- Email: [email protected]
Acute Predict: A Clinician-Led Cardiovascular Disease Quality Improvement Project (Predict-CVD 12)
ORIGINAL ARTICLE
Original Article
Acute Predict: A Clinician-Led Cardiovascular Disease Quality Improvement Project (Predict-CVD 12) Andrew J. Kerr, MBChB a,b,∗ , Jen Li Looi, MBChB a , Daniel Garofalo, MBChB a , Sue Wells, MBChB b and Andy McLachlan, MN a b
a Department of Cardiology, Middlemore Hospital, Auckland, New Zealand Section of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand
Background: New Zealand data demonstrate major disparities in cardiovascular health, particularly by ethnicity and socioeconomic deprivation. Acute Predict Aim: Acute Predict, the secondary care arm of primary care based PREDICT, is a multidisciplinary project based in the coronary care unit, and is jointly led by nursing and medical staff. The project aim is to ensure patients with acute coronary syndromes (ACS) receive appropriate evidence-based secondary prevention management short- and long-term, regardless of age, socioeconomic status or ethnicity. Methods and Results: Acute Predict utilises an electronic backbone to provide the following (1) guideline-based patient-specific decision support, (2) data collection as part of routine clinical workflow, (3) linkage of patients to cardiac rehabilitation and primary care chronic care management programs, (4) clinical and management data capture, (5) realtime whole group and sub-group Key Performance Indicators reporting with drill-down to individual patient data, and (6) long-term tracking of individual patient outcome via linkage to national databases. Over the four years of the project in-hospital provision of cardiac rehabilitation has improved and appropriate discharge medication is high. There are no differences according to ethnicity. Despite this, Maori patients in the Acute Predict ACS cohort are twice as likely as Europeans to have recurrent events post-discharge, even after adjustment for known risk factors. Conclusions: The built-in real-time data reporting and outcomes/prescribing linkage facilitate monitoring of the quality of CVD prevention activity across the continuum of care. It allows early identification of treatment gaps and of persistent disparities in outcome in our patients. We are learning how best to use this real-time data collection and reporting to support the design and assessment of targeted interventions to close gaps and reduce disparity. (Heart, Lung and Circulation 2010;19:378–383) © 2010 Australasian Society of Cardiac and Thoracic Surgeons and the Cardiac Society of Australia and New Zealand. Published by Elsevier Inc. All rights reserved. Keywords. Indigenous; Acute coronary syndrome; Secondary prevention; Clinical decision support system; Health disparities; Quality improvement
Introduction
N
ew Zealand data demonstrate major disparities in cardiovascular health, particularly by ethnicity and socioeconomic deprivation. PREDICT is a cardiovascular disease (CVD) prevention project spanning primary and secondary care, which uses a clinical decision support system (CDSS) to provide CVD risk assessment and personalised guideline-based management advice, and tracks long-term outcomes by encrypted linkage to the New Zealand Health Information Service database. There
∗ Corresponding author at: Department of Cardiology, Middlemore Hospital, Private Bag 933111, Otahuhu, Auckland, New Zealand. Tel.: +64 9 2760000; fax: +64 9 2709746. E-mail address: [email protected] (A.J. Kerr).
are over 100,000 patients in the Predict cohort, the majority in primary care. We have shown that nearly half of new CVD events occur in those with prior CVD [1]. At least 50% of future CVD events can be prevented in this population with adherence to evidence-based lifestyle, pharmacological and interventional management [2].
Acute Predict Aims The aim of the Acute Predict project is to ensure patients with acute coronary syndromes (ACS) receive appropriate evidence-based medicine (EBM) short- and long-term, regardless of age, gender, socioeconomic status or ethnicity. The project is supported by novel electronic decision support, and real-time key performance indicator (KPI) and outcome reporting.
© 2010 Australasian Society of Cardiac and Thoracic Surgeons and the Cardiac Society of Australia and New Zealand. Published by Elsevier Inc. All rights reserved.
