revolutionizing renal care with predictive analytics for ckd

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Revolutionizing Renal Care with Predictive Analytics for CKD Navdeep Tangri, MD, PhD, FRCP(C) Eleanor Herriman, MD, MBA Dave Garnett May 10, 2016

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Page 1: Revolutionizing Renal Care With Predictive Analytics for CKD

Revolutionizing Renal Care with Predictive Analytics for CKD

Navdeep Tangri, MD, PhD, FRCP(C)Eleanor Herriman, MD, MBADave Garnett

May 10, 2016

Page 2: Revolutionizing Renal Care With Predictive Analytics for CKD

Navdeep Tangri, M.D. Ph.D. FRCP(C)

• Developed and validated the Kidney Failure Risk Equation

• Has published more than 120 manuscripts and presented at several national and international scientific meetings

• Reviews manuscripts for several major medical journals

• Section Editor of Current Opinion in Nephrology and Hypertension• Has active grants from the Canadian Institute of Health Research,

Kidney Foundation of Canada, and Manitoba Health Research Council

Attending Physician, Associate Professor, Division of Nephrology, Department ofMedicine, University of Manitoba: Director, Chronic Disease Innovation Center,Seven Oaks Hospital

• Co-­chairs the scientific committee of the Canadian Society of Nephrology

• Member of the American Society of Nephrology and the International Society of Nephrology

Page 3: Revolutionizing Renal Care With Predictive Analytics for CKD

L. Eleanor J. Herriman, M.D.,M.B.A.

• Physician executive with 20 years of varied healthcare experience

• Former faculty member at Harvard Business School’s Institute for Strategy and Competitiveness

• Market research and strategy services to the pathology and laboratory industries at G2 Intelligence

• Healthcare strategy consulting at Bain & Company

Education• Doctor of Medicine degree from Baylor College of Medicine• Presidents Scholarship with honors in Neurology, Psychiatry and

Neuropathology

• Masters in Business Administration from Harvard University Graduate School of Business Administration as a Baker Scholar

Chief Medical Informatics Officer, Viewics

Page 4: Revolutionizing Renal Care With Predictive Analytics for CKD

Dave Garnett

• Multi-­time entrepreneur and recognized authority on cloud computing, Big Data analytics, and entrepreneurship

• Leads product management, product marketing, and user experience• Formerly held senior executive management positions at Actuate

Corporation, Gluster, and 24-­7 Customer

• Founder of Axcient, Digital Data Storage, and Feedheed

• Studied Philosophy at Santa Clara University

VP of Products, Viewics

Page 5: Revolutionizing Renal Care With Predictive Analytics for CKD

Agenda

• Chronic Kidney Disease – The Problem (Eleanor Herriman)

• The Kidney Failure Risk Equation – A New Solution (Navdeep Tangri)

• Viewics CKD Management – A KFRE Program (Dave Garnett)

• Discussion and Q&A

Page 6: Revolutionizing Renal Care With Predictive Analytics for CKD

The CKD Problem

Page 7: Revolutionizing Renal Care With Predictive Analytics for CKD

About CKD

Characterized by a gradual loss of kidney function over time

Diagnosis -­ kidney damage (albuminuria) or

decreased kidney function (GFR <60) for 3

months+

Diabetes and hypertension responsible for two-­thirds of cases

Increased risk of cardiovascular disease –heart complications

major cause of death for CKD patients

Early detection and treatment can often slow

progression vs. progression to end stage

failure (dialysis or transplant)

Source: National Kidney Foundation (NKF) and Lancet 2012;; 379: 165–80

Page 8: Revolutionizing Renal Care With Predictive Analytics for CKD

CKD High Incidence and Prevalence

54%

44%

40%Prevalence in Diabetics

Prevalence Over 65 yrs

U.S. Lifetime Incidence for 30 -­ 49 yrs

26M in U.S.

Source: NKF and Hoerger TJ et al. Am J Kidney Dis. 2015;;65(3):403-­411

Page 9: Revolutionizing Renal Care With Predictive Analytics for CKD

Morbidity and Mortality

CKD Cardiovascular disease

Risk factor for medical errors

Acute kidney injury

Anemia

Bone disorders

Nervous system / CNS (malnutrition)

Page 10: Revolutionizing Renal Care With Predictive Analytics for CKD

Economic Burden -­ Medicare

$48 B

$33 B

0

10

20

30

40

50

60

CKD ESRD

2010 CMS Spend In U.S. $B

Source: Hoerger TJ et al. Am J Kidney Dis. 2015;;65(3):403-­411

Page 11: Revolutionizing Renal Care With Predictive Analytics for CKD

Economic Burden -­ Private

$25 K

$46 K

05101520253035404550

Stage 3 CKD Stage 4 CKD

2015 Private Payer Spend Per Patient $K

Workdays missed exceed 10 hours per week for employees with CKD

Page 12: Revolutionizing Renal Care With Predictive Analytics for CKD

Traditional CKD Management –Stages by Glomerular Filtration Rate (GFR)

