Advances in Preventive Medicine
Volume 2019 (2019), Article ID 8392348, 11 pages
https://doi.org/10.1155/2019/8392348

Research Article

A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

Xiaona Jia, Mirza Mansoor Baig, Farhaan Mirza, and Hamid GholamHosseini

School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand

Correspondence should be addressed to Mirza Mansoor Baig; mirzamansoor01@gmail.com

Received 7 November 2018; Accepted 26 March 2019; Published 9 April 2019

Academic Editor: Gerardo E. Guillén Nieto

Copyright © 2019 Xiaona Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

1. Introduction

Cardiovascular disease (CVD) describes various conditions that affect the functioning of heart/cardiovascular [1]. Due to the high rate of disease morbidity, CVD has become the leading cause of mortality around the world [2–4]. In New Zealand, statistics on CVD mortality in 2017 suggests that the percentage of deaths caused by CVD is 33% [4].

Majority of cardiovascular-related deaths are premature and preventable and can be improved by effective health management by employing effective diet plans, lifestyle interventions, and drug intervention [5]. To prevent CVD, a useful approach is to assess CVD risk regularly and then introduce new lifestyle adjustments or clinical treatments accordingly.

In the past decades, a great deal of research has been done on the CVD risk estimation such as the Framingham risk scores from the Framingham Heart Study (FHS) [6, 7], the QRISK equations [8], the Europe SCORE risk equations [9], the ASSIGN scores from the Scottish Heart Health Extended Cohort (SHHEC) [10], the Prospective Cardiovascular Master (PROCAM) equations [11], and the CUORE Cohort Study formulas [12]. These CVD risk prediction models have proved their effectiveness in the health and disease management for clinicians and individuals [13–15]. The new PREDICT CVD risk assessment equation developed for primary health care among the population in New Zealand has been integrated to the electronic health records (EHRs) and a web-based software called PREDICT has been developed to support general practices manage the CVD risk in primary care [13]. The PREDICT has got 400,728 patients assessed with the CVD risk and is becoming a useful tool for decision support and health management for general practitioners.

However, challenges and issues regarding the development of CVD risk estimation models still exist. CVD risk models [16–18] are based on single risk factor which cannot realize the influence of multiple factors simultaneously. Risk models [6, 8, 19] using statistical regression methods [20–22] prefer to use classic risk factors such as age, smoking, diabetes, sex, high blood pressure, and total cholesterol to estimate the risk score. Studies [18, 19, 23–27] applying data mining or machine learning techniques for the CVD risk estimations cannot provide an absolute risk estimation, although some of these models [18, 26] tried to incorporate novel predictors in the risk models. This research aims to identify the novel risk factors for CVD detection by conventional predictors and then enhance the risk estimation by developing a multiple-variable-based risk prediction model that targets the 5-year and 10-year CVD events.

2. Methods

2.1. Study Population

The study population selected from the Framingham Original Cohort study dataset [28, 29]. We obtained the ethics approval from NHLBI [30] and the Auckland University of Technology Ethics Committee (AUTEC) (Ref: 17/385 Early Detection and Self-Management of Cardiovascular Disease Using Artificial Intelligence-Based Model). The data from this cohort study includes a total of 5079 men and women aged 30-74 years free of CVD at the baseline, of them 3189 had CVD events eventually. Details of the CVD events distribution in male and female among the study population are summarized in Table 1.

Table 1: CVD event distribution in male and female.

Count.
CVD Events
Age Range

Male
2294
1560
30 - 74

Female
2785
1629
30 - 74

Total
5079
3189
30 - 74

2.2. Data Extraction

There are 32 exams in the Framingham Original Cohort study dataset, as shown in Appendix A. Data frame collected in the first exam “Exam1” was chosen to develop the CVD prediction model because it has the maximum number of samples 5209 subjects. Data from 130 subjects were removed because of the ethics protection. The other five exams are ranging from 8 to 12, marked with italic font (as shown in Table 7 of Appendix A) and will be used for the validation for the fitted model. Data of candidate risk factors (listed in Table 2) for creating the risk model was extracted.

