Harnessing Evidence And Real-world Data To Improve Cardiovascular Health and Services (HEARTWISE) - The Personalised - CARdiovascular DIsease Risk Assessment for Chinese (P-CARDIAC) Real World Evidence Study - Population-based Study
Conditions: Cardiovascular Diseases, CVD
Sponsor: The University of Hong Kong

 
 
 
 
 
 
 
 
Project title:  
Harnessing Evidence And Real-world Data To Improve 
Cardiovascular Health and Services (HEARTWISE)-The 
Personalised - CARdiovascular Disease Risk Assessment 
for Chinese (P-CARDIAC) Real World Evidence Study - 
Population-based Study 
 
Brief Title: 
 
HEARTWISE - P-CARDIAC for Chinese: Population-based Study   
 
 
 
 
 
HKU/HA HKW IRB: UW 24-064 
Date: 31 October 2023 
 
 
 
 
 
 

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Study Protocol 
 
Abstract 
Background: Cardiovascular disease (CVD) is one of the prominent diseases that affect 
many people. One cost-effective solution is to identify people at higher risk of CVD by 
CVD risk prediction model. China-PAR, TRS-2P, and SMART2 are common risk 
prediction models for prevention. However, these risk scores were mostly based on the 
routinely self-check health information and multivariable regression without time-varying 
consideration. We developed a Machine Learning (ML) based risk prediction model, 
Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC) 
among a predominantly Chinese population in Hong Kong to estimates the 10 years of 
secondary recurrent CVD risk for the high-risk individuals. Objective: The objective is to 
evaluate the accuracy of the P-CARDIAC performance in practice among a large-scale 
Hong Kong population in medicine specialist outpatient clinic (SOPC) and cardiac clinic.  
Participants: Individuals aged over 18 years old and above who visited the medical or 
cardiology SOPC clinics under HA.  
Method: The study has 2 parts. Part 1 is a prospective population-based cohort study. We 
hypothesize that patients with a higher frequency or larger number of types of cardiac 
event symptoms are likely to have a higher P-CARDIAC risk score. The results will 
reassure cardiologists that the P-CARDIAC risk score is sensitive to the heart disease 
symptoms. Part 2 of the study will involve Delphi technique to determine P-CARDIAC 
risk thresholds with reference to current clinical management guideline such as 
AHA/ACC and ESC/EAS. Researchers will collect insights from cardiologists regarding 
the interpretability of performance metrics and acceptable threshold of model 
performance for clinical practice.  
Expected results: We anticipate that the results may help us to facilitate P-CARDIAC in 
clinical setting and provide more practical information with the development of P-
CARDIAC. 
 
 
 
 

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1. Introduction 
1.1 Background 
Cardiovascular disease (CVD) is one of the prominent diseases that affect many people in 
their middle or old age and result in fatal complications worldwide [1]. It has been 
regarded as a global disease burden and a leading cause of mortality, especially in 
developing countries [2, 3]. From 2009 to 2021, the number of persons diagnosed with 
heart disease increased by 21% according to the Census and Statistics Department's 
Thematic Household Surveys in Hong Kong [4]. According to a report from World 
Health Organization, 80% of premature heart attacks and strokes are preventable [5]. To 
prevent heart disease, one cost-effective solution is to identify people at higher risk of 
CVD and offer them appropriate advice for earlier prevention [6]. China-PAR (Prediction 
for ASCVD Risk in China) [7], Thrombolysis in Myocardial Infarction risk score for 
secondary prevention (TRS-2P) [8], and Secondary Manifestations of ARTerial disease 
(SMART2) [9] risk score are common risk prediction models for the prevention and 
management of CVD. These risk prediction models will serve as a framework for 
integrating electronic patient health records with the use of standard statistical modelling 
and then simplified into a points system for clinical use. Results from these risk scores 
provide an important reference to help clinicians give appropriate care to high-risk 
individuals. By initiating preventive treatment, risk prediction outcomes potentially will 
also lower the burden on the public healthcare sector. However, these risk scores were 
mostly based on the routinely self-check health information and multivariable regression 
without time-varying consideration which is not feasible for providing accurate 
prediction. A systematic review recently published in 2022 that summarized 18 CVD risk 
prediction models from 22 studies based on Chinese population [10]. The study 
concluded that although most of the models were developed among Chinese population 
after 2015, their usefulness still remained unclear due to lacking in external validation 
and comparison for the local setting and recurrent episode.  
To address this issue, we developed a Machine Learning (ML) based risk prediction 
model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-
CARDIAC) among a predominantly Chinese population in Hong Kong. It estimates the 
10 years of secondary recurrent CVD risk for the high-risk individuals with consideration 
of an array of risk variables and concurrent medication effect under time-varying effect.  
 
