Admission Neutrophil-to-Platelet Ratio as an Independent Predictor of Mortality in Critically Ill Patients with AECOPD: A MIMIC-IV Database Analysis

Admission Neutrophil-to-Platelet Ratio as an Independent Predictor of Mortality in Critically Ill Patients with AECOPD: A MIMIC-IV Database Analysis | 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 Admission Neutrophil-to-Platelet Ratio as an Independent Predictor of Mortality in Critically Ill Patients with AECOPD: A MIMIC-IV Database Analysis Huihua Hong, Rundi Gao, Suqun Zheng, Jiayan Zhong, Junchao Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8607276/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 18 You are reading this latest preprint version Abstract Background Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are critical events with high mortality. The neutrophil-to-platelet ratio (NPR) has emerged as a promising systemic inflammatory marker, but its prognostic value in AECOPD remains underexplored. Methods This retrospective cohort study utilized data from the MIMIC-IV 3.1 database (2008–2022). Patients with AECOPD were categorized into quartiles based on admission NPR. The primary outcome was in-hospital mortality; the secondary outcome was 28-day mortality. Multivariable Cox regression, restricted cubic splines (RCS), and subgroup analyses with False Discovery Rate (FDR) adjustment were performed. Results Among 1,137 patients, in-hospital and 28-day mortality rates were 15.5% and 14.1%, respectively. In the fully adjusted model (Model IV), the highest NPR quartile (Q4, > 0.06) was associated with a 3.01-fold increased risk of in-hospital mortality (HR 3.01, 95% CI 1.31–6.89, P = 0.009) and nearly triple the risk of 28-day mortality (HR 2.91, 95% CI 1.18–7.16, P = 0.020) compared to Q1. RCS confirmed a linear risk trajectory (P_ non-linearity = 0.796). Significant interactions were observed for sepsis status (P_FDR < 0.001), with NPR showing higher sensitivity in non-septic patients (HR 4.66, 95% CI 1.24–17.56). Adding NPR significantly improved model discrimination (C-index: 0.7098 to 0.7182, P = 0.036) and clinical net benefit in decision curve analysis. Conclusions Admission NPR is a potent, independent predictor of mortality in AECOPD, particularly in younger and clinically less complex populations. Incorporating NPR into risk stratification may enhance early clinical decision-making. chronic obstructive pulmonary disease neutrophil-to-platelet ratio in-hospital mortality MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Chronic obstructive pulmonary disease (COPD) remains a leading cause of global morbidity and mortality [ 1 , 2 ]. Acute exacerbations of COPD (AECOPD) are critical clinical events characterized by an acute worsening of respiratory symptoms, leading to a surge in healthcare utilization and a substantial risk of death. In severe COPD cases requiring intensive care unit (ICU) admission, in-hospital mortality rates have been reported to range from 20% to as high as 40%[ 3 ]. Therefore, early identification of high-risk patients is essential for optimizing treatment and improving outcomes. Hematologic biomarkers derived from routine complete blood counts (CBC) have gained interest due to their accessibility and cost-effectiveness[ 4 – 6 ]. Among these, the neutrophil-to-platelet ratio (NPR) has emerged as a promising composite marker of systemic inflammation[ 7 ]. By integrating the absolute neutrophil count (a key effector of innate immunity) and the platelet count (a crucial modulator of coagulation and inflammation), NPR reflects a unique balance of the body's inflammatory burden. Recent studies have established NPR as an independent predictor of mortality in sepsis, sometimes outperforming the Sequential Organ Failure Assessment (SOFA) score[ 8 , 9 ]. The prognostic value of NPR specifically in AECOPD remains underexplored. Given that infection and dysregulated immune responses drive both AECOPD and sepsis, NPR may serve as a valuable tool for risk stratification in AECOPD. we hypothesized that admission NPR would serve as a valuable tool for risk stratification. This study aims to investigate the independent association between admission NPR and both in-hospital and 28-day mortality in critically ill patients with AECOPD, and to evaluate whether the integration of NPR enhances the predictive accuracy of conventional clinical risk models. Methods Data Source This retrospective study used the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 3.1, a large-scale, deidentified dataset of patients admitted to the Beth Israel Deaconess Medical Center between 2008 and 2022[ 10 ]. Access was granted following completion of required certification (ID: 11356022) in compliance with institutional review board (IRB) requirements. Informed consent was waived due to the use of de-identified data. Study Population AECOPD was identified via ICD-9 (491.21, 491.22) and ICD-10 (J44.0, J44.1) codes. Only the first ICU admission was analyzed for each patient. Exclusion criteria were: (1) age < 18 years; (2) missing survival data; (3) ICU stay < 24 hours; and (4) lack of neutrophil or platelet measurements. Data Extraction and Variable Definition Patient data were extracted from the MIMIC-IV database using Structured Query Language (SQL) via PostgreSQL (version 17.2) and pgAdmin4 (version 8.14). The extracted variables were categorized into five domains: Demographics and Vital Signs: Including age, gender, weight, and 24-hour mean values of vital signs (heart rate, respiratory rate, body temperature, systolic blood pressure [SBP], and oxygen saturation [SpO₂]) to represent the overall physiological status. Comorbidities: Identified by ICD-9 and ICD-10 codes, including sepsis (Sepsis-3 criteria), asthma, myocardial infarction, cerebrovascular disease, congestive heart failure, and renal disease. Laboratory Parameters: The first measurement was used, including white blood cell (WBC) count, platelet (PLT) count, neutrophil count, lymphocyte count, hemoglobin, creatinine, blood urea nitrogen, and electrolytes. Clinical Severity Scores: To quantify physiological derangement at admission, the Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), and Acute Physiology Score III (APS III) were calculated using the most extreme (highest) values within the initial 24-hour window. Therapeutic Interventions: Clinical management during the ICU stay, including the use of mechanical ventilation (invasive and non-invasive), vasopressors, and systemic steroids. Admission NPR was calculated as the ratio of the absolute neutrophil count to the platelet count using the first blood sample obtained upon ICU admission. Follow-up extended from the date of ICU admission until hospital discharge or in-hospital death. Grouping and Outcomes The primary outcome was in-hospital mortality; the secondary outcome was 28-day mortality. Patients were divided into four groups based on NPR quartiles. Statistical Analysis Continuous variables were presented as mean ± SD or median (IQR) and compared using independent t-tests or Mann–Whitney U-tests. Categorical variables were presented as frequencies and compared using chi-square tests. Missing data (for variables with 5)[ 11 ]. Kaplan-Meier curves with log-rank tests were used to visualize survival. Four multivariable Cox models estimated hazard ratios (HRs): Model I (unadjusted), Model II (adjusted for demographics and vitals), Model III (Model II + labs and comorbidities), and Model IV (Model III + severity scores and treatments). Proportional hazards assumptions were verified using Schoenfeld residuals. Non-linearity was explored using Restricted Cubic Splines (RCS) with 4 knots and the Wald test. Subgroup analyses were performed with interaction terms, and P-values for interaction were adjusted using the Benjamini-Hochberg False Discovery Rate (FDR) method. Finally, C-index, time-dependent AUC, likelihood ratio tests, and Decision Curve Analysis (DCA) were used to evaluate the incremental predictive value of NPR. Bootstrap resampling (B = 200) was applied to the C-index to account for model optimism[ 12 , 13 ]. Analysis was performed in R (version 4.4.2); two-tailed P < 0.05 or FDR-adjusted P < 0.05 was significant. Results Baseline Characteristics As shown in the study flowchart a total of 1,137 patients were included (Fig. 1 ). patients were categorized into quartiles based on admission NPR: Q1 ( 0.06, n = 284). Baseline characteristics are summarized in Table 1 . Mortality rates increased significantly with NPR quartiles for both in-hospital mortality (21.8% in Q4 vs. 11.6% in Q1, P = 0.003) and 28-day mortality (19.4% in Q4 vs. 10.9% in Q1, P = 0.012). Patients in Q4 exhibited a higher systemic inflammatory burden, with significantly elevated white blood cell counts (17.50 ± 16.04 vs. 9.21 ± 4.88, P < 0.001) and a higher prevalence of sepsis (75.7% vs. 58.9%, P < 0.001). Furthermore, Q4 patients required more intensive clinical support, including higher rates of mechanical ventilation (50.7% vs. 38.2%, P = 0.013) and vasopressor use (41.5% vs. 25.3%, P 0.05). Table 1 Comparison of Patient Characteristics by NPR Quartile Groups NPR Quartile Groups Variable Overall (N = 1137) 1 Q1 (n = 285) 2 Q2 (n = 284) 2 Q3 (n = 284) 2 Q4 (n = 284) 2 P-value 3 Admission Age (years) 72.2 ± 10.8 72.2 ± 10.5 72.3 ± 11.5 72.7 ± 10.9 71.5 ± 10.1 0.645 Gender (male) 573 (50.4%) 137 (48.1%) 142 (50.0%) 144 (50.7%) 150 (52.8%) 0.723 Weight (kg) 81.2 ± 25.3 82.6 ± 24.6 79.6 ± 25.5 79.7 ± 25.4 82.9 ± 25.8 0.222 Race/Ethnicity < 0.001 Asian 21 (1.8%) 6 (2.1%) 6 (2.1%) 5 (1.8%) 4 (1.4%) Black 140 (12.3%) 63 (22.1%) 37 (13.0%) 27 (9.5%) 13 (4.6%) Hispanic/Latino 29 (2.6%) 14 (4.9%) 9 (3.2%) 3 (1.1%) 3 (1.1%) Other 37 (3.3%) 12 (4.2%) 8 (2.8%) 10 (3.5%) 7 (2.5%) Unknown 147 (12.9%) 26 (9.1%) 37 (13.0%) 33 (11.6%) 51 (18.0%) White 763 (67.1%) 164 (57.5%) 187 (65.8%) 206 (72.5%) 206 (72.5%) Admission Type 0.043 Elective 37 (3.3%) 3 (1.1%) 10 (3.5%) 11 (3.9%) 13 (4.6%) Emergency 819 (72.0%) 196 (68.8%) 206 (72.5%) 207 (72.9%) 210 (73.9%) Observation 281 (24.7%) 86 (30.2%) 68 (23.9%) 66 (23.2%) 61 (21.5%) Delirium Positive 559 (49.2%) 139 (48.8%) 138 (48.6%) 132 (46.5%) 150 (52.8%) 0.499 Mean Heart Rate (bpm) 87.4 ± 16.3 86.1 ± 16.6 87.5 ± 16.0 86.8 ± 16.6 89.0 ± 15.9 0.173 Mean Systolic BP (mmHg) 118.7 ± 15.5 121.7 ± 15.9 119.6 ± 15.4 117.4 ± 15.6 116.1 ± 14.3 < 0.001 Mean Diastolic BP (mmHg) 65.5 ± 10.9 66.7 ± 12.1 66.0 ± 10.8 65.0 ± 10.1 64.2 ± 10.6 0.033 Mean Respiratory Rate (breaths/min) 20.8 ± 3.7 20.7 ± 3.5 20.6 ± 3.5 20.7 ± 3.6 21.0 ± 4.0 0.499 Mean Temperature (°C) 36.8 ± 0.4 36.9 ± 0.3 36.8 ± 0.4 