Incremental value of coronary computed tomography-derived fractional flow reserve for the prediction of major adverse cardiovascular events in patients with ischemic stroke

Introduction

Ischemic stroke (IS) is one of the leading causes of disability and death worldwide, with the greatest disease burden in low- and middle-income countries (1,2). The recurrence rate of IS remains high, varying from 6% to 12% cumulatively in the first year (3). IS patients are also prone to other adverse cardiac events and cardiac death, which have become the second leading cause of death, following fatalities directly related to neurological deficiency (4). The development of better risk assessment strategies would be of great clinical importance for IS patients.

Atherosclerosis is the fundamental pathological process underpinning the majority of ischemic vascular diseases, making its assessment essential to risk prediction (5,6). Radiological evaluation of atherosclerotic burden of the cerebral, carotid, vertebrobasilar and coronary circulation should all be taken into consideration, as well as that of the aorta. Some studies have demonstrated that intracranial cerebral atherosclerosis (ICAS) and concurrent extracranial atherosclerosis are both important predictors of poor prognosis in IS patients (5,7-9). Similarly, higher coronary artery calcium scores (CACS), a marker of coronary atherosclerotic burden, are strongly associated with a greater 10-year risk of incident atherosclerotic cardiovascular disease and stroke (10,11). These findings underscore the importance of evaluating the total atherosclerotic burden throughout the important vascular territories. To evaluate the overall atherosclerotic burden more efficiently, the low-dose one-step integrated coronary-carotid-cerebral computed tomography angiography (ICCC-CTA) technique has been introduced (12,13). Morphological data from ICCC-CTA have also been used for prognostic evaluation, showing great potential in the prediction of adverse cardiovascular events in patients after stroke. However, relying solely on morphological features may be insufficient. In addition to morphological data, hemodynamic information can be also extracted from CTA datasets, which may provide incremental value for the prediction of cardiovascular events, especially in those with insignificant morphological changes (14).

Coronary computed tomography-derived fractional flow reserve (CT-FFR) is a non-invasive technique used to assess the flow restriction of lesions and identify ischemic and non-ischemic lesions based on coronary computed tomography angiography (CCTA) data (15,16). A vessel-specific CT-FFR value ≤0.8 has been identified as a strong independent predictor of adverse clinical outcomes in patients with coronary heart diseases (14,17). However, the prognostic value of CT-FFR in IS patients has been rarely demonstrated. Incorporating CT-FFR into the evaluation of coronary arteries and myocardium in addition to morphological data may improve prognostic assessment in IS patients. Hence, we intended to evaluate the incremental value of coronary CT-FFR over morphological atherosclerotic burden in the prognostic evaluation of IS patients. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2504/rc).

Methods

Study population

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (No. 2018065), and informed consent was provided by all individual participants. We consecutively enrolled IS patients with ICCC-CTA within 30 days of symptom onset at our hospital between January 2016 and December 2022. IS diagnosis was confirmed with brain magnetic resonance imaging (MRI) at admission. The exclusion criteria were as follows: (I) pregnant or lactating females; (II) poor image quality or insufficient data to perform CT-FFR; (III) loss to follow-up; (IV) allergy to contrast agents; (V) previous coronary artery bypass grafting, stent placement, pacemaker placement, implantable cardioverter defibrillator, or prosthetic valve; (VI) any condition causing hemodynamic instability; (VII) acute coronary syndrome or clinical instability; and (VIII) previous myocardial infarction within 30 days before the ICCC-CTA examination.

Cerebral atherosclerosis score (CAS) and CACS

As previously reported, all ICCC-CTA examinations were performed using either a 192-slice dual-source computed tomography (Somatom Force; Siemens Healthcare, Erlangen, Germany) or a 256-row multidetector computed tomography (Revolution CT; GE HealthAcre, Boston, MA, USA) (6,18). The reconstruction slice thickness of ICCC-CTA images was 0.6 or 0.625 mm. The ICCC-CTA examination used a prospective electrocardiogram-triggered high pitch spiral scan on the dual-source scanner and a combined axial and spiral scan on the 256-row scanner (19,20). On the dual-source scanner, high-pitch caudocranial scan (Turbo Flash Spiral mode) from the diaphragm to the top was triggered at 30% or 60% of the R-R interval depending on the patient’s heart rate. Other parameters included a detector configuration of 2×96×0.6 mm, rotation time of 250 ms, and a pitch of 3.2, with automated tube current modulation (CARE Dose4D, Siemens) and attenuation-based tube voltage (70–90 kV) selection (CARE kV, Siemens). On the 256-row scanner, prospective electrocardiogram-triggered axial scan between 30% and 80% R-R interval was used for cardiac CTA, with 256×0.625 mm detector configuration, tube voltage at 80–120 kV, and automated tube current modulation. For cervicocerebral CTA, a caudocranial spiral scan continued from the previous range using a pitch of 0.992 and a detector configuration of 128×0.625 mm was performed, with tube voltage ranging from 80 to 100 kV and automatically modulated tube current ranging from 300 to 600 mA.

