Morteza Naghavi, M.D. Founder and Executive Chairman of SHAPE

The 2nd Machine Learning

Vulnerable Patient Symposium

Towards Developing an Artificial Intelligence-Based Forecast System

for

Predicting Short-Term CVD Events

A Satellite Event in Conjunction with 2017 Annual Scientific Sessions of American Heart Association

Let’s Begin with

the End

Goal:

Eradicate Heart

Attacks(Unpredicted CVD Events)

Now Let’s See

Where We Are

Last year2,626,418 people died

in the US

614,348 (23.4%) of them

died due to heart disease and

133,033 (5.1%) due to stroke

Total 747,381 (28.5%)

Unlike Cancer

• Unlike in cancer where oncologists deal

with hundreds of different pathologies and

specific therapeutic strategies, in CVD over

50% of cases we deal with one pathology:

Atherosclerosis(The number 1 killer of mankind)

Unpredicted

In >50% of victims,

the first symptom of

asymptomatic

atherosclerosis is a

sudden cardiac

death or acute MI.

Men

Women

0 10 20 30 40 50 60 70

Patients Diagnosed with CHD (%)Murabito et al Circulation 1993

Sudden Cardiac Death or Acute MI

as Initial Presentation of CHD

62%

42%

Add 10yrs to Life Expectancy of

Mankind

Early detection and treatment of

atherosclerosis to prevent acute CVD

events is likely to increase life

expectancy in excess of 10 years.

That’s HUGE!!!A Vaccination Type Impact on Public Health

How Do We Get There?

A heart-attack free

future

Let’s Draw an Analogy

Heart Attack

vs. Hurricane

Imagine if the weatherman says there

is a 7.5% chance of a category 5 hurricane in

the next 10 years. Do you think people would

take immediate preventive actions like boarding

up their windows, buying hurricane supplies, or

even changing their daily routines?

Imagine if heart attack and stroke were

predicted similar to hurricanes Harvey and Irma

with sufficient short-term alerts to at-risk people

to take preventive actions.

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. HurricaneTHE GALVESTON HORROR

Heart Attack vs. Hurricane10-year Risk Prediction vs. 10-day Risk Prediction

What has SHAPE done?

Naghavi et. al. Circulation Journal

The Vulnerable Patient Consensus Statement

Naghavi et. al. Circulation Journal

The Vulnerable Patient Consensus Statement

SHAPE Task Force Meeting

SHAPE Guidelines Published

Coronary Artery Calcium Score

32

The Writing Sub-Committee of the SHAPE Task Force (left to right): Drs Budoff, Falk, Rumberger, Naghavi,

Fayad, Hecht, and Berman

Atherosclerosis Test

Very Low Risk3

Negative Test• CACS =0

• CIMT <50th percentile

LowerRisk

ModerateRisk

Positive Test• CACS ≥1

• CIMT 50th percentile or Carotid Plaque

ModeratelyHigh Risk

HighRisk

VeryHigh Risk

No Risk Factors5 + Risk Factors • CACS <100 & <75th%

• CIMT <1mm & <75th%

& no Carotid Plaque

• Coronary Artery Calcium Score (CACS)

or

• Carotid IMT (CIMT) & Carotid Plaque4

• CACS 100-399 or >75th%

• CIMT 1mm or >75th%

or <50% Stenotic Plaque

• CACS >100 & >90th%

or CACS 400

• 50% Stenotic Plaque6

LDL

Target

<160 mg/dl <130 mg/dl <130 mg/dl

<100 Optional

<100 mg/dl

<70 Optional

<70 mg/dl

Re-test Interval 5-10 years 5-10 years Individualized Individualized Individualized

All >75y receive unconditional treatment2

Apparently Healthy Population Men>45y Women>55y1

ExitExit

Myocardial

IschemiaTest

NoAngiography

Follow Existing

Guidelines

Yes

The 1st

SHAPE Guidelines

Step 1

Step 2

Step 3Optional

CRP>4mg

ABI<0.9

1: No history of angina, heart attack, stroke, or peripheral arterial disease.