36.8 ± 0.5 36.9 ± 0.4 0.672 Mean SpO₂ (%) 95.3 ± 2.3 95.3 ± 2.3 95.4 ± 2.3 94.9 ± 2.5 95.6 ± 2.1 0.014 Comorbidities Myocardial Infarction 278 (24.5%) 60 (21.1%) 66 (23.2%) 76 (26.8%) 76 (26.8%) 0.278 Congestive Heart Failure 646 (56.8%) 162 (56.8%) 153 (53.9%) 172 (60.6%) 159 (56.0%) 0.451 Peripheral Vascular Disease 164 (14.4%) 41 (14.4%) 41 (14.4%) 43 (15.1%) 39 (13.7%) 0.979 Cerebrovascular Disease 106 (9.3%) 26 (9.1%) 29 (10.2%) 29 (10.2%) 22 (7.7%) 0.711 Dementia 82 (7.2%) 25 (8.8%) 22 (7.7%) 20 (7.0%) 15 (5.3%) 0.428 Rheumatic Disease 60 (5.3%) 8 (2.8%) 11 (3.9%) 16 (5.6%) 25 (8.8%) 0.011 Peptic Ulcer Disease 18 (1.6%) 2 (0.7%) 2 (0.7%) 9 (3.2%) 5 (1.8%) 0.068 Diabetes 385 (33.9%) 101 (35.4%) 97 (34.2%) 92 (32.4%) 95 (33.5%) 0.883 Paraplegia 44 (3.9%) 12 (4.2%) 11 (3.9%) 12 (4.2%) 9 (3.2%) 0.901 Renal Disease 327 (28.8%) 79 (27.7%) 79 (27.8%) 86 (30.3%) 83 (29.2%) 0.900 Malignant Cancer 190 (16.7%) 69 (24.2%) 38 (13.4%) 46 (16.2%) 37 (13.0%) 0.001 Severe Liver Disease 34 (3.0%) 6 (2.1%) 4 (1.4%) 6 (2.1%) 18 (6.3%) 0.003 Metastatic Solid Tumor 65 (5.7%) 20 (7.0%) 12 (4.2%) 21 (7.4%) 12 (4.2%) 0.199 Sepsis-3 737 (64.8%) 168 (58.9%) 164 (57.7%) 190 (66.9%) 215 (75.7%) < 0.001 LODS Score 0.002 Median (Q1, Q3) 5.0 (3.0, 7.0) 5.0 (2.0, 7.0) 5.0 (3.0, 7.0) 5.0 (3.0, 7.0) 5.0 (3.0, 7.5) OASIS Score 0.086 Median (Q1, Q3) 33.0 (27.0, 39.0) 33.0 (27.0, 38.0) 33.0 (27.0, 38.0) 32.0 (27.0, 38.0) 34.0 (28.0, 40.0) SOFA Score < 0.001 Median (Q1, Q3) 5.0 (3.0, 7.0) 4.0 (2.0, 7.0) 4.0 (2.0, 6.0) 4.0 (3.0, 7.0) 6.0 (3.0, 9.0) APS III Score 0.002 Median (Q1, Q3) 43.0 (33.0, 55.0) 42.0 (33.0, 55.0) 42.5 (33.0, 53.0) 41.0 (32.5, 53.0) 47.0 (35.0, 60.0) Initia WBC (×10⁹/L) 12.41 ± 9.70 9.21 ± 4.88 10.65 ± 5.26 12.26 ± 5.261 17.50 ± 16.04 < 0.001 Initial Neutrophil (×10⁹/L) 10.0 ± 6.4 5.5 ± 2.8 8.2 ± 3.1 10.6 ± 4.4 15.8 ± 8.2 < 0.001 Initial Lymphocyte (×10⁹/L) 1.3 ± 6.0 1.4 ± 1.4 1.2 ± 1.0 1.0 ± 1.1 1.7 ± 11.8 0.540 Initial Platelet (×10⁹/L) 215.5 ± 98.9 265.6 ± 114.0 233.5 ± 90.1 202.7 ± 75.5 160.3 ± 79.7 < 0.001 Initial Creatinine (mg/dL) 1.4 ± 1.2 1.4 ± 1.2 1.3 ± 1.1 1.4 ± 1.2 1.5 ± 1.3 0.089 Initial Urea (mg/dL) 30.0 ± 21.6 25.2 ± 18.5 28.7 ± 19.7 30.5 ± 21.0 35.6 ± 25.5 < 0.001 Initial Sodium (mmol/L) 138.6 ± 5.4 138.8 ± 5.0 138.6 ± 5.8 138.6 ± 5.5 138.4 ± 5.3 0.856 Initial Chloride (mmol/L) 99.5 ± 6.6 99.4 ± 6.4 99.4 ± 6.8 99.3 ± 6.6 100.0 ± 6.7 0.572 Initial Potassium (mmol/L) 4.5 ± 0.9 4.5 ± 0.9 4.5 ± 0.9 4.5 ± 0.8 4.5 ± 0.9 0.704 Mechanical Ventilation 489 (43.0%) 109 (38.2%) 119 (41.9%) 117 (41.2%) 144 (50.7%) 0.013 Vasopressor Used 329 (28.9%) 72 (25.3%) 73 (25.7%) 66 (23.2%) 118 (41.5%) < 0.001 Sedative Used 455 (40.0%) 105 (36.8%) 105 (37.0%) 108 (38.0%) 137 (48.2%) 0.015 Opioid Used 225 (19.8%) 49 (17.2%) 51 (18.0%) 42 (14.8%) 83 (29.2%) < 0.001 Steroid Used 736 (64.7%) 176 (61.8%) 176 (62.0%) 184 (64.8%) 200 (70.4%) 0.110 In-hospital Mortality 176 (15.5%) 33 (11.6%) 45 (15.8%) 36 (12.7%) 62 (21.8%) 0.003 28day Mortality 160 (14.1%) 31 (10.9%) 42 (14.8%) 32 (11.3%) 55 (19.4%) 0.012 Los_Hospital (days) 10.09 (5.98, 17.17) 9.76 (5.9, 17.17) 9.08 (5.55, 15.74) 9.24(5.93, 15.56) 12.74 (6.64, 20.02) 0.002 Los_ICU(days) 3.00 (1.80, 6.09) 2.92 (1.79, 5.61) 2.82(1.75, 5.80) 2.99(1.79, 5.78) 3.50(1.91, 6.94) 0.105 1 Mean ± SD; n (%) 2 Continuous variables: Mean ± SD or Median (IQR); Categorical variables: Frequency (%) 3 One-way analysis of means; Fisher's Exact Test for Count Data with simulated p-value Kaplan-Meier Survival Analysis Kaplan-Meier survival analysis with log-rank tests was performed to evaluate the association between NPR levels and mortality. The survival curves separated early and continued to diverge throughout the follow-up period. For the endpoint of in-hospital mortality (Fig. 2 A), the survival curves demonstrated a significant divergence across the four NPR quartiles (Log-rank p = 0.0037). Patients in the highest NPR quartile (Q4) consistently exhibited the worst survival probability throughout the hospitalization period. The risk table confirms a more rapid decline in the number of patients at risk in the Q4 group compared to the lower quartiles, particularly during the initial 40 days of hospitalization, indicating a higher and earlier incidence of mortality. Similarly, for 28-day mortality (Fig. 2 B), a statistically significant difference in survival was observed among the NPR groups (Log-rank p = 0.013). The Kaplan-Meier plot visually illustrates a graded, inverse relationship between NPR levels and survival probability, with the Q4 group showing the lowest cumulative survival rate at 28 days. This trend underscores NPR as a robust early predictor of short-term mortality in our AECOPD cohort. Multivariable Cox Proportional Hazards Analysis The independent association between admission NPR and mortality was evaluated using four progressive multivariable models (Tables 2 and 3 ). In the primary analysis (Model III), higher NPR quartiles were associated with increased mortality risk. For in-hospital mortality, patients in Q4 exhibited a significantly higher risk compared to Q1 (HR 2.18, 95% CI 1.05–4.53, P = 0.038). For 28-day mortality, although the association in Model III for Q4 was not nominally significant (HR 1.92, 95% CI 0.86–4.30, P = 0.11), the hazard ratio remained elevated, suggesting a consistent trend. Table 2 Association between NPR group and in-hospital mortality — Cox models Model I Model II Model III Model IV Characteristic HR 1 95% CI 1 p- value HR 1 95% CI 1 p- value HR 1 95% CI 1 p- value HR 1 95% CI 1 p- value NPR_group Q1 1 1 1 1 Q2 1.39 0.89, 2.18 0.2 1.34 0.85, 2.13 0.2 1.54 0.94, 2.53 0.087 1.79 1.06, 3.03 0.030 Q3 1.10 0.69, 1.77 0.7 0.87 0.53, 1.44 0.6 1.07 0.59, 1.93 0.8 1.35 0.71, 2.58 0.4 Q4 1.98 1.30, 3.02 0.002 1.58 1.01, 2.50 0.047 2.18 1.05, 4.53 0.038 3.01 1.31, 6.89 0.009 1 HR = Hazard Ratio, CI = Confidence Interval "Note: Hazard ratios (HR) and 95% confidence intervals (CI) are shown. ", "Model I: unadjusted. Model II: adjusted for demographics, vitals (variables with VIF > 5 were removed). ", "Model III: Model II + comorbidities and laboratory parameters. Model IV: Model III + treatments and severity scores. " Table 3 Association between NPR group and 28-day mortality — Cox models Model I Model II Model III Model IV Characteristic HR 1 95% CI 1 p-value HR 1 95% CI 1 p-value HR 1 95% CI 1 p-value HR 1 95% CI 1 p-value NPR_group Q1 1 1 1 1 Q2 1.38 0.87, 2.19 0.2 1.30 0.81, 2.10 0.3 1.47 0.87, 2.49 0.14 1.82 1.05, 3.18 0.034 Q3 1.04 0.64, 1.71 0.9 0.84 0.50, 1.42 0.5 0.97 0.51, 1.84 > 0.9 1.34 0.67, 2.70 0.4 Q4 1.85 1.19, 2.88 0.006 1.45 0.90, 2.34 0.12 1.92 0.86, 4.30 0.11 2.91 1.18, 7.16 0.020 1HR = Hazard Ratio, CI = Confidence Interval 1 HR = Hazard Ratio, CI = Confidence Interval "Note: Hazard ratios (HR) and 95% confidence intervals (CI) are shown. ", "Model I: unadjusted. Model II: adjusted for demographics, vitals (variables with VIF > 5 were removed). ", "Model III: Model II + comorbidities and laboratory parameters. Model IV: Model III + treatments and severity scores. " In the fully adjusted Model IV the prognostic impact of NPR became even more pronounced. For in-hospital mortality, Q4 remained a powerful independent predictor with a 3.01-fold increase in the hazard of death (HR 3.01, 95% CI 1.31–6.89, P = 0.009). Patients in Q2 also exhibited a significant risk increase compared to those in Q1 (HR 1.79, 95% CI 1.06–3.03, P = 0.030). Regarding 28-day mortality, the findings in Model IV remained consistent. The risk of death for patients in Q4 nearly tripled compared to those in Q1 (HR 2.91, 95% CI 1.18–7.16, P = 0.020), while Q2 also achieved independent significance (HR 1.82, 95% CI 1.05–3.18, P = 0.034). Intriguingly, the observed increase in the hazard ratio for Q4 from Model III to Model IV suggests that the intrinsic biological risk captured by NPR was previously underestimated due to the confounding effects of life-sustaining treatments. By accounting for these critical clinical interventions and severity scores, the robustness and independence of NPR as a mortality predictor in AECOPD were further validated. RCS Analysis Multivariable-adjusted RCS models were constructed to characterize the dose-response relationship (Fig. 3 ). After adjusting for demographics, comorbidities, and laboratory parameters (Model III), the Wald test confirmed a clear linear risk trajectory (P non-linearity = 0.796). Although the overall Wald test for the continuous NPR term did not reach nominal significance in this high-dimensional model (P overall = 0.874), the significant association observed in quartile-based cox models, combined with the linear trend in RCS, suggests that the risk is particularly pronounced at higher NPR thresholds (Fig. 3 A). This linear assumption remained robust in Model IV (P non-linearity = 0.946), confirming the stability of the association across different adjustment levels. Stratified RCS analyses revealed significant heterogeneity; the risk of mortality escalated more sharply in patients with sepsis (Fig. 3 B) and those requiring mechanical ventilation (Fig. 3 C) compared to their counterparts (both P interaction-adj < 0.001). Similar trend modifications were observed across age and gender strata (Figs. 3 D-E, P interaction-adj = 0.040). Subgroup and Interaction Analyses Subgroup analyses (Q4 vs. Q1) confirmed significant effect size modification (Fig. 4 ). After FDR correction, highly significant interactions were confirmed for sepsis-3 status and mechanical ventilation (both P_FDR < 0.001). Notably, the HR for Q4 was markedly higher in non-septic patients (HR 4.66, 95% CI 1.24–17.56, P_ within = 0.023) compared to the septic group (HR 1.46, 95% CI 0.93–2.28, P_within = 0.098), suggesting NPR is a more sensitive discriminator in patients without overt systemic infection. A significant interaction was also observed for age (P_FDR = 0.049), with a more pronounced effect in patients < 75 years (HR 2.85, 95% CI 1.52–5.34). Incremental Predictive Value and Clinical Utility To quantify the added prognostic value of NPR, we compared the discriminative ability and model fit between baseline and NPR-augmented models (Table 4 ). In the primary analysis (Model III), the addition of NPR quartiles to the clinical baseline significantly improved model fitness (Likelihood Ratio Test χ2 = 8.543, p = 0.036). The C-index increased from 0.7098 (95% CI: 0.680–0.739) to 0.7182 (95% CI: 0.680–0.756). After addressing potential overfitting via Bootstrap resampling (B = 200), the corrected C-index for the augmented Model III remained superior at 0.6803 compared to 0.6720 for the baseline. Time-dependent ROC analysis at 28 days