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Mark T. Gladwin, MD Director, Pittsburgh Heart, Lung, Blood, and Mark T. Gladwin, MD Director, Pittsburgh Heart, Lung, Blood, and

Mark T. Gladwin, MD Director, Pittsburgh Heart, Lung, Blood, and - PowerPoint Presentation

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Mark T. Gladwin, MD Director, Pittsburgh Heart, Lung, Blood, and - PPT Presentation

Vascular Medicine Institute Chairman Department of Medicine University of Pittsburgh Institute for Transfusion Medicine Hemophilia Center of Western Pennsylvania and UPMC Personalized science and medicine ID: 760821

data medicine ejection heart medicine data heart ejection fraction care population subjects analytics pulmonary cost clinical high personalized pressure

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Slide1

Mark T. Gladwin, MD

Director, Pittsburgh Heart, Lung, Blood, and Vascular Medicine InstituteChairman, Department of MedicineUniversity of Pittsburgh, Institute for Transfusion Medicine,Hemophilia Center of Western Pennsylvania, and UPMC

Personalized science and medicine

UPMC Heart and Vascular Institute

Slide2

Drivers for BIG data and analytics in Medicine

Population Health Management

:

Affordable care act and payer/provider mergers drive care models from volume to value; moving medicine to analytics-based population management

High cost/low volume care must be managed or prevented bringing “hot-spotting” to consumer medicine

Personalized (precision) medicine

:

Revolution in Genomic knowledge base has introduced an entire set of tools for refined Phenomes for clinical investigation and therapy

Expanding list of high cost, targeted therapeutics require greater precision in Phenome characterization to select high yield targets

Slide3

Slide4

5%

High

Risk

15-35%

Rising Risk

60-80%

Low Risk

ActiveChronic Conditions

Healthy Minor Acute

4

COST

POPULATION HEALTH CARE PARADOX- Hot Spotting Risk

Analytics Needed to Distinguish

Small Volume / High Cost Population

vs

Large Volume / Low Cost Population

Analytics Critical to Target

Complex Interventions to High Risk

Cost Efficient Interventions to Low Risk

Prevention Strategies Across Risk

Slide5

Drivers for BIG data and analytics in Medicine

Personalized (precision) medicine

:

Revolution in Genomic knowledge base has introduced an entire set of tools for refined Phenomes for clinical investigation and therapy

Expanding list of high cost, targeted therapeutics require greater precision in Phenome characterization to select high yield targets

Slide6

What is personalized medicine?

Slide7

THE NEW PHENOME

RIGHT INTERVENTION to RIGHT PATIENT

Slide8

Slide9

Can Personalized Medicine accelerate this timeline?

Slide10

How personalized medicine will accelerate discovery to therapy

A “clean” phenotype (LDL-C levels) with variable response to therapeutic interventions

Slide11

2003

Slide12

Slide13

Slide14

Slide15

Slide16

Slide17

Structure and SNPs identified in proprotein convertase subtilisin/kexin type 9 (PCSK9)

Slide18

9 Years:

2012

Slide19

Slide20

Slide21

Slide22

How personalized medicine will accelerate discovery to therapy

A “clean” phenotype (LDL-C levels) with variable response to therapeutic interventions

A “complex” phenotype using integrated EMR data

Slide23

Pulmonary hypertension: Deadly vascular disease with enigmatic molecular origins

Enlarged right heart

UPMC Heart and Vascular Institute

Slide24

Slide25

Can we explore new therapies for PH-HFpEF without doing an expensive clinical trials?

First define the EHR phenome: Key to link hemodynamic definition to defined outcomes, mortality and hospitalization

Assess the EHR phenome for outcomes based upon existing interventions (metformin) or new interventions in refined clinical trials

Slide26

Aim : To examine the effect of nitrite and metformin on PH-HFpEF

**

P

< 0.01 vs. Ln-Su;

#

P

< 0.05,

##

P

< 0.01 vs. Ob-Su;

n

= 8

Slide27

Can we explore new therapies for PH-HFpEF without doing an expensive clinical trials?