1443-9506/04/$36.00 doi:10.1016/j.hlc.2010.02.016
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Methods Acute Predict Acute Predict is the secondary care arm of PREDICT. It is a multidisciplinary quality improvement project based in the coronary care unit and cardiology out-patient clinics, and is jointly led by nursing and medical staff [3]. It links into primary care as a component of a region-wide CVD prevention strategy. To date over 5000 patients have been managed through this process. Acute Predict utilises an electronic backbone (see Figs. 1 and 2) to provide the following: (1) CVD prevention decision support: on-line guidelinebased patient-specific decision support is provided by Predict CVDDM electronic clinical decision support (ECDS) (2006 to present), an electronic translation of the New Zealand Guidelines for CVD Risk Assessment and Management [4]. Between 2004 and 2006 PredictCVD decision support was used, an earlier program based on prior interim New Zealand guidelines [5]. The software is integrated with local patient management systems (laboratory data and demographic information) and provides within-seconds evidencebased patient-tailored decision support CVD risk management. Further information on data collected for a Predict CVDDM assessment was reported previously [6]. An editable action plan is generated for clinicians, and a personalised CVD risk factor treatment plan is printed for the patient and used as the basis for in-hospital cardiac rehabilitation [7]. In-hospital cardiac rehabilitation management is recorded electronically via the editable action plan.
Figure 1. The Acute Predict quality improvement process.
(2) Acute Coronary Syndrome database: This is run in parallel with Predict CVDDM and collects data on CCU patients not collected as part of the Predict CVDDM decision support module. This includes risk stratification, diagnostic, investigation, management and in-hospital outcome data. (3) Data collection as part of routine clinical workflow. (4) Linkage of patients to cardiac rehabilitation and primary care chronic care management programs. (5) Real-time whole group and sub-group KPI reporting from the CVDDM and Acute Coronary Syndrome
Figure 2. The process of anonymised linkage of Predict risk assessment data with New Zealand Health Information Service (NZHIS) hospital discharge and mortality data. Each patient in New Zealand has a unique National Health Identifier (NHI) number. For each patient the University of Auckland (U of A) receives a copy of the CVD risk assessment data with an encrypted NHI (eNHI). The NHI and its paired eNHI are linked by NZHIS to the individual patient outcomes. The NHI is then stripped from the data set, and the outcome data with eNHIs are sent to the U of A and combined with the Predict data for analysis.
ORIGINAL ARTICLE
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databases, with drill-down to individual patient data to support clinical audit. (6) Post-discharge tracking of individual patient outcome via encrypted linkage to national databases [1]. Data sources: Predict CVDDM data items are those required for CVD and (Diabetes Mellitus) DM risk prevention and management. Data is available since 2004. The Acute Coroanry Syndrome (ACS) database was added to capture in-hospital investigation, management and outcome data in August 2007. For this report, data was extracted for the first 22 months to May 2009. Currently this system does not capture attendance at post-discharge cardiac rehabilitation. We have audited participation in post-discharge cardiac rehabilitation over 1 year from March 2008 to February 2009 using data from the hospital clinical information system. From August 2009 cardiac rehabilitation will be reported routinely through Predict.
Results Population Predict CVD data from 2004 to 2006 was analysed to understand the relationships between CVD risk factors and demographic variables including ethnicity and socioeconomic status in our ACS population [3]. There were 61.5% ¯ classified as European or other ethnicity, 13.0% NZ Maori, ¯ 15.2% Pacific,and 10.3% South Asian. The Maori, Pacific and South Asian patients were younger than the European/other group by 10.7, 9.3 and 7.3 years respectively,
Heart, Lung and Circulation 2010;19:378–383
and more likely to live in areas of greater deprivation (65%, 83% and 49% in New Zealand Deprivation Index 2001 [8] deciles 9 or 10, respectively, compared with 30% of European/other patients). Compared with the Euro¯ pean/other patients, the Maori and Pacific patients were more likely to smoke (21% vs. 47% and 33%, respectively) and have higher levels of risk factors associated with the metabolic syndrome, including Type II diabetes (18% vs. 32% and 39% respectively), obesity, elevated triglycerides and low HDL (Fig. 3). The burden of these risk factors was ¯ higher in younger Maori and Pacific patients, compared with the European/other groups, but persisted across the age range.
Acute Predict Utilisation Over the 4 years of the project, the in-hospital provision of cardiac rehabilitation to ACS patients using the Predict decision support has improved from 54% over the first 2 years to 85% in the last year. There were no differences in utilisation of the in-patient Predict decision support according to ethnicity or socioeconomic status [3]. In the first 22 months of the ACS database 99% of ACS patients admitted to CCU (n = 1244) have complete ACS data.
