Additive Interaction Between Systemic Inflammation and Functional Dependency on In-Hospital Mortality: A "Double-Hit" Phenotype in 8,409 Geriatric Inpatients

Additive Interaction Between Systemic Inflammation and Functional Dependency on In-Hospital Mortality: A "Double-Hit" Phenotype in 8,409 Geriatric Inpatients | 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 Additive Interaction Between Systemic Inflammation and Functional Dependency on In-Hospital Mortality: A "Double-Hit" Phenotype in 8,409 Geriatric Inpatients Wang Huaping, Zhang Haihong, Zhao Hui, Zhou Qi, Li Aihua, Zhu Weifeng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8599033/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Systemic inflammation and functional status are two critical prognostic domains in geriatric medicine. However, whether these two domains interact synergistically—particularly on an additive scale—to influence short-term outcomes in secondary care geriatric inpatients remains unclear.as regional facilities focusing on subacute care, rehabilitation, and management of chronic multimorbidity, serving as a critical bridge between tertiary acute care and community care. Methods This retrospective cohort study included 8,409 patients aged ≥ 65 years admitted to a secondary geriatric hospital in China between January 2022 and December 2025. Systemic inflammation was assessed using the C-reactive protein-to-albumin ratio (CAR), and functional status was evaluated by the Barthel Index (BI). Both multiplicative and additive interaction analyses were performed, with additive interaction quantified using the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). Restricted cubic spline (RCS) models were applied to explore non-linear associations. Results CAR was independently associated with in-hospital mortality (adjusted OR 1.35, 95% CI 1.31–1.40). Although the multiplicative interaction between CAR and BI was not statistically significant ( P = 0.262), a pronounced additive interaction was observed. Compared with patients with low CAR and high BI, those with both high CAR and severe functional dependency exhibited a markedly increased mortality risk (36.3%; adjusted RR 55.75, 95% CI 33.33–93.26). The RERI was 29.54, AP was 53.0%, and SI was 2.17, indicating that over half of the excess mortality was attributable to their synergistic effect. The combined CAR–BI model demonstrated excellent discrimination (AUC 0.890), outperforming models using either component alone. Conclusion Systemic inflammation and functional dependency exert a strong additive synergistic effect on in-hospital mortality in secondary care geriatric patients. Integrating CAR and BI identifies a clinically meaningful “double-hit” phenotype that substantially improves risk stratification and may guide targeted multidisciplinary interventions. C-reactive protein-to-albumin ratio Barthel Index Functional status Mortality Interaction analysis Inflammaging Figures Figure 1 Figure 2 1. Introduction Population aging has led to a growing burden of frailty, disability, and inflammation-related adverse outcomes among hospitalized older adults[1]. Systemic inflammation, often conceptualized as “inflammaging,” is closely linked to immunosenescence, sarcopenia, and poor clinical prognosis[2]. Meanwhile, functional dependency, commonly assessed using the Barthel Index (BI), reflects accumulated deficits in physiological reserve and has consistently predicted mortality in geriatric populations[3]. C-reactive protein-to-albumin ratio (CAR) is an emerging biomarker integrating inflammatory burden and nutritional status. Previous studies and meta-analyses have demonstrated its prognostic value in elderly and hospitalized patients[4]. Similarly, functional impairment measured by BI has been widely validated as an independent predictor of adverse outcomes. However, most existing studies have evaluated systemic inflammation and functional status as independent risk factors [5,6,7]or have focused solely on multiplicative interaction models, which may underestimate biologically meaningful synergy. Additive interaction analysis, in contrast, better reflects biological interaction and public health relevance, yet remains underexplored in geriatric medicine. Moreover, evidence from secondary care geriatric hospitals, which typically manage patients with higher rates of sarcopenia, multimorbidity, and nutritional deficits than tertiary acute-care settings, is limited. Therefore, this study aimed to investigate the joint and interactive effects of systemic inflammation (CAR) and functional dependency (BI) on in-hospital mortality in secondary care geriatric inpatients, with a particular focus on additive interaction and the identification of a clinically actionable “double-hit” phenotype. 2. Methods 2.1 Study Design and Participants This retrospective cohort study analyzed patients aged ≥ 65 years discharged from Shanghai Pudong New Area Geriatric Hospital between January 2022 and December 2025. Ethical approval was granted by the Medical Ethics Committee (No.: HEC-SP-24-006). Exclusion criteria included: age < 65 years,hospital stay 30%).The detailed process of patient selection and exclusion is shown in Supplemental Figure S1 . 2.2 Data Collection and Measurements Biological Markers: Biological Markers: Serum CRP and albumin were measured within 24 hours of admission. CAR was calculated as CRP (mg/L) / Albumin (g/L). CRP values < 0.5 mg/L were imputed as 0.25 mg/L. Functional Status: Assessed using the Barthel Index (BI) within 24 hours. BI ranges from 0 (total dependence) to 100 (independence). Covariates: Data included age, gender, Charlson Comorbidity Index (CCI), and primary admission diagnosis. 2.3 Statistical Analysis Analyses were performed using Python 3.9. Missing data were handled via Multiple Imputation by Chained Equations (MICE). Predictive Models : Univariate and multivariable logistic regression adjusted for age, gender, CCI, diagnosis type, creatinine, and hemoglobin. ROC Analysis : Used to determine optimal cut-offs (CAR 0.58; BI 60) and compare discriminative ability. Interaction Analysis : Multiplicative: Assessed using the product term (CAR × BI) in regression models. Additive: Assessed using Relative Excess Risk due to Interaction (RERI), Attributable Proportion (AP), and Synergy Index (SI) based on adjusted risk ratios (RR) from Poisson regression [8]. CAR was dichotomized at 0.58 and BI at 60. Non-linear Modeling : Restricted Cubic Splines (RCS) with 4 knots were fitted to visualize the continuous risk trajectory of CAR across BI scores. 3. Results 3.1 Baseline Characteristics Of the 8,409 patients, 1,116 (13.3%) died. Non-survivors were significantly older (83.0 vs. 79.3 years) and had higher CAR levels (median 1.38 vs. 0.35) and lower BI scores (median 10 vs. 65) compared to survivors (Table 1 ). Prealbumin was also significantly lower in non-survivors ( P < 0.001). Table 1 Baseline Characteristics of Survivors and Non-survivors Characteristic Survivors (n = 7293) Non-survivors (n = 1116) P value Demographics Age, years (mean ± SD) 79.30 ± 8.13 83.00 ± 8.28 < 0.001 Male,n(%) 3758 (51.5%) 613 (54.9%) 0.037 Laboratory Markers Albumin, g/L (mean ± SD) 37.99 ± 4.47 32.80 ± 5.17 < 0.001 CRP, mg/L 13.20 (3.20–46.70) 44.20 (12.60–97.90) < 0.001 CAR 0.35 (0.08–1.30) 1.38 (0.35–3.21) < 0.001 Prealbumin, mg/L 162.00 (114.00-208.00) 102.00 (57.75-148.25) < 0.001 Creatinine, µmol/L 76.20 (62.00-94.60) 82.75 (62.00-116.25) < 0.001 Hemoglobin, g/L 123.00 (110.00-135.00) 108.00 (92.00-123.00) < 0.001 Lymphocyte, ×10⁹/L 1.30 (0.90–1.80) 1.10 (0.70–1.50) < 0.001 CCI CCI Score, median (IQR) 1.0 (0.0–1.0) 1.0 (1.0–3.0) < 0.001 CCI ≥ 3, n (%) 1042 (14.3%) 372 (33.3%) < 0.001 Functional Status Barthel index 65.00 (25.00–90.00) 10.00 (5.00–15.00) < 0.001 3.2 Independent Association with Mortality In multivariable analysis (Table 2 ), elevated CAR was independently associated with increased mortality (OR 1.37, 95% CI 1.33–1.41, P < 0.001). Conversely, higher functional status was protective; each 10-point increase in BI reduced mortality risk by approximately 47% (OR 0.53, 95% CI 0.51–0.55). 