2: Population over age 75y is considered high risk and must receive therapy without testing for

atherosclerosis.

3: Must not have any of the following: Chol>200 mg/dl, blood pressure >120/80 mmHg, diabetes,

smoking, family history, metabolic syndrome.

4: Pending the development of standard practice guidelines.

5: High cholesterol, high blood pressure, diabetes, smoking, family history, metabolic syndrome.

6: For stroke prevention, follow existing guidelines.

Existing Guidelines (Status Quo):

• Screen for Risk Factors of Atherosclerosis

• Treat Risk Factors of Atherosclerosis

The SHAPE Guidelines:

• Screen for Atherosclerosis (the Disease) Regardless of Risk Factors

• Treat based on the Severity of the Disease and its Risk Factors

SHAPE v.s. Status Quo

Number

(per year)

Estimated Impact

of SHAPE

(Sensitivity

Analysis Range)

Estimated

Change in

Cost

CVD Deaths 910,600 ↓10%

(5%-25%)

($1.2 b)

MI (prevalence) 7,200,000 ↓ 25%

(5%-35%)

($18.0 b)

Chest Pain Symptoms (ER visits) 6,500,000 ↓ 5%

(2.5%-25%)

($4.1 b)

Hospital Discharge for Primary Diagnosis of CVD 6,373,000 ↑ 10%

(5%-25%)

$3.8 b

Hospital Discharge for Primary Diagnosis of CHD 970,000 ↓ 10%

(5%-25%)

($9.9 b)

Cholesterol Lowering Therapy ↑ 50 %

(50%-65%)

8.00 b

CV Imaging 8,700,000 ↑ 10%

(5%-25%)

$358 m

Angiography 6,800,000 ↑ 15% - CTA

(2.5%-25%)

$600 m

PCI (percutaneous coronary interventions per year) 657,000 ↓ 10%

(5%-50%)

($580 m)

CABS (coronary artery bypass surgeries per year) 515,000 ↓ 5%

(2.5%-50%)

($672 m)

Total Δ in Cost ($21.5 b)

Cost Effectiveness of the SHAPE Guidelines

Heart Attack vs. Hurricane10-year Risk Prediction vs. 10-day Risk Prediction

Long term predictions do not

trigger immediate preventive

actions.

Preventive cardiology needs a

short-term predictor.

Heart Attack vs. Hurricane

Machine Learning Vulnerable

Patient Project.

http://shapesociety.org/videos-2/

http://shapesociety.org/videos/

The Big Idea:Developing an Artificial Intelligence-based Forecast System for

Prediction of Heart Attacks within 12 Months

Use machine learning to create new algorithms to detect who will experience

a CHD event within a year (The Vulnerable Patient). Algorithms will be

based on banked biospecimen and information collected days up to 12

months prior to the event. We will utilize existing cohorts such as MESA,

Heinz Nixdorf Recall Study, Framingham Heart Study, BioImage Study and

the Dallas Heart Study. External validation to test for discrimination and

calibration will be conducted using other longitudinal observational studies

that provide adjudicated cardiovascular event information such as the

MiHeart, JHS, DANRISK and ROBINSCA. Additionally, we will use machine

learning to characterize individuals who, despite high conventional risk, have

lived over 80 years with no CHD events (The Invulnerable). We expect to

discover new targets for drug and possibly vaccine development. We will

make the algorithms available as an open source tool to collect additional data

over time and increase its predictive value.

What a great idea, what are you

waiting for?

Funding!

Will Super Intelligent Computers Replace

Physicians?

Will Super Intelligent Computers

Replace Physicians?

Absolutely Yes

When and in What Areas?

Umm let’s discuss

Inspired by IBM Watson

Google DeepMind

49

Machine vs. Cardiologist