1.2 Rational and research gap of the risk prediction models validation on local 
clinical setting 
In 2016, a developed risk prediction model designed for Chinese adults in multiple 
contemporary cohorts, China-PAR, was published [7]. China-PAR were recommended 
by the updated guideline on the assessment and management of cardiovascular risk, 
however, a study conducted among a large-scale Chinese population in real-world 

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indicated that it underestimated the risk for woman in southern Chinese population due to 
the geographic developed economic level.  China-PAR fairly estimated 5-year ASCVD 
risk in low- and medium-risk but underestimated the high-risk participants with older 
age, socioeconomically disadvantaged, higher prevalence of diabetes mellitus, and with 
higher proportion of lipid-lowing medications. It implied that this guideline among 
southern Chinese population might not be appropriate for the individuals with established 
CVD or high-risk risk factors.  
The risk of recurrent CVD events and death increases with age, smoking, hypertension, 
and diabetes mellitus in individuals with established CVD [11]. Symptoms of a heart 
attack includes pain or discomfort in the centre of the chest, pain or discomfort in the 
arms, the left shoulder, elbows, jaw, or back. Moreover, individuals may experience 
difficulty in breathing or shortness of breath; nausea or vomiting; light-headedness or 
faintness; a cold sweat; and turning pale. Other common symptom of a stroke is sudden 
weakness of the face, arm, or leg, most often on one side of the body [12]. Recurrent 
CVD events are also more likely to occur in patients who survive non-ST-elevation 
myocardial infarction (NSTEMI) [13-15]. However, limited data exist on long-term 
secondary atherothrombotic risk stratification. A recently observational study conducted 
to validated TRS 2P among post-NSTEMI patients for risk of recurrent cardiovascular 
events among Hong Kong population for its application in real world [16]. Results 
showed that TRS 2P was able to stratified the patient with high risk of recurrent CVD, 
however, the outcomes didn’t include full spectrum of composite CVD, such as non-fatal 
ischaemic stroke.  
Since P-CARDIAC is a newly developed model, it had not been sufficiently validated 
externally in the real-world yet, and the performance in diverse geographical populations 
for secondary prevention is in doubt. For the availability and feasibility of P-CARDIAC 
performance in local clinical setting, a validation in the local clinical setting is necessary 
to be conducted.   
 
2. Research aims and objectives 
The objective of this study is to evaluate the accuracy of the P-CARDIAC performance in 
practice among a large-scale Hong Kong population in medicine specialist outpatient 
clinic (SOPC) and cardiac clinic managed by the Hospital Authority (HA) in Hong Kong. 
We anticipate that the results may help us to facilitate P-CARDIAC in clinical setting and 
provide more practical information with the development of P-CARDIAC. The main 
objectives are: 
1) To map P-CARDIAC score with 2-year CVD outcomes using electronic health 
record and patients reported symptoms. 
2) To map P-CARDIAC score with clinician’s judgement, international treatment 
guideline identifies thresholds.  