Key Performance Indicators Key performance indicators include, appropriate prescription of CVD medication at discharge, in-hospital angiography and percutaneous coronary intervention
Figure 3. The percentage of smokers and patients with diabetes by age group and ethnicity in ACS patients presenting to Middlemore CCU.
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Table 1. Key Performance Indicators by Ethnicity in ACS Patients. All (n = 1244)
¯ Maori (n = 114)
Pacific (n = 224)
Indian (n = 157)
European/other (n = 749)
p-Valuea
Aspirin Statin Beta-blocker ACEi/ARB Angiogram In-hospital PCI CABG referral Total revasc. PredictCVDDM
97% 95% 86% 63% 81% 43% 17% 60% 81%
96% 96% 85% 64% 79% 38% 18% 56% 84%
99% 97% 85% 72% 76% 22% 25% 47% 80%
96% 97% 89% 65% 77% 39% 21% 59% 78%
98% 94% 86% 61% 84% 51% 14% 65% 82%
0.27 0.23 0.77 0.025 0.024 <0.001 0.003 <0.001 0.63
Attended CR
54%
71%
38%
52%
56%
0.022
ACEi; angiotensin converting enzyme inhibitor, ARB; angiotensin receptor antagonist, PCI; percutaneous coronary intervention, CABG; coronary artery bypass grafting, CR; cardiac rehabilitation. a Chi-square test.
(PCI) and referral for coronary artery bypass grafting (CABG) the use of PREDICT CDSS and cardiac rehabilitation attendance (Table 1). Appropriate discharge medication is high across ethnic groups. Rates of coronary angiography are high in all groups but slightly higher in ¯ European compared with Maori patients (84% and 79%, respectively). Whilst use of PCI is highest in European ¯ patients the rate of CABG is higher in Maori and Pacific patients. The overall revascularisation rate is higher in ¯ European than Maori. Overall revascularisation rates are lowest in the Pacific patients, driven by the low rates of PCI in this group. In-hospital cardiac rehabilitation using Predict CVDDM ECDS was high in all ethnic groups. Overall participation by ACS patients in post-discharge cardiac rehabilitation was 54% over the last year. Atten¯ dance rates were higher for Maori than European, and lowest for Pacific patients (see Table 1).
was defined for the composite endpoint of cardiovascular mortality, rehospitalisation with MI or stroke. The Kaplan–Meier event free survival for this outcome set at 4.2 years is 75% [9] (Fig. 4). Cox proportional hazards modelling was used to investigate predictors of the composite “hard” CVD endpoint of CVD mortality and MI/stroke. The following variables ¯ were assessed – age, gender, ethnicity (European, Maori, Pacific, Indian), NZDep01, diabetes mellitus, prior CVD, BMI, waist circumference, family history of CVD, TC/HDL, systolic BP and smoking status. For the ethnicity variable, ¯ risk was assessed relative Europeans. Maori patients in the Acute Predict ACS cohort are twice as likely as Europeans to have recurrent events post-discharge, even after adjustment for known risk factors (see Table 2).
Discussion Post-Discharge Outcomes A total of 1923 patients discharged alive after ACS admission (2004–2008) were identified. An ICD10 coding set
The Counties-Manukau area, the catchment for Middlemore Hospital, is ethnically and culturally diverse, and has one of the highest levels of deprivation in
Figure 4. Kaplan–Meier event free survival after ACS discharge using the composite endpoint of recurrent MI, stroke or CVD death. The numbers at risk (number of first events) are displayed.