Table 2: Description of candidate predictors.

ORDERS
PREDICTORS
UNITS
TYPES

1
AGE
YEARS
CONTINUOUS

2
SEX
0001 MALE
0002 FEMALE
CATEGORICAL

3
BMI
KG/M2
CONTINUOUS

4
HYPERTENSION
0000 NEGATIVE
0001 TRANSIENT
0002 PERMANENT
0003 TYPE UNKNOWN
0008 DOUBTFUL
CATEGORICAL

5
HISTORY OF NERVOUS HEART
0000 NO
0001 YES, DEFINITE
CATEGORICAL

6
HISTORY OF PERICARDITIS
0000 NO
0001 YES, DEFINITE
CATEGORICAL

7
HISTORY OF OTHER CVD
0000 NO
0001 YES, DEFINITE
CATEGORICAL

8
PREMATURE BEATS
0000 NO
0001 YES, DEFINITE
0002 YES, DOUBTFUL
CATEGORICAL

9
HISTORY OF ATRIOVENTRICULAR BLOCK
0000 NO
0001 YES, DEFINITE
0002 YES, DOUBTFUL
CATEGORICAL

10
HISTORY OF RHEUMATIC FEVER
0000 NONE
0001 YES
0008 DOUBTFUL
CATEGORICAL

11
HISTORY OF ALLERGY OR ASTHMA
0000 NEGATIVE
0001 ALLERGY, ALONE
0002 BRONCHIAL ASTHMA, ALONE, 0003 ALLERGY AND ASTHMA, TOGETHER
CATEGORICAL

12
HISTORY OF THYROID DISEASE
0000 NEGATIVE
0001 HYPERTHYROID ONLY
0002 HYPOTHYROID ONLY
CATEGORICAL

13
HISTORY OF SUBACUTE ENDOCARDITIS
0000 NO
0001 YES
CATEGORICAL

14
BLOOD PRESSURE SYSTOLIC
MM HG
CONTINUOUS

15
BLOOD PRESSURE DIASTOLIC
MM HG
CONTINUOUS

16
CIGARETTES PER DAY
LAPSE, FORM 8/50
CONTINUOUS

17
CIGARS PER DAY
LAPSE, FORM 8/50
CONTINUOUS

18
PIPERS PER DAY
LAPSE, FORM 8/50
CONTINUOUS

19
PULSE RATE
PER MINUTE
CONTINUOUS

20
DIABETES
0000 NO
0001 YES, DEFINITE
CATEGORICAL

2.3. Statistical Analysis

Cox proportional hazard regression analysis [22] was selected for developing the proposed risk model (one of the most accurate method belonging to the semiparametric statistical method). This research aims to develop a prediction model using multiple parameters to estimate the probability of developing CVD for an individual. There are mainly three statistical approaches in survival analysis, i.e., nonparametric, semiparametric, and parametric [31]. The nonparametric approaches can only perform univariate analysis with single predictor and therefore are not suitable for the study of continuous variables [22, 32]. Both parametric and semiparametric approaches can perform multiple parameter analysis. They assume that the predictors and the log hazard rate have a linear relationship between [33]. However, the Cox proportional hazard model has an advantage that only the rank orderings of the failure and censoring times are used to estimate and test the regression coefficients [22]. The Cox model is more efficient even though the assumption of the parametric models is met. When the assumptions are not met, the Cox regression analysis can still be used efficiently with an extended Cox regression from [34], but a parametric model such as Weibull survival distribution would be a null model.

Statistical analyses were performed in R Studio platform [35]. Missing values for candidate risk factors listed in Table 2 were imputed using Multiple Imputation [36]. Continuous and categorical variables were transformed and imputed using algorithms modified from Maximum Generalized Variance (MGV) in the SAS PRINQUAL procedure [37]. R function transcan inside the “Hmisc” package was used [35].