further confirmed this trend, with the AUC improving from 0.731 to 0.736 (Fig. 5 A). Table 4 Comparison of Predictive Performance and Incremental Value of NPR for In-hospital Mortality Model Description C-index (95% CI) Corrected C-index* LRT χ2 LRT P-value AUC (28-day) Primary Analysis ─ Baseline Model (Model 3) 0.7098 (0.680–0.739) 0.6720 — — 0.731 Primary Analysis ─ Augmented Model (+ NPR) 0.7182 (0.680–0.756) 0.6803 8.543 0.036 0.736 Sensitivity Analysis ─ Baseline Model (Model 4) 0.7365 0.7004 — — 0.759 Sensitivity Analysis ─ Augmented Model (+ NPR) 0.7413 0.7052 7.379 0.061 0.766 Value of NPR for In-hospital Mortality Note: * Corrected C-index was calculated using Bootstrap resampling (B=200) to account for model optimism. In a more stringent sensitivity analysis (Model IV), which accounted for critical ICU interventions including mechanical ventilation, vasopressor support, and steroid therapy, the NPR-augmented model exhibited robust performance. The C-index reached 0.7413, with a Bootstrap-corrected C-index of 0.7052, maintaining a threshold above the 0.70 mark, which indicates good discriminative power. The AUC for Model IV increased from 0.759 to 0.766 upon the addition of NPR (Fig. 5 C). Although the incremental improvement in this saturated model reached marginal statistical significance (P_LRT = 0.061), the stable upward trajectory of all performance metrics and the corrected C-index of 0.7052 underscore the stable prognostic contribution of NPR. Finally, Decision Curve Analysis (DCA) was employed to evaluate the clinical net benefit. For both Model III and Model IV, the NPR-integrated models provided a higher or stable net benefit compared to baseline strategies across a wide range of threshold probabilities (Fig. 5 B and 5 D). These findings suggest that incorporating NPR into risk stratification protocols for AECOPD patients facilitates more accurate clinical decision-making without compromising model stability. Discussion In this retrospective cohort study based on the MIMIC-IV database, we identified a robust and independent association between an elevated admission NPR and increased risk of in-hospital and 28-day mortality in patients with AECOPD. Notably, patients in the highest NPR quartile (Q4) exhibited a 3.01-fold higher hazard of death compared to the lowest quartile, even after rigorous adjustment for demographic factors, comorbidities, laboratory parameters, and intensive ICU interventions. The linear dose-response relationship confirmed by RCS analysis, coupled with the stable predictive performance (corrected C-index of 0.7052), underscores NPR as a reliable prognostic marker in critical care settings. The superiority of NPR as a prognostic marker lies in its ability to integrate two central pillars of AECOPD pathophysiology: neutrophilic inflammation and hemostatic dysfunction[ 14 – 16 ]. Neutrophils are the primary effectors in the airway and systemic inflammation of COPD. During exacerbations, neutrophils undergo a massive "burst," releasing serine proteases such as neutrophil elastase (NE) and matrix metalloproteinases (MMPs) that exacerbate airway remodeling and tissue damage[ 15 , 17 ]. Furthermore, activated neutrophils release neutrophil extracellular traps, which contribute to lung injury and facilitate systemic pro-thrombotic states[ 9 , 18 ]. On the other hand, platelets are no longer viewed merely as coagulation fragments but as critical immune modulators. In our cohort, we observed a progressive decline in platelet counts across NPR quartiles, a finding that warrants careful interpretation. While reactive thrombocytosis is common in stable COPD due to chronic inflammation [ 16 , 19 ], the relative thrombocytopenia observed in our high-risk ICU patients likely reflects "consumptive coagulopathy". Platelets are consumed during the formation of microthrombi in the pulmonary vasculature - a phenomenon often termed "immunethrombosis"[ 7 , 8 ]. Therefore, a high NPR captures the lethal combination of an overwhelming inflammatory surge ( high neutrophils ) and a decompensated hemostatic response (low platelets), providing a more comprehensive reflection of systemic stress than other biomarkers like procalcitonin or neutrophil-to-Lymphocyte ratio (NLR), which primarily reflect inflammatory magnitude but lack insight into the concurrent coagulopathic state[ 4 – 6 , 20 , 21 ]. A pivotal finding of our study is the significant interaction between NPR and sepsis status (P_ interaction = 0.016). NPR showed a remarkably higher hazard ratio in non-septic AECOPD patients (HR 4.66) compared to those with sepsis (HR 1.46). This "Sepsis Paradox" suggests a ceiling effect of inflammatory biomarkers in patients with overwhelming infection. In septic patients, the extreme dysregulation of all hematological parameters may saturate the predictive power of NPR[ 9 ]. Conversely, in patients who do not meet the Sepsis criteria, an elevated NPR serves as a highly sensitive "sentinel" marker. It may identify a subgroup of AECOPD patients experiencing occult systemic inflammatory response syndrome (SIRS) or microvascular failure that has not yet manifested as overt organ dysfunction[ 14 , 16 ]. From a clinical perspective, this is where NPR adds the most value: it helps clinicians identify "high-risk" individuals among patients who might otherwise be perceived as "lower-risk" due to the absence of a sepsis diagnosis. Our study confirms that adding NPR to a baseline model significantly improves the C-index (from 0.7098 to 0.7182). While the absolute increase may appear modest, it was statistically significant (P = 0.036), and the DCA demonstrated a superior net benefit across a wide range of threshold probabilities. Given that NPR is derived from a routine and inexpensive CBC, it offers a cost-effective strategy for risk stratification in resource-limited settings or during the early "golden hour" of ICU admission. Patients identified in the highest quartile upon ICU admission may require more aggressive monitoring, early pharmacological intervention or prioritized respiratory support to mitigate the high risk of mortality. Despite its strengths, this study has limitations. First, as a retrospective analysis of the MIMIC-IV database, we could not account for all potential confounders, such as long-term smoking history or baseline lung function, which were not consistently recorded. Second, NPR was only measured at admission; the prognostic impact of dynamic changes in NPR during the ICU stay remains to be explored. Finally, while we used Bootstrap internal validation, external validation in prospective, multi-center cohorts is necessary to confirm the generalizability of our findings across different healthcare systems. Conclusions Elevated admission NPR is a potent, independent predictor of mortality in ICU patients with AECOPD. Its predictive value is particularly pronounced in patients without sepsis, offering a window for early intervention. Incorporating NPR into routine clinical assessment enhances risk stratification and provides incremental value over traditional clinical models, facilitating more personalized management of AECOPD in the intensive care unit. Abbreviations APS III Acute Physiology Score III CBC complete blood counts COPD Chronic obstructive pulmonary disease DCA Decision Curve Analysis FDR False Discovery Rate HRs hazard ratios ICU intensive care unit IRB institutional review board MICE multiple imputation by chained equations MIMIC-IV Medical Information Mart for Intensive Care IV MMPs matrix metalloproteinases NE proteases such as neutrophil elastase NLR neutrophil-to-Lymphocyte ratio PLT platelet RCS Restricted Cubic Splines SAPS II Physiology Score II SBP systolic blood pressure SIRS systemic inflammatory response syndrome SOFA Sequential Organ Failure Assessment SpO₂ oxygen saturation SQL Structured Query Language VIF Variance Inflation Factor WBC white blood cell Declarations Ethics approval and consent to participate Ethical approval was not required for this study as all data were extracted from the publicly available and de-identified MIMIC-IV database, with appropriate certification (certification ID: 11356022). Consent for publication Not applicable. Availability of data and materials Available from the corresponding author on reasonable request Competing interests The authors declare no competing interests Funding This work received main funding from the National Key Research and Development Program of China (NO.2023YFC3502601). Authors' contributions HH and RG contributed equally to this work. HH was responsible for the statistical analysis of the data and drafted the initial manuscript. RD contributed to data extraction and curation, and critically revised the manuscript. SZ performed data verification. JZ conducted the literature review. JY acquired the funding and provided final supervision and approval of the manuscript. All authors read and approved the final manuscript. Acknowledgements No References Prevalence. and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med, 2020. 8(6): pp. 585–96. de Oca MM, et al. The global burden of COPD: epidemiology and effect of prevention strategies. Lancet Respir Med. 2025;13(8):709–24. Prediletto I, Giancotti G, Nava S. COPD Exacerbation: Why It Is Important to Avoid ICU Admission. J Clin Med, 2023. 12(10). Zinellu A et al. Clinical significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute exacerbations of COPD: present and future. Eur Respir Rev, 2022. 31(166). Cai C, et al. Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective, Observational Study. Int J Chron Obstruct Pulmon Dis. 2024;19:933–43. Yao C, Liu X, Tang Z. Prognostic role of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio for hospital mortality in patients with AECOPD. Int J Chron Obstruct Pulmon Dis. 2017;12:2285–90. Zhang N, et al. Risk factors for illness severity in patients with COVID-19 pneumonia: a prospective cohort study. Int J Med Sci. 2021;18(4):921–8. Zhang Y, Peng W, Zheng X. The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis. Sci Rep. 2024;14(1):15075. Zhu J, et al. Association between neutrophil-platelet ratio and 28-day mortality in patients with sepsis: a retrospective analysis based on MIMIC-IV database. BMC Infect Dis. 2025;25(1):685. Johnson AEW, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1. Austin PC, et al. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021;37(9):1322–31. Steyerberg EW, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565–74. Shapiro SD. Neutrophil elastase: path clearer, pathogen killer, or just pathologic? Am J Respir Cell Mol Biol. 2002;26(3):266–8. Stockley RA. Neutrophils and the pathogenesis of COPD. Chest. 