The Pan-Immune-Inflammation Value (PIV) predicts major adverse cardiovascular events in elderly patients undergoing percutaneous coronary intervention: a real-world study

The Pan-Immune-Inflammation Value (PIV) predicts major adverse cardiovascular events in elderly patients undergoing percutaneous coronary intervention: a real-world study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Pan-Immune-Inflammation Value (PIV) predicts major adverse cardiovascular events in elderly patients undergoing percutaneous coronary intervention: a real-world study Junying Duan, Xue Zhang, Zizhao Zhang, Mengzhu Zhou, Kangyin Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7917290/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose The Pan-Immune-Inflammation Value (PIV), a novel inflammatory marker primarily studied in cancer, remains underinvestigated in elderly PCI patients. This study evaluates PIV's prognostic value for risk stratification and personalized treatment in this population. Patients and methods: In our study, we enrolled 1426 elderly PCI patients (age ≥ 75 years) between 2019 and 2023. Patients were divided into low- and high-PIV groups based on the optimal cut-off value determined by receiver operating characteristic (ROC) curve analysis. The primary endpoint was the incidence of major adverse cardiovascular events (MACE), comprising cardiac death, recurrent myocardial infarction, and target vessel revascularization. Secondary endpoints included the individual components of MACE. Cox regression and ROC analyses were employed to evaluate the independent prognostic value of PIV. Results Patients in the high-PIV group had a more adverse clinical profile at baseline. During a median follow-up of 362 days, the high-PIV group experienced a significantly higher incidence of MACE (10.8% vs. 5.1%, P < 0.001), cardiac death (7.1% vs. 2.8%, P < 0.001), and all-cause mortality (10.8% vs. 4.5%, P < 0.001). Multivariable Cox regression confirmed PIV as an independent predictor of MACE after adjusting for confounders (Model 3: HR 1.572, 95% CI 1.040,2.377, P = 0.032). ROC analysis showed that PIV had superior predictive ability for MACE (AUC = 0.641) compared to models combining PIV with age ≥ 80 years. Conclusion PIV serves as a simple, potent, and independent prognostic biomarker for MACE in elderly patients following PCI. Its integration into clinical risk stratification could help identify high-risk patients who may benefit from more intensive management. Cardiovascular events Percutaneous coronary intervention Inflammation Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular disease (CVD) is a series of cardiovascular disorders caused by the formation of plaques and thrombosis within blood vessels. The World Health Organization (WHO) has classified it as a very serious non-communicable disease, aiming to reduce the mortality rate related to CVD by 25% by 2025. 1 To achieve this goal, it is particularly important to predict the occurrence of subsequent cardiovascular events in high-risk populations for CVD. Atherosclerosis (AS) is the primary pathological basis of coronary artery disease and represents a chronic inflammatory state. Numerous clinical and experimental studies have confirmed that inflammation is a key driving factor in its progression. 2 – 4 The accumulation and migration of inflammatory cells play an indispensable role in the formation and development of arterial plaques. 5 – 7 With the intensification of global aging, elderly populations are increasingly exposed to heightened cardiovascular risks. 8 – 10 However, they remain underrepresented in clinical studies, particularly concerning outcomes following percutaneous coronary intervention (PCI). Current guidelines lack specific diagnostic and therapeutic strategies tailored for this vulnerable group. Although composite inflammatory indices, 11,12 such as the Pan-Immune-Inflammation Value (PIV), have shown prognostic value in oncology and ST-elevation myocardial infarction (STEMI) patients, 13,14 their utility in predicting cardiovascular events after PCI in elderly patients remains unclear. 15 Therefore, this study aimed to comprehensively investigate the association between baseline PIV and long-term cardiovascular outcomes in a large cohort of elderly PCI patients, aiming to fill a critical gap in risk stratification for this vulnerable population. Materials and methods Study Population This retrospective study included 1426 elderly PCI patients (2019–2023). Exclusion criteria included: previous CABG/PCI; missing laboratory values; renal insufficiency; age 90 years; lost to follow-up. The study was approved by the institutional ethics committee. Data Collection The collection of baseline data was carried out in a systematic and standardized manner. Clinical variables, such as systolic blood pressure and diastolic blood pressure (measured at least twice every 5 minutes using an Omron HEM-7120 automatic oscillograph and taking the average), were carefully recorded. Biochemical results, including fasting blood glucose, lipid spectrum, liver and kidney function tests, and inflammatory markers (C-reactive protein), were all obtained from the central laboratory. All measurements were conducted using standardized automated analyzers, following the manufacturer's guidelines and quality control procedures. Information on discharge medications, including antiplatelet drugs, statins, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, and calcium channel blockers, was detailed, with the dosage and frequency recorded. Coronary angiography is used to determine the diseased blood vessels, based on the diseased coronary arteries and the degree of stenosis. Complications such as atrial fibrillation, hypertension, diabetes, and stroke have all been carefully verified through medical records and clinical literature. All patients underwent echocardiography with a standard ultrasound system (PHILIPS iE33), and Left Ventricular Ejection Fraction (LVEF) was measured using a modified biplane Simpson’s method. Left atrial anterior and posterior diameter (LAD), interventricular septum thickness (IVS), left ventricular end-diastolic diameter (LVEDD), and left ventricular end-systolic diameter (LVESD) were measured from the parasternal long-axis view. Endpoints All the patients underwent regular outpatient review and telephone follow-up until June 2024; the mean follow-up time was 362 ± 281 days. The primary endpoint was the major adverse cardiovascular events (MACE), and the secondary clinical end events were: cardiac death (CD), recurrent myocardial infarction (ReMI), target lesion revascularization (TLR), target vessel revascularization (TVR), and revascularization. In this study, CD was directly attributable to cardiac causes such as MI, heart failure, severe arrhythmia, and sudden cardiac death. MACE was the composite endpoint of CD, ReMI, and TVR. Statistics Based on PIV at admission, the baseline characteristics of the study population were divided into two groups: low PIV and high PIV. Continuous variables were represented as mean ± standard deviation or median and interquartile range, which is based on a continuous distribution of data: Student’s-test or Analysis of Variance (ANOVA) is used for normal distribution, and the Mann-Whitney test is used for abnormal distribution. Categorical variables were represented as absolute numbers and percentages, which were used the Chi-square test or Fisher’s exact test. To enhance the statistical robustness of the study, we performed a sample size power analysis based on prior effect size estimates and established significance thresholds. The diagnostic value of PIV to identify MACE, CD, and ReMI was investigated and compared via the areas under the curves (AUC) of receiver operating characteristic (ROC) curves. We used the Cox proportional hazards regression model to evaluate the prognostic value of PIV and corrected for potential confounding factors through multiple Cox stepwise regression analyses. Log-rank tests for the Kaplan-Meier survival curves were performed according to the stratification of different PIV levels. All data were analyzed by SPSS statistical software (SPSS 26.0) (IBM, New York, United States) and R programming version 4.1.1 (R Foundation, Vienna, Austria). In all statistical analyses, a two-tailed P 99.9% power to detect the observed effect (Cohen’s *d* = -0.69, α = 0.05), far exceeding the conventional 80% threshold. This ensures high confidence in our statistical