The HALP–CONUT Integrated Score Predicts Survival and Postoperative Complications in Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy: Evidence from a Multicenter Retrospective Cohort Study

The HALP–CONUT Integrated Score Predicts Survival and Postoperative Complications in Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy: Evidence from a Multicenter Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The HALP–CONUT Integrated Score Predicts Survival and Postoperative Complications in Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy: Evidence from a Multicenter Retrospective Cohort Study Hao Chen, Xuan Huang, Zerui Lin, Yuanpu Wei, Chun Chen, Bin Zheng, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8184513/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The prognostic role of nutrition–inflammation indices in esophageal squamous cell carcinoma (ESCC) requires further investigation. We developed the HALP–CONUT Integrated Score (HCIS) and evaluated its ability to predict survival and postoperative complications in locally advanced ESCC patients undergoing neoadjuvant therapy. Methods This multicenter retrospective study analyzed 410 patients. HCIS was calculated by integrating the HALP index and CONUT score. Patients were stratified into risk groups. Survival was analyzed using Kaplan–Meier and Cox regression. Logistic regression assessed associations with complications and pathologic complete response (pCR). Predictive performance was evaluated using ROC curves, C-index, and decision curve analysis. Results Patients with high HCIS scores had significantly worse overall survival (OS) compared with those in lower-risk groups (p < 0.001). Multivariate Cox analysis confirmed HCIS as an independent prognostic factor for OS (HR 4.59, 95% CI 2.64–7.97, p < 0.001). Higher HCIS scores were also independently associated with increased risk of major postoperative complications (OR 7.144, 95% CI 3.698–13.813, p < 0.001) and a lower likelihood of achieving pCR (OR 6.470, 95% CI 3.577–12.40, p = 0.005). Predictive models incorporating HCIS demonstrated superior discrimination compared with models based on HALP or CONUT alone (AUC for OS: 0.789 vs 0.711 and 0.703, respectively). Conclusion HCIS is a robust biomarker that integrates hematologic, nutritional, and inflammatory parameters. It independently predicts survival, postoperative complications, and pCR, providing a valuable tool for risk stratification and individualized treatment planning in locally advanced ESCC. HALP CONUT HCIS esophageal squamous cell carcinoma neoadjuvant therapy prognosis complications Figures Figure 1 Figure 2 Figure 3 Figure 4 1.Introduction Esophageal cancer remains one of the leading causes of cancer-related mortality worldwide, with esophageal squamous cell carcinoma (ESCC) being the predominant histological type in East Asia [ 1 ] . Despite advances in multimodal therapy, including neoadjuvant chemoradiotherapy and surgery, the prognosis of locally advanced ESCC remains unsatisfactory, with high rates of recurrence and postoperative complications [ 2 ],[ 3 ] . Reliable biomarkers for risk stratification and outcome prediction are urgently needed to guide individualized treatment. Malnutrition and systemic inflammation have been recognized as critical determinants of treatment tolerance and long-term outcomes in cancer patients [ 4 ] . Several composite indices derived from hematological and nutritional parameters, such as the Controlling Nutritional Status (CONUT) score and the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) index, have shown prognostic value in various malignancies [ 5 ],[ 6 ] . However, each index has limitations when applied independently, as they fail to capture the multidimensional interplay between nutrition, systemic inflammation, and immunity [ 7 ],[ 8 ] . To overcome these limitations, we developed the HALP–CONUT Integrated Score (HCIS), a novel index combining HALP and CONUT to provide a more comprehensive reflection of host nutritional–inflammatory status. Preliminary evidence suggests that integrated indices may improve predictive accuracy compared with single measures [ 9 ],[ 10 ] . However, the prognostic and predictive role of HCIS in patients with locally advanced ESCC undergoing neoadjuvant therapy has not yet been investigated. Therefore, this multicenter retrospective cohort study aimed to evaluate the predictive value of HCIS for overall survival, postoperative complications, and pathological complete response (pCR) in patients with locally advanced ESCC after neoadjuvant therapy. We further compared HCIS with existing indices to determine whether this integrated score provides superior risk stratification and clinical utility. 2. Materials And Methods 2.1 Data Collection This multicenter retrospective study was approved by the ethics committee of the lead institution, Fujian Medical University Union Hospital, and received either exemption or approval from the ethics committees of the Provincial Hospital Affiliated to Fuzhou University, Fuzhou Pulmonary Hospital, and Zhangzhou Second Hospital. Written informed consent was obtained from all participants. The study included adult patients who underwent total esophagectomy between July 2012 and November 2019 at the four participating hospitals. 2.2 Participant Selection Inclusion criteria were: (1) histopathologically confirmed esophageal squamous cell carcinoma after surgery; (2) patients who received neoadjuvant therapy prior to surgery; (3) resectable tumor lesions after neoadjuvant therapy; and (4) presence of a single tumor lesion. Exclusion criteria included: (1) CONUT score > 4; (2) history within one month before surgery of pulmonary infection, severe chronic obstructive pulmonary disease, pulmonary bullae, respiratory or cardiac failure, hyperglycemia, acute or chronic renal insufficiency, significant malnutrition, inflammatory or thrombotic diseases, or liver failure; (3) multiple primary malignancies; (4) missing preoperative or postoperative laboratory or clinical data; (5) loss to follow-up or refusal of follow-up after surgery; and (6) missing neoadjuvant-surgery interval or an interval exceeding six weeks. Initially, 817 patients diagnosed with ESCC were identified from the databases of the four hospitals. Among them, 213 patients with advanced disease were deemed ineligible for surgical intervention. After initial screening, 604 patients remained as potential candidates. After applying stringent inclusion and exclusion criteria, 410 patients with locally advanced ESCC who underwent surgery after neoadjuvant therapy were ultimately included(Fig. 1 ). 2.3 Operational Definitions Based on preoperative CONUT scores, patients were classified into a normal group (n = 124) and a mildly abnormal group (n = 286). The HALP score was calculated as: HALP = Hb (g/L) × Albumin (g/L) × Lymphocyte (10⁹/L) / Platelet (10⁹/L). The optimal HALP cutoff value of 39.78 was determined using the maximum Youden index (sensitivity + specificity – 1) (AUC = 0.711, 95% CI: 0.661–0.762). To evaluate the stability of this cutoff, internal validation was performed using Bootstrap resampling (1000 repetitions). Results showed a concentrated Bootstrap distribution (mean: 40.3) with a 95% confidence interval (36.37–45.08) highly consistent with the original cutoff (39.78), indicating good consistency and stability (Figure S1 ). Patients were divided into high HALP (n = 220) and low HALP (n = 190) groups. Combining CONUT and HALP, patients were further stratified into three groups: Low group : High HALP and normal CONUT Intermediate group : High HALP and mildly abnormal CONUT, or low HALP and normal CONUT High group : Low HALP and mildly abnormal CONUT All minimally invasive esophagectomy (MIE) procedures were performed by senior thoracic surgeons with extensive experience in MIE. Operative protocols were standardized across surgical approaches. The decision for postoperative discharge was based on the attending physician’s assessment and fulfillment of discharge criteria: (1) optimal general health with adapted enteral nutrition and restored bowel function; (2) smooth recovery from surgical stress with effective pain control; (3) normal body temperature, unremarkable thoracic/abdominal examinations, and laboratory results within or near normal ranges; (4) early ambulation leading to improved activities of daily living after discharge; (5) absence of wound infection symptoms and complete removal of drainage tubes; and (6) postoperative CT showing satisfactory anastomotic healing. The primary outcome was overall survival (OS), calculated from the date of discharge to the date of death or the last follow-up for surviving patients. 2.4 Measured Variables Preoperative demographic and clinical data were collected, including blood samples, age, sex, body mass index (BMI), OS, length of hospital stay (LOS), smoking history (lifetime cumulative smoking ≥ 100 cigarettes), alcohol use (at least one drink in the past 12 months), preoperative tumor imaging, tumor location, postoperative TNM stage, presence of neural or vascular invasion, and postoperative complications. Fasting peripheral blood samples were collected 1–7 days before surgery. The normal BMI range was defined as 18.5–23.9 kg/m². Established reference intervals for blood biomarkers included: hemoglobin (female: 110–150 g/L; male: 120–165 g/L), albumin (40–55 g/L), total cholesterol (131.48–235.89 mg/dL), neutrophil count (1.5–7.0 × 10⁹/L), lymphocyte count (0.8–4.0 × 10⁹/L), monocyte count (0.12–0.8 × 10⁹/L), platelet count (100–300 × 10⁹/L), carcinoembryonic antigen (0–5 ng/mL), and CA19-9 (0–37 U/mL). 2.5 Statistical Analysis Statistical analyses were performed using R software (version 4.5.0). Continuous variables are expressed as median (interquartile range), and categorical variables as frequency (percentage). Intergroup comparisons were conducted using the Kruskal–Wallis test or χ² test, as appropriate. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the predictive performance of HCIS, HALP, and CONUT for 5-year survival, and differences in area under the curve (AUC) were compared using DeLong’s test. Category-free NRI and IDI were computed with 1,000 bootstrap resamples using the survival IDINRI package (R 4.5.0). Survival analysis was performed using the Kaplan–Meier method with log-rank test. Multivariate survival analysis was carried out using Cox proportional hazards models, and logistic regression models were used to identify independent predictors of complications and pathological complete response (pCR). To mitigate potential confounding, propensity score matching (PSM) was implemented using 1:1:1 nearest-neighbor matching with a caliper width of 0.05 standard deviations. A nomogram was constructed and validated through calibration curves, concordance index (C-index), time-dependent ROC analysis, and decision curve analysis (DCA). Results with borderline statistical significance (i.e., P-values approaching 0.05) should be interpreted with caution. A P-value < 0.05 was considered statistically significant. Note on multiple comparisons : The multivariate Cox and logistic regression models in this study were designed to validate the independent predictive value of prespecified core variables (such as HCIS score and pathological stage) based on prior research and clinical rationale, rather than to perform exploratory testing of a large number of hypotheses. Given the limited number of variables included in the final models (Cox model: 4; complication logistic model: 2; pCR logistic model: 3), the risk of Type I error inflation due to multiple comparisons was considered low. Therefore, to maintain consistency with comparable studies and to directly report adjusted effect estimates, no additional multiplicity adjustment was applied to the P-values in the multivariate models. Sensitivity analyses were subsequently conducted to assess the robustness of the findings (see Sections 3.4 , 3.5 , and 3.6 ). 3. Results 3.1 Baseline Characteristics and Treatment Analysis of Esophageal Cancer Patients We analyzed 410 patients with locally advanced ESCC treated with neoadjuvant therapy followed by esophagectomy; cohort demographics, tumor location, comorbidity burden, and laboratory indices are summarized in Table S1 . The cohort was predominantly male with a median age of 60 years, and tumors most commonly arose in the middle thoracic esophagus. By preoperative HCIS stratification, 46.10% were low-risk, 31.22% intermediate-risk, and 22.68% high-risk (Table S1 ). Across HCIS strata, baseline and treatment features differed for BMI (p = 0.012), sex (p = 0.004), Charlson index (p = 0.013), clinical T and N stage (both p ≤ 0.006), surgical approach (p = 0.026), number of retrieved nodes (p = 0.019), and adjuvant regimen (p < 0.001), with higher HCIS aligning with lower BMI and more advanced cT/cN (Table S2 ). All patients underwent esophagectomy (McKeown and Ivor-Lewis were the most common approaches) with a median of 34 lymph nodes examined (Table S1 ). A pathologic complete response was achieved in 10.49% overall. Group-wise matched comparisons are presented in Table S3 . 3.2 Comparison of Predictive Performance of HCIS, HALP, and CONUT Scores for 5-Year Survival Using ROC analysis with DeLong’s test for between-model comparison, HCIS demonstrated the highest discriminative ability for 5-year overall survival, with an AUC of 0.789, compared with 0.711 for HALP and 0.703 for CONUT (Figure S2 ). To quantify reclassification and discrimination gains, we calculated NRI and IDI. Versus HALP, HCIS improved reclassification by 0.124 (95% CI 0.021–0.235, P < 0.05) and discrimination by an IDI of 0.143 (95% CI 0.070–0.213, P < 0.05). Versus CONUT, NRI was 0.485 (95% CI 0.404–0.564, P < 0.001) and IDI was 0.097 (95% CI 0.038–0.160, P < 0.05). Collectively, these metrics indicate that HCIS outperforms its component indices not only in overall accuracy but also in patient-level risk reclassification, supporting its use for prognostic stratification after neoadjuvant therapy. 3.3 Survival Differences by HCIS Stratification and Impact of Adjuvant Therapy Patients were stratified by preoperative HCIS into low- (n = 189, 46.1%), intermediate- (n = 128, 31.2%), and high-score (n = 93, 22.7%) groups. Baseline characteristics differed significantly across the three strata for BMI, sex, Charlson comorbidity index, clinical stage, surgical approach, number of lymph nodes retrieved, and adjuvant regimen (all p < 0.05; Table S2 ). To reduce confounding, propensity score matching (PSM) was performed, yielding 64 well-balanced patients per group (192 total; Table S3 ). After matching, all covariates were adequately balanced, confirming good model calibration. Kaplan–Meier analysis demonstrated significant differences in overall survival (OS) among HCIS groups after matching (p < 0.001; Figure S3 A). Patients with higher HCIS had progressively poorer OS, validating HCIS as a strong prognostic stratification tool. Length-of-stay (LOS) analyses further showed longer postoperative hospitalization in the high HCIS group, whereas low HCIS patients experienced the shortest LOS (Figure S3 B). Within-group evaluation of adjuvant therapy effects revealed no survival advantage in the low- or intermediate-HCIS groups ( p = 0.72 and 0.88 , respectively). In contrast, adjuvant therapy significantly improved OS in high-HCIS patients ( p = 0.0041 ), suggesting that those with the most unfavorable inflammatory-nutritional profiles derive the greatest benefit. Kaplan–Meier curves depicting these differences are presented in Figures S4 A-C. 3.4 Predictors of Overall Survival On univariate analysis, multiple clinicopathologic and hematologic variables were associated with overall survival (OS), including sex, BMI, smoking status, ypT stage, ypN stage, perineural and lymphovascular invasion, HCIS category, neutrophil and monocyte counts, hemoglobin, albumin, and total cholesterol (all P < 0.05). In the multivariable Cox model, four factors remained independently prognostic (Figure S5 ). HCIS category: Relative to the low-risk group, the intermediate-risk group had a 90% higher mortality risk ( HR 1.898, 95% CI 1.256–2.868, P = 0.002), and the high-risk group had an approximately 4.6-fold higher risk ( HR 4.588, 95% CI 2.461–8.533, P < 0.001). ypT stage: Mortality risk increased stepwise with higher pathologic T stage versus ypT0—ypT1 ( HR 2.323, 95% CI 1.147–4.704, P = 0.019); ypT2 ( HR 2.956, 95% CI 1.584–5.517, P < 0.001); ypT3 ( HR 3.331, 95% CI 1.849–6.002, P < 0.001); ypT4 ( HR 3.729, 95% CI 2.158–7.467, P = 0.015). ypN stage: A graded association was also observed versus ypN0—ypN1 ( HR 1.420, 95% CI 1.121–2.184, P = 0.005); ypN2 ( HR 1.721, 95% CI 1.080–2.743, P = 0.022); ypN3 ( HR 2.492, 95% CI 1.266–4.905, P = 0.008). BMI: BMI > 23.9 kg/m² correlated with shorter OS ( HR 1.470, 95% CI 1.042–2.072, P = 0.028). The detail results are summarized in Table S4 . To account for multiple comparisons, we applied false discovery rate (FDR) correction to the four significant signals from the multivariable model. All associations remained significant after adjustment, supporting robustness of the primary findings: HCIS (intermediate vs low: P FDR = 0.015; high vs low: P FDR = 0.009), ypT (ypT1 vs ypT0: P FDR = 0.027; ypT2: P FDR = 0.043; ypT3: P FDR = 0.036; ypT4: P FDR = 0.028), ypN (ypN1 vs ypN0: P FDR = 0.033; ypN2: P FDR = 0.028; ypN3: P FDR = 0.041), and BMI ( P FDR = 0.034). Subgroup analyses stratified by sex, BMI, surgical approach, adjuvant-therapy type, tumor location, and clinical T/N stage showed a consistent gradient of risk across HCIS strata: compared with the low-HCIS group, both intermediate- and high-HCIS groups exhibited higher OS mortality across most subgroups, with the high-HCIS group uniformly at greatest risk (Figure S6 ). For clinical translation, we integrated HCIS, ypT, ypN, and BMI into a nomogram derived from the multivariable model (C-index 0.747; 95% CI 0.713–0.781) (Fig. 2 A). Internal validation demonstrated close agreement between predicted and observed 3- and 5-year OS (Figs. 2 B-C). Using the scoring system, total score 90% 3-year OS, while total score 90% 5-year OS. Time-dependent ROC analyses confirmed the nomogram’s superior discrimination versus traditional ypTNM across time points (AUC 0.777–0.875 vs 0.571–0.690) (Fig. 2 D). Decision curve analysis further showed a consistently higher net clinical benefit for the nomogram across threshold probabilities of 0.20–0.80 (Fig. 2 E). Collectively, these findings indicate that the nomogram offers a more accurate and clinically practical approach to survival prediction after neoadjuvant therapy than conventional staging. 