European Journal of Cardiovascular Medicine

Atherosclerotic cardiovascular disease (ASCVD) is recognized globally for its high prevalence and associated mortality. In 2016, ASCVD accounted for around 2.4 million deaths, representing nearly 25% of total mortality cases. One of the pathological features of atherosclerosis is coronary calcification.1,2

For asymptomatic subjects presenting with risk factors—like advanced age, positive family history, smoking, diabetes, hypertension, and dyslipidemia—coronary computed tomography angiography (CCTA) may be considered by clinicians as a noninvasive tool to assess coronary artery condition.3

In recent years, using calcium score became a new method to quantify calcification and predict coronary artery stenosis.4 Research found that more calcium score values are correlated with greater stenosis severity,5 and that increasing score is associated with worsening prognoses.6.

According to current revascularization guidelines, significant stenosis is defined as ≥70% diameter narrowing in non-left main arteries and ≥50% in the left main coronary artery.7 Palumbo et al. proposed CACS as a potential pre-screening measure prior to CCTA.8 Alshumrani investigated various calcium threshold scores for detecting ≥50% and ≥70% coronary stenosis, finding that symptomatic patients with calcium score ≥250 showed ≥50% stenosis.5 But this study did not use coronary angiography (CAG) to confirm stenosis severity, which is a limitation.

It is important to consider that more calcium score threshold might overestimate the degree of coronary stenosis.9

Objectives:

The objectives of this study are:

To evaluate the diagnostic performance of calcium score in detecting coronary artery stenosis using Gensini score.

To examine the effect of age on CT calcium score

The current study was conducted in the Department of Cardiology, Katuri Medical College, Andhra Pradesh, India.

Study period: 18 months- January 2023- June 2024

Type of study: Prospective study.

Sampling Type: Convenience Sampling

Sample size calculation

Prevalence: 0.01%

n=Z2×p×(1−p)/d2

Prevalence: Error: 2%

Using the Cochran formula for a single‐proportion prevalence estimate (with Z = 1.96 for 95% CI), and assuming a CAD prevalence of 1 %, a margin of error of ±2% yields a required sample size of approximately 100 subjects.10

So, we included 100 patients finally.

Inclusion and exclusion criteria:

Participants were either symptomatic or asymptomatic but had suspected coronary artery disease (CAD).

Symptoms included angina, shortness of breath, syncope, or palpitations were included.

Patients were excluded if they had a history of percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), or if their calcium score data were missing.

Methodology:

Baseline clinical information collected include demographic data, medical and surgical history, laboratory results, calcium score, Gensini score and CAG findings. Comorbidities like diabetes mellitus and hypertension, were identified using diagnostic codes from the 10th revision of the International Classification of Diseases (ICD-10).

CAG assessment and stenosis grading were done by a cardiologist and blined to CT calcium score findings.

Computed Tomography Calcium Scoring Protocol

To perform coronary artery calcium (CAC) scoring, the research assistant (RA) retrieved CT scan data from the local database and loaded participant folders into the TeraRecon workstation.

After verifying the patient’s identifiers, RA selected “coronary recon

2.5/35” series for analysis. The images were imported into calcium scoring module, where the RA did calibration using phantom rods placed beneath the participant, representing known calcium densities. RA reviewed each CT slice to identify and score calcified lesions in the coronary arteries (LMA, LAD, LCX, RCA) based on Hounsfield Unit thresholds. Each lesion was marked, labeled by location, and quantified with outputs including the Agatston score, volume, density, and mineral mass.

Advantage of this CT system is its ability to produce high- resolution images without requiring beta-blockers. So it is suitable for patients with high or irregular heart rates. Imaging data were transferred to dedicated workstations for post-processing, including multiplanar reconstruction (MPR), maximum intensity projection (MIP), and three-dimensional volume rendering. Agatston’s method was used to calculate the coronary calcium score.

Statistical Analysis

Data were analyzed using SPSS version 17.0. Continuous variables were expressed as mean ± SD, and categorical variables as frequencies and percentages. Diagnostic performance was assessed using sensitivity, specificity, PPV, NPV, accuracy, and ROC analysis. A p-value < 0.05 was considered significant.

