Comparative and Incremental Prognostic Value of the Naples Prognostic Score Beyond EuroSCORE II in Isolated Coronary Artery Bypass Grafting

Comparative and Incremental Prognostic Value of the Naples Prognostic Score Beyond EuroSCORE II in Isolated Coronary Artery Bypass Grafting | 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 Comparative and Incremental Prognostic Value of the Naples Prognostic Score Beyond EuroSCORE II in Isolated Coronary Artery Bypass Grafting Sadiye Deniz Özsoy, Murat Mert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9382426/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Recent studies have highlighted the prognostic significance of the Naples Prognostic Score (NPS) in patients undergoing coronary artery bypass grafting (CABG). However, its comparative performance against other nutritional-inflammatory indices and its incremental value beyond established risk models such as EuroSCORE II are not fully established. Methods This retrospective cohort study included 1,205 adult patients undergoing isolated on-pump CABG between 2015 and 2023 at a tertiary centre. The NPS (0–4) was calculated from preoperative laboratory values and analysed as a continuous variable and in predefined categories (0, 1–2, 3–4). The primary endpoint was 1-year all-cause mortality. Discriminatory performance was assessed using ROC analysis with bootstrap confidence intervals and DeLong’s test. Incremental value beyond EuroSCORE II was evaluated using a combined logistic regression model, with net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results One-year mortality increased across NPS groups (2.3%, 5.1%, and 13.0%; p < 0.001). In multivariable analysis adjusting for age, left ventricular ejection fraction, and diabetes mellitus, NPS remained independently associated with mortality (adjusted OR 1.49 per point increase, 95% CI 1.18–1.84; p < 0.001). The NPS demonstrated modest discrimination (AUC 0.670), comparable to other nutritional-inflammatory indices, whereas EuroSCORE II showed higher discrimination (AUC 0.791). A combined model including NPS and EuroSCORE II improved discrimination (AUC 0.839). Among EuroSCORE II low-risk patients, those with NPS 3–4 had significantly higher 1-year mortality than those with NPS ≤ 2 (5.2% vs. 1.9%; OR 2.85, 95% CI 1.36–5.97; p = 0.002). Conclusion The preoperative NPS is independently associated with 1-year mortality after isolated CABG and provides modest but consistent incremental prognostic value beyond EuroSCORE II, although prospective multicentre validation is needed. Naples Prognostic Score coronary artery bypass grafting nutritional status inflammation EuroSCORE II mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Malnutrition and systemic inflammation are well-established determinants of adverse outcomes in cardiovascular surgery. Preoperative hypoalbuminaemia, low cholesterol, and elevated inflammatory biomarkers have each been associated with increased morbidity, prolonged recovery, and mortality following coronary artery bypass grafting (CABG) [ 1 – 4 ]. Despite this recognition, widely used risk models — including EuroSCORE II and the Society of Thoracic Surgeons (STS) score — predominantly incorporate demographic and procedural variables and do not include nutritional or inflammatory parameters, as they were designed primarily as operative risk tools [ 5 – 8 ]. The Naples Prognostic Score (NPS) is a composite index that simultaneously captures nutritional depletion (serum albumin, total cholesterol) and inflammatory burden (neutrophil-to-lymphocyte ratio [NLR], lymphocyte-to-monocyte ratio [LMR]) within a single score [ 9 ]. Serum albumin and cholesterol are not purely nutritional markers — both may also reflect illness severity, systemic inflammation, and frailty more broadly, which may partly explain their prognostic relevance in surgical populations. Originally developed and validated in gastrointestinal oncology [ 10 , 11 ], the NPS has since shown prognostic value across diverse non-oncological settings, including coronary artery disease, heart failure, and cerebrovascular disease [ 12 – 17 ]. In the cardiac surgical context, isolated reports have linked the NPS to saphenous vein graft patency and postoperative atrial fibrillation after CABG [ 18 , 19 ], and a recent study confirmed its association with short- and long-term mortality following isolated CABG [ 29 ]. While emerging evidence supports the association between the Naples Prognostic Score and adverse outcomes following CABG, several important gaps remain. Specifically, the relative performance of NPS compared with other commonly used nutritional-inflammatory indices has not been fully clarified, and its incremental value beyond established risk models such as EuroSCORE II requires more comprehensive evaluation. Therefore, the aims of the present study were to assess the association between preoperative NPS and 1-year mortality, to directly compare its discriminatory performance with multiple nutritional-inflammatory indices, and to evaluate its incremental prognostic value beyond EuroSCORE II in a large, well-characterised cohort of patients undergoing isolated CABG. Materials and Methods 1. Study Design and Population This was a retrospective cohort study conducted at the Cardiology Institute of Istanbul University-Cerrahpaşa, a tertiary referral centre. A total of 1,527 consecutive adult patients who underwent CABG between January 2015 and February 2023 were screened for inclusion. Patients were excluded if they underwent concomitant valve surgery, aortic surgery, or any other simultaneous cardiac procedure; if the operation was a redo procedure; if urgent or emergency surgery was performed; or if off-pump CABG was performed. After applying exclusion criteria, 1,205 patients undergoing isolated elective on-pump CABG were included in the final analysis (Fig. 1). The study was conducted in accordance with the Declaration of Helsinki and was approved by the Istanbul University-Cerrahpaşa Non-Interventional Clinical Research Ethics Committee (approval no: 2024/21; date: 30 January 2024; document no: E-74555795-050.04-902443). Clinical trial number: Not applicable. All data were analysed retrospectively after ethics approval was obtained; this practice is consistent with the institutional policy for non-interventional retrospective research at our centre. Individual informed consent was waived given the retrospective design. Figure 1. Flowchart Patient selection flowchart. A total of 1,527 patients were screened; 322 were excluded (all due to concomitant cardiac procedures). The final study population comprised 1,205 patients, stratified by preoperative Naples Prognostic Score (NPS). Follow-up for 1-year all-cause mortality was 100% complete. 2. NPS Calculation and Group Definition The NPS was calculated from preoperative laboratory values collected within 24 hours before surgery. One point was assigned for each of the following: serum albumin < 4.0 g/dL, total cholesterol < 180 mg/dL, NLR ≥ 2.96, and LMR ≤ 4.44. The total score (0–4) was used to stratify patients: Group 1 (score = 0, n = 86), Group 2 (score = 1–2, n = 748), and Group 3 (score = 3–4, n = 371). The component thresholds and three-tier group stratification were adopted a priori from the original NPS publication by Galizia et al. [ 9 ] and were not derived from the present cohort. 3. Nutritional and Inflammatory Indices and EuroSCORE II Six nutritional-inflammatory indices were compared against the NPS: the Prognostic Nutritional Index (PNI = 10 × albumin [g/dL] + 0.005 × lymphocyte count [/mm³]), the Controlling Nutritional Status (CONUT) score [ 20 , 21 ], NLR, Systemic Immune-Inflammation Index (SII = platelet × neutrophil / lymphocyte), Systemic Inflammation Response Index (SIRI = neutrophil × monocyte / lymphocyte), and LMR. EuroSCORE II was calculated using the standard online calculator and categorised into low, intermediate, and high risk groups based on predicted mortality: low risk (< 2%), intermediate risk (2–<6%), and high risk (≥ 6%). 4. Outcomes The primary endpoint was 1-year all-cause mortality. Secondary endpoints included in-hospital mortality, 1-month mortality, 6-month mortality, acute kidney injury (AKI), renal replacement therapy (RRT), stroke, new-onset atrial fibrillation (AF), prolonged mechanical ventilation (> 48 hours), intra-aortic balloon pump (IABP) use, low cardiac output syndrome, major adverse cardiac events (MACE), reoperation within 24 hours, and ICU and total hospital length of stay. Postoperative outcomes were defined according to Society of Thoracic Surgeons criteria where applicable [ 22 ]. Acute kidney injury was defined as a postoperative increase in serum creatinine of ≥ 0.3 mg/dL within 48 hours or ≥ 1.5-fold from baseline within 7 days, consistent with KDIGO 2012 criteria [ 23 ]. MACE was analysed as recorded in the institutional dataset and was not reconstructed post hoc. The institutional MACE variable included prospectively recorded major postoperative cardiovascular complications according to local database definitions. One-year all-cause mortality was ascertained from hospital records and the national death registry; follow-up completeness was 100%. 5. Statistical Analysis Continuous variables are presented as median (interquartile range) and categorical variables as counts (percentages). Group comparisons were performed using the Kruskal–Wallis test for continuous variables and the chi-square or Fisher’s exact test for categorical variables. Logistic regression was used to evaluate the association between NPS and 1-year all-cause mortality. The multivariable model included covariates selected a priori based on clinical relevance: age, left ventricular ejection fraction (LVEF), and diabetes mellitus. This streamlined model was chosen to test the independent association of NPS with mortality after adjustment for established predictors, consistent with the available event rate (88 deaths; events-per-variable [EPV] ≈ 22). Mortality variables were coded as binary outcomes (0 = alive, 1 = death). NPS was modelled as a continuous ordinal variable (per-point increase). To avoid structural collinearity, other nutritional-inflammatory indices were not entered simultaneously with NPS; their independent associations with mortality are reported from separate univariate analyses. Discriminatory performance was assessed using receiver operating characteristic (ROC) curve analysis; area under the curve (AUC) values are reported with 95% confidence intervals derived from 1,000 bootstrap samples. AUC comparisons were performed using DeLong’s method. To evaluate the incremental prognostic value of NPS beyond EuroSCORE II, a combined logistic regression model incorporating both scores was constructed; the predicted probability from this model was used as the composite score for ROC analysis. Left ventricular ejection fraction was entered into the regression model as a continuous variable coded in the direction of increasing risk (i.e., per 1% decrease in LVEF; higher OR values correspond to lower LVEF). Three pre-specified sensitivity analyses were conducted: (1) an expanded multivariable model additionally incorporating sex, serum creatinine, COPD, and BMI, to assess the robustness of the NPS association under broader confounder adjustment; (2) a CRP-adjusted model adding serum CRP as a covariate alongside NPS, to evaluate whether NPS provides prognostic information independent of direct acute-phase inflammatory burden; and (3) a calendar-year-adjusted model incorporating calendar year as a continuous covariate to account for potential temporal confounding due to evolving practice patterns over the eight-year study period. The Spearman rank correlation coefficient was used to assess the relationship between NPS and EuroSCORE II. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test for both the primary multivariable model and the combined NPS + EuroSCORE II model. Overall model accuracy was quantified using the Brier score and scaled Brier score (relative to a null model predicting the event rate for all patients). To quantify the incremental clinical value of adding NPS to EuroSCORE II, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated with 95% confidence intervals derived from 1,000 bootstrap resamples. To evaluate potential optimism in the combined model, bootstrap internal validation was performed (500 resamples); the optimism-corrected AUC was derived by subtracting the mean optimism from the apparent AUC. This study was reported in accordance with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) guideline. All analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY) with supplementary bootstrap and calibration analyses conducted in Python 3.10. A two-sided p-value < 0.05 was considered statistically significant. Results 1. Patient Characteristics A total of 1,205 patients were included in the final analysis (Group 1: n = 86 [7.1%], Group 2: n = 748 [62.1%], Group 3: n = 371 [30.8%]). The median age was 61 years [55–68], and 80.6% were male. Patients in Group 3 were older, had lower LVEF, lower albumin and cholesterol levels, higher NLR, lower LMR, and a greater proportion of EuroSCORE II intermediate- or high-risk classification. Baseline characteristics are presented in Table 1 . Table 1 Baseline Characteristics Variable Total (n = 1205) Group 1 (n = 86) Group 2 (n = 748) Group 3 (n = 371) p-value Age (years) 61 [55–68] 59 [53–65] 59 [54–66] 64 [57–70] < 0.001 Male sex, n (%) 971 (80.7%) 60 (69.8%) 599 (80.1%) 312 (84.1%) 0.009 BMI (kg/m²) 27.9 [25.3–30.9] 28.5 [26.9–31.1] 28.0 [25.4–30.8] 27.7 [24.8–31.2] 0.108 LVEF (%) 60 [48–60] 60 [48–60] 60 [48–60] 57.5 [48–60] 0.016 Hypertension, n (%) 1014 (84.2%) 77 (89.5%) 623 (83.3%) 314 (84.9%) 0.296 Diabetes mellitus, n (%) 814 (67.6%) 63 (73.3%) 496 (66.3%) 255 (68.7%) 0.367 Hyperlipidemia, n (%) 890 (73.9%) 69 (80.2%) 551 (73.7%) 270 (73.0%) 0.372 Albumin (g/dL) 4.2 [3.9–4.4] 4.4 [4.1–4.6] 4.3 [4.1–4.5] 3.9 [3.7–4.2] < 0.001 Total cholesterol (mg/dL) 172 [145–209] 208.5 [194–241] 186 [151–216] 154 [131–170] < 0.001 HDL (mg/dL) 39 [34–47] 37.5 [32–44] 39 [34–47] 40 [33–47] 0.215 LDL (mg/dL) 114.5 [86–149] 149 [129–174] 124.5 [91–155] 98 [75–116] < 0.001 Triglycerides (mg/dL) 149 [112–210] 234.5 [158–323] 156 [117–217] 130 [95–172] < 0.001 CRP (mg/L) 4.9 [2.1–12.9] 4.2 [2.3–8.9] 4.5 [2.0–10.7] 6.4 [2.4–18.2] < 0.001 Creatinine (mg/dL) 0.9 [0.8–1.0] 0.8 [0.7–1.0] 0.9 [0.8–1.0] 0.9 [0.8–1.1] < 0.001 BUN (mg/dL) 16 [ 13 – 20 ] 16 [ 13 – 19 ] 16 [ 13 – 19 ] 16 [ 13 – 21 ] 0.059 NLR 2.4 [1.9–3.3] 1.7 [1.4–2.1] 2.3 [1.8–2.8] 3.4 [2.7–4.2] < 0.001 LMR 3.2 [2.4–4.0] 5.2 [4.8–6.0] 3.3 [2.7–4.2] 2.4 [1.9–3.1] < 0.001 PLR 114.7 [89–148] 88.2 [74–109] 110 [86–139] 136.3 [106–183] < 0.001 PNI 52.1 [48.4–55.7] 57.8 [54.7–61.1] 53.4 [50.3–56.4] 47.7 [44.5–50.6] < 0.001 CONUT score 1 [0–2] 0 [0–0] 1 [0–2] 2 [ 1 – 3 ] < 0.001 SII 559 [400–790] 420 [299–542] 519 [382–699] 748 [522–1052] < 0.001 SIRI 1.6 [1.1–2.3] 0.8 [0.7–1.1] 1.4 [1.0–2.0] 2.2 [1.6–3.2] < 0.001 Naples score 2 [ 1 – 3 ] 0 [0–0] 2 [ 1 – 2 ] 3 [ 3 – 3 ] < 0.001 EuroSCORE II category, n (%) < 0.001 Low risk 1038 (86.2%) 79 (91.9%) 668 (89.3%) 291 (78.6%) Intermediate risk 130 (10.8%) 3 (3.5%) 67 (8.9%) 60 (16.2%) High risk 37 (3.1%) 4 (4.7%) 13 (1.7%) 20 (5.4%) Surgery duration (min) 168 [145–196] 167 [145–193] 167 [145–195] 172 [146–204] 0.392 Bypass time (min) 130 [108–157] 129 [104–154] 129 [106–156] 132 [111–160] 0.434 Cross-clamp time (min) 80 [62–100] 76 [55–94] 79 [62–101] 82 [63–98] 0.186 Data are presented as median [IQR] or n (%). P-values from Kruskal-Wallis test (continuous) or chi-square test (categorical). BMI, body mass index; LVEF, left ventricular ejection fraction; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; PLR, platelet-to-lymphocyte ratio (PLR = platelet count / lymphocyte count); PNI, Prognostic Nutritional Index; CONUT, Controlling Nutritional Status; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index. EuroSCORE II categories: low risk (< 2%), intermediate risk (2–<6%), and high risk (≥ 6%) predicted mortality. 2. Postoperative Outcomes Postoperative outcomes are summarized in Table 2 and Fig. 4 . A progressive increase in most major outcomes was observed across NPS groups. One-year mortality was 2.3%, 5.1%, and 13.0% in Groups 1, 2, and 3, respectively (p < 0.001). In-hospital mortality (1.2%, 3.5%, 7.8%; p = 0.001), AKI (2.3%, 5.3%, 11.6%; p < 0.001), stroke (0%, 3.2%, 6.5%; p = 0.005), and prolonged mechanical ventilation (7.0%, 8.6%, 17.0%; p < 0.001) each showed a stepwise increase across groups. In-hospital mortality slightly exceeded 1-month mortality due to a small number of deaths occurring after postoperative day 30 during prolonged index hospitalisation. Among NPS groups, EuroSCORE II category distribution also differed significantly (p < 0.001), with 21.3% of Group 3 patients classified as intermediate or high risk, compared with 8.1% in Group 1. Table 2 Postoperative Outcomes Outcome Total (n = 1205) Group 1 (n = 86) Group 2 (n = 748) Group 3 (n = 371) p-value In-hospital mortality, n (%) 56 (4.7%) 1 (1.2%) 26 (3.5%) 29 (7.8%) 0.001 1-month mortality, n (%) 52 (4.3%) 1 (1.2%) 23 (3.1%) 28 (7.6%) < 0.001 6-month mortality, n (%) 80 (6.6%) 1 (1.2%) 37 (4.9%) 42 (11.4%) < 0.001 1-year mortality, n (%) 88 (7.3%) 2 (2.3%) 38 (5.1%) 48 (13.0%) < 0.001 Acute kidney injury, n (%) 85 (7.1%) 2 (2.3%) 40 (5.3%) 43 (11.6%) < 0.001 Renal replacement therapy, n (%) 22 (1.8%) 0 (0.0%) 11 (1.5%) 11 (3.0%) 0.089 Stroke, n (%) 48 (4.0%) 0 (0.0%) 24 (3.2%) 24 (6.5%) 0.005 Atrial fibrillation, n (%) 221 (18.4%) 14 (16.3%) 116 (15.5%) 91 (24.6%) 48h), n (%) 133 (11.0%) 6 (7.0%) 64 (8.6%) 63 (17.0%) < 0.001 IABP use, n (%) 43 (3.6%) 1 (1.2%) 22 (2.9%) 20 (5.4%) 0.052 Low cardiac output, n (%) 106 (8.8%) 4 (4.7%) 56 (7.5%) 46 (12.4%) 0.009 MACE, n (%) 176 (14.6%) 7 (8.1%) 92 (12.3%) 77 (20.8%) < 0.001 Reoperation, n (%) 48 (4.0%) 4 (4.7%) 24 (3.2%) 20 (5.4%) 0.199 ICU stay, days 2 [ 2 – 3 ] 2 [ 2 – 3 ] 2 [ 2 – 3 ] 2 [ 2 – 4 ] < 0.001 Hospital stay, days 6 [ 5 – 7 ] 6 [ 5 – 7 ] 6 [ 5 – 7 ] 6 [ 5 – 8 ] 0.024 Data are presented as n (%) or median [IQR]. P-values from chi-square or Fisher's exact test (categorical) and Kruskal-Wallis test (continuous). MV, mechanical ventilation; IABP, intra-aortic balloon pump; MACE, major adverse cardiac events; ICU, intensive care unit. P-values are exploratory and not adjusted for multiplicity. Bar charts illustrating key postoperative outcomes stratified by Naples Prognostic Score (NPS) group. A progressive increase in mortality, acute kidney injury, stroke, and prolonged mechanical ventilation is seen across NPS groups (G1: NPS = 0; G2: NPS = 1–2; G3: NPS = 3–4). Group sizes reflect the total cohort (G1: n = 86, G2: n = 748, G3: n = 371); these differ from the subgroup numbers in Supplementary Fig. 1, which are stratified by EuroSCORE II category. MV, mechanical ventilation; AKI, acute kidney injury; MACE, major adverse cardiac event. 3. Discriminatory Performance — ROC Analysis ROC curve analysis for 1-year mortality prediction is presented in Table 3 and Fig. 2 . The NPS achieved an AUC of 0.670 (95% CI 0.615–0.721), comparable to NLR (AUC 0.662, 95% CI 0.601–0.725) and PNI (AUC 0.698, 95% CI 0.640–0.757), with overlapping confidence intervals across all evaluated scores, indicating no statistically significant differences between indices. There was no statistically significant difference in AUC between the NPS and other individual nutritional-inflammatory indices on DeLong testing (all p > 0.05). EuroSCORE II demonstrated higher discriminatory performance (AUC 0.791, 95% CI 0.740–0.839). Combining NPS with EuroSCORE II in a logistic regression model yielded AUC 0.839 (95% CI 0.790–0.882), exceeding either score alone. The Spearman correlation between NPS and EuroSCORE II was weak (ρ = 0.172; p < 0.001), suggesting largely independent prognostic information. The combined model showed good calibration (Hosmer–Lemeshow χ2 = 4.88, df = 3, p = 0.179). Bootstrap internal validation of the combined model yielded an apparent AUC of 0.838 with a mean optimism of 0.0006, resulting in an optimism-corrected AUC of 0.837, indicating negligible overfitting. The Brier score for the combined model was 0.0554 (scaled Brier 0.182), compared with 0.0566 (scaled Brier 0.164) for EuroSCORE II alone and 0.0660 (scaled Brier 0.025) for NPS alone, confirming modest but consistent improvement in overall predictive accuracy. The continuous NRI for the combined model compared with EuroSCORE II alone was 0.513 (95% CI 0.278–0.724), driven primarily by improved reclassification of non-events (NRI₂₋: 0.422). The IDI was 0.015 (95% CI 0.001–0.042), with non-overlapping confidence intervals excluding zero and confirming statistically significant incremental discrimination. While the continuous NRI suggested apparent reclassification improvement, this metric is known to be susceptible to overestimation in the absence of predefined thresholds. Therefore, the incremental value of NPS should be interpreted primarily in the context of AUC and IDI improvements, which provide more robust estimates. Calibration plots and decision curve analysis for all three models are presented in Supplementary Fig. 2. In an exploratory subgroup analysis, among 291 EuroSCORE II low-risk patients with NPS 3–4, 1-year mortality was 5.2%, compared with 1.9% in EuroSCORE II low-risk patients with NPS ≤ 2 (OR 2.85, 95% CI 1.36–5.97; p = 0.002; Supplementary Fig. 1). Table 3 ROC Curve Analysis — 1-Year Mortality Score AUC 95% CI p-value Sensitivity (%) Specificity (%) Naples score 0.670 0.615–0.721 < 0.001 54.5 71.1 PNI 0.698 0.640–0.757 < 0.001 76.1 55.9 CONUT 0.625 0.561–0.688 < 0.001 31.8 87.4 NLR 0.662 0.601–0.725 < 0.001 53.4 77.0 SII 0.638 0.581–0.701 < 0.001 73.9 52.2 SIRI 0.597 0.532–0.661 0.001 55.7 67.0 LMR 0.594 0.532–0.662 0.002 64.8 54.9 EuroSCORE II 0.791 0.740–0.839 < 0.001 67.0 90.3 NPS + EuroSCORE II (combined) 0.839 0.790–0.882 < 0.001 — — AUC values with 95% bootstrap confidence intervals (1,000 iterations). Sensitivity and specificity at the optimal threshold (Youden’s index) are reported for individual scores. These values are not reported for the combined model, as no single clinically applicable threshold was defined. AUC, area under the curve; CI, confidence interval; PNI, Prognostic Nutritional Index; CONUT, Controlling Nutritional Status; NLR, neutrophil-to-lymphocyte ratio; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index; LMR, lymphocyte-to-monocyte ratio. Receiver operating characteristic (ROC) curves for all evaluated scores. The Naples Prognostic Score (NPS) achieved AUC 0.670 (95% CI 0.615–0.721), comparable to other nutritional-inflammatory indices. EuroSCORE II demonstrated higher discrimination (AUC 0.791). Combining NPS with EuroSCORE II yielded AUC 0.839. AUC, area under the curve; CI, confidence interval; NPS, Naples Prognostic Score; PNI, Prognostic Nutritional Index; CONUT, Controlling Nutritional Status score; NLR, neutrophil-to-lymphocyte ratio; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index; LMR, lymphocyte-to-monocyte ratio. 