conclusions. A total of 1426 elderly patients receiving primary PCI for coronary heart disease were included in this study (Fig. 1 ). The clinical baseline characteristics of the present study cohort are shown in Table 1 . Compared with the low-PIV group, men had a higher proportion of smokers, a higher history of combined MI and stroke, more use of diuretics, calcium receptor blockers, angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor antagonist (ARB), and angiotensin receptor enkephalin inhibitor (ARNI), and lower LVEF (P < 0.05, respectively). In terms of laboratory indicators, troponin I, CK, NT-pro BNP, and D-dimer levels were higher in the high-PIV group than in the low-PIV group, while fasting glucose and CK-MB total protein levels were lower than those in the low-PIV group (P < 0.01, respectively). Table 1 Clinical baseline characteristics of patients according to different levels of PIV Indicators Low PIV group (n = 713) High PIV group (n = 713) P-value Age (years) 80.36 ± 4.25 79.90 ± 3.86 0.014 Gender, male (%) 352(49.40) 370(51.60) 0.368 Systolic blood pressure (mmHg) 138.64 ± 20.51 141.00 ± 21.80 0.376 Diastolic blood pressure (mmHg) 76.05 ± 12.07 76.88 ± 11.90 0.626 Smoking history(%) 181(25.40) 143(20.00) 0.015 Medical history Atrial fibrillation (%) 57(8.00) 62(8.70) 0.644 Hypertension (%) 578(81.20) 570(79.80) 0.521 Diabetes mellitus(%) 240(33.70) 263(36.80) 0.217 MI(%) 393(55.20) 191(26.80) < 0.001 Stroke (%) 125(17.60) 177(24.80) 0.001 Echocardiographic parameters LVEF (%) 57.64 ± 10.01 54.12 ± 11.30 < 0.001 LAD (mm) 41.20 ± 5.90 41.39 ± 5.95 0.792 LVEDD (mm) 49.23 ± 5.84 49.09 ± 6.18 0.182 LVESD (mm) 30.65 ± 8.31 29.86 ± 8.20 0.444 IVS (mm) 9.55 ± 1.79 9.58 ± 1.79 0.532 Discharge medications ARNI (%) 365(51.30) 234(32.80) < 0.001 ACEI or ARB (%) 115(16.20) 246(34.50) < 0.001 Beta-blocker (%) 402(56.50) 421(59.00) 0.339 Statins (%) 661(92.80) 665(93.10) 0.824 Diuretics (%) 330(46.30) 270(37.80) 0.001 Calcium channel blocker (%) 274(34.70) 364(51.00) < 0.001 Biomarkers Glucose 7.21(5.91,9.73) 6.74(5.65,9.11) 0.001 Hemoglobin (g/L) 125.74 ± 19.30 126.59 ± 18.93 0.142 Troponin I (ng/ml) 0.019(0.01,0.22) 2.79(0.03,16.47) < 0.001 CK (U/L) 78(54.50,123.20) 111.10(61.00,309.85) < 0.001 CK-MB (U/L) 19.05(11.60,41.95) 12.25(9.00,16.90) < 0.001 NT-proBNP (ng/L) 430.90(157.68,1640.95) 1084.40(328.30,3915.60) < 0.001 D-dimer (ng/L) 197.18(0.43,730.52) 579.51(358.13,953.11) < 0.001 Total cholesterol (mmol/L) 4.44 ± 1.12 4.43 ± 1.13 0.281 Triglyceride(mmol/L) 1.22(0.92,1.63) 1.28(0.95,1.81) 0.060 LDL-C (mmol/l) 2.81 ± 0.91 2.76 ± 0.91 0.524 HDL-C (mmol/l) 1.13 ± 0.31 1.15 ± 0.31 0.526 TP (g/l) 65.65 ± 6.65 62.90 ± 6.31 0.029 Cr (µmol/l) 81.35(64.70,107.15) 78.8(64.25,100.68) 0.070 UA (µmol/L) 343.85(285.43,417.48) 343.90(279.00,422.73) 0.775 Diseased blood vessel Left main coronary artery (%) 69(9.70) 59(8.30) 0.346 Left artery descending (%) 682(95.80) 686(96.10) 0.780 Left circumflex (%) 617(86.70) 595(83.30) 0.079 Right coronary artery (%) 651(91.40) 665(93.10) 0.228 Multi vessel (%) 675(94.80) 678(95.00) 0.895 Prognosis of patients with different levels of PIV The median follow-up for this study was 362 ± 281 days, during which the incidence of the primary endpoint event MACE was higher in the high-PIV group than in the low-PIV group (high-PIV group 10.8% vs low-PIV group 5.1%, P<0.001). Moreover, in terms of secondary endpoint events, cardiac death occurred in the high-PIV group (high-PIV group 7.1% vs low-PIV group 2.8%, P<0.001), recurrence of ST-elevation myocardial infarction (high-PIV group 1.3% vs low-PIV group 0.1%, P=0.011), and all-cause mortality (high-PIV group 10.8% vs low-PIV group 4.5%, P=0.011) than in the low-PIV group. While the incidence of recurrent ST-elevation myocardial infarction (STEMI) was higher in the high-PIV group, there was no statistically significant difference between the two groups in the broader endpoint of recurrent myocardial infarction (ReMI), nor in target vessel revascularization (TVR) (Table 2, Figure 2). Table 2 Prognosis of patients with different levels of PIV PIV Low-PIV group (n = 713) High-PIV group (n = 713) P-value MACE 36(5.1) 77(10.8) < 0.001 CD 20(2.8) 51(7.1) < 0.001 STEMI 1(0.1) 9(1.3) 0.011 ACD 32(4.5) 77(10.8) < 0.001 TLR 5(0.7) 12(1.7) 0.088 TVR 8(1.1) 14(2.0) 0.198 ReMI 11(1.5) 17(2.4) 0.252 Prognostic Value of PIV By drawing ROC curves for predicting patient MACE, CD, and ReMI events, we found that PIV predicted MACE and CD over other indicators [MACE: 0.641 (95% CI: 0.588-0.694), CD: 0.661 (95%CI: 0.593-0.729)], ACD: 0.660 (95%CI: 0.604-0.716) (Figure 3). To further assess the predictive power, two models were developed: Model 1 combined a high PIV with the high-risk level in GRACE, while Model 2 combined a high PIV with an age of 80 years or above. The GRACE score consistently demonstrated the strongest predictive ability among all endpoints (MACE: 0.718, CD: 0.773, ReMI: 0.663). Although GRACE remained the strongest predictor, PIV alone showed comparable performance to composite models. The two composite models demonstrated moderate performance: Model 1 achieved AUCs of 0.626 (MACE), 0.655 (CD), and 0.589 (ReMI). Model 2 shows slightly lower AUC (0.606, 0.624, and 0.569, respectively). Notably, the performance of the simple PIV alone is comparable to that of the two composite models, especially in terms of ReMI prediction (PIV: 0.592 vs Model 1: 0.589 vs Model 2: 0.569) (Table 3). Table 3 Efficacy of different models in the diagnosis of MACE, CD, and ReMI AUC(95%CI) MACE CD ReMI PIV 0.641(0.588,0.694) 0.661(0.593,0.729) 0.592(0.491,0.692) GRACE 0.718(0.669,0.767) 0.773(0.718,0.827) 0.663(0.580,0.746) Age > = 80years 0.575(0.503,0.647) 0.613(0.524,0.703) 0.515(0.373,0.657) MODEL1 0.626(0.571,0.682) 0.655(0.588,0.722) 0.589(0.480,0.699) MODEL2 0.606(0.548,0.663) 0.624(0.554,0.695) 0.569(0.457,0.681) Multivariate regression analysis of COX associated with MACE Included covariates age, gender, and PIV, to build Model 1, multivariate Cox regression suggested that the PIV index (adjusted HR 2.089, 95% CI 1.403,3.112; P<0.001) is a risk factor for the primary endpoint event MACE (Table 4), after continued inclusion of patient prior history based on Model 1, building up the Model 2, suggesting that the PIV index is still a risk factor for MACE time (Model 2: adjusted HR 1.826, 95% CI 1.219,2.735; P=0.003); Then, based on Model 2, inclusion of patient laboratory indicators and medication during hospitalization, to build up the Model 3, the result prompt, the PIV index remains a risk factor for MACE events (HR1.572, 95% CI =1.040,2.377; P=0.032). Table 4 Multivariate Cox regression analysis of PIV index and MACE after PCI in elderly patients Model1 Model2 Model3 Variable HR(95%CI) P -value Adjusted HR(95%CI) P -value Adjusted HR(95%CI) P -value Low-PIV group 1(ref) 1(ref) 1(ref) High-PIV group 2.089 (1.403,3.112) < 0.001 1.826 (1.219,2.735) 0.003 1.572 (1.040,2.377) 0.032 Notes : Model 1:adjusted for age, and gender; Modle 2: adjusted for age, gender, hypertension, hyperlipidemia, previous MI, previous PCI, previous atrial fibrillation, and previous tumors; Modle 3: adjusted for age, gender, hypertension, hyperlipidemia, previous MI, previous PCI, previous atrial fibrillation, previous tumors, polyvascular lesions, TC, LDL-C, HDL-C, TG, aspirin, ACEI/ARB, calcium channel blocker, diuretics, and nicorandil. Abbreviations: HR, hazard ratio; CI, confidence interval; ref, reference; PCI, percutaneous coronary intervention; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blockers. Subgroups analyzed by PIV Subgroup analysis was conducted for clinically relevant factors, including age (= 80 years), gender (male or female), previous hypertension, diabetes, stroke, dyslipemia, and use of diuretics, nicorandil, and CCB during hospitalization. For the primary endpoint, the high-PIV group exhibited a comparable risk of MACE in the subgroup when compared to the low-PIV group. Notably, there was an interaction between the high PIV group and the history of diabetes mellitus on the occurrence of MACE events (interaction P=0.028). The risk of MACE events was increased 1.9-fold in patients who were treated with diuretics during hospitalization (HR 1.9, 95% CI 1.13,3.20; P=0.015) in the high-PIV group and 2.57-fold (HR 2.57, 95% CI 1.50,4.40; P=0.001) in patients who did not use CCB; However, the incidence of MACE was increased in both males and females, with or without a history of stroke in the high PIV group (P<0.05) (Figure4). Discussion