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Table 2. Independent Predictors of “Hard” CVD Events in the ACS Cohort Post-Discharge. ¯ NZ Maori Pacific Age (/10 years) Diabetes Prior CVD
Hazard Ratio
SE
p
95% CI
2.1 1.51 1.34 1.37 1.45
0.36 0.23 0.05 0.17 0.16
<.001 0.008 <0.001 0.01 0.001
1.5, 2.93 1.12, 2.05 1.22, 1.43 1.08, 1.75 1.16, 1.80
New Zealand [10]. The Acute Predict project aims to ensure that all patients receive evidence-based investigation and management. A flexible approach is required to assist individuals and particular groups to meet the ¯ evidence-based targets. For example the Maori patients have in-hospital support from the Whaanau Support team, ¯ a provider arm of Te Kaahui Ora (Maori Health Unit) and are actively enrolled into post-discharge home-based cardiac rehabilitation via the Heart Guide Aotearoa program [11]. This cognitive behavioural approach to cardiac rehabilitation is based on effective prevention programs in the United Kingdom [12] and has been piloted specifically for ¯ Maori in New Zealand. Patients who smoke are identified early for an in-hospital/post-discharge smoking cessation program. Real-time reporting systems allow us to identify treatment gaps at individual and group levels. For example, the data in this paper showing that Pacific people have poorer attendance at rehabilitation has triggered a collaborative project with the Community Self Management Education Coordinator and the Pacific Cultural Resource Unit to address this issue. The observation that ¯ Maori and Pacific people, compared with European/other people, have similar high rates of evidence-based prevention medication and invasive coronary angiography but a different pattern of revascularisation (more CABG and less PCI), and lower overall rate of revascularisation, has led to a project to investigate this difference further. One important likely contributor to the observed differences is the much higher incidence of ¯ diabetes in the Maori and Pacific patients. Diabetic coronary disease is often multi-vessel and less appropriate for PCI. ¯ Long-term outcomes remain worse in Maori and Pacific patients. The reasons for this are complex and include preexisting high risk lifestyle, family/whanau support, socioeconomic factors, health behaviours, ability of the health system to engage with patients and vice versa. Significant changes to lifestyle and management can be achieved at a single hospital admission and short-term cardiac rehabilitation, but this has to continue into the community and primary care over the long-term. To support this chronic management we are developing systems to (1) routinely track individual patient post-discharge medication use via linkage to the national database, (2) track attendance and completion at out-patient cardiac rehabilitation, and (3) link to an annual CVD “get checked” program via the community based chronic care management program.
Conclusion The built-in real-time data reporting and outcomes/ prescribing linkage facilitate monitoring of the quality of CVD prevention activity across the continuum of care. It allows early identification of treatment gaps and of persistent disparities in outcome in our patients. We are learning how best to use this real-time data collection and reporting to support the design and assessment of targeted interventions to close gaps in evidence-based therapy and reduce disparity.
Acknowledgements The authors would like to thank CCU medical and nursing staff and their patients. Predict CVDDM was developed by a collaboration of clinical epidemiologists at the University of Auckland, IT specialists at Enigma Publishing Ltd. (a private provider of on-line health knowledge systems) and group of clinicians and support staff from Middlemore Hospital, Counties Manukau District Health Board, ProCare Health Ltd., National Heart Foundation, New Zealand Guidelines Group and the Ministry of Health. PREDICT software platform is owned by Enigma Publishing Ltd. (PREDICT is a trademark of Enigma Publishing Ltd.). The Acute PREDICT software platform is a version of Predict CVDDM. It was adapted and enhanced for secondary care services collaboratively by Enigma Publishing Ltd. and the Department of Cardiology, Middlemore Hospital. Funding: The PREDICT research project is supported by a grant HRC 03/183 from the Health Research Council.
References [1] Kerr AJ, Broad J, Wells S, Riddell T, Jackson R. Should the first priority in cardiovascular risk management be those with prior cardiovascular disease? Heart 2009;95:125–9. [2] Wald NJ, Law MR. A strategy to reduce cardiovascular disease by more than 80%. British Medical Journal 2003;326:1419. [3] Kerr AJ, McLachlan A, Furness S, Broad J, Riddell T, Jackson R, Wells S. The burden of modifiable cardiovascular risk factors in the coronary care unit by age, ethnicity, and socioeconomic status—PREDICT CVD-9. New Zealand Medical Journal 2008;121:20–33. [4] New Zealand Guidelines Group. The assessment and management of cardiovascular risk. New Zealand: Wellington; 2003. [5] Wells S, Jackson R. Online management of cardiovascular risk in New Zealand with PREDICT—getting evidence to the “Moment of Care”. Health Care & Informatics Review Online 2005. [6] Colvine K, Kerr AJ, McLachlan A, Gow P, Kumar S, Ly J, Wiltshire C, Robinson E, Dalbeth N. Cardiovascular disease risk factor assessment and management in gout: an analysis using guideline-based electronic clinical decision support. New Zealand Medical Journal 2008;121:73–81. [7] New Zealand Guideline Group. Cardiac rehabilitation guidelines: Wellington, NZ; 2002. [8] Salmond C, Crampton P. NZDep2001 Index of Deprivation Users Manual: Department of Public Health, Wellington School of Medicine and Health Sciences, 2002.
