JMIR MEDICAL INFORMATICS

Bae et al

Original Paper

Coronary Artery Computed Tomography Angiography for
Preventing Cardio-Cerebrovascular Disease: Observational Cohort
Study Using the Observational Health Data Sciences andInformatics’ Common Data Model
Woo Kyung Bae1*, MPH, MD; Jihoon Cho2*, MS; Seok Kim2, MPH; Borham Kim2, BSN, RN; Hyunyoung Baek2,
MPH, RN; Wongeun Song2, MS; Sooyoung Yoo2, PhD
1

Department of Family Medicine, Health Promotion Center, Seoul National University Bundang Hospital, Republic of Korea, Bundang-gu, Seongnam-si,
Gyeonggi-do, Republic of Korea
2

Healthcare Information and Communication Technology Research Center, Office of eHealth Research and Business, Seoul National University Bundang
Hospital, Republic of Korea, Seongnam-si, Republic of Korea*

these authors contributed equally

Corresponding Author:
Sooyoung Yoo, PhD
Healthcare Information and Communication Technology Research Center, Office of eHealth Research and BusinessSeoul National University Bundang Hospital
Republic of Korea
172, Dolma-ro, Bundang-gu
Seongnam-si, 13605
Republic of Korea
Phone: 82 010 9053 7094
Email: yoosoo0@snubh.org

Abstract
Background: Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2%
of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes
mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, canbe used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation
exposure.
Objective: The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using theObservational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model(OMOP-CDM) data and the population-level estimation method.
Methods: Using electronic health record–based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74
years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA
(target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk
groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD)
risk score and Framingham risk score (FRS) for subgroup analyses.
Results: The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787
participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were
found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between
the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97; FRS: P=.85). However, the CCTA group
showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03; FRS: P=.02).
Conclusions: Although we could not confirm a significant difference in the preventive effects of CCTA screening for CVDs
over a long period of 5 years, it may have a beneficial effect on risk score management over 2 years.
(JMIR Med Inform 2022;10(10):e41503) doi: 10.2196/41503

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KEYWORDS
cardiovascular diseases; coronary artery computed tomography angiography; observational study; common data model; populationlevel estimation; cardiology; vascular disease; medical informatics; computed tomography; angiography; electronic health record;risk score; health data science; data modeling

Introduction

Methods

Cardio-cerebrovascular diseases (CVDs) result in 17.5 million
deaths annually worldwide, accounting for 46.2% of
noncommunicable causes of death, and are the leading causeof death, followed by cancer, respiratory disease, and diabetes
mellitus [1]. CVDs involve demographic factors (age, sex, andfamily history), pre-existing conditions (hypertension, diabetes
mellitus, and hyperlipidemia), and lifestyle and environmental
factors. Unlike demographic characteristics, lifestyle factors,
such as an inappropriate diet, a lack of exercise, smoking, stress,and excessive drinking, can be improved to reduce the risk of
CVDs [2].