In this large, real-world cohort study of elderly patients (age ≥75 years) undergoing PCI, we investigated the prognostic utility of the novel inflammatory biomarker, the Pan-Immune-Inflammation Value (PIV). The principal findings are as follows: (1) when compared to the low PIV group, Patients in the high PIV group combined with poor baseline levels, (2) Patients in the high PIV group had a higher incidence of endpoint events during the follow-up period, (3) The predictive accuracy of PIV for MACE and cardiac death, while moderate, was comparable to that of models combining PIV with other clinical features such as age ≥80 years, (4) After comprehensive adjustment for a wide array of potential confounders, including demographics, medical history, laboratory parameters, and discharge medications, a high PIV remained an independent predictor of MACE, (5) Subgroup analyses demonstrated the consistent association between high PIV and increased MACE risk across most clinically relevant subgroups. Atherosclerosis, the primary pathological basis of coronary artery disease, is characterized by chronic inflammation of the arterial wall. 15,1 6 Previous studies have shown that inflammatory processes contribute to atherosclerotic plaque formation, STEMI occurrence via inflammatory cell infiltration, myocardial dysfunction, accelerated fibrosis, and elevated pro-inflammatory cytokine release. 17,18 Moreover, acute coronary syndrome often results from plaque rupture and subsequent thrombosis, 19 a process exacerbated by increased circulating platelets and platelet activation–driven chemokine secretion, which further fuels local inflammation and thrombus formation. 2 0-23 According to the World Health Organization, individuals aged ≥75 years are classified as the “senior elderly. ” 24 The prevalence of coronary artery disease increases with age, 25-27 and elderly patients often present with multiple chronic conditions, leading to elevated risks of physical and cognitive impairment. 2 8,29 In recent years, composite inflammatory markers have gained attention for their prognostic value in various diseases. 13 However, data on post-PCI prognostic indicators specifically in the elderly remain scarce. Unlike traditional single inflammatory markers, PIV integrates neutrophil, lymphocyte, and platelet counts, offering a more comprehensive reflection of systemic inflammatory status and enhancing prognostic assessment in cardiovascular disease. 30,31 Initially applied in oncology, PIV has recently been validated in cardiovascular settings. 32-34 A meta-analysis in cancer patients indicated that elevated PIV was associated with significantly reduced survival. 35 In our study, patients with higher PIV were younger, had longer smoking histories, higher rates of prior myocardial infarction and stroke, lower ejection fractions, and less favorable laboratory profiles at admission. To our knowledge, this is the first study to systematically evaluate PIV’s prognostic value in elderly patients after PCI. Our results demonstrate that PIV outperforms other inflammatory indices such as the Systemic Immune-Inflammation Index (SII), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR) in predicting MACE. Previous studies have also linked high PIV with early-onset myocardial infarction, 36-38 corroborating our findings that elevated PIV is associated with higher risks of MACE, cardiac death, recurrent ST-segment elevation myocardial infarction, and all-cause mortality post-PCI. In multivariate Cox regression analyses, PIV remained a significant risk factor for MACE across sequential adjustment models: Model 1 (adjusted for age and gender): HR 2.089, 95% CI 1.403,3.112 P < 0.001; Model 2 (additionally adjusted for medical history): HR 1.826, 95% CI 1.219,2.735, P = 0.003; Model 3 (further adjusted for laboratory parameters and discharge medications): HR 1.572, 95% CI 1.040,2.377 P = 0.032. These results confirm that PIV is an independent predictor of MACE in elderly PCI patients. Furthermore, subgroup analyses consistently revealed higher MACE incidence in the high PIV group across most patient strata. It provides feasible and reliable prognostic information for elderly patients following PCI, highlighting its potential utility in risk stratification and long-term management in this vulnerable population. Limitations This study has several limitations that should be acknowledged. First, its single-center and retrospective design inherently carries the risk of selection bias and unmeasured confounding, which limits the generalizability of our findings. External validation in multi-center, prospective cohorts is essential to confirm the prognostic role of PIV in elderly PCI patients. Second, we only evaluated the baseline PIV value. The prognostic implications of dynamic changes in PIV after PCI and during follow-up remain unknown and represent an important avenue for future research. Finally, and most critically for clinical translation, the current lack of a universally validated and standardized PIV cut-off value significantly hinders its immediate broad clinical application. Our study used a data-driven cut-off, which may not be generalizable. Future large-scale studies must prioritize the establishment of standardized cut-offs before PIV can be recommended for routine clinical use. Conclusion In conclusion, our study establishes PIV as a readily available and independent prognostic marker for MACE in elderly patients undergoing PCI. It demonstrates moderate predictive value, which is comparable to, but not superior to, other composite inflammatory indices. Although its predictive ability is less robust than the GRACE score, elevated PIV levels consistently identified patients at higher risk. Therefore, PIV shows potential as a simple and complementary tool for risk stratification in this vulnerable population. Its integration into clinical practice, alongside and not in replacement of established scores, could help identify high-risk elderly PCI patients who may benefit from more intensive management and follow-up. Abbreviations PIV, Pan-Immune-Inflammation Value; PCI, percutaneous coronary intervention; MACE, major adverse cardiovascular events; CABG, Coronary Artery Bypass Grafting; LVEF, left ventricular ejection fraction; LAD, left atrial anterior and posterior diameter; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; IVS, interventricular septum thickness; ARNI, angiotensin receptor-neprilysin inhibitors; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TP, total protein; NT-proBNP, N-terminal prohormone brain natriuretic peptide; Cr, creatinine; UA, uric acid; TC, total cholesterol; TG, triglycerides; STEMI, ST-elevated myocardial infarction; ACD, all-cause death; TLR, target lesion revascularization; TVR, target vessel revascularization; ReMI, recurrent myocardial infarction; LMR: lymphocyte to monocyte ratio; MLR: monocyte to lymphocyte ratio; SII: systemic immune-inflammation index; PLR: platelet to lymphocyte ratio; HR, hazard ratio; CI, confidence interval; ref, reference. Declarations Ethics approval and consent to participate The studies involving human participants received approval from the Ethics Committee of the Second Hospital of Tianjin Medical University (KY2023053-01). Informed consent was waived due to the retro-spective nature of the study. Consent for publication Not applicable. Availability of data and materials The datasets generated during and/or analyzed during the current study are not publicly available as the data also forms part of another ongoing study but are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. Funding This research was supported by the National Natural Science Foundation of China (82370332, 82470527, 82100342), Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-006B). Authors' contributions J.D., T.L., and X.Z. contributed to the conception or design of the work. J.D. and M.Z. contributed to the acquisition, analysis, or interpretation of data for the work. J.D. and Z.Z. drafted the manuscript. L.C., K.C., and T.L. critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of work, ensuring integrity and accuracy. Acknowledgments We are grateful to the subjects who participated in the study and to the physicians' assistance in this study. References Neumann JT, Thao LTP, Callander E, et al. Cardiovascular risk prediction in healthy older people. GeroScience. 