All reconstructed CTA images were transferred to the Syngo.via workstation (MMWP, Syngo.via, Siemens) to analyze CAS and CACS. Curved planar reformation, maximum intensity projection, multiplanar reformation, and volume rendering images were used to aid in the evaluation of the carotid, cerebrovascular, and coronary arteries. As described in the previous study, CAS is based on the number of atherosclerotic cerebral arteries, defined by stenosis of 50% or more in diameter, and the degree of atherosclerosis (21,22). The degree of atherosclerosis was visually scored as follows: stenosis <50% as 0, 50–99% as 1, and complete occlusion as 2. Arteries including the intracranial (middle cerebral, anterior cerebral, posterior cerebral, basilar, intracranial carotid, and intracranial vertebral arteries) and extracranial cerebral arteries (extracranial carotid, extracranial vertebral, innominate, and subclavian arteries) were evaluated. Hypoplasia of the vertebral artery or the proximal segments of the anterior cerebral artery is not considered a form of stenosis. CAS is defined as the total sum of the scores for each artery. The degree of atherosclerosis was evaluated jointly by two experienced reviewers (with 5 and 15 respective years of specialized experience) who were blinded to the patient’s clinical information. In the event of a disagreement, the opinion of a third reviewer with more than 20 years of experience was sought.

According to the Agatston method, we calculated the CACS using the automated program on the Syngo.via workstation, which included all calcified plaques on the coronary arteries (23). CACS was assigned to different levels as follows: Level 0 (CACS =0), Level 1 (0< CACS ≤100), Level 2 (100< CACS ≤300), and Level 3 (CACS >300) (10). These levels represent a numerical reclassification of the CACS, where each level is treated as a discrete numerical variable, preserving the quantitative aspects of the original score for analysis. Significant coronary stenosis is defined as a stenotic arterial lumen of ≥50% or occlusion in any of the three coronary arteries.

CT-FFR

CT-FFR was analyzed and calculated for all patients. CCTA images were manually transferred to the dedicated research software (Shukun CT-FFR, Shukun Technology, Beijing, China). Calculation of CT-FFR consists of two major steps: (I) automated coronary artery reconstruction (post-processing of coronary images) and (II) CT-FFR calculation (24). The lesion-specific CT-FFR value was measured 2 cm distal to the lesion for all epicardial stenoses in major epicardial vessels ≥2 mm in diameter. CT-FFR values were recorded at the most distal site of vessels ≥1.5 mm in diameter for vessels without significant stenosis. The minimum coronary vessel CT-FFR value was used for patient-based analysis, and any coronary artery CT-FFR value ≤0.80 was considered hemodynamically significant.

Clinical follow-up and study outcomes

All participants were followed up for 1 year via telephone by a physician with more than two years of experience. Major adverse cardiovascular event (MACE) is defined as the occurrence of a new non-fatal IS, non-fatal myocardial infarction, and vascular death (6). Vascular deaths consisted of fatal strokes, fatal myocardial infarctions, cardiac deaths, deaths following vascular interventions or pulmonary embolisms, and any other deaths with a clear vascular cause or hemorrhages (6). The outcome of interest was the time to the first occurrence of MACE in enrolled stroke patients. The primary aim of our study was to explore whether CT-FFR is a predictive factor for MACE in stroke patients. The secondary aim was to develop a risk stratification model for MACE by integrating clinical risk factors, imaging parameters of atherosclerotic burden (CAS and CACS), and CT-FFR.