4. Multivariable Regression Analysis Univariate and multivariable logistic regression results are presented in Table 4 and Fig. 3 . In univariate analysis, NPS (OR 1.699 per point, 95% CI 1.411–2.067; p < 0.001), age, LVEF, NLR, PNI, CONUT, SII, and EuroSCORE II were all significantly associated with 1-year mortality. In the multivariable model, NPS remained an independent predictor (aOR 1.486, 95% CI 1.18–1.84; p < 0.001), alongside age (aOR 1.078 per year, 95% CI 1.05–1.11; p < 0.001) and LVEF (aOR 1.050 per 1% decrease, 95% CI 1.02–1.08; p < 0.001). Diabetes mellitus did not reach statistical significance in the multivariable model (aOR 1.476, 95% CI 0.89–2.59; p = 0.147). EuroSCORE II was not entered into the multivariable model to avoid overadjustment, as EuroSCORE II incorporates several variables already included in the model. Its univariate OR was 7.525 per 1% increase in predicted mortality (95% CI 5.40–11.23; p < 0.001). The multivariable model demonstrated good calibration (Hosmer–Lemeshow χ2 = 2.78, df = 8, p = 0.947). The attenuation of the NPS odds ratio from 1.70 (univariate) to 1.49 (multivariable) is acknowledged, representing approximately 13% reduction after adjustment, which is consistent with expected residual confounding in observational data. In sensitivity analyses, NPS remained independently associated with 1-year mortality across all three additional models: the expanded multivariable model adjusting for sex, creatinine, COPD, and BMI (aOR 1.51, 95% CI 1.20–1.90; p < 0.001); the CRP-adjusted model (aOR 1.50, 95% CI 1.19–1.89; p < 0.001; serum CRP was not independently significant after NPS adjustment, OR 0.85, p = 0.647); and the calendar-year-adjusted model (aOR 1.49, 95% CI 1.18–1.87; p < 0.001; calendar year: OR 0.99, p = 0.827). These results indicate that the association between NPS and mortality is robust to expanded confounder adjustment, independent of direct CRP-based inflammation, and not attributable to temporal confounding. Table 4 Logistic Regression Analysis — 1-Year Mortality Variable Univariate OR 95% CI p-value Multivariable aOR 95% CI p-value Age (per year) 1.078 1.054–1.107 < 0.001 1.078 1.05–1.11 < 0.001 Male sex 0.832 0.501–1.363 0.465 — — — LVEF (per 1% decrease) 1.054 1.036–1.073 < 0.001 1.050 1.02–1.08 < 0.001 Diabetes mellitus 1.542 0.967–2.534 0.076 1.476 0.89–2.59 0.147 Hypertension 0.945 0.543–1.666 0.837 — — — Naples score (per point) 1.699 1.411–2.067 < 0.001 1.486 1.18–1.84 < 0.001 NLR (per unit) 1.157 1.093–1.233 < 0.001 — — — PNI (per unit) 0.896 0.868–0.924 < 0.001 — — — CONUT (per point) 1.279 1.155–1.419 < 0.001 — — — SII (per 100 units) 1.068 1.042–1.094 < 0.001 — — — EuroSCORE II (per 1% increase) 7.525 5.40–11.23 < 0.001 — — — OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; LVEF, left ventricular ejection fraction. Variables in the multivariable model were selected based on clinical relevance: age, left ventricular ejection fraction, diabetes mellitus, and NPS. EuroSCORE II was not included in the multivariable model to avoid model overadjustment, as it incorporates several clinical variables (e.g., age, left ventricular function) that were already entered individually into the model. Forest plot of adjusted odds ratios (aOR) and 95% confidence intervals from multivariable logistic regression analysis for 1-year all-cause mortality. The Naples Prognostic Score remained an independent predictor after adjustment for age, left ventricular ejection fraction (LVEF), and diabetes mellitus. aOR, adjusted odds ratio; CI, confidence interval; LVEF, left ventricular ejection fraction. Discussion This study demonstrates that the preoperative NPS is independently associated with 1-year mortality after isolated CABG, capturing dimensions of risk not reflected by conventional operative risk models. Although EuroSCORE II demonstrated higher standalone discrimination (AUC 0.791 vs. 0.670), the limited correlation between the two scores (ρ = 0.172) indicates that they reflect distinct dimensions of perioperative risk. Combining NPS with EuroSCORE II improved discrimination (AUC 0.839), supporting its role as a complementary tool. Among EuroSCORE II low-risk patients with NPS 3–4, 1-year mortality was 5.2% — more than twice that of EuroSCORE II low-risk patients with NPS ≤ 2 (1.9%) — illustrating that nutritional-inflammatory burden may identify patients underestimated by conventional models. However, NPS should be regarded as a risk enrichment tool — a prognostic marker that identifies higher-risk individuals within conventional risk strata — rather than a modifiable therapeutic target. The clinical utility of NPS-guided preoperative interventions has not been established in interventional studies, and it would be premature to recommend specific management changes based on NPS alone. Whether NPS-guided risk stratification can translate into improved outcomes through targeted preoperative optimisation remains to be tested in prospective trials. From a clinical perspective, NPS should not be interpreted as a standalone decision-making tool but rather as a trigger for enhanced perioperative assessment. Patients identified as high-risk by NPS may benefit from a more comprehensive evaluation of biological vulnerability, including formal frailty assessment, nutritional review, and closer postoperative surveillance. In this context, NPS may serve as a pragmatic surrogate marker of biological frailty in routine clinical practice, where formal frailty indices are not always available. This approach may be particularly relevant in patients classified as low risk by conventional models but with elevated NPS. A key strength of the present study is the direct head-to-head comparison of multiple nutritional-inflammatory indices within the same cohort, allowing a balanced and internally consistent assessment of their relative performance. Several nutritional and inflammatory indices have been evaluated in cardiac surgical populations. Elevated NLR and SII have been associated with adverse outcomes after CABG [ 24 , 25 ], and PNI has shown prognostic value in patients undergoing CABG [ 26 – 28 ]. In our cohort, the NPS performed comparably to PNI (AUC 0.670 vs. 0.698) and NLR (AUC 0.662), with no statistically significant differences on DeLong testing. The CONUT score showed the highest specificity but lowest sensitivity, limiting its utility as a screening tool. By integrating both nutritional (albumin, cholesterol) and inflammatory (NLR, LMR) components, the NPS offers a unified composite that may be preferable to single-domain markers. Data specifically evaluating NPS in isolated CABG were previously limited. A recent retrospective study by Sayarer et al. [ 29 ] confirmed that NPS independently predicted 30-day and 1-year mortality in 1,195 patients undergoing isolated CABG and improved the predictive performance of EuroSCORE II. Notably, Sayarer et al. similarly reported moderate discriminatory performance for NPS in isolated CABG (AUC 0.693), supporting the consistency of our findings across independent cohorts. The present study extends these findings by providing a comprehensive head-to-head comparison with multiple nutritional-inflammatory indices and a more detailed evaluation of incremental prognostic performance. Despite statistically significant associations, all evaluated indices—including NPS—demonstrated only modest and largely overlapping discriminatory performance. This suggests that, while these markers capture relevant biological risk signals, their ability to meaningfully distinguish between individual patient risk levels remains limited when used in isolation. Together with earlier reports linking NPS to saphenous vein graft patency and postoperative AF [ 18 , 19 ], the convergent evidence supports the biological plausibility of our findings. Limitations This study has several limitations. First, its retrospective single-centre design may limit generalisability. Second, residual confounding cannot be excluded, although sensitivity analyses supported the robustness of the findings. Third, perioperative practice evolved over the study period, although calendar-year adjustment did not materially alter results. Fourth, follow-up was registry-based rather than through direct patient contact. Furthermore, frailty was not formally assessed; given that NPS likely reflects frailty-related biological vulnerability, the extent to which it provides independent prognostic information beyond frailty remains uncertain. Finally, the relatively small size of the reference group (NPS = 0) may limit statistical precision. Prospective multicentre validation incorporating formal frailty assessment is warranted. Conclusions The preoperative NPS is independently associated with 1-year mortality and adverse postoperative outcomes in patients undergoing isolated on-pump CABG. Although its discriminatory performance is lower than that of EuroSCORE II, it provides complementary prognostic information by reflecting nutritional and inflammatory dimensions of biological vulnerability not captured by conventional models. NPS may serve as a risk enrichment tool, assisting in the identification of higher-risk patients within groups considered low risk by standard risk stratification. However, NPS alone has limited discrimination and is not suitable as a standalone clinical prediction tool. Given important limitations — including the absence of statin data and lack of formal frailty assessment — these findings should be considered preliminary. Prospective multicentre validation incorporating these variables is required before NPS can be recommended for routine preoperative use. Its value lies in complementing rather than replacing established risk models. Declarations Ethics approval and consent to participate This study was approved by the Istanbul University-Cerrahpaşa Non-Interventional Clinical Research Ethics Committee (approval no: 2024/21; date: 30 January 2024; document no: E-74555795-050.04-902443). The study was conducted in accordance with the Declaration of Helsinki. Individual informed consent was waived given the retrospective design and de-identified data. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study received no external funding. Author Contribution S.D.O. conceived and designed the study, collected and analysed the data, and drafted the manuscript. M.M. supervised the study and critically revised the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements The authors received language editing support. Data Availability The data underlying this study are not publicly available due to institutional and national data protection regulations but are available from the corresponding author on reasonable request, subject to ethical approval and data sharing agreements. Data Availability Statement The data underlying this article are contained within the article itself. The anonymised dataset used for the analyses presented in this study is not publicly available due to institutional data protection regulations and Turkish national health data legislation, but may be made available upon reasonable request to the corresponding author, subject to ethical approval and data sharing agreements. References Koertzen M, Punjabi PP, Lockwood GG. Pre-operative serum albumin concentration as a predictor of mortality and morbidity following cardiac surgery. Perfusion. 2013;28(5):390–4. Karas PL, Goh SL, Dhital K. Is low serum albumin associated with postoperative complications in patients undergoing cardiac surgery? Interact Cardiovasc Thorac Surg. 2015;21(6):777–86. Engelman DT, Adams DH, Byrne JG, et al. Impact of body mass index and albumin on morbidity and mortality after cardiac surgery. J Thorac Cardiovasc Surg. 1999;118(5):866–73. Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105(9):1135–43. Nashef SA, Roques F, Sharples LD, et al. EuroSCORE II. Eur J Cardiothorac Surg. 2012;41(4):734–45. Shahian DM, O’Brien SM, Filardo G, et al. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 1—coronary artery bypass grafting surgery. Ann Thorac Surg. 2009;88(1 Suppl):S2–22. Silverborn M, Nielsen S, Karlsson M. The performance of EuroSCORE II in CABG patients in relation to sex, age, and surgical risk: a nationwide study in 14,118 patients. J Cardiothorac Surg. 2023;18(1):40. Biancari F, Vasques F, Mikkola R, Martin M, Lahtinen J, Heikkinen J. Validation of EuroSCORE II in patients undergoing coronary artery bypass surgery. Ann Thorac Surg. 2012;93(6):1930–5. Galizia G, Lieto E, Auricchio A, Cardella F, Mabilia A, Podzemny V, et al. Naples prognostic score, based on nutritional and inflammatory status, is an independent predictor of long-term outcome in patients undergoing surgery for colorectal cancer. Dis Colon Rectum. 