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© 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