Statistical analysis

The Kolmogorov-Smirnov test was used to determine the normality of the numerical variables. Data were presented as frequencies with proportions for categorical variables, the median with interquartile range for abnormally distributed continuous variables, and the mean ± standard deviation for continuous variables. Continuous variables were compared using the Student’s t-test or Mann-Whitney U test. Categorical variables were compared using the Chi-squared test or Fisher’s exact test. We used the Kaplan-Meier method to analyze the cumulative incidence of MACE and compared its significance using the log-rank test with the reclassified CACS group and the following cutoff value: CT-FFR ≤0.8. Univariate and multivariate Cox proportional hazards regression models were applied to determine the independent prognostic predictors of clinical and imaging variables for the occurrence of MACE.

Based on the regression results, prediction models were constructed according to the statistically significant risk factors. Coronary CT-FFR would not be included in the base prediction model (model 1), whereas it would be included to establish a second model (model 2) if it was an independent risk factor. The concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curve analyses were applied to compare the two models. The calibration curve and the Akaike Information Criterion (AIC) were used to assess the models’ goodness of fit. Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess the discrimination of models and select better model. Decision curve analysis (DCA) was conducted to assess the clinical utility of our models by quantifying the net benefits at different threshold probabilities.

Risk stratification using the nomogram was conducted with MACE probability estimates based on model 2, which includes all significant independent predictive factors identified by the multivariate Cox regression analysis. All P values were two-sided and P<0.05 indicated statistical significance. Statistical analysis was performed utilizing the software SPSS 25.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.1 (http://www.R-project.org/).

Results

Patient characteristics and clinical outcomes

A total of 372 IS patients were initially enrolled from January 2016 to December 2022, all of whom were followed up for 1 year. Some 25 patients were excluded due to the exclusion criteria and 347 patients were ultimately included. The age ranged from 36 to 88 years, with a mean age of 61.7±9.1 years. Men were predominant in our study, accounting for 80.1%. MACE occurred in 11.5% (40/347) of patients. Among these patients, 27 experienced non-fatal ischemic stroke, 7 underwent non-fatal myocardial infarction, and 6 died of vascular events.

Comparison of clinical and imaging characteristics between IS patients with and without MACE

Patients with MACE had statistically different results in age, CAS, CACS, and CT-FFR ≤0.8 compared to patients without MACE (Table 1). Some clinical factors, including age, diabetes history, and coronary artery disease (CAD) history showed statistically significant differences between patients with and without MACE (Table 1). As illustrated in Figure 1A, Kaplan-Meier survival curves showed that patients with CT-FFR ≤0.8 had a significantly higher MACE rate compared to those with CT-FFR >0.8. A similar trend was also observed in patients with higher CACS (Figure 1B).

Figure 1 Cumulative event rate for MACE of CT-FFR (A) and CACS (B). CACS, coronary artery calcium score; CT-FFR, coronary computed tomography-derived fractional flow reserve; MACE, major adverse cardiovascular event.

Development of predictive models

Univariate and multivariate Cox regression analyses were conducted for all clinical and imaging covariates (Table 2). According to univariate Cox regression analysis, age [hazard ratio (HR) =1.05 per year, P=0.005], diabetes history (HR =2.15, P=0.019), CAD history (HR =2.25, P=0.033), CT-FFR ≤0.80 (HR =4.30, P=0.015), fast glucose (HR =1.10, P=0.047), CAS (HR =1.20, P<0.001), and CACS (HR =1.62 per level, P=0.007) were all predictors for MACE. The above factors were subsequently included in a multivariate Cox regression analysis. Integrating clinical risk factors with atherosclerotic burden imaging parameters, we found that age (HR =1.05 per year, P=0.008), diabetes history (HR =2.12, P=0.023), and CAS (HR =1.22, P<0.001) were significant risk factors in multivariate Cox regression analysis, forming the model 1. Furthermore, age (HR =1.06 per year, P=0.005), CT-FFR ≤0.80 (HR =3.52, P=0.037), diabetes history (HR =2.03, P=0.034), and CAS (HR =1.21, P=0.001) were consistently the independent predictors for MACE in the final multivariate Cox regression analysis results. As a result, these four factors in the prediction model 2 were included to construct a nomogram to predict the probability of MACE at 1 year (Figure 2).

Figure 2 Nomogram of model 2 for 1-year probability of MACE. Model 2: with CT-FFR. CAS, cerebral atherosclerosis score; CT-FFR, coronary computed tomography-derived fractional flow reserve; MACE, major adverse cardiovascular event.

