Coronary artery ectasia prediction: the role of the Intermountain Risk Score in identifying patients at risk

ABSTRACT

Aims

This study evaluates the association between the Intermountain Risk Score (IMRS) and coronary artery ectasia (CAE) to determine its predictive value.

Materials & methods

A retrospective study was conducted at a tertiary hospital from January 2019 to January 2024. A total of 446 patients (226 with CAE, 220 controls) were included. Clinical, laboratory, and angiographic data were analyzed. IMRS was calculated based on routine demographic and laboratory parameters. Statistical analyses included logistic regression and receiver operating characteristic (ROC) curve analysis.

Results

Patients with CAE had significantly higher IMRS scores (p = 0.011) and were more likely to fall into high-risk IMRS categories (p = 0.002). Smoking (HR: 3.744, p = 0.045), mean corpuscular volume (HR: 1.105, p = 0.019), and IMRS color category (HR: 5.255, p = 0.016) were independent predictors. ROC analysis showed an AUC of 0.617 for IMRS score and 0.627 for IMRS color category.

Conclusions

IMRS is a significant predictor of CAE and may serve as a practical risk stratification tool. Higher-risk patients could benefit from closer monitoring and targeted interventions. Further validation in prospective studies is needed.

1. Introduction

Coronary artery ectasia (CAE) is a phenomenon marked by an abnormal dilatation of one or more segments of the coronary arteries, commonly defined as any localized or diffuse dilation measuring at least 1.5 times the diameter of an adjacent, ostensibly healthy vessel segment. Unlike focal coronary artery aneurysms, CAE often affects a relatively longer or more diffuse portion of the vessel, which can confer distinctive hemodynamic and clinical implications. The abnormal expansion of arterial walls in CAE has the potential to alter blood flow dynamics, occasionally leading to sluggish circulation, turbulent flow, and subsequent complications such as thrombosis or distal embolization. Clinical presentations vary from stable angina to acute coronary syndromes and, in rare cases, myocardial infarction triggered by the interplay between abnormal vessel geometry and underlying atherosclerotic or inflammatory processes [1].

The prevalence of CAE in the general population remains challenging to ascertain because the condition is frequently underdiagnosed and can be mistaken for other coronary abnormalities. Estimates range broadly from 1.5% to 8.5% of patients undergoing coronary angiography, although certain studies have reported even higher figures, particularly in settings characterized by a higher burden of coronary artery disease (CAD). This variability in reported prevalence underscores the heterogeneity in CAE definitions, diagnostic criteria, and populations studied. In some acute myocardial infarction cohorts, for instance, CAE has been observed in as many as 8.5% of patients, highlighting its potential clinical significance in high-risk groups. Despite this relatively substantial prevalence in selected populations, CAE remains insufficiently recognized and often conflated with other coronary pathologies [2].

The pathophysiologic underpinnings of CAE appear multifactorial, involving both atherosclerosis and non-atherosclerotic factors. Atherosclerotic lesions can weaken the arterial wall through progressive plaque development and associated inflammatory responses. However, some patients with CAE do not exhibit extensive atherosclerosis, suggesting the involvement of additional components, including congenital vessel anomalies and inflammatory or autoimmune processes [3]. In certain geographic regions, conditions like Kawasaki disease or connective tissue disorders have been linked with coronary artery ectatic changes [4]. Genetic predispositions, including familial clustering of CAE or variants in genes involved in connective tissue integrity, have also been proposed as contributing factors, although the precise mechanisms remain to be fully elucidated [5].

From a clinical management standpoint, CAE can pose significant diagnostic and therapeutic dilemmas. On one hand, conventional CAD therapies targeting atherosclerotic lesions – such as antiplatelet agents, lipid-lowering medications, and revascularization interventions – remain relevant if coexisting obstructive disease is present [1]. On the other hand, the unique geometry of ectatic vessels may predispose patients to thrombus formation or distal embolization, rendering them at a higher risk of adverse outcomes if not closely monitored [6]. Some clinicians recommend more aggressive antithrombotic strategies for patients with significant CAE, especially if they exhibit prior thromboembolic events or large ectatic segments [7]. However, standardized guidelines are lacking, and the optimal approach often hinges on individual patient characteristics and clinical judgment.

In light of these complexities, reliable and easily implementable risk stratification methods are urgently needed to guide decision-making and optimize outcomes. One increasingly recognized tool is the Intermountain Risk Score (IMRS), which uses demographic and routinely collected laboratory parameters to generate a numeric risk category [8]. While the IMRS was originally developed to predict mortality and morbidity in broad cardiovascular contexts, emerging evidence suggests that it may also identify individuals at higher risk for specific pathologies, including CAE. By combining age, sex, and common laboratory findings – among other factors – the IMRS provides a multifaceted glimpse into a patient’s overall cardiovascular risk profile.

