Case Study: Acute PREDICT

Cardiovascular Prevention Program andAcute Coronary Syndrome database

Andrew Kerr and Andrew McLachlan, Cardiology Dept

Middlemore Hospital

Themes

• Motivation

• Team approach

• Willingness to learn and adapt

• Data collection as part of the clinicalwork-flow ideally to drive decision support

• Electronic data collection, decision supportand reporting

• Program development driven by results anddata

The Treatment Gap

Discrepancy between ideal

CVD risk management and

what happens in real life

Counties Manukau

55%

40%

62%

36%

50%

22%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Perc

enta

ge a

chie

ved

Prescribed Aspirin Prescribed Statin Systolic BP< 130

Management Indicators in High Risk Patients

CVD or Equivalent Risk > 15%

Sinclair and Kerr, NZMJ 2006

CCU audit highlights an “issue”

• Increasing CCU staff turnover/less experienced nurses

• Shorter but more intensive inpatient stays

• Poor uptake of cardiacrehabilitation

• Primary care input on dischargeunknown/variable.

100%

52%4%

0%

0%

20%

40%

60%

80%

100%

Documented lifestyle interventions

Smoking

Cardiac Rehab

Diet

Activity

Weight loss

BP/lipid lowering

78%

64%

24%

0% 0%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Assessment documented

Smoking addressed

Weight

Height

BMI

Waist circumference

The “golden” 24 hours

• Robinson (2001) argues that healthfunding for health promotion is targetedat primary health care and nurses in acutecare have slowly developed a belief thathealth education is no longer their role.

Acute Predict Project

• To implement an electronic clinical decision supportsystem (Acute Predict™) to more systematically managepeoples cardiovascular disease (CVD) risk factors inacute care.

• Run by nursing and junior medical staff

• To collect and analyse data to help strategically plan aconsidered response.

The PREDICT CVD ECDS Program

• ECDS vision shared by multiple stakeholders» ProCare

» CMDHB/WDHB

» University of Auckland

» Enigma

» Ministry of Health

» National Heart Foundation

» National Cardiovascular Advisory Group

» New Zealand Guidelines Group

Predict Electronic Program

• Use Information Technology to provide on-line CVD riskassessment and management advice which is:

– Fast

– User Friendly

– Guideline and evidence based

– Patient Individualised

• Using the PREDICT tool integrated with PMS– PROMPT

– CMDHB CCM

– Acute Predict– CVD/DM Predict

The Acute PREDICT implementation process

A Plan, Do, Study, Act quality improvement cycle.

2004

2007

Frequent training

sessions including

one on one support

to develop

“CHAMPIONS”

Monthly “user” group

identify issues and

develop action plans

Monthly audit

and feedback

results to CCU

team

Celebrate

Success’s

Regular marketing to

keep Predict in

peoples thoughts

Ongoing development of software

by Enigma publishing Ltd acting

on user feedback

Patient resources and

information

Nurse surveys and

act on feedback

0%

10%

20%

30%

40%

50%

60%

70%

80%

Feb

May

July

Aug

Sept Oc

tNov

Dec

Jan FebMarch

2005-2006

month

% of total

CCU/SDU

population

screened

The things you can do to reduce your risk of heart or stroke problems in the future include:

Stay smokefree

Keep as active as you can , on as many days a week as you are able.

Try and keep your weight stable or lose weight if you are too heavy.

Follow a healthy eating plan

Take the medications you have been prescribed regularly.

Talk to someone if you are stressed or feeling low

Your doctor and nurses are happy to help you

keep as well as you can.

Who carries responsibility for managing your information?

The Acute CVD PREDICT Administrator, who can be contacted C/O

CCU, Middlemore Hospital, Pr ivate Bag 94052, South Auckland Mail Centre, if you have any c

Together we can make a difference.

Cardiovascular disease (CVD) includ ing heart attacks and strokes and diabetes are major problem in New

Zealand today. The good news is that there are many things that both you and your doctors and nurses can do to reduce your risk of heart attack or stroke or the

complications of diabetes.