2002;121(5 Suppl):s151–5. Fawzy A, et al. Association of thrombocytosis with COPD morbidity: the SPIROMICS and COPDGene cohorts. Respir Res. 2018;19(1):20. Fricker M, Lokwani R. COPD: the role of neutrophils in inflammation, pathophysiology, and as drug targets. Clin Sci (Lond). 2025;139(20):1199–214. Kashif AM, et al. NETosis and pyroptosis of immune cells in sepsis. J Transl Int Med. 2025;13(4):318–27. Muñoz-Esquerre M, et al. Impact of acute exacerbations on platelet reactivity in chronic obstructive pulmonary disease patients. Int J Chron Obstruct Pulmon Dis. 2018;13:141–8. Liao QQ, et al. Platelet-to-Lymphocyte Ratio (PLR), Neutrophil-to-Lymphocyte Ratio (NLR), Monocyte-to-Lymphocyte Ratio (MLR), and Eosinophil-to-Lymphocyte Ratio (ELR) as Biomarkers in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD). Int J Chron Obstruct Pulmon Dis. 2024;19:501–18. Chen K, et al. Procalcitonin for Antibiotic Prescription in Chronic Obstructive Pulmonary Disease Exacerbations: Systematic Review, Meta-Analysis, and Clinical Perspective. Pulm Ther. 2020;6(2):201–14. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8607276","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621719827,"identity":"9ee40b92-606b-417d-809b-2bfcd179940c","order_by":0,"name":"Huihua Hong","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Huihua","middleName":"","lastName":"Hong","suffix":""},{"id":621719829,"identity":"b94d5319-e0e6-4620-b293-8424581c964c","order_by":1,"name":"Rundi Gao","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Rundi","middleName":"","lastName":"Gao","suffix":""},{"id":621719832,"identity":"6de99bf4-4518-4c74-9ea4-e8f83356202c","order_by":2,"name":"Suqun Zheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Suqun","middleName":"","lastName":"Zheng","suffix":""},{"id":621719834,"identity":"b5f06daf-7a75-4bd1-9c30-4e2b9adde7ce","order_by":3,"name":"Jiayan Zhong","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Jiayan","middleName":"","lastName":"Zhong","suffix":""},{"id":621719836,"identity":"ee0479eb-3f3f-4f1b-b1fd-5a33ae7a64e4","order_by":4,"name":"Junchao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYLACCSA2ADMqoCI8xGs5Q6wWBpgWxjYitBgcP3v4hWXbHXlz9sOHP1jOq5PXnZHA+OBtG4O8OS4tZ/LSLCTbnhnu7ElLMJDcxma47UYCs+HcNgbDnQ04tBzIMTOQbDucAGQYJEhu40kwu5HAJs3bxgAUwaHl/BuolvNvDA5IzpEAaWH/jVfLjRzjB2AtN3IMGyQbDMC2MOPTInnjjRmDxLnDhhtuPEtmkDiWYLjtzMNmyTnnJAw34NDCdz7H+LNE2WF5g/PJhz9L1NTJmx1PPvjhTZmNPC5bFA4wsElLQDnMEAZjAwMkerED+QYG5o8foBzGDzjVjYJRMApGwUgGAEszXqBIrFx1AAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China","correspondingAuthor":true,"prefix":"","firstName":"Junchao","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-01-15 05:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8607276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8607276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106871668,"identity":"52e9a29b-856f-438e-921c-e0f5ec6288f6","added_by":"auto","created_at":"2026-04-14 09:50:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":264354,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection.The diagram illustrates the systematic inclusion and exclusion criteria based on the MIMIC-IV 3.1 database, resulting in the final cohort of 1,137 patients with AECOPD.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8607276/v1/baac386fb7f638d7b4697ab6.png"},{"id":106960898,"identity":"726d6a05-fc9d-4b29-8eea-44ccee32fd50","added_by":"auto","created_at":"2026-04-15 09:23:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":636188,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for in-hospital and 28-day mortality stratified by NPR quartiles. \u003cstrong\u003e(A)\u003c/strong\u003e In-hospital mortality; \u003cstrong\u003e(B)\u003c/strong\u003e 28-day mortality. Shaded areas represent 95% confidence intervals. The log-rank test was used to compare survival distributions among the four NPR groups. Patients in the highest NPR quartile (Q4) exhibited significantly higher mortality risks compared to those in lower quartiles (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8607276/v1/177b4f4446bf8a10ae661964.png"},{"id":106871670,"identity":"fe83080b-7721-4413-8a72-1cddddabe182","added_by":"auto","created_at":"2026-04-14 09:50:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":874103,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) analysis of the association between neutrophil-to-platelet ratio (NPR) and in-hospital mortality. (A) Overall linear relationship in the primary cohort; (B–E) Stratified RCS curves showcasing the risk trajectory across different clinical subgroups. The solid lines represent the hazard ratio (HR), and the shaded areas represent the 95% confidence interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8607276/v1/cee4951f342b3be0c1160e7a.png"},{"id":106871671,"identity":"ff3010de-e1c6-48ca-b2d5-3ecff30e95c8","added_by":"auto","created_at":"2026-04-14 09:50:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173379,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis and interaction effects for the association between NPR and in-hospital mortality.The forest plot displays HRs and 95% CIs comparing the highest versus lowest NPR quartiles. P-values for interaction were adjusted using the False Discovery Rate (FDR) method. Significant interactions were observed for sepsis-3 status, age, and mechanical ventilation (P_FDR \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8607276/v1/944fddb58e60ab68b267a45d.png"},{"id":106871672,"identity":"619e5300-1b37-4027-bd72-64d7cab52ca1","added_by":"auto","created_at":"2026-04-14 09:50:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35876,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive performance and clinical utility of the NPR-augmented models. (A) Time-dependent ROC curves for Model III; (B) Decision curve analysis (DCA) for Model III; (C) Time-dependent ROC curves for Model IV, adjusting for ICU interventions; (D) DCA for Model IV. These curves demonstrate consistent discriminative power and clinical net benefit of NPR across different levels of adjustment.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8607276/v1/e29213c9a1ac4e7c8028c24f.png"},{"id":106994425,"identity":"3b0b40fb-59ce-44a8-9f8a-0a42c650c31b","added_by":"auto","created_at":"2026-04-15 15:08:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3714194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8607276/v1/b2843a1c-336a-4211-b8dd-a9e740649e7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Admission Neutrophil-to-Platelet Ratio as an Independent Predictor of Mortality in Critically Ill Patients with AECOPD: A MIMIC-IV Database Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) remains a leading cause of global morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Acute exacerbations of COPD (AECOPD) are critical clinical events characterized by an acute worsening of respiratory symptoms, leading to a surge in healthcare utilization and a substantial risk of death. In severe COPD cases requiring intensive care unit (ICU) admission, in-hospital mortality rates have been reported to range from 20% to as high as 40%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, early identification of high-risk patients is essential for optimizing treatment and improving outcomes.\u003c/p\u003e \u003cp\u003eHematologic biomarkers derived from routine complete blood counts (CBC) have gained interest due to their accessibility and cost-effectiveness[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among these, the neutrophil-to-platelet ratio (NPR) has emerged as a promising composite marker of systemic inflammation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. By integrating the absolute neutrophil count (a key effector of innate immunity) and the platelet count (a crucial modulator of coagulation and inflammation), NPR reflects a unique balance of the body's inflammatory burden. Recent studies have established NPR as an independent predictor of mortality in sepsis, sometimes outperforming the Sequential Organ Failure Assessment (SOFA) score[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prognostic value of NPR specifically in AECOPD remains underexplored. Given that infection and dysregulated immune responses drive both AECOPD and sepsis, NPR may serve as a valuable tool for risk stratification in AECOPD. we hypothesized that admission NPR would serve as a valuable tool for risk stratification. This study aims to investigate the independent association between admission NPR and both in-hospital and 28-day mortality in critically ill patients with AECOPD, and to evaluate whether the integration of NPR enhances the predictive accuracy of conventional clinical risk models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData Source\u003c/p\u003e \u003cp\u003eThis retrospective study used the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 3.1, a large-scale, deidentified dataset of patients admitted to the Beth Israel Deaconess Medical Center between 2008 and 2022[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Access was granted following completion of required certification (ID: 11356022) in compliance with institutional review board (IRB) requirements. Informed consent was waived due to the use of de-identified data.\u003c/p\u003e \u003cp\u003eStudy Population\u003c/p\u003e \u003cp\u003eAECOPD was identified via ICD-9 (491.21, 491.22) and ICD-10 (J44.0, J44.1) codes. Only the first ICU admission was analyzed for each patient. Exclusion criteria were: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) missing survival data; (3) ICU stay\u0026thinsp;\u0026lt;\u0026thinsp;24 hours; and (4) lack of neutrophil or platelet measurements.\u003c/p\u003e \u003cp\u003eData Extraction and Variable Definition\u003c/p\u003e \u003cp\u003ePatient data were extracted from the MIMIC-IV database using Structured Query Language (SQL) via PostgreSQL (version 17.2) and pgAdmin4 (version 8.14). The extracted variables were categorized into five domains:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDemographics and Vital Signs: Including age, gender, weight, and 24-hour mean values of vital signs (heart rate, respiratory rate, body temperature, systolic blood pressure [SBP], and oxygen saturation [SpO₂]) to represent the overall physiological status.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eComorbidities: Identified by ICD-9 and ICD-10 codes, including sepsis (Sepsis-3 criteria), asthma, myocardial infarction, cerebrovascular disease, congestive heart failure, and renal disease.