Data Sources

Coronary artery computed tomography angiography (CCTA)
detects calcification in the coronary arteries and can be used todetect asymptomatic but serious vascular disease. It allows fornoninvasive and quick testing despite involving radiation
exposure [3,4]. For these reasons, many studies haveinvestigated the early detection of CVDs by using CCTA, whichenables prompt treatment and results in better outcomes.
In recent years, there has been debate about whether screening
via CCTA helps prevent CVDs in populations with varying
degrees of risk. CCTA has been recommended to predict CVDs
in patients with cancer [2,5], but among asymptomatic
individuals, the evidence about its effectiveness is inconsistent.
We aimed to study the effectiveness of CCTA screening byanalyzing observational health checkup data from electronic
health records (EHRs) in the form of the Observational Medical
Outcomes Partnership Common Data Model (OMOP-CDM),
using a cohort study design [6]. The OMOP-CDM standardizes
disparate data and enables the analysis of deidentified,large-scale observational data in a distributed research data
network. Moreover, as the data are standardized, the same
analytical codes can be used to conduct efficient analyses
through the data network. Observational Health Data Sciences
and Informatics (OHDSI)—an open international collaborative
community—provides an open-source analytics tool for
OMOP-CDM data that produces scientific, reliable, andreproducible evidence.
Using the OHDSI analytics tool, we performed a comparative
effectiveness study of CVD outcomes in asymptomatic patients
without a history of CVD who underwent a health checkup at
a tertiary university hospital. The conventional assessments of
CVD risk, namely assessments of the Framingham risk score(FRS) and the American College of Cardiology/American Heart
Association (ACC/AHA) atherosclerotic cardiovascular disease(ASCVD) risk score, were used to stratify the participants into
high-risk and low-risk groups for stratified analyses. Although
the risk of CVD increases with age, we compared differences
between the two groups after 2 and 5 years to assess theshort-term benefits of CCTA-based screening and whether it
can help prevent CVDs.
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The study site was the Seoul National University Bundang
Hospital (SNUBH), which is located in the Seoul metropolitan
area. The SNUBH collected OMOP-CDM version 5.3 data
based on EHRs from 2003 to 2020. The data included patients’demographic information, clinical information (diagnoses,
medications, tests, surgeries and procedures, family histories,
past histories, and nursing flowcharts), and health questionnaire
responses. The health questionnaire responses about medical
history, family history, socioeconomic status, medication
history, marital status, exercise and physical activity status, anddepression assessment results were converted to OMOP-CDM
data. In this study, we used the deidentified OMOP-CDM datathat the SNUBH collected from over 2 million patients,
including outpatients, inpatients, and emergency department
visits.

Ethical Considerations
This study adhered to the relevant guidelines and regulationsof the SNUBH Institutional Review Board (IRB). As the
OMOP-CDM is a deidentified data set, the study was exemptedfrom review by the SNUBH IRB (IRB number:
X-2202-736-903).

Study Design
This was a retrospective, observational, comparative cohort
study that used OMOP-CDM–formatted EHR data. We analyzed
data from adults aged 30 to 74 years who underwent a healthcheckup between April 1, 2003, and December 31, 2015, andwere followed up for at least 5 years. Only those who responded
to the questionnaire item about medical history in the healthcheckup survey were included. Individuals with a history of
CVD were excluded from this study. The index date was set as
the date of completing the health checkup questionnaire at ahealth checkup visit for the first time. CVDs that occurred within
60 days of the index date were considered as cases in which
patients were diagnosed during the health checkup, and theseCVD events were excluded as CVD outcomes. Thus, theoutcome was defined as CVD events that occurred 60 days after
the index date, and follow-ups ended on the date that CVD
events occurred (ie, within 5 years from the index date), thedate of the final hospital visit, or the date of death. As such, thetime-at-risk period was set as 61 days after the index date to 5
years after the index date.
The primary outcome was the comparison of CVD hazard ratios(HRs) between the group that underwent CCTA (target group)and the group that did not undergo CCTA at the health checkup
visit (comparison group).
In the subgroup analyses, the CVD HRs, which were based on
the ACC/AHA ASCVD risk score and the FRS, were analyzed.
The patients were stratified into the nonrisk and low-risk group
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JMIR MEDICAL INFORMATICSor the high-risk group based on a cutoff score of 10 for the FRS[7] and 5 for the ASCVD risk score [8].
The secondary outcome was the difference between the risk
scores of patients who underwent health checkups 2 years and5 years after the index date. The differences between the risk
scores at the index date and those at the times of subsequentexaminations were used for comparative analyses.

