Association between Stress Hyperglycemia Ratio and Mortality in Critically Ill COPD Patients: A Mediation Analysis of White Blood Cell Count

Association between Stress Hyperglycemia Ratio and Mortality in Critically Ill COPD Patients: A Mediation Analysis of White Blood Cell Count | 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 Association between Stress Hyperglycemia Ratio and Mortality in Critically Ill COPD Patients: A Mediation Analysis of White Blood Cell Count Xuanmei Ye, Huibo Wang, Guosong Jiang, Xiaoxiao Qu, Suipeng Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7448763/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: The stress hyperglycemia ratio (SHR), derived from admission glucose and HbA1c, reflects acute glycemic excursions. This study investigates the association between SHR and mortality in critically ill patients with chronic obstructive pulmonary disease (COPD), and explores the mediating role of white blood cell (WBC) count. Methods: A retrospective cohort analysis was conducted using the MIMIC-IV database (2008–2022). Adult ICU patients with COPD and available glucose and HbA1c data were included. SHR was categorized into tertiles (T1–T3). Primary and secondary outcomes were 28-day and 365-day all-cause mortality, respectively. Cox regression, restricted cubic spline (RCS) analysis, and Kaplan–Meier curves assessed associations. Mediation analysis evaluated the indirect effect of WBC count. Results: A total of 873 patients were included. Higher SHR (T3) was independently associated with increased 28-day (HR=1.35, 95% CI: 1.04–1.73, p=0.024) and 365-day mortality (HR=1.33, 95% CI: 1.11–1.59, p=0.002). RCS analysis revealed a linear relationship between SHR and mortality risk. Kaplan–Meier curves showed lower survival in the highest SHR group. WBC count partially mediated the effect of SHR on 28-day mortality (ACME, p<0.01), accounting for 4.45% of the total effect. Conclusions: SHR is an independent predictor of short- and long-term mortality in critically ill COPD patients. The association may be partially mediated by inflammation, as reflected by WBC count. SHR could serve as a simple tool for early risk stratification in this population. Clinical trial number: not applicable. COPD stress hyperglycemia ratio mortality mediation analysis MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction COPD is a progressive respiratory disorder characterized by persistent airflow limitation and chronic airway inflammation. Both its prevalence and mortality rates increase significantly with age, posing a growing health and socioeconomic burden, particularly among elderly populations [ 1 , 2 ]. Despite advances in therapeutic interventions, the prognosis of COPD patients remains poor, especially for those requiring intensive care unit (ICU) admission. Multiple clinical phenotypes and comorbidities (such as frequent acute exacerbations, cardiovascular disease, and diabetes) are associated with poor prognosis in COPD patients [ 3 , 4 ]. Among these, metabolic disturbances have attracted particular attention due to their bidirectional interaction with systemic inflammation. The Stress Hyperglycemia Ratio (SHR), defined as the ratio of admission blood glucose to the average glucose levels estimated from glycated hemoglobin (HbA1c), has emerged as a novel biomarker reflecting acute glucose dysregulation under stress conditions[ 5 , 6 ]. SHR has been significantly associated with adverse outcomes in various critical illnesses, including sepsis, myocardial infarction, and stroke [ 7 – 9 ]. However, its prognostic value in COPD patients - particularly those in ICU settings - remains insufficiently explored. Furthermore, the underlying pathophysiological mechanisms linking SHR to COPD mortality are not fully understood. Inflammation, as indicated by elevated white blood cell (WBC) counts, may serve as a crucial mediating factor, given the well-established inflammatory nature of both hyperglycemia and COPD pathogenesis [ 10 , 11 ]. To fill this knowledge gap, this study conducted a large-scale retrospective cohort study based on the MIMIC-IV database to explore the relationship between SHR and all-cause mortality in COPD patients in the ICU, and further analyze whether WBC count mediates this association, thereby revealing the potential metabolic-inflammation axis mechanism in the prognosis of COPD. The study results may provide new ideas for risk stratification and therapeutic targets in critically ill COPD populations. Materials and methods Data source This study conducted a retrospective cohort analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, a comprehensive and publicly available repository containing clinical data from patients admitted to the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center between 2008 and 2022. Data were accessed after certification (ID: 69192644). The study adhered to the STROBE guidelines for observational epidemiological reporting and was approved by the ethics committees of the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center. Informed consent was waived due to the use of de-identified data. Study Population The study included adult patients aged ≥ 18 years with a confirmed diagnosis of COPD. COPD was defined based on ICD-9 and ICD-10 codes (see Supplementary Table S1 ). Data extracted included demographic information, clinical data, laboratory test results, and comorbidities of the patients. The inclusion criteria were: (1) patients admitted to the intensive care unit (ICU) for the first time, and (2) patients aged 18 years or older. The exclusion criteria included: (1) patients with an ICU stay of less than 24 hours (n = 761), (2) patients with missing admission blood glucose or glycated hemoglobin (HbA1c) data at the time of admission (n = 2950). A total of 873 patients were eligible for the study (Fig. 1 ). Demographical and laboratory variables Structured Query Language (SQL) was used to collect patients' demographic information (including age and sex), medical history (such as diabetes, atrial fibrillation, etc.), initial laboratory indicators (such as magnesium, calcium, hemoglobin, platelet count, international normalized ratio, prothrombin time), vital signs (such as heart rate, respiratory rate (RR), and body temperature), and survival time. Variables with a missing rate of ≥ 20% were excluded to reduce potential bias, while variables with a missing rate of < 20% were imputed using multiple imputation methods (see Table S2 ). All laboratory parameters were recorded. Exposure Variable and Definitions The primary exposure measure in this study was the SHR index, which was calculated based on the blood glucose level at admission and the composite measurement of HbA1c. Therefore, the formula for calculating SHR is: SHR = Admission blood glucose (mmol/L) / [1.59 × HbA1c (%) − 2.59][ 5 , 12 ]. Outcomes The primary outcome was all-cause mortality at 28 days after ICU admission, and the secondary outcome was all-cause mortality at 365 days. The status of death was determined through in-hospital records and follow-up data from the database. Statistical analysis In this study, patients diagnosed with COPD were divided into three groups (T1, T2, T3) based on the tertiles of the SHR index: T1 (0.28 ≤ SHR < 0.90), T2 (0.90 ≤ SHR < 1.19), and T3 (1.19 ≤ SHR < 5.62). The basic characteristics of each group were described. Continuous variables were presented as mean ± standard deviation or median (interquartile range), while categorical variables were presented as counts and frequency percentages (%). To compare categorical variables between different groups, one-way analysis of variance (ANOVA), Kruskal-Wallis H test (for non-normally distributed continuous variables), or chi-square test (for categorical variables) were used as appropriate. Kaplan-Meier (K-M) survival analysis was used to assess the incidence of endpoint events at different levels of SHR, with differences evaluated by the log-rank test to compare survival between groups. To explore the relationship between SHR and all-cause mortality at 28 days and 365 days, a Cox proportional hazards model was used to determine the relationship between the SHR index and study endpoints, providing hazard ratios (HR) and 95% confidence intervals (CI). Covariates were included in the model if their addition changed the matched hazard ratio by at least 10%, or based on prior studies and clinical considerations. To adjust for confounding factors, three models were used: Model 1 (adjusted for age and sex), Model 2 (adjusted for laboratory indicators, including Mg, Ca, RBC, Hb, PLT, INR, PT, in addition to Model 1), and Model 3 (adjusted for diabetes and atrial fibrillation, in addition to Model 2). Group T1 was used as the reference group for all models. To explore the potential linear relationship between SHR levels and all-cause mortality at 28 days and 365 days, restricted cubic spline models were employed. Subgroup analyses were conducted based on age (< 65 years or ≥ 65 years), sex, diabetes, and atrial fibrillation, with interaction effects assessed by p-values. These findings were presented in the form of forest plots. Bootstrap methods (with 5000 replications) were used for mediation analysis to assess the mediating effect of WBC count on the relationship between SHR and 28-day mortality. All analyses were conducted using FreeStatistics V2.1.1 software, and a p-value below 0.05 was considered statistically significant. Results