Abstract
Background
Accumulating evidence suggests that inflammatory and nutritional status play key roles in the prognosis of cardiovascular diseases (CVD). The Inflammatory Prognostic Index (IPI), integrating both inflammatory and nutritional metrics, has been linked to multiple subtypes of coronary artery disease (CAD). However, the association between IPI and long-term prognosis in patients with chronic total occlusion (CTO) after successful PCI remains unclear. Therefore, we aimed to investigate the prognostic role of IPI in patients with CTO who underwent successful PCI.
Methods
A total of 525 patients with CTO who underwent successful PCI were enrolled in this study. Participants were stratified into a MACE group and a control group, as well as three subgroups (T1, T2, T3) by IPI tertiles. Baseline characteristics were compared, and Cox regression analysis were performed. The receiver operating characteristic curve (ROC) was used to determine the predictive value of IPI for major adverse cardiovascular events (MACE).
Results
A total of 76 patients with 82 MACE were recorded during the follow-up. Patients with MACE exhibited significantly higher C-reactive protein (CRP) and IPI levels (p < 0.05). Patients in T3 (high IPI group) had higher white blood cell count, neutrophil count, CRP, creatinine, and IPI, while they had lower lymphocyte count, albumin (ALB), and left ventricular ejection fraction (LVEF) (p < 0.05). In addition, patients in the highest IPI tertile exhibited a higher risk of target vessel revascularization (TVR) and composite MACE (p < 0.05). Multivariate Cox regression analysis demonstrated that IPI was an independent predictor of MACE in patients with CTO who underwent successful PCI (p < 0.05). Moreover, patients in the highest IPI tertile had a 2.484-fold higher risk of MACE (p < 0.05).
Conclusion
IPI may serve as a supplementary prognostic tool due to its simplicity and low cost. However, given its modest predictive performance, it should be combined with existing risk models to improve predictive value and facilitate clinical decision-making.
Keywords: Inflammatory Prognostic Index, Chronic total occlusion, Percutaneous coronary intervention, Major adverse cardiovascular events, Prognosis
Introduction
Coronary artery disease (CAD) is a leading cause of morbidity and mortality in China and worldwide [1]. PCI is the most widely used technique for the revascularization of the vessels involved. Despite substantial advances in PCI techniques and interventional devices, revascularization for chronic total occlusion (CTO) remains one of the most challenging procedures in contemporary interventional cardiology. In addition, accumulating evidence suggests that CTO-PCI is associated with an increased risk of procedural complications and a poor prognosis compared with non-CTO lesions [2]. Even after successful PCI for CTO, the 10-year cardiovascular mortality rate remains as high as 10.4% [3]. Therefore, it is clinically critical to identify a reliable biomarker for predicting MACE and stratifying cardiovascular risk in this population.
Inflammation is increasingly recognized as a key determinant of CAD onset, progression, and long-term prognosis [4]. Evidence suggests that multiple inflammatory biomarkers have been proven as prognostic indicators for patients with CTO after successful PCI [5–8]. The nutritional status is also a key element for the prognosis of CVD. Prior studies have demonstrated that low serum albumin and other nutritional markers correlate with adverse outcomes in patients with CTO undergoing PCI [9, 10]. The Inflammatory Prognostic Index (IPI), calculated as C-reactive protein (CRP) × Neutrophil count/lymphocyte count/albumin, was first linked to the prognosis of small-cell lung cancer [11]. IPI integrates the inflammatory and nutritional information, which could comprehensively reflect the inflammatory and nutritional status of the human body. In recent years, IPI has also been suggested as a prognostic indicator for patients with non-ST-segment elevation myocardial infarction (NSTEMI) undergoing PCI [12]. Although IPI has been widely discussed in various diseases, the association between IPI and the prognosis of CTO-PCI patients is rarely discussed. Therefore, in the present study, we aimed to explore the prognostic value of IPI in patients with CTO undergoing successful PCI.