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLaboratory Parameters: The first measurement was used, including white blood cell (WBC) count, platelet (PLT) count, neutrophil count, lymphocyte count, hemoglobin, creatinine, blood urea nitrogen, and electrolytes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eClinical Severity Scores: To quantify physiological derangement at admission, the Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), and Acute Physiology Score III (APS III) were calculated using the most extreme (highest) values within the initial 24-hour window.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTherapeutic Interventions: Clinical management during the ICU stay, including the use of mechanical ventilation (invasive and non-invasive), vasopressors, and systemic steroids.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAdmission NPR was calculated as the ratio of the absolute neutrophil count to the platelet count using the first blood sample obtained upon ICU admission. Follow-up extended from the date of ICU admission until hospital discharge or in-hospital death.\u003c/p\u003e \u003cp\u003eGrouping and Outcomes\u003c/p\u003e \u003cp\u003eThe primary outcome was in-hospital mortality; the secondary outcome was 28-day mortality. Patients were divided into four groups based on NPR quartiles.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) and compared using independent t-tests or Mann\u0026ndash;Whitney U-tests. Categorical variables were presented as frequencies and compared using chi-square tests. Missing data (for variables with \u0026lt;\u0026thinsp;30% missingness) were handled via multiple imputation by chained equations (MICE). Multicollinearity was assessed using the Variance Inflation Factor (VIF\u0026thinsp;\u0026gt;\u0026thinsp;5)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKaplan-Meier curves with log-rank tests were used to visualize survival. Four multivariable Cox models estimated hazard ratios (HRs): Model I (unadjusted), Model II (adjusted for demographics and vitals), Model III (Model II\u0026thinsp;+\u0026thinsp;labs and comorbidities), and Model IV (Model III\u0026thinsp;+\u0026thinsp;severity scores and treatments). Proportional hazards assumptions were verified using Schoenfeld residuals.\u003c/p\u003e \u003cp\u003eNon-linearity was explored using Restricted Cubic Splines (RCS) with 4 knots and the Wald test. Subgroup analyses were performed with interaction terms, and P-values for interaction were adjusted using the Benjamini-Hochberg False Discovery Rate (FDR) method.\u003c/p\u003e \u003cp\u003eFinally, C-index, time-dependent AUC, likelihood ratio tests, and Decision Curve Analysis (DCA) were used to evaluate the incremental predictive value of NPR. Bootstrap resampling (B\u0026thinsp;=\u0026thinsp;200) was applied to the C-index to account for model optimism[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Analysis was performed in R (version 4.4.2); two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003cp\u003eAs shown in the study flowchart a total of 1,137 patients were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). patients were categorized into quartiles based on admission NPR: Q1 (\u0026lt;\u0026thinsp;0.03, n\u0026thinsp;=\u0026thinsp;285), Q2 (0.03\u0026ndash;0.04, n\u0026thinsp;=\u0026thinsp;284), Q3 (0.04\u0026ndash;0.06, n\u0026thinsp;=\u0026thinsp;284), and Q4 (\u0026gt;\u0026thinsp;0.06, n\u0026thinsp;=\u0026thinsp;284). Baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mortality rates increased significantly with NPR quartiles for both in-hospital mortality (21.8% in Q4 vs. 11.6% in Q1, P\u0026thinsp;=\u0026thinsp;0.003) and 28-day mortality (19.4% in Q4 vs. 10.9% in Q1, P\u0026thinsp;=\u0026thinsp;0.012). Patients in Q4 exhibited a higher systemic inflammatory burden, with significantly elevated white blood cell counts (17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;16.04 vs. 9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a higher prevalence of sepsis (75.7% vs. 58.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, Q4 patients required more intensive clinical support, including higher rates of mechanical ventilation (50.7% vs. 38.2%, P\u0026thinsp;=\u0026thinsp;0.013) and vasopressor use (41.5% vs. 25.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed in age or gender across groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Patient Characteristics by NPR Quartile Groups\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNPR Quartile Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;1137)\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;285)\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;284)\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;284)\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;284)\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdmission Age (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.6\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.7\u0026thinsp;\u0026plusmn;\u0026thinsp;25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (1.4%)\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\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (4.6%)\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\u003eHispanic/Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (1.1%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (2.5%)\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (18.0%)\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\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e763 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206 (72.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e206 (72.5%)\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\u003cb\u003eAdmission Type\u003c/b\u003e\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 \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (4.6%)\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\u003eEmergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e819 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206 (72.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207 (72.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e210 (73.9%)\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\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61 (21.5%)\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\u003cb\u003eDelirium Positive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e559 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Heart Rate (bpm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Systolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Diastolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Respiratory Rate (breaths/min)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Temperature (\u0026deg;C)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean SpO₂ (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\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\u003cb\u003eMyocardial Infarction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongestive Heart Failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e646 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (53.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeripheral Vascular Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerebrovascular Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDementia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRheumatic Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeptic Ulcer Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParaplegia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignant Cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSevere Liver Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetastatic Solid Tumor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e737 (64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLODS Score\u003c/b\u003e\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 \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (3.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (2.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (3.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0 (3.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0 (3.0, 7.5)\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\u003cb\u003eOASIS Score\u003c/b\u003e\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 \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0 (27.0, 39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.0 (27.0, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.0 (27.0, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.0 (27.0, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.0 (28.0, 40.0)\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\u003cb\u003eSOFA Score\u003c/b\u003e\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (3.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (2.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (2.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (3.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0 (3.0, 9.0)\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\u003cb\u003eAPS III Score\u003c/b\u003e\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 \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.0 (33.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0 (33.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.5 (33.0, 53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.0 (32.5, 53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.0 (35.0, 60.0)\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\u003cb\u003eInitia WBC (\u0026times;10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.26\u0026thinsp;\u0026plusmn;\u0026thinsp;5.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;16.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Neutrophil (\u0026times;10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Lymphocyte (\u0026times;10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Platelet (\u0026times;10⁹/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215.5\u0026thinsp;\u0026plusmn;\u0026thinsp;98.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265.6\u0026thinsp;\u0026plusmn;\u0026thinsp;114.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233.5\u0026thinsp;\u0026plusmn;\u0026thinsp;90.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202.7\u0026thinsp;\u0026plusmn;\u0026thinsp;75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160.3\u0026thinsp;\u0026plusmn;\u0026thinsp;79.