Study Population
From April 2003 to December 2015, a total of 69,334 patients
aged 30 to 74 years were enrolled for a health checkup. Of thesepatients, only 49,496 responded to the questionnaire, and only
46,087 patients had no cardiovascular history. A total of 42,489
patients for whom we could calculate the risk score—a key
indicator of this study—were selected as the initial cohort.
Initially, of the 42,489 patients who were included in theanalysis, 12,661 underwent CCTA (target group), and 29,828

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did not (comparison group). Of these patients, 1514 from thetarget group and 1519 from the comparison group with a historyof CVD before the index date were excluded from the analysis.
In addition, 1783 patients from the target group and 5004
patients from the comparison group who did not fulfill theminimum observation period of 1 day during the time-at-risk
window were excluded. The remaining 9364 patients from thetarget group and 23,305 patients from the comparator group
underwent 1:1 propensity score matching. During 1:1 propensity
score matching, 577 people who did not match the comparatorgroup were excluded from the target group because matching
was performed to maximize the minority group, and 14,518
people were excluded from the comparator group. Finally, 8787
of the 12,661 patients (69.4%) from the initial target cohort
were selected as the final target group, and 8787 of the 29,828
patients (29.5%) from the initial comparator cohort were used
for the analysis as the final comparator group (Figure 1).

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Figure 1. The flowchart of the study population. CCTA: coronary artery computed tomography angiography.

Covariates
Approximately 13,000 variables were used as covariates forpropensity score matching. These covariates included patient
clinical data that were obtained at any time prior to the index
date and health checkup data that were obtained on the index
date. The patient clinical covariates included the condition era,
the condition group era, the drug group era, observations,
measurements, procedures, the Charlson Comorbidity Index
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score, the Diabetes Complications Severity Index score, theCHADS2 (Congestive Heart Failure, Hypertension, Age,
Diabetes, Previous Stroke/Transient Ischemic Attack [2 points])
score, the CHA2DS2-VASc (Congestive Heart Failure,
Hypertension, Age≥75 [Doubled], Diabetes, Stroke [Doubled],
Vascular Disease, Age 65 to 74, and Sex Category [Female])score, and the hospital frailty risk score. The covariates thatwere measured at the index date included demographic data,such as sex, age, education level, average monthly income, andJMIR Med Inform 2022 | vol. 10 | iss. 10 | e41503 | p. 4
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marital status; health questionnaire data, such as any history ofcancer and chronic diseases (hypertension, diabetes, andhyperlipidemia), medication history (antihypertensive drugs,
antidiabetic drugs, antihyperlipidemic drugs, and aspirin),smoking status, and family history; and health checkup data,such as height, weight, BMI, blood pressure (systolic anddiastolic), waist circumference, glucose levels, uric acid levels,
aspartate aminotransferase levels, alanine aminotransferase
levels, triglyceride levels, total cholesterol, high-density
lipoprotein cholesterol levels, low-density lipoprotein cholesterollevels, and glycated hemoglobin A1c levels.

Outcomes
The outcome of this study was the first registered CVD event,which was based on a CVD diagnosis during the observation
period. A CVD event was defined based on International
Classification of Diseases, 10th Revision (ICD-10) codes I20
to I25 (ischemic heart disease), I50 (heart failure), I60 to I69
and G45 to G46 (stroke), and E78 (hypercholesterolemia). As
we intended to assess the HRs of CVDs resulting fromarteriosclerotic diseases only, we excluded cardiogenic diseases,
such as atrial fibrillation and aneurysm (I42-I43, I48, I71, I62,
and I68), and diseases caused by external accidental factors (I60
and I62). The ICD-10 codes that were chosen as the outcomes
were reviewed by 1 clinical specialist and 1 nurse.

Statistical Analysis
We used the population-level estimation methodology and an
open-source tool provided by OHDSI [9]. All analyses were
performed by using R version 4.0.3 (R Foundation for StatisticalComputing) [10]. Large-scale propensity score matching [11]was performed to adjust for potential confounding and to resolve
the imbalance between the target and comparison cohorts causedby selection bias—a result of the retrospective observational
nature of this study. The propensity score–matched model,
which used approximately 13,000 covariates, was fitted throughregularized regression, and the propensity score was calculated
as the probability of a patient undergoing CCTA based on thecovariates. Target and comparison group patients with similar
propensity scores were matched to create a balanced cohort. To
establish a matched cohort, we performed 1:1 propensity scorematching by using a caliper width of 0.2 of the SD of the logit.
The conditional Cox proportional hazards model was used toestimate HRs for the target group, in relation to the comparison