Goal 1 - Increase use

of PREDICTGoal 2- Improve workflow

process

Goal 3- Close loop-

Get PREDICT info to GP

ACS data base within AcutePredict

KPIs• Finalised patients on Statin

• Finalised patients on Aspirin

• Finalised IHD/CABG/PTCA patients on Beta

• Finalised patients on ACE and/or ATII blocker

• Current smokers who got smoking cessation advice

• Door to balloon < 90 min for primary PCI STEMI

• Door to needle < 30 min for thrombolysed STEMI

• Revascularisation (PCI or CABG referral)

• Aspirin< 24h

• ACE and/or ATII blocker in patients with EF <40%at D/C

• Beta-blocker in patients with EF <40% at D/C

Cardiac out patients

Innovations increasingscreening

COP monthly PREDICT screenings

0

10

20

30

40

50

60

70

80

12/06. 1/07. 2/07. 3/07. 4/07. 5/07. 6/07. 7/07.

DM

non DM

CVD risk

questionnaire

introduced

Gout clinics

Patients with >15% CVD risk on management

n = 42

0%

20%

40%

60%

80%

100%

Aspirin Statin BP therapy

non DM n=26

DM n=16

Over 50% >15%/High risk

Significant treatment gaps

Incidence of ACS admission to CCU perhead of CMDHB population

0

2

4

6

8

10

12

35-44 y 45- 54 y 55 - 64 y

NZ Euro/Other NZ Maori Pacific All Asian

Modifiable risk: Smoking

0

10

20

30

40

50

60

70

15-44 45-54 55-64 65-74

Sm

oki

ng

(%

)

Other

Maori

Pacific

Indian

Modifiable risk: BMI

20

22

24

26

28

30

32

34

36

38

40

15-44 45-54 55-64 65-74

Me

an

BM

I

Other

Maori

Pacific

Indian

Modifiable risk: Diabetes

0

10

20

30

40

50

60

15-44 45-54 55-64 65-74

Dia

be

tes

(%

)

Other

Maori

Pacific

Indian

Predict database

NHI + Predict data

Encrypted NHI

+ baseline data

NHI & eNHI

NZHIS

eNHI is linked to outcomes data

(hospital admissions, deaths)

eNHI

+ outcomes data

University of Auckland

Combines Predict data

with NZHIS outcomes

GP practice

RA and

management advice

Linkage of risk to outcomesapproved by Multicentre Ethics Committee

Results: events in risk groups in first30,878 patients

Risk <10%

68%

Risk 10-<15%

11%

Risk 15+%

9%

Hx CVD

12%

47%

27%26%

74% of events occur in 32% of the people, 26% in low-risk people

The Framingham score & NZ Guidelineadjustments: useful for CVD risk

prediction in NZ?

J B Broad, R J Marshall, S Wells,

A J Kerr, T Riddell, R Jackson

on behalf of HRC-Predict Co-Investigators

Work in progress: Preliminary results

Results: est. 5-year incidence

For prior CVD 5-year risk is: 20 + 1.3*Framingham score

Mean est. 5-year incidence for Hx CVD is 28.4% (95%CI 26.3 to 30.4)

0

10

20

30

40

50

60

<5 5-<10 10-<15 15-<20 20+

Framingham score (5-year risk, %)

Cu

mu

lati

ve

in

cid

en

ce

(%

)

of

CV

D e

ve

nt

in 5

ye

ars

(9

5%

CI)

for Hx CVD

no Hx CVD

Gotta keep moving…

Where to next?

• Support CVD risk screening in other areas of thehospital

• Support development of nursing workforce toassess and manage CVD risk as part of anadvanced role

• Next audit loop – use data reports/exceptionreporting to identify problem areas and designprograms to address these

• Move towards electronic integration of PREDICTwith primary care

• Develop HF/AF eCDS

• ? Electronic care planning

Acknowledgements• School of Population Health – particularly Rod Jackson,

Sue Wells, Tania Riddell, Jo Broad

• Many many GPs, practice nurses and clinicalmedical/nursing specialists

• Primary Healthcare organisations –esp ProCare,HealthWest,

• Enigma Publishing

• New Zealand Guidelines Group

• National Cardiovascular Advisory Group

• Maori Cardiovascular Group

• Ministry of Health Clinical Services Directorate

• National Heart Foundation

• Diabetes NZ

• Counties Manukau District Health Board esp ChronicCare Management programme, Middlemore HospitalCardiology and Diabetes Services

• Medtech Global Ltd

• Health Research Council