3.5 Predictive Factors for Postoperative Complications To develop a risk tool for severe postoperative complications (Clavien–Dindo grade ≥ III), we performed univariate and multivariable logistic regression (Table 1 ). On univariate analysis, lymphocyte count, hemoglobin, albumin, and HCIS category were associated with complications. After multivariable adjustment, only lymphocyte count ( OR 1.729, 95% CI 1.114–2.682, P = 0.015) and HCIS category (intermediate vs low: OR 2.393, 95% CI 1.128–5.076; high vs low: OR 7.771, 95% CI 3.521–17.150; both P < 0.05) remained independently predictive. Table 1 Univariate and multivariate logistic regression analyses assessing the association between clinical factors and postoperative Clavien-Dindo grade ≥ III complications, presented as OR (95% CI) and P-value. Univariate analysis Multivariate analysis OR(95%CI) P OR(95%CI) P Age(years) <60 1.00(Reference) 1.00(Reference) ≥ 60 1.622(0.973–2.701) 0.063 1.612(0.915–2.838) 0.098 BMI(kg/m²) 1.010(0.929–1.098) 0.824 1.065(0.971–1.167) 0.18 Sex Female 1.00(Reference) 1.00(Reference) Male 1.143(0.614–2.126) 0.674 1.989(0.826–4.786) 0.125 Smoke No 1.00(Reference) 1.00(Reference) Yes 1.013(0.612–1.677) 0.959 1.344(0.629–2.874) 0.445 Drink No 1.00(Reference) 1.00(Reference) Yes 0.894(0.546–1.465) 0.658 0.712(0.363-1.400) 0.325 Surgery Ivor-Lewis 1.00(Reference) 1.00(Reference) McKeown 1.204(0.733–1.978) 0.464 0.951(0.491–1.840) 0.881 Number of retrieved nodes(n) 1.014(0.986–1.043) 0.318 1.006(0.970–1.044) 0.742 Platelet counts(×109/L) 1.000(0.996–1.003) 0.854 0.998(0.993–1.002) 0.356 Neutrophil counts (×109/L) 1.110(0.962–1.281) 0.154 1.041(0.869–1.247) 0.661 Lymphocyte count (×10 9 /L) 1.583(1.090–2.298) 0.016 1.729(1.114–2.682) 0.015 Monocyte count (×10 9 /L) 1.594(0.540–4.704) 0.398 1.034(0.231–4.627) 0.965 Hemoglobin(g/L) 0.973(0.957–0.989) 0.001 0.984(0.963–1.005) 0.137 Albumin(g/L) 0.933(0.876–0.993) 0.030 1.018(0.943–1.099) 0.646 Total cholesterol(mg/dl) 0.995(0.990–1.001) 0.083 1.002(0.996–1.009) 0.424 HCIS Low 1.00(Reference) 1.00(Reference) Intermediate 2.624(1.339–5.145) 0.005 2.393(1.128–5.076) 0.023 High 7.144(3.695–13.813) <0.001 7.771(3.521–17.150) <0.001 To address multiplicity, FDR correction was applied to the two significant signals; both retained statistical significance (lymphocyte count: P FDR = 0.030; HCIS—intermediate vs low: P FDR = 0.046; high vs low: P FDR < 0.001), supporting their robustness. A two-factor nomogram incorporating lymphocyte count and HCIS demonstrated good performance (C-index/AUC 0.733, 95% CI 0.675–0.796) with excellent calibration (Figs. 3 A-C). Decision-curve analysis showed higher net clinical benefit than “treat-all” or “treat-none” strategies for threshold probabilities 0.20–0.60 (Fig. 3 D). This model provides a concise, practical tool for preoperative risk stratification and individualized perioperative management in locally advanced ESCC after neoadjuvant therapy. 3.6 Predictive Factors for Pathological Complete Response (pCR) Univariate logistic regression identified neutrophil count, total cholesterol, and HCIS category as significant correlates of pathological complete response (pCR). In multivariable analysis, all three remained independent predictors: neutrophil count ( OR 1.520, P = 0.017), total cholesterol ( OR 0.990, P = 0.020), and HCIS (intermediate vs low: OR 4.930, P = 0.003; high vs low: OR 6.470, P = 0.005) (Table 2 ). Table 2 Univariate and Multivariate Logistic Regression Analyses of Pathological Complete Response (pCR) in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy. Univariate analysis Multivariate analysis OR(95%CI) P OR(95%CI) P Age(years) <60 1.00(Reference) 1.00(Reference) ≥ 60 0.687(0.358–1.317) 0.258 0.671(0.321-1.400) 0.287 Sex Female 1.00(Reference) 1.00(Reference) Male 0.997(0.459–2.167) 0.994 1.161(0.342–3.943) 0.811 cT 1 1.00(Reference) 1.00(Reference) 2 1.040(0.439–2.466) 0.087 1.581(0.547–3.573) 0.398 3 1.139(0.508–2.555) 0.751 0.838(0.330–2.126) 0.710 4 2.329(0.884–6.136) 0.928 0.741(0.444–2.031) 0.560 cN 0 1.00(Reference) 1.00(Reference) 1 1.743(1.140–3.529) 0.015 1.650(0.660–4.150) 0.283 2 3.176(1.367–7.378) 0.007 1.321(0.664–2.743) 0.409 3 3.207(0.740–4.107) 0.204 1.524(0.986–2.471) 0.749 Smoke No 1.00(Reference) 1.00(Reference) Yes 1.417(1.014–2.547) 0.294 1.339(1.147–2.146) 0.057 Drink No 1.00(Reference) 1.00(Reference) Yes 0.992(0.527–1.866) 0.979 1.707(0.724–4.027) 0.222 CEA(ng/ml) 1.016(0.903–1.144) 0.786 0.966(0.846–1.104) 0.616 CA199(U/ml) 1.008(0.997–1.019) 0.170 1.010(0.997–1.022) 0.126 Platelet counts(×10 9 /L) 1.001(0.996–1.006) 0.805 0.998(0.992–1.004) 0.558 Neutrophil counts (×10 9 /L) 1.373(1.051–1.794) 0.020 1.520(1.078–2.144) 0.017 Monocyte count (×10 9 /L) 1.717(1.313–2.411) 0.533 1.810(1.195–3.472) 0.602 Hemoglobin(g/L) 1.001(0.980–1.022) 0.922 1.016(0.986–1.048) 0.302 Albumin(g/L) 0.999(0.920–1.086) 0.989 1.057(0.940–1.189) 0.351 Total cholesterol(mg/dl) 0.992(0.986–0.998) 0.013 0.990(0.982–0.998) 0.020 HCIS Low 1.00(Reference) 1.00(Reference) Intermediate 3.030(1.293–7.099) 0.011 4.930(2.733–8.146) 0.003 High 3.767(1.289–11.011) 0.015 6.470(3.577–12.40) 0.005 After FDR correction, significance persisted for neutrophil count ( P FDR = 0.043) and HCIS (intermediate vs low and high vs low, each P FDR = 0.015), while total cholesterol approached significance ( P FDR = 0.060). These findings indicate that elevated neutrophil levels and higher inflammatory–nutritional burden, reflected by HCIS, are robust predictors of reduced pCR probability. A three-factor nomogram incorporating neutrophil count, total cholesterol, and HCIS achieved a C-index of 0.717 (95% CI 0.650–0.798) (Fig. 4 A). The calibration curve showed close concordance between predicted and observed pCR probabilities (Fig. 4 B). ROC analysis confirmed good discrimination with an AUC of 0.717 (95% CI 0.650–0.798) (Fig. 4 C). Finally, decision-curve analysis (DCA) demonstrated a clear net clinical benefit for the nomogram across a decision-threshold range of 0.10–0.50, outperforming “treat-all” and “treat-none” strategies (Fig. 4 D). Taken together, this model offers a concise, interpretable, and clinically applicable tool for preoperative identification of patients most likely to achieve pCR following neoadjuvant therapy in locally advanced ESCC. 4. Discussion In this multicenter retrospective cohort of locally advanced ESCC treated with neoadjuvant therapy followed by esophagectomy, we show that the HALP–CONUT Integrated Score (HCIS) offers reproducible and clinically meaningful risk stratification. HCIS outperformed HALP and CONUT for 5-year survival discrimination and remained independently associated with overall survival after adjustment for pathologic factors. Importantly, HCIS also predicted major postoperative complications and pathologic complete response (pCR), indicating utility across perioperative safety and oncologic efficacy domains. Together, these results support HCIS as a pragmatic biomarker for individualized risk assessment in ESCC. Our findings fit within a broad evidence base linking systemic inflammation and nutritional depletion to adverse cancer outcomes and treatment intolerance [ 11 ]−[ 15 ] . By integrating hematologic and nutritional components, HCIS captures the inflammation–malnutrition–immune dysregulation axis that shapes host–tumor interactions [ 7 ]−[ 10 ],[ 16 ] . Elevated neutrophils and relative lymphopenia are associated with immunosuppressive tumor microenvironments [ 17 ],[ 18 ] , while hypoalbuminemia and disordered lipid metabolism reflect impaired reserve and repair [ 4 ],[ 19 ],[ 20 ] ; these mechanisms plausibly underlie the observed associations between higher HCIS and worse OS, greater complication burden, and lower pCR. In this context, HCIS provides a single, accessible index that consolidates signals previously reported across multiple inflammation–nutrition metrics [ 3 ],[ 5 ],[ 6 ],[ 8 ],[ 10 ],[ 21 ] . We also observed heterogeneity of adjuvant benefit by baseline inflammatory–nutritional risk: adjuvant therapy conferred no measurable advantage in low- and intermediate-HCIS strata but was associated with significantly improved survival among high-HCIS patients. This pattern aligns with literature suggesting that systemic inflammation and malnutrition amplify perioperative risk and blunt treatment efficacy, yet may also identify patients who derive outsized benefit from intensified postoperative strategies when appropriately selected [ 21 ]−[ 23 ] . Clinically, these data argue for risk-adapted pathways: (i) prehabilitation and targeted nutritional/immunonutritional optimization for patients with elevated HCIS [ 21 ],[ 24 ] ; and (ii) shared decision-making around adjuvant intensification that prioritizes the high-HCIS subgroup [ 2 ],[ 22 ],[ 23 ] . To facilitate translation, we combined HCIS, ypT, ypN, and BMI into a parsimonious nomogram that showed strong discrimination, excellent calibration at 3 and 5 years, and greater net clinical benefit than ypTNM across relevant thresholds. Complementary models for severe complications and pCR achieved acceptable accuracy and decision-analytic utility, supporting use of HCIS for preoperative counseling and optimization as well as postoperative treatment selection. The robustness of the principal associations after FDR correction further mitigates concerns about multiplicity. Our study has several strengths: (i) a relatively large, multicenter cohort representative of real-world ESCC care; (ii) comprehensive endpoints spanning survival, perioperative safety, and treatment response; (iii) rigorous statistics, including propensity score matching, internal validation, and decision-curve analysis; and (iv) a clinically deployable tool that synthesizes host and tumor information. Limitations should also be acknowledged. The retrospective design introduces potential residual confounding despite matching and multivariable adjustment. External validation was not performed, limiting generalizability beyond participating centers. Laboratory indices were assessed at baseline; whether dynamic HCIS changes during treatment further refine prediction warrants investigation. Additionally, while we explored heterogeneity of the adjuvant effect across HCIS strata, treatment allocation was not randomized. These findings collectively position HCIS as a pragmatic biomarker to bridge preoperative assessment and postoperative decision-making in ESCC. Future work should prospectively validate HCIS and the derived nomograms across diverse populations, assess dynamic (time-updated) trajectories of HCIS during neoadjuvant and adjuvant therapy, and test HCIS-guided strategies—such as targeted nutritional optimization, immunonutrition, and risk-adapted adjuvant intensification—in randomized or adaptive trial designs. If confirmed, HCIS-based stratification could help clinicians identify patients who will benefit most from perioperative optimization and adjuvant therapy while sparing low-risk patients from unnecessary toxicity. 