[9] Looi J, McLachlan A, Lee M, Pylypchuk R, Kerr A. Anonymised electronic tracking of ACS patient outcome and linkage to CVD risk factors in South Auckland: proof of concept. Cardiac Society of Australiia and New Zealand. Sydney; 2009. [10] Counties Manukau District Health Board. Primary Care Plan: http://www.cmdhb.org.nz/About CMDHB/Planning/PrimaryCare-Plan/CMDHB-PHCplan-2007-2010.pdf; 2007.
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[11] Cuthill JC, Brown M. Sweet southern success for Heart Guide Aotearoa. Heart Lung & Circulation 2008;17:S5. [12] Jolly K, Taylor RS, Lip GYH, et al. The Birmingham Rehabilitation Uptake Maximisation Study (BRUM). Homebased compared with hospital-based cardiac rehabilitation in a multi-ethnic population: cost-effectiveness and patient adherence. Health Technology Assessment 2007;11:1–118.
ORIGINAL ARTICLE
Heart, Lung and Circulation 2010;19:378–383
Original Article
Acute Predict: A Clinician-Led Cardiovascular Disease Quality Improvement Project (Predict-CVD 12) Andrew J. Kerr, MBChB a,b,∗ , Jen Li Looi, MBChB a , Daniel Garofalo, MBChB a , Sue Wells, MBChB b and Andy McLachlan, MN a b
a Department of Cardiology, Middlemore Hospital, Auckland, New Zealand Section of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand
Background: New Zealand data demonstrate major disparities in cardiovascular health, particularly by ethnicity and socioeconomic deprivation. Acute Predict Aim: Acute Predict, the secondary care arm of primary care based PREDICT, is a multidisciplinary project based in the coronary care unit, and is jointly led by nursing and medical staff. The project aim is to ensure patients with acute coronary syndromes (ACS) receive appropriate evidence-based secondary prevention management short- and long-term, regardless of age, socioeconomic status or ethnicity. Methods and Results: Acute Predict utilises an electronic backbone to provide the following (1) guideline-based patient-specific decision support, (2) data collection as part of routine clinical workflow, (3) linkage of patients to cardiac rehabilitation and primary care chronic care management programs, (4) clinical and management data capture, (5) realtime whole group and sub-group Key Performance Indicators reporting with drill-down to individual patient data, and (6) long-term tracking of individual patient outcome via linkage to national databases. Over the four years of the project in-hospital provision of cardiac rehabilitation has improved and appropriate discharge medication is high. There are no differences according to ethnicity. Despite this, Maori patients in the Acute Predict ACS cohort are twice as likely as Europeans to have recurrent events post-discharge, even after adjustment for known risk factors. Conclusions: The built-in real-time data reporting and outcomes/prescribing linkage facilitate monitoring of the quality of CVD prevention activity across the continuum of care. It allows early identification of treatment gaps and of persistent disparities in outcome in our patients. We are learning how best to use this real-time data collection and reporting to support the design and assessment of targeted interventions to close gaps and reduce disparity. (Heart, Lung and Circulation 2010;19:378–383) © 2010 Australasian Society of Cardiac and Thoracic Surgeons and the Cardiac Society of Australia and New Zealand. Published by Elsevier Inc. All rights reserved. Keywords. Indigenous; Acute coronary syndrome; Secondary prevention; Clinical decision support system; Health disparities; Quality improvement
Introduction
N
ew Zealand data demonstrate major disparities in cardiovascular health, particularly by ethnicity and socioeconomic deprivation. PREDICT is a cardiovascular disease (CVD) prevention project spanning primary and secondary care, which uses a clinical decision support system (CDSS) to provide CVD risk assessment and personalised guideline-based management advice, and tracks long-term outcomes by encrypted linkage to the New Zealand Health Information Service database. There
∗ Corresponding author at: Department of Cardiology, Middlemore Hospital, Private Bag 933111, Otahuhu, Auckland, New Zealand. Tel.: +64 9 2760000; fax: +64 9 2709746. E-mail address: [email protected] (A.J. Kerr).
are over 100,000 patients in the Predict cohort, the majority in primary care. We have shown that nearly half of new CVD events occur in those with prior CVD [1]. At least 50% of future CVD events can be prevented in this population with adherence to evidence-based lifestyle, pharmacological and interventional management [2].