Methods
Study population
We consecutively enrolled 612 patients with CTO who underwent successful PCI between December 2017 and February 2022. Successful CTO-PCI was defined as residual stenosis < 30% with Thrombolysis in Myocardial Infarction (TIMI) grade 3 flow, without causing any severe complications. The inclusion criteria were as follows: 1) > 18 years old; 2) had the indications for CTO-PCI and received successful PCI without severe complications; 3) written informed consent was obtained, and participants agreed to receive a long-term follow-up. The exclusion criteria were as follows: 1) myocardial infarction [MI] on admission; 2) severe heart failure with NYHA 3–4; 3) moderate to severe valvular disease; 4) severe hepatic or renal dysfunction; 5) systemic immune diseases; 6) acute or chronic inflammatory diseases; 7) use of corticosteroids, immunosuppressants, or nonsteroidal anti-inflammatory drugs; 8) failure of CTO-PCI or had severe complications; 9) malignant tumor; 10) refused to participate or receive long-term follow-up. Eventually 525 patients were included in this study. Patients were stratified into a MACE group and a control group, as well as T1, T2, and T3 subgroups based on IPI tertiles.
Data collection and Definition of IPI
The data used in this study were acquired from the medical records or patient interviews. Demographic characteristics (age, gender, body mass index [BMI], current smoking status, diabetes mellitus, hypertension, and prior histories of stroke, myocardial infarction [MI], PCI, and coronary artery bypass grafting [CABG]), laboratory parameters (blood pressure values, presence of heart failure, and atrial fibrillation), laboratory findings (routine blood tests, biochemical parameters, lipid profile, C-reactive protein [CRP], and albumin levels), angiographic findings (CTO location, multivessel disease, et al.), and medications were carefully recorded. IPI was calculated as C-reactive protein (mg/L) × Neutrophil (109/L)/lymphocyte (109/L)/albumin (g/L) [11].
Follow‑up and endpoints
Patients included received a long-term follow-up at 1, 3, 6, 12 months, thereafter once a year after discharge. The follow-up was conducted via telephone interviews, online questionnaires, or outpatient/inpatient hospital visits. MACE was defined as the composite endpoint events of all-cause mortality, target vessel revascularization (TVR) and nonfatal myocardial infarction (MI). TVR was defined as repeat revascularization of the target vessel or its major branches. In our analysis, for the primary time-to-event analysis, only the first MACE event for each patient was considered. 76 patients developed their first MACE during follow-up. For descriptive purposes, we also recorded all recurrent events, which amounted to 82 events in total.
Statistical analysis
Continuous data were presented as the mean ± standard deviation or median with interquartile range according to the data distribution, which were compared using the Student's t-test, ANOVA, Mann–Whitney U test, or Kruskal–Wallis H test, as appropriate. Categorical variables were expressed as number (n) and percentage (%), which were compared using the chi-square test or Fisher’s exact test. To explore the parameters potentially associated with MACE, univariate analyses were initially performed. The multivariate Cox proportional hazards regression model was used to analyze the time to the first occurrence of MACE. Variables with univariate P < 0.10 or established clinical relevance were entered into the multivariable model to identify independent predictors of MACE in patients with successful CTO-PCI. Before fitting the final model, we evaluated multicollinearity among these candidates using the Variance Inflation Factor (VIF), excluding variables with a VIF > 5.0 to maintain model stability. The proportional hazards assumption was tested using Schoenfeld residuals and was not violated for the variables in the final model. Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of IPI and other parameters in predicting MACE in patients with CTO undergoing successful PCI.
Results
Baseline and clinical characteristics when divided by MACE
A total of 525 patients with CTO undergoing successful PCI were included in this study. The study flowchart is shown in Fig. 1. The baseline characteristics are shown in Table 1. The demographic characteristics were comparable between the two groups (p > 0.05) (Table 1). The comorbidities including current smoking, diabetes, hypertension, prior PCI or MI were also comparable between patients with MACE and the controls (p > 0.05) (Table 1). We found that patients with MACE had a higher CRP and IPI (p < 0.05) (Table 2). No significant differences were observed in other laboratory indicators including LVEF and the CTO vessels (p > 0.05) (Table 1). The proportion of patients prescribed angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), or angiotensin receptor-neprilysin inhibitors (ARNIs) was significantly higher in the MACE group (p < 0.05) (Table 1).
Fig. 1.
Study flow chart
Table 1.