5. Conclusion In conclusion, HCIS integrates nutritional and inflammatory information into a single, clinically accessible index that outperforms HALP and CONUT, independently predicts survival, major complications, and pCR, and identifies a subgroup most likely to benefit from adjuvant therapy. Used alone or within our nomogram, HCIS provides a practical framework for individualized, evidence-based management of locally advanced ESCC after neoadjuvant therapy. Declarations Supplementary Materials No supplementary materials. Conflict Of Interest Statement: No conflict of interest. Orcid Zhang Yang https://orcid.org/0000-0001-8592-5800 Funding: This research was funded by a grant from Clinical Research Center for Thoracic Tumors of Fujian Province(2022Y2007); National Natural Science Foundation of China (82203307); Fujian provincial health technology project (2022GGA021); Talent Fund Project of Fujian Medical University Union Hospital (2021XH029) and Joint Fund for the innovation of science and Technology, Fujian province (Grant number: 2023Y9204). Author Contribution Conception and design: HC,XH,ZY,CX ; Administrative support: CC,BZ ; Provision of study materials or patients :HC, YW, YL, RH, LC; Collection and assembly of data: HC,XH,ZL ;Data analysis and interpretation: All authors; Manuscript writing: All authors; Final approval of manuscript: All authors. Acknowledgments: We acknowledge the support of this project by a grant from Clinical Research Center for Thoracic Tumors of Fujian Province; National Natural Science Foundation of China (82203307); Fujian provincial health technology project (2022GGA021); Talent Fund Project of Fujian Medical University Union Hospital (2021XH029) and Joint Fund for the innovation of science and Technology, Fujian province (Grant number: 2023Y9204).We extend our gratitude to all colleagues who dedicated their time and effort to this research. 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Supplementary Files Table1.docx Table2.docx FigureS5.pdf FigureS1.pdf FigureS4.pdf TableS14.docx FigureS3.pdf FigureS2.pdf FigureS6.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor assigned by journal 28 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 23 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:27:55","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":268170,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/91fbabd99a9e6b52dbe97362.pdf"},{"id":97669521,"identity":"6dafd2d7-8b2f-4586-985d-a22676e2659f","added_by":"auto","created_at":"2025-12-08 09:28:07","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":557162,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/bd9541f08a8fe9b0dbb953a0.pdf"},{"id":97670707,"identity":"11c50f49-a92e-4b63-8bdc-0b46ee5f4193","added_by":"auto","created_at":"2025-12-08 09:31:11","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":308554,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/0d5314568f9a891467bfc472.pdf"},{"id":97488946,"identity":"893f94a0-4431-4b4c-a4c1-924b7ad0cfe2","added_by":"auto","created_at":"2025-12-05 01:56:26","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":35973,"visible":true,"origin":"","legend":"","description":"","filename":"TableS14.docx","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/62567ccad22a0ebdc7548ae6.docx"},{"id":97488942,"identity":"1f0deb97-f928-498b-bc5e-dacd0e1a6a9b","added_by":"auto","created_at":"2025-12-05 01:56:26","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":266962,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/2c1092f9ada9d8ca087b6e31.pdf"},{"id":97669131,"identity":"cb9a7e78-61aa-461b-9888-89526af20a3e","added_by":"auto","created_at":"2025-12-08 09:27:21","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":202815,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/3460328ca4b6c2d0feffe343.pdf"},{"id":97488940,"identity":"84f3dd91-c992-41b9-936a-8c4f84b386f7","added_by":"auto","created_at":"2025-12-05 01:56:26","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":475423,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8184513/v1/311fa377647408b24b6bac52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The HALP–CONUT Integrated Score Predicts Survival and Postoperative Complications in Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy: Evidence from a Multicenter Retrospective Cohort Study","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eEsophageal cancer remains one of the leading causes of cancer-related mortality worldwide, with esophageal squamous cell carcinoma (ESCC) being the predominant histological type in East Asia\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite advances in multimodal therapy, including neoadjuvant chemoradiotherapy and surgery, the prognosis of locally advanced ESCC remains unsatisfactory, with high rates of recurrence and postoperative complications\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Reliable biomarkers for risk stratification and outcome prediction are urgently needed to guide individualized treatment.\u003c/p\u003e\u003cp\u003eMalnutrition and systemic inflammation have been recognized as critical determinants of treatment tolerance and long-term outcomes in cancer patients\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Several composite indices derived from hematological and nutritional parameters, such as the Controlling Nutritional Status (CONUT) score and the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) index, have shown prognostic value in various malignancies\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, each index has limitations when applied independently, as they fail to capture the multidimensional interplay between nutrition, systemic inflammation, and immunity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo overcome these limitations, we developed the HALP\u0026ndash;CONUT Integrated Score (HCIS), a novel index combining HALP and CONUT to provide a more comprehensive reflection of host nutritional\u0026ndash;inflammatory status. Preliminary evidence suggests that integrated indices may improve predictive accuracy compared with single measures\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e],[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, the prognostic and predictive role of HCIS in patients with locally advanced ESCC undergoing neoadjuvant therapy has not yet been investigated.\u003c/p\u003e\u003cp\u003eTherefore, this multicenter retrospective cohort study aimed to evaluate the predictive value of HCIS for overall survival, postoperative complications, and pathological complete response (pCR) in patients with locally advanced ESCC after neoadjuvant therapy. We further compared HCIS with existing indices to determine whether this integrated score provides superior risk stratification and clinical utility.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Collection\u003c/h2\u003e\u003cp\u003e This multicenter retrospective study was approved by the ethics committee of the lead institution, Fujian Medical University Union Hospital, and received either exemption or approval from the ethics committees of the Provincial Hospital Affiliated to Fuzhou University, Fuzhou Pulmonary Hospital, and Zhangzhou Second Hospital. Written informed consent was obtained from all participants. The study included adult patients who underwent total esophagectomy between July 2012 and November 2019 at the four participating hospitals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participant Selection\u003c/h2\u003e\u003cp\u003eInclusion criteria were: (1) histopathologically confirmed esophageal squamous cell carcinoma after surgery; (2) patients who received neoadjuvant therapy prior to surgery; (3) resectable tumor lesions after neoadjuvant therapy; and (4) presence of a single tumor lesion.\u003c/p\u003e\u003cp\u003eExclusion criteria included: (1) CONUT score\u0026thinsp;\u0026gt;\u0026thinsp;4; (2) history within one month before surgery of pulmonary infection, severe chronic obstructive pulmonary disease, pulmonary bullae, respiratory or cardiac failure, hyperglycemia, acute or chronic renal insufficiency, significant malnutrition, inflammatory or thrombotic diseases, or liver failure; (3) multiple primary malignancies; (4) missing preoperative or postoperative laboratory or clinical data; (5) loss to follow-up or refusal of follow-up after surgery; and (6) missing neoadjuvant-surgery interval or an interval exceeding six weeks.\u003c/p\u003e\u003cp\u003eInitially, 817 patients diagnosed with ESCC were identified from the databases of the four hospitals. Among them, 213 patients with advanced disease were deemed ineligible for surgical intervention. After initial screening, 604 patients remained as potential candidates. After applying stringent inclusion and exclusion criteria, 410 patients with locally advanced ESCC who underwent surgery after neoadjuvant therapy were ultimately included(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Operational Definitions\u003c/h2\u003e\u003cp\u003eBased on preoperative CONUT scores, patients were classified into a normal group (n\u0026thinsp;=\u0026thinsp;124) and a mildly abnormal group (n\u0026thinsp;=\u0026thinsp;286). The HALP score was calculated as: HALP\u0026thinsp;=\u0026thinsp;Hb (g/L) \u0026times; Albumin (g/L) \u0026times; Lymphocyte (10⁹/L) / Platelet (10⁹/L). The optimal HALP cutoff value of 39.78 was determined using the maximum Youden index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026ndash; 1) (AUC\u0026thinsp;=\u0026thinsp;0.711, 95% CI: 0.661\u0026ndash;0.762). To evaluate the stability of this cutoff, internal validation was performed using Bootstrap resampling (1000 repetitions). Results showed a concentrated Bootstrap distribution (mean: 40.3) with a 95% confidence interval (36.37\u0026ndash;45.08) highly consistent with the original cutoff (39.78), indicating good consistency and stability (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Patients were divided into high HALP (n\u0026thinsp;=\u0026thinsp;220) and low HALP (n\u0026thinsp;=\u0026thinsp;190) groups. Combining CONUT and HALP, patients were further stratified into three groups:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLow group\u003c/b\u003e: High HALP and normal CONUT\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntermediate group\u003c/b\u003e: High HALP and mildly abnormal CONUT, or low HALP and normal CONUT\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHigh group\u003c/b\u003e: Low HALP and mildly abnormal CONUT\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll minimally invasive esophagectomy (MIE) procedures were performed by senior thoracic surgeons with extensive experience in MIE. Operative protocols were standardized across surgical approaches.