For candidate predictors listed in Table 2, two steps of variables selection from the list were performed. The first step was conducted in a “Forward Selection” manner [38]; i.e., the univariate Cox analysis was applied to all candidate variables. Insignificant predictors were filtered out based on a significance level p value >0.05. In the second step, all selected variables from the univariate analysis were entered into the multivariate Cox regression analysis to see how the risk factors jointly impact the incidence rate for CVD. Risk factors with a p value less than 0.05 will be finally decided.

In the validation stage, two approaches were undertaken to assess the predictive ability of our fitted model, statistical validation, and empirical validation. The statistical validation was performed with respect to both discrimination and calibration. The empirical validation was defined as an empirical comparison with a general CVD risk prediction model (the Framingham office-based risk equation [6]) in a horizontal and longitudinal perspective. The horizontal comparison was conducted by comparing with the Framingham prognostic model using data collected from multiple samples at the same time point. The longitudinal comparison was conducted by comparing with the Framingham prognostic model using data collected from specific examples at different time-points (fixed time intervals follow-up) and seeing the risk trend for an individual over time.

3. Results

3.1. Derivation of a 10-Year Risk Score for CVD

Risk factors included in the risk model are age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, the status of diabetes. Characteristics of risk factors were listed in Table 3. Statistics of “Min.”, “1st Qu.”, “Median”, “Mean”, “3rd Qu.”, and “Max.” of these risk factors are summarized.

Table 3: Summary statistics for risk factors used in risk model.

Predictors
Variables
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.

AGE
Age
28
37
44
44.15
51
74

SEX
Sex
1
1
2
1.548
2
2

BMI
Bmi
14.12
22.66
25.17
25.61
27.92
56.68

HYPERTENSION
Hyp
0
0
0
0.147
0
1

BLOOD PRESSURE SYSTOLIC
Bps
84
122
136
138.6
150
270

CIGARETTES PER DAY
Cgrpd
0
5
20
16.26
20
60

PULSE RATE
Pr
37
67
75
75.61
83
170

DIABETES
Dia
0
0
0
0.0197
0
1

The regression coefficients, hazard ratios, and their corresponding upper and lower 95% confidence intervals (CI) were estimated, as presented in Table 4. Values of the baseline hazard rate where the time point is ten years were estimated as well, shown in Table 5. The 10-year baseline hazard rate is 0.1023354 at mean values of all covariates, 0.001863652 at all covariates equal to zero. Corresponding, the survival probability () is 0.9027267 at mean values and 0.9981381 at all covariates equal to zero.

Table 4: Regression coefficients and hazard ratios in risk model.

Predictors
Variables
coef
Hazard Ratio
lower .95
upper .95

AGE
log of age
2.083643
8.033686
6.4082
10.0716

SEX
sex
-0.469719
0.625178
0.5787
0.6754

BMI
log of bmi
0.608864
1.838342
1.4368
2.3521

HYPERTENSION
hyp
0.241461
1.273108
1.1342
1.429

BLOOD PRESSURE SYSTOLIC
log of bps
1.682571
5.37937
3.7938
7.6277

CIGARETTES PER DAY
cgrpd
0.009669
1.009716
1.0065
1.013

PULSE RATE
log of pr
-0.30209
0.739271
0.5879
0.9297

DIABETES
dia
1.087501
2.96685
2.3244
3.7869

Estimated regression coefficient.

Table 5: Baseline hazard and survival at 10 years.

Covariates at mean value
Covariates equal to zero

Baseline hazard estimate
0.1023354
0.001863652

Baseline survival estimate
0.9027267
0.9981381

The Cox model has an exponential form (see Equation (1)), where t represents the time that the event occurs; is the hazard function for a subject at time t, determined by a set of m covariates (); are the regression coefficients that measure the effect size of covariates; exp is the exponential function (); is the baseline hazard rate, an arbitrary (unknown) function, corresponding to the value of the hazard when all equal zero.