• PCP• “Low risk”• Very low awareness of disease

Stage 1-­2

• Variability in nephrologist referral• Effective Rx available • Progression unpredictable

Stage 3 • Nephrologist• Significant numbers actually lower risk

Stage 4

• Discuss dialysis options / implant fistula• 66% of ESRD not referred in time for dialysis

Stage 5

Page 13: Revolutionizing Renal Care With Predictive Analytics for CKD

The Volume – Risk Dilemma

Young Patients

Higher Risk of Progressing

Few Patients with CKD

Risk

Volume

Elderly

Most at Low Risk of Progressing

Many Patients with CKD(50% over 70 yo)

Risk

Volume

Need Predictive Model to Identify Progressors

Page 14: Revolutionizing Renal Care With Predictive Analytics for CKD

Current CKD Management Suboptimal

“With current treatment, nearly half of patients progress tounfavorable renal and cardiovascular outcomes. … Attention totraditional measures of kidney function (eg, eGFR) is no longeradequate to optimally manage and care for patients with CKD.”

Lee Ann Braun et al., RTI Health Solutions, Ann Arbor, MI, and Mitsubishi Tanabe Pharma America, Inc,

Warren, NJ, USA

Source: International Journal of Nephrology and Renovascular Disease 2012:5 151–163

Page 15: Revolutionizing Renal Care With Predictive Analytics for CKD

Challenges of CKD

Patients diagnosed later in disease spectrum –poor awareness

High variability of condition and patient risk of progression – difficult

to manage

Treatments and specialty care can impact

outcomes, but must be targeted appropriately

(clinical and $)

Page 16: Revolutionizing Renal Care With Predictive Analytics for CKD

Kidney Failure Risk Equation

Page 17: Revolutionizing Renal Care With Predictive Analytics for CKD

Why CKD risk prediction

• Early and appropriate nephrology care

• Prognostic Information for patient and provider

• Clinical trial enrollment

• Dialysis resource management

Source: Tangri et al. Curr Opinion Nephrology Hypertension 2013

Page 18: Revolutionizing Renal Care With Predictive Analytics for CKD

Care for Advanced CKD• Progression challenges

– Not all patients with CKD Stage 4 progress

– Some patients with CKD Stage 3 are at a higher risk for progression

• Managing care by GFR– GFR based care for advanced CKD misses the mark– Care should be aligned with risk rather than eGFR

• Complexity of advanced care– Patients with CKD Stages 4-­5 are at high risk of adverse events including progression to

kidney failure– Advanced care patients need education about treatment options, modality planning and

specialized nutrition

– Interdisciplinary care in clinics is delivered by health care teams (nurses, dieticians, pharmacist & nephrologist) -­ $ 1600 annually

Page 19: Revolutionizing Renal Care With Predictive Analytics for CKD

Ideal Model

• Across spectrum of chronic kidney disease

• Electronic ascertainment and reporting

• Improve discrimination and reclassification beyond standard of care

• Externally validated in diverse patient populations

Source: Tangri et al. Annals of Internal Medicine 2013

Page 20: Revolutionizing Renal Care With Predictive Analytics for CKD

Published Online FirstApril 11, 2011

Available atwww.jama.com

Page 21: Revolutionizing Renal Care With Predictive Analytics for CKD

A prediction model for progression of CKD to Kidney Failure

• Patients with CKD Stages 3 – 5

• Followed by nephrologists in Ontario and British Columbia, Canada

• 8,391 participants with 1,563 kidney failure events

• Multiple lab based prediction models

Page 22: Revolutionizing Renal Care With Predictive Analytics for CKD

Risk Prediction Models

Page 23: Revolutionizing Renal Care With Predictive Analytics for CKD

Kidney Failure Risk Equation (KFRE)

• We developed laboratory based prediction models that accurately predict the progression of CKD (C statistics 0.84 – 0.91)

• Our preferred models use routinely collected laboratory data– 3 variable KFRE – Age, Sex, eGFR– 4 variable KFRE – Age, Sex, eGFR, ACR– 8 variable KFRE – + Calcium, Phosphorous, Bicarbonate and Albumin

Source: Tangri et al. JAMA 2011

Page 24: Revolutionizing Renal Care With Predictive Analytics for CKD

External Validation

• Initially valid in two Canadian cohorts of patients referred to nephrologists

• Concerns re: validity in other ethnicities and health systems

• External validation needed prior to global adoption

Page 25: Revolutionizing Renal Care With Predictive Analytics for CKD

Tangri et al. JAMA 2016

Page 26: Revolutionizing Renal Care With Predictive Analytics for CKD

Study Design

• A validation study of the KFRE in patients with CKD 3-­5 (referred and unreferred) from the CKD Prognosis Consortium (CKD-­PC)

• Individual level meta-­analysis of beta coefficients, discrimination, calibration and net reclassification improvement