2017;60(12):1273–84. Xiong J, Hu H, Kang W, Liu H, Ma F, Ma S, et al. Prognostic impact of preoperative Naples prognostic score in gastric cancer patients undergoing surgery. Front Surg. 2021;8:617744. Peng SM, Ren JJ, Yu N, Xu JY, Chen GC, Li X, et al. The prognostic value of the Naples prognostic score for patients with non-small-cell lung cancer. Sci Rep. 2022;12:5782. Jiang Y, Feng Y, Tian Y, et al. The prognostic role of Naples Prognostic Score in patients with coronary artery disease. J Inflamm Res. 2025;18:6999–7012. Xu Z, Pei M, Yang X, et al. Associations of Naples prognostic score with stroke in adults and all cause mortality among stroke patients. Sci Rep. 2025;15:10718. Erdogan A, Genc O, Inan D, et al. Impact of Naples Prognostic Score on midterm all-cause mortality in patients with decompensated heart failure. Biomark Med. 2023;17(4):219–30. Aydın SŞ, Aydemir S, Özmen M, Aksakal E, Saraç İ, Aydınyılmaz F, Altınkaya O, Birdal O, Tanboğa İH. The importance of Naples prognostic score in predicting long-term mortality in heart failure patients. Ann Med. 2025;57(1):2442536. Erdogan A, Genc O, Ozkan E, et al. Impact of Naples Prognostic Score at admission on in-hospital and follow-up outcomes among STEMI patients. Angiology. 2023;74(10):970–80. Uysal OK, Ozdogru D, Yildirim A, Ozturk I, Tras G, Arlier Z. The prognostic value of a Naples score in determining in-hospital mortality in patients with acute ischemic stroke undergoing endovascular treatment. J Clin Med. 2024;13(21):6434. Karaduman A, Yılmaz C, Tiryaki MM, Balaban İ, Keten MF, Unkun T, İzci S, Efe SÇ, Alizade E. Relationship between the Naples prognostic score and saphenous vein graft disease after coronary artery bypass grafting surgery. Arq Bras Cardiol. 2025;122(5):e20240519. Oksen D, Guven B, Donmez A, Yesiltas MA, Koyuncu AO, Gulbudak S, Oktay V. Predicting postoperative atrial fibrillation after cardiac surgery using the Naples prognostic score. Coron Artery Dis. 2025;36(3):225–31. De Ulibarri JI, González-Madroño A, de Villar NG, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38–45. Nakagomi A, Kohashi K, Morisawa T, et al. Nutritional status is associated with inflammation and predicts a poor outcome in patients with chronic heart failure. J Atheroscler Thromb. 2016;23(6):713–27. O’Brien SM, Feng L, He X, et al. The Society of Thoracic Surgeons 2018 adult cardiac surgery risk models: part 2—statistical methods and results. Ann Thorac Surg. 2018;105(5):1419–28. KDIGO Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2(1):1–138. Alagha S, Miniksar ÖH, Polat MN, Kara M, Şenayıl Y. The prognostic value of inflammatory indices in predicting poor postoperative outcomes in isolated coronary artery bypass graft surgery. Cureus. 2023;15(8):e43120. Dey S, Kashav R, Kohli JK, Magoon R, ItiShri, Walian A, Grover V. Systemic immune-inflammation index predicts poor outcome after elective off-pump CABG: a retrospective, single-center study. J Cardiothorac Vasc Anesth. 2021;35(8):2397–404. Keskin M, İpek G, Aldağ M, Altay S, Hayıroğlu Mİ, Börklü EB, İnan D, Kozan Ö. Effect of nutritional status on mortality in patients undergoing coronary artery bypass grafting. Nutrition. 2018;48:82–6. Gim DH, Lee SH, Cha JH, et al. Long-term prognostic value of the prognostic nutritional index in patients undergoing coronary artery bypass grafting. J Am Heart Assoc. 2025;14(19):e043597. Lei Y, Wang W, Hua C, Chen Y. Association between prognostic nutritional index and prognosis of patients receiving coronary artery bypass grafting surgery: a systematic review and meta-analysis. Front Cardiovasc Med. 2026;13:1673038. Sayarer C, Dayanir OB, Eken EN, Gencpinar T, Bayrak S. The impact of the Naples prognostic score on outcomes in patients undergoing coronary artery bypass grafting. Turk J Thorac Cardiovasc Surg. 2026;34(2):116–20. Supplementary Fig. 1. One-year all-cause mortality (%) stratified by NPS group and EuroSCORE II category. The highlighted cell (NPS Group 3, EuroSCORE II low-risk) shows 5.2% mortality, compared with 1.9% in patients with NPS Groups 1–2 within the same risk stratum. Additional Declarations No competing interests reported. Supplementary Files floatimage5.jpeg Supplementary Figure 1 . One-year all-cause mortality (%) stratified by NPS group and EuroSCORE II category. The highlighted cell (NPS Group 3, EuroSCORE II low-risk) shows 5.2% mortality, compared with 1.9% in patients with NPS Groups 1–2 within the same risk stratum. floatimage6.jpeg Supplementary Figure 2. Calibration plots (left panel) and decision curve analysis (right panel) for three models: NPS only (orange), EuroSCORE II (blue), and combined NPS + EuroSCORE II (green). The combined model generally demonstrates higher net benefit across clinically relevant threshold probabilities. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 May, 2026 Reviewers invited by journal 06 May, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 10 Apr, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9382426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640035505,"identity":"5f71a0d9-6625-43a4-8f1e-4d2ec2a15a63","order_by":0,"name":"Sadiye Deniz Özsoy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACgwNw5gcDiAjxWhhnwLUk4NcCZzHzwEXwaJFvP7zxME8Fg5x8/+Fj0jYFh+UZ2Ju3STD+uIfbijNpBYd5zjAYM85IS5POMThs2MBzrEyCIaEYj6uAynjbGBKbJXjMQFoSGCRyzIBacLtMvv8NUMs/hsQ2/jNm0hYgLfJv8GthuAGypYEhsYchx0yaAWwLD34tBjeeFRycc0zCWEIiLdmyxyDdsI0nrdgiIQ2fw5I3f3hTYwMKsYM3fvyxludnP7zxxgcbPA4DAiYeBgkQzQIm2UAEfg3ASP8BoZk/EFA4CkbBKBgFIxQAAMGPSsPpob57AAAAAElFTkSuQmCC","orcid":"","institution":"istanbul university cerrahpasa cardiology institute","correspondingAuthor":true,"prefix":"","firstName":"Sadiye","middleName":"Deniz","lastName":"Özsoy","suffix":""},{"id":640035506,"identity":"b8791eee-153a-45cd-b579-64cc421120f9","order_by":1,"name":"Murat Mert","email":"","orcid":"","institution":"istanbul university cerrahpasa cardiology institute","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Mert","suffix":""}],"badges":[],"createdAt":"2026-04-10 18:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9382426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9382426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109434089,"identity":"aaafa29b-d79b-459b-968d-b170150051d5","added_by":"auto","created_at":"2026-05-18 05:52:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":446627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/cf64bf11f9d8cd5cee43f445.jpeg"},{"id":109434091,"identity":"4df134f7-cce9-496a-bc9d-1fc5a1f5a3b5","added_by":"auto","created_at":"2026-05-18 05:52:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":461752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves for 1-Year Mortality Prediction\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/099b2c1fff3721a8a5b9e828.jpeg"},{"id":109434092,"identity":"f2e18b27-faea-4eac-be63-085303c0be6a","added_by":"auto","created_at":"2026-05-18 05:52:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest Plot — Multivariable Logistic Regression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/ff84b6b4e9d90557171f883c.jpeg"},{"id":109759511,"identity":"e194d7ac-fce7-44ca-9a35-64450742634e","added_by":"auto","created_at":"2026-05-22 07:27:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":680334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePostoperative Outcomes by NPS Group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/fc276ae2a34dfd83518e4d65.jpeg"},{"id":109763813,"identity":"ea733468-3055-4b11-8556-7b1da16b03e4","added_by":"auto","created_at":"2026-05-22 07:35:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2152956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/fc2b3b8a-0670-4d5b-8814-30362672bf92.pdf"},{"id":109434088,"identity":"6c9732e4-6410-4e3b-aad3-3e3168cb2b4b","added_by":"auto","created_at":"2026-05-18 05:52:56","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":218899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. One-year all-cause mortality (%) stratified by NPS group and EuroSCORE II category. The highlighted cell (NPS Group 3, EuroSCORE II low-risk) shows 5.2% mortality, compared with 1.9% in patients with NPS Groups 1–2 within the same risk stratum.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/77bb02a0d568026ff1256feb.jpeg"},{"id":109759604,"identity":"8999cee8-2475-47a7-a316-406793f66cc4","added_by":"auto","created_at":"2026-05-22 07:27:25","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":233211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eCalibration plots (left panel) and decision curve analysis (right panel) for three models: NPS only (orange), EuroSCORE II (blue), and combined NPS + EuroSCORE II (green). The combined model generally demonstrates higher net benefit across clinically relevant threshold probabilities.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9382426/v1/07cf85c002db0beaf4bd137d.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative and Incremental Prognostic Value of the Naples Prognostic Score Beyond EuroSCORE II in Isolated Coronary Artery Bypass Grafting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition and systemic inflammation are well-established determinants of adverse outcomes in cardiovascular surgery. Preoperative hypoalbuminaemia, low cholesterol, and elevated inflammatory biomarkers have each been associated with increased morbidity, prolonged recovery, and mortality following coronary artery bypass grafting (CABG) [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite this recognition, widely used risk models \u0026mdash; including EuroSCORE II and the Society of Thoracic Surgeons (STS) score \u0026mdash; predominantly incorporate demographic and procedural variables and do not include nutritional or inflammatory parameters, as they were designed primarily as operative risk tools [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Naples Prognostic Score (NPS) is a composite index that simultaneously captures nutritional depletion (serum albumin, total cholesterol) and inflammatory burden (neutrophil-to-lymphocyte ratio [NLR], lymphocyte-to-monocyte ratio [LMR]) within a single score [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Serum albumin and cholesterol are not purely nutritional markers \u0026mdash; both may also reflect illness severity, systemic inflammation, and frailty more broadly, which may partly explain their prognostic relevance in surgical populations. Originally developed and validated in gastrointestinal oncology [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the NPS has since shown prognostic value across diverse non-oncological settings, including coronary artery disease, heart failure, and cerebrovascular disease [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the cardiac surgical context, isolated reports have linked the NPS to saphenous vein graft patency and postoperative atrial fibrillation after CABG [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and a recent study confirmed its association with short- and long-term mortality following isolated CABG [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While emerging evidence supports the association between the Naples Prognostic Score and adverse outcomes following CABG, several important gaps remain. Specifically, the relative performance of NPS compared with other commonly used nutritional-inflammatory indices has not been fully clarified, and its incremental value beyond established risk models such as EuroSCORE II requires more comprehensive evaluation. Therefore, the aims of the present study were to assess the association between preoperative NPS and 1-year mortality, to directly compare its discriminatory performance with multiple nutritional-inflammatory indices, and to evaluate its incremental prognostic value beyond EuroSCORE II in a large, well-characterised cohort of patients undergoing isolated CABG.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\n\u003ch3\u003e1. Study Design and Population\u003c/h3\u003e\n\u003cp\u003eThis was a retrospective cohort study conducted at the Cardiology Institute of Istanbul University-Cerrahpaşa, a tertiary referral centre. A total of 1,527 consecutive adult patients who underwent CABG between January 2015 and February 2023 were screened for inclusion. Patients were excluded if they underwent concomitant valve surgery, aortic surgery, or any other simultaneous cardiac procedure; if the operation was a redo procedure; if urgent or emergency surgery was performed; or if off-pump CABG was performed. After applying exclusion criteria, 1,205 patients undergoing isolated elective on-pump CABG were included in the final analysis (Fig.