First define the EHR phenome: Key to link hemodynamic definition to defined outcomes, mortality and hospitalization

Project lead by Melissa Saul and Rebecca

Vanderpool

Slide28

Mining Electronic Health Records

Jensen PB et al., Nature Reviews

Genetics 2012

Patient encounter

Patient linked Data

Electronic Health Records

Time

Specific Databases

Right Heart Catheterizations

1/19/2005

9/26/2012

20,200 incidences10,577 subjects

Project’s starting point

Slide29

1.) Right Heart Catheterizations

Data Sources

What about additional clinical data? Mortality?

Hospitalizations?

1/19/2005

9/26/2012

20,200

RHCs10,577 subjects

Starting point

Slide30

1.) Right Heart Catheterization

4.) Labs

5.) Pulmonary Function Tests

2.) Ejection Fraction

MRI

CT

Echo

3.) Administrative data, admin, discharge, ICD 9 diagnosis and procedure codes,

CPT codes

Patient linked data Sources

Slide31

1.) Right Heart Catheterization

4.) Labs

5.) Pulmonary Function Tests

2.) Ejection Fraction

3.) Administrative data, admin, discharge, ICD 9 diagnosis and procedure codes, CPT codes

Data Files

1/19/2005

9/26/2012

20,200 incidences10,577 subjects

Echo headings11/1/1993 – 7/6/2015252,097 entriesVolumetric derived EF (CT, MRI)3/13/1992 – 8/18/20155359 entries

12/13/1990 – 10/15/2014 652,425 entries

1/5/1998 – 5/29/2015819,163 entries

1/1/1998 – 12/31/2014>8.1 million entries

MRI

CT

Slide32

I WANT ALL THE DATA!!

Clinician scientists

provide key clinical characteristics and (hopefully!)

specific

research questions

Data scientists

build upon strong bioinformatics tools and a strong knowledge base in medical data structure and relationships to extract accurate data and link disparate information

Data validation requires a strong

collaborative

process between the clinical scientist and the data scientist

Slide33

Right Heart Catheterizations UPMC Presbyterian

Study Characteristics: Includes all RHCs between 1/19/2005 and 9/26/2012All patients were followed until death (last visit) or 10/15/2014

20200 RHC Incidences(1/2005 – 9/2012)

20128 RHC incidences

72 incidences excluded due to no follow-up

10,023 subjects

No PH

mPAP

< 25 mmHg

PAH

mPAP

≥ 25 mmHgPCWP ≤ 15 mmHg PH-LHDmPAP ≥ 25 mmHgPCWP > 15 mmHg

866 incidences excluded due missing PA pressure or cardiac output

19262

RHC incidences

Slide34

Sources of Ejection Fraction

All reported ejection fractions in Echocardiography reports and Volumetric derived EF (MRI, CT) were tabulated. Ejection Fraction closest the date of the RHC was used. Priority was given to volumetric derived EFs. Search text/progress note for a reported EF in the 13.6% with no Echo or Volumetric EFPerformed preliminary analysis on subjects with a measured EF

Echo headings

11/1/1993 – 7/6/2015252,097 entriesVolumetric derived EF (CT, MRI)3/13/1992 – 8/18/20155359 entries

MRI

CT

No EF

2615

RHCs

Echo. EF

14,455

RHCs

Vol. EF2192 RHCs

19262

RHC incidences

Slide35

Subtype PH-LHD

Subjects with an Ejection Fraction ≥ 45% were defined as having a preserved Ejection Fraction

(

pEF

)

or diastolic dysfunction based on ESC 2012 and ACC/AHA 2013 heart failure guidelines.

McMurray JJ, et al. EHJ, 2012.

Yancy

CW, et al. JACC, 2013.

Subjects with an ejection fraction < 45% were classified as having a reduced ejection fraction

(

rEF

)

or systolic dysfunction

Slide36

Distribution of Ejection Fractions in the whole population

2141 subjects have PH-

HFpEFcurrently no exclusion for valvular disease

pEF – preserved Ejection Fraction (≥ 45%)rEF – reduced Ejection Fraction (< 45%)

1475 subjects have PH-HFrEF

1005 subjects have not been linked to an ejection fraction

Slide37

With a defined and validated phenotype in hand we can ask question:

Does pulmonary hypertension affect outcomes (hospitalization and mortality) in patients with

HFpEF

or

HFrEF

?

