Abstract
Background
The Coronary Artery Tree Description and Lesion Evaluation (CatLet) angiographic scoring system is a newly developed vascular scoring for assessing the degree of coronary artery stenosis. It has unique advantages in reflecting coronary artery variability as compared to Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score. Preliminary studies support its superiority over SYNTAX in predicting clinical outcomes after percutaneous coronary intervention (PCI) in patients with chronic coronary syndrome (CCS). This study aimed to determine whether the CatLet score incorporating three clinical variables (CVs)—age, ejection fraction, and creatinine—is a better predictor of clinical outcomes in patients treated with PCI for CCS as compared to the CatLet score.
Methods
A total of 222 patients who were diagnosed with CCS, underwent coronary drug-eluting stent (DES) implantation, and had a calculable CatLet score were retrospectively selected from the Second Affiliated Hospital of Wannan Medical College in China between April 2019 and June 2020. The primary endpoint was major adverse cardiac events (MACEs), including myocardial infarction, recurrent angina, cardiac death, heart failure, and ischemia-driven revascularization, and was stratified according to CatLet score tertiles as follows: >0 and ≤23= CatLet low (n=72), 24–43= CatLet mid (n=76), and ≥44= CatLet top (n=74).
Results
The CatLet score predicted long-term prognosis, with a 4.5-year-follow-up and a median of 3.4 years. Of the 222 patients analyzed, the rates of MACEs, cardiac death, and reangina were 27.03%, 3.60%, and 18.02%, respectively. In the Kaplan-Meier analysis, as the tertiles of the CatLet score increased, so did the cumulative incidence event rates for all endpoints (log-rank test for trend P<0.05). The area under the curve (AUC) of the CatLet score was 0.73, 0.76, and 0.73 for MACEs, cardiac death, and reangina, respectively, while the AUCs for CV-adjusted CatLet score models were 0.78, 0.88, and 0.74, respectively. Alone or after adjustments for risk factors, the multivariable-adjusted hazard ratio/unit higher score was 6.22 [95% confidence interval (CI): 2.40–16.13] for MACEs, 4.84 (95% CI: 2.52–9.32) for cardiac death, and 8.59 (95% CI: 2.53–29.10) for heart failure.
Conclusions
As compared with CatLet score alone, the model incorporating the CatLet score and three CVs can provide superior prediction ability.
Keywords: Coronary Artery Tree Description and Lesion Evaluation score (CatLet score), chronic coronary syndrome (CCS), prognosis, risk factors
Introduction
Certain clinical variables (CVs), such as age, renal function, left ventricular ejection fraction (LVEF), and flow-limiting lesions in the coronary artery, significantly impact the prognosis of patients with coronary artery disease (CAD) (1-3). The Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) is a commonly used coronary anatomical score index which is broadly used to assess the complexity and severity of coronary artery lesions and determine the prognosis of patients with CAD (4,5). However, the SYNTAX score, based solely on dichotomous left or right coronary dominance, fails to adequately describe coronary anatomical variability, much less semiquantify the extent of the coronary artery blood supply (6,7). The newly developed Coronary Artery Tree Description and Lesion Evaluation (CatLet) scoring system uniquely integrates the coronary anatomy variation, lesion severity in diseased arteries, and semiquantification of myocardial blood supply territory to affected vessels (8-10).
The predictive capacity of the CatLet score can potentially be enhanced through integrating CVs of age, LVEF, and serum creatinine in patients with acute myocardial infarction (AMI) (11). Our previous study demonstrated that this combination can be used to predict clinical outcomes after percutaneous coronary intervention (PCI) in patients with chronic coronary syndrome (CCS) (12). Thus, we hypothesized that the CatLet score incorporating the three CVs of age, ejection fraction, and creatinine can provide superior prediction of the clinical outcomes in patients with CCS treated by PCI. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-171/rc).
Methods
Study patients
Consecutive patients with CCS undergoing coronary drug-eluting stent (DES) implantation at The Second Affiliated Hospital of Wannan Medical College from April 2019 to June 2020 were retrospectively enrolled in this study. All patients received medications according to standard guideline-directed medical therapy (GDMT) post-PCI. The exclusion criteria included (I) AMI, (II) abnormal coronary anatomy, (III) normal coronary angiography (CAG) results, and (IV) insufficient clinical and angiographic data at baseline and follow-up.