\u003c/p\u003e\u003cp\u003eThe decision for postoperative discharge was based on the attending physician\u0026rsquo;s assessment and fulfillment of discharge criteria: (1) optimal general health with adapted enteral nutrition and restored bowel function; (2) smooth recovery from surgical stress with effective pain control; (3) normal body temperature, unremarkable thoracic/abdominal examinations, and laboratory results within or near normal ranges; (4) early ambulation leading to improved activities of daily living after discharge; (5) absence of wound infection symptoms and complete removal of drainage tubes; and (6) postoperative CT showing satisfactory anastomotic healing. The primary outcome was overall survival (OS), calculated from the date of discharge to the date of death or the last follow-up for surviving patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Measured Variables\u003c/h2\u003e\u003cp\u003ePreoperative demographic and clinical data were collected, including blood samples, age, sex, body mass index (BMI), OS, length of hospital stay (LOS), smoking history (lifetime cumulative smoking\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes), alcohol use (at least one drink in the past 12 months), preoperative tumor imaging, tumor location, postoperative TNM stage, presence of neural or vascular invasion, and postoperative complications. Fasting peripheral blood samples were collected 1\u0026ndash;7 days before surgery. The normal BMI range was defined as 18.5\u0026ndash;23.9 kg/m\u0026sup2;. Established reference intervals for blood biomarkers included: hemoglobin (female: 110\u0026ndash;150 g/L; male: 120\u0026ndash;165 g/L), albumin (40\u0026ndash;55 g/L), total cholesterol (131.48\u0026ndash;235.89 mg/dL), neutrophil count (1.5\u0026ndash;7.0 \u0026times; 10⁹/L), lymphocyte count (0.8\u0026ndash;4.0 \u0026times; 10⁹/L), monocyte count (0.12\u0026ndash;0.8 \u0026times; 10⁹/L), platelet count (100\u0026ndash;300 \u0026times; 10⁹/L), carcinoembryonic antigen (0\u0026ndash;5 ng/mL), and CA19-9 (0\u0026ndash;37 U/mL).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R software (version 4.5.0). Continuous variables are expressed as median (interquartile range), and categorical variables as frequency (percentage). Intergroup comparisons were conducted using the Kruskal\u0026ndash;Wallis test or χ\u0026sup2; test, as appropriate. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the predictive performance of HCIS, HALP, and CONUT for 5-year survival, and differences in area under the curve (AUC) were compared using DeLong\u0026rsquo;s test. Category-free NRI and IDI were computed with 1,000 bootstrap resamples using the survival IDINRI package (R 4.5.0). Survival analysis was performed using the Kaplan\u0026ndash;Meier method with log-rank test. Multivariate survival analysis was carried out using Cox proportional hazards models, and logistic regression models were used to identify independent predictors of complications and pathological complete response (pCR).\u003c/p\u003e\u003cp\u003eTo mitigate potential confounding, propensity score matching (PSM) was implemented using 1:1:1 nearest-neighbor matching with a caliper width of 0.05 standard deviations. A nomogram was constructed and validated through calibration curves, concordance index (C-index), time-dependent ROC analysis, and decision curve analysis (DCA).\u003c/p\u003e\u003cp\u003eResults with borderline statistical significance (i.e., P-values approaching 0.05) should be interpreted with caution. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNote on multiple comparisons\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe multivariate Cox and logistic regression models in this study were designed to validate the independent predictive value of prespecified core variables (such as HCIS score and pathological stage) based on prior research and clinical rationale, rather than to perform exploratory testing of a large number of hypotheses. Given the limited number of variables included in the final models (Cox model: 4; complication logistic model: 2; pCR logistic model: 3), the risk of Type I error inflation due to multiple comparisons was considered low. Therefore, to maintain consistency with comparable studies and to directly report adjusted effect estimates, no additional multiplicity adjustment was applied to the P-values in the multivariate models. Sensitivity analyses were subsequently conducted to assess the robustness of the findings (see Sections \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e, \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e, and \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics and Treatment Analysis of Esophageal Cancer Patients\u003c/h2\u003e\u003cp\u003eWe analyzed 410 patients with locally advanced ESCC treated with neoadjuvant therapy followed by esophagectomy; cohort demographics, tumor location, comorbidity burden, and laboratory indices are summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The cohort was predominantly male with a median age of 60 years, and tumors most commonly arose in the middle thoracic esophagus.\u003c/p\u003e\u003cp\u003eBy preoperative HCIS stratification, 46.10% were low-risk, 31.22% intermediate-risk, and 22.68% high-risk (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Across HCIS strata, baseline and treatment features differed for BMI (p\u0026thinsp;=\u0026thinsp;0.012), sex (p\u0026thinsp;=\u0026thinsp;0.004), Charlson index (p\u0026thinsp;=\u0026thinsp;0.013), clinical T and N stage (both p\u0026thinsp;\u0026le;\u0026thinsp;0.006), surgical approach (p\u0026thinsp;=\u0026thinsp;0.026), number of retrieved nodes (p\u0026thinsp;=\u0026thinsp;0.019), and adjuvant regimen (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher HCIS aligning with lower BMI and more advanced cT/cN (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll patients underwent esophagectomy (McKeown and Ivor-Lewis were the most common approaches) with a median of 34 lymph nodes examined (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A pathologic complete response was achieved in 10.49% overall. Group-wise matched comparisons are presented in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparison of Predictive Performance of HCIS, HALP, and CONUT Scores for 5-Year Survival\u003c/h2\u003e\u003cp\u003eUsing ROC analysis with DeLong\u0026rsquo;s test for between-model comparison, HCIS demonstrated the highest discriminative ability for 5-year overall survival, with an AUC of 0.789, compared with 0.711 for HALP and 0.703 for CONUT (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To quantify reclassification and discrimination gains, we calculated NRI and IDI. Versus HALP, HCIS improved reclassification by 0.124 (95% CI 0.021\u0026ndash;0.235, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and discrimination by an IDI of 0.143 (95% CI 0.070\u0026ndash;0.213, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Versus CONUT, NRI was 0.485 (95% CI 0.404\u0026ndash;0.564, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and IDI was 0.097 (95% CI 0.038\u0026ndash;0.160, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Collectively, these metrics indicate that HCIS outperforms its component indices not only in overall accuracy but also in patient-level risk reclassification, supporting its use for prognostic stratification after neoadjuvant therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Survival Differences by HCIS Stratification and Impact of Adjuvant Therapy\u003c/h2\u003e\u003cp\u003ePatients were stratified by preoperative HCIS into low- (n\u0026thinsp;=\u0026thinsp;189, 46.1%), intermediate- (n\u0026thinsp;=\u0026thinsp;128, 31.2%), and high-score (n\u0026thinsp;=\u0026thinsp;93, 22.7%) groups. Baseline characteristics differed significantly across the three strata for BMI, sex, Charlson comorbidity index, clinical stage, surgical approach, number of lymph nodes retrieved, and adjuvant regimen (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To reduce confounding, propensity score matching (PSM) was performed, yielding 64 well-balanced patients per group (192 total; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). After matching, all covariates were adequately balanced, confirming good model calibration.