Baseline characteristics of study subjects A total of 873 COPD patients were included in this study, comprising 345 males (39.5%) and 528 females (60.5%), with a median age of 70.6 years, as shown in Table 1 . Based on the SHR index, participants were divided into tertile groups (T1, T2, T3): T1 (0.28 ≤ SHR < 0.90), T2 (0.90 ≤ SHR < 1.19), and T3 (1.19 ≤ SHR < 5.62), with 291 individuals in each group. These groups were compared in terms of sex, age, magnesium (Mg), calcium (Ca), red blood cells (RBC), hemoglobin (Hb), platelets (PLT), international normalized ratio (INR), prothrombin time (PT), diabetes status, and atrial fibrillation status. Statistical differences were described using p-values. The baseline table showed significant differences in Mg and diabetes status among the groups (P 0.005) (Table 1 ). Table 1 Baseline Characteristics of Participants Stratified by Tertiles of the Stress Hyperglycemia Ratio (SHR) Variables Total (N = 873) T1(0.28 ~ 0.90) (N = 291) T2(0.90 ~ 1.19) (N = 291) T3 (1.19–5.62) (N = 291) P value Gender, n (%) 0.678 Female 345 (39.5) 109 (37.5) 118 (40.5) 118 (40.5) Male 528 (60.5) 182 (62.5) 173 (59.5) 173 (59.5) Admission Age (years) 70.6 ± 10.4 70.3 ± 10.5 71.3 ± 10.1 70.2 ± 10.7 0.359 Magnesium (mmol/L) 2.1 ± 0.6 2.2 ± 0.5 2.1 ± 0.6 2.1 ± 0.6 0.024 Calcium (mmol/L) 8.5 ± 0.8 8.5 ± 0.9 8.5 ± 0.7 8.4 ± 0.8 0.439 RBC (×10 12 /L) 3.6 ± 0.8 3.7 ± 0.8 3.6 ± 0.8 3.6 ± 0.8 0.250 Hemoglobin (g/L) 10.8 ± 2.4 10.8 ± 2.4 10.7 ± 2.4 10.8 ± 2.4 0.726 Platelet Count (×10 9 /L) 204.0 ± 91.7 200.6 ± 88.8 199.8 ± 94.1 211.5 ± 92.0 0.233 INR 1.4 ± 0.7 1.4 ± 0.8 1.4 ± 0.6 1.4 ± 0.7 0.951 PT (s) 15.6 ± 7.5 15.6 ± 8.9 15.7 ± 6.2 15.5 ± 7.3 0.982 Diabetes, n (%) < 0.001 No 454 (52.0) 130 (44.7) 184 (63.2) 140 (48.1) Yes 419 (48.0) 161 (55.3) 107 (36.8) 151 (51.9) Atrial Fibrillation, n (%) 0.546 No 504 (57.7) 162 (55.7) 175 (60.1) 167 (57.4) Yes 369 (42.3) 129 (44.3) 116 (39.9) 124 (42.6) Note: RBC = Red blood cell count; INR = International normalized ratio; PT = Prothrombin time. SHR and mortality In the univariate Cox regression analysis, the impact of various factors on the 28-day and 365-day mortality of COPD (chronic obstructive pulmonary disease) patients is presented. The hazard ratio (HR) for each factor, along with its 95% confidence interval (CI) and the P-value from the Wald test, are listed. Age, magnesium levels, INR, and PT significantly affect the 28-day and 365-day mortality of COPD patients (P < 0.05). Sex, calcium, red blood cell count, hemoglobin, platelets, and atrial fibrillation do not significantly impact the risk of death. Diabetes has an effect on 365-day mortality that approaches the level of significance (see Table S3). To evaluate the independent effect of SHR on mortality, we constructed two Cox proportional hazards models, each comprising four columns: unadjusted, Model 1, Model 2, and Model 3. Models 2 and 3 were analyzed using multiply imputed data. Model 1 adjusted for age and sex; Model 2 additionally adjusted for laboratory parameters (magnesium, calcium, red blood cells, hemoglobin, platelet count, and plateletcrit); and Model 3 further incorporated adjustments for diabetes and atrial fibrillation. When SHR was analyzed as a continuous variable, the unadjusted model demonstrated that higher SHR levels were significantly associated with increased 28-day mortality (hazard ratio [HR] 1.46, 95% confidence interval [CI] 1.15–1.85) and 365-day mortality (HR 1.43, 95% CI 1.21–1.85). These associations remained robust after adjusting for age and sex (Model 1) and further adjustment for laboratory parameters (Model 2) (see Table 2 ). When SHR was analyzed as a categorical variable, compared with the T1 group, higher SHR levels showed significant associations with both 28-day mortality (HR 1.83, 95% CI 1.13–2.97, P < 0.015) and 365-day mortality (HR 1.53, 95% CI 1.12–2.09, P < 0.009). Patients in the highest tertile (T3) exhibited significantly increased mortality risk across all models (see Table 2 ). The SHR index and T3 group (1.19–5.62) showed significant associations with both 28-day and 365-day mortality in all models, with statistically significant mortality differences across tertiles. Table 2 Multivariable Cox Regression Analyses for 28-Day and 365-Day Mortality in COPD Patients Categories Unadjusted Model Model 1 Model 2 Model 3 HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value 28-day Mortality SHR 1.46 (1.15 ~ 1.85) 0.002 1.39 (1.11 ~ 1.76) 0.005 1.35 (1.05 ~ 1.75) 0.022 1.35 (1.04 ~ 1.73) 0.024 Tertile T1 (0.28 ~ 0.90) 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) T2 (0.90 ~ 1.19) 1.28 (0.78 ~ 2.10) 0.334 1.24 (0.76 ~ 2.05) 0.389 1.15 (0.69 ~ 1.90) 0.594 1.19 (0.70 ~ 2.00) 0.513 T3 (1.19–5.62) 2.00 (1.27 ~ 3.16) 0.003 2.06 (1.30 ~ 3.27) 0.002 1.78 (1.11 ~ 2.86) 0.018 1.83 (1.13 ~ 2.97) 0.015 P for trend 0.002 0.001 0.012 0.012 365-day Mortality SHR 1.43 (1.21 ~ 1.69) < 0.001 1.37 (1.16 ~ 1.61) < 0.001 1.33 (1.11 ~ 1.59) 0.002 1.33 (1.11 ~ 1.59) 0.002 Tertile T1 (0.28 ~ 0.90) 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) T2 (0.90 ~ 1.19) 1.32 (0.96 ~ 1.81) 0.088 1.28 (0.93 ~ 1.76) 0.127 1.23 (0.89 ~ 1.70) 0.206 1.24 (0.89 ~ 1.72) 0.200 T3 (1.19–5.62) 1.70 (1.25 ~ 2.31) 0.001 1.71 (1.26 ~ 2.32) 0.001 1.53 (1.12 ~ 2.10) 0.008 1.53 (1.12 ~ 2.09) 0.009 P for trend 0.001 0.001 0.009 0.008 Note: T = Tertile; SHR = Stress hyperglycemia ratio; HR = Hazard ratio; CI = Confidence interval; Ref. = Reference. Model 1: Adjusted for age and sex. Model 2: Adjusted as in Model 1 plus magnesium, calcium, red blood cell count, hemoglobin, platelet count, international normalized ratio (INR), and prothrombin time (PT). Model 3: Adjusted as in Model 2 plus diabetes and atrial fibrillation. In the study population, sensitivity analyses were performed by dividing SHR into quartiles and quintiles to explore its relationship with 28-day and 365-day mortality in COPD (see Tables S4 and S5). Each section includes results from unadjusted and three adjusted models (Model 1, Model 2, and Model 3). When SHR was treated as a continuous variable, higher SHR levels were significantly associated with increased 28-day (HR 1.46, 95% CI 1.15–1.85, P < 0.002) and 365-day mortality (HR 1.43, 95% CI 1.21–1.85, P < 0.001). This association remained significant after adjusting for age and sex (Model 1) and further adjusting for laboratory indicators (Model 2) (see Tables S4 and S5). When SHR was analyzed as a categorical variable, T4 (quartile) and T5 (quintile) showed significantly higher risk ratios for 28-day and 365-day mortality compared to T1. Specifically, T4 had a HR of 1.93 (95% CI 1.12–3.33, P < 0.021) for 28-day and 1.64 (95% CI 1.13–2.38, P < 0.009) for 365-day mortality. T5 had a HR of 1.99 (95% CI 1.09–3.61, P < 0.027) for 28-day and 1.86 (95% CI 1.23–2.82, P < 0.004) for 365-day mortality. Overall, there was a significant increasing trend in mortality risk with higher SHR quartiles and quintiles (see Tables S4 and S5). A Kaplan-Meier survival analysis was conducted for mortality by SHR index tertiles, as shown in Fig. 3 . This figure illustrates the association between different SHR groups and all-cause mortality at 28 days (Panel A) and 365 days (Panel B) in patients with chronic obstructive pulmonary disease (COPD). It displays the Kaplan-Meier survival curves for the three groups (T1, T2, and T3), revealing significant differences in survival rates among them. The results show that both cumulative risk and all-cause mortality at 28 days (Panel A) and 365 days (Panel B) indicate relatively higher and slower-declining survival rates in Group T1, whereas Group T3 has the fastest-declining and lowest survival rate compared with the other groups. The statistical significance is demonstrated by log-rank P-values of 0.0061 and 0.0027, respectively (see Fig. 3 ). In addition, the analysis of restricted cubic spline regression models shows that there is a linear relationship between SHR index levels and the mortality risk at 28 and 365 days, where Figure A represents the all - cause mortality at 28 days and Figure B represents the all - cause mortality at 365 days. For detailed results, see Figure S1 . Subgroup analysis Subgroup analysis of the SHR index for risk stratification of the primary outcome across various subgroups (sex, age, diabetes, heart failure) of the study population is presented in Fig. 2 . Figure A and B show the hazard ratios (HRs) and 95% confidence intervals (CIs) for each subgroup, reflecting treatment - effect differences across populations. P - Interaction values are also shown to test the significance of differences between subgroups. Figure A corresponds to 28 - day all - cause mortality and Figure B to 365 - day all - cause mortality. In both figures, the unadjusted group had a significantly higher risk than the adjusted group. No significant differences were found between subgroups (age, sex, diabetes, atrial fibrillation), as all P - interaction values exceeded 0.05, suggesting no significant interaction between them. Mediation analysis In the mediation causal effect analysis, white blood cell count (WBC) was used as a mediator to examine the relationship between SHR and 28-day all-cause mortality in COPD patients (see Fig. 4 ). In the mediation model shown, SHR is the independent variable, COPD the dependent variable, and WBC the mediator. Figure 4 shows a mediation - effect model. In the mediation analysis, path a is the regression coefficient linking SHR to WBC. Path b is