Ethical Aspects

The study was approved by the Institutional Ethics Committee. Written informed consent was obtained from all participants.

Descriptives

Average afe was 62.12 years. Age ranged from 43 to 84 years. Mean CT Calcium score was 607

Mean Gensini score was 36.2 in the current study.

Table 1: Descriptive statistics

Gender:

28% of the patients were female.

Table 2: Sex of patients

Correlation between calcium score and severity of CAD:

There is a significant positive correlation between CT calcium score and Gensini score. (r=0.34)

Table 3: CT calcium score and Gensini score

Graph 1: Calcium score and Gensini score

CAG:

25% of the patients had double vessel disease(DVD), 31% of the patients had single vessel disease (SVD). 7% had no CAD. 8% had triple vessel disease(TVD).

Table 4: CAG findings among patients Treatment: 15% of the patients underwent CABG, 47% underwent medical management (MM). 38% underwent PTCA- Percutaneous Transluminal Coronary Angioplasty

Table 5: Treatment status among patients DIAGNOSTIC ACCURACY: Area under curve(AUC) is 0.11 Diagnostic accuracy of CT calcium score in detecting Gensini score above 10(severe CAD) was given below:

Graph 2: Diagnostic accuracy of CT calciumscore

In our study cohort of 100 patients (mean age 62.1 ± 9.5 years), the mean CT calcium score was 607.3 and mean Gensini score was 36.3, indicating a moderate burden of coronary atherosclerosis. We found a significant positive correlation between CT calcium score and Gensini score (r = 0.345, p < 0.001). This implies that more coronary calcification was associated with more severe angiographic disease.

This relationship was similar with previous studies. One CT-based study done on 202 patients, found a strong correlation (r = 0.636, p < 0.001) between calcium score and Gensini score. They found that increasing calcium scores with more number of diseased vessels.11

One study done on 351 patients reported a moderate correlation between CACS and Gensini score (r = 0.405, p < 0.0001). This shows that calcium score is a reliable marker of CAD severity.12

Contrastingly, some studies reported weaker correlation. In a hypertensive cohort, the correlation between CAC and Gensini scores was lower (r = 0.244, p < 0.001), suggesting that while both scores predicted adverse cardiovascular events, they might have reflected different aspects of disease burden.13

This underscored that calcification and stenosis were related but not wholly overlapping phenomena.

Our correlation coefficient (0.345) was modest compared to others (0.405–0.636), likely due to differences in population characteristics (e.g., symptomatic vs. mixed cohorts), CT calcium scoring protocols, and scoring methodology.

Our sample included both symptomatic and asymptomatic patients, but other studies focused mainly on patients with suspected CAD and higher clinical suspicion—potentially amplifying correlation strength.

Important determinants of calcium score and Gensini scores are: age and gender.

Yildiz et al. found that CACS correlated with age and male sex and independently predicted both Gensini and SYNTAX scores. 11

Our population, predominantly male (72%) and with a mean age over 60, was consistent with risk profiles that potentiated calcification and angiographic disease.

Yildiz et al. used ROC analysis to define cutoffs for high SYNTAX scores (e.g., CAC > 809 predicted SS > 32), and dual-source CT study found an optimal calcium score cutoff of 350 for distinguishing CAD (sensitivity 83%, specificity 70%). 11

The relationship between calcification and luminal stenosis had also some clinical limitations. Calcification imples chronic, stable plaque and did not always equate to flow-limiting stenosis. Uncalcified “vulnerable” plaques could precipitate acute coronary events and might not have been fully captured by CACS.

Also, more calcium load can exaggerated stenosis severity when using CT-derived assessments due to beam hardening, though our study used invasive angiography as the gold standard.

Limitations

Retrospective design and single-center setting may introduce selection bias.The sample   size        (n = 100)       limited   generalizability,  and         lack        of longitudinal follow-up prevented assessment of clinical outcomes.

Our results corroborate existing literature demonstrating a statistically significant correlation between CACS and Gensini score, albeit of moderate strength. This supports the use of CACS in initial CAD severity stratification, ideally integrated with other clinical or imaging metrics. Future prospective studies with larger cohorts and outcome data are warranted. 

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