\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier analysis demonstrated significant differences in overall survival (OS) among HCIS groups after matching (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Patients with higher HCIS had progressively poorer OS, validating HCIS as a strong prognostic stratification tool. Length-of-stay (LOS) analyses further showed longer postoperative hospitalization in the high HCIS group, whereas low HCIS patients experienced the shortest LOS (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin-group evaluation of adjuvant therapy effects revealed no survival advantage in the low- or intermediate-HCIS groups (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.72 and 0.88\u003c/em\u003e, respectively). In contrast, adjuvant therapy significantly improved OS in high-HCIS patients (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0041\u003c/em\u003e), suggesting that those with the most unfavorable inflammatory-nutritional profiles derive the greatest benefit. Kaplan\u0026ndash;Meier curves depicting these differences are presented in Figures \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA-C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Predictors of Overall Survival\u003c/h2\u003e\u003cp\u003eOn univariate analysis, multiple clinicopathologic and hematologic variables were associated with overall survival (OS), including sex, BMI, smoking status, ypT stage, ypN stage, perineural and lymphovascular invasion, HCIS category, neutrophil and monocyte counts, hemoglobin, albumin, and total cholesterol (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn the multivariable Cox model, four factors remained independently prognostic (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHCIS category: Relative to the low-risk group, the intermediate-risk group had a 90% higher mortality risk (\u003cem\u003eHR\u003c/em\u003e 1.898, 95% CI 1.256\u0026ndash;2.868, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and the high-risk group had an approximately 4.6-fold higher risk (\u003cem\u003eHR\u003c/em\u003e 4.588, 95% CI 2.461\u0026ndash;8.533, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eypT stage: Mortality risk increased stepwise with higher pathologic T stage versus ypT0\u0026mdash;ypT1 (\u003cem\u003eHR\u003c/em\u003e 2.323, 95% CI 1.147\u0026ndash;4.704, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019); ypT2 (\u003cem\u003eHR\u003c/em\u003e 2.956, 95% CI 1.584\u0026ndash;5.517, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); ypT3 (\u003cem\u003eHR\u003c/em\u003e 3.331, 95% CI 1.849\u0026ndash;6.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); ypT4 (\u003cem\u003eHR\u003c/em\u003e 3.729, 95% CI 2.158\u0026ndash;7.467, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eypN stage: A graded association was also observed versus ypN0\u0026mdash;ypN1 (\u003cem\u003eHR\u003c/em\u003e 1.420, 95% CI 1.121\u0026ndash;2.184, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005); ypN2 (\u003cem\u003eHR\u003c/em\u003e 1.721, 95% CI 1.080\u0026ndash;2.743, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022); ypN3 (\u003cem\u003eHR\u003c/em\u003e 2.492, 95% CI 1.266\u0026ndash;4.905, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBMI: BMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9 kg/m\u0026sup2; correlated with shorter OS (\u003cem\u003eHR\u003c/em\u003e 1.470, 95% CI 1.042\u0026ndash;2.072, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). The detail results are summarized in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTo account for multiple comparisons, we applied false discovery rate (FDR) correction to the four significant signals from the multivariable model. All associations remained significant after adjustment, supporting robustness of the primary findings: HCIS (intermediate vs low: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.015; high vs low: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.009), ypT (ypT1 vs ypT0: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.027; ypT2: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.043; ypT3: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.036; ypT4: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.028), ypN (ypN1 vs ypN0: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.033; ypN2: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.028; ypN3: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.041), and BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.034).\u003c/p\u003e\u003cp\u003eSubgroup analyses stratified by sex, BMI, surgical approach, adjuvant-therapy type, tumor location, and clinical T/N stage showed a consistent gradient of risk across HCIS strata: compared with the low-HCIS group, both intermediate- and high-HCIS groups exhibited higher OS mortality across most subgroups, with the high-HCIS group uniformly at greatest risk (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor clinical translation, we integrated HCIS, ypT, ypN, and BMI into a nomogram derived from the multivariable model (C-index 0.747; 95% CI 0.713\u0026ndash;0.781) (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Internal validation demonstrated close agreement between predicted and observed 3- and 5-year OS (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). Using the scoring system, total score\u0026thinsp;\u0026lt;\u0026thinsp;100 identified patients with \u0026gt;\u0026thinsp;90% 3-year OS, while total score\u0026thinsp;\u0026lt;\u0026thinsp;40 corresponded to \u0026gt;\u0026thinsp;90% 5-year OS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTime-dependent ROC analyses confirmed the nomogram\u0026rsquo;s superior discrimination versus traditional ypTNM across time points (AUC 0.777\u0026ndash;0.875 vs 0.571\u0026ndash;0.690) (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Decision curve analysis further showed a consistently higher net clinical benefit for the nomogram across threshold probabilities of 0.20\u0026ndash;0.80 (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Collectively, these findings indicate that the nomogram offers a more accurate and clinically practical approach to survival prediction after neoadjuvant therapy than conventional staging.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Predictive Factors for Postoperative Complications\u003c/h2\u003e\u003cp\u003eTo develop a risk tool for severe postoperative complications (Clavien\u0026ndash;Dindo grade\u0026thinsp;\u0026ge;\u0026thinsp;III), we performed univariate and multivariable logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On univariate analysis, lymphocyte count, hemoglobin, albumin, and HCIS category were associated with complications. After multivariable adjustment, only lymphocyte count (\u003cem\u003eOR\u003c/em\u003e 1.729, 95% CI 1.114\u0026ndash;2.682, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and HCIS category (intermediate vs low: \u003cem\u003eOR\u003c/em\u003e 2.393, 95% CI 1.128\u0026ndash;5.076; high vs low: \u003cem\u003eOR\u003c/em\u003e 7.771, 95% CI 3.521\u0026ndash;17.150; both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) remained independently predictive.\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\u003eUnivariate and multivariate logistic regression analyses assessing the association between clinical factors and postoperative Clavien-Dindo grade\u0026thinsp;\u0026ge;\u0026thinsp;III complications, presented as OR (95% CI) and P-value.\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=\"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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\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\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.622(0.973\u0026ndash;2.701)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.612(0.915\u0026ndash;2.838)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.098\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.010(0.929\u0026ndash;1.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.065(0.971\u0026ndash;1.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.143(0.614\u0026ndash;2.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.989(0.826\u0026ndash;4.786)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.013(0.612\u0026ndash;1.677)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.344(0.629\u0026ndash;2.874)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.894(0.546\u0026ndash;1.465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.712(0.363-1.400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIvor-Lewis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMcKeown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.204(0.733\u0026ndash;1.978)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.951(0.491\u0026ndash;1.840)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of retrieved nodes(n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.014(0.986\u0026ndash;1.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.006(0.970\u0026ndash;1.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet counts(\u0026times;109/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.996\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.998(0.993\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil counts (\u0026times;109/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.110(0.962\u0026ndash;1.281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.041(0.869\u0026ndash;1.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte count (\u0026times;10\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.583(1.090\u0026ndash;2.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.729(1.114\u0026ndash;2.682)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte count (\u0026times;10\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.594(0.540\u0026ndash;4.704)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.034(0.231\u0026ndash;4.627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.973(0.957\u0026ndash;0.989)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.984(0.963\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.933(0.876\u0026ndash;0.993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.018(0.943\u0026ndash;1.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.646\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.995(0.990\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.002(0.996\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCIS\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.624(1.339\u0026ndash;5.