Baseline characteristics of the study population by MACE
| Variables | MACE group (n = 76) |
Control group (n = 449) |
P-value |
|---|---|---|---|
| Age(years) | 64.63 ± 12.91 | 62.75 ± 12.69 | 0.271 |
| Gender(male), n(%) | 50(65.79) | 279(62.14) | 0.543 |
| BMI (kg/m2) | 24.94 ± 3.88 | 25.05 ± 3.62 | 0.817 |
| Current smoker, n(%) | 35(46.05) | 180(40.09) | 0.328 |
| Diabetes Mellitus, n(%) | 37(48.68) | 229(51.00) | 0.709 |
| Hypertension, n(%) | 41(53.95) | 263(58.57) | 0.450 |
| Prior MI, n(%) | 18(23.68) | 123(27.39) | 0.500 |
| Prior PCI, n(%) | 60(78.95) | 348(77.51) | 0.780 |
| WBC count, 109/L | 7.52 ± 1.93 | 7.33 ± 2.09 | 0.557 |
| Neutrophils count, 109/L | 5.26 ± 2.04 | 5.07 ± 2.19 | 0.422 |
| Lymphocytes count, 109/L | 1.60 ± 0.55 | 1.73 ± 0.62 | 0.082 |
| NLR | 3.65 ± 1.82 | 3.37 ± 2.17 | 0.289 |
| CRP, mg/L | 1.47 ± 1.03 | 1.20 ± 0.72 | 0.006 |
| ALB, g/L | 39.39 ± 3.35 | 39.34 ± 3.41 | 0.910 |
| FBG, mmol/L | 6.43 ± 2.04 | 6.24 ± 1.76 | 0.394 |
| Cr, mmol/L | 76.19 ± 19.38 | 71.94 ± 20.73 | 0.097 |
| Uric acid, mmol/L | 357.55 ± 116.27 | 357.05 ± 96.78 | 0.461 |
| TC, mmol/L | 4.57 ± 1.15 | 4.47 ± 1.09 | 0.454 |
| TG, mmol/L | 1.66 ± 1.49 | 1.73 ± 1.48 | 0.703 |
| LDL-C, mmol/L | 2.88 ± 0.92 | 2.83 ± 0.85 | 0.671 |
| HDL-C, mmol/L | 1.02 ± 0.27 | 1.06 ± 0.44 | 0.389 |
| NT-proBNP, pg/ml | 647.43 ± 1070.20 | 589.42 ± 1175.77 | 0.687 |
| IPI | 0.14 ± 0.13 | 0.11 ± 0.11 | < 0.001 |
| Aspirin, n (%) | 76(100.0) | 449(100.0) | 1 |
| Clopidogrel, n (%) | 41 (53.95) | 259 (57.68) | 0.616 |
| Ticagrelor, n (%) | 35 (46.05%) | 190 (42.32) | |
| ACEI/ARB/ARNI, n (%) | 51 (67.11) | 245 (54.57) | 0.041 |
| b-blocker, n (%) | 58 (76.32) | 334 (74.39) | 0.721 |
| Statines, n (%) | 76(100.0) | 449(100.0) | 1 |
| Calcium channel blockers, n (%) | 22 (28.95) | 101 (22.49) | 0.219 |
| SGLT-2i, n (%) | 18 (23.68) | 131 (29.18) | 0.326 |
| LVEF(%) | 49.93 ± 7.12 | 51.14 ± 6.95 | 0.154 |
| LAD | 34 (44.74) | 175 (38.98) | 0.125 |
| LCX | 21 (27.63) | 96 (21.38) | |
| RCA | 21 (27.63) | 178 (39.64) | |
| ISR-CTO | 16(21.05) | 104(23.16) | 0.685 |
| Multivessel disease | 55(72.37) | 341(75.95) | 0.503 |
Abbreviations: BMI body mass index, MI myocardial infarction, PCI percutaneous coronary intervention, WBC white blood cell, NLR neutrophil to lymphocyte ratio, CRP C-reactive protein, FBG fasting blood glucose, Cr creatinine, ALB albumin, CAR C-reactive protein to albumin ratio, TC total cholesterol, TG Triglyceride, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, NT-proBNP N-terminal brain natriuretic peptide, IPI inflammatory prognostic index, ACEI angiotensin-converting enzyme inhibitor, ARB angiotensin II receptor blocker, ARNI angiotensin receptor enkephalinase inhibitor, CCB Calcium channel blockers, SGLT-2i sodium-glucose cotransporter 2 inhibitor, LVEF left ventricular ejection fraction, LAD left anterior descending artery, LCX left circumflex artery, RCA right coronary artery, ISR-CTO in-stent restenotic chronic total occlusion
Table 2.