3.3 Predictive Performance The combined model (CAR + BI) achieved an AUC of 0.890, superior to CAR (0.683) or BI (0.875) alone (Fig. 1 ). Table 2 Multivariable Binary Logistic Regression Analysis for In-hospital Mortality Exposure Model OR (95% CI) P value CAR Model 1 (Unadjusted) 1.353 (1.312–1.396) < 0.001 CAR Model 2 (Age, Sex adjusted) 1.369 (1.326–1.414) < 0.001 CAR Model 3 (Fully adjusted, including CCI) 1.357 (1.313–1.403) < 0.001 BI (per 10-unit increase) Model 1 (Unadjusted) 0.537 (0.514–0.560) < 0.001 BI (per 10-unit increase) Model 2 (Age, Sex adjusted) 0.530 (0.507–0.553) < 0.001 BI (per 10-unit increase) Model 3 (Fully adjusted, including CCI) 0.535 (0.512–0.560) < 0.001 Note: Model 3 adjusted for CCI, Creatinine, Hemoglobin, and Lymphocytes. Table 3 Incremental Predictive Value of CAR Beyond BI and Clinical Covariates Metric Value P-value AUC of Baseline Model (BI + Covariates) 0.888 - AUC of Full Model (BI + CAR + Covariates) 0.899 - Delta AUC 0.0106 - Likelihood Ratio Test (LRT) LR χ² = 190.31 < 0.001 Integrated Discrimination Improvement (IDI) 0.0389 < 0.001 3.5 Interaction Analysis 3.5.1 Multiplicative Interaction The interaction term (CAR × BI) was not statistically significant (OR 0.99, P = 0.262), indicating CAR predicts mortality consistently across functional levels in a multiplicative model (Table 4). Table 4 Multiplicative Interaction Between CAR and Barthel Index Variable OR (95% CI) P-value CAR (per unit increase) 1.36 (1.28–1.44) < 0.001 Barthel index (per 10-point increase) 0.55 (0.52–0.59) 0.05 for all subgroups) (Table 5). Table 5 Subgroup Analysis of CAR and Mortality Subgroup No. Patients Events (%) Adj OR (95% CI) P Value P Interaction Overall 8409 1116 (13.3) 1.35 (1.31–1.40) < 0.001 - Age 0.278 < 79 years 3869 337 (8.7) 1.46 (1.39–1.54) < 0.001 ≥ 79 years 4540 779 (17.2) 1.31 (1.25–1.36) < 0.001 Sex 0.941 Male 4371 613 (14.0) 1.35 (1.30–1.40) < 0.001 Female 4038 503 (12.5) 1.36 (1.30–1.44) 25 5539 145 (2.6) 1.27 (1.17–1.37) < 0.001 BI ≤ 25 2870 971 (33.8) 1.40 (1.34–1.46) < 0.001 Restricted Cubic Spline (RCS) Analysis RCS modeling (Figure 2) revealed a J-shaped curve. The risk associated with CAR was lowest at a nadir of BI = 64.3 (OR=1.21). A critical risk surge point was identified as BI approaches 0, where the OR rose sharply to 1.42, confirming a step-wise amplification of risk with worsening dependency. 3.5.2 Additive Interaction Analysis In contrast, additive interaction analysis revealed a pronounced synergistic effect (Table 6). Compared with the reference group (Low CAR + High BI), patients with both high CAR and severe functional dependency exhibited a markedly elevated adjusted RR of 55.75 (95% CI 33.33–93.26). The relative excess risk due to interaction (RERI) was 29.54, the attributable proportion (AP) was 53.0%, and the synergy index (SI) was 2.17, indicating that 53% of the excess mortality risk in the “double-hit” group was attributable to the synergistic interaction. Table 6 Additive Interaction Measures Between CAR and Barthel Index on In-hospital Mortality Exposure group Adjusted RR (95% CI) n (deaths) Low CAR + High BI (CAR = 60) 1.00 (Reference) 2502 (15) High CAR + High BI (CAR > = 0.58 & BI > = 60) 2.95 (1.60–5.45) 1706 (31) Low CAR + Low BI (CAR < 0.58 & BI = 0.58 & BI < 60) 55.75 (33.33–93.26) 2011 (731) Interaction metrics: RERI = 29.54; AP = 0.530 (53.0%); SI = 2.17 Adjusted for: age, sex, creatinine, hemoglobin, lymphocyte count, primary admission diagnosis categories, Charlson Comorbidity Index. Stratified Analysis by Infection Status Stratified analysis showed the additive interaction was particularly pronounced in patients with infection-related diagnoses (RERI 25.07). In contrast, the interaction was attenuated and not statistically significant in the non-infection subgroup, where all exposure groups exhibited similarly high adjusted RRs around 1.00 (Table 7). This suggests that in non-infectious contexts, the mortality risk may be predominantly driven by severe functional dependency and comorbidities themselves, with a relatively smaller marginal contribution from elevated inflammation as measured by CAR. Alternatively, this null finding warrants cautious interpretation due to potential model limitations in this subgroup. Table 7 Table 7 : Stratified Additive Interaction Measures (Infectious vs. Non-Infectious) Subgroup / Exposure Group Adjusted RR (95% CI) n (deaths) Interaction Metrics Infection-related Diagnosis Low CAR + High BI 1.00 (Reference) 1241 (3) RERI: 25.07 AP: 0.234 SI: 1.31 High CAR + High BI 2.10 (0.53–8.36) 1082 (6) Low CAR + Low BI 80.95 (25.47–257.28) 498 (128) High CAR + Low BI 107.11 (33.74–340.00) 769 (303) Non-infection Diagnosis Low CAR + High BI 1.00 (Reference) 1261 (12) RERI: 0.00 AP: 0.000 SI: nan High CAR + High BI 1.00 (0.91–1.10) 624 (25) Low CAR + Low BI 1.00 (0.93–1.08) 1692 (211) High CAR + Low BI 1.00 (0.93–1.08) 1242 (428) Adjusted for: age, sex, CCI, creatinine, hemoglobin, lymphocyte count. 4. Discussion 4.1 The “Double-Hit” Phenotype: Inflammation and Disability Our findings identify a "double-hit" high-risk phenotype defined by high CAR (≥0.58) and severe functional dependency (BI <60), with 53% of excess mortality attributable to additive synergy (RERI 29.54, SI 2.17). This phenotype reflects a pathophysiological vicious cycle[9,10] mediated by three key pathways: (1) Systemic inflammation (high CAR) induces anabolic resistance, inhibiting skeletal muscle protein synthesis via mTOR pathway suppression—exacerbating sarcopenia and reducing BI scores[11]; (2) Severe functional dependency (low BI) leads to immobilization, gut microbiota dysbiosis, and malnutrition, activating the NF-κB pathway[9] to promote pro-inflammatory cytokine (IL-6, TNF-α) release and further elevate CAR[12]; (3) This cycle is amplified by acute stressors such as infection, as evidenced by stronger additive interaction in infection-related diagnoses (RERI 25.07 vs. 0.00 in non-infection cases). The 55-fold higher mortality risk in the "double-hit" group underscores the clinical significance of this synergy, which cannot be captured by individual markers alone. 4.2 Functional Status as a Baseline for Risk Stratification The robust predictive value of BI in our cohort reaffirms functional status as a cornerstone of geriatric assessment. However, our study adds a crucial layer to this established knowledge: CAR remains a powerful predictor even within the same functional strata. While international guidelines rightly emphasize ADL assessment, relying solely on functional status may miss “hidden” risks in patients who appear functionally stable but harbor high systemic inflammation. Therefore, CAR serves as a vital biological complement to the functional assessment provided by BI. 4.3 Clinical Implications Based on these findings, we propose a tiered risk stratification framework: Low Risk: BI >64.3 (the identified nadir) and low CAR, suitable for standard geriatric care; Intermediate Risk: High CAR or moderate dependency, requiring targeted nutritional or anti-inflammatory support; High Risk (“Double-Hit” Phenotype): High CAR (≥0.58) and severe dependency (BI <60). This group faces a 55-fold risk increase, necessitating immediate multidisciplinary intervention to break the inflammation-disability cycle, such as enhanced protein intake (e.g., 1.2–1.5 g/kg/day via oral supplements), anti-inflammatory rehabilitation protocols, and early palliative consultation if appropriate. 4.4 Attenuated Additive Interaction in Non-infection Cases Notably, the additive interaction between CAR and BI was attenuated in non-infection-related diagnoses (RERI 0.00, AP 0.00%). This finding likely reflects three key factors: (1) Higher baseline mortality risk in non-infection patients due to advanced chronic diseases (e.g., end-stage organ failure), masking the incremental risk of inflammation-functional dependency synergy; (2) Chronic inflammation in non-infection cases is often low-grade and persistent, differing from the acute inflammatory response in infection; (3) Non-infection subgroups included a higher proportion of patients receiving palliative care, where mortality risk is driven by underlying disease rather than inflammation or functional status. These results highlight that the "inflammation-functional disability" cycle is most lethal in the context of acute infectious stress, guiding targeted intervention prioritization. 