So, the Cox model can be written as a survival function:

A general formula for computing risk estimates has the following form:

where H(t) is the CVD risk estimated for an individual; S0(t) is baseline survival rate at follow-up time t, where t = 10 years (see Table 5), βi is the regression coefficient (see Table 4), is the value of the risk factor (if is continuous it is the log-transformed value), is the corresponding mean, and k denotes the number of risk factors. The CVD risk function could be derived from (3), using regression coefficients from Table 4 and the baseline hazard rates from Table 5; hence, we computed the probability of developing any type of CVD for an individual. A case of computing the absolute risk score in 10 years was demonstrated in Appendix C.

3.2. Nomograms

A nomogram is a two-dimensional diagram to represent a mathematical function involving several predictors [39]. It is a simple graphical illustration to approximately predict a particular event based on conventional statistical regression methods such as Cox proportional hazards model for survival analysis [40]. A nomogram is accomplishing the estimation of individual survivals in 10 years and the median survival time by years was depicted in Figure 1.

Figure 1: Nomogram for predicting overall survival in 10 years.

In Figure 1, each predictor has a set of n scales, and there is a mapping between each scale and the “Points” scale. The bottoms are the corresponding 10-year survival estimates, and the median survival time (years). By accumulating the total points corresponding to the specific configuration of covariates for a patient, a clinician can then manually obtain the predicted value of the event for that patient.

3.3. Validation

The validation of the proposed predictive risk model was performed using traditional statistics. C-index (also called receiver operating characteristic (ROC) area) [41] was used to assess the goodness of the risk model based on a bootstrap internal resampling validation. From the statistical validation analysis, we got a C-index (area under the receiver operator curve [AUROC]) of 0.71 indicating moderately good discrimination.

Then, we performed an empirical validation by comparing our risk model with the Framingham Heart Study model in an external dataset horizontally and longitudinally over time. In the horizontal validation process, there were 2786 samples in the external dataset, and 1693 samples have got a CVD event. Risk scores using the FHS model and the proposed risk model were computed separately. Statistics of min (lower whisker), 1st quartile (the lower hinge), median, 3rd quartile (the upper hinge), and max (the extreme of the upper whisker) of estimated risks for all samples are depicted in Figure 2. This box-whisker graph in Figure 2 shows that the risks assessed by our Cox model are higher than the risk calculated by the Framingham model, but the error for five statistics (min, 1st Qu, median, mean, 3rd Qu., max) is within 0.02. For example, the median values of the FHS model and the Cox model are 0.1429475 and 0.1661985, respectively. For subjects with CVD event, the Cox model is much more accurate than the FHS model whereas for subjects without CVD, the Cox risk model overestimates the risk rate. Overall, the risk scale of the Cox model is consistent with the Framingham model, which highlights that the proposed Cox model is par with the FHS model.

Figure 2: Horizontal comparison between Cox model and FHS model.

In the longitudinal validation process, we selected four sex-specific subjects with or without CVD at the end of the Framingham Study. A summary of these four subjects is listed in Table 6 to confirm the longitudinal validation of the predicted CVD event.

Table 6: Data summary for samples in the longitudinal validation.

Samples
Gender
CVD
Diabetes

Sample 1
Male
✘
✘

Sample 2
Male
✓
✓

Sample 3
Female
✘
✘

Sample 4
Female
✓
✓

Table 7: Exams in the Framingham Original Cohort study data set.