2022;44(1):403–13. Nayor M, Brown KJ, Vasan RS. The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk. Circ Res. 2021;128(2):287–303. Jebari-Benslaiman S, Galicia-García U, Larrea-Sebal A, et al. Pathophysiology of Atherosclerosis. Int J Mol Sci. 2022;23(6):3346. Wolf D, Ley K. Immunity and Inflammation in Atherosclerosis. Circ Res. 2019;124(2):315–27. Ajoolabady A, Pratico D, Lin L, et al. 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Murat B, Murat S, Ozgeyik M, Comparison of pan-immune-inflammation value with other inflammation markers of long-term survival after ST-segment elevation myocardial infarction. Eur J Clin Invest., Duignan JM et al. IJ. Zhang N, Aiyasiding X, Li WJ, et al. Neutrophil degranulation and myocardial infarction. Cell Commun Signal. 2022;20(1):50. Fucà G, Guarini V, Antoniotti C, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. Br J Cancer. 2020;123(3):403–9. Ligorio F, Fucà G, Zattarin E, et al. The Pan-Immune-Inflammation-Value Predicts the Survival of Patients with Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Advanced Breast Cancer Treated with First-Line Taxane-Trastuzumab-Pertuzumab. Cancers (Basel). 2021;13(8):1964. Wang Q, Zhong W, Xiao Y, et al. Pan-immune-inflammation value predicts immunotherapy response and reflects local antitumor immune response in rectal cancer. Cancer Sci. 2025;116(2):350–66. Guven DC, Sahin TK, Erul E, et al. The Association between the Pan-Immune-Inflammation Value and Cancer Prognosis: A Systematic Review and Meta-Analysis. Cancers (Basel). 2022;14(11):2675. Ma R, Ren J, Chen X, Li X, Zhao Y, Ding Y. Association between pan-immune-inflammation value and coronary heart disease in elderly population: a cross-sectional study. Front Cardiovasc Med. 2025;12:1538643. Akkaya E. Association of RDW-Albumin Ratio, TG-Glucose Index, and PIV with Coronary Artery Disease. J Clin Med. 2024;13(23):7003. Han W, Xiong N, Zhong R, et al. CYP2C19 Poor Metabolizer Status and High System Inflammation Response Index are Independent Risk Factors for Premature Myocardial Infarction: A Hospital-Based Retrospective Study. Int J Gen Med. 2024;17:4959–69. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 27 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Editor invited by journal 30 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 29 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7917290","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546421355,"identity":"95ea5029-f0a6-46a9-a4c1-fc4a3787f2ea","order_by":0,"name":"Junying Duan","email":"","orcid":"","institution":"Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease，, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junying","middleName":"","lastName":"Duan","suffix":""},{"id":546421357,"identity":"6004a9ea-73dd-455d-b6a2-30f13e089919","order_by":1,"name":"Xue Zhang","email":"","orcid":"","institution":"Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease，, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Zhang","suffix":""},{"id":546421358,"identity":"79532e1c-f103-4c4c-8de6-7852863c53c1","order_by":2,"name":"Zizhao Zhang","email":"","orcid":"","institution":"Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease，, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zizhao","middleName":"","lastName":"Zhang","suffix":""},{"id":546421359,"identity":"ef2f437c-64cb-45ac-8373-5f5785fff023","order_by":3,"name":"Mengzhu Zhou","email":"","orcid":"","institution":"Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease，, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengzhu","middleName":"","lastName":"Zhou","suffix":""},{"id":546421361,"identity":"3cba7c24-edd2-4b21-8030-093452d3c2ee","order_by":4,"name":"Kangyin Chen","email":"","orcid":"","institution":"Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease，, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kangyin","middleName":"","lastName":"Chen","suffix":""},{"id":546421362,"identity":"c8a464f0-21ec-4cfb-b93f-989e2ff6ffa2","order_by":5,"name":"Tong Liu","email":"","orcid":"","institution":"Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease，, the Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Liu","suffix":""},{"id":546421363,"identity":"0f8b3fd1-ea94-45b7-85ec-f898bb37ae4a","order_by":6,"name":"Lijun 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11:41:10","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28271,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/0669146bf1914ce8904de7a5.png"},{"id":96555932,"identity":"afbf5241-bc88-48c3-a44c-000df65cc873","added_by":"auto","created_at":"2025-11-23 11:41:11","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95424,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/8af7180dc4ac5a7dd1f1b3fe.png"},{"id":96604676,"identity":"d3776bab-963c-496b-99be-cf7aca653959","added_by":"auto","created_at":"2025-11-24 09:14:33","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102900,"visible":true,"origin":"","legend":"","description":"","filename":"75178ffd6d8a45b2aef9a19984b7e0511structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/88e53d8fa7e5a768490422ee.xml"},{"id":96604487,"identity":"4127eb29-1d5e-4bd1-857c-fa0534b58383","added_by":"auto","created_at":"2025-11-24 09:14:02","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112188,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/9af3359e0b98dedc367c8a8d.html"},{"id":96555919,"identity":"4e286a47-42dc-44be-915b-f30c939d867b","added_by":"auto","created_at":"2025-11-23 11:41:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175580,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e PCI, Percutaneous Coronary Intervention; PIV, Pan-immune-inflammation value; CABG, Coronary Artery Bypass Grafting.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/46972a433dbd6880a7b36967.png"},{"id":96555921,"identity":"9a1d1cde-0069-48fc-9039-4f7cba6062f9","added_by":"auto","created_at":"2025-11-23 11:41:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31140,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve in terms of cardiovascular events (MACE) (A), cardiac death (CD) (B), ST-elevation myocardial infarction (STEMI) (C), all-cause death (ACD) (D) in different PIV levels in elderly patients after PCI.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/ba8ee07ffe0f3e7769e0303c.jpeg"},{"id":96555929,"identity":"f811ef69-ab4d-4665-b488-d4901e17acd7","added_by":"auto","created_at":"2025-11-23 11:41:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93728,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic (ROC) curve of the diagnostic ability for Adverse Cardiac Events major adverse cardiovascular events (MACE) (A), cardiac death (CD) (B), all-cause death (ACD) (C) of PIV, and other indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e LMR: lymphocyte to monocyte ratio; MLR: monocyte to lymphocyte ratio; SII: systemic immune-inflammation index; PLR: platelet to lymphocyte ratio.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/a93648f0dab3165c07afff0f.jpeg"},{"id":96555924,"identity":"555c19b8-c4de-4212-80a3-8b2820d052db","added_by":"auto","created_at":"2025-11-23 11:41:10","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98181,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the predictive value of high and low PIV for MACE events in elderly patients after PCI.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/cab90f519b211fc6e2919dd2.jpeg"},{"id":104250868,"identity":"12b35254-5611-4ddf-a7bc-9f8516bfbb87","added_by":"auto","created_at":"2026-03-09 16:10:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7917290/v1/7cdbb443-4060-494b-8895-c42ec055c717.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Pan-Immune-Inflammation Value (PIV) predicts major adverse cardiovascular events in elderly patients undergoing percutaneous coronary intervention: a real-world study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is a series of cardiovascular disorders caused by the formation of plaques and thrombosis within blood vessels. The World Health Organization (WHO) has classified it as a very serious non-communicable disease, aiming to reduce the mortality rate related to CVD by 25% by 2025.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e To achieve this goal, it is particularly important to predict the occurrence of subsequent cardiovascular events in high-risk populations for CVD. Atherosclerosis (AS) is the primary pathological basis of coronary artery disease and represents a chronic inflammatory state. Numerous clinical and experimental studies have confirmed that inflammation is a key driving factor in its progression.