This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Second Affiliated Hospital of Wannan Medical College (approval No. WYEFYLS2025018). The requirement for informed consent was waived due to the retrospective nature of the analysis.
CatLet score and lesion evaluation
A comprehensive description of the CatLet angiographic scoring system is available elsewhere (8), and a tutorial on its use and related calculators are available online (http://www.CatLetscore.com). This novel scoring system is based on a 17-segment model of the myocardium, the law of competitive supply, and the law of conservation of flow. The characteristic feature of this scoring system is that the assessment of CAD takes into account the anatomical variability of the coronary artery and the blood supply range of the diseased blood vessels. According to the scoring scheme, the right coronary artery type is divided into 6 categories, the left anterior descending branch types into 3 categories, and the diagonal branch types into 3 categories, resulting in 54 (6×3×3) types of coronary circulation patterns. The score for each lesion is the product of the weighting factor for that lesion and the coefficient of stenosis, with a coefficient of stenosis of 2.0 for a stenosis of 50% to 99% of the diameter of the vessel and a coefficient of 5.0 for a complete occlusion of the vessel; the sum of the scores for all the lesions is the total coronary artery lesion score. The system scores only lesions with vessels ≥1.5 mm in diameter and ≥50% diameter stenosis and corrects the score if necessary, whereas adverse lesion characteristics are recorded objectively and not scored. For nonocclusive lesions, there is no occlusion status scoring; for a total occlusive lesion, there are three types of status scores: (I) occlusive score, (II) guidewire passing/nonocclusion score after balloon dilation, and (III) score after stenosis removal (13). An example of the calculation of the CatLet Coronary Artery Score is shown in Figure 1. In this study, coronary angiographic findings in all included cases were scored by two physicians with experience in coronary intervention, and in case of disagreement, a third analyst was involved in the discussion. The physicians were blinded to patients’ clinical findings and outcomes.
Figure 1.
An example of the CatLet score (the nonocclusive lesion on mid-RCA). The arrow indicates the site of vascular stenosis. CatLet, Coronary Artery Tree Description and Lesion Evaluation; Dx, diagonal; LAD, left anterior descending artery; RCA, right coronary artery.
Disease definitions and endpoints
CCS is defined as the presence of atherosclerotic cardiovascular disease or associated risk factors, and most patients can be diagnosed based on a typical history of angina (14). Unless a noncardiac cause was definitively identified (clinically or at autopsy), deaths with undetermined etiology were deemed to be cardiac-related (15). A diagnosis of chronic obstructive pulmonary disease (COPD) required the demonstration of persistent airflow limitation, defined as a postbronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity <70% (16). The primary endpoint was the occurrence of major adverse cardiac events (MACEs), defined as myocardial infarction, recurrent angina, cardiac death, heart failure, or ischemia-driven revascularization.
Follow-up
All surviving patients or their immediate family members were interviewed by telephone and asked specific questions regarding MACEs. All patients were followed-up until the date of MACE or the end of the study period (November 2024), whichever came first. The medical records, discharge records, and imaging data of patients experiencing adverse events were retrieved and reviewed. Death information was obtained from the hospitals or next of kin.
Statistical analysis
Continuous variables were expressed as the median and interquartile range (IQR), while categorical variables were expressed as the frequency (percentage). The detection of trends in the incidence of events at all levels was completed via Stata software (StataCorp, College Station, TX, USA). Missing values were handled via multiple interpolation. Kaplan-Meier analysis was used to generate the survival curves, and comparison were conducted with the log-rank test. To determine independent predictors of clinical outcomes, Cox regression survival analyses were performed. The performance of the model was evaluated in terms of discrimination and calibration. The comparison of the CatLet score and the CV-adjusted CatLet score was conducted via receiver operating characteristic (ROC) curves. The z-test (17) was applied to examine the interaction between the CatLet’s continuous score and other risk factors. All analyses were performed by via Stata version 16. All statistical tests were performed bilaterally, and the difference was statistically significant with P<0.05.