Acute Predict Aims The aim of the Acute Predict project is to ensure patients with acute coronary syndromes (ACS) receive appropriate evidence-based medicine (EBM) short- and long-term, regardless of age, gender, socioeconomic status or ethnicity. The project is supported by novel electronic decision support, and real-time key performance indicator (KPI) and outcome reporting.
© 2010 Australasian Society of Cardiac and Thoracic Surgeons and the Cardiac Society of Australia and New Zealand. Published by Elsevier Inc. All rights reserved.
1443-9506/04/$36.00 doi:10.1016/j.hlc.2010.02.016
Kerr et al. Predict and Quality Improvement
379
Methods Acute Predict Acute Predict is the secondary care arm of PREDICT. It is a multidisciplinary quality improvement project based in the coronary care unit and cardiology out-patient clinics, and is jointly led by nursing and medical staff [3]. It links into primary care as a component of a region-wide CVD prevention strategy. To date over 5000 patients have been managed through this process. Acute Predict utilises an electronic backbone (see Figs. 1 and 2) to provide the following: (1) CVD prevention decision support: on-line guidelinebased patient-specific decision support is provided by Predict CVDDM electronic clinical decision support (ECDS) (2006 to present), an electronic translation of the New Zealand Guidelines for CVD Risk Assessment and Management [4]. Between 2004 and 2006 PredictCVD decision support was used, an earlier program based on prior interim New Zealand guidelines [5]. The software is integrated with local patient management systems (laboratory data and demographic information) and provides within-seconds evidencebased patient-tailored decision support CVD risk management. Further information on data collected for a Predict CVDDM assessment was reported previously [6]. An editable action plan is generated for clinicians, and a personalised CVD risk factor treatment plan is printed for the patient and used as the basis for in-hospital cardiac rehabilitation [7]. In-hospital cardiac rehabilitation management is recorded electronically via the editable action plan.
Figure 1. The Acute Predict quality improvement process.
(2) Acute Coronary Syndrome database: This is run in parallel with Predict CVDDM and collects data on CCU patients not collected as part of the Predict CVDDM decision support module. This includes risk stratification, diagnostic, investigation, management and in-hospital outcome data. (3) Data collection as part of routine clinical workflow. (4) Linkage of patients to cardiac rehabilitation and primary care chronic care management programs. (5) Real-time whole group and sub-group KPI reporting from the CVDDM and Acute Coronary Syndrome
Figure 2. The process of anonymised linkage of Predict risk assessment data with New Zealand Health Information Service (NZHIS) hospital discharge and mortality data. Each patient in New Zealand has a unique National Health Identifier (NHI) number. For each patient the University of Auckland (U of A) receives a copy of the CVD risk assessment data with an encrypted NHI (eNHI). The NHI and its paired eNHI are linked by NZHIS to the individual patient outcomes. The NHI is then stripped from the data set, and the outcome data with eNHIs are sent to the U of A and combined with the Predict data for analysis.
ORIGINAL ARTICLE
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databases, with drill-down to individual patient data to support clinical audit. (6) Post-discharge tracking of individual patient outcome via encrypted linkage to national databases [1]. Data sources: Predict CVDDM data items are those required for CVD and (Diabetes Mellitus) DM risk prevention and management. Data is available since 2004. The Acute Coroanry Syndrome (ACS) database was added to capture in-hospital investigation, management and outcome data in August 2007. For this report, data was extracted for the first 22 months to May 2009. Currently this system does not capture attendance at post-discharge cardiac rehabilitation. We have audited participation in post-discharge cardiac rehabilitation over 1 year from March 2008 to February 2009 using data from the hospital clinical information system. From August 2009 cardiac rehabilitation will be reported routinely through Predict.