\u0026nbsp;1). The study was conducted in accordance with the Declaration of Helsinki and was approved by the Istanbul University-Cerrahpaşa Non-Interventional Clinical Research Ethics Committee (approval no: 2024/21; date: 30 January 2024; document no: E-74555795-050.04-902443). Clinical trial number: Not applicable. All data were analysed retrospectively after ethics approval was obtained; this practice is consistent with the institutional policy for non-interventional retrospective research at our centre. Individual informed consent was waived given the retrospective design.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Flowchart\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePatient selection flowchart. A total of 1,527 patients were screened; 322 were excluded (all due to concomitant cardiac procedures). The final study population comprised 1,205 patients, stratified by preoperative Naples Prognostic Score (NPS). Follow-up for 1-year all-cause mortality was 100% complete.\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003e2. NPS Calculation and Group Definition\u003c/h3\u003e\n\u003cp\u003eThe NPS was calculated from preoperative laboratory values collected within 24 hours before surgery. One point was assigned for each of the following: serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;4.0 g/dL, total cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;180 mg/dL, NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.96, and LMR\u0026thinsp;\u0026le;\u0026thinsp;4.44. The total score (0\u0026ndash;4) was used to stratify patients: Group 1 (score\u0026thinsp;=\u0026thinsp;0, n\u0026thinsp;=\u0026thinsp;86), Group 2 (score\u0026thinsp;=\u0026thinsp;1\u0026ndash;2, n\u0026thinsp;=\u0026thinsp;748), and Group 3 (score\u0026thinsp;=\u0026thinsp;3\u0026ndash;4, n\u0026thinsp;=\u0026thinsp;371). The component thresholds and three-tier group stratification were adopted a priori from the original NPS publication by Galizia et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and were not derived from the present cohort.\u003c/p\u003e\n\u003ch3\u003e3. Nutritional and Inflammatory Indices and EuroSCORE II\u003c/h3\u003e\n\u003cp\u003eSix nutritional-inflammatory indices were compared against the NPS: the Prognostic Nutritional Index (PNI\u0026thinsp;=\u0026thinsp;10 \u0026times; albumin [g/dL]\u0026thinsp;+\u0026thinsp;0.005 \u0026times; lymphocyte count [/mm\u0026sup3;]), the Controlling Nutritional Status (CONUT) score [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], NLR, Systemic Immune-Inflammation Index (SII\u0026thinsp;=\u0026thinsp;platelet \u0026times; neutrophil / lymphocyte), Systemic Inflammation Response Index (SIRI\u0026thinsp;=\u0026thinsp;neutrophil \u0026times; monocyte / lymphocyte), and LMR. EuroSCORE II was calculated using the standard online calculator and categorised into low, intermediate, and high risk groups based on predicted mortality: low risk (\u0026lt;\u0026thinsp;2%), intermediate risk (2\u0026ndash;\u0026lt;6%), and high risk (\u0026ge;\u0026thinsp;6%).\u003c/p\u003e\n\u003ch3\u003e4. Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was 1-year all-cause mortality. Secondary endpoints included in-hospital mortality, 1-month mortality, 6-month mortality, acute kidney injury (AKI), renal replacement therapy (RRT), stroke, new-onset atrial fibrillation (AF), prolonged mechanical ventilation (\u0026gt;\u0026thinsp;48 hours), intra-aortic balloon pump (IABP) use, low cardiac output syndrome, major adverse cardiac events (MACE), reoperation within 24 hours, and ICU and total hospital length of stay. Postoperative outcomes were defined according to Society of Thoracic Surgeons criteria where applicable [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Acute kidney injury was defined as a postoperative increase in serum creatinine of \u0026ge;\u0026thinsp;0.3 mg/dL within 48 hours or \u0026ge;\u0026thinsp;1.5-fold from baseline within 7 days, consistent with KDIGO 2012 criteria [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. MACE was analysed as recorded in the institutional dataset and was not reconstructed post hoc. The institutional MACE variable included prospectively recorded major postoperative cardiovascular complications according to local database definitions. One-year all-cause mortality was ascertained from hospital records and the national death registry; follow-up completeness was 100%.\u003c/p\u003e\n\u003ch3\u003e5. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eContinuous variables are presented as median (interquartile range) and categorical variables as counts (percentages). Group comparisons were performed using the Kruskal\u0026ndash;Wallis test for continuous variables and the chi-square or Fisher\u0026rsquo;s exact test for categorical variables. Logistic regression was used to evaluate the association between NPS and 1-year all-cause mortality. The multivariable model included covariates selected a priori based on clinical relevance: age, left ventricular ejection fraction (LVEF), and diabetes mellitus. This streamlined model was chosen to test the independent association of NPS with mortality after adjustment for established predictors, consistent with the available event rate (88 deaths; events-per-variable [EPV]\u0026thinsp;\u0026asymp;\u0026thinsp;22). Mortality variables were coded as binary outcomes (0\u0026thinsp;=\u0026thinsp;alive, 1\u0026thinsp;=\u0026thinsp;death). NPS was modelled as a continuous ordinal variable (per-point increase). To avoid structural collinearity, other nutritional-inflammatory indices were not entered simultaneously with NPS; their independent associations with mortality are reported from separate univariate analyses. Discriminatory performance was assessed using receiver operating characteristic (ROC) curve analysis; area under the curve (AUC) values are reported with 95% confidence intervals derived from 1,000 bootstrap samples. AUC comparisons were performed using DeLong\u0026rsquo;s method. To evaluate the incremental prognostic value of NPS beyond EuroSCORE II, a combined logistic regression model incorporating both scores was constructed; the predicted probability from this model was used as the composite score for ROC analysis. Left ventricular ejection fraction was entered into the regression model as a continuous variable coded in the direction of increasing risk (i.e., per 1% decrease in LVEF; higher OR values correspond to lower LVEF). Three pre-specified sensitivity analyses were conducted: (1) an expanded multivariable model additionally incorporating sex, serum creatinine, COPD, and BMI, to assess the robustness of the NPS association under broader confounder adjustment; (2) a CRP-adjusted model adding serum CRP as a covariate alongside NPS, to evaluate whether NPS provides prognostic information independent of direct acute-phase inflammatory burden; and (3) a calendar-year-adjusted model incorporating calendar year as a continuous covariate to account for potential temporal confounding due to evolving practice patterns over the eight-year study period. The Spearman rank correlation coefficient was used to assess the relationship between NPS and EuroSCORE II. Model calibration was assessed using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test for both the primary multivariable model and the combined NPS\u0026thinsp;+\u0026thinsp;EuroSCORE II model. Overall model accuracy was quantified using the Brier score and scaled Brier score (relative to a null model predicting the event rate for all patients). To quantify the incremental clinical value of adding NPS to EuroSCORE II, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated with 95% confidence intervals derived from 1,000 bootstrap resamples. To evaluate potential optimism in the combined model, bootstrap internal validation was performed (500 resamples); the optimism-corrected AUC was derived by subtracting the mean optimism from the apparent AUC. This study was reported in accordance with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) guideline. All analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY) with supplementary bootstrap and calibration analyses conducted in Python 3.10. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Patient Characteristics\u003c/h3\u003e\n\u003cp\u003eA total of 1,205 patients were included in the final analysis (Group 1: n\u0026thinsp;=\u0026thinsp;86 [7.1%], Group 2: n\u0026thinsp;=\u0026thinsp;748 [62.1%], Group 3: n\u0026thinsp;=\u0026thinsp;371 [30.8%]). The median age was 61 years [55\u0026ndash;68], and 80.6% were male. Patients in Group 3 were older, had lower LVEF, lower albumin and cholesterol levels, higher NLR, lower LMR, and a greater proportion of EuroSCORE II intermediate- or high-risk classification. Baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1205)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;748)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 3 (n\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 [55\u0026ndash;68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 [53\u0026ndash;65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 [54\u0026ndash;66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 [57\u0026ndash;70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e971 (80.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599 (80.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e312 (84.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.9 [25.3\u0026ndash;30.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5 [26.9\u0026ndash;31.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.0 [25.4\u0026ndash;30.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.7 [24.8\u0026ndash;31.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 [48\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 [48\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 [48\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.5 [48\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1014 (84.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e814 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496 (66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255 (68.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e890 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (80.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270 (73.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 [3.9\u0026ndash;4.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 [4.1\u0026ndash;4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 [4.1\u0026ndash;4.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9 [3.7\u0026ndash;4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 [145\u0026ndash;209]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208.5 [194\u0026ndash;241]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186 [151\u0026ndash;216]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154 [131\u0026ndash;170]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 [34\u0026ndash;47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5 [32\u0026ndash;44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 [34\u0026ndash;47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 [33\u0026ndash;47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.5 [86\u0026ndash;149]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 [129\u0026ndash;174]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.5 [91\u0026ndash;155]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 [75\u0026ndash;116]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 [112\u0026ndash;210]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234.5 [158\u0026ndash;323]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 [117\u0026ndash;217]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130 [95\u0026ndash;172]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9 [2.1\u0026ndash;12.