4.5 Limitations This study has several limitations that should be considered. First, the single-center retrospective design limits generalizability to other secondary care geriatric hospitals. Future multicenter prospective cohort studies are needed to validate the CAR-BI risk stratification framework. Second, the high mortality rate in the severe dependency group (36.3%) may include patients receiving palliative care, potentially confounding the association—though sensitivity analyses excluding palliative care patients confirmed result robustness. Third, competing risks (e.g., non-mortality discharge) were not accounted for; future studies should use Fine-Gray models to address this. Fourth, the extremely low mortality in the reference group (0.6%) may reflect selection bias, though internal validation (split-sample analysis) showed consistent cutoff stability. Fifth, we lacked direct measures of frailty, sarcopenia, and gut microbiota—key mediators of the inflammation-functional dependency cycle. Future studies should include these variables to clarify mediating pathways. Finally, we did not evaluate the effectiveness of the proposed MDT intervention; randomized controlled trials are needed to confirm whether targeting the "double-hit" phenotype reduces mortality. 5. Conclusion In conclusion, CAR and functional dependency (assessed via BI) are robust independent predictors of in-hospital mortality in secondary care geriatric inpatients, with a strong additive synergistic effect defining a "double-hit" high-risk phenotype. The combined CAR-BI assessment is a low-cost, feasible tool that significantly improves risk stratification beyond single indicators. Integrating these two measures into routine geriatric care enables targeted MDT interventions to break the inflammation-functional disability cycle, ultimately improving outcomes for the most vulnerable older adults. Future studies should validate this framework in multicenter cohorts and evaluate the effectiveness of targeted interventions. Declarations Ethical Approval and Informed This study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of Shanghai Pudong New Area Geriatric Hospital (Approval No.: HEC-SP-24-006).Due to the retrospective nature of the study, which involved the collection and analysis of routine clinical data from medical records, the requirement for written informed consent from individual patients was waived by the Medical Ethics Committee. All patient data were anonymized and de-identified prior to analysis to ensure strict confidentiality and protect patient privacy. Funding This study was supported by the People's Livelihood Research Project of Pudong New Area Science and Technology and Economic Committee (Grant No. PKJ2024-Y82). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions Conceptualization & Study Design Yang Zhoujian developed the overall research framework and defined the study objectives, specifically focusing on the additive interaction between systemic inflammation (CAR) and functional dependency (BI) on in-hospital mortality. He designed the methodological protocol, including the retrospective cohort design, selection of covariates, and the statistical analysis plan for both multiplicative and additive interactions. Data Collection & Curation Wang Huaping, Zhang Haihong, Zhao Hui, Li Aihua , Zhu Weifeng and Zhou Qi coordinated the data retrieval from the electronic medical records (EMR) of Shanghai Pudong New Area Geriatric Hospital. They extracted and validated data on demographics, laboratory markers (CRP, albumin, prealbumin, etc.), functional status (Barthel Index), and clinical outcomes. Ye Jin managed data cleaning and the Multiple Imputation by Chained Equations (MICE) process for missing values. Data Analysis & Interpretation Wang Xiaodan and Ye Jin performed the statistical analyses using Python 3.9. This included conducting restricted cubic spline (RCS) modeling, logistic regression, ROC curve analysis, and calculating interaction metrics (RERI, AP, SI). All authors participated in interpreting the results, particularly the clinical implications of the "double-hit" phenotype. Manuscript Preparation & Revision Wang Huaping and Zhang Haihong drafted the initial manuscript and created the tables and figures. Yang Zhoujian and Wang Shanjin critically revised the manuscript for important intellectual content, ensuring the accuracy of the discussion regarding biological mechanisms and public health relevance. All authors read and approved the final manuscript. Declaration of Competing Interest 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. Acknowledgments We sincerely thank all the participants and their families for their involvement in this study. We extend our gratitude to the medical staff at Shanghai Pudong New Area Geriatric Hospital for their dedication to patient care and their assistance in data acquisition. We also appreciate the valuable administrative support provided by the hospital’s Medical Ethics Committee and the Department of Geriatrics. Data Availability Statement Data: All raw data supporting the findings of this study, including patient demographics, laboratory results, and Barthel Index scores, are securely stored by the research team in compliance with the hospital’s data privacy regulations. De-identified data can be made available to qualified researchers for academic purposes upon reasonable request. To access the data, please contact the corresponding author, Yang Zhoujian (Email: [email protected] ) or Wang Shanjin (Email: [email protected] ). Materials: The study utilized routine clinical laboratory assays for CRP and albumin, and the standard Barthel Index for functional assessment. No proprietary software, algorithms, or unpublished questionnaires were used. The statistical code (Python 3.9) used for analysis is available from the corresponding author upon request. References World Health Organization. Decade of Healthy Ageing 2020–2030. Geneva: WHO; 2020. https://www.who.int/publications/i/item/9789240017900 Baechle JJ, Chen N, Makhijani P, Winer S, Furman D, Winer DA. Chronic inflammation and the hallmarks of aging. Mol Metab. 2023;74:101755. https://doi.org/10.1016/j.molmet.2023.101755 Mahoney FI, Barthel DW. Functional evaluation: the Barthel Index. 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Lancet. 2019;393(10191):2636–2646. https://doi.org/10.1016/S0140-6736(19)31138-9 Soysal P, Stubbs B, Lucato P, et al. Inflammation and frailty in the elderly: A systematic review and meta-analysis. Ageing Res Rev. 2016;31:1–8. https://doi.org/10.1016/j.arr.2016.08.006 Additional Declarations No competing interests reported. Supplementary Files SupplementalFigureS1.png STROBE.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor invited by journal 20 Jan, 2026 Editor assigned by journal 18 Jan, 2026 Submission checks completed at journal 18 Jan, 2026 First submitted to journal 14 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Xiaodan","suffix":""},{"id":590912650,"identity":"5372389d-ea1e-4943-95e4-dbdbdd25acf2","order_by":8,"name":"Yang Zhoujian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDCCAxCKGUx+IFILYwNMC+MMUrRArOIhRgff8ebnDz7uqWM3OH728GubMmsG/vbuBLxaJM8cM2yc8YyN2eBMXpp1zrl0BokzZzfg1WJwI4exmecAD7PBgRwz49y2wwwGErlEaZFgNjj/xszYkgQtBsxAhvFjRmK0gPwyc8aBBGbJG2/MGHvOpfMQ9AswxB58+HCgLpnvfI7xhx9l1nL87b34tcBAssIBBjYJBjbiogYM7OQbGJg/ALUQrWMUjIJRMApGDgAAJh5LgA2f8EoAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Pudong New Area Geriatric Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhoujian","suffix":""},{"id":590912651,"identity":"62ad495a-e0aa-4c9d-b757-e5a46cd185d3","order_by":9,"name":"Wang Shanjin","email":"","orcid":"","institution":"Shanghai Pudong New Area Geriatric Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Shanjin","suffix":""}],"badges":[],"createdAt":"2026-01-14 07:54:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8599033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8599033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102828761,"identity":"583f0222-43c2-4854-b6fe-86b967840400","added_by":"auto","created_at":"2026-02-17 09:26:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":498817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves with Optimal Cut-off for In-hospital Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure legend:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003el Combined (CAR + BI): AUC = 0.890, Cut-off 0.17, Sensitivity 85.0%, Specificity 76.7%.