Association between Advanced lung cancer inflammation index and all- cause mortality in critically ill patients with sepsis: analysis of the MIMIC- IV database

Association between Advanced lung cancer inflammation index and all- cause mortality in critically ill patients with sepsis: analysis of the MIMIC- IV database | 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 Article Association between Advanced lung cancer inflammation index and all- cause mortality in critically ill patients with sepsis: analysis of the MIMIC- IV database Lei Zhang, Minye Li, Jianfei Liu, Zhanwei Zhao, Lijun Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5772539/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background This study aimed to explore the association between the advanced lung cancer inflammation (ALI) index and the risk of mortality in critically ill patients with sepsis. Methods This retrospective study included 6489 critically ill patients with sepsis from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The participants were grouped into four groups according to the ALI index quartiles. The outcome was in-hospital mortality and ICU mortality. Cox proportional hazards regression analysis and restricted cubic spline regression was used to evaluate the association between the ALI index and clinical outcomes in critically ill patients with sepsis. Results A total of 6489 patients (59.1% male) were included in the study. The in-hospital and intensive care unit (ICU) mortality were 25.4% and 19.0%, respectively. Multivariate Cox proportional hazards analysis showed that the ALI index was independently associated with to all-cause mortality. After confounders adjusting, patients with an elevated ALI index had a significant association with hospital mortality (adjusted hazard ratio, 0.990; 95% confidence interval, 0.985–0.996; P < 0.001) and ICU mortality (adjusted hazard ratio, 0.991; 95% confidence interval, 0.985–0.997; P = 0.004). Restricted cubic splines revealed a non-linear association between ALI and all-cause mortality in sepsis patients. Conclusion Our study indicates that the ALI index has a significant association with hospital and ICU all-cause mortality in critically ill sepsis patients. However, further confirmation of these findings necessitates larger prospective studies. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Health sciences/Signs and symptoms Advanced lung cancer inflammation index All-cause mortality Sepsis MIMIC-IV database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sepsis is a life-threatening condition resulting from a dysregulated immune response to infection, which can lead to severe organ dysfunction and, if untreated, potentially fatal organ failure[ 1 ]. It remains a significant contributor to morbidity and mortality in intensive care units (ICUs), with short-term mortality rates reaching up to 50%, depending on the severity of the illness[ 2 ]. Despite continuous efforts, the incidence and mortality of sepsis have seen minimal improvement over the past decades[ 3 ]. there is an urgent requirement for new predictive indicators to evaluate the prognosis of sepsis. The advanced lung cancer inflammation index (ALI) is a comprehensive index developed in recent years to assess the nutritional and inflammatory status of patients, encompassing parameters such as albumin, BMI, and neutrophil to lymphocyte ratio (NLR)[ 4 , 5 ]. This blend of nutritional and inflammatory indicators makes ALI index an effective tool for assessing the prognosis of cancer patients[ 6 – 8 ]. In addition, studies have found ALI index to be associated with prognosis in a variety of inflammatory diseases, such as heart failure, coronary artery disease, hypertension, and diabetes[ 9 – 12 ]. However, the relationship between ALI index and prognosis of sepsis is currently not well understood. The aim of this study was to assess the role of the ALI index in predicting all-cause mortality in critically ill patients with sepsis by analysing the Medical Information Mart for Intensive Care IV (MIMIC-IV) Materials and methods Study population This study employed a retrospective observational design, utilizing data from the publicly available Medical Information Mart for Intensive Care IV (MIMIC-IV) database, specifically the records of ICU patients at the Beth Israel Deaconess Medical Center, located in the United States [ 13 ]. In order to comply with relevant regulations, the author Lei Zhang obtained both a Collaborative Institutional Training Initiative (CITI) license (Record ID 64101469), along with the necessary permissions to utilize the MIMIC-IV database. This project adhered to the regulations outlined in the Helsinki Declaration. Since the database has already undergone anonymization, the Ethical Committee of the Sixth Medical Center of Chinese PLA General Hospital, China, waived the necessity for informed consent and ethical approval. Study participants met the sepsis 3.0 diagnostic criteria were included in this study, which with infection and Sequential Organ Failure Assessment (SOFA) score ≥ 2[ 14 ]. The exclusion criteria were: (1) patients aged less than 18 years at the time of first admission; (2) length of stay in ICU was less than 48h; (3) multiple admissions to the ICU for sepsis, for whom only data from the first admission were extracted; (4) missing BMI, Albumin, neutrophil and lymphocyte counts within 24h of admission. The flowchart of this study is presented in Fig. 1 . Variable extraction The software PostgresSQL (version 16.1.0) and Navicate Premium (version 17.1.9) were used to extract information with a running Structured Query Language (SQL). Data extracted from the MIMIC-IV database on the frst 24h of ICU admission, which encompassed patient demographics ( age, gender, body mass index (BMI), race), vital signs (heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP)), severity