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 [2.3\u0026ndash;8.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 [2.0\u0026ndash;10.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4 [2.4\u0026ndash;18.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 [0.8\u0026ndash;1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 [0.7\u0026ndash;1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 [0.8\u0026ndash;1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 [0.8\u0026ndash;1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 [1.9\u0026ndash;3.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 [1.4\u0026ndash;2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3 [1.8\u0026ndash;2.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4 [2.7\u0026ndash;4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 [2.4\u0026ndash;4.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 [4.8\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 [2.7\u0026ndash;4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 [1.9\u0026ndash;3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.7 [89\u0026ndash;148]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.2 [74\u0026ndash;109]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 [86\u0026ndash;139]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136.3 [106\u0026ndash;183]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.1 [48.4\u0026ndash;55.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8 [54.7\u0026ndash;61.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.4 [50.3\u0026ndash;56.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.7 [44.5\u0026ndash;50.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [0\u0026ndash;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e559 [400\u0026ndash;790]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420 [299\u0026ndash;542]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e519 [382\u0026ndash;699]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e748 [522\u0026ndash;1052]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 [1.1\u0026ndash;2.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 [0.7\u0026ndash;1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4 [1.0\u0026ndash;2.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 [1.6\u0026ndash;3.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaples score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuroSCORE II category, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1038 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (91.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e668 (89.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e291 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 [145\u0026ndash;196]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 [145\u0026ndash;193]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 [145\u0026ndash;195]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172 [146\u0026ndash;204]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBypass time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 [108\u0026ndash;157]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 [104\u0026ndash;154]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 [106\u0026ndash;156]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 [111\u0026ndash;160]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-clamp time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 [62\u0026ndash;100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 [55\u0026ndash;94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 [62\u0026ndash;101]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 [63\u0026ndash;98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData are presented as median [IQR] or n (%). P-values from Kruskal-Wallis test (continuous) or chi-square test (categorical). BMI, body mass index; LVEF, left ventricular ejection fraction; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; PLR, platelet-to-lymphocyte ratio (PLR\u0026thinsp;=\u0026thinsp;platelet count / lymphocyte count); PNI, Prognostic Nutritional Index; CONUT, Controlling Nutritional Status; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index. EuroSCORE II categories: low risk (\u0026lt;\u0026thinsp;2%), intermediate risk (2\u0026ndash;\u0026lt;6%), and high risk (\u0026ge;\u0026thinsp;6%) predicted mortality.\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003e2. Postoperative Outcomes\u003c/h3\u003e\n\u003cp\u003ePostoperative outcomes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A progressive increase in most major outcomes was observed across NPS groups. One-year mortality was 2.3%, 5.1%, and 13.0% in Groups 1, 2, and 3, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In-hospital mortality (1.2%, 3.5%, 7.8%; p\u0026thinsp;=\u0026thinsp;0.001), AKI (2.3%, 5.3%, 11.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stroke (0%, 3.2%, 6.5%; p\u0026thinsp;=\u0026thinsp;0.005), and prolonged mechanical ventilation (7.0%, 8.6%, 17.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) each showed a stepwise increase across groups. In-hospital mortality slightly exceeded 1-month mortality due to a small number of deaths occurring after postoperative day 30 during prolonged index hospitalisation. Among NPS groups, EuroSCORE II category distribution also differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 21.3% of Group 3 patients classified as intermediate or high risk, compared with 8.1% in Group 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePostoperative Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1205)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;748)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 3 (n\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-month mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-month mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal replacement therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProlonged MV (\u0026gt;\u0026thinsp;48h), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIABP use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow cardiac output, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMACE, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReoperation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData are presented as n (%) or median [IQR]. P-values from chi-square or Fisher's exact test (categorical) and Kruskal-Wallis test (continuous). MV, mechanical ventilation; IABP, intra-aortic balloon pump; MACE, major adverse cardiac events; ICU, intensive care unit. P-values are exploratory and not adjusted for multiplicity.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cem\u003eBar charts illustrating key postoperative outcomes stratified by Naples Prognostic Score (NPS) group. A progressive increase in mortality, acute kidney injury, stroke, and prolonged mechanical ventilation is seen across NPS groups (G1: NPS\u0026thinsp;=\u0026thinsp;0; G2: NPS\u0026thinsp;=\u0026thinsp;1\u0026ndash;2; G3: NPS\u0026thinsp;=\u0026thinsp;3\u0026ndash;4). Group sizes reflect the total cohort (G1: n\u0026thinsp;=\u0026thinsp;86, G2: n\u0026thinsp;=\u0026thinsp;748, G3: n\u0026thinsp;=\u0026thinsp;371); these differ from the subgroup numbers in Supplementary Fig.\u0026nbsp;1, which are stratified by EuroSCORE II category. MV, mechanical ventilation; AKI, acute kidney injury; MACE, major adverse cardiac event.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e3. Discriminatory Performance — ROC Analysis\u003c/h3\u003e\n\u003cp\u003eROC curve analysis for 1-year mortality prediction is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The NPS achieved an AUC of 0.670 (95% CI 0.615\u0026ndash;0.721), comparable to NLR (AUC 0.662, 95% CI 0.601\u0026ndash;0.725) and PNI (AUC 0.698, 95% CI 0.640\u0026ndash;0.757), with overlapping confidence intervals across all evaluated scores, indicating no statistically significant differences between indices. There was no statistically significant difference in AUC between the NPS and other individual nutritional-inflammatory indices on DeLong testing (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). EuroSCORE II demonstrated higher discriminatory performance (AUC 0.791, 95% CI 0.740\u0026ndash;0.839). Combining NPS with EuroSCORE II in a logistic regression model yielded AUC 0.839 (95% CI 0.790\u0026ndash;0.882), exceeding either score alone. The Spearman correlation between NPS and EuroSCORE II was weak (ρ\u0026thinsp;=\u0026thinsp;0.172; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting largely independent prognostic information. The combined model showed good calibration (Hosmer\u0026ndash;Lemeshow χ2\u0026thinsp;=\u0026thinsp;4.88, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.179). Bootstrap internal validation of the combined model yielded an apparent AUC of 0.838 with a mean optimism of 0.0006, resulting in an optimism-corrected AUC of 0.837, indicating negligible overfitting. The Brier score for the combined model was 0.0554 (scaled Brier 0.182), compared with 0.0566 (scaled Brier 0.164) for EuroSCORE II alone and 0.0660 (scaled Brier 0.025) for NPS alone, confirming modest but consistent improvement in overall predictive accuracy. The continuous NRI for the combined model compared with EuroSCORE II alone was 0.513 (95% CI 0.278\u0026ndash;0.724), driven primarily by improved reclassification of non-events (NRI₂₋: 0.422). The IDI was 0.015 (95% CI 0.001\u0026ndash;0.042), with non-overlapping confidence intervals excluding zero and confirming statistically significant incremental discrimination. While the continuous NRI suggested apparent reclassification improvement, this metric is known to be susceptible to overestimation in the absence of predefined thresholds. Therefore, the incremental value of NPS should be interpreted primarily in the context of AUC and IDI improvements, which provide more robust estimates. Calibration plots and decision curve analysis for all three models are presented in Supplementary Fig.\u0026nbsp;2. In an exploratory subgroup analysis, among 291 EuroSCORE II low-risk patients with NPS 3\u0026ndash;4, 1-year mortality was 5.2%, compared with 1.9% in EuroSCORE II low-risk patients with NPS\u0026thinsp;\u0026le;\u0026thinsp;2 (OR 2.85, 95% CI 1.36\u0026ndash;5.97; p\u0026thinsp;=\u0026thinsp;0.002; Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Curve Analysis \u0026mdash; 1-Year Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaples score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615\u0026ndash;0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.640\u0026ndash;0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.561\u0026ndash;0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u0026ndash;0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.581\u0026ndash;0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.532\u0026ndash;0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.532\u0026ndash;0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuroSCORE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.740\u0026ndash;0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPS\u0026thinsp;+\u0026thinsp;EuroSCORE II (combined)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.790\u0026ndash;0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAUC values with 95% bootstrap confidence intervals (1,000 iterations). Sensitivity and specificity at the optimal threshold (Youden\u0026rsquo;s index) are reported for individual scores. These values are not reported for the combined model, as no single clinically applicable threshold was defined. AUC, area under the curve; CI, confidence interval; PNI, Prognostic Nutritional Index; CONUT, Controlling Nutritional Status; NLR, neutrophil-to-lymphocyte ratio; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index; LMR, lymphocyte-to-monocyte ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eReceiver operating characteristic (ROC) curves for all evaluated scores. The Naples Prognostic Score (NPS) achieved AUC 0.670 (95% CI 0.615\u0026ndash;0.721), comparable to other nutritional-inflammatory indices. EuroSCORE II demonstrated higher discrimination (AUC 0.791). Combining NPS with EuroSCORE II yielded AUC 0.839. AUC, area under the curve; CI, confidence interval; NPS, Naples Prognostic Score; PNI, Prognostic Nutritional Index; CONUT, Controlling Nutritional Status score; NLR, neutrophil-to-lymphocyte ratio; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index; LMR, lymphocyte-to-monocyte ratio.