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Bae et algroup. The balance of the covariates between the cohorts wasassessed based on the standardized difference of the mean(<0.1). Statistical significance was evaluated at P<.05 for
2-tailed tests.
To explain any residual bias after controlling for the measured
covariates, we used negative control outcomes that were unlikely
to be induced or prevented by undergoing CCTA; thus, theactual HR was anticipated to be 1. The negative control
outcomes were selected by a clinical specialist through a manual
review of the outcomes that were used in a previous OHDSI
study [12] (Table S1 in Multimedia Appendix 1). The same
study design was used to estimate the outcomes of interest andcalculate the HR estimate for the negative control group, andall HR estimates were presented with 95% CIs and P values,
along with the empirical null distribution and adjustment[13,14]. The empirical equivalence of the two cohorts wasassessed by using the propensity score distribution. We also
reported the power analysis; propensity score; cohort balance
before and after propensity score matching; fitted null
distribution; calibration chart for negative control outcomes;
and Kaplan-Meier curve, which shows the proportional hazards
assumption over time.
To confirm the changes in the differences in ASCVD risk scoresand FRSs, we used the 2-group comparison method. The
normality of the amount of change was confirmed by using theShapiro-Wilk test, and the changes in the two groups wereconfirmed by using the Wilcoxon rank-sum test.

Results
Characteristics of Study Participants
Table 1 shows the baseline characteristics of the patients before
and after propensity score matching. The table shows thepatients’ age groups, sex, and BMIs; the number of patients in
the risk score groups; and the follow-up periods. For mostdemographic characteristics, the differences between groups
decreased after matching. The standardized difference of themean for the covariates decreased from 0.4 to 0.07 after
propensity score matching, which is lower than the conventional
standard of 0.1, thereby confirming that propensity scorematching was performed correctly (Figure 2). This can also be
observed in Figure 3, which compares the distributions frombefore and after propensity score matching.

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Table 1. The baseline characteristics of the study population before and after propensity score matching.
Characteristics

Before matching

After matching

CCTAa group(n=12,661)

Non-CCTA group(n=29,828)

Standard difference

CCTA group

Non-CCTA group(n=8787)(n=8787)

Standard difference30-34

226 (1.8)

2442 (8.2)−0.26

150 (1.7)

155 (1.8)

0

35-39

1043 (8.2)

4319 (14.5)−0.19

761 (8.7)700 (8)

0.03

40-44

1870 (14.8)

5257 (17.6)−0.081406 (16)1263 (14.4)

0.05

45-49

2516 (19.9)

5134 (17.2)

0.071846 (21)1697 (19.3)

0.04

50-54

2435 (19.2)

4617 (15.5)

0.10

1702 (19.4)

1678 (19.1)

0.01

55-59

2084 (16.5)

3322 (11.1)

0.16

1373 (15.6)

1438 (16.4)

−0.02

60-64

1468 (11.6)

2275 (7.6)

0.14

908 (10.3)

1050 (11.9)

−0.05

65-69

734 (5.8)1564 (5.2)

0.02471 (5.4)

568 (6.5)

−0.05

70-74

285 (2.3)

898 (3.0)−0.05

170 (1.9)

238 (2.7)−0.054757 (37.6)

12,650 (42.4)−0.103561 (40.5)

3368 (38.3)

0.047904 (62.4)

17,178 (57.6)

0.105226 (59.5)

5419 (61.7)−0.04

24.2 (3.1)

23.7 (0.2)

0.18

24.0 (3.1)

24.1 (3.1)

−0.03

b

Age group (years), n (%)

Sexb, n (%)
FemaleMaleb

2

BMI (kg/m ), mean (SD)ACC/AHAc ASCVDd risk scoree, n (%)
High (≥5)