Results
Initially, 300 patients were included in the study, 78 of whom were excluded due to meeting the exclusion criteria, resulting in 222 patients being included in the study. The mean age was 66.74±10.97 years, and 71.62% (159/222) were male. The CatLet score ranged from 0 to 120.5, with a mean of 35.83 (SD 19.98) and a median of 32.75. Patients were divided into three groups according to the CatLet tertiles as follows: CatLet low, 0–23; CatLet middle (CatLet mid), 24–43; and CatLet top, ≥44.
Baseline characteristics
Patient baseline characteristics, including clinical and angiographic variables, are presented in Table 1 and Table 2. Patients with higher CatLet score tertiles were more likely to be male and have a lower LVEF and a higher apolipoprotein B (ApoB):apolipoprotein A1 (ApoA1) ratio, paradoxically, the higher a patient’s CatLet score was, the less likely they were to smoke.
Table 1. Baseline clinical characteristics of the study population.
| Variable | CatLet low [0–23] | CatLet mid [24–43] | CatLet top [44–120.5] | P value |
|---|---|---|---|---|
| No. of cases | 72 | 76 | 74 | |
| Age (years) | 65.35±10.34 | 67.55±10.77 | 67.27±11.75 | 0.42 |
| Male | 49 (68%) | 48 (63%) | 62 (84%) | 0.014 |
| BMI (kg/m2) | 23.59±3.06 | 23.45±4.17 | 23.36±4.21 | 0.94 |
| Medical history | ||||
| Diabetes | 17 (24%) | 24 (32%) | 23 (31%) | 0.49 |
| Hypertension | 49 (68%) | 61 (80%) | 47 (64%) | 0.066 |
| COPD | 10 (14%) | 11 (14%) | 15 (20%) | 0.51 |
| CKD | 3 (4%) | 7 (9%) | 5 (7%) | 0.47 |
| Smoking | 0.60 | |||
| Current | 26 (36%) | 31 (41%) | 36 (49%) | |
| Never | 35 (49%) | 34 (45%) | 27 (36%) | |
| Past | 11 (15%) | 11 (14%) | 11 (15%) | |
| Alcohol consumption | 0.82 | |||
| Current | 14 (19%) | 13 (17%) | 18 (24%) | |
| Never | 54 (75%) | 58 (76%) | 53 (72%) | |
| Past | 4 (6%) | 5 (7%) | 3 (4%) | |
| Laboratory results | ||||
| LVEF | 0.61±0.09 | 0.58±0.10 | 0.54±0.10 | <0.001 |
| Creatinine (μmol/L) | 99.75±36.98 | 104.12±49.11 | 120.45±97.31 | 0.14 |
| UA (μmol/L) | 376.30±112.31 | 379.19±100.30 | 403.09±115.15 | 0.27 |
| TG (mmol/L) | 1.75±1.04 | 1.82±1.08 | 1.51±0.71 | 0.12 |
| TC (mmol/L) | 4.56±1.07 | 4.39±1.10 | 4.45±1.06 | 0.60 |
| HDL-C (mmol/L) | 1.14±0.39 | 1.07±0.31 | 1.04±0.29 | 0.18 |
| LDL-C (mmol/L) | 2.63±0.94 | 2.53±0.84 | 2.66±0.90 | 0.65 |
| FBG (mmol/L) | 6.01±2.11 | 6.93±3.10 | 6.46±2.28 | 0.088 |
| ApoB/ApoA1 (mmol/L) | 0.61±0.22 | 0.63±0.19 | 0.71±0.27 | 0.019 |
| NLR | 3.10±2.19 | 3.76±3.42 | 4.40±4.39 | 0.079 |
Data are expressed as the mean ± standard deviation or n (%). BMI, body mass index; CatLet, Coronary Artery Tree Description and Lesion Evaluation; CatLet low, bottom tertile of CatLet score; CatLet mid, middle tertile of CatLet score; CatLet top, top tertile of CatLet score; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; NLR, neutrophil:lymphocyte ratio; TC, total cholesterol; TG, triglyceride; UA, uric acid.