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3. Methods 
3.1 Study design  
The study has two parts: 
Part 1 
Part 1 is a prospective population-based cohort study which will address objective 1. We 
hypothesize that patients with a higher frequency or larger number of types of cardiac 
event symptoms are likely to have a higher P-CARDIAC risk score. The correlation 
between P-CARDIAC risk score and the increasing in number of types and in number of 
times a patient reported to have heart disease symptoms, e.g. chest pain, shortness of 
breath, fatigue, swelling in legs, ankles or feet will be examined [12]. The results reassure 
cardiologists that the P-CARDIAC risk score is sensitive to the heart disease symptoms. 
Patients who are at a higher risk of recurrent CVD event are likely to have more 
medications and to have a sooner follow up appointment.  At such, we hypothesize a 
positive relationship between P-CARDIAC risk score and the number of class of 
medications prescribed and a negative correlation between P-CARDIAC risk score and 
the time to next SOPC follow up appointment.  
The application of P-CARDIAC will be firstly introduced to our collaborated clinicians. 
Collaborating clinicians will help to randomly select patients who visited medical / 
cardiology SOPC to identify eligible patients with stabled CVD risk or of low CVD risk. 
If time allows, the participating clinicians may introduce the project to the participants 
during the consultation. Research personnel will then recruit and obtain the written 
consent from the eligible patients, our researchers (not limited to: research personnel, 
pharmacy or nursing students or study helpers) will ask any CVD related symptom such 
as chest pain and its frequency, and medication for the baseline records through 
structured interview using a questionnaire. We will also obtain consent to collect their 
personal identifier (such as Hospitalization Number, Accidence and Emergency Number, 
HKID or Out-Patient appointment reference) to access their electronic health records 
(eHR) from the Clinical Management System and Hospital Authority Clinical Data 
Analysis and Reporting System (CDARS) and inputted to the P-CARDIAC model for 
recurrent CVD risk calculation. The medical records of recruited patients will be 
collected every 3 to 6 months after until end of follow-up period to observe any CVD 
outcomes and relevant change in risk factors.   
 
Part 2 
Part 2 of the study will involve Delphi technique [17] to determine P-CARDIAC risk 
thresholds with reference to current clinical management guideline such as American 
Heart Association (AHA)/American College of Cardiology (ACC) Multisociety 

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Guideline (AHA/ACC) and European Society of Cardiology (ESC) and the European 
Atherosclerosis Society (EAS) (ESC/EAS), and this part will not involve patient contact. 
This part of the study will address Objective 2. We will compare the P-CARDIAC risk 
score and the clinician’s rating of a patient’s recurrent CVD risk on a 10% random 
subgroup of patients from part 1 in a “silent deployment” [18] approach, ie, an individual 
patient’s P-CARDIAC risk score will not be communicated to cardiologists. 
Cardiologists will be invited to rate if they think the patient is at high or low risk 
according to existing risk scores and the proposed treatment management and target 
based on treatment guidelines based on their experience. Each patient profile will be 
reviewed by at least 2 cardiologists and discussed to reach a consensus. We will then 
evaluate the sensitivity of P-CARDIAC risk score with clinician’s judgment.  Prior to the 
commencement of Part 2, researchers will collect insights from cardiologists regarding 
the interpretability of performance metrics and acceptable threshold of model 
performance for clinical practice. In addition, through this exercise, HKU team will assist 
cardiologists to draft a guideline for follow up for patients at different risk level.   
A total of 10% of the recruitment patients will be included in Part 2. Clinicians are 
invited to review the medical records of the sub-cohort at recruitment to determine their 
CVD risk using existing risk scores such PCE and Thrombolysis in Myocardial Infarction 
(TIMI) Risk Score for Secondary Prevention (TRS‐2P). The P-CARDIAC score will not 
be provided to ensure objective assessment. Clinicians will then be asked to map to 
relevant clinical management plan according to local or international guidelines such as 
AHA/ACC and ESC/EAS. A Delphi study will then be conducted to align the clinical 
judgement with the P-CARDIAC scores to establish the risk thresholds.   
 