Baseline characteristics of the study population by tertiles of IPI
| Variables | Tertile 1 (n = 175) |
Tertile 2 (n = 175) |
Tertile 3 (n = 175) |
P-value |
|---|---|---|---|---|
| Age, years | 62.97 ± 12.79 | 61.89 ± 12.78 | 64.19 ± 12.57 | 0.239 |
| Gender(male), n(%) | 105 (60.00%) | 109 (62.29%) | 115 (65.71%) | 0.539 |
| BMI | 25.01 ± 3.60 | 25.32 ± 3.56 | 24.77 ± 3.79 | 0.371 |
| Current smoker, n(%) | 65 (37.14%) | 73 (41.71%) | 77 (44.00%) | 0.414 |
| Diabetes Mellitus, n(%) | 98 (56.00%) | 91 (52.00%) | 77 (44.00%) | 0.073 |
| Hypertension, n(%) | 107 (61.14%) | 98 (56.00%) | 99 (56.57%) | 0.565 |
| Prior MI | 47 (26.86%) | 38 (21.71%) | 56 (32.00%) | 0.095 |
| Prior PCI | 138 (78.86%) | 136 (77.71%) | 134 (76.57%) | 0.876 |
| WBC count, 109/L | 6.76 ± 1.70 | 7.45 ± 2.01 | 7.85 ± 2.30 | < 0.001 |
| Neutrophils count, 109/L | 3.93 ± 1.28 | 5.00 ± 1.68 | 6.35 ± 2.59 | < 0.001 |
| Lymphocytes count, 109/L | 2.03 ± 0.59 | 1.73 ± 0.56 | 1.38 ± 0.51 | < 0.001 |
| NLR | 2.06 ± 0.77 | 3.11 ± 1.30 | 5.07 ± 2.56 | < 0.001 |
| CRP, mg/L | 0.79 ± 0.34 | 1.17 ± 0.42 | 1.76 ± 1.03 | < 0.001 |
| ALB, g/L | 39.86 ± 3.54 | 39.62 ± 3.17 | 38.56 ± 3.34 | < 0.001 |
| FBG, mmol/L | 6.36 ± 1.98 | 6.46 ± 1.94 | 5.98 ± 1.49 | 0.031 |
| Cr, mmol/L | 68.32 ± 17.92 | 72.86 ± 20.17 | 76.46 ± 22.63 | < 0.001 |
| Uric acid, mmol/L | 352.45 ± 93.72 | 358.56 ± 99.24 | 360.36 ± 106.19 | 0.740 |
| TC, mmol/L | 4.48 ± 1.18 | 4.61 ± 1.13 | 4.36 ± 0.97 | 0.104 |
| TG, mmol/L | 1.84 ± 1.92 | 1.74 ± 1.29 | 1.58 ± 1.10 | 0.251 |
| LDL-C, mmol/L | 2.79 ± 0.86 | 2.98 ± 0.90 | 2.74 ± 0.80 | 0.028 |
| HDL-C, mmol/L | 1.09 ± 0.60 | 1.03 ± 0.29 | 1.04 ± 0.27 | 0.394 |
| NT-proBNP, pg/ml | 137.73 ± 17.79 | 138.42 ± 17.14 | 133.94 ± 18.83 | 0.043 |
| IPI | 0.04 ± 0.01 | 0.08 ± 0.01 | 0.21 ± 0.14 | < 0.001 |
| LVEF(%) | 52.79 ± 6.10 | 50.42 ± 7.41 | 49.67 ± 7.02 | < 0.001 |
| Aspirin, n (%) | 175(100.0) | 175(100.0) | 175(100.0) | 1 |
| Clopidogrel, n (%) | 107 (61.14) | 93 (53.14) | 101 (57.71) | 0.316 |
| Ticagrelor, n (%) | 68 (38.86) | 82 (46.86) | 74 (42.29) | |
| ACEI/ARB/ARNI, n (%) | 101 (57.71) | 100 (57.14) | 95 (54.29) | 0.787 |
| b-blocker, n (%) | 125 (71.43) | 134 (76.57) | 133 (76.00) | 00.479 |
| Statines, n (%) | 175(100.0) | 175(100.0) | 175(100.0) | 1 |
| CCB, n (%) | 38 (21.71) | 42 (24.00) | 43 (24.57) | 0.800 |
| SGLT-2i, n (%) | 45 (25.71) | 51 (29.14) | 53 (30.29) | 0.614 |
| LAD | 78 (44.57%) | 61 (34.86%) | 70 (40.00%) | 0.132 |
| LCX | 43 (24.57%) | 36 (20.57%) | 39 (22.29%) | |
| RCA | 54 (30.86%) | 78 (44.57%) | 66 (37.71%) | |
| ISR-CTO | 36 (20.57%) | 40 (22.86%) | 44 (25.14%) | 0.595 |
| Multivessel disease | 130 (74.29%) | 134 (76.57%) | 132 (75.43%) | 0.884 |
Abbreviations: BMI body mass index, MI myocardial infarction, PCI percutaneous coronary intervention, WBC white blood cell, NLR neutrophil to lymphocyte ratio, CRP C-reactive protein, FBG fasting blood glucose, Cr creatinine, ALB albumin, CAR C-reactive protein to albumin ratio, TC total cholesterol, TG Triglyceride, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, NT-proBNP N-terminal brain natriuretic peptide, IPI inflammatory prognostic index, ACEI angiotensin-converting enzyme inhibitor, ARB angiotensin II receptor blocker, ARNI angiotensin receptor enkephalinase inhibitor, CCB Calcium channel blockers, SGLT-2i sodium-glucose cotransporter 2 inhibitor, LVEF left ventricular ejection fraction, LAD left anterior descending artery, LCX left circumflex artery, RCA right coronary artery, ISR-CTO in-stent restenotic chronic total occlusion