Table 2. Baseline angiographic characteristics of the study population.
| Variables | CatLet low [0–23] | CatLet mid [24–43] | CatLet top [44–120.5] | P value |
|---|---|---|---|---|
| No. of cases | 72 | 76 | 74 | |
| Diagonal size | 0.62 | |||
| Small | 7 (10%) | 6 (8%) | 7 (9%) | |
| Intermediate | 25 (35%) | 19 (25%) | 22 (30%) | |
| Large | 40 (56%) | 51 (67%) | 45 (61%) | |
| LAD length | 0.81 | |||
| Short | 20 (28%) | 18 (24%) | 16 (22%) | |
| Average | 29 (40%) | 28 (37%) | 32 (43%) | |
| Long | 23 (32%) | 30 (39%) | 26 (35%) | |
| RCA dominance | 0.45 | |||
| PDA zero | 4 (6%) | 1 (1%) | 2 (3%) | |
| PDA only | 5 (7%) | 4 (5%) | 6 (8%) | |
| Small RCA | 26 (36%) | 23 (30%) | 17 (23%) | |
| Average RCA | 29 (40%) | 38 (50%) | 39 (53%) | |
| Large RCA | 7 (10%) | 10 (13%) | 9 (12%) | |
| Super RCA | 1 (1%) | 0 (0%) | 1 (1%) | |
| Coronary artery treated | ||||
| LM | 0 (0%) | 1 (1%) | 7 (9%) | 0.004 |
| LAD | 53 (74%) | 44 (58%) | 48 (65%) | 0.13 |
| LCX | 12 (17%) | 20 (26%) | 10 (14%) | 0.11 |
| RCA | 12 (17%) | 25 (33%) | 25 (34%) | 0.035 |
| Culprit vessels | ||||
| LAD | 49 (68%) | 37 (49%) | 44 (59%) | 0.056 |
| LCX | 9 (13%) | 13 (17%) | 4 (5%) | 0.081 |
| RCA | 12 (17%) | 24 (32%) | 21 (28%) | 0.094 |
| Lesion characteristics | ||||
| Bifurcation | 43 (60%) | 45 (59%) | 53 (72%) | 0.21 |
| Medina 1.1.1 | 3 (4%) | 8 (11%) | 20 (27%) | <0.001 |
| Medina 1.1.0 | 3 (4%) | 3 (4%) | 3 (4%) | 1.00 |
| Medina 1.0.1 | 3 (4%) | 7 (9%) | 3 (4%) | 0.31 |
| Medina 1.0.0 | 7 (10%) | 4 (5%) | 2 (3%) | 0.19 |
| Medina 0.1.1 | 5 (7%) | 11 (14%) | 15 (20%) | 0.067 |
| Medina 0.1.0 | 20 (28%) | 11 (14%) | 9 (12%) | 0.030 |
| Medina 0.0.1 | 2 (3%) | 1 (1%) | 1 (1%) | 0.75 |
| Tortuosity | 20 (28%) | 28 (37%) | 43 (58%) | <0.001 |
| Calcification | 2 (3%) | 3 (4%) | 14 (19%) | <0.001 |
| Ostial lesion | 6 (8%) | 8 (11%) | 18 (24%) | 0.011 |
| Angulation <70 | 38 (53%) | 42 (55%) | 40 (54%) | 0.96 |
| CatLet score | 15.43±5.63 | 32.42±5.68 | 59.16±13.03 | <0.001 |
| Lesion length >20 mm | 43 (60%) | 66 (87%) | 63 (85%) | <0.001 |
| Total stent number | 1.08±0.40 | 1.36±0.71 | 1.65±0.69 | <0.001 |
| Minimal stent diameter (mm) | 3.06±0.48 | 2.99±0.40 | 2.92±0.44 | 0.18 |
| Total lesion number | 1.60±0.71 | 2.59±0.64 | 3.09±0.60 | <0.001 |
Data are expressed as the mean ± standard deviation or as n (%). CatLet, Coronary Artery Tree Description and Lesion Evaluation; CatLet low, bottom tertile of CatLet score; CatLet mid, middle tertile of CatLet score; CatLet top, top tertile of CatLet score; LAD, left anterior descending artery; LCX, left circumflex artery; LM, left main artery; RCA, right coronary artery; PDA, posterior descending artery.
