Prognostic Significance of the C-Reactive Protein to Albumin Ratio for Mortality in Critically Ill Adults: A Systematic Review and Meta-analysis Across Heterogeneous ICU Populations

Prognostic Significance of the C-Reactive Protein to Albumin Ratio for Mortality in Critically Ill Adults: A Systematic Review and Meta-analysis Across Heterogeneous ICU Populations | 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 Prognostic Significance of the C-Reactive Protein to Albumin Ratio for Mortality in Critically Ill Adults: A Systematic Review and Meta-analysis Across Heterogeneous ICU Populations Jackson Djuma, Deogratias Mulungulungu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8758904/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Inflammation and metabolic depletion are major determinants of survival in critical illness. The C-reactive protein to albumin ratio (CAR) integrates these two physiologic domains and has emerged as a promising prognostic biomarker. Objective To evaluate the association between CAR and mortality in critically ill adult patients through a systematic review and meta-analysis. Methods We searched six databases (2015–2025) for observational studies reporting CAR and mortality. Two reviewers screened articles, extracted data, and assessed quality using the Newcastle–Ottawa Scale. Random-effects models (DerSimonian–Laird, Hartung-Knapp-Sidik-Jonkman, and REML) were applied separately for odds ratios (OR) and hazard ratios (HR). Meta-regression explored sources of heterogeneity. Results Eleven studies were included. Elevated CAR was associated with increased mortality (OR 1.85, 95% CI 1.23–2.78; HR 1.10, 95% CI 1.06–1.14). Heterogeneity was high (I² ≈ 90%). Meta-regression identified age, albumin, diabetes burden, and severity of illness as significant modifiers. Sensitivity analyses confirmed effect robustness. No publication bias was detected. Conclusion Elevated CAR is consistently associated with higher mortality in critically ill adults. Its accessibility and biological plausibility support CAR as a clinically useful adjunct for early risk stratification. C-reactive protein to albumin ratio critical illness mortality inflammation biomarkers meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION (≈ 220 words) Systemic inflammation and metabolic depletion are central features of critical illness and major determinants of adverse outcomes, including mortality. Biomarkers capable of capturing both physiologic domains are clinically valuable for early risk stratification, particularly in the ICU where rapid triage and prognostic assessment are essential. 1–6 The C-reactive protein to albumin ratio (CAR) integrates two well-established markers: C-reactive protein (CRP), a sensitive indicator of acute inflammation, and serum albumin, a marker of nutritional reserve, capillary leak, and catabolic stress. 7 –12 Elevated CAR has been associated with worse outcomes in sepsis, shock, surgical critical illness, and mixed ICU cohorts 13 – 19 ; however, effect sizes vary substantially, and the determinants of heterogeneity remain unclear. Given its accessibility, low cost, and growing clinical interest 20 – 30 , a rigorous synthesis of available evidence is needed. We therefore conducted a systematic review and meta-analysis to quantify the association between CAR and mortality in critically ill adults, assess heterogeneity, and identify key modifying factors such as age, illness severity, comorbidities, and baseline albumin. The review followed PRISMA 2020 31–35 and MOOSE guidelines and was prospectively registered in PROSPERO ( https://www.crd.york.ac.uk/PROSPERO/view/CRD420251185977 . ). 36 –38 Findings from this study provide an updated, comprehensive evaluation of CAR as a potential prognostic biomarker with direct implications for everyday critical care practice. Methods This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. The study protocol was developed a priori, and all methodological details are fully reported in the Supplementary Appendices. Eligibility Criteria We included observational cohort and case–control studies enrolling adult patients (≥ 18 years) admitted to intensive care units (ICUs) or other high-acuity hospital settings. Eligible studies were required to report the C-reactive protein to albumin ratio (CAR), measured at hospital admission or within the first 24 hours, and to evaluate its association with mortality outcomes. The primary outcome was all-cause mortality, including ICU mortality, in-hospital mortality, and short- to medium-term mortality (28- to 90-day). We excluded studies conducted exclusively in pediatric populations, studies without mortality outcomes, narrative reviews, editorials, conference abstracts, case reports, and studies lacking sufficient data to estimate effect sizes. When multiple publications reported overlapping populations, the most comprehensive or recent study was retained. Literature Search and Study Selection A comprehensive literature search was performed across PubMed/MEDLINE, Embase, Scopus, Web of Science, Europe PMC, and Google Scholar, covering publications from January 2015 to December 2025. The search strategy combined controlled vocabulary and free-text terms related to C-reactive protein, albumin, CAR, critical illness, intensive care, and mortality. The full search strategies for all databases are provided in Supplementary Appendix 1. Two reviewers independently screened titles and abstracts for eligibility, followed by full-text assessment of potentially relevant articles. Discrepancies were resolved by consensus. The study selection process is summarized in the PRISMA 2020 flow diagram (Fig. 1), with detailed reasons for exclusion provided in Supplementary Appendix 2. Data Extraction and Quality Assessment Data extraction was performed independently by two reviewers using a standardized data collection form. Extracted data included study design, country, ICU population characteristics, sample size, CAR definition and thresholds, timing of measurement, mortality endpoints, follow-up duration, and adjusted effect estimates reported as odds ratios (ORs) or hazard ratios (HRs) with corresponding confidence intervals. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) for observational studies, which evaluates selection, comparability, and outcome domains. Studies with NOS scores ≥ 7 were considered high quality. Detailed NOS scoring for each included study is provided in Supplementary Appendix 5. Definition of CAR CAR was defined as the ratio of serum C-reactive protein concentration (mg/L) to serum albumin concentration (g/L). When multiple CAR cut-off values were reported, the threshold used in the primary multivariable model was extracted. Differences in cut-off definitions were explored in sensitivity and meta-regression analyses. 39 –44 Statistical Analysis Quantitative syntheses were conducted separately for studies reporting odds ratios and hazard ratios, in accordance with best methodological practices. For OR-based analyses, pooled estimates were calculated using Review Manager (RevMan Web, Cochrane Collaboration). For HR-based analyses, meta-analyses were conducted using random-effects models. Random-effects pooling was performed using DerSimonian–Laird estimation, with robustness assessed using alternative estimators including Hartung–Knapp–Sidik–Jonkman (HKSJ) and restricted maximum likelihood (REML). Statistical heterogeneity was quantified using the I² statistic, with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively. Meta-regression, Sensitivity, and Publication Bias Pre-specified meta-regression analyses were performed to explore potential sources of heterogeneity, including mean age, baseline albumin levels, prevalence of diabetes, severity of illness scores (APACHE II or SOFA), sample size, and CAR cut-off values. Meta-regression results are presented in Supplementary Appendix 7. Sensitivity analyses included leave-one-out analyses, restriction to high-quality studies, exclusion of small studies, and exclusion of the study reporting both OR and HR estimates. Detailed results of sensitivity analyses are provided in Supplementary Appendix 6. Publication bias was assessed visually using funnel plots and statistically using Egger’s and Begg’s tests when sufficient studies were available. Funnel plots are presented in the main figures, with supporting analyses detailed in the Supplementary Material. 45 –52 RESULTS Study Selection and Characteristics A total of 936 records were identified through systematic database searching. Following duplicate removal and screening of titles and abstracts, 60 full-text articles were assessed for eligibility. Of these, 49 studies were excluded for predefined reasons, including lack of mortality outcomes, ineligible populations, or non-original study designs. Ultimately, 11 observational studies fulfilled the inclusion criteria and were included in the final analysis (Fig. 1). 53 –63 One study (Bender et al., 2020) reported both odds ratios (ORs) and hazard ratios (HRs) for mortality in relation to the C-reactive protein to albumin ratio (CAR) and was therefore included in both quantitative syntheses. In total, six studies contributed to the OR-based meta-analysis, and six studies contributed to the HR-based meta-analysis, resulting in two separate pooled effect estimates (Figs. 2 and 3 ). The included studies evaluated critically ill adult populations admitted to intensive care units, including patients with sepsis, shock, mixed medical–surgical ICU admissions, and postoperative critical illness. Key characteristics of the included studies, including study design, sample size, clinical setting, CAR assessment, effect estimates, and covariate adjustment, are summarized in Table 4 . Pooled Effect Estimates Six studies contributed odds ratios to the OR-based meta-analysis, and six studies contributed hazard ratios to the HR-based meta-analysis. One study reported both effect measures. Higher CAR was significantly associated with increased mortality: • OR = 1.85 (95% CI 1.23–2.78) (Fig. 2 ) • HR = 1.10 (95% CI 1.06–1.14) (Fig. 3 ) These consistent findings across dichotomous and time-to-event endpoints support the robustness of CAR as a prognostic marker. Heterogeneity and Meta-regression Heterogeneity was substantial (I² ≈ 90%). Meta-regression identified four significant effect modifiers: Age (p < 0.01) Baseline albumin (p < 0.01) Diabetes prevalence (p < 0.01) Severity of illness (SOFA/APACHE II) (p < 0.01) Sample size had no measurable effect. These findings suggest that conditions associated with heightened inflammatory response or impaired metabolic reserve amplify the prognostic significance of CAR. Sensitivity Analyses Results were stable across all sensitivity analyses, including HKSJ and REML estimators and leave-one-out approaches. No single study exerted disproportionate influence. Publication Bias Funnel plots for OR (Fig. 4 ) and HR(Fig. 5 )appeared symmetrical, and Egger and Begg tests were non-significant, indicating minimal risk of small-study or public cation bias. DISCUSSION (≈ 1,050 words) This systematic review and meta-analysis demonstrates that elevated CAR is a strong and consistent predictor of mortality among critically ill adults. CAR captures two central physiological domains: systemic inflammation via CRP and metabolic–nutritional reserve via albumin. Elevated CAR thus represents a compounded biomarker of immune activation and physiologic depletion, both of which correlate with adverse outcomes in severe illness. Our results confirm and extend prior smaller analyses by including a broader variety of critical illness populations and by isolating OR- and HR-based evidence separately. The consistency of effects across OR and HR models underscores the prognostic value of CAR irrespective of whether mortality is measured as a fixed outcome or a time-dependent event. The significant modifiers identified in meta-regression point to important clinical interactions. Older age, hypoalbuminemia, comorbid diabetes, and higher severity scores all magnified the association between CAR and mortality. These findings align with established physiological principles: older or metabolically compromised patients have diminished reserves, while high-severity patients experience amplified inflammatory cascades, making CAR more predictive in these contexts. 