Baseline and clinical characteristics when divided by the tertiles of IPI
Patients were stratified into three IPI tertiles: T1 (< 0.058), T2 (0.058–0.108), and T3 (> 0.108) (Table 2). Patients in T3 (High IPI group) had a higher white blood cell count, neutrophils count, neutrophil to lymphocyte ratio (NLR), CRP, Cr, and IPI, while a lower lymphocytes counts, albumin (ALB) and LVEF (p < 0.05) (Table 2). In addition, patients in the highest IPI tertile exhibited lower fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels (p < 0.05) (Table 2). No statistically significant differences were found in other indicators (p > 0.05) (Table 2).
MACE during the 3 years of follow-up
The median follow-up duration was 33.3 months, with 13 patients lost to follow-up. Eventually, 76 patients experienced a first-time MACE during the follow-up. A total of 82 MACE (including recurrent events) were recorded. Patients in high tertiles of IPI tended to encounter an increased risk of TVR and a composite MACE (p < 0.05) (Table 3).
Table 3.
Incidence of MACEs during follow-up in different tertiles of IPI
| Variables | T1 (n = 175) |
T2 (n = 175) |
T3 (n = 175) |
P-value |
|---|---|---|---|---|
| Patients suffered MACEs | 16 (9.1%) | 25 (14.3%) | 35 (20.0%) | 0.015 |
| All cause death, (%) | 2(1.1) | 2(1.1) | 4(2.3) | 0.602 |
| TVR, (%) | 13(7.4) | 21(12.0) | 29(16.6) | 0.031 |
| None fatal MI, (%) | 3(1.7) | 4(2.3) | 4(2.3) | 0.911 |
| Composite MACE, (%), | 18(10.3) | 27(15.4) | 37(21.1) | 0.020 |
Abbreviations: TVR target vessel revascularization, MI myocardial infarction, MACE major adverse cardiovascular events
Independent predictors for MACE
Variables with established prognostic relevance for coronary artery disease and those with p < 0.05 (including age, body mass index, current smoking, hypertension, diabetes mellitus, prior MI, prior PCI, ISR-CTO, LAD-CTO, multivessel disease, Cr, LDL-C, HDL-C, uric acid, FBG, NT-proBNP/100, LVEF and IPI) were included in the univariate analysis. Cox proportional hazards model showed that IPI was an independent predictor for MACE in CTO patients undergoing successful PCI (p < 0.05) (Table 4). The proportional hazards assumption was met for the model variables (global test p = 0.45). In addition, patients in high tertiles of IPI showed an increased risk of MACE (Fig. 2). Moreover, patients in high tertiles of IPI suffered a 2.484-fold increased risk of MACE (p < 0.05) (Table 4). ROC curve analysis identified an optimal IPI cutoff of 0.0767 with moderate predictive performance for MACE, corresponding to an area under the curve (AUC) of 0.6236 (Table 4). The corresponding sensitivity and specificity were 0.724 and 0.501, respectively (Table 4, Fig. 3). Moreover, IPI exhibited numerically superior predictive performance compared with CRP, neutrophil count, lymphocyte count, and ALB alone, though the difference did not reach statistical significance (p > 0.05) (Table 5).