Results Population Predict CVD data from 2004 to 2006 was analysed to understand the relationships between CVD risk factors and demographic variables including ethnicity and socioeconomic status in our ACS population [3]. There were 61.5% ¯ classified as European or other ethnicity, 13.0% NZ Maori, ¯ 15.2% Pacific,and 10.3% South Asian. The Maori, Pacific and South Asian patients were younger than the European/other group by 10.7, 9.3 and 7.3 years respectively,
Heart, Lung and Circulation 2010;19:378–383
and more likely to live in areas of greater deprivation (65%, 83% and 49% in New Zealand Deprivation Index 2001 [8] deciles 9 or 10, respectively, compared with 30% of European/other patients). Compared with the Euro¯ pean/other patients, the Maori and Pacific patients were more likely to smoke (21% vs. 47% and 33%, respectively) and have higher levels of risk factors associated with the metabolic syndrome, including Type II diabetes (18% vs. 32% and 39% respectively), obesity, elevated triglycerides and low HDL (Fig. 3). The burden of these risk factors was ¯ higher in younger Maori and Pacific patients, compared with the European/other groups, but persisted across the age range.
Acute Predict Utilisation Over the 4 years of the project, the in-hospital provision of cardiac rehabilitation to ACS patients using the Predict decision support has improved from 54% over the first 2 years to 85% in the last year. There were no differences in utilisation of the in-patient Predict decision support according to ethnicity or socioeconomic status [3]. In the first 22 months of the ACS database 99% of ACS patients admitted to CCU (n = 1244) have complete ACS data.
Key Performance Indicators Key performance indicators include, appropriate prescription of CVD medication at discharge, in-hospital angiography and percutaneous coronary intervention
Figure 3. The percentage of smokers and patients with diabetes by age group and ethnicity in ACS patients presenting to Middlemore CCU.
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Table 1. Key Performance Indicators by Ethnicity in ACS Patients. All (n = 1244)
¯ Maori (n = 114)
Pacific (n = 224)
Indian (n = 157)
European/other (n = 749)
p-Valuea
Aspirin Statin Beta-blocker ACEi/ARB Angiogram In-hospital PCI CABG referral Total revasc. PredictCVDDM
97% 95% 86% 63% 81% 43% 17% 60% 81%
96% 96% 85% 64% 79% 38% 18% 56% 84%
99% 97% 85% 72% 76% 22% 25% 47% 80%
96% 97% 89% 65% 77% 39% 21% 59% 78%
98% 94% 86% 61% 84% 51% 14% 65% 82%
0.27 0.23 0.77 0.025 0.024 <0.001 0.003 <0.001 0.63
Attended CR
54%
71%
38%
52%
56%
0.022
ACEi; angiotensin converting enzyme inhibitor, ARB; angiotensin receptor antagonist, PCI; percutaneous coronary intervention, CABG; coronary artery bypass grafting, CR; cardiac rehabilitation. a Chi-square test.
(PCI) and referral for coronary artery bypass grafting (CABG) the use of PREDICT CDSS and cardiac rehabilitation attendance (Table 1). Appropriate discharge medication is high across ethnic groups. Rates of coronary angiography are high in all groups but slightly higher in ¯ European compared with Maori patients (84% and 79%, respectively). Whilst use of PCI is highest in European ¯ patients the rate of CABG is higher in Maori and Pacific patients. The overall revascularisation rate is higher in ¯ European than Maori. Overall revascularisation rates are lowest in the Pacific patients, driven by the low rates of PCI in this group. In-hospital cardiac rehabilitation using Predict CVDDM ECDS was high in all ethnic groups. Overall participation by ACS patients in post-discharge cardiac rehabilitation was 54% over the last year. Atten¯ dance rates were higher for Maori than European, and lowest for Pacific patients (see Table 1).
was defined for the composite endpoint of cardiovascular mortality, rehospitalisation with MI or stroke. The Kaplan–Meier event free survival for this outcome set at 4.2 years is 75% [9] (Fig. 4). Cox proportional hazards modelling was used to investigate predictors of the composite “hard” CVD endpoint of CVD mortality and MI/stroke. The following variables ¯ were assessed – age, gender, ethnicity (European, Maori, Pacific, Indian), NZDep01, diabetes mellitus, prior CVD, BMI, waist circumference, family history of CVD, TC/HDL, systolic BP and smoking status. For the ethnicity variable, ¯ risk was assessed relative Europeans. Maori patients in the Acute Predict ACS cohort are twice as likely as Europeans to have recurrent events post-discharge, even after adjustment for known risk factors (see Table 2).