• Individual-­study and pooled measures of model validation

Page 27: Revolutionizing Renal Care With Predictive Analytics for CKD

Discrimination

Page 28: Revolutionizing Renal Care With Predictive Analytics for CKD

Ontario Renal Network and KFRE

• ORN– Responsible for providing all dialysis and advanced CKD care in the province of Ontario, Canada

– Includes 11,000 patients on dialysis, and 17,000 patients with advanced CKD– Annual budget exceeds $ 640 million

• Plan for implementing KFRE– Adopt risk based thresholds as eligibility criteria for interdisciplinary care – Align level of risk with intensity of care– Cost savings of $ 1300 annually per patient– Annualized savings of $ 7 million for the ORN

Page 29: Revolutionizing Renal Care With Predictive Analytics for CKD

Conclusions

• The KFREs accurately predict the risk of kidney failure requiring dialysis in patients with CKD Stages 3-­5 for up to 5 years

• Risk prediction is accurate across multiple countries and subpopulations

• The equation is simple and highly accurate and can be integrated into clinical practice

Page 30: Revolutionizing Renal Care With Predictive Analytics for CKD

Viewics CKD Management™

Page 31: Revolutionizing Renal Care With Predictive Analytics for CKD

Viewics CKD Management™ – Care Protocol

Risk score > xx%* over 5 years• Refer from PCP to nephrologist

Risk score > yy%* over 2 years• Refer to multidisciplinary clinic / hospital support

Risk score > zz%* over 2 years• Discuss dialysis / transplants preparations

Risk score > aa%* over 2 years• Place AVF fistula

* Exact thresholds provided to Viewics customers only

Page 32: Revolutionizing Renal Care With Predictive Analytics for CKD

• Alerts on at risk patients who have not been referred to Nephrologists

• View all CKD patients you provide care for

• Create and send a customized report to the patient– Where they are with disease

progression– Treatment options

– Information about the disease

Viewics CKD Management™: Sample Dashboard -­ Physician View

Page 33: Revolutionizing Renal Care With Predictive Analytics for CKD

• Dashboard showing entire population of CKD patients under their care, their staging information, and their risks of ESRD

• Stoplight for low / medium /high risk

• Alerts for high risk patients • Most recent risk score and date

of testing • Determination of whether or not

care protocol has been followed• Provides traditional staging for

reference

Viewics CKD Management™: Sample Dashboard -­ Nephrologist View

Page 34: Revolutionizing Renal Care With Predictive Analytics for CKD

Source: Navdeep Tangri, MD, FRCPC, http://kidneyfailurerisk.com/

Viewics CKD Management™: Sample Report -­ Patient View

Page 35: Revolutionizing Renal Care With Predictive Analytics for CKD

Viewics CKD Management™: Sample Dashboard -­ Executive View

• View Patients by Risk• Make sure your PCPs are

following the care protocol• Track where patients are

within the system• See trends on CKD

throughout the system• Benchmark against other

systems*• View to date & projected

savings*

Page 36: Revolutionizing Renal Care With Predictive Analytics for CKD

Viewics CKD Management™ -­ Benefits

BENEFITS TO PATIENTS, CLINICIANS AND HEALTH

Personalized risk scores with care management and patient education reports lower patient anxiety levels and improve PCP care.

More appropriate and earlier nephrologist care translates to better patient choices, fewer emergency cases, lower costs, better outcomes.

More accurate and standardized risk prediction improves resource utilization, generating savings. Inaccurate CKD staging causes over and under-­treatment, driving avoidable costs and adverse events.

Validated risk thresholds provide care management pathways. PCPs directed to refer to nephrologists, and nephrologists to discuss dialysis, thus slowing progression and improving Renal Failure outcomes.

Sources: Tangri N et al. JAMA. 2016;;315(2):164-­174;; Personal communication with Navdeep Tangri, MD, FRCPC;; Lee et al. BMC Health Services Research 2012, 12:252;; Lee J, et al. (2014) PLoS ONE 9(6): e99460.

Page 37: Revolutionizing Renal Care With Predictive Analytics for CKD

Viewics CKD Management™ – Expected Savings

$800K savings

Better care slows progression to 3b

$1M savings

Risk scores change 30% of Stage 4s to Stage 3

$210K savings

Helps decrease necessity of dialysis/transplant for patients

Delay in Stage 3 Progression

ReclassifyingStage 4

Earlier Nephrologist Care Pre-­Renal Failure

Sources: Tangri N et al. JAMA. 2016;;315(2):164-­174;; Personal communication with Navdeep Tangri, MD, FRCPC;; Lee et al. BMC Health Services Research 2012, 12:252;; Lee J, et al. (2014) PLoS ONE 9(6): e99460;; Reaven NL et al. Am J Pharm Benefits. 2014;;6(6):e169-­e176.

Total Savings for Health System with Population of 25,000 ~ $2M

Page 38: Revolutionizing Renal Care With Predictive Analytics for CKD

Want to follow up?

Contact Dr. Eleanor Herriman

[email protected]

Download the recording

Click hereor visit

goo.gl/ct6Zm3

For further information and resources on this topic, please visit renalrisk.com