CatLet score and its associations with 4.5-year outcomes
Patients were followed up for a median of 3.4 years. The rates of MACEs, cardiac death. and reangina were 27.03%, 3.60%, and 18.02%, respectively. As the tertiles of the CatLet score increased, Kaplan-Meier analysis indicated a significant upward trend in cumulative event rates for all endpoints (log-rank test for trend P<0.05), as shown in Figure 2. Relative to the CatLet low group, both the CatLet top [hazard ratio (HR) =6.22, 95% confidence interval (CI): 2.40–16.13] and CatLet mid (HR =4.18, 95% CI: 1.58–11.06) groups demonstrated elevated MACE risk. Parallel patterns were observed for reangina and cardiac death (Table 3).
Figure 2.
K-M curves for all endpoints at 4.5 years according to the CatLet score tertiles. CatLet, Coronary Artery Tree Description and Lesion Evaluation; K-M, Kaplan-Meier; MACE, major adverse cardiac event.
Table 3. Univariate or multivariable-adjusted HRs/unit higher for clinical outcomes.
| Outcome | 4.5-year K-M rate, n (%) | Unadjusted HR (95% CI) | Adjusted HR (95% CI) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CatLet low [0–23] | CatLet mid [24–43] | CatLet top >43 | *P value | CatLet low [0–23] | CatLet mid [24–43] | CatLet top >43 | P value for trend | CatLet low [0–23] | CatLet mid [24–43] | CatLet top >43 | P value for trend | |||
| MACE | 5 (6.94) | 22 (28.95) | 33 (44.59) | <0.001 | Reference | 4.67 (1.77–12.35) |
8.01 (3.12–20.55) |
<0.001 | Reference | 4.18 (1.58–11.06) |
6.22 (2.40–16.13) |
<0.001 | ||
| Reangina | 3 (4.17) | 15 (19.74) | 22 (29.73) | <0.001 | Reference | 5.22 (1.51–18.04) |
9.31 (2.79–31.13) |
<0.001 | Reference | 5.10 (1.47–17.65) |
8.59 (2.53–29.10) |
<0.001 | ||
| Cardiac death | 0 | 2 (2.63) | 6 (8.11) | 0.03 | Reference | Reference | 4.98 (2.49–9.96) |
<0.05 | Reference | Reference | 4.84 (2.52–9.32) |
<0.001 | ||
Adjustments were made for age, creatinine, and LVEF. *P, indicates comparison between different CatLet score categories. CatLet, Coronary Artery Tree Description and Lesion Evaluation; CatLet low, bottom tertile of CatLet score; CatLet mid, middle tertile of CatLet score; CatLet top, top tertile of CatLet score; CI, confidence interval; HR, hazard ratio; K-M, Kaplan-Meier; LVEF, left ventricular ejection fraction; MACE, major adverse cardiac event.
Prediction value of the CatLet score adjusted by three CVs
With respect to MACEs, cardiac death, and recurrent angina, the CatLet score yielded AUCs of 0.73 (95% CI: 0.66–0.81), 0.76 (95% CI: 0.63–0.89), and 0.73 (95% CI: 0.65–0.81), respectively; un contrast, the CV-adjusted CatLet score demonstrated superior discriminatory capacity, with AUCs of 0.78 (95% CI: 0.71–0.84), 0.88 (95% CI: 0.77–0.98), and 0.74 (95% CI: 0.66–0.82), respectively, as shown in Figure 3. Figure 3 also shows that the three CV-adjusted CatLet score had significantly better predictive ability than the did CatLet scores alone for all endpoints.
Figure 3.
ROC curves showing a significant difference between CatLet score and adjusted CatLet score for predicting MACEs (A), cardiac death (B), and reangina (C) at 4.5 years. AUC, area under the curve; CatLet, Coronary Artery Tree Description and Lesion Evaluation; CI, confidence interval; CS, CatLet score; Adj-CS, adjusted CatLet score; MACEs, major adverse cardiac events; ROC, receiver operating characteristic.