64 65 66 Although heterogeneity was high, the effect remained robust across all sensitivity analyses, and publication bias was not detected. Taken together, these findings suggest that variation across studies reflects genuine differences in patient case mix rather than methodological flaws. Clinical implications: CAR has several advantages: it is inexpensive, rapidly available, and universally measured in ICU admission panels. It may complement existing scores such as SOFA and APACHE II, helping identify high-risk patients earlier. CAR may also inform decisions regarding monitoring intensity, escalation of care, nutritional interventions, and resource allocation. From a clinical perspective, CAR may complement established severity scores by providing rapid, inexpensive prognostic information available at ICU admission. Its integration into routine assessment could support early risk stratification, monitoring of disease trajectory, and identification of patients at increased risk of adverse outcomes. Limitations: All included studies were observational, limiting causal inference. CAR thresholds varied, timing of measurement was not uniform, and adjustment sets differed across studies. Future prospective and multicenter studies are needed to establish standardized CAR cutoffs, incorporate dynamic changes, and evaluate integration into prognostic scoring systems. CONCLUSION (≈ 100 words) Across diverse critical care populations, elevated CAR is strongly associated with increased mortality. Its biological plausibility, accessibility, and consistent predictive value make CAR a promising adjunct to established severity scores. Incorporating CAR into early clinical assessment may improve risk stratification and guide timely decision-making. Prospective research is warranted to determine standardized thresholds and evaluate the incremental value of CAR within prognostic models. CAR represents a pragmatic and clinically accessible biomarker that may support early prognostic assessment and risk stratification in critically ill adults. Declarations Conflicts of Interest: None declared. Ethical Approval: Not applicable (systematic review). PROSPERO CRD420251185977 CONFLICTS OF INTEREST None declared. Funding: No external funding received. Author Contribution J.R. Djuma: Conceptualization, data extraction, statistical analysis, draftingD. Mulungulungu: Supervision, interpretation, critical revision Acknowledgement The authors thank Prof. Deogratias Mulungulungu and collaborators for their guidance and methodological support. References Oami T, Yamamoto A, Ishida S, et al. Critical Care Nutrition from a Metabolic Point of View: A Narrative Review. Nutrients. 2025;17:1352. 10.3390/nu17081352 . Chadda K, Blakey E, Davies T, Puthucheary Z. Risk factors, biomarkers, and mechanisms for PICS: a systematic review and meta-analysis. Br J Anaesth. 2024;133:538–49. 10.1016/j.bja.2024.03.038 . Rousseau A, Martindale R. Nutritional and metabolic modulation of inflammation in critically ill patients. Ann Intensive Care. 2024;14. 10.1186/s13613-024-01350-x . Berger M, Singer P, Wierzchowska-McNew R, et al. 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TABLES Table 1 Search Strategy Database Search Terms Years Filters PubMed/MEDLINE (“C-reactive protein to albumin ratio” OR “CRP/albumin ratio” OR “CAR”) AND (“mortality” OR “death”) AND (“critical illness” OR ICU OR sepsis OR shock) 2015–2025 Humans, Adults, English Embase (‘C-reactive protein’/exp AND ‘albumin’/exp) AND ‘ratio’ AND (‘critical illness’/exp OR ICU) AND mortality 2015–2025 Humans, Articles Scopus TITLE-ABS-KEY ( “CRP/albumin ratio” OR CAR ) AND (mortality) AND (ICU OR sepsis OR shock) 2015–2025 English Web of Science (SCI) TS= (“C-reactive protein to albumin ratio” OR “CAR”) AND TS= (mortality) AND TS= (ICU OR sepsis OR shock) 2015–2025 Articles Europe PMC CRP albumin ratio AND mortality AND critical 2015–2025 Articles Google Scholar “C-reactive protein to albumin ratio” mortality ICU 2015–2025 None Table 2 Eligibility Category Inclusion Criteria Exclusion Criteria Population Adults ≥ 18 years; critically ill; ICU or high-acuity hospital admission Pediatric patients; non-acute populations Exposure CAR measured at admission or within 24h No CAR measurement Outcome Mortality (ICU, hospital, 30-day, 90-day) Studies without mortality outcomes Study Type Observational (cohort, case-control) Reviews, editorials, case reports, abstracts Language English All non-English Table 3 NOS Study Selection (0–4) Comparability (0–2) Outcome (0–3) Total (0–9) Quality Akpinar et al. 2023 4 2 3 9 High Bender et al. 2020 4 2 2 8 High Cacciola et al. 2022 3 2 3 8 High Kaya et al. 2019 3 2 2 7 High Liu Y. 2024 4 2 2 8 High Liu J. 2025 3 2 3 8 High Moon 2018 3 2 3 8 High Tak Kyu Oh 2018 3 2 2 7 High Per Wandel 2024 4 2 2 8 High Min-Hyung Kim 2015 3 2 2 7 High De Liyis 2024 3 2 3 8 High NOS score ≥ 7 indicates high methodological quality. Table 4 Study Characteristics Author (Year) Country Sample Size Population CAR Threshold Mortality Endpoint Effect Size Reported Akpinar (2023) Turkey 512 Sepsis 2.0 30-day OR Bender (2020) USA 8,095 Emergency/ICU 1.8 In-hospital OR + HR Cacciola (2022) Italy 600 Shock 1.4 90-day OR Kaya (2019) Turkey 300 ICU 2.5 ICU mortality OR Liu Y (2024) China 450 Critical illness 1.9 28-day HR Liu J (2025) China 390 Sepsis 1.7 In-hospital HR Moon (2018) Korea 550 ICU 2.1 In-hospital HR Tak Kyu Oh (2018) Korea 780 Surgery ICU 2.0 30-day HR Per Wandel (2024) Denmark 1,200 ICU 1.6 90-day HR De Liyis (2024) Brazil 400 Sepsis 1.5 28-day OR Kim (2015) Korea 122 ICU 2.3 ICU mortality OR Table 5 Sensitivity Analyses (OR + HR) Model OR (95% CI) HR (95% CI) Interpretation DerSimonian–Laird 1.85 (1.23–2.78) — Primary model OR significant Hartung–Knapp (HKSJ) 1.79 (1.15–2.72) 1.10 (1.05–1.14) More conservative; effect unchanged REML 1.83 (1.20–2.75) 1.10 (1.06–1.14) Estimates stable Leave-one-out All significant All significant No influential outlier Odds ratios andhazard ratios were pooled separately using random-effects models. Table 6 Meta-regression Covariate OR Model β (p-value) HR Model β (p-value) Interpretation Age + 0.045 (0.008) + 0.009 (0.014) Older age strengthens CAR–mortality link Albumin + 0.067 (0.002) + 0.015 (0.003) Low albumin increases effect size Diabetes + 0.012 (0.004) + 0.003 (0.010) Diabetes is an effect modifier Severity (SOFA/APACHE) + 0.058 (0.006) + 0.011 (0.005) Severe cases show stronger associations Sample size NS NS No sample-size effect Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialCar.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Djuma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACxgYGZjDd2AAkPwAxGzspWhhngLQwE7aIGaoVyOKB8/Gpbz/82PBHxR3Z5hnZiZ9tfm2T52NmYPzwMQePw3rSjJN5zjwzbpyRu1k6t++2YRszA7PkzG34/JLDfJix7XAiUMsG6dye24xALWzMvPi09L9hPvgTomXzb8ue2/aEtczIYU7ghWjZJs3w43YiEVqeGRvznDls3Njzdptlb8Pt5DZmxma8fjHsT34s+aPisOzG9tzNN378uW07v7354IeP+LQ0IDMY28A2N+BQDAHyqIw/eBWPglEwCkbBCAUA6RNW6EE1dEAAAAAASUVORK5CYII=","orcid":"","institution":"University of Lubumbashi","correspondingAuthor":true,"prefix":"","firstName":"Jackson","middleName":"","lastName":"Djuma","suffix":""},{"id":588521463,"identity":"ff7e8bc5-5745-48e4-bfd2-f909535b0881","order_by":1,"name":"Deogratias Mulungulungu","email":"","orcid":"","institution":"University of Lubumbashi","correspondingAuthor":false,"prefix":"","firstName":"Deogratias","middleName":"","lastName":"Mulungulungu","suffix":""}],"badges":[],"createdAt":"2026-02-01 21:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8758904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8758904/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102389313,"identity":"25156492-64de-429b-8884-176b96ea9ed6","added_by":"auto","created_at":"2026-02-11 08:27:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1477941,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/49e2276f2651aaeca0d6a687.png"},{"id":102389260,"identity":"9566b8a0-43da-493d-b202-98637c4805aa","added_by":"auto","created_at":"2026-02-11 08:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":402426,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing pooled odds ratios (ORs) for the association between elevated C-reactive protein to albumin ratio (CAR) and mortality using a random-effects model.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/131f92aff04bde9a53fc1552.png"},{"id":102389263,"identity":"7dfaabf2-6e47-499d-b703-51449dfcba1a","added_by":"auto","created_at":"2026-02-11 08:27:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388310,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing pooled hazard ratios (HRs) for the association between elevated C-reactive protein to albumin ratio (CAR) and mortality using a random-effects model.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/7e342d86dd7c4fa7d204f38d.png"},{"id":102389282,"identity":"cea5cd35-2615-4e2c-af1c-9de389f1bb71","added_by":"auto","created_at":"2026-02-11 08:27:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22336,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot assessing publication bias for studies reporting odds ratios (ORs) of mortality associated with elevated C-reactive protein to albumin ratio (CAR).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/8c748182119e2bb902379070.jpeg"},{"id":102389270,"identity":"e29621a8-1196-41a9-85c6-bfc1690ab90a","added_by":"auto","created_at":"2026-02-11 08:27:52","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21452,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot assessing publication bias for studies reporting hazard ratios (HRs) of mortality associated with elevated C-reactive protein to albumin ratio (CAR).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/9a381fc7f1e7bfc2d8de04af.jpeg"},{"id":102900255,"identity":"44f7ec68-68cb-4680-bfb9-ca6a6ff377a4","added_by":"auto","created_at":"2026-02-18 07:56:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3176835,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/62523a57-2a89-4b74-983c-7f3108ba7a3c.pdf"},{"id":102389271,"identity":"47414fe1-18a7-4353-ae5d-62cd84e95046","added_by":"auto","created_at":"2026-02-11 08:27:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18267,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialCar.docx","url":"https://assets-eu.researchsquare.com/files/rs-8758904/v1/bc778a223158c7d8f52eb7d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Significance of the C-Reactive Protein to Albumin Ratio for Mortality in Critically Ill Adults: A Systematic Review and Meta-analysis Across Heterogeneous ICU Populations","fulltext":[{"header":"INTRODUCTION (≈ 220 words)","content":"\u003cp\u003eSystemic inflammation and metabolic depletion are central features of critical illness and major determinants of adverse outcomes, including mortality. Biomarkers capable of capturing both physiologic domains are clinically valuable for early risk stratification, particularly in the ICU where rapid triage and prognostic assessment are essential. \u003csup\u003e1\u0026ndash;6\u003c/sup\u003e The C-reactive protein to albumin ratio (CAR) integrates two well-established markers: C-reactive protein (CRP), a sensitive indicator of acute inflammation, and serum albumin, a marker of nutritional reserve, capillary leak, and catabolic stress. \u003csup\u003e7 \u0026ndash;12\u003c/sup\u003e Elevated CAR has been associated with worse outcomes in sepsis, shock, surgical critical illness, and mixed ICU cohorts \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e \u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; however, effect sizes vary substantially, and the determinants of heterogeneity remain unclear.\u003c/p\u003e \u003cp\u003eGiven its accessibility, low cost, and growing clinical interest \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e \u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, a rigorous synthesis of available evidence is needed. We therefore conducted a systematic review and meta-analysis to quantify the association between CAR and mortality in critically ill adults, assess heterogeneity, and identify key modifying factors such as age, illness severity, comorbidities, and baseline albumin. The review followed PRISMA 2020 \u003csup\u003e31\u0026ndash;35\u003c/sup\u003e and MOOSE guidelines and was prospectively registered in PROSPERO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251185977\u003c/span\u003e\u003cspan address=\"https://www.crd.york.ac.uk/PROSPERO/view/CRD420251185977\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003csup\u003e36 \u0026ndash;38\u003c/sup\u003e Findings from this study provide an updated, comprehensive evaluation of CAR as a potential prognostic biomarker with direct implications for everyday critical care practice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. The study protocol was developed a priori, and all methodological details are fully reported in the Supplementary Appendices.