Assessing the effectiveness of the IMRS in CAE prediction is of particular interest given the condition’s heterogeneous etiology and the difficulty in pinpointing individuals most likely to develop ectatic changes. If the IMRS can reliably differentiate higher-risk patients, then clinicians might tailor more thorough imaging strategies, implement aggressive medical management, or provide closer follow-up. Such a targeted approach could mitigate the underdiagnosis of CAE and expedite interventions that potentially forestall serious complications such as myocardial infarction or heart failure.

Against this backdrop, the current study aimed to explore how the IMRS correlates with CAE in a retrospective cohort of patients who underwent invasive coronary angiography. Specifically, this investigation sought to elucidate the demographic, clinical, and laboratory parameters that characterize CAE and determine whether an elevated IMRS independently predicts its presence. By doing so, the study provides greater clarity on the role of IMRS in identifying patients at risk, while also contributing to the broader understanding of CAE, a condition that straddles the spectrum between conventional atherosclerotic disease and unique vascular remodeling processes.

In summary, CAE stands as a complex, under-recognized entity within the domain of coronary artery anomalies. Its prevalence, potentially high morbidity, and coexistence with typical atherosclerotic disease emphasize the necessity for improved diagnostic and prognostic tools. The adoption of the IMRS for CAE risk stratification could translate into more strategic clinical interventions, delivering better prognoses and reduced complication rates in susceptible patients. Through this study, we aim to dissect the various clinical and laboratory correlates of CAE and present evidence that might solidify the IMRS as a robust mechanism for identifying patients at elevated risk of this distinct coronary pathology.

2. Materials and methods

2.1. Study design and population

This research employed a retrospective, observational design to investigate the relationship between the Intermountain Risk Score (IMRS) and coronary artery ectasia (CAE). The cohort encompassed individuals undergoing invasive coronary angiography at Health Sciences University Sultan 2. Abdülhamid Han Training and Research Hospital over a five-year span, from January 2019 to January 2024. Patients were selected based on coronary angiography findings consistent with CAE coronary arteries without ectasia. The primary endpoint was the presence of CAE, allowing comparisons between ectatic patients and an age- and sex-matched control group without ectasia.

All study protocols adhered to the ethical principles delineated in the Declaration of Helsinki. Ethical approval was obtained from the Health Sciences University Hamidiye School of Medicine Ethics Board on 14 November 2024 (registration number 24/553). Patient confidentiality was ensured by deidentifying data, and all analyses were conducted solely for the purpose of scientific inquiry.

Patients included in the ectatic group met the following principal criterion: the presence of CAE defined as a coronary artery segment measuring at least 1.5 times the diameter of the nearest segment deemed normal. Patients with evidence of obstructive coronary artery disease (≥50% stenosis on visual assessment), significant congenital coronary anomalies, prior surgical or percutaneous coronary revascularization, or incomplete medical and laboratory records were excluded. This approach was adopted to isolate CAE as the central pathology of interest and reduce confounding effects introduced by advanced revascularization procedures.

Propensity score matched controls were drawn from the larger angiography database and met the same exclusion criteria but did not present with CAE. Matching was performed to ensure comparability between groups, limiting the confounding influence of demographic factors. Ultimately, two groups – ectatic and non-ectatic – were established, and their clinical, laboratory, and angiographic parameters were compared.

2.2. Data collection

Data were systematically retrieved from electronic medical records. Relevant demographic information included age, sex, and traditional cardiovascular risk factors such as hypertension, diabetes mellitus, dyslipidemia, and smoking history. Laboratory data comprised the complete blood count (CBC) with differential, lipid panel, blood glucose, inflammatory markers, and serum electrolytes (e.g., calcium levels). Angiographic information encompassed specific coronary artery involvement (right coronary artery, left anterior descending artery, or left circumflex artery), the extent of ectatic segments, and any additional pertinent findings noted during the procedure.

A central focus of data collection was the Intermountain Risk Score (IMRS), which integrates a range of laboratory and clinical factors into a single numeric value. The IMRS was calculated according to standardized protocols, using patient data available at the time of angiography. The IMRS was calculated according to the 30-day mortality column of the table in the original IMRS study. (Appendices Table A1). The results were then categorized into three different risk strata (green, yellow and red as stated in the study by Horne et al. [9]) (Appendices Table B1)

2.3. Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 27.0 (Armonk, NY: IBM Corp). Descriptive statistics were employed to delineate group characteristics, with continuous variables presented as means ± standard deviation (SD) or medians (interquartile range), depending on data distribution. Categorical variables were expressed as frequencies or percentages. Normality was assessed through the Kolmogorov-Smirnov test, guiding the choice of parametric or non-parametric statistical techniques.