\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The accumulation and migration of inflammatory cells play an indispensable role in the formation and development of arterial plaques.\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e With the intensification of global aging, elderly populations are increasingly exposed to heightened cardiovascular risks.\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, they remain underrepresented in clinical studies, particularly concerning outcomes following percutaneous coronary intervention (PCI). Current guidelines lack specific diagnostic and therapeutic strategies tailored for this vulnerable group. Although composite inflammatory indices,\u003csup\u003e11,12\u003c/sup\u003e such as the Pan-Immune-Inflammation Value (PIV), have shown prognostic value in oncology and ST-elevation myocardial infarction (STEMI) patients, \u003csup\u003e13,14\u003c/sup\u003e their utility in predicting cardiovascular events after PCI in elderly patients remains unclear.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Therefore, this study aimed to comprehensively investigate the association between baseline PIV and long-term cardiovascular outcomes in a large cohort of elderly PCI patients, aiming to fill a critical gap in risk stratification for this vulnerable population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis retrospective study included 1426 elderly PCI patients (2019\u0026ndash;2023). Exclusion criteria included: previous CABG/PCI; missing laboratory values; renal insufficiency; age\u0026thinsp;\u0026lt;\u0026thinsp;75 or \u0026gt;\u0026thinsp;90 years; lost to follow-up. The study was approved by the institutional ethics committee.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThe collection of baseline data was carried out in a systematic and standardized manner. Clinical variables, such as systolic blood pressure and diastolic blood pressure (measured at least twice every 5 minutes using an Omron HEM-7120 automatic oscillograph and taking the average), were carefully recorded. Biochemical results, including fasting blood glucose, lipid spectrum, liver and kidney function tests, and inflammatory markers (C-reactive protein), were all obtained from the central laboratory. All measurements were conducted using standardized automated analyzers, following the manufacturer's guidelines and quality control procedures. Information on discharge medications, including antiplatelet drugs, statins, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, and calcium channel blockers, was detailed, with the dosage and frequency recorded. Coronary angiography is used to determine the diseased blood vessels, based on the diseased coronary arteries and the degree of stenosis. Complications such as atrial fibrillation, hypertension, diabetes, and stroke have all been carefully verified through medical records and clinical literature. All patients underwent echocardiography with a standard ultrasound system (PHILIPS iE33), and Left Ventricular Ejection Fraction (LVEF) was measured using a modified biplane Simpson\u0026rsquo;s method. Left atrial anterior and posterior diameter (LAD), interventricular septum thickness (IVS), left ventricular end-diastolic diameter (LVEDD), and left ventricular end-systolic diameter (LVESD) were measured from the parasternal long-axis view.\u003c/p\u003e\n\u003ch3\u003eEndpoints\u003c/h3\u003e\n\u003cp\u003eAll the patients underwent regular outpatient review and telephone follow-up until June 2024; the mean follow-up time was 362\u0026thinsp;\u0026plusmn;\u0026thinsp;281 days. The primary endpoint was the major adverse cardiovascular events (MACE), and the secondary clinical end events were: cardiac death (CD), recurrent myocardial infarction (ReMI), target lesion revascularization (TLR), target vessel revascularization (TVR), and revascularization. In this study, CD was directly attributable to cardiac causes such as MI, heart failure, severe arrhythmia, and sudden cardiac death. MACE was the composite endpoint of CD, ReMI, and TVR.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eBased on PIV at admission, the baseline characteristics of the study population were divided into two groups: low PIV and high PIV. Continuous variables were represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median and interquartile range, which is based on a continuous distribution of data: Student\u0026rsquo;s-test or Analysis of Variance (ANOVA) is used for normal distribution, and the Mann-Whitney test is used for abnormal distribution. Categorical variables were represented as absolute numbers and percentages, which were used the Chi-square test or Fisher\u0026rsquo;s exact test. To enhance the statistical robustness of the study, we performed a sample size power analysis based on prior effect size estimates and established significance thresholds.\u003c/p\u003e\u003cp\u003eThe diagnostic value of PIV to identify MACE, CD, and ReMI was investigated and compared via the areas under the curves (AUC) of receiver operating characteristic (ROC) curves. We used the Cox proportional hazards regression model to evaluate the prognostic value of PIV and corrected for potential confounding factors through multiple Cox stepwise regression analyses. Log-rank tests for the Kaplan-Meier survival curves were performed according to the stratification of different PIV levels. All data were analyzed by SPSS statistical software (SPSS 26.0) (IBM, New York, United States) and R programming version 4.1.1 (R Foundation, Vienna, Austria). In all statistical analyses, a two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eA post hoc power analysis confirmed that our sample size (n\u0026thinsp;=\u0026thinsp;713 per group) provided \u0026gt;\u0026thinsp;99.9% power to detect the observed effect (Cohen\u0026rsquo;s *d* = -0.69, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05), far exceeding the conventional 80% threshold. This ensures high confidence in our statistical conclusions. A total of 1426 elderly patients receiving primary PCI for coronary heart disease were included in this study (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The clinical baseline characteristics of the present study cohort are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with the low-PIV group, men had a higher proportion of smokers, a higher history of combined MI and stroke, more use of diuretics, calcium receptor blockers, angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor antagonist (ARB), and angiotensin receptor enkephalin inhibitor (ARNI), and lower LVEF (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). In terms of laboratory indicators, troponin I, CK, NT-pro BNP, and D-dimer levels were higher in the high-PIV group than in the low-PIV group, while fasting glucose and CK-MB total protein levels were lower than those in the low-PIV group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical baseline characteristics of patients according to different levels of PIV\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow PIV\u003c/p\u003e\n \u003cp\u003egroup (n\u0026thinsp;=\u0026thinsp;713)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh PIV group\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;713)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, male (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352(49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370(51.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood\u003c/p\u003e\n \u003cp\u003epressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.64\u0026thinsp;\u0026plusmn;\u0026thinsp;20.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.00\u0026thinsp;\u0026plusmn;\u0026thinsp;21.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood\u003c/p\u003e\n \u003cp\u003epressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181(25.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMedical history\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtrial fibrillation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(8.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e578(81.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e570(79.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240(33.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263(36.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMI(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e393(55.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191(26.