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEligibility Criteria\u003c/h2\u003e \u003cp\u003e We included observational cohort and case\u0026ndash;control studies enrolling adult patients (\u0026ge;\u0026thinsp;18 years) admitted to intensive care units (ICUs) or other high-acuity hospital settings. Eligible studies were required to report the C-reactive protein to albumin ratio (CAR), measured at hospital admission or within the first 24 hours, and to evaluate its association with mortality outcomes. The primary outcome was all-cause mortality, including ICU mortality, in-hospital mortality, and short- to medium-term mortality (28- to 90-day).\u003c/p\u003e \u003cp\u003eWe excluded studies conducted exclusively in pediatric populations, studies without mortality outcomes, narrative reviews, editorials, conference abstracts, case reports, and studies lacking sufficient data to estimate effect sizes. When multiple publications reported overlapping populations, the most comprehensive or recent study was retained.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLiterature Search and Study Selection\u003c/h3\u003e\n\u003cp\u003eA comprehensive literature search was performed across PubMed/MEDLINE, Embase, Scopus, Web of Science, Europe PMC, and Google Scholar, covering publications from January 2015 to December 2025. The search strategy combined controlled vocabulary and free-text terms related to C-reactive protein, albumin, CAR, critical illness, intensive care, and mortality. The full search strategies for all databases are provided in Supplementary Appendix 1.\u003c/p\u003e \u003cp\u003eTwo reviewers independently screened titles and abstracts for eligibility, followed by full-text assessment of potentially relevant articles. Discrepancies were resolved by consensus. The study selection process is summarized in the PRISMA 2020 flow diagram (Fig.\u0026nbsp;1), with detailed reasons for exclusion provided in Supplementary Appendix 2.\u003c/p\u003e\n\u003ch3\u003eData Extraction and Quality Assessment\u003c/h3\u003e\n\u003cp\u003eData extraction was performed independently by two reviewers using a standardized data collection form. Extracted data included study design, country, ICU population characteristics, sample size, CAR definition and thresholds, timing of measurement, mortality endpoints, follow-up duration, and adjusted effect estimates reported as odds ratios (ORs) or hazard ratios (HRs) with corresponding confidence intervals.\u003c/p\u003e \u003cp\u003eRisk of bias was assessed using the Newcastle\u0026ndash;Ottawa Scale (NOS) for observational studies, which evaluates selection, comparability, and outcome domains. Studies with NOS scores\u0026thinsp;\u0026ge;\u0026thinsp;7 were considered high quality. Detailed NOS scoring for each included study is provided in Supplementary Appendix 5.\u003c/p\u003e\n\u003ch3\u003eDefinition of CAR\u003c/h3\u003e\n\u003cp\u003eCAR was defined as the ratio of serum C-reactive protein concentration (mg/L) to serum albumin concentration (g/L). When multiple CAR cut-off values were reported, the threshold used in the primary multivariable model was extracted. Differences in cut-off definitions were explored in sensitivity and meta-regression analyses. \u003csup\u003e39 \u0026ndash;44\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e Quantitative syntheses were conducted separately for studies reporting odds ratios and hazard ratios, in accordance with best methodological practices. For OR-based analyses, pooled estimates were calculated using Review Manager (RevMan Web, Cochrane Collaboration). For HR-based analyses, meta-analyses were conducted using random-effects models.\u003c/p\u003e \u003cp\u003eRandom-effects pooling was performed using DerSimonian\u0026ndash;Laird estimation, with robustness assessed using alternative estimators including Hartung\u0026ndash;Knapp\u0026ndash;Sidik\u0026ndash;Jonkman (HKSJ) and restricted maximum likelihood (REML). Statistical heterogeneity was quantified using the I\u0026sup2; statistic, with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeta-regression, Sensitivity, and Publication Bias\u003c/h2\u003e \u003cp\u003ePre-specified meta-regression analyses were performed to explore potential sources of heterogeneity, including mean age, baseline albumin levels, prevalence of diabetes, severity of illness scores (APACHE II or SOFA), sample size, and CAR cut-off values. Meta-regression results are presented in Supplementary Appendix 7.\u003c/p\u003e \u003cp\u003eSensitivity analyses included leave-one-out analyses, restriction to high-quality studies, exclusion of small studies, and exclusion of the study reporting both OR and HR estimates. Detailed results of sensitivity analyses are provided in Supplementary Appendix 6.\u003c/p\u003e \u003cp\u003ePublication bias was assessed visually using funnel plots and statistically using Egger\u0026rsquo;s and Begg\u0026rsquo;s tests when sufficient studies were available. Funnel plots are presented in the main figures, with supporting analyses detailed in the Supplementary Material. \u003csup\u003e45 \u0026ndash;52\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection and Characteristics\u003c/h2\u003e \u003cp\u003eA total of 936 records were identified through systematic database searching. Following duplicate removal and screening of titles and abstracts, 60 full-text articles were assessed for eligibility. Of these, 49 studies were excluded for predefined reasons, including lack of mortality outcomes, ineligible populations, or non-original study designs. Ultimately, 11 observational studies fulfilled the inclusion criteria and were included in the final analysis (Fig.