Comparisons between the ectatic and non-ectatic groups were conducted using chi-square tests for categorical variables and independent samples t-tests or Mann-Whitney U tests for continuous variables, depending on distribution normality. Variables of interest included age, sex, smoking status, IMRS score, IMRS color category, platelet indices (e.g., mean platelet volume, MPV), red blood cell distribution width (RDW), and serum monocyte counts, among others.

Subsequent univariate Cox regression analysis (using the Enter method) was employed to evaluate the association between each clinical or laboratory parameter and the presence of CAE. Candidate variables with a p-value <0.200 in univariate models were then entered into a multivariate logistic regression. This step adjusted for potential confounders, such as age, sex, and conventional cardiovascular risk factors. Final hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. A p-value <0.05 was considered statistically significant.

Receiver operating characteristic (ROC) curve analyses were conducted for selected parameters, including mean corpuscular volume (MCV), IMRS score, and IMRS color category. The area under the curve (AUC) was determined for each variable to gauge discriminatory power, with optimal cutoff points identified with Youden method based on maximizing the sum of sensitivity and specificity. The ultimate objective was to assess whether the IMRS, or specific components of it, serve as reliable predictors of CAE.

3. Results

A total of 446 patients met the inclusion criteria, with 226 in the ectatic group and 220 in the non-ectatic (control) group. Propensity score matching minimized demographic imbalances, though subtle differences remained. Patients in the ectatic group were older on average compared to those in the non-ectatic group (p < 0.05), suggesting that age may be a contributory factor in the development of ectasia. Moreover, the ectatic group contained a higher proportion of men (77%) compared to the non-ectatic group (67.7%) (p = 0.029). These findings align with broader cardiovascular disease patterns, wherein advanced age and male sex are frequently encountered risk factors. Table 1 summarizes key demographic parameters.

Table 1.

Demographic properties of groups.

	Ectasia (-) n=220	Ectasia (+) n=226	P value
Age (years)	62±10	62±10	0.915
Gender (male) n(%)	149%67,7)	174%77)	0.029
Hypertension, n(%)	120%54,5)	114%50,9)	0.441
Diabetes mellitus, n (%)	55%25)	51%22,8)	0.581
Hyperlipidemia, n (%)	90%41)	101%44,7)	0.385
Smoking, n (%)	16%7,3)	43%19,2)	<0.001

Values are mean ± SD or n (%) unless otherwise stated.

With regard to traditional cardiovascular risk factors, smoking emerged as a notable differentiator. Nearly one in five ectatic patients (19.2%) reported active or recent smoking, whereas only 7.3% of the non-ectatic group carried the same history (p < 0.001). Other common risk factors – such as hypertension, hyperlipidemia, and diabetes – were distributed between groups, but only smoking achieved statistical significance in association with CAE in subsequent analyses.

Laboratory indices revealed intriguing discrepancies between the ectatic and non-ectatic groups. As depicted in Table 2, red cell distribution width (RDW) was significantly higher in ectatic patients (14.3% vs. 13.4%, p < 0.001), intimating an inflammatory or hematologic disturbance. Mean platelet volume (MPV) likewise exceeded normal or near-normal values in the ectatic group (9.8 vs. 9.3, p < 0.001), hinting at heightened platelet activation. A modest but significant elevation in monocyte counts was also observed in the ectatic cohort (0.51 vs. 0.47, p = 0.047), supporting a role for cellular components of inflammation in CAE pathogenesis.

Table 2.

Examination of laboratory and indices.