the regression coefficient linking WBC to COPD. Path c is SHR's direct effect on COPD. The total effect (TE) of SHR on COPD is 0.0343 (95% CI: 0.0046–0.0560), statistically significant. The indirect effect (IE) through WBC is 0.0328 (95% CI: 0.0014–0.0541), also statistically significant. WBC mediates 4.45% of SHR's effect on COPD. The model shows WBC plays a role in the SHR - COPD relationship. E- value and Unmeasured Confounding Analysis The bias plots of confounding - adjusted RRs show confounders' impact on exposure - confounder relationships for 28 - day (A) and 365 - day (B) mortality (see Figure S2 ). When RREU = 2.04 (28 - day) and 1.99 (365 - day), confounders' influence on these relationships is minimal, indicating the least bias. This clarifies how confounders affect RRs in different exposure - confounder relationships (see Figure S2 ). Discussion Study used the MIMIC database to thoroughly assess the link between the SHR and short - and long - term all - cause mortality in COPD patients. Results show that the SHR is an independent predictor of short - and long - term all - cause mortality in critically ill COPD patients,potentially valuable in prognosis. This association is partly mediated by white blood cell count. SHR is a routine and cost - effective indicator, so it can be used for risk stratification and treatment decisions. The study has two main findings: firstly, after adjusting for key variables, the link remains significant and is robust across various analyses. Secondly, white blood cell (WBC) count partly mediates the relationship between SHR and 28 - day mortality, indicating a possible metabolic - inflammatory pathway. These findings highlight SHR's potential as a prognostic marker in acute COPD management. This findings align with prior evidence on SHR's prognostic role in critical illness and extend existing research. Previous studies have linked SHR to adverse outcomes in sepsis [ 13 ], myocardial infarction [ 8 , 14 ], stroke [ 15 ], atrial fibrillation [ 7 , 16 ]and coronary artery disease [ 17 , 18 ]. But few have focused on COPD. A recent study indicated that SHR better predicts mortality in acute - exacerbation COPD patients than admission glucose or HbA1c [ 19 ], yet it didn't explore mechanisms or conduct mediation analysis. By incorporating direct - and indirect - effect models, our study offers stronger evidence for SHR's prognostic relevance and uncovers potential pathophysiological processes. Acute stress - induced hyperglycemia can trigger cortisol and catecholamine release, boost oxidative stress, and cause immune dysfunction[ 20 ]. For COPD patients with systemic inflammation, these metabolic disturbances may intensify the inflammatory response, accelerate disease progression, and worsen prognosis [ 21 , 22 ]. Also, COPD is linked to glucocorticoid resistance. This mainly happens because oxidative stress inhibits histone deacetylase − 2 (HDAC2), weakening glucocorticoids' anti - inflammatory effects [ 23 – 25 ]. This resistance can worsen hyperglycemia's harmful impacts and reduce treatment effectiveness. The intermediary effect of white blood cell (WBC) count observed in our analysis supports this interconnected inflammatory - metabolic mechanism. COPD a chronic inflammatory lung disease, has inflammation as a key factor in its pathogenesis. Research shows that multiple inflammation - related composite blood biomarkers have potential clinical value in COPD progression, prognosis, and survival [ 26 ]. SII, SIRI, PIV, NLR, and PLR are emerging composite inflammatory indices derived from peripheral immune cell counts, including neutrophils, monocytes, platelets, and lymphocytes [ 27 ]. They are closely related to body - wide inflammation and immune regulation. Composite inflammatory markers are not only inexpensive and easily accessible but also more accurately reflect the body's inflammatory responses and immune regulation under pathological conditions than single biomarkers [ 28 ].The SHR, reflecting blood glucose changes during stress, is significantly associated with mortality risk in COPD patients. Monitoring SHR can thus better assess disease severity and prognosis in COPD patients, offering a basis for clinical intervention. Research on SHR in COPD has expanded its clinical applications. Future studies can explore SHR’s specific mechanisms in different diseases and its potential as a therapeutic target in COPD. This study explores the link between the SHR and 28 - day and 365 - day all - cause mortality in COPD patients. Study has several strengths, including a large sample size, standardized data collection, and thorough adjustment for potential confounders. While most studies indicate SHR is related to poor prognosis, some haven’t found such a link. This discrepancy may be due to differences in study design, sample characteristics, or analysis methods. Despite the convincing results of the study, it is imperative to recognize the limitations of observational data in establishing causality. Study is a retrospective cohort study prone to measurement errors and influenced by baseline data changes, with limited causal inference. It didn't account for hormonal interventions or assess dynamic blood glucose changes. As a secondary analysis, it relies on the quality and integrity of existing database data. Future research should include more diverse groups and comprehensive data to further solidify and validate these findings. Conclusion Study shows that the SHR, after adjusting for confounders, is linked to a higher risk of 28 - day and 365 - day mortality in COPD patients and can predict their prognosis and risk. The causal relationship and intervention value of SHR in COPD need confirmation in prospective studies. The elevated SHR is significantly associated with short - and long - term mortality in COPD patients, partly mediated by white blood cells, highlighting the metabolic - inflammatory pathway’s role in COPD prognosis. SHR has the potential to be a simple risk - prediction tool for clinical use. Declarations Ethical approval: The Medical Information Mart for Intensive Care IV database (MIMIC-IV, version 3.1) is a publicly available resource developed and maintained by the Laboratory for Computational Physiology at the Massachusetts Institute of Technology (MIT). This database contains anonymized medical records of more than 60,000 adult patients admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and December 2021. This study adheres to the STROBE guidelines for observational epidemiological research reporting. Consent for publication: Not applicable Data availability: The data from the Medical Information Mart for Intensive Care (MIMIC-IV v3.1) database can be publicly accessed at https://physionet.org/ content/mimiciv/3.1/. Competing interests: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Author contributions: XMY: data collection, data analysis, manuscript writing. HBW: data analysis, manuscript editing. GSJ: data analysis, manuscript editing. XXQ: data analysis, manuscript editing. SPC: data analysis, manuscript editing. XJY: data analysis, manuscript editing. QPX: project development, data analysis, manuscript writing, manuscript editing. All authors have read and approved this manuscript. This study did not receive any funding. Funding ： There is no funding to report. Consent to Participate declaration: not applicable. Acknowledgments: We acknowledge the contributions of the MIMIC-IV (version 3.1) program team for the development and maintenance of the MIMIC-IV database. References Negewo, N.A., P.G. Gibson, and V.M. 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Chen, Y., et al., Assessment of stress hyperglycemia ratio to predict all-cause mortality in patients with critical cerebrovascular disease: a retrospective cohort study from the MIMIC-IV database. Cardiovasc Diabetol, 2025. 24 (1): p. 58. Leuppi, J.D., et al., Short-term vs conventional glucocorticoid therapy in acute exacerbations of chronic obstructive pulmonary disease: the REDUCE randomized clinical trial. Jama, 2013. 309 (21): p. 2223-31. Thomsen, M., et al., Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. Jama, 2013. 309 (22): p. 2353-61. Mador, M.J. and S. Sethi, Systemic inflammation in predicting COPD exacerbations. Jama, 2013. 309 (22): p. 2390-1. 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). Groenewegen, K.H., et al., Increased systemic inflammation is a risk factor for COPD exacerbations. Chest, 2008. 