\u0026nbsp;1). \u003csup\u003e53 –63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne study (Bender et al., 2020) reported both odds ratios (ORs) and hazard ratios (HRs) for mortality in relation to the C-reactive protein to albumin ratio (CAR) and was therefore included in both quantitative syntheses. In total, six studies contributed to the OR-based meta-analysis, and six studies contributed to the HR-based meta-analysis, resulting in two separate pooled effect estimates (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe included studies evaluated critically ill adult populations admitted to intensive care units, including patients with sepsis, shock, mixed medical–surgical ICU admissions, and postoperative critical illness. Key characteristics of the included studies, including study design, sample size, clinical setting, CAR assessment, effect estimates, and covariate adjustment, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePooled Effect Estimates\u003c/h2\u003e \u003cp\u003eSix studies contributed odds ratios to the OR-based meta-analysis, and six studies contributed hazard ratios to the HR-based meta-analysis. One study reported both effect measures. Higher CAR was significantly associated with increased mortality:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e• OR = 1.85 (95% CI 1.23–2.78) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e• HR = 1.10 (95% CI 1.06–1.14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThese consistent findings across dichotomous and time-to-event endpoints support the robustness of CAR as a prognostic marker.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity and Meta-regression\u003c/h2\u003e \u003cp\u003eHeterogeneity was substantial (I² ≈ 90%). Meta-regression identified four significant effect modifiers:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAge\u003c/b\u003e (p \u0026lt; 0.01)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBaseline albumin\u003c/b\u003e (p \u0026lt; 0.01)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiabetes prevalence\u003c/b\u003e (p \u0026lt; 0.01)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSeverity of illness (SOFA/APACHE II)\u003c/b\u003e (p \u0026lt; 0.01)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSample size had no measurable effect. These findings suggest that conditions associated with heightened inflammatory response or impaired metabolic reserve amplify the prognostic significance of CAR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e \u003cp\u003eResults were stable across all sensitivity analyses, including HKSJ and REML estimators and leave-one-out approaches. No single study exerted disproportionate influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias\u003c/h2\u003e \u003cp\u003eFunnel plots for OR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and HR(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e)appeared symmetrical, and Egger and Begg tests were non-significant, indicating minimal risk of small-study or public cation bias.\u003c/p\u003e "},{"header":"DISCUSSION (≈ 1,050 words)","content":"\u003cp\u003eThis systematic review and meta-analysis demonstrates that elevated CAR is a strong and consistent predictor of mortality among critically ill adults. CAR captures two central physiological domains: systemic inflammation via CRP and metabolic–nutritional reserve via albumin. Elevated CAR thus represents a compounded biomarker of immune activation and physiologic depletion, both of which correlate with adverse outcomes in severe illness.\u003c/p\u003e\u003cp\u003eOur results confirm and extend prior smaller analyses by including a broader variety of critical illness populations and by isolating OR- and HR-based evidence separately. The consistency of effects across OR and HR models underscores the prognostic value of CAR irrespective of whether mortality is measured as a fixed outcome or a time-dependent event.\u003c/p\u003e\u003cp\u003eThe significant modifiers identified in meta-regression point to important clinical interactions. Older age, hypoalbuminemia, comorbid diabetes, and higher severity scores all magnified the association between CAR and mortality. These findings align with established physiological principles: older or metabolically compromised patients have diminished reserves, while high-severity patients experience amplified inflammatory cascades, making CAR more predictive in these contexts. \u003csup\u003e64 65 66\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAlthough heterogeneity was high, the effect remained robust across all sensitivity analyses, and publication bias was not detected. Taken together, these findings suggest that variation across studies reflects genuine differences in patient case mix rather than methodological flaws.\u003c/p\u003e\u003ch2\u003eClinical implications:\u003c/h2\u003e\u003cp\u003eCAR has several advantages: it is inexpensive, rapidly available, and universally measured in ICU admission panels. It may complement existing scores such as SOFA and APACHE II, helping identify high-risk patients earlier. CAR may also inform decisions regarding monitoring intensity, escalation of care, nutritional interventions, and resource allocation.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, CAR may complement established severity scores by providing rapid, inexpensive prognostic information available at ICU admission. Its integration into routine assessment could support early risk stratification, monitoring of disease trajectory, and identification of patients at increased risk of adverse outcomes.\u003c/p\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e\u003cp\u003eAll included studies were observational, limiting causal inference. CAR thresholds varied, timing of measurement was not uniform, and adjustment sets differed across studies. Future prospective and multicenter studies are needed to establish standardized CAR cutoffs, incorporate dynamic changes, and evaluate integration into prognostic scoring systems.\u003c/p\u003e"},{"header":"CONCLUSION (≈ 100 words)","content":"\u003cp\u003eAcross diverse critical care populations, elevated CAR is strongly associated with increased mortality. Its biological plausibility, accessibility, and consistent predictive value make CAR a promising adjunct to established severity scores. Incorporating CAR into early clinical assessment may improve risk stratification and guide timely decision-making. Prospective research is warranted to determine standardized thresholds and evaluate the incremental value of CAR within prognostic models. CAR represents a pragmatic and clinically accessible biomarker that may support early prognostic assessment and risk stratification in critically ill adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eNone declared.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval:\u003c/strong\u003e \u003cp\u003eNot applicable (systematic review).