Does exposure to metformin modulate this outcome?

Slide38

Diffusion Capacity and Mortality in Patients With Pulmonary Hypertension Due to Heart Failure With Preserved Ejection Fraction

Hoeper

MM, et al., JACC Heart Failure. 2016.

Diastolic Pressure Gradient Predicts Outcome in Patients With Heart Failure and Preserved Ejection Fraction Zotter-Tufaro C, et al. J Am Coll Cardiol. 2015; 66:1308-1310.Diastolic Pressure GradientUnivariable Analysis: HR (95%CI): 1.078 (1.018–1.141), P-value: 0.01Multivariable Analysis: HR (95%CI): 1.057 (1.017–1.097), P-value: 0.004

Right Ventricular Function in Heart Failure With Preserved Ejection Fraction

Mohammed SF, et al., Circulation. 2014; 130:2310-2320

Pulmonary Arterial Capacitance Is an Important Predictor of Mortality in Heart Failure With a Preserved Ejection Fraction

Al-

Naamani

N, et al., JACC Heart Failure. 2015; 3:467-474

PA Ca

≥ 1.1

PA Ca

< 1.1

Slide39

Univariate predictors of mortality in PH-HFpEF

Slide40

Elevated TPG, PVR and PVR associate with increased mortality in PH-HFpEF

Slide41

Drivers for BIG data and analytics in Medicine

Population Health Management

:

Affordable care act and payer/provider mergers drive care models from volume to value; moving medicine to analytics-based population management

High cost/low volume care must be managed or prevented bringing “hot-spotting” to consumer medicine

Slide42

Example: Where are PAH patients managed and what are their outcomes?

Can we use clinical analytics

to find patients outside of specialty medical homes and hot spot them?

Use to compare outcomes between specialty and primary care

models to

evaluate need for specialty

vs

general medical homes?

Slide43

Manage the PH Population

43

330 Patients

538 Visits

3857 Patients7236 Visits

364 Patients1608 Visits

Clinic*

Non-Clinic

*Clinic defined as CVI PUH HBC & PULM HBC HYPERTENSION

EPIC pts between1/2014 to 12/2014Any office visit where a patient had one of the 3 dx codes on that visit:416.0, 416.8, 416.9PA Pressure: Looked at max PA Pressure of any patient found in initial population

PA Pressure>=6019960.3%<6011534.8%N/A164.8%

PA Pressure>=60108028.0%<60185448.1%N/A92323.9%

PA Pressure

>=60

255

70.1%

<60

104

28.6%

N/A

5

1.4%

Slide44

Patients with PH but not seen in the clinic

44

Office

n

CVI GRNVL OFC

318

GMC INTERNAL MED

308

BMA SMC FAM PRAC

299

HORIZON PULMONOLOGY

297

BMA SMC CARDIOLOGY

297

PGH CARDIOLOGY SHDYS

197

RFP PENN HILLS

197

MEDICOR ASSOC ERIE

184

CVI PASSAVANT HBC

177

BMA SMC INTERNAL MED

175

BMA SMC PULMONOLOGY

172

PULM HBC OAKLAND

165

CVI LATROBE OFFICE

164

HORIZON FMLY HLTHCARE

138

MURRYSVILLE EXTENDED

124

CVI SHADYSIDE

121

GMC FAMILY PRACTICE

105

RHMS PRIMARY CARE PRT

103

METRO ENT WEXFORD

103

FPN PULMONARY

102

PIMA CASTLE SHANNON

101

PULMONARY PART PASSVNT

97

CCP-HAMOT WEST

97

PARTNERS LEVEL GREEN

94

NORTHERN MED WEXFORD

93

Slide45

Pulmonary Hypertension--Outcomes

45

Slide46

Healthcare is at a pivotal junctureAffordable care act and payer/provider mergers will drive care models from volume to value; movement to analytics-based population managementPersonalized (precision medicine) is critical for successful population management and discovery and translation of new therapeutics for the right patientPitt and UPMC are ground zero for discovering the way forward and serving as a national model to provide better care for the right patient at a lower cost - with personalized care

46

Slide47

We want you!