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125(17.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177(24.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEchocardiographic parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.64\u0026thinsp;\u0026plusmn;\u0026thinsp;10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.20\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.23\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.09\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVESD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVS (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDischarge medications\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARNI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365(51.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234(32.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEI or ARB (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115(16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246(34.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta-blocker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402(56.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421(59.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e661(92.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e665(93.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiuretics (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330(46.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270(37.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium channel\u003c/p\u003e\n \u003cp\u003eblocker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274(34.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364(51.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBiomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.21(5.91,9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.74(5.65,9.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.74\u0026thinsp;\u0026plusmn;\u0026thinsp;19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.59\u0026thinsp;\u0026plusmn;\u0026thinsp;18.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTroponin I (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019(0.01,0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79(0.03,16.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(54.50,123.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.10(61.00,309.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK-MB (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.05(11.60,41.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.25(9.00,16.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNT-proBNP (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430.90(157.68,1640.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1084.40(328.30,3915.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD-dimer (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197.18(0.43,730.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e579.51(358.13,953.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003cp\u003e(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22(0.92,1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28(0.95,1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.90\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr (\u0026micro;mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.35(64.70,107.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.8(64.25,100.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343.85(285.43,417.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343.90(279.00,422.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDiseased blood vessel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft main coronary\u003c/p\u003e\n \u003cp\u003eartery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(9.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft artery\u003c/p\u003e\n \u003cp\u003edescending (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682(95.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e686(96.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft circumflex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e617(86.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e595(83.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight coronary\u003c/p\u003e\n \u003cp\u003eartery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651(91.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e665(93.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti vessel (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e675(94.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e678(95.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrognosis of patients with different levels of PIV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe median follow-up for this study was 362 \u0026plusmn; 281 days, during which the incidence of the primary endpoint event MACE was higher in the high-PIV group than in the low-PIV group (high-PIV group 10.8% vs low-PIV group 5.1%, P\u0026lt;0.001). Moreover, in terms of secondary endpoint events, cardiac death occurred in the high-PIV group (high-PIV group 7.1% vs low-PIV group 2.8%, P\u0026lt;0.001), recurrence of ST-elevation myocardial infarction (high-PIV group 1.3% vs low-PIV group 0.1%, P=0.011), and all-cause mortality (high-PIV group 10.8% vs low-PIV group 4.5%, P=0.011) than in the low-PIV group. While the incidence of recurrent ST-elevation myocardial infarction (STEMI) was higher in the high-PIV group, there was no statistically significant difference between the two groups in the broader endpoint of recurrent myocardial infarction (ReMI), nor in target vessel revascularization (TVR) (Table 2, Figure 2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrognosis of patients with different levels of PIV\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePIV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow-PIV group (n\u0026thinsp;=\u0026thinsp;713)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh-PIV group (n\u0026thinsp;=\u0026thinsp;713)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrognostic Value of PIV\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBy drawing ROC curves for predicting patient MACE, CD, and ReMI events, we found that PIV predicted MACE and CD over other indicators [MACE: 0.641 (95% CI: 0.588-0.694), CD: 0.661 (95%CI: 0.593-0.729)], ACD: 0.660 (95%CI: 0.604-0.716) (Figure 3). To further assess the predictive power, two models were developed: Model 1 combined a high PIV with the high-risk level in GRACE, while Model 2 combined a high PIV with an age of 80 years or above. The GRACE score consistently demonstrated the strongest predictive ability among all endpoints (MACE: 0.718, CD: 0.773, ReMI: 0.663). Although GRACE remained the strongest predictor, PIV alone showed comparable performance to composite models. The two composite models demonstrated moderate performance: Model 1 achieved AUCs of 0.626 (MACE), 0.655 (CD), and 0.589 (ReMI). Model 2 shows slightly lower AUC (0.606, 0.624, and 0.569, respectively). Notably, the performance of the simple PIV alone is comparable to that of the two composite models, especially in terms of ReMI prediction (PIV: 0.592 vs Model 1: 0.589 vs Model 2: 0.569) (Table 3).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEfficacy of different models in the diagnosis of MACE, CD, and ReMI\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAUC(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReMI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.641(0.588,0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.661(0.593,0.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592(0.491,0.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.718(0.669,0.767)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.773(0.718,0.827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.663(0.580,0.746)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;80years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.575(0.503,0.647)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.613(0.524,0.703)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.515(0.373,0.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMODEL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.626(0.571,0.682)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.655(0.588,0.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589(0.480,0.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMODEL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.606(0.548,0.