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePROSPERO\u003c/strong\u003e \u003cp\u003eCRD420251185977\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCONFLICTS OF INTEREST\u003c/h2\u003e \u003cp\u003eNone declared.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo external funding received.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.R. Djuma: Conceptualization, data extraction, statistical analysis, draftingD. Mulungulungu: Supervision, interpretation, critical revision\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Prof. Deogratias Mulungulungu and collaborators for their guidance and methodological support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOami T, Yamamoto A, Ishida S, et al. Critical Care Nutrition from a Metabolic Point of View: A Narrative Review. Nutrients. 2025;17:1352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu17081352\u003c/span\u003e\u003cspan address=\"10.3390/nu17081352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChadda K, Blakey E, Davies T, Puthucheary Z. Risk factors, biomarkers, and mechanisms for PICS: a systematic review and meta-analysis. 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Rev Gastroenterol Mex. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rgmxen.2022.06.007\u003c/span\u003e\u003cspan address=\"10.1016/j.rgmxen.2022.06.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"TABLES","content":"\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSearch Strategy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSearch Terms\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFilters\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePubMed/MEDLINE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(“C-reactive protein to albumin ratio” OR “CRP/albumin ratio” OR “CAR”) AND (“mortality” OR “death”) AND (“critical illness” OR ICU OR sepsis OR shock)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015–2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHumans, Adults, English\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmbase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(‘C-reactive protein’/exp AND ‘albumin’/exp) AND ‘ratio’ AND (‘critical illness’/exp OR ICU) AND mortality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015–2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHumans, Articles\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScopus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTITLE-ABS-KEY ( “CRP/albumin ratio” OR CAR ) AND (mortality) AND (ICU OR sepsis OR shock)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015–2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeb of Science (SCI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS= (“C-reactive protein to albumin ratio” OR “CAR”) AND TS= (mortality) AND TS= (ICU OR sepsis OR shock)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015–2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticles\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEurope PMC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRP albumin ratio AND mortality AND critical\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015–2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArticles\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGoogle Scholar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e“C-reactive protein to albumin ratio” mortality ICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015–2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEligibility\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion Criteria\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion Criteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdults ≥ 18 years; critically ill; ICU or high-acuity hospital admission\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePediatric patients; non-acute populations\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAR measured at admission or within 24h\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo CAR measurement\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMortality (ICU, hospital, 30-day, 90-day)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies without mortality outcomes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStudy Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservational (cohort, case-control)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReviews, editorials, case reports, abstracts\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll non-English\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNOS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelection (0–4)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComparability (0–2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutcome (0–3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (0–9)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkpinar et al. 2023\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBender et al. 2020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCacciola et al. 2022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaya et al. 2019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu Y. 2024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu J. 2025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoon 2018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTak Kyu Oh 2018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer Wandel 2024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin-Hyung Kim 2015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe Liyis 2024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNOS score ≥ 7 indicates high methodological quality.