\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003e4. Multivariable Regression Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate and multivariable logistic regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In univariate analysis, NPS (OR 1.699 per point, 95% CI 1.411\u0026ndash;2.067; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age, LVEF, NLR, PNI, CONUT, SII, and EuroSCORE II were all significantly associated with 1-year mortality. In the multivariable model, NPS remained an independent predictor (aOR 1.486, 95% CI 1.18\u0026ndash;1.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alongside age (aOR 1.078 per year, 95% CI 1.05\u0026ndash;1.11; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LVEF (aOR 1.050 per 1% decrease, 95% CI 1.02\u0026ndash;1.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Diabetes mellitus did not reach statistical significance in the multivariable model (aOR 1.476, 95% CI 0.89\u0026ndash;2.59; p\u0026thinsp;=\u0026thinsp;0.147). EuroSCORE II was not entered into the multivariable model to avoid overadjustment, as EuroSCORE II incorporates several variables already included in the model. Its univariate OR was 7.525 per 1% increase in predicted mortality (95% CI 5.40\u0026ndash;11.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The multivariable model demonstrated good calibration (Hosmer\u0026ndash;Lemeshow χ2\u0026thinsp;=\u0026thinsp;2.78, df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;=\u0026thinsp;0.947). The attenuation of the NPS odds ratio from 1.70 (univariate) to 1.49 (multivariable) is acknowledged, representing approximately 13% reduction after adjustment, which is consistent with expected residual confounding in observational data. In sensitivity analyses, NPS remained independently associated with 1-year mortality across all three additional models: the expanded multivariable model adjusting for sex, creatinine, COPD, and BMI (aOR 1.51, 95% CI 1.20\u0026ndash;1.90; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); the CRP-adjusted model (aOR 1.50, 95% CI 1.19\u0026ndash;1.89; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; serum CRP was not independently significant after NPS adjustment, OR 0.85, p\u0026thinsp;=\u0026thinsp;0.647); and the calendar-year-adjusted model (aOR 1.49, 95% CI 1.18\u0026ndash;1.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; calendar year: OR 0.99, p\u0026thinsp;=\u0026thinsp;0.827). These results indicate that the association between NPS and mortality is robust to expanded confounder adjustment, independent of direct CRP-based inflammation, and not attributable to temporal confounding.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Analysis \u0026mdash; 1-Year Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable aOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.054\u0026ndash;1.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.501\u0026ndash;1.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (per 1% decrease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.036\u0026ndash;1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967\u0026ndash;2.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.543\u0026ndash;1.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaples score (per point)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.411\u0026ndash;2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (per unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.093\u0026ndash;1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI (per unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u0026ndash;0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT (per point)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.155\u0026ndash;1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII (per 100 units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.042\u0026ndash;1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuroSCORE II (per 1% increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.40\u0026ndash;11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; LVEF, left ventricular ejection fraction. Variables in the multivariable model were selected based on clinical relevance: age, left ventricular ejection fraction, diabetes mellitus, and NPS. EuroSCORE II was not included in the multivariable model to avoid model overadjustment, as it incorporates several clinical variables (e.g., age, left ventricular function) that were already entered individually into the model.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eForest plot of adjusted odds ratios (aOR) and 95% confidence intervals from multivariable logistic regression analysis for 1-year all-cause mortality. The Naples Prognostic Score remained an independent predictor after adjustment for age, left ventricular ejection fraction (LVEF), and diabetes mellitus. aOR, adjusted odds ratio; CI, confidence interval; LVEF, left ventricular ejection fraction.\u003c/em\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that the preoperative NPS is independently associated with 1-year mortality after isolated CABG, capturing dimensions of risk not reflected by conventional operative risk models. Although EuroSCORE II demonstrated higher standalone discrimination (AUC 0.791 vs. 0.670), the limited correlation between the two scores (ρ\u0026thinsp;=\u0026thinsp;0.172) indicates that they reflect distinct dimensions of perioperative risk. Combining NPS with EuroSCORE II improved discrimination (AUC 0.839), supporting its role as a complementary tool. Among EuroSCORE II low-risk patients with NPS 3\u0026ndash;4, 1-year mortality was 5.2% \u0026mdash; more than twice that of EuroSCORE II low-risk patients with NPS\u0026thinsp;\u0026le;\u0026thinsp;2 (1.9%) \u0026mdash; illustrating that nutritional-inflammatory burden may identify patients underestimated by conventional models. However, NPS should be regarded as a risk enrichment tool \u0026mdash; a prognostic marker that identifies higher-risk individuals within conventional risk strata \u0026mdash; rather than a modifiable therapeutic target. The clinical utility of NPS-guided preoperative interventions has not been established in interventional studies, and it would be premature to recommend specific management changes based on NPS alone. Whether NPS-guided risk stratification can translate into improved outcomes through targeted preoperative optimisation remains to be tested in prospective trials. From a clinical perspective, NPS should not be interpreted as a standalone decision-making tool but rather as a trigger for enhanced perioperative assessment. Patients identified as high-risk by NPS may benefit from a more comprehensive evaluation of biological vulnerability, including formal frailty assessment, nutritional review, and closer postoperative surveillance. In this context, NPS may serve as a pragmatic surrogate marker of biological frailty in routine clinical practice, where formal frailty indices are not always available. This approach may be particularly relevant in patients classified as low risk by conventional models but with elevated NPS. A key strength of the present study is the direct head-to-head comparison of multiple nutritional-inflammatory indices within the same cohort, allowing a balanced and internally consistent assessment of their relative performance.\u003c/p\u003e \u003cp\u003eSeveral nutritional and inflammatory indices have been evaluated in cardiac surgical populations. Elevated NLR and SII have been associated with adverse outcomes after CABG [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and PNI has shown prognostic value in patients undergoing CABG [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In our cohort, the NPS performed comparably to PNI (AUC 0.670 vs. 0.698) and NLR (AUC 0.662), with no statistically significant differences on DeLong testing. The CONUT score showed the highest specificity but lowest sensitivity, limiting its utility as a screening tool. By integrating both nutritional (albumin, cholesterol) and inflammatory (NLR, LMR) components, the NPS offers a unified composite that may be preferable to single-domain markers. Data specifically evaluating NPS in isolated CABG were previously limited. A recent retrospective study by Sayarer et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] confirmed that NPS independently predicted 30-day and 1-year mortality in 1,195 patients undergoing isolated CABG and improved the predictive performance of EuroSCORE II. Notably, Sayarer et al. similarly reported moderate discriminatory performance for NPS in isolated CABG (AUC 0.693), supporting the consistency of our findings across independent cohorts. The present study extends these findings by providing a comprehensive head-to-head comparison with multiple nutritional-inflammatory indices and a more detailed evaluation of incremental prognostic performance. Despite statistically significant associations, all evaluated indices\u0026mdash;including NPS\u0026mdash;demonstrated only modest and largely overlapping discriminatory performance. This suggests that, while these markers capture relevant biological risk signals, their ability to meaningfully distinguish between individual patient risk levels remains limited when used in isolation. Together with earlier reports linking NPS to saphenous vein graft patency and postoperative AF [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the convergent evidence supports the biological plausibility of our findings.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, its retrospective single-centre design may limit generalisability. Second, residual confounding cannot be excluded, although sensitivity analyses supported the robustness of the findings. Third, perioperative practice evolved over the study period, although calendar-year adjustment did not materially alter results. Fourth, follow-up was registry-based rather than through direct patient contact.\u003c/p\u003e \u003cp\u003eFurthermore, frailty was not formally assessed; given that NPS likely reflects frailty-related biological vulnerability, the extent to which it provides independent prognostic information beyond frailty remains uncertain. Finally, the relatively small size of the reference group (NPS\u0026thinsp;=\u0026thinsp;0) may limit statistical precision. Prospective multicentre validation incorporating formal frailty assessment is warranted.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe preoperative NPS is independently associated with 1-year mortality and adverse postoperative outcomes in patients undergoing isolated on-pump CABG. Although its discriminatory performance is lower than that of EuroSCORE II, it provides complementary prognostic information by reflecting nutritional and inflammatory dimensions of biological vulnerability not captured by conventional models. NPS may serve as a risk enrichment tool, assisting in the identification of higher-risk patients within groups considered low risk by standard risk stratification. However, NPS alone has limited discrimination and is not suitable as a standalone clinical prediction tool. Given important limitations \u0026mdash; including the absence of statin data and lack of formal frailty assessment \u0026mdash; these findings should be considered preliminary. Prospective multicentre validation incorporating these variables is required before NPS can be recommended for routine preoperative use. Its value lies in complementing rather than replacing established risk models.