Time-dependent ROC analyses showed that model 1 had an area under the curve (AUC) of 0.732 [95% confidence interval (CI): 0.657–0.807] at 1 year, whereas model 2 achieved an AUC of 0.749 (95% CI: 0.678–0.820) (Figure 3). The C-index was also higher in model 2 (0.742, 95% CI: 0.677–0.807) compared to that in model 1 (0.713, 95% CI: 0.642–0.785) (Table 3). The calibration curve did not deviate significantly from the reference line in either model 1 or model 2 (Figure 4), with model 2 showing better agreement between predicted and actual outcomes than model 1 (Figure 4). As shown in the DCA of the nomogram, compared to model 1 (Figure 5A), model 2 (Figure 5B) provided additional net benefits, demonstrating its elevated clinical utility. After calculating NRI, IDI, and AIC, model 2 was shown to be better than model 1 (Table 3).

Figure 3 Time-dependent ROC curves comparing the prognostic accuracy of model 1 and model 2. Model 1: without CT-FFR; Model 2: with CT-FFR. AUC, area under the curve; CI, confidence interval; CT-FFR, coronary computed tomography-derived fractional flow reserve; ROC, receiver operating characteristic.

Figure 4 Calibration curves of model 1 and model 2. Model 1: without CT-FFR; Model 2: with CT-FFR. CT-FFR, coronary computed tomography-derived fractional flow reserve.

Figure 5 Decision curve analysis for model 1 (A) and model 2 (B) to predict MACE in patients with IS. Model 1: without CT-FFR; Model 2: with CT-FFR. CT-FFR, coronary computed tomography-derived fractional flow reserve; IS, ischemic stroke; MACE, major adverse cardiovascular event.

Discussion

Morphological changes of aortic and coronary atherosclerosis have been recognized as risk factors in IS patients. However, the functional changes of the vessels had not been studied in IS. To the best of our knowledge, the present study is the first to evaluate the prognostic value of functional changes of coronary circulation for MACE in IS patients and to investigate the incremental prognostic value of CT-FFR for endpoint outcomes compared to traditional imaging parameters, including cerebral and coronary atherosclerotic burden parameters. The major findings were as follows: First, CAS, CACS, and CT-FFR were independent predictive factors for unfavorable outcomes in IS patients. Second, a model incorporating clinical risk factors, CAS, and CT-FFR demonstrated the prognostic value of MACE in IS patients. Third, the combination of information on cerebral atherosclerotic loads, traditional clinical risk factors, and the addition of CT-FFR contributes to providing additional prognostic value.

In the context of stroke management, the recurrence of stroke and the incidence of post-stroke MACE significantly influence patient prognosis, ranking as the top two causes of mortality among stroke survivors (4,25,26). This enhances the critical need for strategies to prevent post-stroke MACE. The basis of such preventive strategies is the ability to accurately stratify patient risk. In our study, 72.0% of the patients had large-artery atherosclerosis, whereas the others had small-artery diseases. These IS subtypes share common risk factors and clinical manifestations, distinguishing them from other subtypes (27). The symptomatic status and the degree of stenosis in both intracranial and extracranial vessels are pivotal in guiding evidence-based interventions. Patients with concurrent intracranial atherosclerosis and extracranial carotid artery disease, or those with systemic atherosclerosis and multi-territorial vascular disease, are at higher risk for poor outcomes (5,9,28,29). To address this, we employed the ICCC-CTA scan protocol to assess the systemic atherosclerosis burden, which includes CAS, CACS, and significant coronary artery stenosis.

Previous studies by Kim et al. (21) and Lee et al. (22) have established CAS as an independent risk factor for stroke occurrence and a valuable imaging parameter for assessing the impact of cerebral arterial atherosclerosis on stroke recurrence. Our study emphasized these findings by demonstrating that CAS is also an independent risk factor for MACE in both univariate and multivariate regression analyses. Moreover, several studies have suggested that patients with systemic atherosclerosis may have multiple underlying stroke mechanisms, and a comprehensive evaluation of overlapping diseases can enhance risk stratification (6,30). In addition to CAS, CACS has been recognized as a marker of subclinical coronary atherosclerosis (11). In our cohort, 73.8% of patients exhibited significant coronary artery stenosis. Interestingly, our univariate regression analysis revealed CACS, rather than significant coronary stenosis, as a potential independent predictor of MACE. This is in line with reports by Mitchell et al. (31), who linked the presence and severity of coronary artery calcification with MACE in young individuals without traditional cardiovascular risk factors. Similarly, Budoff et al. (10) highlighted coronary artery calcium as a strong predictor of 10-year risk for incident atherosclerotic cardiovascular disease. Despite these findings, in the construction of model 1 and model 2, CAS emerged as the independent risk factor in the multivariate Cox regression analysis, superseding CACS. This is consistent with the notion that CACS, as an indicator of coronary atherosclerosis burden, is associated with atherosclerosis in other vascular territories (32). Consequently, CAS was retained as a key predictor in both model 1 and model 2, rather than CACS.