133 (2): p. 350-7. Additional Declarations No competing interests reported. Supplementary Files SupplementalFigures20250825.pptx Figure S1. Restricted cubic spline plots illustrating the association between SHR and all-cause mortality in critically ill COPD patients. Figure S2. Sensitivity analysis using E-values to assess the potential impact of unmeasured confounding on the association between SHR and mortality. SupplementalTables20250825.docx Table S1. ICD Codes Used to Define COPD in This Study Table S2. Overview of Missing Data in the Study Sample Table S3. Univariate Cox Regression Analysis for 28-Day and 365-Day Mortality Table S4. Sensitivity Analyses Using SHR Quartiles to Evaluate the Association between SHR and Mortality in COPD Patients Table S5. Sensitivity Analyses Using SHR Quartiles and Quintiles to Evaluate the Association between SHR and Mortality in COPD Patients Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Oct, 2025 Editor invited by journal 09 Sep, 2025 Editor assigned by journal 06 Sep, 2025 Submission checks completed at journal 06 Sep, 2025 First submitted to journal 24 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7448763","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531186867,"identity":"91351223-b3f3-4181-bd14-4fda40d30650","order_by":0,"name":"Xuanmei Ye","email":"","orcid":"","institution":"The Second Affiliated Hospital \u0026 Yuying Children’s Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuanmei","middleName":"","lastName":"Ye","suffix":""},{"id":531186868,"identity":"55668d74-c5c8-4f0e-8dee-21a12ba2a974","order_by":1,"name":"Huibo Wang","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huibo","middleName":"","lastName":"Wang","suffix":""},{"id":531186871,"identity":"9ecbbc0f-d9ef-463a-b6dd-9ca0198db946","order_by":2,"name":"Guosong Jiang","email":"","orcid":"","institution":"The 1st People's Hospital of Zhaotong City \u0026 The Zhaotong Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guosong","middleName":"","lastName":"Jiang","suffix":""},{"id":531186872,"identity":"9dfe13db-5e65-4110-92ab-3f823e0ef2ac","order_by":3,"name":"Xiaoxiao Qu","email":"","orcid":"","institution":"The Second Affiliated Hospital \u0026 Yuying Children’s Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiao","middleName":"","lastName":"Qu","suffix":""},{"id":531186874,"identity":"1408c4a1-b4af-4e71-9637-7cf93010888f","order_by":4,"name":"Suipeng Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital \u0026 Yuying Children’s Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Suipeng","middleName":"","lastName":"Chen","suffix":""},{"id":531186876,"identity":"4742662f-232a-41a2-aed9-7912a0a2b7f4","order_by":5,"name":"Xunjun Yang","email":"","orcid":"","institution":"The Second Affiliated Hospital \u0026 Yuying Children’s Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xunjun","middleName":"","lastName":"Yang","suffix":""},{"id":531186877,"identity":"64af37f2-1141-4b6c-879c-89c9ee4f9d46","order_by":6,"name":"Qipeng Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYJACCQYGGx429uYDBz78IF5Lmhw/z7HEgzN7iNdy2FhyRo7xYQ42IpSbs589eOPjDubEDQdyPhxm4GGQ5xc7gF+LZU9esuXMM2xALWc3HC6wYDCcOTsBvxaDAzlm0rxtPIkbDvZuODyDhyHB4DYhLeffgLRIJG44zPPgMA8bMVpugG0xMJZs42EgTovljDfGljPbEoCBzGYADGQJwn4x588xvPGx7T8Pm/zjxx8+/LCR55cm5DA0vgR+5di0jIJRMApGwSjABACmaEdlaA5kLgAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital \u0026 Yuying Children’s Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qipeng","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2025-08-25 01:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7448763/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7448763/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93942357,"identity":"99a1d6d0-af11-427f-b9b3-9362cdc644a1","added_by":"auto","created_at":"2025-10-20 13:48:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84340,"visible":true,"origin":"","legend":"","description":"","filename":"COPD20250825.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/272ea76dea54c782b0219ca5.docx"},{"id":93942358,"identity":"73b622f3-95d5-4d4f-ba93-69fa2f7de7d6","added_by":"auto","created_at":"2025-10-20 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13:40:25","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93406,"visible":true,"origin":"","legend":"","description":"","filename":"db029488f39b4087a7cdac76d89bde551structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/1c7768e6af473e4cb3e68fdc.xml"},{"id":93941629,"identity":"91ffada1-8f76-48b2-ab23-8a35d771be41","added_by":"auto","created_at":"2025-10-20 13:40:25","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102814,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/c62905f3bf60744873c12102.html"},{"id":93941616,"identity":"5d1237ff-ee26-4685-97de-48f882acf544","added_by":"auto","created_at":"2025-10-20 13:40:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Participant Selection in the MIMIC-IV Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure presents a schematic overview of the inclusion and exclusion criteria used to derive the final analytical cohort from the MIMIC-IV database.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/08bac8bcbf97d2ca13fcf882.png"},{"id":93941618,"identity":"b9eea988-5d80-48c9-af3b-fa20394bcbbf","added_by":"auto","created_at":"2025-10-20 13:40:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup Analysis of the Association between SHR and All-Cause Mortality in COPD Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure shows a subgroup analysis of the association between the stress hyperglycemia ratio (SHR) and all-cause mortality across different patient subgroups, including sex, age, diabetes status, and heart failure. In all subgroups, the P-value for interaction exceeded 0.05, indicating no significant effect modification.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/f17a23278b391a27293cfebf.png"},{"id":93941617,"identity":"36377b0b-3ba2-43b0-b245-6ea0706bde4e","added_by":"auto","created_at":"2025-10-20 13:40:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier Survival Curves for SHR Tertiles and Their Association with 28-Day and 365-Day Mortality in COPD Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure illustrates the relationship between SHR tertiles and all-cause mortality at 28 days (Panel A) and 365 days (Panel B) in patients with COPD. Patients in the highest SHR tertile (T3) had the lowest survival probabilities at both time points. The log-rank tests yielded P-values of 0.0061 and 0.0027, indicating statistically significant differences among the groups.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/80724e4b23200f3aa39fa8e8.png"},{"id":93941622,"identity":"28bae301-d4fe-4987-9496-b7f9ef658371","added_by":"auto","created_at":"2025-10-20 13:40:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation Analysis of the Association between SHR and 28-Day Mortality in COPD Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Mediation results showing the total effect (TE), average direct effect (ADE), and average causal mediation effect (ACME) of SHR on 28-day mortality, with WBC as the mediator. (B) Mediation model diagram. SHR is the independent variable, WBC the mediator, and COPD mortality the outcome. WBC mediated 4.45% of the total effect. All effects were statistically significant.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/fb4573eed134ffd17dfdc230.png"},{"id":93943640,"identity":"2430bbb1-1108-4234-aa37-fe6ca02e32b0","added_by":"auto","created_at":"2025-10-20 13:56:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1402535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/13208696-d540-425e-b53b-9407cf00c886.pdf"},{"id":93941624,"identity":"f3806ad5-9bcc-4d41-b1cd-113b9a41e7e0","added_by":"auto","created_at":"2025-10-20 13:40:25","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":427486,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Restricted cubic spline plots illustrating the association between SHR and all-cause mortality in critically ill COPD patients.\u003c/p\u003e\n\u003cp\u003eFigure S2. Sensitivity analysis using E-values to assess the potential impact of unmeasured confounding on the association between SHR and mortality.\u003c/p\u003e","description":"","filename":"SupplementalFigures20250825.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/97a2a4c99b651c64d496de35.pptx"},{"id":93941620,"identity":"e11123d0-976e-493e-a880-12b347e7619a","added_by":"auto","created_at":"2025-10-20 13:40:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32644,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. ICD Codes Used to Define COPD in This Study\u003c/p\u003e\n\u003cp\u003eTable S2. Overview of Missing Data in the Study Sample\u003c/p\u003e\n\u003cp\u003eTable S3. Univariate Cox Regression Analysis for 28-Day and 365-Day Mortality\u003c/p\u003e\n\u003cp\u003eTable S4. Sensitivity Analyses Using SHR Quartiles to Evaluate the Association between SHR and Mortality in COPD Patients\u003c/p\u003e\n\u003cp\u003eTable S5. Sensitivity Analyses Using SHR Quartiles and Quintiles to Evaluate the Association between SHR and Mortality in COPD Patients\u003c/p\u003e","description":"","filename":"SupplementalTables20250825.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448763/v1/680009f61cf858b91fb280cc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Stress Hyperglycemia Ratio and Mortality in Critically Ill COPD Patients: A Mediation Analysis of White Blood Cell Count","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCOPD is a progressive respiratory disorder characterized by persistent airflow limitation and chronic airway inflammation. Both its prevalence and mortality rates increase significantly with age, posing a growing health and socioeconomic burden, particularly among elderly populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advances in therapeutic interventions, the prognosis of COPD patients remains poor, especially for those requiring intensive care unit (ICU) admission.