Laboratory	Ectasia (-) n=220	Ectasia (+) n=226	P value
WBC (103/µL)	8,03±2,49	8,11±2,68	0.761
Hemoglobin (g/dL)	13,6±1,6	13,8±1,9	0.126
Hematocrit	40,7±4,7	41,6±5,03	0.069
MCV (fL)	87±5,1	87,5±5,8	0.129
MCHC (g/dL)	33,2±1,42	33,2±1,47	0.889
RDW %	13,4±1,2	14,3±3,02	<0.001
MPV (fL)	9,3 (8,5–10,2)	9,8 (9,1–10,6)	<0.001
Platelet count (103/µL)	234 (194–274)	236 (200–281)	0.316
Neutrophils (103/µL)	4,6 (3,7–5,7)	4,5 (3,8–5,5)	0.881
Lymphocytes (103/µL)	2,1 (1,7–2,5)	2,3 (1,7–2,8)	0.067
Monocyte (103/µL)	0,47 (0,37–0,62)	0,51 (0,41–0,62)	0.047
Creatinine(mg/dL)	0,95 (0,84–1,15)	0,98 (0,79–1,12)	0.839
Sodium (mmol/L)	139±3	139±3	0.963
Potassium (mmol/L)	4,4±0,4	4,45±0,47	0.951
Calcium (mg/dL)	9,4±0,6	9,3±0,53	0.001
Bicarbonate (mmol/L)	25,8±2,7	25,7±3,6	0.816
Glucose(mg/dL)	104 (93–133)	104 (92–127)	0.708
CRP (mg/dL)	2,4 (2–5,1)	3 (2–10)	0.385
Sedimentasyon	20 (12–34)	16 (8–32)	0.244
Albumin (g/L)	42±4,6	41±4,1	0.269
Total cholesterol (mg/dL)	186 (152–223)	192 (160–218)	0.822
LDL-C (mg/dL)	111 (84–141)	117 (89–143)	0.205
HDL-C (mg/dL)	41 (35–53)	41 (34–49)	0.375
Triglyceride (mg/dL)	150 (101–208)	132 (100–176)	0.049
Indices
NLR	2,09 (1,7–2,82)	1,98 (1,61–2,78)	0.177
LMR	4,5 (3,44–5,86)	4,5 (3,33–5,62)	0.589
PLR	111 (84–140)	106 (81–138)	0.416
PNI	53,6 (49,3–56,9)	53,5 (48,5–57,7)	0.891
SII index	485 (352–699)	455 (347–694)	0.468
CAR	0,57 (0,45–1,25)	0,72 (0,43–2,14)	0.284
IMRS score	8,5 (7–11)	10 (8–13)	0.011
IMRS category	1 (1–2)	2 (1–2)	0.002
TyG index	1,29 (0,88–1,85)	1,21 (0,83–1,71)	0.296

Values are mean ± SD or median (interquartile range) unless otherwise stated.

WBC: white blood cell, MCV: Mean corpuscular volume, MCHC: Mean corpuscular hemoglobin concentration, RDW: red cell distribution width, MPV: mean platelet volume, CRP: C- reactive protein, LDL-C: low-density lipoprotein cholesterol, HDL-C: high-density lipoprotein cholesterol INDICES: CAR: C-Reactive Protein-Albumin Ratio, NLR: Neutrocyte-lymphocyte-ratio, LMR: Lymphocyte-to-Monocyte Ratio, PLR: Platelet-to-lymphocyte ratio, SII: Systemic Inflammation Index, PNI: Prognostic nutritional index, IMRS: Intermountain Mortality Risk Score, IMRS category: IMRS category, TyG: Triglyceride- Glucose Ratio.

Serum calcium levels, albeit within physiologic ranges, were marginally lower in ectatic compared to non-ectatic patients (9.3 vs. 9.4, p = 0.001). While the absolute clinical impact of this difference is debatable, it raises provocative questions regarding the interplay between mineral homeostasis and vascular remodeling.

The IMRS appeared as a powerful discriminator of ectasia. On average, the IMRS was significantly higher in the ectatic group (mean: 10) than in the non-ectatic group (mean: 8.5, p = 0.011). Additionally, when analyzed by color category, ectatic patients were more likely to fall into a higher-risk category (e.g., yellow) than their non-ectatic counterparts (predominantly green), with a p-value of 0.002. This result underscores the potential utility of IMRS stratification in flagging those at elevated risk for CAE. Because the IMRS calculation incorporates an array of routinely measured laboratory variables and demographic information, it functions as a feasible, cost-effective tool in many clinical environments.

Angiographic data corroborated prior epidemiologic observations: the right coronary artery (RCA) was the most frequently involved vessel, followed by the left anterior descending (LAD) artery and left circumflex (LCx) artery. This preferential involvement of the RCA is consistent with several studies, though the pathophysiologic rationale for why ectatic changes localize most frequently in the RCA remains partially speculative. Hypotheses include an inherent susceptibility of the RCA’s muscular layer to dilation or distinctive hemodynamic forces that predispose the vessel to ectasia. Table 3 provides a detailed breakdown of these angiographic findings.

Table 3.

Vascular involvement and extent of ectatic vessels.