Table 4.
Independent predictors of MACE
| Univariate analysis | multivariate analysis | |||||
|---|---|---|---|---|---|---|
| HR | 95% CI | P value | HR | 95% CI | P value | |
| Age | 1.011 | 0.993–1.029 | 0.245 | 1.010 | 0.991–1.030 | 0.291 |
| BMI | 0.851 | 0.934–1.058 | 0.851 | 0.998 | 0.937–1.062 | 0.943 |
| Current smoking | 0.804 | 0.512–1.262 | 0.342 | 0.828 | 0.513–1.338 | 0.441 |
| Hypertension | 1.212 | 0.772–1.903 | 0.403 | 1.200 | 0.751–1.918 | 0.445 |
| Diabetes mellitus | 1.086 | 0.693–1.703 | 0.719 | 1.331 | 0.736–2.407 | 0.344 |
| Prior MI | 1.212 | 0.714–2.057 | 0.476 | 1.199 | 0.688–2.112 | 0.530 |
| Prior PCI | 0.930 | 0.536–1.615 | 0.797 | 0.827 | 0.463–1.477 | 0.521 |
| ISR-CTO | 1.133 | 0.653–1.966 | 0.658 | 1.253 | 0.688–2.282 | 0.462 |
| LAD-CTO | 0.796 | 0.507–1.251 | 0.323 | |||
| Multi-vessel disease | 1.207 | 0.730–1.996 | 0.463 | 1.189 | 0.699–2.024 | 0.523 |
| Cr | 1.008 | 0.998–1.018 | 0.113 | 1.006 | 0.995–1.018 | 0.268 |
| LDL-C | 1.058 | 1.007–1.377 | 0.048 | 1.036 | 0.787–1.365 | 0.799 |
| HDL-C | 0.703 | 0.324–1.527 | 0.373 | |||
| Uric acid | 1.000 | 0.998–1.002 | 0.982 | 0.999 | 0.997–1.002 | 0.584 |
| FBG | 1.051 | 0.936–1.180 | 0.400 | 1.098 | 0.945–1.227 | 0.221 |
| NT-proBNP/100 | 1.003 | 0.985–1.021 | 0.746 | 0.994 | 0.975–1.014 | 0.585 |
| LVEF | 0.980 | 0.950–1.011 | 0.194 | 0.981 | 0.948–1.016 | 0.287 |
| IPI | 5.115 | 1.210–21.624 | 0.026 | 4.367 | 1.052–20.938 | 0.044 |
| Tertile 1 | 1.0 | |||||
| Tertile 2 | 1.656 | 0.851–3.223 | 0.138 | |||
| Tertile 3 | 2.484 | 1.318–4.681 | 0.049 | |||
Abbreviations: BMI body mass index, MI myocardial infarction, PCI percutaneous coronary intervention, ISR-CTO in-stent restenotic chronic total occlusion, LAD left anterior descending artery, Cr creatinine, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, FBG fasting blood glucose, NT-proBNP N-terminal brain natriuretic peptide, IPI inflammatory prognostic index
Fig. 2.
The Cox regression model analysis in different tertiles of IPI
Fig. 3.
ROC curve analysis for the predictive values of different biomarkers
Table 5.