Recently, as an emerging technique for the functional assessment of coronary ischemia, the value of CT-FFR in predicting adverse vascular events has been validated (14,33). Many studies have suggested that CT-FFR, as an innovative and non-invasive method, can be used to identify coronary flow-limiting lesions and observe hemodynamic abnormalities (34,35). This makes it a viable alternative to FFR, which is considered the gold standard for detecting blood flow abnormalities (36). Myocardial ischemia was indicated if the FFR was ≤0.8 (35). Therefore, we used CT-FFR of 0.8 as the threshold in the present study. Although CT-FFR is still the hot topic of cardiac CT studies, the prognostic value of coronary CT-FFR has not been explored in IS patients. According to univariate and multivariate Cox regression analyses, CT-FFR was shown to be a robust and independent predictor of MACE in our study. In the Kaplan-Meier analysis, the occurrence of MACE at the 1-year follow-up was statistically significantly higher in patients with CT-FFR ≤0.8 compared to those with CT-FFR >0.8. The potential reasons for this outcome may be attributed to the following factors. First, patients with decreased CT-FFR had a higher risk of cardiac adverse events due to better discrimination of true flow-restricting lesions compared to conventional coronary stenosis assessment (14). This has been supported by many studies on coronary heart disease. Another underlying mechanism may be related to inconspicuous impairment of cardiac function in patients with decreased CT-FFR. Previous research indicated that chronic heart failure was more common in patients with decreased FFR, suggesting continued effects on cardiac function by myocardial ischemia (37), whereas cerebral blood flow could be directly determined by cardiac function due to impaired cerebral autoregulation in IS patients (38). It was also found that hypoperfusion could be one of the coexisting mechanisms of stroke recurrence in intracranial atherosclerotic disease (39). These findings may potentially explain the elevated risk of stroke recurrence in patients with decreased CT-FFR. In our study, the majority of patients exhibited stroke subtypes attributable to large-artery atherosclerosis. We inferred that in certain patients, both coronary and cerebral arteries may be affected by ischemia, which intensifies the ischemic condition of both cardiac and cerebral tissues and, in turn, contributes to the progression of MACE. Future research is needed to further explore the relationship between cerebrovascular hemodynamics, CT-FFR, and the occurrence of MACE.

Our study successfully developed a predictive model that integrates clinical factors and imaging parameters, including the combination of cerebral atherosclerotic burden and CT-FFR. It provides good risk stratification in the assessment of MACE in IS patients. By incorporating atherosclerotic burden alongside clinical variables, the addition of CT-FFR significantly improves the model’s ability to identify IS patients at elevated risk for MACE.

There were some limitations in this study. Firstly, some patients were excluded due to the exclusion criteria, which may be a potential source of selection bias. Secondly, the absence of an external validation set limits the generalizability of our model. Multicenter studies with external validation are necessary to further investigate these issues and to test the model’s broader applicability. Additionally, although CT-FFR assesses the hemodynamic status of the major epicardial coronary arteries, it cannot reflect coronary microvascular dysfunction, which may significantly impact the prognosis of IS patients and can only be evaluated with additional functional imaging modalities. Finally, we only evaluated the follow-up results at one year. However, functional changes of the coronary circulation may have a greater impact on longer-term prognosis compared to structural changes. Continued follow-up of the present cohort would be needed to further assess the prognostic value of coronary CT-FFR in IS patients.

Conclusions

Coronary CT-FFR ≤0.80, indicating hemodynamic ischemia, emerged as a powerful independent predictor of MACE in IS patients. Incorporating CT-FFR into a model that already includes atherosclerotic burden enhances the stratification of MACE risk in IS patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2504/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2504/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2504/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (No. 2018065) and informed consent was provided by all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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