Calibration
In terms of calibration, the three CV-adjusted CatLet score model was robust (Figure 4). In the multivariable analysis, the CatLet score remained an independent predictor of clinical outcomes even after adjustments were made for additional risk factors demonstrating univariate significance (Table S1).
Figure 4.
Calibration plots for the cross-validation of clinical outcomes predicted by the CatLet score alone (A-C) and the adjusted CatLet score (D-F). The circles indicate the observed frequencies by tertile of predicted probabilities with a 95% CI. There was good agreement between the observed and predicted incidence for MACEs, cardiac death, and recurrent angina. The intercept is also known as the CITL. Lowess smoothing curves (red) are superimposed on calibration plots. Perfect prediction is mathematically defined by intercept =0 and slope =1. AUC, area under the curve; CatLet, Coronary Artery Tree Description and Lesion Evaluation; CI, confidence interval; CITL, calibration-in-the-large; E:O, estimation risk/observed risk; MACEs, major adverse cardiac events.
Subgroup and sensitivity analyses
Subgroup and sensitivity analyses demonstrated a continuous association between CatLet score and clinical and imaging results, without significant interactions between the different subgroups (Figure 5).
Figure 5.
Hazard ratios for MACEs per 1 unit higher CatLet score stratified by risk factors, categorically or medially, and adjusted for age, serum creatinine, and LVEF. CatLet, Coronary Artery Tree Description and Lesion Evaluation; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MACEs, major adverse cardiac events; TC, total cholesterol; TG, triglyceride.
Discussion
The key findings of this study are as follows: (I) the stand-alone CatLet score is an independent predictor of long-term prognosis in patients with CCS who have undergone primary PCI, and (II) the three CV-adjusted CatLet model had a better predictive power than did the conventional CatLet model.
Predictably, patients with higher CatLet score tertiles were more likely to be male. Other features associated with a higher CatLet score were as follows: a lower LVEF, a higher ApoB:ApoA1 ratio, a higher number of lesions, adverse lesion features, bifurcated lesions, the left main and right coronary arteries lesions, severe tortuosity or calcification, ostial lesion involvement, higher stent number, and lesion length >20 mm. These findings are consistent with those reported previously (11,12,18-21). Conversely, a higher CatLet score was associated with a lower likelihood of smoking, a phenomenon known as “smoking paradox”, which has been observed in a few studies on SYNTAX scores (22,23). The possible explanation for this is that smokers are younger and have fewer cardiovascular risk factors than do nonsmokers (24), and age is the strongest prognosticator for adverse cardiovascular outcomes.
CatLet score
The CatLet angiographic scoring system accounts for variations in coronary anatomy, severity of flow-limiting lesions, and corresponding myocardial territories (8). Its ability to predict clinical prognosis in AMI has been demonstrated in previous studies (9,11,18,21). In the current cohort, each unit increase in CatLet score was associated with a 6.22-fold higher MACE risk (P<0.001). This is consistent with our previous CCS research (12). The SYNTAX score quantifies coronary lesion complexity, stratifies CAD severity, and predicts clinical outcomes in patients with CAD. In previous studies, models incorporating both anatomical and clinical factors outperformed other models in outcome prediction (25). For more severe events such as cardiac death, the CV-adjusted CatLet scoring model was particularly beneficial in improving discrimination and calibration, implying that such events are especially affected by these CVs. One study found that CVs significantly enhance 1-year all-cause mortality prediction beyond anatomical SYNTAX scores alone (26). The CatLet score performs better than the SYNTAX score overall in predicting long-term prognosis (9,11,12). This is because (I) the CatLet score takes full account of the diversity of the coronary tree, whereas the SYNTAX score is based only on dichotomous right-or-left coronary predominance, and (II) it reflects both aspects of the lesion: the degree of stenosis and the area of the myocardium covered by the stenotic coronary arteries (weights).