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624(0.554,0.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569(0.457,0.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMultivariate regression analysis of COX associated with MACE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIncluded covariates age, gender, and PIV, to build Model 1, multivariate Cox regression suggested that the PIV index (adjusted HR 2.089, 95% CI 1.403,3.112; P\u0026lt;0.001) is a risk factor for the primary endpoint event MACE (Table 4), after continued inclusion of patient prior history based on Model 1, building up the Model 2, suggesting that the PIV index is still a risk factor for MACE time (Model 2: adjusted HR 1.826, 95% CI 1.219,2.735; P=0.003); Then, based on Model 2, inclusion of patient laboratory indicators and medication during hospitalization, to build up the Model 3, the result prompt, the PIV index remains a risk factor for MACE events (HR1.572, 95% CI =1.040,2.377; P=0.032).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate Cox regression analysis of PIV index and MACE after PCI in elderly patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted HR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted HR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-PIV group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-PIV group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.089\u003c/p\u003e\n \u003cp\u003e(1.403,3.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.826 (1.219,2.735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.572 (1.040,2.377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Model 1:adjusted for age, and gender; Modle 2: adjusted for age, gender, hypertension, hyperlipidemia, previous MI, previous PCI, previous atrial fibrillation, and previous tumors; Modle 3: adjusted for age, gender, hypertension, hyperlipidemia, previous MI, previous PCI, previous atrial fibrillation, previous tumors, polyvascular lesions, TC, LDL-C, HDL-C, TG, aspirin, ACEI/ARB, calcium channel blocker, diuretics, and nicorandil.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHR, hazard ratio; CI, confidence interval; ref, reference; PCI, percutaneous coronary intervention; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blockers.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubgroups analyzed by PIV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSubgroup analysis was conducted for clinically relevant factors, including age (\u0026lt; 80 years or \u0026gt;= 80 years), gender (male or female), previous hypertension, diabetes, stroke, dyslipemia, and use of diuretics, nicorandil, and CCB during hospitalization. For the primary endpoint, the high-PIV group exhibited a comparable risk of MACE in the subgroup when compared to the low-PIV group. Notably, there was an interaction between the high PIV group and the history of diabetes mellitus on the occurrence of MACE events (interaction P=0.028). The risk of MACE events was increased 1.9-fold in patients who were treated with diuretics during hospitalization (HR 1.9, 95% CI 1.13,3.20; P=0.015) in the high-PIV group and 2.57-fold (HR 2.57, 95% CI 1.50,4.40; P=0.001) in patients who did not use CCB; However, the incidence of MACE was increased in both males and females, with or without a history of stroke in the high PIV group (P\u0026lt;0.05) (Figure4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, real-world cohort study of elderly patients (age ≥75 years) undergoing PCI, we investigated the prognostic utility of the novel inflammatory biomarker, the Pan-Immune-Inflammation Value (PIV). The principal findings are as follows: (1) when compared to the low PIV group, Patients in the high PIV group combined with poor baseline levels, (2) Patients in the high PIV group had a higher incidence of endpoint events during the follow-up period, (3) The predictive accuracy of PIV for MACE and cardiac death, while moderate, was comparable to that of models combining PIV with other clinical features such as age ≥80 years, (4) After\u0026nbsp;comprehensive adjustment for a wide array of potential confounders, including demographics, medical history, laboratory parameters, and discharge medications, a high PIV remained an independent predictor of MACE,\u0026nbsp;(5) Subgroup analyses demonstrated the consistent association between high PIV and increased MACE risk across most clinically relevant subgroups.\u003c/p\u003e\n\u003cp\u003eAtherosclerosis, the primary pathological basis of coronary artery disease, is characterized by chronic inflammation of the arterial wall.\u003csup\u003e15,1\u003c/sup\u003e\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ePrevious studies have shown that inflammatory processes contribute to atherosclerotic plaque formation, STEMI occurrence via inflammatory cell infiltration, myocardial dysfunction, accelerated fibrosis, and elevated pro-inflammatory cytokine release.\u003csup\u003e17,18\u003c/sup\u003e Moreover, acute coronary syndrome often results from plaque rupture and subsequent thrombosis,\u003csup\u003e19\u003c/sup\u003e a process exacerbated by increased circulating platelets and platelet activation–driven chemokine secretion, which further fuels local inflammation and thrombus formation.\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e0-23\u003c/sup\u003e According to the World Health Organization, individuals aged ≥75 years are classified as the “senior elderly. ”\u003csup\u003e24\u003c/sup\u003e The prevalence of coronary artery disease increases with age,\u003csup\u003e25-27\u003c/sup\u003e and elderly patients often present with multiple chronic conditions, leading to elevated risks of physical and cognitive impairment.\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e8,29\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, composite inflammatory markers have gained attention for their prognostic value in various diseases.\u003csup\u003e13\u003c/sup\u003e However, data on post-PCI prognostic indicators specifically in the elderly remain scarce. Unlike traditional single inflammatory markers, PIV integrates neutrophil, lymphocyte, and platelet counts, offering a more comprehensive reflection of systemic inflammatory status and enhancing prognostic assessment in cardiovascular disease.\u003csup\u003e30,31\u003c/sup\u003e Initially applied in oncology, PIV has recently been validated in cardiovascular settings.\u003csup\u003e32-34\u0026nbsp;\u003c/sup\u003eA meta-analysis in cancer patients indicated that elevated PIV was associated with significantly reduced survival.\u003csup\u003e35\u003c/sup\u003e In our study, patients with higher PIV were younger, had longer smoking histories, higher rates of prior myocardial infarction and stroke, lower ejection fractions, and less favorable laboratory profiles at admission.\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first study to systematically evaluate PIV’s prognostic value in elderly patients after PCI. Our results demonstrate that PIV outperforms other inflammatory indices such as the Systemic Immune-Inflammation Index (SII), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR) in predicting MACE. Previous studies have also linked high PIV with early-onset myocardial infarction,\u003csup\u003e36-38\u003c/sup\u003e corroborating our findings that elevated PIV is associated with higher risks of MACE, cardiac death, recurrent ST-segment elevation myocardial infarction, and all-cause mortality post-PCI. In multivariate Cox regression analyses, PIV remained a significant risk factor for MACE across sequential adjustment models: Model 1 (adjusted for age and gender): HR 2.089, 95% CI 1.403,3.112 P \u0026lt; 0.001; Model 2 (additionally adjusted for medical history): HR 1.826, 95% CI 1.219,2.735, P = 0.003; Model 3 (further adjusted for laboratory parameters and discharge medications): HR 1.572, 95% CI 1.040,2.377 P = 0.032. These results confirm that PIV is an independent predictor of MACE in elderly PCI patients. Furthermore, subgroup analyses consistently revealed higher MACE incidence in the high PIV group across most patient strata. It provides feasible and reliable prognostic information for elderly patients following PCI, highlighting its potential utility in risk stratification and long-term management in this vulnerable population.