Exams
Exam Date Range
Age Range
Mean Age
Attendees

Exam 1
1948 - 1953
28 - 74
44
5209

Exam 2
1950 - 1955
31 - 65
46
4792

Exam 3
1952 - 1956
32 - 67
48
4416

Exam 4
1954 - 1958
34 - 69
50
4541

Exam 5
1956 - 1960
37 - 70
52
4421

Exam 6
1958 - 1963
38 - 72
54
4259

Exam 7
1960 - 1964
40 - 74
55
4191

Exam 8
1962 - 1966
42 - 76
57
4030

Exam 9
1964 - 1968
44 - 78
59
3833

Exam 10
1966 - 1970
46 - 80
61
3595

Exam 11
1968 - 1971
49 - 81
62
2955

Exam 12
1971 - 1974
50 - 83
64
3261

Exam 13
1972 - 1976
53 - 85
66
3133

Exam 14
1975 - 1978
55 - 88
68
2871

Exam 15
1977 - 1979
57 - 89
69
2632

Exam 16
1979 - 1982
59 - 91
70
2351

Exam 17
1981 - 1984
61 - 93
72
2179

Exam 18
1983 - 1985
63 - 94
74
1825

Exam 19
1985 - 1988
65 - 96
75
1541

Exam 20
1986 - 1990
67 - 97
77
1401

Exam 21
1988 - 1992
69 - 99
79
1319

Exam 22
1990 - 1994
72 - 101
80
1166

Exam 23
1992 - 1996
73 - 101
81
1026

Exam 24
1995 - 1998
76 - 103
83
831

Exam 25
1997 - 1999
78 - 104
84
703

Exam 26
1999 - 2001
79 - 103
86
558

Exam 27
2002 - 2003
82 - 104
87
414

Exam 28
2004 - 2005
84 - 104
89
303

Exam 29
2006 - 2007
85 - 102
91
218

Exam 30
2008 - 2010
88 - 102
92
141

Exam 31
2010 - 2011
90 - 99
92
91

Exam 32
2012 - 2014
93 - 106
96
40

For each sample, data with fixed time intervals (approximately two years) from longitudinal time follow-up are extracted. The data from five exams (Exam 8, Exam 9, Exam 10, Exam 11, and Exam 12) are extracted for comparison. Data summary for sample 1, sample 2, sample 3, and sample 4 are listed in Appendix B. For each sample, the risks of developing CVD in 10 years related to the selected five exams data are separately computed using the Cox model and the Framingham model. Then the trend of risk over the years with 5% error is depicted, as shown in Figure 3. This figure shows that the trend of risks of these two models are consistent and risks for a specific sample increase over time, the dotted trend lines in each graph represent the increase in the CVD risk over time. Also, samples (both male and female) with diabetes that developed CVD will have a higher risk than the ones with no developed CVD.

Figure 3: Longitudinal validation.

4. Discussion

It is widely accepted that CVD has become one of the significant public health issue globally [42, 43] and contributes significantly to the annual deaths globally. Previous studies have noted the importance of identifying associated risk factors and the early detection and intervention of CVDs [44–48] and investigated reducing the risk of developing CVD in early stages. Consequently, CVD risk prediction tools based on a single variable or multiple variables have been devised to yield estimates of the CVD risk [6, 8, 9, 14, 49–51].

Motivated by the objective of early detection and risk estimation of CVD, the present study was designed to identify novel CVD risk factors, determine the effect of these factors, and then develop a risk prediction model based on the identified factors. Although risk factors could vary from one specific CVD component to another, there is sufficient evidence that different types of CVD have commonalities of risk factors. We developed and validated a 10-year risk equation for CVD risk using follow-up data rigorously measured by the Framingham Heart Study.

This investigation extends the number of risk factors by the previous general CVD risk formulations, incorporating heart rate to estimate absolute CVD risk. The approach used in this research is based on advanced statistical techniques that allow reducing the bias in the assessment of true CVD risk. The whole process of data analysis strictly follows the guideline of regression modelling strategies and survival analysis [34, 52].