\u003c/p\u003e\n\u003cp\u003el Barthel Index: AUC = 0.875, Cut-off 25.00.\u003c/p\u003e\n\u003cp\u003el CAR: AUC = 0.683, Cut-off 0.58, Sensitivity 68.3%, Specificity 59.6%.\u003c/p\u003e\n\u003cp\u003el CCI Score: AUC = 0.675.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8599033/v1/af411428763363e35991877c.png"},{"id":102828759,"identity":"2e468142-0d88-4cf5-a04d-ac79cdd89ae0","added_by":"auto","created_at":"2026-02-17 09:26:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":342425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline (RCS) visualization of the non-linear interaction between C-reactive protein-to-albumin ratio (CAR) and functional status (Barthel Index) on in-hospital mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003el Curve Shape: J-shaped relationship (P for interaction \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003el Stable Phase: Risk is stable/lowest at Nadir (BI = 64.3, OR = 1.21).\u003c/p\u003e\n\u003cp\u003el Risk Surge: Risk rises sharply below BI = 20, peaking at BI = 0 (OR = 1.42).\u003c/p\u003e\n\u003cp\u003el Interpretation: Patients with high functional independence possess physiological reserve that buffers inflammation; this reserve collapses as BI approaches 0.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8599033/v1/22707dfa33cb44af59f7765a.png"},{"id":102829048,"identity":"c7ef7d0e-36ad-4909-8028-1d388ce72a79","added_by":"auto","created_at":"2026-02-17 09:27:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1975225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8599033/v1/b4b5614c-5c35-4427-95d0-b1b08911e184.pdf"},{"id":102828766,"identity":"2f93917f-51b6-453c-85b5-1c60281207f9","added_by":"auto","created_at":"2026-02-17 09:26:07","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":43784,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-8599033/v1/f90dcf47608dcbefbe1ebd83.png"},{"id":102828900,"identity":"03b7cdc0-9de4-4dc0-ba0b-035749956766","added_by":"auto","created_at":"2026-02-17 09:26:37","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16956,"visible":true,"origin":"","legend":"","description":"","filename":"STROBE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8599033/v1/34a7afa84e0c535354a1b5d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Additive Interaction Between Systemic Inflammation and Functional Dependency on In-Hospital Mortality: A \"Double-Hit\" Phenotype in 8,409 Geriatric Inpatients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePopulation aging has led to a growing burden of frailty, disability, and inflammation-related adverse outcomes among hospitalized older adults[1]. Systemic inflammation, often conceptualized as \u0026ldquo;inflammaging,\u0026rdquo; is closely linked to immunosenescence, sarcopenia, and poor clinical prognosis[2]. Meanwhile, functional dependency, commonly assessed using the Barthel Index (BI), reflects accumulated deficits in physiological reserve and has consistently predicted mortality in geriatric populations[3].\u003c/p\u003e \u003cp\u003eC-reactive protein-to-albumin ratio (CAR) is an emerging biomarker integrating inflammatory burden and nutritional status. Previous studies and meta-analyses have demonstrated its prognostic value in elderly and hospitalized patients[4]. Similarly, functional impairment measured by BI has been widely validated as an independent predictor of adverse outcomes.\u003c/p\u003e \u003cp\u003eHowever, most existing studies have evaluated systemic inflammation and functional status as independent risk factors [5,6,7]or have focused solely on multiplicative interaction models, which may underestimate biologically meaningful synergy. Additive interaction analysis, in contrast, better reflects biological interaction and public health relevance, yet remains underexplored in geriatric medicine.\u003c/p\u003e \u003cp\u003eMoreover, evidence from secondary care geriatric hospitals, which typically manage patients with higher rates of sarcopenia, multimorbidity, and nutritional deficits than tertiary acute-care settings, is limited.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the joint and interactive effects of systemic inflammation (CAR) and functional dependency (BI) on in-hospital mortality in secondary care geriatric inpatients, with a particular focus on additive interaction and the identification of a clinically actionable \u0026ldquo;double-hit\u0026rdquo; phenotype.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study analyzed patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years discharged from Shanghai Pudong New Area Geriatric Hospital between January 2022 and December 2025. Ethical approval was granted by the Medical Ethics Committee (No.: HEC-SP-24-006). Exclusion criteria included: age\u0026thinsp;\u0026lt;\u0026thinsp;65 years,hospital stay\u0026thinsp;\u0026lt;\u0026thinsp;24 hours, missing CRP/albumin data not imputable, BI not assessed within 24h, or severe missing clinical data (\u0026gt;\u0026thinsp;30%).The detailed process of patient selection and exclusion is shown in Supplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection and Measurements Biological Markers:\u003c/h2\u003e \u003cp\u003eBiological Markers: Serum CRP and albumin were measured within 24 hours of admission. CAR was calculated as CRP (mg/L) / Albumin (g/L). CRP values\u0026thinsp;\u0026lt;\u0026thinsp;0.5 mg/L were imputed as 0.25 mg/L.\u003c/p\u003e \u003cp\u003eFunctional Status: Assessed using the Barthel Index (BI) within 24 hours. BI ranges from 0 (total dependence) to 100 (independence).\u003c/p\u003e \u003cp\u003eCovariates: Data included age, gender, Charlson Comorbidity Index (CCI), and primary admission diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were performed using Python 3.9. Missing data were handled via Multiple Imputation by Chained Equations (MICE).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePredictive Models\u003c/b\u003e: Univariate and multivariable logistic regression adjusted for age, gender, CCI, diagnosis type, creatinine, and hemoglobin.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eROC Analysis\u003c/b\u003e: Used to determine optimal cut-offs (CAR 0.58; BI 60) and compare discriminative ability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInteraction Analysis\u003c/b\u003e:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMultiplicative: Assessed using the product term (CAR \u0026times; BI) in regression models.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdditive: Assessed using Relative Excess Risk due to Interaction (RERI), Attributable Proportion (AP), and Synergy Index (SI) based on adjusted risk ratios (RR) from Poisson regression [8]. CAR was dichotomized at 0.58 and BI at 60.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNon-linear Modeling\u003c/b\u003e: Restricted Cubic Splines (RCS) with 4 knots were fitted to visualize the continuous risk trajectory of CAR across BI scores.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\n\u003cp\u003eOf the 8,409 patients, 1,116 (13.3%) died. Non-survivors were significantly older (83.0 vs. 79.3 years) and had higher CAR levels (median 1.38 vs. 0.35) and lower BI scores (median 10 vs. 65) compared to survivors (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Prealbumin was also significantly lower in non-survivors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline Characteristics of Survivors and Non-survivors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eSurvivors (n\u0026thinsp;=\u0026thinsp;7293)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;1116)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eDemographics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e79.30\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e83.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eMale,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e3758 (51.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e613 (54.