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations that should be acknowledged. First, its single-center and retrospective design inherently carries the risk of selection bias and unmeasured confounding, which limits the generalizability of our findings. External validation in multi-center, prospective cohorts is essential to confirm the prognostic role of PIV in elderly PCI patients. Second, we only evaluated the baseline PIV value. The prognostic implications of dynamic changes in PIV after PCI and during follow-up remain unknown and represent an important avenue for future research. Finally, and most critically for clinical translation, the current lack of a universally validated and standardized PIV cut-off value significantly hinders its immediate broad clinical application. Our study used a data-driven cut-off, which may not be generalizable. Future large-scale studies must prioritize the establishment of standardized cut-offs before PIV can be recommended for routine clinical use.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study establishes PIV as a readily available and independent prognostic marker for MACE in elderly patients undergoing PCI. It demonstrates moderate predictive value, which is comparable to, but not superior to, other composite inflammatory indices. Although its predictive ability is less robust than the GRACE score, elevated PIV levels consistently identified patients at higher risk. Therefore, PIV shows potential as a simple and complementary tool for risk stratification in this vulnerable population. Its integration into clinical practice, alongside and not in replacement of established scores, could help identify high-risk elderly PCI patients who may benefit from more intensive management and follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePIV, Pan-Immune-Inflammation Value; PCI, percutaneous coronary intervention; MACE, major adverse cardiovascular events; CABG, Coronary Artery Bypass Grafting; LVEF, left ventricular ejection fraction; LAD, left atrial anterior and posterior diameter; LVEDD, left ventricular end-diastolic diameter; LVESD, left ventricular end-systolic diameter; IVS, interventricular septum thickness; ARNI, angiotensin receptor-neprilysin inhibitors; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TP, total protein; NT-proBNP, N-terminal prohormone brain natriuretic peptide; Cr, creatinine; UA, uric acid; TC, total cholesterol; TG, triglycerides; STEMI, ST-elevated myocardial infarction; ACD, all-cause death; TLR, target lesion revascularization; TVR, target vessel revascularization; ReMI, recurrent myocardial infarction; LMR: lymphocyte to monocyte ratio; MLR: monocyte to lymphocyte ratio; SII: systemic immune-inflammation index; PLR: platelet to lymphocyte ratio; HR, hazard ratio; CI, confidence interval; ref, reference.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants received approval from the Ethics Committee of the Second Hospital of Tianjin Medical University (KY2023053-01). Informed consent was waived due to the retro-spective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available as the data also forms part of another ongoing study but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China\u0026nbsp;(82370332,\u0026nbsp;82470527, 82100342),\u0026nbsp;Tianjin Key Medical Discipline Construction Project\u0026nbsp;(TJYXZDXK-3-006B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D., T.L.,\u0026nbsp;and X.Z. contributed to the conception or design of the work. J.D.\u0026nbsp;and M.Z. contributed to the acquisition, analysis, or interpretation of data for the work. J.D. and Z.Z. drafted the manuscript. L.C., K.C., and T.L. critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of work, ensuring integrity and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are grateful to the subjects who participated in the study and to the physicians' assistance in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNeumann JT, Thao LTP, Callander E, et al. Cardiovascular risk prediction in healthy older people. GeroScience. 2022;44(1):403\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNayor M, Brown KJ, Vasan RS. The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk. 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Int J Gen Med. 2024;17:4959\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular events, Percutaneous coronary intervention, Inflammation, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7917290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7917290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThe Pan-Immune-Inflammation Value (PIV), a novel inflammatory marker primarily studied in cancer, remains underinvestigated in elderly PCI patients. This study evaluates PIV's prognostic value for risk stratification and personalized treatment in this population.\u003c/p\u003e\u003ch2\u003ePatients and methods:\u003c/h2\u003e\u003cp\u003eIn our study, we enrolled 1426 elderly PCI patients (age\u0026thinsp;\u0026ge;\u0026thinsp;75 years) between 2019 and 2023. Patients were divided into low- and high-PIV groups based on the optimal cut-off value determined by receiver operating characteristic (ROC) curve analysis. The primary endpoint was the incidence of major adverse cardiovascular events (MACE), comprising cardiac death, recurrent myocardial infarction, and target vessel revascularization. Secondary endpoints included the individual components of MACE. Cox regression and ROC analyses were employed to evaluate the independent prognostic value of PIV.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePatients in the high-PIV group had a more adverse clinical profile at baseline. During a median follow-up of 362 days, the high-PIV group experienced a significantly higher incidence of MACE (10.8% vs. 5.1%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cardiac death (7.1% vs. 2.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and all-cause mortality (10.8% vs. 4.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariable Cox regression confirmed PIV as an independent predictor of MACE after adjusting for confounders (Model 3: HR 1.572, 95% CI 1.040,2.377, P\u0026thinsp;=\u0026thinsp;0.032). ROC analysis showed that PIV had superior predictive ability for MACE (AUC\u0026thinsp;=\u0026thinsp;0.641) compared to models combining PIV with age\u0026thinsp;\u0026ge;\u0026thinsp;80 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePIV serves as a simple, potent, and independent prognostic biomarker for MACE in elderly patients following PCI. Its integration into clinical risk stratification could help identify high-risk patients who may benefit from more intensive management.\u003c/p\u003e","manuscriptTitle":"The Pan-Immune-Inflammation Value (PIV) predicts major adverse cardiovascular events in elderly patients undergoing percutaneous coronary intervention: a real-world study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:41:06","doi":"10.21203/rs.3.rs-7917290/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-27T05:15:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T16:56:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T15:48:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T12:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269630978987992712845347102528006126301","date":"2025-11-12T11:14:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62446770654461340075172732641710876704","date":"2025-11-11T20:54:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257427422273652676523332707993540619733","date":"2025-11-11T20:52:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T20:14:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T20:10:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-30T05:12:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T10:49:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-10-29T10:24:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c615290b-8200-4dbe-a448-d5f0710f0fe0","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:06:15+00:00","versionOfRecord":{"articleIdentity":"rs-7917290","link":"https://doi.org/10.1186/s12872-026-05679-y","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2026-03-04 15:59:02","publishedOnDateReadable":"March 4th, 2026"},"versionCreatedAt":"2025-11-23 11:41:06","video":"","vorDoi":"10.1186/s12872-026-05679-y","vorDoiUrl":"https://doi.org/10.1186/s12872-026-05679-y","workflowStages":[]},"version":"v1","identity":"rs-7917290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7917290","identity":"rs-7917290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}