Distribution of ectasia n=226
LMCA	23%10,2)
LAD	88%38,9)
CX	84%37,2)
RCA	99%43,8)
Number of ectatic vessels n=226
One vessel	118%52,2)
Two vessels	45%19,9)
Three vessels	23%10,2)
Distribution of coronary slow flow n=226
LAD	49%21,7)
CX	18%8)
RCA	47%20,8)

Values are n (%) unless otherwise stated.

LMCA: left main coronary artery, LAD: left anterior descending artery, CX: circumflex artery, RCA: right coronary artery.

Multivariate logistic regression integrated several statistically significant predictors, highlighting smoking, MCV, and IMRS color category as independent correlates of CAE (Table 4). Smoking was significant with a hazard ratio (HR) of 3.744 (95% CI: 1.026–13.653, p = 0.045). MCV (mean corpuscular volume) was associated with an HR of 1.105 (95% CI: 1.016–1.202, p = 0.019). IMRS color category displayed an HR of 5.255 (95% CI: 1.349–20.464, p = 0.016).

Table 4.

Independent predictors of angiographic ectasia in univariate and multivariate logistic regression analysis.

	Univariate regression analysis			Multivariate regression analysis
	HR	%95 Cl	P value	HR	%95 Cl	p value
Gender (male) n(%)	0.627	0.412–0.953	0.028
Smoking, n (%)	3.014	1.641–5.535	<0.001	3.744	1.026–13.653	0.045
Hemoglobin (g/dL)	1.086	0.976–1.209	0.125
Hematocrit	1.036	0.997–1.077	0.068
MCV (fL)	1.027	0.992–1.063	0.129	1.105	1.016–1.202	0.019
RDW %	1.406	1.204–1.642	< 0.0001
MPV (fL)	0.991	0.956–1.027	0.611
Lymphocytes (103/µL)	1.207	0.965–1.508	0.093
Monocytes (103/µL)	2.786	0.964–8.050	0.025
Calcium (mg/dL)	0.556	0.386–0.801	0.001
Triglyceride (mg/dL)	0.998	0.996–1.000	0.186
NLR	0.957	0.872–1.050	0.352
IMRS score	1.138	1.034–1.252	0.006
IMRS category	2.656	1.502–4.695	<0.001	5.255	1.349–20.464	0.016

MCV: Mean corpuscular volume, RDW: red cell distribution width, MPV: mean platelet volume.

INDEXES: NLR: Neutrocyte-lymphocyte-ratio, IMRS: Intermountain Mortality Risk Score, IMRS category: IMRS category.

These results underscore the multifaceted nature of CAE risk, merging traditional behavioral risk factors like smoking with subtle hematologic indicators. ROC analyses yielded areas under the curve (AUC) as follows: 0.548 for MCV, 0.617 for IMRS score, and 0.627 for IMRS color category (Figure 1). Cut-off values highlighting optimal sensitivity and specificity were 89 fL for MCV, >9 for the IMRS score, and > 1 for IMRS color category, suggesting that while none of these parameters alone offer perfect discrimination, each can provide valuable risk stratification information

Figure 1.

Analysis of the ROC (receiver operating characteristics) Curves.

4. Discussion

The present study offers robust evidence supporting the utility of the Intermountain Risk Score (IMRS) in predicting coronary artery ectasia (CAE). Among the various laboratory and clinical factors assessed, elevated IMRS values stood out as a statistically significant indicator of ectasia, pointing toward a broader, systemic risk phenotype in patients who develop CAE. The discovery of distinct laboratory profiles – exemplified by increased RDW, MPV, and monocyte counts – reinforces the growing consensus that inflammation and vascular remodeling dynamics are integral to CAE pathogenesis.

The notable differences in RDW, MPV, and monocyte levels between ectatic and non-ectatic patients converge on a theme of heightened inflammatory and rheologic activation. RDW, a measure of red blood cell size variability, has become increasingly recognized as a potential biomarker of systemic inflammation, stress, and subclinical disease states. Elevated RDW has been linked to worse outcomes in heart failure [10], acute coronary syndromes [11], and now, CAE. Likewise, elevated MPV aligns with accentuated platelet activation [12], which can catalyze both thrombosis and inflammatory signaling within ectatic vessels. The higher monocyte counts in the ectatic group further underline the role of immune cells in orchestrating vascular changes, from initiating atherosclerosis to sustaining chronic inflammation that fosters ectatic remodeling [13].

Our multivariate analyses identified mean corpuscular volume (MCV) as an independent predictor of CAE, suggesting that subtle changes in red blood cell indices, even beyond RDW, warrant attention. Elevated MCV has been associated with alcohol use, vitamin deficiencies, and thyroid disorders, but it may also reflect broader metabolic or inflammatory disturbances that potentiate vascular pathology [14]. The precise pathophysiologic connection between MCV and CAE remains to be fully defined and merits future mechanistic exploration.