\u003c/p\u003e\u003cp\u003eMultiple clinical phenotypes and comorbidities (such as frequent acute exacerbations, cardiovascular disease, and diabetes) are associated with poor prognosis in COPD patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, metabolic disturbances have attracted particular attention due to their bidirectional interaction with systemic inflammation. The Stress Hyperglycemia Ratio (SHR), defined as the ratio of admission blood glucose to the average glucose levels estimated from glycated hemoglobin (HbA1c), has emerged as a novel biomarker reflecting acute glucose dysregulation under stress conditions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSHR has been significantly associated with adverse outcomes in various critical illnesses, including sepsis, myocardial infarction, and stroke [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, its prognostic value in COPD patients - particularly those in ICU settings - remains insufficiently explored. Furthermore, the underlying pathophysiological mechanisms linking SHR to COPD mortality are not fully understood. Inflammation, as indicated by elevated white blood cell (WBC) counts, may serve as a crucial mediating factor, given the well-established inflammatory nature of both hyperglycemia and COPD pathogenesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo fill this knowledge gap, this study conducted a large-scale retrospective cohort study based on the MIMIC-IV database to explore the relationship between SHR and all-cause mortality in COPD patients in the ICU, and further analyze whether WBC count mediates this association, thereby revealing the potential metabolic-inflammation axis mechanism in the prognosis of COPD. The study results may provide new ideas for risk stratification and therapeutic targets in critically ill COPD populations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003e This study conducted a retrospective cohort analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, a comprehensive and publicly available repository containing clinical data from patients admitted to the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center between 2008 and 2022. Data were accessed after certification (ID: 69192644). The study adhered to the STROBE guidelines for observational epidemiological reporting and was approved by the ethics committees of the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center. Informed consent was waived due to the use of de-identified data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study included adult patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with a confirmed diagnosis of COPD. COPD was defined based on ICD-9 and ICD-10 codes (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Data extracted included demographic information, clinical data, laboratory test results, and comorbidities of the patients. The inclusion criteria were: (1) patients admitted to the intensive care unit (ICU) for the first time, and (2) patients aged 18 years or older. The exclusion criteria included: (1) patients with an ICU stay of less than 24 hours (n\u0026thinsp;=\u0026thinsp;761), (2) patients with missing admission blood glucose or glycated hemoglobin (HbA1c) data at the time of admission (n\u0026thinsp;=\u0026thinsp;2950). A total of 873 patients were eligible for the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDemographical and laboratory variables\u003c/h3\u003e\n\u003cp\u003eStructured Query Language (SQL) was used to collect patients' demographic information (including age and sex), medical history (such as diabetes, atrial fibrillation, etc.), initial laboratory indicators (such as magnesium, calcium, hemoglobin, platelet count, international normalized ratio, prothrombin time), vital signs (such as heart rate, respiratory rate (RR), and body temperature), and survival time. Variables with a missing rate of \u0026ge;\u0026thinsp;20% were excluded to reduce potential bias, while variables with a missing rate of \u0026lt;\u0026thinsp;20% were imputed using multiple imputation methods (see Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). All laboratory parameters were recorded.\u003c/p\u003e\n\u003ch3\u003eExposure Variable and Definitions\u003c/h3\u003e\n\u003cp\u003eThe primary exposure measure in this study was the SHR index, which was calculated based on the blood glucose level at admission and the composite measurement of HbA1c. Therefore, the formula for calculating SHR is:\u003c/p\u003e\u003cp\u003eSHR\u0026thinsp;=\u0026thinsp;Admission blood glucose (mmol/L) / [1.59 \u0026times; HbA1c (%) \u0026minus;\u0026thinsp;2.59][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality at 28 days after ICU admission, and the secondary outcome was all-cause mortality at 365 days. The status of death was determined through in-hospital records and follow-up data from the database.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eIn this study, patients diagnosed with COPD were divided into three groups (T1, T2, T3) based on the tertiles of the SHR index: T1 (0.28\u0026thinsp;\u0026le;\u0026thinsp;SHR\u0026thinsp;\u0026lt;\u0026thinsp;0.90), T2 (0.90\u0026thinsp;\u0026le;\u0026thinsp;SHR\u0026thinsp;\u0026lt;\u0026thinsp;1.19), and T3 (1.19\u0026thinsp;\u0026le;\u0026thinsp;SHR\u0026thinsp;\u0026lt;\u0026thinsp;5.62). The basic characteristics of each group were described. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), while categorical variables were presented as counts and frequency percentages (%). To compare categorical variables between different groups, one-way analysis of variance (ANOVA), Kruskal-Wallis H test (for non-normally distributed continuous variables), or chi-square test (for categorical variables) were used as appropriate.\u003c/p\u003e\u003cp\u003eKaplan-Meier (K-M) survival analysis was used to assess the incidence of endpoint events at different levels of SHR, with differences evaluated by the log-rank test to compare survival between groups. To explore the relationship between SHR and all-cause mortality at 28 days and 365 days, a Cox proportional hazards model was used to determine the relationship between the SHR index and study endpoints, providing hazard ratios (HR) and 95% confidence intervals (CI). Covariates were included in the model if their addition changed the matched hazard ratio by at least 10%, or based on prior studies and clinical considerations. To adjust for confounding factors, three models were used: Model 1 (adjusted for age and sex), Model 2 (adjusted for laboratory indicators, including Mg, Ca, RBC, Hb, PLT, INR, PT, in addition to Model 1), and Model 3 (adjusted for diabetes and atrial fibrillation, in addition to Model 2). Group T1 was used as the reference group for all models.\u003c/p\u003e\u003cp\u003eTo explore the potential linear relationship between SHR levels and all-cause mortality at 28 days and 365 days, restricted cubic spline models were employed. Subgroup analyses were conducted based on age (\u0026lt;\u0026thinsp;65 years or \u0026ge;\u0026thinsp;65 years), sex, diabetes, and atrial fibrillation, with interaction effects assessed by p-values. These findings were presented in the form of forest plots.\u003c/p\u003e\u003cp\u003eBootstrap methods (with 5000 replications) were used for mediation analysis to assess the mediating effect of WBC count on the relationship between SHR and 28-day mortality.\u003c/p\u003e\u003cp\u003eAll analyses were conducted using FreeStatistics V2.1.1 software, and a p-value below 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of study subjects\u003c/h2\u003e\u003cp\u003eA total of 873 COPD patients were included in this study, comprising 345 males (39.5%) and 528 females (60.5%), with a median age of 70.6 years, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Based on the SHR index, participants were divided into tertile groups (T1, T2, T3): T1 (0.28\u0026thinsp;\u0026le;\u0026thinsp;SHR\u0026thinsp;\u0026lt;\u0026thinsp;0.90), T2 (0.90\u0026thinsp;\u0026le;\u0026thinsp;SHR\u0026thinsp;\u0026lt;\u0026thinsp;1.19), and T3 (1.19\u0026thinsp;\u0026le;\u0026thinsp;SHR\u0026thinsp;\u0026lt;\u0026thinsp;5.62), with 291 individuals in each group. These groups were compared in terms of sex, age, magnesium (Mg), calcium (Ca), red blood cells (RBC), hemoglobin (Hb), platelets (PLT), international normalized ratio (INR), prothrombin time (PT), diabetes status, and atrial fibrillation status. Statistical differences were described using p-values. The baseline table showed significant differences in Mg and diabetes status among the groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant differences were observed in other baseline covariates between groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of Participants Stratified by Tertiles of the Stress Hyperglycemia Ratio (SHR)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;873)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1(0.28\u0026thinsp;~\u0026thinsp;0.90) (N\u0026thinsp;=\u0026thinsp;291)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT2(0.90\u0026thinsp;~\u0026thinsp;1.19) (N\u0026thinsp;=\u0026thinsp;291)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3 (1.19\u0026ndash;5.62) (N\u0026thinsp;=\u0026thinsp;291)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e345 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e118 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e118 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e528 (60.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e173 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission Age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (\u0026times;10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet Count (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204.0\u0026thinsp;\u0026plusmn;\u0026thinsp;91.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e200.6\u0026thinsp;\u0026plusmn;\u0026thinsp;88.