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Creatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Urea (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Sodium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Chloride (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial Potassium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMechanical Ventilation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e489 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasopressor Used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSedative Used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOpioid Used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSteroid Used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e736 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184 (64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e28day Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLos_Hospital (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.09 (5.98, 17.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.76 (5.9, 17.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.08 (5.55, 15.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.24(5.93, 15.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.74 (6.64, 20.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLos_ICU(days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 (1.80, 6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92 (1.79, 5.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82(1.75, 5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.99(1.79, 5.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.50(1.91, 6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMean \u0026plusmn; SD; n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"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\u003csup\u003e2\u003c/sup\u003eContinuous variables:\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or Median (IQR); Categorical variables: Frequency (%)\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\u003csup\u003e3\u003c/sup\u003eOne-way analysis of means; Fisher's Exact Test for Count Data with simulated p-value\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier Survival Analysis\u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis with log-rank tests was performed to evaluate the association between NPR levels and mortality. The survival curves separated early and continued to diverge throughout the follow-up period.\u003c/p\u003e \u003cp\u003eFor the endpoint of in-hospital mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), the survival curves demonstrated a significant divergence across the four NPR quartiles (Log-rank p\u0026thinsp;=\u0026thinsp;0.0037). Patients in the highest NPR quartile (Q4) consistently exhibited the worst survival probability throughout the hospitalization period. The risk table confirms a more rapid decline in the number of patients at risk in the Q4 group compared to the lower quartiles, particularly during the initial 40 days of hospitalization, indicating a higher and earlier incidence of mortality. Similarly, for 28-day mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), a statistically significant difference in survival was observed among the NPR groups (Log-rank p\u0026thinsp;=\u0026thinsp;0.013). The Kaplan-Meier plot visually illustrates a graded, inverse relationship between NPR levels and survival probability, with the Q4 group showing the lowest cumulative survival rate at 28 days. This trend underscores NPR as a robust early predictor of short-term mortality in our AECOPD cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariable Cox Proportional Hazards Analysis\u003c/p\u003e \u003cp\u003eThe independent association between admission NPR and mortality was evaluated using four progressive multivariable models (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the primary analysis (Model III), higher NPR quartiles were associated with increased mortality risk. For in-hospital mortality, patients in Q4 exhibited a significantly higher risk compared to Q1 (HR 2.18, 95% CI 1.05\u0026ndash;4.53, P\u0026thinsp;=\u0026thinsp;0.038). For 28-day mortality, although the association in Model III for Q4 was not nominally significant (HR 1.92, 95% CI 0.86\u0026ndash;4.30, P\u0026thinsp;=\u0026thinsp;0.11), the hazard ratio remained elevated, suggesting a consistent trend.\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\u003eAssociation between NPR group and in-hospital mortality \u0026mdash; Cox models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eModel IV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep-\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep-\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep-\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPR_group\u003c/b\u003e\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\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \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 \u003cp\u003e1\u003c/p\u003e \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\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89, 2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85, 2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.94, 2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.06, 3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69, 1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53, 1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.59, 1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.71, 2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30, 3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01, 2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05, 4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.31, 6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\"Note: Hazard ratios (HR) and 95% confidence intervals (CI) are shown. \",\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\"Model I: unadjusted. Model II: adjusted for demographics, vitals (variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 were removed). \",\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\"Model III: Model II\u0026thinsp;+\u0026thinsp;comorbidities and laboratory parameters. Model IV: Model III\u0026thinsp;+\u0026thinsp;treatments and severity scores. \"\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between NPR group and 28-day mortality \u0026mdash; Cox models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eModel IV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPR_group\u003c/b\u003e\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\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \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 \u003cp\u003e1\u003c/p\u003e \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\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87, 2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81, 2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87, 2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05, 3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64, 1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50, 1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51, 1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67, 2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19, 2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90, 2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.86, 4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.18, 7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1HR\u0026thinsp;=\u0026thinsp;Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003e1\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\"Note: Hazard ratios (HR) and 95% confidence intervals (CI) are shown. \",\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\"Model I: unadjusted. Model II: adjusted for demographics, vitals (variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 were removed). \",\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\"Model III: Model II\u0026thinsp;+\u0026thinsp;comorbidities and laboratory parameters. Model IV: Model III\u0026thinsp;+\u0026thinsp;treatments and severity scores. \"\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the fully adjusted Model IV the prognostic impact of NPR became even more pronounced. For in-hospital mortality, Q4 remained a powerful independent predictor with a 3.01-fold increase in the hazard of death (HR 3.01, 95% CI 1.31\u0026ndash;6.89, P\u0026thinsp;=\u0026thinsp;0.009). Patients in Q2 also exhibited a significant risk increase compared to those in Q1 (HR 1.79, 95% CI 1.06\u0026ndash;3.03, P\u0026thinsp;=\u0026thinsp;0.030).\u003c/p\u003e \u003cp\u003eRegarding 28-day mortality, the findings in Model IV remained consistent. The risk of death for patients in Q4 nearly tripled compared to those in Q1 (HR 2.91, 95% CI 1.18\u0026ndash;7.16, P\u0026thinsp;=\u0026thinsp;0.020), while Q2 also achieved independent significance (HR 1.82, 95% CI 1.05\u0026ndash;3.18, P\u0026thinsp;=\u0026thinsp;0.034). Intriguingly, the observed increase in the hazard ratio for Q4 from Model III to Model IV suggests that the intrinsic biological risk captured by NPR was previously underestimated due to the confounding effects of life-sustaining treatments. By accounting for these critical clinical interventions and severity scores, the robustness and independence of NPR as a mortality predictor in AECOPD were further validated.