We use continuous variables (age, BMI, SBP, and pulse rate) to generate the model that performs better than other similar models developed using categorical variables. Compared with simpler approaches that try to make inferences of 5-year and 10-year risk models such as the model based on logistic regression analysis [53] and the CVD risk model using Kaplan-Meier and log-rank test [46], the proposed Cox risk model is more adequate and will avoid severe errors of underestimation or overestimation [22, 34]. Moreover, this model was developed based on a more substantial number of samples and events, suggesting a valid estimation of the real risk.

4.1. Comparison with Other CVD Risk Prediction Tools

The old version Framingham general CVD risk function [53] is useful for identifying persons at high risk of CVD, but it was based on a limited number of risk factors (serum cholesterol, SBP, smoking history, electrocardiogram, and glucose intolerance). The new Framingham laboratory-test-based formula [6] included HDL cholesterol in the risk function. The QRISK study investigators incorporated family history as a novel risk factor by the Framingham general formulas [8]. Although researchers have published risk scores [6, 8, 53] for predicting general CVDs, these functions did not include heart rate in the risk model.

Risk models formulated by using machine learning or data mining techniques have incorporated heart rate as a risk factor but tools that can predict CVD absolute risk are fewer. For example, a prediction tool [54] focuses on the classification of CVD event by employing the ANN and the Bayesian classifier based on heart rate variability. The diagnosis CVD model [27] categorizes the CVD risk as different levels but an absolute risk score cannot be obtained. Even though a supportive tool [19] will generate the estimate of a risk score, but the user can not know how many years the score is targeting.

Some equations only focused on specific CVD outcomes. The Europe SCORE project equations were developed for the fatal cardiovascular event [9]. These risk estimation tools [7, 14, 30] are just for coronary heart disease. Also, there are some risk models aiming stroke [16, 55]. Compared with these disease-specific models to estimate the risk of developing specific CVD outcomes, the present study generated a general CVD risk tool that could predict a global CVD risk as well as the risk of developing individual components.

Moreover, compared with the laboratory-based algorithms, the present research proposed a more straightforward way to estimate 10-year CVD risk based on risk factors. An individual can assess his or her CVD risk during an office visit or his monitoring of the combination of risk factors in the risk model, either manually or use some devices like wearable sensors.

4.2. Implication

The CVD risk prediction model could be implemented at the primary care for population analysis and identifying the high-risk individual. This would be a transformation in healthcare management of CVD at an individual as well as at a population level. However, with a small event size of diabetes, caution must be applied to the practice of this risk model. Even though we have used multiple imputation methods to impute the missing values for diabetes, the original feature of data in-balance, which decides that the imputed data frame for the “diabetes” might still have a data in-balance there. Advanced imputation methods need to be considered in the future for avoiding unexpected outcome caused by the diabetes data in-balance.

Our research aims to provide a CVD prediction model based on key risk factors, so that it can be used at the point-of-care for better and informed decision making. Thus, risk factors based on a clinical test such as total cholesterol, HDL cholesterol were not included, but some of these risk factors have a substantial effect on the development of CVD. We have provided a valid framework for creating a risk model using the Cox regression model; future work should consider risk factors not included in our model at this moment. Thus, expanding more predictors into the risk model is an important issue for future research.

5. Conclusion

The proposed study devised a risk prediction model based on multivariable predictors. A novel risk factor “heart rate” was incorporated into this risk equation by conventional risk factors. A satisfying predictive ability with C-index (AUROC) of 0.71 was obtained, which ensures the accuracy of estimating risk scores. Compared with studies focusing on specific diseases, the proposed algorithm can be applied to measure the 10-year risk of CVD. Health care professionals, public health physicians, practice managers, and individuals can run the proposed model to quantify risk at a population level, during patient consultation and identify high-risk individuals for further preventive health care for the entire practice.

Appendix

A. Exams in the Framingham Original Cohort Study Dataset

See Table 7.

B. Data Summary for Samples

See Tables 8–11.