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e199.8\u0026thinsp;\u0026plusmn;\u0026thinsp;94.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e211.5\u0026thinsp;\u0026plusmn;\u0026thinsp;92.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e454 (52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e184 (63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e140 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e419 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e151 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial Fibrillation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e504 (57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e162 (55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e175 (60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e167 (57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e369 (42.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129 (44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e116 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e124 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: RBC\u0026thinsp;=\u0026thinsp;Red blood cell count; INR\u0026thinsp;=\u0026thinsp;International normalized ratio; PT\u0026thinsp;=\u0026thinsp;Prothrombin time.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSHR and mortality\u003c/h2\u003e\u003cp\u003eIn the univariate Cox regression analysis, the impact of various factors on the 28-day and 365-day mortality of COPD (chronic obstructive pulmonary disease) patients is presented. The hazard ratio (HR) for each factor, along with its 95% confidence interval (CI) and the P-value from the Wald test, are listed. Age, magnesium levels, INR, and PT significantly affect the 28-day and 365-day mortality of COPD patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Sex, calcium, red blood cell count, hemoglobin, platelets, and atrial fibrillation do not significantly impact the risk of death. Diabetes has an effect on 365-day mortality that approaches the level of significance (see Table S3).\u003c/p\u003e\u003cp\u003eTo evaluate the independent effect of SHR on mortality, we constructed two Cox proportional hazards models, each comprising four columns: unadjusted, Model 1, Model 2, and Model 3. Models 2 and 3 were analyzed using multiply imputed data. Model 1 adjusted for age and sex; Model 2 additionally adjusted for laboratory parameters (magnesium, calcium, red blood cells, hemoglobin, platelet count, and plateletcrit); and Model 3 further incorporated adjustments for diabetes and atrial fibrillation. When SHR was analyzed as a continuous variable, the unadjusted model demonstrated that higher SHR levels were significantly associated with increased 28-day mortality (hazard ratio [HR] 1.46, 95% confidence interval [CI] 1.15\u0026ndash;1.85) and 365-day mortality (HR 1.43, 95% CI 1.21\u0026ndash;1.85). These associations remained robust after adjusting for age and sex (Model 1) and further adjustment for laboratory parameters (Model 2) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When SHR was analyzed as a categorical variable, compared with the T1 group, higher SHR levels showed significant associations with both 28-day mortality (HR 1.83, 95% CI 1.13\u0026ndash;2.97, P\u0026thinsp;\u0026lt;\u0026thinsp;0.015) and 365-day mortality (HR 1.53, 95% CI 1.12\u0026ndash;2.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.009). Patients in the highest tertile (T3) exhibited significantly increased mortality risk across all models (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The SHR index and T3 group (1.19\u0026ndash;5.62) showed significant associations with both 28-day and 365-day mortality in all models, with statistically significant mortality differences across tertiles.\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\u003eMultivariable Cox Regression Analyses for 28-Day and 365-Day Mortality in COPD Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnadjusted Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day Mortality\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.46 (1.15\u0026thinsp;~\u0026thinsp;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39 (1.11\u0026thinsp;~\u0026thinsp;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35 (1.05\u0026thinsp;~\u0026thinsp;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35 (1.04\u0026thinsp;~\u0026thinsp;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 (0.28\u0026thinsp;~\u0026thinsp;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 (0.90\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28 (0.78\u0026thinsp;~\u0026thinsp;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24 (0.76\u0026thinsp;~\u0026thinsp;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15 (0.69\u0026thinsp;~\u0026thinsp;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.19 (0.70\u0026thinsp;~\u0026thinsp;2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3 (1.19\u0026ndash;5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.27\u0026thinsp;~\u0026thinsp;3.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.06 (1.30\u0026thinsp;~\u0026thinsp;3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.78 (1.11\u0026thinsp;~\u0026thinsp;2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.83 (1.13\u0026thinsp;~\u0026thinsp;2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e365-day Mortality\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43 (1.21\u0026thinsp;~\u0026thinsp;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37 (1.16\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33 (1.11\u0026thinsp;~\u0026thinsp;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.33 (1.11\u0026thinsp;~\u0026thinsp;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 (0.28\u0026thinsp;~\u0026thinsp;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 (0.90\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32 (0.96\u0026thinsp;~\u0026thinsp;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.93\u0026thinsp;~\u0026thinsp;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.23 (0.89\u0026thinsp;~\u0026thinsp;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.24 (0.89\u0026thinsp;~\u0026thinsp;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3 (1.19\u0026ndash;5.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.70 (1.25\u0026thinsp;~\u0026thinsp;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.71 (1.26\u0026thinsp;~\u0026thinsp;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.53 (1.12\u0026thinsp;~\u0026thinsp;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.53 (1.12\u0026thinsp;~\u0026thinsp;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: T\u0026thinsp;=\u0026thinsp;Tertile; SHR\u0026thinsp;=\u0026thinsp;Stress hyperglycemia ratio; HR\u0026thinsp;=\u0026thinsp;Hazard ratio; CI\u0026thinsp;=\u0026thinsp;Confidence interval; Ref. = Reference.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: Adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: Adjusted as in Model 1 plus magnesium, calcium, red blood cell count, hemoglobin, platelet count, international normalized ratio (INR), and prothrombin time (PT).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3: Adjusted as in Model 2 plus diabetes and atrial fibrillation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the study population, sensitivity analyses were performed by dividing SHR into quartiles and quintiles to explore its relationship with 28-day and 365-day mortality in COPD (see Tables S4 and S5). Each section includes results from unadjusted and three adjusted models (Model 1, Model 2, and Model 3). When SHR was treated as a continuous variable, higher SHR levels were significantly associated with increased 28-day (HR 1.46, 95% CI 1.15\u0026ndash;1.85, P\u0026thinsp;\u0026lt;\u0026thinsp;0.002) and 365-day mortality (HR 1.43, 95% CI 1.21\u0026ndash;1.85, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained significant after adjusting for age and sex (Model 1) and further adjusting for laboratory indicators (Model 2) (see Tables S4 and S5). When SHR was analyzed as a categorical variable, T4 (quartile) and T5 (quintile) showed significantly higher risk ratios for 28-day and 365-day mortality compared to T1. Specifically, T4 had a HR of 1.93 (95% CI 1.12\u0026ndash;3.33, P\u0026thinsp;\u0026lt;\u0026thinsp;0.021) for 28-day and 1.64 (95% CI 1.13\u0026ndash;2.38, P\u0026thinsp;\u0026lt;\u0026thinsp;0.009) for 365-day mortality. T5 had a HR of 1.99 (95% CI 1.09\u0026ndash;3.61, P\u0026thinsp;\u0026lt;\u0026thinsp;0.027) for 28-day and 1.86 (95% CI 1.23\u0026ndash;2.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.004) for 365-day mortality. Overall, there was a significant increasing trend in mortality risk with higher SHR quartiles and quintiles (see Tables S4 and S5).