5036 (39.8)

8576 (28.8)

N/Af3062 (34.8)

3493 (39.8)

N/A

Low (<5)7625 (60.2)

21,252 (71.2)

N/A

5725 (65.2)

5294 (60.2)

N/A

Framingham risk scoree, n (%)
High (≥10)

4996 (39.5)

8155 (27.3)

N/A

3030 (34.5)

3381 (38.5)

N/A

Low (<10)7665 (60.5)

21,673 (72.7)

N/A

5757 (65.5)

5406 (61.5)

N/A

2220.3 (1731.6)

1928.9 (1675.5)

N/A2604 (1594.4)

2583.1 (1657.0)

N/A

Follow-up period (days)e,mean (SD)a

CCTA: coronary artery computed tomography angiography.

b

Variables used in propensity score matching.

c

ACC/AHA: American College of Cardiology/American Heart Association.

dASCVD: atherosclerotic cardiovascular disease.

eVariables not used in propensity score matching.

f

N/A: not applicable.

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Figure 2. Standardized difference of means between the two groups of covariates before and after propensity score matching.

Figure 3. Distribution of propensity scores in each group (A) before and (B) after propensity score matching.

Effect of CCTA on CVDs
The Cox proportional hazards model was used to estimate andcompare the HRs of CVDs among the target and comparisongroups after propensity score matching, and no statisticallysignificant differences were found between the two groups. The

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Kaplan-Meier analysis revealed that the HR was 1.048 (95%
CI 0.960-1.144), which was not statistically significant (P=.30).
The calibration P value, which was adjusted by using a negative
control and was the most important indicator in our analysis,
was .37, indicating no statistical significance (Figure 4).

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Figure 4. (A) Kaplan-Meier curve plot and (B) rejection area plot with negative outcome controls applied in the main analysis. HR: hazard ratio.

Subgroup Analysis
The study population was stratified based on the cutoff scoresfor the ACC/AHA ASCVD risk score and FRS for subgroup
analyses. Table 2 presents the results of each analysis. In eachsubgroup, the standardized difference of the mean dropped to<0.1 after propensity score matching. Figure S1 in Multimedia
Appendix 1 shows the propensity score distributions, and Figure
S2 in Multimedia Appendix 1 shows the standardized differenceof the mean among groups of covariates before and after
propensity score matching.
In the ASCVD high-risk subgroup (risk score≥5), 3149 patientswere included in both the target group and comparison group.
In the low-risk subgroup (risk score<5), 5524 patients were

included in both the target group and comparison group. In thehigh-risk and low-risk subgroups, the calibration P value, which
was adjusted by using negative controls, was .39 and .50,
respectively, showing no significant differences in the HRs of
CVDs among the target and comparison groups.
In the FRS high-risk subgroup (FRS≥10), 3110 participants
were included in both the target group and comparison group.
In the low-risk subgroup (FRS<10), 5602 patients were included
in both the target group and comparison group. The calibration
P value, which was adjusted by using negative controls, was.13 and .57 in the high-risk and low-risk subgroups, respectively,
indicating no significant differences in the HRs of CVDs among
the target and comparison groups (Figure S3 in Multimedia
Appendix 1).

Table 2. The risk of cardio-cerebrovascular disease at 5 years in each subgroup based on the American College of Cardiology/American Heart Association(ACC/AHA) atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS).
Hazard ratio (95% CI)

P valuea

Calibration P valueb

High (≥5)

1.113 (0.984-1.259)

.09

.39

Low (<5)0.999 (0.881-1.133)

.99

.50

High (≥10)

1.166 (1.031-1.321)

.02

.13

Low (<10)1.004 (0.883-1.141)

.96

.57

ACC/AHA ASCVD risk score

FRS

a

Kaplan-Meier analysis P value.

b

Calibration P value that was adjusted by using a negative control.