As in conventional CAD, smoking conferred a substantially elevated risk of CAE in our cohort, echoing its well-documented capacity to promote endothelial dysfunction, inflammation, and plaque progression [15]. The association between smoking and ectasia may be partially explained by the chronic inflammatory stimulus that smoking exerts [16], paving the way for more pronounced arterial remodeling. This finding highlights an actionable risk factor in the clinical management of CAE. Smoking cessation strategies may hold promise for reducing CAE progression or minimizing associated adverse events, although prospective research is needed to verify this hypothesis.

A growing number of serum biomarkers have recently been explored in the setting of coronary artery disease and especially CAE. For example, Yavcin et al. evaluated serum copeptin for its predictive value in CAE. They found no difference in serum copeptin levels between CAD patients with and without CAE [17].

The IMRS’s consistent association with CAE underscores the premise that a multi-parameter risk algorithm can reveal subclinical vulnerabilities in individuals prone to ectatic transformations. By incorporating routine laboratory metrics – like RDW, hemoglobin levels, and platelet indices – along with demographic factors, the IMRS can capture the confluence of inflammatory, metabolic, and hematologic derangements that predispose to cardiovascular disease. The IMRS color category (e.g., green, yellow, or red) can convey at-a-glance risk assessments, particularly useful in high-volume clinical settings where immediate triage is crucial. In the case of suspected or emerging CAE, an elevated IMRS might prompt more vigilant imaging follow-up, the initiation of anti-inflammatory or antithrombotic therapy, or intensified lifestyle interventions.

It is worth noting that while the areas under the ROC curves for IMRS score and color category were modest (0.617 and 0.627, respectively), such values are not unusual for risk tools applied in multifactorial cardiovascular conditions. As with most risk algorithms, the IMRS should be interpreted as part of a comprehensive clinical evaluation rather than an isolated diagnostic measure. Nevertheless, the specificity and sensitivity at the identified cutoff points (>9 for IMRS score and > 1 for IMRS color category) provide clinically relevant guidance for identifying patients at heightened risk.

Although the precise pathophysiology linking an elevated IMRS to CAE remains partially speculative, it is plausible that the same inflammatory and atherosclerotic drivers that propel coronary artery disease also operate in CAE. Chronic inflammation weakens the structural integrity of the arterial wall by degrading elastin and collagen, processes often mediated by matrix metalloproteinases secreted by inflammatory cells [18]. Over time, the vessel may undergo eccentric or diffuse dilations, culminating in CAE. Moreover, systemic risk factors – captured in part by the IMRS – could amplify these local destructive processes.

Conversely, a subset of patients may exhibit ectatic changes without extensive atherosclerosis. In such cases, the IMRS may still be elevated due to non-atherosclerotic inflammation, genetic predispositions, or concurrent conditions that the IMRS encapsulates (e.g., anemia, abnormal platelet indices, or subclinical inflammatory dysregulation). The present findings thus spotlight the need for more granular research to disentangle the multifarious pathways by which the IMRS aligns with CAE risk.

These results carry significant implications for everyday cardiology practice. First, clinicians should recognize CAE as a potentially high-risk entity deserving of diligent surveillance and management, particularly in the presence of other recognized cardiovascular risk factors. Second, the IMRS, given its reliance on standard lab tests, represents a pragmatic solution for risk stratification, enabling earlier detection of patients prone to ectatic changes. In the future, integrated risk models that incorporate imaging findings (e.g., coronary computed tomography angiography features of vascular wall morphology or plaque characterization) alongside the IMRS could further refine the predictive accuracy for CAE.

Longitudinal and prospective studies will be essential to confirm the stability and reproducibility of our findings over extended time horizons. Evaluating whether patients with a high IMRS truly experience more rapid ectatic progression or higher rates of complications – such as myocardial infarction or sudden cardiac events – will yield deeper insights into the natural history of CAE. Moreover, randomized clinical trials examining targeted anti-inflammatory or antithrombotic therapies, guided by IMRS risk categorization, could illuminate whether aggressive medical interventions can curb the clinical impact of CAE.

5. Study limitations

Several limitations inherent to our retrospective design warrant attention. First, reliance on electronic medical records may introduce biases, particularly if data completeness varied across the study period. While the matching process aimed to minimize demographic discrepancies between groups, residual confounding may persist. The findings should also be interpreted in light of the single-center setting, as variations in patient population characteristics and local referral patterns could influence prevalence and risk factor associations. The omission of the lesion characteristics of both groups was another limitation. Finally, the observational nature of our study precludes definitive conclusions regarding causality. The associations observed, while robust, should be tested in prospective cohorts before firm causal inferences are made.