\u003c/p\u003e\u003cp\u003eA Kaplan-Meier survival analysis was conducted for mortality by SHR index tertiles, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This figure illustrates the association between different SHR groups and all-cause mortality at 28 days (Panel A) and 365 days (Panel B) in patients with chronic obstructive pulmonary disease (COPD). It displays the Kaplan-Meier survival curves for the three groups (T1, T2, and T3), revealing significant differences in survival rates among them. The results show that both cumulative risk and all-cause mortality at 28 days (Panel A) and 365 days (Panel B) indicate relatively higher and slower-declining survival rates in Group T1, whereas Group T3 has the fastest-declining and lowest survival rate compared with the other groups. The statistical significance is demonstrated by log-rank P-values of 0.0061 and 0.0027, respectively (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, the analysis of restricted cubic spline regression models shows that there is a linear relationship between SHR index levels and the mortality risk at 28 and 365 days, where Figure A represents the all - cause mortality at 28 days and Figure B represents the all - cause mortality at 365 days. For detailed results, see Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eSubgroup analysis of the SHR index for risk stratification of the primary outcome across various subgroups (sex, age, diabetes, heart failure) of the study population is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure A and B show the hazard ratios (HRs) and 95% confidence intervals (CIs) for each subgroup, reflecting treatment - effect differences across populations. P - Interaction values are also shown to test the significance of differences between subgroups. Figure A corresponds to 28 - day all - cause mortality and Figure B to 365 - day all - cause mortality. In both figures, the unadjusted group had a significantly higher risk than the adjusted group. No significant differences were found between subgroups (age, sex, diabetes, atrial fibrillation), as all P - interaction values exceeded 0.05, suggesting no significant interaction between them.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMediation analysis\u003c/h2\u003e\u003cp\u003eIn the mediation causal effect analysis, white blood cell count (WBC) was used as a mediator to examine the relationship between SHR and 28-day all-cause mortality in COPD patients (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the mediation model shown, SHR is the independent variable, COPD the dependent variable, and WBC the mediator. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a mediation - effect model. In the mediation analysis, path a is the regression coefficient linking SHR to WBC. Path b is the regression coefficient linking WBC to COPD. Path c is SHR's direct effect on COPD. The total effect (TE) of SHR on COPD is 0.0343 (95% CI: 0.0046\u0026ndash;0.0560), statistically significant. The indirect effect (IE) through WBC is 0.0328 (95% CI: 0.0014\u0026ndash;0.0541), also statistically significant. WBC mediates 4.45% of SHR's effect on COPD. The model shows WBC plays a role in the SHR - COPD relationship.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eE- value and Unmeasured Confounding Analysis\u003c/h2\u003e\u003cp\u003eThe bias plots of confounding - adjusted RRs show confounders' impact on exposure - confounder relationships for 28 - day (A) and 365 - day (B) mortality (see Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). When RREU\u0026thinsp;=\u0026thinsp;2.04 (28 - day) and 1.99 (365 - day), confounders' influence on these relationships is minimal, indicating the least bias. This clarifies how confounders affect RRs in different exposure - confounder relationships (see Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudy used the MIMIC database to thoroughly assess the link between the SHR and short - and long - term all - cause mortality in COPD patients. Results show that the SHR is an independent predictor of short - and long - term all - cause mortality in critically ill COPD patients,potentially valuable in prognosis. This association is partly mediated by white blood cell count. SHR is a routine and cost - effective indicator, so it can be used for risk stratification and treatment decisions. The study has two main findings: firstly, after adjusting for key variables, the link remains significant and is robust across various analyses. Secondly, white blood cell (WBC) count partly mediates the relationship between SHR and 28 - day mortality, indicating a possible metabolic - inflammatory pathway. These findings highlight SHR's potential as a prognostic marker in acute COPD management.\u003c/p\u003e\u003cp\u003eThis findings align with prior evidence on SHR's prognostic role in critical illness and extend existing research. Previous studies have linked SHR to adverse outcomes in sepsis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], myocardial infarction [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], stroke [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], atrial fibrillation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]and coronary artery disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. But few have focused on COPD. A recent study indicated that SHR better predicts mortality in acute - exacerbation COPD patients than admission glucose or HbA1c [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], yet it didn't explore mechanisms or conduct mediation analysis. By incorporating direct - and indirect - effect models, our study offers stronger evidence for SHR's prognostic relevance and uncovers potential pathophysiological processes.\u003c/p\u003e\u003cp\u003eAcute stress - induced hyperglycemia can trigger cortisol and catecholamine release, boost oxidative stress, and cause immune dysfunction[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For COPD patients with systemic inflammation, these metabolic disturbances may intensify the inflammatory response, accelerate disease progression, and worsen prognosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Also, COPD is linked to glucocorticoid resistance. This mainly happens because oxidative stress inhibits histone deacetylase \u0026minus;\u0026thinsp;2 (HDAC2), weakening glucocorticoids' anti - inflammatory effects [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This resistance can worsen hyperglycemia's harmful impacts and reduce treatment effectiveness. The intermediary effect of white blood cell (WBC) count observed in our analysis supports this interconnected inflammatory - metabolic mechanism.\u003c/p\u003e\u003cp\u003eCOPD a chronic inflammatory lung disease, has inflammation as a key factor in its pathogenesis. Research shows that multiple inflammation - related composite blood biomarkers have potential clinical value in COPD progression, prognosis, and survival [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. SII, SIRI, PIV, NLR, and PLR are emerging composite inflammatory indices derived from peripheral immune cell counts, including neutrophils, monocytes, platelets, and lymphocytes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. They are closely related to body - wide inflammation and immune regulation. Composite inflammatory markers are not only inexpensive and easily accessible but also more accurately reflect the body's inflammatory responses and immune regulation under pathological conditions than single biomarkers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].The SHR, reflecting blood glucose changes during stress, is significantly associated with mortality risk in COPD patients. Monitoring SHR can thus better assess disease severity and prognosis in COPD patients, offering a basis for clinical intervention. Research on SHR in COPD has expanded its clinical applications. Future studies can explore SHR\u0026rsquo;s specific mechanisms in different diseases and its potential as a therapeutic target in COPD.\u003c/p\u003e\u003cp\u003eThis study explores the link between the SHR and 28 - day and 365 - day all - cause mortality in COPD patients. Study has several strengths, including a large sample size, standardized data collection, and thorough adjustment for potential confounders. While most studies indicate SHR is related to poor prognosis, some haven\u0026rsquo;t found such a link. This discrepancy may be due to differences in study design, sample characteristics, or analysis methods. Despite the convincing results of the study, it is imperative to recognize the limitations of observational data in establishing causality.\u003c/p\u003e\u003cp\u003eStudy is a retrospective cohort study prone to measurement errors and influenced by baseline data changes, with limited causal inference. It didn't account for hormonal interventions or assess dynamic blood glucose changes. As a secondary analysis, it relies on the quality and integrity of existing database data. Future research should include more diverse groups and comprehensive data to further solidify and validate these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eStudy shows that the SHR, after adjusting for confounders, is linked to a higher risk of 28 - day and 365 - day mortality in COPD patients and can predict their prognosis and risk. The causal relationship and intervention value of SHR in COPD need confirmation in prospective studies. The elevated SHR is significantly associated with short - and long - term mortality in COPD patients, partly mediated by white blood cells, highlighting the metabolic - inflammatory pathway\u0026rsquo;s role in COPD prognosis. SHR has the potential to be a simple risk - prediction tool for clinical use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThe Medical Information Mart for Intensive Care IV database (MIMIC-IV, version 3.1) is a publicly available resource developed and maintained by the Laboratory for Computational Physiology at the Massachusetts Institute of Technology (MIT). This database contains anonymized medical records of more than 60,000 adult patients admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and December 2021. This study adheres to the STROBE guidelines for observational epidemiological research reporting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data from the Medical Information Mart for Intensive Care (MIMIC-IV v3.1) database can be publicly accessed at\u0026nbsp;https://physionet.org/ content/mimiciv/3.1/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e XMY: data collection, data analysis, manuscript writing. HBW: data analysis, manuscript editing. GSJ: data analysis, manuscript editing. XXQ: data analysis, manuscript editing. SPC: data analysis, manuscript editing. XJY: data analysis, manuscript editing. QPX: project development, data analysis, manuscript writing, manuscript editing. All authors have read and approved this manuscript. This study did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThere is no funding to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe acknowledge the contributions of the MIMIC-IV (version 3.1) program team for the development and maintenance of the MIMIC-IV database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNegewo, N.A., P.G. Gibson, and V.M. 