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory Markers\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eAlbumin, g/L (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e37.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e32.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCRP, mg/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e13.20 (3.20\u0026ndash;46.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e44.20 (12.60\u0026ndash;97.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e0.35 (0.08\u0026ndash;1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.38 (0.35\u0026ndash;3.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003ePrealbumin, mg/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e162.00 (114.00-208.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e102.00 (57.75-148.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e76.20 (62.00-94.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e82.75 (62.00-116.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e123.00 (110.00-135.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e108.00 (92.00-123.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eLymphocyte, \u0026times;10⁹/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.30 (0.90\u0026ndash;1.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.10 (0.70\u0026ndash;1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eCCI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCCI Score, median (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.0 (0.0\u0026ndash;1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.0 (1.0\u0026ndash;3.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCCI\u0026thinsp;\u0026ge;\u0026thinsp;3, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1042 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e372 (33.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBarthel index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e65.00 (25.00\u0026ndash;90.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e10.00 (5.00\u0026ndash;15.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Independent Association with Mortality\u003c/h2\u003e\n\u003cp\u003eIn multivariable analysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), elevated CAR was independently associated with increased mortality (OR 1.37, 95% CI 1.33\u0026ndash;1.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, higher functional status was protective; each 10-point increase in BI reduced mortality risk by approximately 47% (OR 0.53, 95% CI 0.51\u0026ndash;0.55).\u003c/p\u003e\n\u003ch2\u003e3.3 Predictive Performance\u003c/h2\u003e\n\u003cp\u003eThe combined model (CAR\u0026thinsp;+\u0026thinsp;BI) achieved an AUC of 0.890, superior to CAR (0.683) or BI (0.875) alone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable Binary Logistic Regression Analysis for In-hospital Mortality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eModel 1 (Unadjusted)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.353 (1.312\u0026ndash;1.396)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eModel 2 (Age, Sex adjusted)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.369 (1.326\u0026ndash;1.414)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eModel 3 (Fully adjusted, including CCI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.357 (1.313\u0026ndash;1.403)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBI (per 10-unit increase)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eModel 1 (Unadjusted)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.537 (0.514\u0026ndash;0.560)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBI (per 10-unit increase)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eModel 2 (Age, Sex adjusted)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.530 (0.507\u0026ndash;0.553)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBI (per 10-unit increase)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eModel 3 (Fully adjusted, including CCI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.535 (0.512\u0026ndash;0.560)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eNote: Model 3 adjusted for CCI, Creatinine, Hemoglobin, and Lymphocytes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eIncremental Predictive Value of CAR Beyond BI and Clinical Covariates\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eMetric\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eValue\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eAUC of Baseline Model (BI\u0026thinsp;+\u0026thinsp;Covariates)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e0.888\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eAUC of Full Model (BI\u0026thinsp;+\u0026thinsp;CAR\u0026thinsp;+\u0026thinsp;Covariates)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e0.899\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eDelta AUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e0.0106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eLikelihood Ratio Test (LRT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eLR \u0026chi;\u0026sup2; = 190.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eIntegrated Discrimination Improvement (IDI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e0.0389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Interaction Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 Multiplicative Interaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interaction term (CAR \u0026times; BI) was not statistically significant (OR 0.99, P = 0.262), indicating CAR predicts mortality consistently across functional levels in a multiplicative model (Table 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultiplicative Interaction Between CAR and Barthel Index\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCAR (per unit increase)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.36 (1.28\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBarthel index (per 10-point increase)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.55 (0.52\u0026ndash;0.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eCAR \u0026times; Barthel index (interaction term)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e0.262\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubgroup analysis confirmed consistent associations across age and sex, with no significant interaction detected (P interaction \u0026gt; 0.05 for all subgroups) (Table 5).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSubgroup Analysis of CAR and Mortality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eSubgroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eNo. Patients\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eEvents (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eAdj OR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eP Interaction\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e8409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1116 (13.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.35 (1.31\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.278\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;79 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e3869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e337 (8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.46 (1.39\u0026ndash;1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;79 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e4540\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e779 (17.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.31 (1.25\u0026ndash;1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.941\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e4371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e613 (14.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.35 (1.30\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e4038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e503 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.36 (1.30\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.330\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBI\u0026thinsp;\u0026gt;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e5539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e145 (2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.27 (1.17\u0026ndash;1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eBI\u0026thinsp;\u0026le;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e2870\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e971 (33.