Table 8: Exam data for Sample 1: male without CVD.

Exams
age
bmi
bps
pr
cgrpd
trt
hyp
dia
smk

Exam 8
44
26.386894
120
82
40
0
0
0
1

Exam 9
45
26.826676
120
80
0
0
0
0
0

Exam 10
47
27.467643
118
70
20
0
0
0
1

Exam 11
49
28.222249
110
76
44
0
0
0
1

Exam 12
52
28.675012
110
80
50
0
0
0
1

Table 9: Exam data for Sample 2: male with CVD and diabetes.

Exams
age
bmi
bps
pr
cgrpd
trt
hyp
dia
smk

Exam 8
45
27.74258
132
83
20
0
0
0
1

Exam 9
47
26.26118
124
80
20
0
0
0
1

Exam 10
49
27.664352
130
78
20
0
1
0
1

Exam 11
51
27.121914
130
90
20
0
1
0
1

Exam 12
53
24.816551
122
82
20
0
0
1
1

Table 10: Exam data for Sample 3: female without CVD.

Exams
age
bmi
bps
pr
cgrpd
trt
hyp
dia
smk

Exam 8
44
20.776333
110
70
20
0
0
0
1

Exam 9
46
20.265439
120
70
20
0
0
0
1

Exam 10
48
22.312012
118
73
20
0
0
0
1

Exam 11
50
21.797119
114
82
20
0
0
0
1

Exam 12
52
21.797119
130
76
20
0
0
0
1

Table 11: Exam data for Sample 4: female with CVD and diabetes.

Exams
age
bmi
bps
pr
cgrpd
trt
hyp
dia
smk

Exam 8
46
21.793044
130
65
3
0
1
0
1

Exam 9
48
21.967388
170
75
16
0
1
0
1

Exam 10
50
22.494583
140
60
8
0
1
0
1

Exam 11
53
22.31746
140
63
8
0
1
0
1

Exam 12
54
23.380197
160
58
2
1
1
1
1

C. Computation of Absolute Risk

Here, we take a specific subject to illustrate the process of risk score calculation. This sample is a 44-year-old man not having diabetes and hypertension. He has a systolic blood pressure of 120 mm Hg, pulse rate of 82 per minute, BMI of 26.38689413 kg/ and is a current smoker smoking 40 lapses per day, as shown in Table 12.

Table 12: Data summary for the subject 15018644.

PREDICTORS
VALUES
UNITS

AGE
44
YEARS

SEX
1
MALE

BMI
26.38689413
KG/M2

HYPERTENSION
0
NO

TREATMENT OF HYPERTENSION
0
NO

BLOOD PRESSURE SYSTOLIC
120
MM HG

CIGARETTES PER DAY
40
LAPSE

SMOKING
1
YES

PULSE RATE
82
PER MINUTE

DIABETES
0
NO

COX MODEL RISK
12.57

FHS MODEL RISK
11.86

The risk estimate based on the Cox model is calculated as follows:

Data Availability

The cardiovascular disease (CVD) data used to support the findings of this study were supplied by Framingham Heart Study-Cohort (FHS-Cohort) under license and so cannot be made freely available. Requests for access to these data should be made with Open BioLINCC Studies Group through this website https://biolincc.nhlbi.nih.gov/studies/framcohort/.

Additional Points

The main contribution of the present study is developing a risk prediction model for early detection of CVD. More specifically, the contribution can be summarized in four major respects: firstly, a novel risk factor “heart rate” was identified as significant for the development of CVD; secondly, an CVD risk prediction model aiming for early detection of CVD was developed based on various risk factors; thirdly, an absolute risk score in 10 years of CVD can be calculated using this risk model; lastly, multiple forms of the risk estimation of CVD, namely risk equation and nomogram, were also developed.

Conflicts of Interest

Authors declare no conflicts of interest.

Authors’ Contributions

All authors contributed equally.

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