • The 12-lead ECGs have 100% sensitivity and specificity for atrial fibrillation (if a person is in atrial fibrillation at the time of the test).

  • For 48% of people with paroxysmal atrial fibrillation the episode will have stopped by the time a 12‑lead ECG is done (2 or 14 days after the initial primary care consultation when an irregular pulse is detected). This is based on data from Israel et al. (2004).

  • Holter testing for paroxysmal atrial fibrillation is assumed to have 100% sensitivity and specificity (if atrial fibrillation occurs during testing). Holter testing is assumed to be for 7 days and 70% people with atrial fibrillation are assumed to have an episode in that time (based on data from Kirchoff et al. 2006).

  • In the standard diagnostic pathway, 50% of people who have a negative 12‑lead ECG have Holter testing. In the lead‑I ECG pathway, 80% of people who have a negative lead‑I ECG have a 12‑lead ECG. If the 12-lead ECG is negative, 50% of people have Holter testing. Of the 20% of people who are not referred for a 12‑lead ECG after a negative lead‑I ECG, 50% have Holter testing.

  • Only people who are diagnosed with atrial fibrillation and who have a CHA2DS2-VASc score of 2 or more have anticoagulants. There are 82.4% of people with atrial fibrillation assumed to have a CHA2DS2-VASc score of 2 or more, and 81.2% of these are assumed to take anticoagulants (based on NHS Quality and Outcomes Framework 2016/2017 indicator AF007).

  • People having anticoagulants have apixaban (simplifying assumption).

  • Treatment with anticoagulants starts immediately after a positive lead-I ECG result (simplifying assumption).

  • People whose atrial fibrillation is undetected and who have a cerebrovascular event are assumed to have their atrial fibrillation diagnosed as part of treatment.

Base-case results

4.41 The EAG produced 4 base cases, depending on when and where 12‑lead ECGs were done:

  • base case 1: 12-lead ECG in primary care (2 days later)

  • base case 2: 12-lead ECG in primary care (14 days later)

  • base case 3: 12-lead ECG in secondary care (2 days later)

  • base case 4: 12-lead ECG in secondary care (14 days later).

4.42 In pairwise analyses, all the lead-I ECG devices were compared independently with the standard pathway (that is, no use of a lead-I ECG device). Results were similar across the 4 base cases, and in probabilistic analyses. The results from base-case 1 are shown in table 8.

Table 8 Base case 1: Pairwise cost-effectiveness analysis (compared with standard pathway)

Total costs (£)

Total QALYs

Incremental costs (£)

Incremental QALYs

ICER (£)

Standard pathway

514,187

447.963

Kardia Mobile

515,551

449.249

1,364

1.286

1,060

imPulse

530,745

448.987

16,557

1.024

16,165

MyDiagnostick

521,233

449.024

7,046

1.061

6,638

Zenicor-ECG

518,468

449.199

4,281

1.236

3,462

4.43 In fully incremental analyses across all the base cases, all lead‑I ECG devices were dominated by Kardia Mobile (that is, Kardia Mobile cost less but produced more quality-adjusted life years [QALYs]). The incremental cost-effectiveness ratios (ICERs) for Kardia Mobile compared with the standard pathway were the same as for the pairwise comparison (less than £1,100 per QALY gained). At consultation, the company who makes MyDiagnostick proposed new costs for their device. The EAG ran the base-case analysis again using these costs in an addendum to the diagnostics assessment report. This resulted in lower costs for MyDiagnostick, but did not affect the EAG's overall conclusions on the pairwise cost-effectiveness analysis.

Analysis of alternative scenarios

4.44 The EAG investigated the effect of varying some of the base-case assumptions in scenario analyses. This included assessing the effect of adding the cost of a smartphone or tablet (including the cost of a data network) for Kardia Mobile in a threshold analysis. The EAG commented that a smartphone or tablet would need to cost more than £2,850 for Kardia Mobile to no longer dominate the other lead-I ECG devices. The ICER for Kardia Mobile compared with the standard pathway remained less than £20,000 per QALY gained if a smartphone or tablet costs less than £24,362. Using alternative accuracy estimates for MyDiagnostick and Kardia Mobile (using results from electrophysiologist 2 from Desteghe et al.) resulted in Kardia Mobile having an ICER of £5,503 per QALY gained compared with MyDiagnostick. Compared with the standard pathway MyDiagnostick dominated. The Zenicor‑ECG was no longer dominated, but had an ICER of £242,994 per QALY gained when compared with Kardia Mobile.

Deterministic sensitivity analysis

4.45 The model was most sensitive to the proportion of patients whose atrial fibrillation was paroxysmal (assumed to be 50% in the base case) in one-way analyses for all of the lead-I ECG devices. Cost effectiveness improved as the proportion of paroxysmal atrial fibrillation increased. Conversely, lower estimates of the proportion of paroxysmal atrial fibrillation made the devices less cost effective (increased incremental costs and decreased incremental QALYs).

Probabilistic sensitivity analysis

4.46 In a probabilistic sensitivity analysis (done in base case 1) all other lead‑I ECG devices were dominated by Kardia Mobile in a fully incremental analysis. In pairwise comparisons with the standard pathway, ICERs were similar to the deterministic results, and all were less than £17,000 per QALY gained.