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.393(1.128\u0026ndash;5.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.144(3.695\u0026ndash;13.813)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.771(3.521\u0026ndash;17.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\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\u003eTo address multiplicity, FDR correction was applied to the two significant signals; both retained statistical significance (lymphocyte count: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.030; HCIS\u0026mdash;intermediate vs low: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.046; high vs low: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting their robustness.\u003c/p\u003e\u003cp\u003eA two-factor nomogram incorporating lymphocyte count and HCIS demonstrated good performance (C-index/AUC 0.733, 95% CI 0.675\u0026ndash;0.796) with excellent calibration (Figs.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Decision-curve analysis showed higher net clinical benefit than \u0026ldquo;treat-all\u0026rdquo; or \u0026ldquo;treat-none\u0026rdquo; strategies for threshold probabilities 0.20\u0026ndash;0.60 (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). This model provides a concise, practical tool for preoperative risk stratification and individualized perioperative management in locally advanced ESCC after neoadjuvant therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Predictive Factors for Pathological Complete Response (pCR)\u003c/h2\u003e\u003cp\u003eUnivariate logistic regression identified neutrophil count, total cholesterol, and HCIS category as significant correlates of pathological complete response (pCR). In multivariable analysis, all three remained independent predictors: neutrophil count (\u003cem\u003eOR\u003c/em\u003e 1.520, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), total cholesterol (\u003cem\u003eOR\u003c/em\u003e 0.990, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), and HCIS (intermediate vs low: \u003cem\u003eOR\u003c/em\u003e 4.930, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; high vs low: \u003cem\u003eOR\u003c/em\u003e 6.470, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eUnivariate and Multivariate Logistic Regression Analyses of Pathological Complete Response (pCR) in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\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\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.687(0.358\u0026ndash;1.317)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.671(0.321-1.400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.997(0.459\u0026ndash;2.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.161(0.342\u0026ndash;3.943)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecT\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.040(0.439\u0026ndash;2.466)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.581(0.547\u0026ndash;3.573)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.139(0.508\u0026ndash;2.555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.838(0.330\u0026ndash;2.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.329(0.884\u0026ndash;6.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.741(0.444\u0026ndash;2.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecN\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.743(1.140\u0026ndash;3.529)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.650(0.660\u0026ndash;4.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.176(1.367\u0026ndash;7.378)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.321(0.664\u0026ndash;2.743)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.207(0.740\u0026ndash;4.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.524(0.986\u0026ndash;2.471)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.417(1.014\u0026ndash;2.547)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.339(1.147\u0026ndash;2.146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.992(0.527\u0026ndash;1.866)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.707(0.724\u0026ndash;4.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA(ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.016(0.903\u0026ndash;1.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.966(0.846\u0026ndash;1.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA199(U/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.008(0.997\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.010(0.997\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet counts(\u0026times;10\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.001(0.996\u0026ndash;1.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.998(0.992\u0026ndash;1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil counts (\u0026times;10\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.373(1.051\u0026ndash;1.794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.520(1.078\u0026ndash;2.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte count (\u0026times;10\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.717(1.313\u0026ndash;2.411)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.810(1.195\u0026ndash;3.472)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.001(0.980\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.016(0.986\u0026ndash;1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999(0.920\u0026ndash;1.086)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.057(0.940\u0026ndash;1.189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.992(0.986\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.990(0.982\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCIS\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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\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=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.030(1.293\u0026ndash;7.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.930(2.733\u0026ndash;8.146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.767(1.289\u0026ndash;11.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.470(3.577\u0026ndash;12.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter FDR correction, significance persisted for neutrophil count (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.043) and HCIS (intermediate vs low and high vs low, each \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.015), while total cholesterol approached significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.060). These findings indicate that elevated neutrophil levels and higher inflammatory\u0026ndash;nutritional burden, reflected by HCIS, are robust predictors of reduced pCR probability.\u003c/p\u003e\u003cp\u003eA three-factor nomogram incorporating neutrophil count, total cholesterol, and HCIS achieved a C-index of 0.717 (95% CI 0.650\u0026ndash;0.798) (Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The calibration curve showed close concordance between predicted and observed pCR probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). ROC analysis confirmed good discrimination with an AUC of 0.717 (95% CI 0.650\u0026ndash;0.798) (Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Finally, decision-curve analysis (DCA) demonstrated a clear net clinical benefit for the nomogram across a decision-threshold range of 0.10\u0026ndash;0.50, outperforming \u0026ldquo;treat-all\u0026rdquo; and \u0026ldquo;treat-none\u0026rdquo; strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Taken together, this model offers a concise, interpretable, and clinically applicable tool for preoperative identification of patients most likely to achieve pCR following neoadjuvant therapy in locally advanced ESCC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this multicenter retrospective cohort of locally advanced ESCC treated with neoadjuvant therapy followed by esophagectomy, we show that the HALP\u0026ndash;CONUT Integrated Score (HCIS) offers reproducible and clinically meaningful risk stratification. HCIS outperformed HALP and CONUT for 5-year survival discrimination and remained independently associated with overall survival after adjustment for pathologic factors. Importantly, HCIS also predicted major postoperative complications and pathologic complete response (pCR), indicating utility across perioperative safety and oncologic efficacy domains. Together, these results support HCIS as a pragmatic biomarker for individualized risk assessment in ESCC.