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e1.40 (1.34\u0026ndash;1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eRestricted Cubic Spline (RCS) Analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRCS modeling (Figure 2) revealed a J-shaped curve. The risk associated with CAR was lowest at a nadir of BI = 64.3 (OR=1.21). A critical risk surge point was identified as BI approaches 0, where the OR rose sharply to 1.42, confirming a step-wise amplification of risk with worsening dependency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 Additive Interaction Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast, additive interaction analysis revealed a pronounced synergistic effect (Table 6). Compared with the reference group (Low CAR + High BI), patients with both high CAR and severe functional dependency exhibited a markedly elevated adjusted RR of 55.75 (95% CI 33.33\u0026ndash;93.26). The relative excess risk due to interaction (RERI) was 29.54, the attributable proportion (AP) was 53.0%, and the synergy index (SI) was 2.17, indicating that 53% of the excess mortality risk in the \u0026ldquo;double-hit\u0026rdquo; group was attributable to the synergistic interaction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAdditive Interaction Measures Between CAR and Barthel Index on In-hospital Mortality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eExposure group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eAdjusted RR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003en (deaths)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eLow CAR\u0026thinsp;+\u0026thinsp;High BI (CAR\u0026thinsp;\u0026lt;\u0026thinsp;0.58 \u0026amp; BI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e1.00 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e2502 (15)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eHigh CAR\u0026thinsp;+\u0026thinsp;High BI (CAR\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.58 \u0026amp; BI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e2.95 (1.60\u0026ndash;5.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e1706 (31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eLow CAR\u0026thinsp;+\u0026thinsp;Low BI (CAR\u0026thinsp;\u0026lt;\u0026thinsp;0.58 \u0026amp; BI\u0026thinsp;\u0026lt;\u0026thinsp;60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e24.26 (14.43\u0026ndash;40.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e2190 (339)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003eHigh CAR\u0026thinsp;+\u0026thinsp;Low BI (CAR\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.58 \u0026amp; BI\u0026thinsp;\u0026lt;\u0026thinsp;60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e55.75 (33.33\u0026ndash;93.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left; height: 35px;\"\u003e\n\u003cp\u003e2011 (731)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" colspan=\"3\"\u003eInteraction metrics: RERI\u0026thinsp;=\u0026thinsp;29.54; AP\u0026thinsp;=\u0026thinsp;0.530 (53.0%); SI\u0026thinsp;=\u0026thinsp;2.17\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdjusted for: age, sex, creatinine, hemoglobin, lymphocyte count, primary admission diagnosis categories, Charlson Comorbidity Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified Analysis by Infection Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStratified analysis showed the additive interaction was particularly pronounced in patients with infection-related diagnoses (RERI 25.07). In contrast, the interaction was attenuated and not statistically significant in the non-infection subgroup, where all exposure groups exhibited similarly high adjusted RRs around 1.00 (Table 7). This suggests that in non-infectious contexts, the mortality risk may be predominantly driven by severe functional dependency and comorbidities themselves, with a relatively smaller marginal contribution from elevated inflammation as measured by CAR. Alternatively, this null finding warrants cautious interpretation due to potential model limitations in this subgroup.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e: Stratified Additive Interaction Measures (Infectious vs. Non-Infectious)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e \u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eSubgroup / Exposure Group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eAdjusted RR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003en (deaths)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eInteraction Metrics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"text-align: left;\"\u003e\n\u003cp\u003eInfection-related Diagnosis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eLow CAR\u0026thinsp;+\u0026thinsp;High BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.00 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1241 (3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\" rowspan=\"4\"\u003e\n\u003cp\u003eRERI: 25.07\u003c/p\u003e\n\u003cp\u003eAP: 0.234\u003c/p\u003e\n\u003cp\u003eSI: 1.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eHigh CAR\u0026thinsp;+\u0026thinsp;High BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e2.10 (0.53\u0026ndash;8.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1082 (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eLow CAR\u0026thinsp;+\u0026thinsp;Low BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e80.95 (25.47\u0026ndash;257.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e498 (128)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eHigh CAR\u0026thinsp;+\u0026thinsp;Low BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e107.11 (33.74\u0026ndash;340.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e769 (303)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon-infection Diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eLow CAR\u0026thinsp;+\u0026thinsp;High BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.00 (Reference)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1261 (12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\" rowspan=\"4\"\u003e\n\u003cp\u003eRERI: 0.00\u003c/p\u003e\n\u003cp\u003eAP: 0.000\u003c/p\u003e\n\u003cp\u003eSI: nan\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eHigh CAR\u0026thinsp;+\u0026thinsp;High BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.00 (0.91\u0026ndash;1.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e624 (25)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eLow CAR\u0026thinsp;+\u0026thinsp;Low BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.00 (0.93\u0026ndash;1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1692 (211)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003eHigh CAR\u0026thinsp;+\u0026thinsp;Low BI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1.00 (0.93\u0026ndash;1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"text-align: left;\"\u003e\n\u003cp\u003e1242 (428)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eAdjusted for: age, sex, CCI, creatinine, hemoglobin, lymphocyte count.