Predictive value of related indicators
| Variables | AUC | 95% CI | Cut off value | specificity | sensitivity |
|---|---|---|---|---|---|
| CRP | 0.6025 | 0.538–0.667 | 0.8650 | 0.345 | 0.816 |
| Neutrophils count | 0.5288 | 0.458–0.600 | 7.010 | 0.871 | 0.211 |
| Lymphocytes count | 0.5624 | 0.498–0.627 | 1.715 | 0.459 | 0.724 |
| ALB | 0.5044 | 0.436–0.573 | 40.150 | 0.388 | 0.671 |
| IPI | 0.6236 | 0.560–0.687 | 0.0767 | 0.501 | 0.724 |
Abbreviations: CRP C-reactive protein, ALB albumin, IPI inflammatory prognostic index
Discussion
In this study, we found that patients with MACE showed higher levels of CRP and IPI. When divided according to the tertiles of IPI, we found that patients in high tertiles of IPI tended to encounter an increased risk of TVR and a composite MACE. Multivariate Cox regression analysis showed that IPI is an independent predictor for MACE in CTO patients undergoing successful PCI. Moreover, patients in high tertiles of IPI suffered a 2.484-fold increased risk of MACE compared with low tertiles of IPI. Our study suggested a modest prognostic value of IPI in patients with CTO undergoing successful PCI. Therefore, it should be combined with existing risk models to improve predictive value and facilitate clinical decision-making.
The management of CTO is challenging due to the complex procedural techniques, low recanalization success rates, and elevated risks of procedural complications. In addition, patients with CTO are typically older and carry a heavier burden of comorbidities [5]. Indeed, patients with CTO suffered an increased risk of procedural complications and a poor prognosis compared with non-CTO patients [2]. Despite an optimal medical treatment and a well performed PCI, there is still a 10.4% 10 year cardiovascular mortality in these patients [3]. The initiation and development of atherosclerosis have been suggested as a chronic, low-grade inflammatory process, which plays a key role in the pathogenesis of CVD [13]. In recent years, the associations between various inflammatory parameters and the prognosis of CTO patients have been widely discussed in the literature. The systemic immune-inflammation index (SII) was calculated as platelets count × neutrophil-to-lymphocyte ratio (NLR), providing a comprehensive reflection of the inflammatory state compared with single inflammatory markers[14]. Initially SII was used as a prognostic indicator for cancer [14]. In recent years, SII is also suggested to associate with the development of CAD. As a novel inflammatory indicator, higher SII has been linked to an increased risk of MACE in patients with CTO and prior CABG [5]. SII could be used as a reliable predictor for MACE and may be useful for high-risk patients identification in these patients [5]. A large sample study from China, which included 1482 non-diabetic patients who underwent successful CTO PCI, also suggested that high level of SII is significant predictor of poor prognosis in non-diabetic CTO PCI patients [15]. Neutrophil to lymphocyte ratio (NLR) has been widely used as an inflammatory indicator, which is also associated with the prognosis of CAD [16]. Prof Ge suggested that higher NLR and its periprocedural change could be used for the prediction of MACE in CTO patients who underwent PCI [17]. In the present study, we discovered that patients with MACE had a higher NLR, but there was no statistical difference. We hypothesize that the limited sample size, sole reliance on routine blood biomarkers, and absence of gold-standard inflammatory assays may prevent single laboratory markers from fully capturing patients’ true systemic inflammatory burden. Therefore, a better parameter integrating more inflammatory indicators such as CRP, ALB, NLR is warranted.
CRP is a well established biomarker for reflecting the degree of inflammation and higher levels of CRP are linked to an increased risk of CAD [18]. In addition, CRP was also proven as a reliable predictor for cardiovascular events [18]. The clinical significance of CRP in the prognosis of CVD has been increasingly emphasized. In the present study, we found that patients in MACE group had an increased level of CRP, suggesting the important role of inflammation in the prognosis of CTO-PCI patients. ALB, synthesized by hepatocytes, is the most abundant protein in the human body. ALB is a marker used for the evaluation both of the nutritional and the inflammatory status of the human body. Its synthesis is suppressed by both malnutrition and chronic inflammatory states. Evidence suggested that low ALB is linked to an increased risk of cardiovascular and all-cause mortality [19, 20]. A recent study suggested that low ALB is an independent predictor for MACE in patients with CTO, indicating the inflammatory and nutritional role in the prognosis of CTO [10].