3.2 Recruitment 
Inclusion and exclusion  
Individuals aged 18 – 80 years old who visit the medical or cardiology SOPC clinics at 
the Queen Mary Hospital (QMH), and experienced prior CVD event (such as peripheral 
artery diseases, coronary heart diseases, myocardial infarction, stroke, revascularization) 
before the scheduled SOPC clinic visit date (i.e. index date) are eligible to participate in 
this study. Participants will be also able to comprehend English or Traditional Chinese as 
well. People with history of dementia, psychiatric diseases or physical impairment will be 
excluded in this study as they are less likely to benefit from the medication education and 
counselling service. Part 1 and 2 will share the same inclusion criteria as a random subset 
of Part 2 participants will be selected from Part 1. 
We will adopt a 2-steps approach to obtain informed consent. The research team will, in 
collaboration with the patient support groups such as Care For Your Heart and the Hong 
Kong Stroke Association, to disseminate an study invitation pack to their members. The 
study invitation pack will include a flyer with the above inclusion and exclusion criteria 

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clearly outlined, a study consent form, an information sheet and a stamped to be returned 
to the research team. A QR code link to the study registration site will also be included in 
the invitation pack for participants to register them intend to participate in this study. In 
this first step of recruitment, we will obtain their consent to collect their contact 
information and the next SOPC visit for scheduling.       
The research team will also prepare a flyer, which may also serve as an advertisement in 
online platform, e.g. Facebook to recruit participants.   
The research team will recruit also use the flyer to recruit at the medicine SOPC or 
cardiac clinics of the QMH between 1 February 2024 to 31st January 2025 (up to 12 
months) with a possibility to extend the recruitment period depending on recruitment 
progress. The follow-up period will be up to 2 years after recruitment (31st January 
2027).  During their SOPC visit, our research personnel will perform the 2nd step of 
recruitment which is to obtain participants’ written consent to collect their personal 
identifier to access their eHR from the Clinical Management System and Hospital 
Authority Clinical Data Analysis and Reporting System (CDARS) and inputted to the P-
CARDIAC model for recurrent CVD risk calculation. An interview on their symptoms 
will be collected through questionnaire. We will assess CDARS to conduct pragmatic 
prospective follow-up of the recruitment patients. 
 
3.3 Sample size consideration  
Considering the services allocation and the traffic of SOPC appointment in practice, we 
aim to recruit no more than 800 participants at Queen Mary Hospital for Part 1. 10% of 
the participants, i.e. no more than 80 patients will be included in Part 2.   
 
3.4 Informed Consent 
Informed consent will be obtained in written form at recruitment and before the start of 
each part of the study. Informed consent can be obtained by either clinician or research 
assistant if necessarily. Consent form will be available in English or Traditional Chinese 
with detailed information of the study including study aim, procedures and usage of 
collected data. Participants will be given time to read through and understand the purpose 
of the study before giving their consent.  
All research personnel will be provided relevant training on the study objectives, 
questionnaire, information sheet and consent form before commencement of duty.  
 
3.5 Data collection and outcome 

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With the use of their personalized identifier (such as Hospitalization Number, Accidence 
and Emergency Number, HKID or Out-Patient appointment reference), we will retrieve 
their eHR from CDARS. Health parameters from eHR will be extracted for the P-
CARDIAC risk calculation. All participants will be followed up to 2 years in Part 1 and 
2. Data will be retrieved from CDARS every 3 to 6 months until end of follow-up period. 
The outcomes will include the recurrent CVD event between the follow-up period, 
including a composite of coronary heart disease, ischemic or hemorrhagic stroke, 
peripheral artery disease, and revascularization which will be compliance with P-
CARDIAC outcomes by ICD 9 codes. The ICD 9 code confirmation will be considered 
as the standard and confirmed by the independent clinicians for adjudication to ascertain 
the date of diagnosis for conflicting records. Any cause-specific (cardiovascular-related 
or other) death will be confirmed by death certificate in the health information system. 
During the followed-up period, other risk parameters, such as hospitalization, healthcare 
service utilization and medication records, will also be extracted from CDARS. Other 
potential outcome may include but limited to parameters included in P-CARDIAC such 
as blood pressure, blood cholesterol, blood glucose, medications, and healthcare 
utilization (such as A&E admission, hospitalization, SOPD visit not limited to medical / 
cardiology clinics). Patient reported symptoms will also be collected in Part 1. 
 