\u003c/p\u003e\u003cp\u003eOur findings fit within a broad evidence base linking systemic inflammation and nutritional depletion to adverse cancer outcomes and treatment intolerance\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u0026minus;[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. By integrating hematologic and nutritional components, HCIS captures the inflammation\u0026ndash;malnutrition\u0026ndash;immune dysregulation axis that shapes host\u0026ndash;tumor interactions\u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u0026minus;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Elevated neutrophils and relative lymphopenia are associated with immunosuppressive tumor microenvironments \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e],[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, while hypoalbuminemia and disordered lipid metabolism reflect impaired reserve and repair\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e],[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e; these mechanisms plausibly underlie the observed associations between higher HCIS and worse OS, greater complication burden, and lower pCR. In this context, HCIS provides a single, accessible index that consolidates signals previously reported across multiple inflammation\u0026ndash;nutrition metrics\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e],[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe also observed heterogeneity of adjuvant benefit by baseline inflammatory\u0026ndash;nutritional risk: adjuvant therapy conferred no measurable advantage in low- and intermediate-HCIS strata but was associated with significantly improved survival among high-HCIS patients. This pattern aligns with literature suggesting that systemic inflammation and malnutrition amplify perioperative risk and blunt treatment efficacy, yet may also identify patients who derive outsized benefit from intensified postoperative strategies when appropriately selected\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u0026minus;[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Clinically, these data argue for risk-adapted pathways: (i) prehabilitation and targeted nutritional/immunonutritional optimization for patients with elevated HCIS\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e; and (ii) shared decision-making around adjuvant intensification that prioritizes the high-HCIS subgroup \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo facilitate translation, we combined HCIS, ypT, ypN, and BMI into a parsimonious nomogram that showed strong discrimination, excellent calibration at 3 and 5 years, and greater net clinical benefit than ypTNM across relevant thresholds. Complementary models for severe complications and pCR achieved acceptable accuracy and decision-analytic utility, supporting use of HCIS for preoperative counseling and optimization as well as postoperative treatment selection. The robustness of the principal associations after FDR correction further mitigates concerns about multiplicity.\u003c/p\u003e\u003cp\u003e Our study has several strengths: (i) a relatively large, multicenter cohort representative of real-world ESCC care; (ii) comprehensive endpoints spanning survival, perioperative safety, and treatment response; (iii) rigorous statistics, including propensity score matching, internal validation, and decision-curve analysis; and (iv) a clinically deployable tool that synthesizes host and tumor information. Limitations should also be acknowledged. The retrospective design introduces potential residual confounding despite matching and multivariable adjustment. External validation was not performed, limiting generalizability beyond participating centers. Laboratory indices were assessed at baseline; whether dynamic HCIS changes during treatment further refine prediction warrants investigation. Additionally, while we explored heterogeneity of the adjuvant effect across HCIS strata, treatment allocation was not randomized.\u003c/p\u003e\u003cp\u003eThese findings collectively position HCIS as a pragmatic biomarker to bridge preoperative assessment and postoperative decision-making in ESCC. Future work should prospectively validate HCIS and the derived nomograms across diverse populations, assess dynamic (time-updated) trajectories of HCIS during neoadjuvant and adjuvant therapy, and test HCIS-guided strategies\u0026mdash;such as targeted nutritional optimization, immunonutrition, and risk-adapted adjuvant intensification\u0026mdash;in randomized or adaptive trial designs. If confirmed, HCIS-based stratification could help clinicians identify patients who will benefit most from perioperative optimization and adjuvant therapy while sparing low-risk patients from unnecessary toxicity.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, HCIS integrates nutritional and inflammatory information into a single, clinically accessible index that outperforms HALP and CONUT, independently predicts survival, major complications, and pCR, and identifies a subgroup most likely to benefit from adjuvant therapy. Used alone or within our nomogram, HCIS provides a practical framework for individualized, evidence-based management of locally advanced ESCC after neoadjuvant therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eSupplementary Materials\u003c/h2\u003e\u003cp\u003eNo supplementary materials.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict Of Interest Statement:\u003c/h2\u003e\u003cp\u003eNo conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eOrcid\u003c/h2\u003e\u003cp\u003eZhang Yang \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://orcid.org/0000-0001-8592-5800\u003c/span\u003e\u003cspan address=\"https://orcid.org/0000-0001-8592-5800\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was funded by a grant from Clinical Research Center for Thoracic Tumors of Fujian Province(2022Y2007); National Natural Science Foundation of China (82203307); Fujian provincial health technology project (2022GGA021); Talent Fund Project of Fujian Medical University Union Hospital (2021XH029) and Joint Fund for the innovation of science and Technology, Fujian province (Grant number: 2023Y9204).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: HC,XH,ZY,CX ; Administrative support: CC,BZ ; Provision of study materials or patients :HC, YW, YL, RH, LC; Collection and assembly of data: HC,XH,ZL ;Data analysis and interpretation: All authors; Manuscript writing: All authors; Final approval of manuscript: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eWe acknowledge the support of this project by a grant from Clinical Research Center for Thoracic Tumors of Fujian Province; National Natural Science Foundation of China (82203307); Fujian provincial health technology project (2022GGA021); Talent Fund Project of Fujian Medical University Union Hospital (2021XH029) and Joint Fund for the innovation of science and Technology, Fujian province (Grant number: 2023Y9204).We extend our gratitude to all colleagues who dedicated their time and effort to this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al. 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Clin Nutr. 2017;36(1):11\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2016.07.015\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2016.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HALP, CONUT, HCIS, esophageal squamous cell carcinoma, neoadjuvant therapy, prognosis, complications","lastPublishedDoi":"10.21203/rs.3.rs-8184513/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8184513/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe prognostic role of nutrition\u0026ndash;inflammation indices in esophageal squamous cell carcinoma (ESCC) requires further investigation. We developed the HALP\u0026ndash;CONUT Integrated Score (HCIS) and evaluated its ability to predict survival and postoperative complications in locally advanced ESCC patients undergoing neoadjuvant therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis multicenter retrospective study analyzed 410 patients. HCIS was calculated by integrating the HALP index and CONUT score. Patients were stratified into risk groups. Survival was analyzed using Kaplan\u0026ndash;Meier and Cox regression. Logistic regression assessed associations with complications and pathologic complete response (pCR). Predictive performance was evaluated using ROC curves, C-index, and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePatients with high HCIS scores had significantly worse overall survival (OS) compared with those in lower-risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate Cox analysis confirmed HCIS as an independent prognostic factor for OS (HR 4.59, 95% CI 2.64\u0026ndash;7.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher HCIS scores were also independently associated with increased risk of major postoperative complications (OR 7.144, 95% CI 3.698\u0026ndash;13.813, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a lower likelihood of achieving pCR (OR 6.470, 95% CI 3.577\u0026ndash;12.40, p\u0026thinsp;=\u0026thinsp;0.005). Predictive models incorporating HCIS demonstrated superior discrimination compared with models based on HALP or CONUT alone (AUC for OS: 0.789 vs 0.711 and 0.703, respectively).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHCIS is a robust biomarker that integrates hematologic, nutritional, and inflammatory parameters. It independently predicts survival, postoperative complications, and pCR, providing a valuable tool for risk stratification and individualized treatment planning in locally advanced ESCC.\u003c/p\u003e","manuscriptTitle":"The HALP–CONUT Integrated Score Predicts Survival and Postoperative Complications in Locally Advanced Esophageal Squamous Cell Carcinoma Following Neoadjuvant Therapy: Evidence from a Multicenter Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 01:56:21","doi":"10.21203/rs.3.rs-8184513/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T18:36:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T10:31:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336913238172169247394272580412391667060","date":"2026-03-22T01:32:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T09:52:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164295004233091735657480037206284441501","date":"2026-03-07T09:04:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284258211098364545070202883460296489869","date":"2025-12-02T21:29:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T12:27:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-28T08:17:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-25T01:13:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-11-23T09:11:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2970e8a-6449-47d9-9860-7212e6c5fee2","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T16:25:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 01:56:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8184513","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8184513","identity":"rs-8184513","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}