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor (Year)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCAR Threshold\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMortality Endpoint\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect Size Reported\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkpinar (2023)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30-day\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBender (2020)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,095\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmergency/ICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn-hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR + HR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCacciola (2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShock\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90-day\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaya (2019)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICU mortality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu Y (2024)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical illness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28-day\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu J (2025)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn-hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoon (2018)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn-hospital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTak Kyu Oh (2018)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurgery ICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30-day\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer Wandel (2024)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90-day\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe Liyis (2024)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28-day\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKim (2015)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICU mortality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity Analyses (OR + HR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDerSimonian–Laird\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.85 (1.23–2.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e—\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimary model OR significant\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHartung–Knapp (HKSJ)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.79 (1.15–2.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.10 (1.05–1.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMore conservative; effect unchanged\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREML\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.83 (1.20–2.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.10 (1.06–1.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimates stable\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeave-one-out\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo influential outlier\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eOdds ratios andhazard ratios were pooled separately using random-effects models.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR Model β (p-value)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR Model β (p-value)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ 0.045 (0.008)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ 0.009 (0.014)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlder age strengthens CAR–mortality link\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ 0.067 (0.002)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ 0.015 (0.003)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow albumin increases effect size\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ 0.012 (0.004)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ 0.003 (0.010)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiabetes is an effect modifier\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity (SOFA/APACHE)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ 0.058 (0.006)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+ 0.011 (0.005)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere cases show stronger associations\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo sample-size effect\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein to albumin ratio, critical illness, mortality, inflammation, biomarkers, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-8758904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8758904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInflammation and metabolic depletion are major determinants of survival in critical illness. The C-reactive protein to albumin ratio (CAR) integrates these two physiologic domains and has emerged as a promising prognostic biomarker.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo evaluate the association between CAR and mortality in critically ill adult patients through a systematic review and meta-analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe searched six databases (2015\u0026ndash;2025) for observational studies reporting CAR and mortality. Two reviewers screened articles, extracted data, and assessed quality using the Newcastle\u0026ndash;Ottawa Scale. Random-effects models (DerSimonian\u0026ndash;Laird, Hartung-Knapp-Sidik-Jonkman, and REML) were applied separately for odds ratios (OR) and hazard ratios (HR). Meta-regression explored sources of heterogeneity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEleven studies were included. Elevated CAR was associated with increased mortality (OR 1.85, 95% CI 1.23\u0026ndash;2.78; HR 1.10, 95% CI 1.06\u0026ndash;1.14). Heterogeneity was high (I\u0026sup2; \u0026asymp; 90%). Meta-regression identified age, albumin, diabetes burden, and severity of illness as significant modifiers. Sensitivity analyses confirmed effect robustness. No publication bias was detected.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated CAR is consistently associated with higher mortality in critically ill adults. Its accessibility and biological plausibility support CAR as a clinically useful adjunct for early risk stratification.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of the C-Reactive Protein to Albumin Ratio for Mortality in Critically Ill Adults: A Systematic Review and Meta-analysis Across Heterogeneous ICU Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 08:27:20","doi":"10.21203/rs.3.rs-8758904/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5bbe71d-a376-41bc-9b77-7f3ba32f3485","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T07:55:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 08:27:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8758904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8758904","identity":"rs-8758904","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}