© 2015 iRhythm Technologies, Inc. Content is Confidential and Proprietary.

Big Data in Arrhythmia Detection: Real or an Illusion?

Presentation to Cardiac Safety Research Consortium

December 2, 2015

Judith Lenane, RN, MHA

Chief Clinical Officer & EVP, Operations

December 3, 2015©2015 iRhythm Technologies, Inc. Content is Confidential and Proprietary.2

Once we know something, we find it hard to imagine what it was like not to know it.

Chip & Dan Heath, Authors of Made to Stick, Switch

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.3

Big Data AnalyticsNovel Biosensor

iRhythm Pioneering Digital Health: Novel Biosensor

Combined With Big Data Analytics

Rapidly Scaling

Service

Disruptive

Technology

Strong Clinical

Evidence

• Single-use patient wearable

biosensor

• Proprietary cloud-based data

analytic engine

• 14 peer reviewed studies

• Proven to change clinical

diagnosis and treatment

• Payer coverage for >275

million lives in the US

• Prescribed for ~400,000

patients to date

• World’s largest repository

of ECG & patient data

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.4

Wearable Biosensors Replacing Older Monitoring

Modalities

• Cumbersome for patients and physicians

• Ineffective - only allows 24-48 hour monitoring

• Costly - low diagnostic yield of 20% drives

follow on studies1

• Easy-to-wear biosensor preferred by patients

• Continuous monitoring & analysis of every

heart beat for up to 14 days

• >2x detection of arrhythmias vs. Holter2-4

• High diagnostic yield of ~60 to 80% Sources:

1. Podrid, P.J., Zimetbaum, P.J. & Downey, B.C. (Feb 2012). Ambulatory monitoring in the assessment of cardiac arrhythmias.

2.) Rosenberg, M., Samuel, M., Thosani, A. & Zimetbaum, P. (2012). Use of a non-invasive continuous monitoring device in the management of atrial fibrillation.

3.) Barrett, P., Komatireddy, R., Haaser, S. et al. (2013). Comparison of 24 hour Holter monitoring versus 14 day novel adhesive patch electrocardiographic monitoring.

4.) Schreiber, D., Attar A., Drigalla D., and Higgins S. Ambulatory cardiac monitoring for discharged emergency department patients with possible cardiac arrhythmias.

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.5

Analytical “Curation” Engine

& Data RepositoryPatient Reported Health Info

iRhythm Digital Healthcare Leader Due To Analytical Engine and Data Repository

Unprecedented database of annotated, long term continuous ECG recordings with contextual patient information … 85M+ hours of curated data

Patient Data Sources Digital Report for

Diagnosis and Management

~2 million heartbeats/ patient

Long Term Continuous Monitoring

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.6

Q3 2015 Results Demonstrates ZIO® Patch Positive

Performance in Clinical Use

ZIO Patch Reports with Data Posted in Q3 2015

● Generalizable Population with Mean Age of 61 years (Median 66 years)

– 51% of Patients Female

● Mean Wear Time: 10 Days (Median 11.8 Days, Max 14.0 Days)

● High Diagnostic Yield: 73% had at least one Arrhythmia

● High Mean Analyzable Time: 96.1% (Median 99.3%)

● 74.2% With Symptomatic Trigger Utilized at Least Once

Source: iRhythm Database

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.7

High Diagnostic Yield: 73% Had At Least One Arrhythmia

• 24% of records had multiple arrhythmias

• 6% of arrhythmias met critical value notification

1.2%

AV Block

Q3 2015 Experience Demonstrates High Diagnostic

Yield of Long Term Continuous Monitoring

Source: iRhythm Database

8.3%

Paroxysmal

Atrial

Fibrillation

44.8%

Supraventricular

Tachycardia

(>8 Beats)

22.2%

Ventricular

Tachycardia

(>4 Beats)

4.2%

Pause

(>3 Sec)

17.6%

Symptomatic

Arrhythmias

Detected 6%

Supraventricular

Tachycardia

(> 30 Sec)

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.8

Q3’ 15 Diagnostic Yield for Arrhythmia (%) Per Day

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Source: iRhythm Database – Diagnostic Yield Excluding 100% AFib

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.9

Future Considerations

• Clinical Insights in Arrhythmia Detection – Power of the Raw Data

• Long Term Continuous QTcAnalysis

• Predictive Modeling in Arrhythmia Detection

• Machine Learning

• Deep Learning

© iRhythm Technologies, Inc. Content is Confidential and Proprietary.10

Big Data and Arrhythmia Detection --

If you can’t explain it simply, you don’t understand it well enough.

Albert Einstein, Physicist