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 The “Double-Hit” Phenotype: Inflammation and Disability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings identify a \"double-hit\" high-risk phenotype defined by high CAR (≥0.58) and severe functional dependency (BI \u0026lt;60), with 53% of excess mortality attributable to additive synergy (RERI 29.54, SI 2.17). This phenotype reflects a pathophysiological vicious cycle[9,10] mediated by three key pathways: (1) Systemic inflammation (high CAR) induces anabolic resistance, inhibiting skeletal muscle protein synthesis via mTOR pathway suppression—exacerbating sarcopenia and reducing BI scores[11]; (2) Severe functional dependency (low BI) leads to immobilization, gut microbiota dysbiosis, and malnutrition, activating the NF-κB pathway[9] to promote pro-inflammatory cytokine (IL-6, TNF-α) release and further elevate CAR[12]; (3) This cycle is amplified by acute stressors such as infection, as evidenced by stronger additive interaction in infection-related diagnoses (RERI 25.07 vs. 0.00 in non-infection cases). The 55-fold higher mortality risk in the \"double-hit\" group underscores the clinical significance of this synergy, which cannot be captured by individual markers alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Functional Status as a Baseline for Risk Stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe robust predictive value of BI in our cohort reaffirms functional status as a cornerstone of geriatric assessment. However, our study adds a crucial layer to this established knowledge: CAR remains a powerful predictor even within the same functional strata. While international guidelines rightly emphasize ADL assessment, relying solely on functional status may miss “hidden” risks in patients who appear functionally stable but harbor high systemic inflammation. Therefore, CAR serves as a vital biological complement to the functional assessment provided by BI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Clinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on these findings, we propose a tiered risk stratification framework:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eLow Risk:\u003c/strong\u003e BI \u0026gt;64.3 (the identified nadir) and low CAR, suitable for standard geriatric care;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntermediate Risk:\u003c/strong\u003e High CAR or moderate dependency, requiring targeted nutritional or anti-inflammatory support;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHigh Risk (“Double-Hit” Phenotype):\u0026nbsp;\u003c/strong\u003eHigh CAR (≥0.58) and severe dependency (BI \u0026lt;60). This group faces a 55-fold risk increase, necessitating immediate multidisciplinary intervention to break the inflammation-disability cycle, such as enhanced protein intake (e.g., 1.2–1.5 g/kg/day via oral supplements), anti-inflammatory rehabilitation protocols, and early palliative consultation if appropriate.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Attenuated Additive Interaction in Non-infection Cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotably, the additive interaction between CAR and BI was attenuated in non-infection-related diagnoses (RERI 0.00, AP 0.00%). This finding likely reflects three key factors: (1) Higher baseline mortality risk in non-infection patients due to advanced chronic diseases (e.g., end-stage organ failure), masking the incremental risk of inflammation-functional dependency synergy; (2) Chronic inflammation in non-infection cases is often low-grade and persistent, differing from the acute inflammatory response in infection; (3) Non-infection subgroups included a higher proportion of patients receiving palliative care, where mortality risk is driven by underlying disease rather than inflammation or functional status. These results highlight that the \"inflammation-functional disability\" cycle is most lethal in the context of acute infectious stress, guiding targeted intervention prioritization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be considered. First, the single-center retrospective design limits generalizability to other secondary care geriatric hospitals. Future multicenter prospective cohort studies are needed to validate the CAR-BI risk stratification framework. Second, the high mortality rate in the severe dependency group (36.3%) may include patients receiving palliative care, potentially confounding the association—though sensitivity analyses excluding palliative care patients confirmed result robustness. Third, competing risks (e.g., non-mortality discharge) were not accounted for; future studies should use Fine-Gray models to address this. Fourth, the extremely low mortality in the reference group (0.6%) may reflect selection bias, though internal validation (split-sample analysis) showed consistent cutoff stability. Fifth, we lacked direct measures of frailty, sarcopenia, and gut microbiota—key mediators of the inflammation-functional dependency cycle. Future studies should include these variables to clarify mediating pathways. Finally, we did not evaluate the effectiveness of the proposed MDT intervention; randomized controlled trials are needed to confirm whether targeting the \"double-hit\" phenotype reduces mortality.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, CAR and functional dependency (assessed via BI) are robust independent predictors of in-hospital mortality in secondary care geriatric inpatients, with a strong additive synergistic effect defining a \"double-hit\" high-risk phenotype. The combined CAR-BI assessment is a low-cost, feasible tool that significantly improves risk stratification beyond single indicators. Integrating these two measures into routine geriatric care enables targeted MDT interventions to break the inflammation-functional disability cycle, ultimately improving outcomes for the most vulnerable older adults. Future studies should validate this framework in multicenter cohorts and evaluate the effectiveness of targeted interventions.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Informed\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of Shanghai Pudong New Area Geriatric Hospital (Approval No.: HEC-SP-24-006).Due to the retrospective nature of the study, which involved the collection and analysis of routine clinical data from medical records, the requirement for written informed consent from individual patients was waived by the Medical Ethics Committee. All patient data were anonymized and de-identified prior to analysis to ensure strict confidentiality and protect patient privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the People's Livelihood Research Project of Pudong New Area Science and Technology and Economic Committee (Grant No. PKJ2024-Y82). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization \u0026amp; Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Zhoujian developed the overall research framework and defined the study objectives, specifically focusing on the additive interaction between systemic inflammation (CAR) and functional dependency (BI) on in-hospital mortality. He designed the methodological protocol, including the retrospective cohort design, selection of covariates, and the statistical analysis plan for both multiplicative and additive interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection \u0026amp; Curation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWang Huaping, Zhang Haihong, Zhao Hui, Li Aihua , Zhu Weifeng and Zhou Qi coordinated the data retrieval from the electronic medical records (EMR) of Shanghai Pudong New Area Geriatric Hospital. They extracted and validated data on demographics, laboratory markers (CRP, albumin, prealbumin, etc.), functional status (Barthel Index), and clinical outcomes. Ye Jin managed data cleaning and the Multiple Imputation by Chained Equations (MICE) process for missing values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis \u0026amp; Interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWang Xiaodan and Ye Jin performed the statistical analyses using Python 3.9. This included conducting restricted cubic spline (RCS) modeling, logistic regression, ROC curve analysis, and calculating interaction metrics (RERI, AP, SI). All authors participated in interpreting the results, particularly the clinical implications of the \"double-hit\" phenotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManuscript Preparation \u0026amp; Revision\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWang Huaping and Zhang Haihong drafted the initial manuscript and created the tables and figures. Yang Zhoujian and Wang Shanjin critically revised the manuscript for important intellectual content, ensuring the accuracy of the discussion regarding biological mechanisms and public health relevance. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\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\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the participants and their families for their involvement in this study. We extend our gratitude to the medical staff at Shanghai Pudong New Area Geriatric Hospital for their dedication to patient care and their assistance in data acquisition. We also appreciate the valuable administrative support provided by the hospital’s Medical Ethics Committee and the Department of Geriatrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData: All raw data supporting the findings of this study, including patient demographics, laboratory results, and Barthel Index scores, are securely stored by the research team in compliance with the hospital’s data privacy regulations. De-identified data can be made available to qualified researchers for academic purposes upon reasonable request. To access the data, please contact the corresponding author, Yang Zhoujian (Email: [email protected]) or Wang Shanjin (Email: [email protected]).\u003c/p\u003e\n\u003cp\u003eMaterials: The study utilized routine clinical laboratory assays for CRP and albumin, and the standard Barthel Index for functional assessment. No proprietary software, algorithms, or unpublished questionnaires were used. The statistical code (Python 3.9) used for analysis is available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e World Health Organization. Decade of Healthy Ageing 2020\u0026ndash;2030. Geneva: WHO; 2020. https://www.who.int/publications/i/item/9789240017900\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Baechle JJ, Chen N, Makhijani P, Winer S, Furman D, Winer DA. Chronic inflammation and the hallmarks of aging. Mol Metab. 2023;74:101755. https://doi.org/10.1016/j.molmet.2023.101755\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Mahoney FI, Barthel DW. Functional evaluation: the Barthel Index. Md State Med J. 1965;14:61\u0026ndash;65. https://pubmed.ncbi.nlm.nih.gov/14258950/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Oh TK, Ji E, Na HS, Min B, Jeon YT, Do SH, Song IA, Park HP, Hwang JW. C-Reactive Protein to Albumin Ratio Predicts 30-Day and 1-Year Mortality in Postoperative Patients after Admission to the Intensive Care Unit. J Clin Med. 2018;7(3):39. https://doi.org/10.3390/jcm7030039. PMID: 29495423; PMCID: PMC5867565.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Koozi H, Lengquist M, Frigyesi A. C-reactive protein as a prognostic factor in intensive care admissions for sepsis: A Swedish multicenter study. J Crit Care. 2020;56:73\u0026ndash;79. https://doi.org/10.1016/j.jcrc.2019.12.009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Proctor MJ, McMillan DC, Horgan PG, Fletcher CD, Talwar D, Morrison DS. Systemic inflammation predicts all-cause mortality: a glasgow inflammation outcome study. PLoS One. 2015;10(3):e0116206. https://doi.org/10.1371/journal.pone.0116206\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Reuben DB, Rubenstein LV, Hirsch SH, Hays RD. Value of functional status as a predictor of mortality: results of a prospective study. Am J Med. 1992;93(6):663\u0026ndash;669. https://doi.org/10.1016/0002-9343(92)90200-u. Erratum in: Am J Med 1993;94(2):232; Am J Med 1993;94(5):560. PMID: 1466363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Wolinsky FD, Callahan CM, Fitzgerald JF, Johnson RJ. Changes in functional status and the risks of subsequent nursing home placement and death. J Gerontol. 1993;48(3):S94\u0026ndash;S101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol. 2012;41(2):514\u0026ndash;520. https://doi.org/10.1093/ije/dyr218\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Calder\u0026oacute;n-Larra\u0026ntilde;aga A, Vetrano DL, Ferrucci L, et al. Multimorbidity and functional impairment\u0026ndash;bidirectional interplay, synergistic effects and common pathways. J Intern Med. 2019;285(3):255\u0026ndash;271. https://doi.org/10.1111/joim.12843\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Tong Y, Jia Y, Gong A, Li F, Zeng R. Systemic inflammation in midlife is associated with late-life functional limitations. Sci Rep. 2024;14(1):17434. https://doi.org/10.1038/s41598-024-68724-w\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet. 2019;393(10191):2636\u0026ndash;2646. https://doi.org/10.1016/S0140-6736(19)31138-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Soysal P, Stubbs B, Lucato P, et al. Inflammation and frailty in the elderly: A systematic review and meta-analysis. Ageing Res Rev. 2016;31:1\u0026ndash;8. https://doi.org/10.1016/j.arr.2016.08.006\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein-to-albumin ratio, Barthel Index, Functional status, Mortality, Interaction analysis, Inflammaging","lastPublishedDoi":"10.21203/rs.3.rs-8599033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8599033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSystemic inflammation and functional status are two critical prognostic domains in geriatric medicine. However, whether these two domains interact synergistically\u0026mdash;particularly on an additive scale\u0026mdash;to influence short-term outcomes in secondary care geriatric inpatients remains unclear.as regional facilities focusing on subacute care, rehabilitation, and management of chronic multimorbidity, serving as a critical bridge between tertiary acute care and community care.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included 8,409 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years admitted to a secondary geriatric hospital in China between January 2022 and December 2025. Systemic inflammation was assessed using the C-reactive protein-to-albumin ratio (CAR), and functional status was evaluated by the Barthel Index (BI). Both multiplicative and additive interaction analyses were performed, with additive interaction quantified using the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). Restricted cubic spline (RCS) models were applied to explore non-linear associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCAR was independently associated with in-hospital mortality (adjusted OR 1.35, 95% CI 1.31\u0026ndash;1.40). Although the multiplicative interaction between CAR and BI was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.262), a pronounced additive interaction was observed. Compared with patients with low CAR and high BI, those with both high CAR and severe functional dependency exhibited a markedly increased mortality risk (36.3%; adjusted RR 55.75, 95% CI 33.33\u0026ndash;93.26). The RERI was 29.54, AP was 53.0%, and SI was 2.17, indicating that over half of the excess mortality was attributable to their synergistic effect. The combined CAR\u0026ndash;BI model demonstrated excellent discrimination (AUC 0.890), outperforming models using either component alone.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSystemic inflammation and functional dependency exert a strong additive synergistic effect on in-hospital mortality in secondary care geriatric patients. Integrating CAR and BI identifies a clinically meaningful \u0026ldquo;double-hit\u0026rdquo; phenotype that substantially improves risk stratification and may guide targeted multidisciplinary interventions.\u003c/p\u003e","manuscriptTitle":"Additive Interaction Between Systemic Inflammation and Functional Dependency on In-Hospital Mortality: A \"Double-Hit\" Phenotype in 8,409 Geriatric Inpatients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 09:23:44","doi":"10.21203/rs.3.rs-8599033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-22T22:33:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25351924115969001772756562518056834161","date":"2026-02-13T13:09:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T08:43:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T14:06:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T02:53:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T02:51:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-01-14T07:44:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c81d72a3-5f9f-41d3-9121-3456c4046d02","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T09:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 09:23:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8599033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8599033","identity":"rs-8599033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}