\u003c/p\u003e \u003cp\u003eRCS Analysis\u003c/p\u003e \u003cp\u003eMultivariable-adjusted RCS models were constructed to characterize the dose-response relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for demographics, comorbidities, and laboratory parameters (Model III), the Wald test confirmed a clear linear risk trajectory (P non-linearity\u0026thinsp;=\u0026thinsp;0.796). Although the overall Wald test for the continuous NPR term did not reach nominal significance in this high-dimensional model (P overall\u0026thinsp;=\u0026thinsp;0.874), the significant association observed in quartile-based cox models, combined with the linear trend in RCS, suggests that the risk is particularly pronounced at higher NPR thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This linear assumption remained robust in Model IV (P non-linearity\u0026thinsp;=\u0026thinsp;0.946), confirming the stability of the association across different adjustment levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratified RCS analyses revealed significant heterogeneity; the risk of mortality escalated more sharply in patients with sepsis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and those requiring mechanical ventilation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) compared to their counterparts (both P interaction-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar trend modifications were observed across age and gender strata (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E, P interaction-adj\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e \u003cp\u003eSubgroup and Interaction Analyses\u003c/p\u003e \u003cp\u003eSubgroup analyses (Q4 vs. Q1) confirmed significant effect size modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). After FDR correction, highly significant interactions were confirmed for sepsis-3 status and mechanical ventilation (both P_FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the HR for Q4 was markedly higher in non-septic patients (HR 4.66, 95% CI 1.24\u0026ndash;17.56, P_ within =\u0026thinsp;0.023) compared to the septic group (HR 1.46, 95% CI 0.93\u0026ndash;2.28, P_within\u0026thinsp;=\u0026thinsp;0.098), suggesting NPR is a more sensitive discriminator in patients without overt systemic infection. A significant interaction was also observed for age (P_FDR\u0026thinsp;=\u0026thinsp;0.049), with a more pronounced effect in patients\u0026thinsp;\u0026lt;\u0026thinsp;75 years (HR 2.85, 95% CI 1.52\u0026ndash;5.34).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIncremental Predictive Value and Clinical Utility\u003c/p\u003e \u003cp\u003eTo quantify the added prognostic value of NPR, we compared the discriminative ability and model fit between baseline and NPR-augmented models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the primary analysis (Model III), the addition of NPR quartiles to the clinical baseline significantly improved model fitness (Likelihood Ratio Test χ2\u0026thinsp;=\u0026thinsp;8.543, p\u0026thinsp;=\u0026thinsp;0.036). The C-index increased from 0.7098 (95% CI: 0.680\u0026ndash;0.739) to 0.7182 (95% CI: 0.680\u0026ndash;0.756). After addressing potential overfitting via Bootstrap resampling (B\u0026thinsp;=\u0026thinsp;200), the corrected C-index for the augmented Model III remained superior at 0.6803 compared to 0.6720 for the baseline. Time-dependent ROC analysis at 28 days further confirmed this trend, with the AUC improving from 0.731 to 0.736 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison of Predictive Performance and Incremental\u003c/b\u003e Value of NPR for In-hospital Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-index (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrected C-index*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLRT χ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLRT P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC (28-day)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Analysis\u003c/b\u003e─ Baseline Model \u003cb\u003e(Model 3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7098 (0.680\u0026ndash;0.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Analysis\u003c/b\u003e─ Augmented Model (+\u0026thinsp;NPR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7182 (0.680\u0026ndash;0.756)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensitivity Analysis\u003c/b\u003e─ Baseline Model \u003cb\u003e(Model 4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.7365\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.7004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensitivity Analysis\u003c/b\u003e─ Augmented Model (+\u0026thinsp;NPR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.7413\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.7052\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.061\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.766\u003c/b\u003e\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\u003eValue of NPR for In-hospital Mortality\u003c/p\u003e\n\u003cp\u003eNote: \u003cstrong\u003e*\u0026nbsp;\u003c/strong\u003eCorrected C-index was calculated using Bootstrap resampling (B=200) to account for model optimism.\u003c/p\u003e \u003cp\u003eIn a more stringent sensitivity analysis (Model IV), which accounted for critical ICU interventions including mechanical ventilation, vasopressor support, and steroid therapy, the NPR-augmented model exhibited robust performance. The C-index reached 0.7413, with a Bootstrap-corrected C-index of 0.7052, maintaining a threshold above the 0.70 mark, which indicates good discriminative power. The AUC for Model IV increased from 0.759 to 0.766 upon the addition of NPR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Although the incremental improvement in this saturated model reached marginal statistical significance (P_LRT\u0026thinsp;=\u0026thinsp;0.061), the stable upward trajectory of all performance metrics and the corrected C-index of 0.7052 underscore the stable prognostic contribution of NPR.\u003c/p\u003e \u003cp\u003eFinally, Decision Curve Analysis (DCA) was employed to evaluate the clinical net benefit. For both Model III and Model IV, the NPR-integrated models provided a higher or stable net benefit compared to baseline strategies across a wide range of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These findings suggest that incorporating NPR into risk stratification protocols for AECOPD patients facilitates more accurate clinical decision-making without compromising model stability.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study based on the MIMIC-IV database, we identified a robust and independent association between an elevated admission NPR and increased risk of in-hospital and 28-day mortality in patients with AECOPD. Notably, patients in the highest NPR quartile (Q4) exhibited a 3.01-fold higher hazard of death compared to the lowest quartile, even after rigorous adjustment for demographic factors, comorbidities, laboratory parameters, and intensive ICU interventions. The linear dose-response relationship confirmed by RCS analysis, coupled with the stable predictive performance (corrected C-index of 0.7052), underscores NPR as a reliable prognostic marker in critical care settings.\u003c/p\u003e \u003cp\u003eThe superiority of NPR as a prognostic marker lies in its ability to integrate two central pillars of AECOPD pathophysiology: neutrophilic inflammation and hemostatic dysfunction[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Neutrophils are the primary effectors in the airway and systemic inflammation of COPD. During exacerbations, neutrophils undergo a massive \"burst,\" releasing serine proteases such as neutrophil elastase (NE) and matrix metalloproteinases (MMPs) that exacerbate airway remodeling and tissue damage[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, activated neutrophils release neutrophil extracellular traps, which contribute to lung injury and facilitate systemic pro-thrombotic states[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, platelets are no longer viewed merely as coagulation fragments but as critical immune modulators. In our cohort, we observed a progressive decline in platelet counts across NPR quartiles, a finding that warrants careful interpretation. While reactive thrombocytosis is common in stable COPD due to chronic inflammation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the relative thrombocytopenia observed in our high-risk ICU patients likely reflects \"consumptive coagulopathy\". Platelets are consumed during the formation of microthrombi in the pulmonary vasculature - a phenomenon often termed \"immunethrombosis\"[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, a high NPR captures the lethal combination of an overwhelming inflammatory surge ( high neutrophils ) and a decompensated hemostatic response (low platelets), providing a more comprehensive reflection of systemic stress than other biomarkers like procalcitonin or neutrophil-to-Lymphocyte ratio (NLR), which primarily reflect inflammatory magnitude but lack insight into the concurrent coagulopathic state[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA pivotal finding of our study is the significant interaction between NPR and sepsis status (P_ interaction\u0026thinsp;=\u0026thinsp;0.016). NPR showed a remarkably higher hazard ratio in non-septic AECOPD patients (HR 4.66) compared to those with sepsis (HR 1.46). This \"Sepsis Paradox\" suggests a ceiling effect of inflammatory biomarkers in patients with overwhelming infection. In septic patients, the extreme dysregulation of all hematological parameters may saturate the predictive power of NPR[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, in patients who do not meet the Sepsis criteria, an elevated NPR serves as a highly sensitive \"sentinel\" marker. It may identify a subgroup of AECOPD patients experiencing occult systemic inflammatory response syndrome (SIRS) or microvascular failure that has not yet manifested as overt organ dysfunction[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. From a clinical perspective, this is where NPR adds the most value: it helps clinicians identify \"high-risk\" individuals among patients who might otherwise be perceived as \"lower-risk\" due to the absence of a sepsis diagnosis.\u003c/p\u003e \u003cp\u003eOur study confirms that adding NPR to a baseline model significantly improves the C-index (from 0.7098 to 0.7182). While the absolute increase may appear modest, it was statistically significant (P\u0026thinsp;=\u0026thinsp;0.036), and the DCA demonstrated a superior net benefit across a wide range of threshold probabilities. Given that NPR is derived from a routine and inexpensive CBC, it offers a cost-effective strategy for risk stratification in resource-limited settings or during the early \"golden hour\" of ICU admission. Patients identified in the highest quartile upon ICU admission may require more aggressive monitoring, early pharmacological intervention or prioritized respiratory support to mitigate the high risk of mortality.\u003c/p\u003e \u003cp\u003eDespite its strengths, this study has limitations. First, as a retrospective analysis of the MIMIC-IV database, we could not account for all potential confounders, such as long-term smoking history or baseline lung function, which were not consistently recorded. Second, NPR was only measured at admission; the prognostic impact of dynamic changes in NPR during the ICU stay remains to be explored. Finally, while we used Bootstrap internal validation, external validation in prospective, multi-center cohorts is necessary to confirm the generalizability of our findings across different healthcare systems.