6. Conclusion

In this retrospective analysis, patients with coronary artery ectasia (CAE) exhibited discernible differences in inflammatory and hematologic markers, including red cell distribution width (RDW), mean platelet volume (MPV), and monocyte counts, when compared to matched controls. The elevated Intermountain Risk Score (IMRS) among ectatic patients presented as a significant predictor of CAE, underscoring the potential of this multifactorial tool in stratifying cardiovascular risk and highlighting individuals who might warrant closer surveillance. Smoking, in particular, was strongly associated with CAE, reinforcing the importance of addressing modifiable lifestyle risk factors.

By illuminating the intersection of inflammation, vessel remodeling, and systemic risk profiles, this study underscores the multifaceted nature of CAE and identifies avenues for more targeted clinical interventions. The IMRS, calculated using data routinely available in clinical practice, may offer a cost-effective and efficient method to identify patients at heightened risk for CAE. As such, the IMRS can be easily integrated into existing workflows, especially in high-throughput settings like emergency departments and outpatient cardiology clinics.

Future studies should aim to validate these findings in larger, prospective cohorts and explore whether interventions guided by IMRS-based risk stratification – such as more frequent imaging, intensified medical therapy, or lifestyle modifications – can alter the course of CAE and improve long-term cardiovascular outcomes. Exploring deeper into the molecular mechanisms linking inflammation and vascular remodeling will further clarify the basis of ectatic changes and pave the way for precision medicine approaches that adapt treatment to an individual’s unique risk factors and pathophysiology. Ultimately, recognizing and accurately characterizing CAE is a critical component of comprehensive cardiovascular care, and the IMRS holds promise as a practical tool to help clinicians meet this challenge.

Appendices.  

Table A1.

Intermountain risk score calculation table.

Component		Female 30-d	Female 1-y	Female 5-y	Male 30-d	Male 1-y	Male 5-y
Hematocrit	≤ 34.6	1	1	2	3	3	3
	34.7–38.2	0	0	1	2	2	3
	38.3–41.0	0	0	0	1	1	2
	41.1–44.1	0	0	0	0	1	1
	≥44.2	0	0	1	0	0	0
White blood cell count	≤5.9	0	0	0	0	1	0
	6.0–7.3	0	0	0	0	0	0
	7.4–8.9	1	0	0	0	1	1
	9.0–11.2	2	1	1	2	2	1
	≥11.3	4	3	2	4	3	2
Platelet count	≤183	1	0	0	1	0	0
	184–220	1	0	1	0	0	0
	221–254	1	0	1	1	1	0
	255–300	0	0	1	1	1	1
	≥301	0	0	1	1	1	1
Mean corpuscular volume	≤86.3	0	0	0	0	0	0
	86.4–89.1	0	0	0	0	0	0
	89.2–91.4	1	0	0	0	0	0
	91.5–94.0	0	0	1	0	0	0
	≥94.1	1	1	1	1	1	1
Mean corpuscular hemoglobin concentration	≤33.3	1	1	0	1	1	0
	33.4–33.8	0	0	0	0	1	0
	33.9–34.2	1	0	0	0	0	0
	34.3–34.6	0	0	0	0	0	1
	≥34.7	0	0	0	0	0	1
Red cell distribution width	≤12.5	0	0	0	0	0	0
	12.6–13.0	2	1	1	1	0	0
	13.1–13.5	1	1	2	1	1	2
	13.6–14.3	3	2	2	2	2	3
	≥14.4	4	4	5	3	3	4
Mean platelet volume	≤7.5	1	1	1	1	1	0
	7.6–8.0	1	0	1	1	0	0
	8.1–8.4	1	0	0	2	0	0
	8.5–9.1	0	0	0	0	0	0
	≥9.2	0	0	0	1	0	0
Sodium	≤138	1	1	2	1	1	2
	139	1	1	2	1	1	2
	140–141	0	0	1	0	0	0
	142	0	0	0	1	0	0
	≥143	1	1	0	2	1	0
Potassium	≤3.7	1	1	1	2	0	0
	3.8–3.9	1	1	1	2	0	0
	4.0–4.1	0	0	1	1	0	0
	4.2–4.4	0	0	0	0	0	0
	≥4.5	1	0	1	1	0	0
Bicarbonate	≤23	3	1	1	4	2	1
	24–25	1	0	0	2	0	0
	26	1	0	0	1	0	0
	27–28	0	0	0	0	0	0
	≥29	2	1	1	1	1	1
Calcium	≤8.5	4	3	3	1	2	2
	8.6–8.9	2	2	2	0	1	2
	9.0–9.2	2	1	1	0	0	1
	9.3–9.5	0	0	0	0	1	0
	≥9.6	1	1	0	0	0	0
Glucose	≤85	1	0	0	1	1	0
	86–94	0	0	0	0	0	0
	95–104	1	0	1	1	1	0
	105–125	1	1	1	2	1	1
	≥126	3	2	2	3	2	1
Creatinine	≤0.8	0	1	1	2	3	2
	0.9	0	0	1	1	1	1
	1.0	0	0	0	0	1	0
	1.1–1.2	1	1	1	0	0	0
	≥1.3	2	2	3	2	2	1
Age (y)	18–29	−3	−5	−5	1	0	0
	30–39	−2	−1	−1	1	0	0
	40–49	0	0	0	0	0	0
	50–59	1	1	1	1	1	1
	60–69	2	2	3	1	1	2
	70–79	2	3	4	2	2	3
	≥80	5	6	8	4	5	7
Sex	Female	0	0	0	–	–	–
	Male	–	–	0	0	0	0