Risk Scores at 2 and 5 Years
The 2-year median change in the ASCVD risk scores and the
FRSs of the non-CCTA group was 0.23 and 0.60, respectively.
In contrast, the ASCVD risk scores and the FRSs of the CCTA
group changed by 0.17 and 0.39, respectively. There was astatistically significant difference for both risk scores, with P
values of .03 and .02, respectively.

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The 5-year median change in the ASCVD risk scores and the
FRSs of the non-CCTA group was 1.06 and 1.61, respectively.
In contrast, the ASCVD risk scores and the FRSs of the CCTA
group changed by 1.10 and 1.66, respectively. There was no
statistically significant difference for both risk scores, with P
values of .97 and .85, respectively (Table 3).

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Table 3. Changes in the differences in American College of Cardiology/American Heart Association (ACC/AHA) atherosclerotic cardiovascular disease(ASCVD) risk scores and Framingham risk score (FRSs) from baseline at 2 and 5 years.
CCTAa group
Patients, n

Non-CCTA group

Change in score, median (IQR) Patients, nChange in score, median (IQR)

P valueb

Differences in risk scores from baseline at 2 years
ACC/AHA ASCVD risk scores

1330

0.17 (−0.16 to 1.08)

1691

0.23 (−0.10 to 1.30)

.03

FRSs

1330

0.39 (−0.80 to 1.96)

1691

0.60 (−0.69 to 2.26).02

Differences in risk scores from baseline at 5 yearsa

ACC/AHA ASCVD risk scores1232

1.10 (0.08 to 1.57)

1372

1.06 (0 to 2.79)

.97

FRSs

1232

1.66 (0.04 to 3.92)

1372

1.61 (0.09 to 4.11).85

CCTA: coronary artery computed tomography angiography.

b

Wilcoxon rank-sum test P value.

Discussion
Principal Results

underwent the same levels of examination and counseling, whichwere conducted by the cardiovascular health screening programof the national service in 1 hospital.

From our population-level estimation study, which compared
the CVD HRs of a health checkup group that was undergoing
CCTA with those of a group that was not undergoing CCTA
over 5 years, although some benefits were observed at 2 years,
we found no significant difference (calibration P=.37) in the
final risk of CVD events between the two groups. It seems that
CCTA has no beneficial effect on CVD prevention for long
periods of time.

Smoking status, blood pressure, and blood lipid concentration,
which are major factors in the FRS and ASCVD pooled cohort
equations score, are closely related to lifestyle changes. Similar
to previous studies, the effect of a single coronary computed
tomography scan and the results of counseling decreased overtime, and the differences that were observed after 2 years
disappeared after 5 years.

Communication about medical examinations and examination
results through counseling has been reported to improve healthindicators, such as CVD risk. In the Korean national healthinsurance service screening program, the group that underwent
cardiovascular health screening for 40-year-olds had higher
rates of new hypertension, diabetes, and hyperlipidemia, whereas
the incidence of CVD mortality, all-cause mortality, and major
adverse cardiovascular events was lower [15]. Per the resultsof an analysis of the same data, the group that received
counseling after the health checkup had higher motivation stagesof health behavior change than those of the group that received
only the checkup [16]. The smoking cessation rate was higher
after 2 years when compared to that of the group who received
only the checkup [17]. Engberg et al [18] reported thatcardiovascular risk scores, BMIs, and serum cholesterol levels
were lower in the intervention groups than those in the control
group after 5 years’ worth of health screenings andconsultations.