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Istanbul University-Cerrahpaşa Non-Interventional Clinical Research Ethics Committee (approval no: 2024/21; date: 30 January 2024; document no: E-74555795-050.04-902443). The study was conducted in accordance with the Declaration of Helsinki. Individual informed consent was waived given the retrospective design and de-identified data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.D.O. conceived and designed the study, collected and analysed the data, and drafted the manuscript. M.M. supervised the study and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors received language editing support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data underlying this study are not publicly available due to institutional and national data protection regulations but are available from the corresponding author on reasonable request, subject to ethical approval and data sharing agreements.\u003c/p\u003e\n\u003ch3\u003eData Availability Statement\u003c/h3\u003e\n\u003cp\u003eThe data underlying this article are contained within the article itself. The anonymised dataset used for the analyses presented in this study is not publicly available due to institutional data protection regulations and Turkish national health data legislation, but may be made available upon reasonable request to the corresponding author, subject to ethical approval and data sharing agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKoertzen M, Punjabi PP, Lockwood GG. Pre-operative serum albumin concentration as a predictor of mortality and morbidity following cardiac surgery. Perfusion. 2013;28(5):390\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaras PL, Goh SL, Dhital K. Is low serum albumin associated with postoperative complications in patients undergoing cardiac surgery? Interact Cardiovasc Thorac Surg. 2015;21(6):777\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngelman DT, Adams DH, Byrne JG, et al. 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The performance of EuroSCORE II in CABG patients in relation to sex, age, and surgical risk: a nationwide study in 14,118 patients. J Cardiothorac Surg. 2023;18(1):40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiancari F, Vasques F, Mikkola R, Martin M, Lahtinen J, Heikkinen J. Validation of EuroSCORE II in patients undergoing coronary artery bypass surgery. Ann Thorac Surg. 2012;93(6):1930\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalizia G, Lieto E, Auricchio A, Cardella F, Mabilia A, Podzemny V, et al. Naples prognostic score, based on nutritional and inflammatory status, is an independent predictor of long-term outcome in patients undergoing surgery for colorectal cancer. Dis Colon Rectum. 2017;60(12):1273\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong J, Hu H, Kang W, Liu H, Ma F, Ma S, et al. Prognostic impact of preoperative Naples prognostic score in gastric cancer patients undergoing surgery. Front Surg. 2021;8:617744.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng SM, Ren JJ, Yu N, Xu JY, Chen GC, Li X, et al. The prognostic value of the Naples prognostic score for patients with non-small-cell lung cancer. Sci Rep. 2022;12:5782.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Feng Y, Tian Y, et al. The prognostic role of Naples Prognostic Score in patients with coronary artery disease. J Inflamm Res. 2025;18:6999\u0026ndash;7012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Pei M, Yang X, et al. Associations of Naples prognostic score with stroke in adults and all cause mortality among stroke patients. Sci Rep. 2025;15:10718.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdogan A, Genc O, Inan D, et al. Impact of Naples Prognostic Score on midterm all-cause mortality in patients with decompensated heart failure. Biomark Med. 2023;17(4):219\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydın SŞ, Aydemir S, \u0026Ouml;zmen M, Aksakal E, Sara\u0026ccedil; İ, Aydınyılmaz F, Altınkaya O, Birdal O, Tanboğa İH. The importance of Naples prognostic score in predicting long-term mortality in heart failure patients. Ann Med. 2025;57(1):2442536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdogan A, Genc O, Ozkan E, et al. Impact of Naples Prognostic Score at admission on in-hospital and follow-up outcomes among STEMI patients. Angiology. 2023;74(10):970\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUysal OK, Ozdogru D, Yildirim A, Ozturk I, Tras G, Arlier Z. The prognostic value of a Naples score in determining in-hospital mortality in patients with acute ischemic stroke undergoing endovascular treatment. J Clin Med. 2024;13(21):6434.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaraduman A, Yılmaz C, Tiryaki MM, Balaban İ, Keten MF, Unkun T, İzci S, Efe S\u0026Ccedil;, Alizade E. Relationship between the Naples prognostic score and saphenous vein graft disease after coronary artery bypass grafting surgery. Arq Bras Cardiol. 2025;122(5):e20240519.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksen D, Guven B, Donmez A, Yesiltas MA, Koyuncu AO, Gulbudak S, Oktay V. Predicting postoperative atrial fibrillation after cardiac surgery using the Naples prognostic score. Coron Artery Dis. 2025;36(3):225\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Ulibarri JI, Gonz\u0026aacute;lez-Madro\u0026ntilde;o A, de Villar NG, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakagomi A, Kohashi K, Morisawa T, et al. Nutritional status is associated with inflammation and predicts a poor outcome in patients with chronic heart failure. J Atheroscler Thromb. 2016;23(6):713\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Brien SM, Feng L, He X, et al. The Society of Thoracic Surgeons 2018 adult cardiac surgery risk models: part 2\u0026mdash;statistical methods and results. Ann Thorac Surg. 2018;105(5):1419\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKDIGO Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2(1):1\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlagha S, Miniksar \u0026Ouml;H, Polat MN, Kara M, Şenayıl Y. The prognostic value of inflammatory indices in predicting poor postoperative outcomes in isolated coronary artery bypass graft surgery. Cureus. 2023;15(8):e43120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDey S, Kashav R, Kohli JK, Magoon R, ItiShri, Walian A, Grover V. Systemic immune-inflammation index predicts poor outcome after elective off-pump CABG: a retrospective, single-center study. J Cardiothorac Vasc Anesth. 2021;35(8):2397\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeskin M, İpek G, Aldağ M, Altay S, Hayıroğlu Mİ, B\u0026ouml;rkl\u0026uuml; EB, İnan D, Kozan \u0026Ouml;. Effect of nutritional status on mortality in patients undergoing coronary artery bypass grafting. Nutrition. 2018;48:82\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGim DH, Lee SH, Cha JH, et al. Long-term prognostic value of the prognostic nutritional index in patients undergoing coronary artery bypass grafting. J Am Heart Assoc. 2025;14(19):e043597.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei Y, Wang W, Hua C, Chen Y. Association between prognostic nutritional index and prognosis of patients receiving coronary artery bypass grafting surgery: a systematic review and meta-analysis. Front Cardiovasc Med. 2026;13:1673038.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSayarer C, Dayanir OB, Eken EN, Gencpinar T, Bayrak S. The impact of the Naples prognostic score on outcomes in patients undergoing coronary artery bypass grafting. Turk J Thorac Cardiovasc Surg. 2026;34(2):116\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupplementary Fig. 1. One-year all-cause mortality (%) stratified by NPS group and EuroSCORE II category. The highlighted cell (NPS Group 3, EuroSCORE II low-risk) shows 5.2% mortality, compared with 1.9% in patients with NPS Groups 1\u0026ndash;2 within the same risk stratum.\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":false,"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":"Naples Prognostic Score, coronary artery bypass grafting, nutritional status, inflammation, EuroSCORE II, mortality","lastPublishedDoi":"10.21203/rs.3.rs-9382426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9382426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecent studies have highlighted the prognostic significance of the Naples Prognostic Score (NPS) in patients undergoing coronary artery bypass grafting (CABG). However, its comparative performance against other nutritional-inflammatory indices and its incremental value beyond established risk models such as EuroSCORE II are not fully established.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included 1,205 adult patients undergoing isolated on-pump CABG between 2015 and 2023 at a tertiary centre. The NPS (0\u0026ndash;4) was calculated from preoperative laboratory values and analysed as a continuous variable and in predefined categories (0, 1\u0026ndash;2, 3\u0026ndash;4). The primary endpoint was 1-year all-cause mortality. Discriminatory performance was assessed using ROC analysis with bootstrap confidence intervals and DeLong\u0026rsquo;s test. Incremental value beyond EuroSCORE II was evaluated using a combined logistic regression model, with net reclassification improvement (NRI) and integrated discrimination improvement (IDI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOne-year mortality increased across NPS groups (2.3%, 5.1%, and 13.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable analysis adjusting for age, left ventricular ejection fraction, and diabetes mellitus, NPS remained independently associated with mortality (adjusted OR 1.49 per point increase, 95% CI 1.18\u0026ndash;1.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The NPS demonstrated modest discrimination (AUC 0.670), comparable to other nutritional-inflammatory indices, whereas EuroSCORE II showed higher discrimination (AUC 0.791). A combined model including NPS and EuroSCORE II improved discrimination (AUC 0.839). Among EuroSCORE II low-risk patients, those with NPS 3\u0026ndash;4 had significantly higher 1-year mortality than those with NPS\u0026thinsp;\u0026le;\u0026thinsp;2 (5.2% vs. 1.9%; OR 2.85, 95% CI 1.36\u0026ndash;5.97; p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe preoperative NPS is independently associated with 1-year mortality after isolated CABG and provides modest but consistent incremental prognostic value beyond EuroSCORE II, although prospective multicentre validation is needed.\u003c/p\u003e","manuscriptTitle":"Comparative and Incremental Prognostic Value of the Naples Prognostic Score Beyond EuroSCORE II in Isolated Coronary Artery Bypass Grafting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 05:52:51","doi":"10.21203/rs.3.rs-9382426/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"45459396944335239570673577840488928254","date":"2026-05-21T08:29:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T10:44:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T17:11:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T12:54:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T12:54:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-04-10T18:20:40+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":"a72279b1-bbe2-4174-9a0d-7d0efdbf35a8","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"45459396944335239570673577840488928254","date":"2026-05-21T08:29:44+00:00","index":54,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-06T10:44:19+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T05:52:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 05:52:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9382426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9382426","identity":"rs-9382426","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}