3.6 Data analysis 
Continuous and categorical baseline characteristics of participants will be summarized by 
mean (standard deviation) or numbers (percentage), respectively. Student's t-test or one-
way ANOVA was used to compare baseline clinical characteristics. Considering 
differences in health status and survival for participants with and without missing data, 
our main analysis will be conducted on the dataset by imputation to reduce selection bias. 
We used the multiple imputation by chained equations (MICE) method as the 
development of P-CARDIAC to replace missing values.  
For each participant, observed recurrent CVD risk will be calculated using the Kaplan-
Meier method with 95% confidence interval (CI) and predicted recurrent CVD risk will 
be calculated using original model among the whole study population. Model 
performance will be assessed by Harrell’s C-statistics for discrimination and Hosmer-
Lemeshow chi-square for calibration. Calibration was also evaluated using calibration 
slope, a shrinkage factor that closed to 1 for well-calibrated equations and visualized by 
plotting the predicted risk against the observed risk for each decile of the predicted risk.  
In addition, to assess the robustness of the results, we excluded participants with missing 
data of any predictors included in model and then evaluated the performance of P-
CARDIAC in sensitivity analyses. We further restricted the analyses to participants 
without any predictors in mandatory field at baseline to evaluate their effects on the 
discordance between observed and predicted risk [23]. 

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All analyses will be conducted using R (version 4.1.2) or statistical software deemed 
appropriate with a statistical significance level of P < 0.05.  
 
3.7 Data handling and record keeping 
Given the personal data will be collected, protective measures will be done to prevent any 
data leakage. To protect all the collected data, data will be stored in password-protected 
server located in a locked server room within the HKU premise and identifiable 
information will be stored separately from medical records to minimize risk of 
confidentiality breach. Only authorized researchers have the access to the datasets. The 
collected data is confidential for this study research use only. The data will be kept for 
five years after the completion of study in case there might have a need of analyzing the 
collected data in the future. 
 
3.8 Ethical Consideration  
This study protocol will seek the ethics approval by the Institutional Review Board of the 
University of Hong Kong/Hospital Authority Hong Kong West Cluster. Participant’s 
confidentiality and data leakage will be the major risk and concern of the study. Although 
there are personal data will be collected, protective measures will be done to prevent any 
data leakage. Besides, informed consent process will be done before the start of the study 
in compliance of the Declaration of Helsinki. To protect the participant, all the data 
collected in this study will be kept confidential throughout the whole study period. All 
the participants are voluntary to join and able to drop out any time during the study 
without any prejudice. All data will be destroyed according to HA guidelines five years 
after the completion of study. 
 
3.9 Risk management 
We believe the imposed risk to patients will be minimal for Part 1 and 2 as no 
intervention will be given to patients.  
 
3.10 Funding 
This study received no grant by any funding agencies. 
 
4. Limitation 

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Limitations should be acknowledged and include the limited length of follow-up in the 
validation. The long-term incidence of CVD would have been the optimal primary 
outcome measure for our study. However, time and budgetary restrictions prevented us 
from using this 10-years endpoint. The study period was 2 years prospective followed-up 
cohort, we understand the loss-to followed-up rate is inevitable. Nevertheless, as for all 
registry and multi-center studies, patient inclusion is always subject to bias. We will try 
to review hospital records and laboratory results carefully to ascertain the diagnosis 
standard for our collaborated clinicians but potential confounding still cannot be 
excluded.  
 
 

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