Discussion Post-Discharge Outcomes A total of 1923 patients discharged alive after ACS admission (2004–2008) were identified. An ICD10 coding set
The Counties-Manukau area, the catchment for Middlemore Hospital, is ethnically and culturally diverse, and has one of the highest levels of deprivation in
Figure 4. Kaplan–Meier event free survival after ACS discharge using the composite endpoint of recurrent MI, stroke or CVD death. The numbers at risk (number of first events) are displayed.
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Heart, Lung and Circulation 2010;19:378–383
Table 2. Independent Predictors of “Hard” CVD Events in the ACS Cohort Post-Discharge. ¯ NZ Maori Pacific Age (/10 years) Diabetes Prior CVD
Hazard Ratio
SE
p
95% CI
2.1 1.51 1.34 1.37 1.45
0.36 0.23 0.05 0.17 0.16
<.001 0.008 <0.001 0.01 0.001
1.5, 2.93 1.12, 2.05 1.22, 1.43 1.08, 1.75 1.16, 1.80
New Zealand [10]. The Acute Predict project aims to ensure that all patients receive evidence-based investigation and management. A flexible approach is required to assist individuals and particular groups to meet the ¯ evidence-based targets. For example the Maori patients have in-hospital support from the Whaanau Support team, ¯ a provider arm of Te Kaahui Ora (Maori Health Unit) and are actively enrolled into post-discharge home-based cardiac rehabilitation via the Heart Guide Aotearoa program [11]. This cognitive behavioural approach to cardiac rehabilitation is based on effective prevention programs in the United Kingdom [12] and has been piloted specifically for ¯ Maori in New Zealand. Patients who smoke are identified early for an in-hospital/post-discharge smoking cessation program. Real-time reporting systems allow us to identify treatment gaps at individual and group levels. For example, the data in this paper showing that Pacific people have poorer attendance at rehabilitation has triggered a collaborative project with the Community Self Management Education Coordinator and the Pacific Cultural Resource Unit to address this issue. The observation that ¯ Maori and Pacific people, compared with European/other people, have similar high rates of evidence-based prevention medication and invasive coronary angiography but a different pattern of revascularisation (more CABG and less PCI), and lower overall rate of revascularisation, has led to a project to investigate this difference further. One important likely contributor to the observed differences is the much higher incidence of ¯ diabetes in the Maori and Pacific patients. Diabetic coronary disease is often multi-vessel and less appropriate for PCI. ¯ Long-term outcomes remain worse in Maori and Pacific patients. The reasons for this are complex and include preexisting high risk lifestyle, family/whanau support, socioeconomic factors, health behaviours, ability of the health system to engage with patients and vice versa. Significant changes to lifestyle and management can be achieved at a single hospital admission and short-term cardiac rehabilitation, but this has to continue into the community and primary care over the long-term. To support this chronic management we are developing systems to (1) routinely track individual patient post-discharge medication use via linkage to the national database, (2) track attendance and completion at out-patient cardiac rehabilitation, and (3) link to an annual CVD “get checked” program via the community based chronic care management program.
Conclusion The built-in real-time data reporting and outcomes/ prescribing linkage facilitate monitoring of the quality of CVD prevention activity across the continuum of care. It allows early identification of treatment gaps and of persistent disparities in outcome in our patients. We are learning how best to use this real-time data collection and reporting to support the design and assessment of targeted interventions to close gaps in evidence-based therapy and reduce disparity.
Acknowledgements The authors would like to thank CCU medical and nursing staff and their patients. Predict CVDDM was developed by a collaboration of clinical epidemiologists at the University of Auckland, IT specialists at Enigma Publishing Ltd. (a private provider of on-line health knowledge systems) and group of clinicians and support staff from Middlemore Hospital, Counties Manukau District Health Board, ProCare Health Ltd., National Heart Foundation, New Zealand Guidelines Group and the Ministry of Health. PREDICT software platform is owned by Enigma Publishing Ltd. (PREDICT is a trademark of Enigma Publishing Ltd.). The Acute PREDICT software platform is a version of Predict CVDDM. It was adapted and enhanced for secondary care services collaboratively by Enigma Publishing Ltd. and the Department of Cardiology, Middlemore Hospital. Funding: The PREDICT research project is supported by a grant HRC 03/183 from the Health Research Council.
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ORIGINAL ARTICLE
Heart, Lung and Circulation 2010;19:378–383