This study has some limitations. First, the follow-up period was
5 years, and the risk scores were not observed for a longer period(eg, 10 years, as CVDs can last for >10 years). A follow-up
study for identifying a risk score that is suitable for CVD
prediction over longer periods can be conducted in the future.
Second, as this was a single-center study, some of the outcomesmay not be generalizable. Multicenter studies that use OHDSI
data networks can provide more generalizable evidence. Third,
this study included patients who visited the health promotion
center multiple times; those who did not undergo CCTA at thefirst visit but underwent CCTA during subsequent visits were
included in the comparison group. Therefore, the differences
between the groups might have been attenuated. This can be
avoided by conducting a prospective cohort study. Lastly,observational research that uses EHR data has the limitationthat it cannot fully capture the entirety of a patient's healthinformation [21]. This study converted EHR data into common
data model data, and it has the same limitation. If the participantsof this study underwent examinations and treatments outsideof the hospital, there was a disadvantage that the records forthese procedures were not recorded in the database.
Additionally, with regard to drugs, the SNUBH common data
model converted data on prescription drugs for outpatients andadministration drugs for inpatients. Thus, it was not known
whether the drugs ordered for the outpatients were taken ontime by the patients. As such, selection bias may have occurred
due to information not being recorded in the database. Although
it is possible to reduce channeling bias through large-scale
propensity score matching, which we used in this study, there

In existing studies that require lifestyle modifications, such as
modifications for obesity, smoking cessation, and substanceabuse, the effects of 1-time interventions or short-term
interventions, interviews, and counseling tend to weaken overtime. In a study that used the motivational interview techniquefor people with substance abuse issues, the positive effectobserved at 3 months disappeared at 12 months [19], and inanother study, the effect of smoking cessation treatment
continued for 10 weeks and gradually slowed down at 3, 6, and12 months [20].

Limitations

Our study compared patients who did or did not undergo
additional coronary computed tomography. Both groups
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may still be the limitation that such matching cannot reduce
selection bias [22].

Comparison With Prior Work
Waugh et al [23] conducted a systematic review andmeta-analysis of 5 studies and reported that computed
tomography has no benefits as a screening tool for the potential
onset of CVDs. However, a closer review revealed that all 5
included studies were inappropriate in terms of their findings
about the prophylactic benefits of CCTA. All of these studies
investigated the association between coronary artery calcium(CAC) and the onset of CVDs or death after a specific follow-up
period in patients who underwent CCTA screening. They useda short follow-up period and analyzed the results in the context
of the presence of CAC as opposed to CCTA findings.
Therefore, the conclusion of the meta-analysis by Waugh et al[23]—CCTA screening is not effective—was based on thefinding that the risk of heart disease was not elevated in people
undergoing a CAC assessment via CCTA, as opposed to anassessment of the prophylactic benefits of CCTA itself. Further,since the measurement of CAC is regarded as a reliable method
for CVD risk assessment, a study claimed that CCTA should
be introduced for the screening of asymptomatic individuals[24]. However, other studies claim that CCTA iscost-ineffective, although these admit that CAC, when observed
via CCTA, is a better predictor of CVD than the FRS [25]. We
supplemented these studies by comparing groups that underwent
CCTA with those that did not undergo CCTA.
McEvoy et al [26] examined the differences in the incidence ofcoronary artery disease between CCTA and comparison groups
after a fixed follow-up period. The authors matched thepropensity scores of 1000 individuals who underwent CCTA
for a health checkup with those of 1000 individuals who did
not undergo CCTA (ie, the comparison group) and compared
the incidence of coronary artery disease at the 90-day and18-month follow-ups. The study reported that CCTA-based
screening was significantly associated with an increased rate ofinvasive tests and medication use but was not associated with
the incidence of coronary artery disease, concluding that CCTA
is not recommended for screening purposes. However, the study
was limited by the small number of cases and the short follow-up
periods.