C-reactive protein to albumin ratio (CAR) is an emerging biomarker for assessing systemic inflammation and nutrition. It is an easily obtained and calculated indicator from routine biochemical tests. The composite biomarker provides better predictive value for clinical diagnosis than the parameters alone as a result of its impact on disease from multiple dimensions. Previous studies demonstrated that CAR is superior to CRP or ALB alone in predicting the severity and prognosis of CVD [8]. Composite inflammatory or nutritional indicators have been suggested as a more reliable indicator for the prognosis of CTO patients. Prof Fan suggested that patients with higher CAR showed a poor prognosis with all-cause death and cardiovascular death in CTO patients [8]. A more recent study from China suggested that CAR is an independent predictor for MACE in patients with CTO and provides superior prognostic value compared with CRP or ALB alone [10]. In our study, we also found that patients who suffered MACE had a higher CAR, suggesting its prognostic role in CTO patients.
IPI, integrating the data of nutrition and inflammation, has been suggested as a predictor for contrast-induced nephropathy in patients undergoing PCI [21, 22] and in-stent re-stenosis after PCI [23]. Moreover, IPI was used as a prognostic indicator for acute coronary syndrome [24], non-ST-segment elevation myocardial infarction undergoing PCI [12] and patients receiving surgical aortic valve replacement [25]. In this study, we found a moderate predictive value for MACE in patients with CTO undergoing PCI. When divided according to the tertiles of IPI, we found that patients in high tertiles of IPI tended to encounter an increased risk of TVR and a composite MACE. Moreover, patients in high tertiles of IPI suffered a 2.484-fold increased risk of MACE compared with low tertiles of IPI. The potential mechanism of the prognostic value of IPI for CTO-PCI patients included both the inflammatory and the nutritional conditions. Firstly, IPI could be re-calculated as NLR*CAR, while both NLR and CAR have been suggested as predictors for MACE in CTO patients. Secondly, a higher IPI means a higher CRP, Neutrophil, while a lower lymphocyte or albumin. Lower serum albumin means a status of malnutrition. Under the conditions of malnutrition, immune system activity is impaired, resulting in multiple complications and an increased level of inflammatory reaction. Inflammation, in turn, has a negative effect on albumin synthesis. Therefore, the inflammation and malnutrition together form a vicious circle, resulting in a poor prognosis of the patients with CTO undergoing PCI. We assessed the association between IPI and the prognosis of CTO patients and found a modest prognostic value of IPI in patients with CTO undergoing successful PCI. So we recommend that IPI should be used in conjunction with established risk models to optimize predictive accuracy and aid clinical decision-making.
This study has several limitations. First, it is a single-center investigation with a relatively small sample size. The procedures were performed by the experienced hands with high volume of PCI each year. Therefore, selection bias may occur and this may limit the generalizability of our findings. Second, IPI was calculated solely using admission laboratory data, and we did not perform dynamic serial IPI measurements during follow-up. Third, unmeasured residual confounding factors may persist even after adjustment via Cox regression models. Fourth, key procedural variables—including antegrade/retrograde recanalization approach, intravascular imaging utilization, and deployment of drug-eluting balloons (DEBs) or drug-eluting stents (DESs)—were not incorporated into our analyses, potentially introducing residual bias. Lastly, large-sample, multi-center prospective studies in independent cohorts are warranted to confirm our study.
Conclusion
We found that patients with MACE showed higher levels of CRP and IPI. When divided according to the tertiles of IPI, we found that patients in high tertiles of IPI tended to encounter an increased risk of TVR and a composite MACE. Multivariate Cox regression analysis showed that IPI is an independent predictor for MACE in CTO patients undergoing successful PCI. Moreover, patients in high tertiles of IPI suffered a 2.484-fold increased risk of MACE compared with low tertiles of IPI. Our findings demonstrate that IPI provides modest predictive ability for MACE among patients with CTO following successful PCI. The IPI may serve as a supplementary prognostic tool due to its simplicity and low cost. However, given its modest predictive performance, it should be combined with existing risk models to improve predictive value and facilitate clinical decision-making.
Acknowledgements
Not applicable.
Authors’ contributions
HLW: Conceptualization, Methodology and Project administration; XJZ, BL: Visualization, Investigation, Software, Writing, Review and Editing; JJL: Investigation, Software, Data collection, Writing, Review and Editing. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
This study received no specific funding support.
Data availability
The datasets analyzed in the present study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This trial was approved by the Ethics Committee of the People’s Hospital of Liaoning Province and conducted in compliance with the ethical principles outlined in the 1964 Declaration of Helsinki and its subsequent amendments. All study participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed in the present study are available from the corresponding author upon reasonable request.