Table B1.

Intermountain risk score color (risk) category threshold table.

Sex	Low-Risk (Green)	Moderate-Risk (Yellow)	High-Risk (Red)
Female	<15	15–19	≥=20
Male	<15	15–18	≥=19

Funding Statement

This paper was not funded.

Article highlights

Coronary artery ectasia (CAE) is an under-recognized vascular abnormality characterized by abnormal coronary artery dilation, which can lead to altered blood flow, thrombosis, and adverse cardiac events.

The prevalence of CAE varies widely (1.5%–8.5%) in coronary angiography populations, with certain high-risk groups exhibiting even greater frequencies.

Pathophysiology of CAE involves both atherosclerotic and non-atherosclerotic mechanisms, including inflammation, connective tissue disorders, and genetic predispositions.

Current diagnostic challenges arise from the heterogeneous nature of CAE, leading to underdiagnosis and limited consensus on optimal management strategies.

The Intermountain Risk Score (IMRS), a validated risk stratification tool incorporating routine demographic and laboratory parameters, may serve as an effective predictor of CAE.

This study retrospectively analyzed 446 patients, including 226 with CAE and 220 controls, assessing clinical, laboratory, and angiographic parameters to evaluate the predictive power of IMRS.

Key findings:

Patients with CAE had significantly higher IMRS scores than controls (p = 0.011).

Higher IMRS categories correlated with increased CAE risk (p = 0.002).

Smoking (HR: 3.744, p = 0.045), mean corpuscular volume (HR: 1.105, p = 0.019), and IMRS category (HR: 5.255, p = 0.016) independently predicted CAE.

ROC analysis demonstrated modest but significant predictive value (AUC = 0.617 for IMRS score, 0.627 for IMRS category).

Clinical implications:

IMRS could help identify high-risk patients for closer monitoring and early intervention.

Routine laboratory markers, including red cell distribution width (RDW) and platelet indices, may provide additional risk stratification insights.

Smoking remains a modifiable risk factor for CAE, emphasizing the need for aggressive smoking cessation efforts.

Future research should focus on:

Prospective validation of IMRS as a CAE risk stratification tool.

The potential role of anti-inflammatory or antithrombotic therapies in CAE management.

Advanced imaging techniques to refine CAE detection and risk assessment.

Conclusion: IMRS is a promising, cost-effective risk stratification tool for CAE detection. Identifying high-risk individuals may allow for earlier intervention, potentially improving cardiovascular outcomes.

Author contribution

Şahhan KILIÇ and Süha ASAL contributed to the conception and design of the study. Mert BABAOĞLU, Samet YAVUZ, Hatice ALTINIŞIK, Yusuf Turan GÜL and Mohaddeseh Davoudi BİLEHSAVAR were responsible for data collection and analysis. Şahhan KILIÇ and Süha ASAL contributed to the literature review and manuscript drafting. Mustafa OĞUZ, Selami DOĞAN, and Ahmet Lütfullah ORHAN, MD, critically reviewed and revised the manuscript for intellectual content.

All authors have made a significant contribution to this work, have reviewed and approved all versions of the manuscript, and take responsibility for its accuracy and integrity.

Disclosure statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval (the Health Sciences University Hamidiye School of Medicine Ethics Board on 14th of November 2024 with the registration number 24/553) and/or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.