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eElevated admission NPR is a potent, independent predictor of mortality in ICU patients with AECOPD. Its predictive value is particularly pronounced in patients without sepsis, offering a window for early intervention. Incorporating NPR into routine clinical assessment enhances risk stratification and provides incremental value over traditional clinical models, facilitating more personalized management of AECOPD in the intensive care unit.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPS III \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute Physiology Score III\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; complete blood counts\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic obstructive pulmonary disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision Curve Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; False Discovery Rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHRs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; hazard ratios\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;intensive care unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIRB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;institutional review board\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMICE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; multiple imputation by chained equations\u003c/p\u003e\n\u003cp\u003eMIMIC-IV \u0026nbsp; \u0026nbsp; \u0026nbsp;Medical Information Mart for Intensive Care IV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMMPs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; matrix metalloproteinases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;proteases such as neutrophil elastase\u003c/p\u003e\n\u003cp\u003eNLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;neutrophil-to-Lymphocyte ratio\u003c/p\u003e\n\u003cp\u003ePLT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; platelet\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Restricted Cubic Splines\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAPS II \u0026nbsp; \u0026nbsp; \u0026nbsp; Physiology Score II\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; systolic blood pressure\u003c/p\u003e\n\u003cp\u003eSIRS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; systemic inflammatory response syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Sequential Organ Failure Assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpO₂ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;oxygen saturation\u003c/p\u003e\n\u003cp\u003eSQL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Structured Query Language\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVIF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Variance Inflation Factor\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;white blood cell\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study as all data were extracted from the publicly available and de-identified MIMIC-IV database, with appropriate certification (certification ID: 11356022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailable from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received main funding from the National Key Research and Development Program of China (NO.2023YFC3502601).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;HH and RG contributed equally to this work. HH was responsible for the statistical analysis of the data and drafted the initial manuscript. RD contributed to data extraction and curation, and critically revised the manuscript. SZ performed data verification. JZ conducted the literature review. JY acquired the funding and provided final supervision and approval of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrevalence. \u003cem\u003eand\u003c/em\u003e attributable health burden of chronic respiratory diseases, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med, 2020. 8(6): pp. 585\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oca MM, et al. 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Association between neutrophil-platelet ratio and 28-day mortality in patients with sepsis: a retrospective analysis based on MIMIC-IV database. BMC Infect Dis. 2025;25(1):685.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson AEW, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustin PC, et al. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021;37(9):1322\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteyerberg EW, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro SD. Neutrophil elastase: path clearer, pathogen killer, or just pathologic? Am J Respir Cell Mol Biol. 2002;26(3):266\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockley RA. Neutrophils and the pathogenesis of COPD. Chest. 2002;121(5 Suppl):s151\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFawzy A, et al. Association of thrombocytosis with COPD morbidity: the SPIROMICS and COPDGene cohorts. Respir Res. 2018;19(1):20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFricker M, Lokwani R. COPD: the role of neutrophils in inflammation, pathophysiology, and as drug targets. Clin Sci (Lond). 2025;139(20):1199\u0026ndash;214.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashif AM, et al. NETosis and pyroptosis of immune cells in sepsis. J Transl Int Med. 2025;13(4):318\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu\u0026ntilde;oz-Esquerre M, et al. Impact of acute exacerbations on platelet reactivity in chronic obstructive pulmonary disease patients. Int J Chron Obstruct Pulmon Dis. 2018;13:141\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao QQ, et al. Platelet-to-Lymphocyte Ratio (PLR), Neutrophil-to-Lymphocyte Ratio (NLR), Monocyte-to-Lymphocyte Ratio (MLR), and Eosinophil-to-Lymphocyte Ratio (ELR) as Biomarkers in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD). Int J Chron Obstruct Pulmon Dis. 2024;19:501\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen K, et al. Procalcitonin for Antibiotic Prescription in Chronic Obstructive Pulmonary Disease Exacerbations: Systematic Review, Meta-Analysis, and Clinical Perspective. Pulm Ther. 2020;6(2):201\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chronic obstructive pulmonary disease, neutrophil-to-platelet ratio, in-hospital mortality, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-8607276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8607276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute exacerbations of chronic obstructive pulmonary disease (AECOPD) are critical events with high mortality. The neutrophil-to-platelet ratio (NPR) has emerged as a promising systemic inflammatory marker, but its prognostic value in AECOPD remains underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized data from the MIMIC-IV 3.1 database (2008\u0026ndash;2022). Patients with AECOPD were categorized into quartiles based on admission NPR. The primary outcome was in-hospital mortality; the secondary outcome was 28-day mortality. Multivariable Cox regression, restricted cubic splines (RCS), and subgroup analyses with False Discovery Rate (FDR) adjustment were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 1,137 patients, in-hospital and 28-day mortality rates were 15.5% and 14.1%, respectively. In the fully adjusted model (Model IV), the highest NPR quartile (Q4, \u0026gt;\u0026thinsp;0.06) was associated with a 3.01-fold increased risk of in-hospital mortality (HR 3.01, 95% CI 1.31\u0026ndash;6.89, P\u0026thinsp;=\u0026thinsp;0.009) and nearly triple the risk of 28-day mortality (HR 2.91, 95% CI 1.18\u0026ndash;7.16, P\u0026thinsp;=\u0026thinsp;0.020) compared to Q1. RCS confirmed a linear risk trajectory (P_ non-linearity\u0026thinsp;=\u0026thinsp;0.796). Significant interactions were observed for sepsis status (P_FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with NPR showing higher sensitivity in non-septic patients (HR 4.66, 95% CI 1.24\u0026ndash;17.56). Adding NPR significantly improved model discrimination (C-index: 0.7098 to 0.7182, P\u0026thinsp;=\u0026thinsp;0.036) and clinical net benefit in decision curve analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAdmission NPR is a potent, independent predictor of mortality in AECOPD, particularly in younger and clinically less complex populations. Incorporating NPR into risk stratification may enhance early clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Admission Neutrophil-to-Platelet Ratio as an Independent Predictor of Mortality in Critically Ill Patients with AECOPD: A MIMIC-IV Database Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 09:50:00","doi":"10.21203/rs.3.rs-8607276/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T12:58:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:51:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T09:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241221796015699218320092409555040672763","date":"2026-04-20T03:06:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T23:20:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T09:19:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T23:05:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311218585683127880540079398836657660235","date":"2026-04-14T22:37:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221183123392445171185731917346191084996","date":"2026-04-14T09:00:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208251518913502788588144035242930571231","date":"2026-04-11T03:39:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T12:24:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196946200090022460089710513529091778220","date":"2026-04-09T11:52:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62811867211586524514601968331663126214","date":"2026-04-07T11:46:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T09:09:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-15T09:35:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T06:44:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T06:43:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-01-15T05:46:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"461c9462-59b5-45ed-8805-04c4f7dc5df6","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T12:58:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:51:06+00:00","index":75,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T13:10:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 09:50:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8607276","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8607276","identity":"rs-8607276","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}