Bae et al
Our study presents reliable evidence about CCTA, which was
obtained by performing large-scale propensity score matching
and using EHR and health checkup questionnaire responses
from OMOP-CDM data. We studied a large study sample overa longer study period than those used by previous studies.
Although past studies used either 90-day follow-ups or 18-monthfollow-ups, we observed the patients from 60 days after theindex date to 5 years after the index date to analyze the CVD
HRs in relation to CCTA. Moreover, while previous studies
had approximately 1000 patients in both the target group andcomparison group, we included 8787 patients in each group.
The data were also standardized, which enabled us to perform
an efficient analysis across organizations and use the same
analysis codes. Future studies can investigate the effects of
CCTA and CVD in larger populations over long follow-up
periods, in collaboration with organizations that convert healthquestionnaire data into the common data model format.
We also stratified the population into high-risk and low-risk
groups based on the ASCVD risk score and FRS. Even in thehigh-risk group, CCTA screening did not have a significant
effect (ASCVD risk score: calibration P=.39; FRS: calibration
P=.13) on the prevention of CVD.
Based on the changes in risk scores, a significant difference wasobserved between the CCTA and comparison groups after 2
years (change in ASCVD risk scores: P=.03; change in FRSs:
P=.02). However, this difference was not significant after 5
years (change in ASCVD risk score: P=.92; change in FRSs:
P=.85). We speculate that patients are motivated to manage
their risk score factors for a brief period immediately after the
CCTA test; however, the significance decreases over long
periods.

Conclusions
Through a retrospective cohort study that was conducted overa 5-year period, we found that CCTA had no significant
preventive effect on future CVDs. We also demonstrated thepotential of converting health checkup data into OMOP-CDM
data and integrating such data into common data model–based
EHR data for research targeting the health checkup population.
Although we examined the outcomes of CVDs after CCTA,
future studies could examine patients’ health behaviors
following CCTA. It is expected that the use of common data
model data will be expanded to multicenter studies.

Acknowledgments
This work was supported by the Technology Innovation Program (grant 20004927 for “Upgrade of CDM based Distributed
Biohealth Data Platform and Development of Verification Technology”), which is funded by the Ministry of Trade, Industry &Energy (Korea).

Data Availability
Common data model data are designed to support a distributed research network. Thus, access to the data is restricted on internal
private networks, and the data are not publicly available.

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Authors' Contributions
WKB designed this study. JC drafted the manuscript and performed the data analyses. SK, BK, HB, and WS reviewed the data
extraction and study design. WKB, JC, and SY inspected and revised the manuscript. SY supervised this study. All authors haveread and approved the final manuscript.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Supplementary material.
[DOCX File , 2366 KB-Multimedia Appendix 1]

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Abbreviations
ACC/AHA: American College of Cardiology/American Heart Association
ASCVD: atherosclerotic cardiovascular disease
CAC: coronary artery calcium
CCTA: coronary artery computed tomography angiography
CHA2DS2-VASc: Congestive Heart Failure, Hypertension, Age≥75 (Doubled), Diabetes, Stroke (Doubled),
Vascular Disease, Age 65 to 74, and Sex Category (Female)
CHADS2: Congestive Heart Failure, Hypertension, Age, Diabetes, Previous Stroke/Transient Ischemic Attack(2 points)
CVD: cardio-cerebrovascular disease
EHR: electronic health record
FRS: Framingham risk score
HR: hazard ratio
ICD-10: International Classification of Diseases, 10th Revision
IRB: Institutional Review BoardOHDSI: Observational Health Data Sciences and Informatics
OMOP-CDM: Observational Medical Outcomes Partnership Common Data Model
SNUBH: Seoul National University Bundang Hospital

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Edited by C Lovis; submitted 28.07.22; peer-reviewed by S Chang, K Adapa; comments to author 18.08.22; revised version received
04.09.22; accepted 24.09.22; published 13.10.22
Please cite as:
Bae WK, Cho J, Kim S, Kim B, Baek H, Song W, Yoo S
Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study
Using the Observational Health Data Sciences and Informatics’ Common Data Model
JMIR Med Inform 2022;10(10):e41503
URL: https://medinform.jmir.org/2022/10/e41503
doi: 10.2196/41503
PMID:©Woo Kyung Bae, Jihoon Cho, Seok Kim, Borham Kim, Hyunyoung Baek, Wongeun Song, Sooyoung Yoo. Originally publishedin JMIR Medical Informatics (https://medinform.jmir.org), 13.10.2022. This is an open-access article distributed under the termsof the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly
cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as thiscopyright and license information must be included.

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