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Qi, \u003cem\u003ePrognostic value of composite inflammatory markers in patients with chronic obstructive pulmonary disease: A retrospective cohort study based on the MIMIC-IV database.\u003c/em\u003e PLoS One, 2025. \u003cstrong\u003e20\u003c/strong\u003e(1): p. e0316390.\u003c/li\u003e\n\u003cli\u003eBeitland, S., et al., \u003cem\u003eBlood leucocyte cytokine production after LPS stimulation at different concentrations of glucose and/or insulin.\u003c/em\u003e Acta Anaesthesiol Scand, 2009. \u003cstrong\u003e53\u003c/strong\u003e(2): p. 183-9.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Segade, S., et al., \u003cem\u003eTranslating the A1C assay into estimated average glucose values: response to Nathan et al.\u003c/em\u003e Diabetes Care, 2009. \u003cstrong\u003e32\u003c/strong\u003e(1): p. e10; author reply e12.\u003c/li\u003e\n\u003cli\u003eZhou, Y., et al., \u003cem\u003eThe association between stress hyperglycemia ratio and clinical outcomes in patients with sepsis-associated acute kidney injury: a secondary analysis of the MIMIC-IV database.\u003c/em\u003e BMC Infect Dis, 2024. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 1263.\u003c/li\u003e\n\u003cli\u003eLi, X.H., et al., \u003cem\u003ePredicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.\u003c/em\u003e Cardiovasc Diabetol, 2025. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 10.\u003c/li\u003e\n\u003cli\u003eHuang, M., et al., \u003cem\u003eAssociation between stress hyperglycemia ratio (SHR) and long-term mortality in patients with ischemic stroke: a retrospective cohort study.\u003c/em\u003e Cardiovasc Diabetol, 2025. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 180.\u003c/li\u003e\n\u003cli\u003eLuo, J., et al., \u003cem\u003eAssociation of stress hyperglycemia ratio with in-hospital new-onset atrial fibrillation and long-term outcomes in patients with acute myocardial infarction.\u003c/em\u003e Diabetes Metab Res Rev, 2024. \u003cstrong\u003e40\u003c/strong\u003e(2): p. e3726.\u003c/li\u003e\n\u003cli\u003eHe, H.M., et al., \u003cem\u003eSimultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database.\u003c/em\u003e Cardiovasc Diabetol, 2024. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 61.\u003c/li\u003e\n\u003cli\u003eWang, F., et al., \u003cem\u003eCombined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning.\u003c/em\u003e Cardiovasc Diabetol, 2025. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 199.\u003c/li\u003e\n\u003cli\u003eYang, C.J., et al., \u003cem\u003eGlycated hemoglobin A1c-based adjusted glycemic variables in patients with diabetes presenting with acute exacerbation of chronic obstructive pulmonary disease.\u003c/em\u003e Int J Chron Obstruct Pulmon Dis, 2017. \u003cstrong\u003e12\u003c/strong\u003e: p. 1923-1932.\u003c/li\u003e\n\u003cli\u003eMonnier, L., et al., \u003cem\u003eActivation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes.\u003c/em\u003e Jama, 2006. \u003cstrong\u003e295\u003c/strong\u003e(14): p. 1681-7.\u003c/li\u003e\n\u003cli\u003eTan, M.Y., et al., \u003cem\u003eThe prognostic significance of stress hyperglycemia ratio in evaluating all-cause and cardiovascular mortality risk among individuals across stages 0-3 of cardiovascular-kidney-metabolic syndrome: evidence from two cohort studies.\u003c/em\u003e Cardiovasc Diabetol, 2025. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 137.\u003c/li\u003e\n\u003cli\u003eDong, K., et al., \u003cem\u003ePredictive role of neutrophil percentage-to-albumin ratio, neutrophil-to-lymphocyte ratio, and systemic immune-inflammation index for mortality in patients with MASLD.\u003c/em\u003e Sci Rep, 2024. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 30403.\u003c/li\u003e\n\u003cli\u003eChen, Y., et al., \u003cem\u003eAssessment of stress hyperglycemia ratio to predict all-cause mortality in patients with critical cerebrovascular disease: a retrospective cohort study from the MIMIC-IV database.\u003c/em\u003e Cardiovasc Diabetol, 2025. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 58.\u003c/li\u003e\n\u003cli\u003eLeuppi, J.D., et al., \u003cem\u003eShort-term vs conventional glucocorticoid therapy in acute exacerbations of chronic obstructive pulmonary disease: the REDUCE randomized clinical trial.\u003c/em\u003e Jama, 2013. \u003cstrong\u003e309\u003c/strong\u003e(21): p. 2223-31.\u003c/li\u003e\n\u003cli\u003eThomsen, M., et al., \u003cem\u003eInflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease.\u003c/em\u003e Jama, 2013. \u003cstrong\u003e309\u003c/strong\u003e(22): p. 2353-61.\u003c/li\u003e\n\u003cli\u003eMador, M.J. and S. Sethi, \u003cem\u003eSystemic inflammation in predicting COPD exacerbations.\u003c/em\u003e Jama, 2013. \u003cstrong\u003e309\u003c/strong\u003e(22): p. 2390-1.\u003c/li\u003e\n\u003cli\u003eZinellu, A., et al., \u003cem\u003eClinical significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute exacerbations of COPD: present and future.\u003c/em\u003e Eur Respir Rev, 2022. \u003cstrong\u003e31\u003c/strong\u003e(166).\u003c/li\u003e\n\u003cli\u003eGroenewegen, K.H., et al., \u003cem\u003eIncreased systemic inflammation is a risk factor for COPD exacerbations.\u003c/em\u003e Chest, 2008. \u003cstrong\u003e133\u003c/strong\u003e(2): p. 350-7.\u003c/li\u003e\n\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":"COPD, stress hyperglycemia ratio, mortality, mediation analysis, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-7448763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7448763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The stress hyperglycemia ratio (SHR), derived from admission glucose and HbA1c, reflects acute glycemic excursions. This study investigates the association between SHR and mortality in critically ill patients with chronic obstructive pulmonary disease (COPD), and explores the mediating role of white blood cell (WBC) count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective cohort analysis was conducted using the MIMIC-IV database (2008–2022). Adult ICU patients with COPD and available glucose and HbA1c data were included. SHR was categorized into tertiles (T1–T3). Primary and secondary outcomes were 28-day and 365-day all-cause mortality, respectively. Cox regression, restricted cubic spline (RCS) analysis, and Kaplan–Meier curves assessed associations. Mediation analysis evaluated the indirect effect of WBC count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 873 patients were included. Higher SHR (T3) was independently associated with increased 28-day (HR=1.35, 95% CI: 1.04–1.73, p=0.024) and 365-day mortality (HR=1.33, 95% CI: 1.11–1.59, p=0.002). RCS analysis revealed a linear relationship between SHR and mortality risk. Kaplan–Meier curves showed lower survival in the highest SHR group. WBC count partially mediated the effect of SHR on 28-day mortality (ACME, p\u0026lt;0.01), accounting for 4.45% of the total effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e SHR is an independent predictor of short- and long-term mortality in critically ill COPD patients. The association may be partially mediated by inflammation, as reflected by WBC count. SHR could serve as a simple tool for early risk stratification in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Association between Stress Hyperglycemia Ratio and Mortality in Critically Ill COPD Patients: A Mediation Analysis of White Blood Cell Count","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 13:40:20","doi":"10.21203/rs.3.rs-7448763/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-07T11:24:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T11:20:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-06T09:52:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-06T09:51:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-08-25T01:25:34+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":"641defa1-4f4d-4201-9e6e-6dd6e41ee9e0","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T13:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 13:40:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7448763","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7448763","identity":"rs-7448763","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}