The CatLet score adjusted by three CVs
Age, creatinine, and ejection fraction (ACEF) are clinically important factors affecting the clinical prognosis of patients with CAD. They have been widely used in predicting the clinical prognosis of patients with cardiovascular disease, demonstrating superior efficacy than the EuroSCORE, the Parsonnet score, and the Northern New England score (27-29). The CV-adjusted CatLet score examined in this study outperformed the stand-alone CatLet score in terms of outcome prediction. In a study series on elective cardiac surgery, serum creatinine was identified as one of the three best predictors of surgical mortality, along with age and LVEF (29). Glomerular filtration rate (GFR) is a highly indicative marker of renal function (30). Nevertheless, the AGEF score, which includes age, GFR, and LVEF components, has not been proven to be superior to ACEF in predicting cardiovascular outcomes (31). A possible explanation for this is that creatinine clearance calculated according to the Diet Adjustment Equation for Kidney Disease or the Cockcroft-Gault formula may introduce mathematical coupling and collinearity bias into the model, as age is already an integral part of this formula (32). Therefore, we used the serum creatinine instead of the GFR to adjust the CatLet model. Finally, LVEF is a well-established predictor of clinical outcomes in cardiovascular disease (29,33,34). In our study, LVEF was the most powerful predictor of clinical outcomes and can be used in refining an optimal cardiovascular adjustment model.
Limitations
This research involved several restrictions that should be addressed. First, the CatLet score validation test employed an observational design, and confounding was inevitable. These findings should be viewed as preliminary, particularly given the retrospective, single-center design and limited sample size, which constrain the statistical power and broader applicability of the conclusions. However, according to published data, each 1-unit increase in the CatLet score is associated with a 1.05-fold increased risk of adverse cardiovascular events, with a standard deviation of 12 (9). Therefore, a minimum sample size of 205 could ensure a statistical power of 0.90 in drawing reliable conclusions at a two-sided significance level of α=0.05. A total of 222 participants were enrolled in our study, providing sufficient statistical power to support the research conclusions. Third, while our study GDMT post-PCI, we did not collect detailed longitudinal pharmacotherapy data (e.g., drug dosage and adherence). This decision was based on the standardized GDMT protocol mandated across all participating centers for patients with CCS as per contemporary guidelines. Nevertheless, real-world variations in drug titration, patient compliance, or unrecorded treatment discontinuations could act as unmeasured confounders, particularly over extended follow-up. The CatLet score’s independent prognostic value observed in our analysis should thus be interpreted in the context of presumed optimal medical therapy. Future studies should incorporate prospective medication monitoring to quantify its interaction with anatomical complexity scoring. Fourth, beyond conventional pharmacotherapy, nutraceuticals may modulate cardiovascular risk in patients with CCS, as highlighted by Scicchitano et al. (35). These agents can improve lipid profiles, endothelial function, and inflammation—factors potentially influencing the CatLet score’s prognostic targets. However, our study did not systematically collect data on nutraceutical use or dietary patterns. This omission is a recognized limitation, as such interventions might attenuate residual risk unaccounted for by our model. Fifth, in the CatLet score, lesions with vascular stenosis ≥50% are scored. This scoring system demonstrates good diagnostic efficacy for myocardial ischemia, but its clinical value in guiding revascularization still requires further investigation (36). Finally, we must acknowledge that this sample size was small, and therefore the findings cannot inform personalized risk assessment. Further studies involving a larger cohort patients will address this issue.
Conclusions
The CatLet score was found to be an independent predictor of long-term clinical outcomes in patients with CCS after PCI. Moreover, the CatLet score incorporating three CVs demonstrated better risk stratification for the long-term outcomes of these patients than did the conventional CatLet score.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Wannan Medical College (approval No. WYEFYLS2025018). The requirement for informed consent was waived due to the retrospective nature of the analysis.
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-171/rc
Funding: This work was in part supported by the College of Natural Science Funds (No. WK2023ZQNZ68) and the Wuhu City Program for the Cultivation of High-Level Innovative Health Talents.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-171/coif). The authors have no conflicts of interest to declare.
(English Language Editor: J. Gray)
Data Sharing Statement
Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-171/dss
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