at admission (measured by Simplified acute physiological score II (SAPSII), Glasgow Coma Scale (GCS) score, Acute physiology score III (APSIII), Oxford Acute Severity of Illness Score (OASIS) and the SOFA Other relevant data, including laboratory test results, clinical outcomes, and comorbidities were obtained. All laboratory parameters extracted from the MIMIC-IV database were measured on the frst time after ICU admission. Follow-up began on the date of admission and ended on the date of death. ALI index upon admission was calculated using the following formula: ALI index = BMI × Alb / NLR, in which BMI represents weight in kilogrammes divided by height in metres squared, Alb stands for serum albumin in grammes perdecilitre and NLR is the ratio of absolute neutrophil count to absolute lymphocyte count. To avoid possible bias, variables were excluded if they had more than 20% missing values. Variables with missing data less than 20% were processed by multiple imputation using a random forest algorithm (trained by other non-missing variables) by the “mice” package of R software (Additional file 1: Table S1 ) Clinical outcomes The primary endpoint of the present study was hospital all-cause mortality, and the second endpoint included ICU mortality. Patient mortality information for discharged patients was accessed from the US Social Security Death Index. Statistical analysis ALI index was divided into four groups according to quartiles. Categorical variables were evaluated using Fisher’s exact or chi-square tests and are presented as counts (percentages). For continuous variables, the Wilcoxon rank-sum test, Student’s t-test, or one-way analysis of variance was employed. Kaplan-Meier survival analysis was employed to assess the incidence rate of endpoints among groups based on different levels of the ALI index, and their differences were assessed through log-rank tests. Cox proportional hazards models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) between the ALI index and endpoints, and also adjusted for some models. Confounding variables included variables selected based on p value < 0.05 in univariate analysis. And clinically relevant and prognosis-associated variables were also enrolled in the multivariate model: model 1: unadjusted; model 2: adjusted for age,gender, race; model 3 adjusted for age, gender atrial fibrillation, diabetes, heart failure, hypertension, myocardial infarction, renal failure, sofa, Platelets, WBC, Alp, Ptt, Ast, Inr, Pt, Hemoglobin, Sodium, Alt. Further, we also analyzed the nonlinear association between baseline ALI index with hospital all-cause mortality, ICU mortality using a restricted cubic spline regression model. The receiver operating characteristic (ROC) curves were analyzed to determine the cutoff value of the ALI index. The ALI index was entered into the models as continuous variables or ordinal variables (the first quartile of the ALI index was taken as a reference group). The P values for trends were calculated using the quartile level. Subgroup analyses were conducted to explore potential differences across various subgroups based on age (< 65 and ≥ 65 years), gender, BMI (< 30 and ≥ 30 kg/ m2), diabetes, hypertension, atrial fibrillation, heart failure, myocardial infarction, renal failure to identify the consistency of the prognostic value of the ALI index for primary outcomes. The interactions between ALI index and variables used for stratification were examined with likelihood ratio tests. Data processing and analysis were carried out via R version 4.4.2, with statistical significance set at P < 0.05 for two-tailed tests. Results Baseline characteristics A total of 6489 patients were included in the final data analysis. The median age of the included patients was 65.14 (IQR: 53.92–76.21) years, and 3837 (59.1%) were men. Te in-hospital, ICU, 30-day, and 90-day mortality were 25.40%, 19.00%, 28.000%, and 37.00% respectively. Patients were categorized into four groups based on the quartile of ALI index as follows: Q1 (ALI < 4.6; n = 1623), Q2 (4.6.1–8.8; n = 1622), Q3 (8.8–16.3; n = 1622) and Q3 ((ALI ≥16.3; n = 1622). The baseline characteristics of these patients were shown in Table 1. Patients with the lowest ALI (Q1) exhibited lower values in BMI, temperature, SBP, DBP, Spo2, hematocrit, lymphocyte, albumin, bicarbonate, chloride, sodium, basophil, eosinophil, calcium and hemoglobin. Additionally, Q1 had higher values of age, SOFA, APS III, SAPS II, OASISl, heart rate, respiration rate, WBC, neutrophil, aniongap, creatinine, total bilirubin, alp, inr, pt, bun, in- hospital, ICU, 30-day, and 90-day mortality. More patients in Q1 had atrial fibrillation and renal failure. In Table 2, patients in the non-survivor group were more likely to be older, and have higher severity of illness scores, higher prevalence of atrial fibrillation, heart failure, myocardial infarction, renal failure, higher value of WBC, monocyte, neutrophil, aniongap, creatinine, potassium, total bilirubin, alt, alp, ptt, ast, inr, pt, calcium, bun. The ALI index levels in the non-survivor group were significantly lower than those in the survivor group (7.33 vs. 9.39, P＜0.001). Primary outcomes The Kaplan-Meier survival analysis curves were employed to analyze incidence of primary outcomes among groups, based on the ALI index quartiles are presented in Fig.2. Patients with a lower ALI index had a higher risk of hospital and ICU death. There were significant differences during the short-term of 30 days and 90 days (log-rank P all <0.001). We evaluated the ALI index clinical efficacy using the ROC analysis. However, the the AUC of ALI index was not good enough (in hospital death AUC:0.571, P < 0.001; ICU death AUC: 0.560, P < 0.001 ). The cutoff value of ALI index was 7.78 and 7.18 for hospital death and ICU death, respectively. Multivariate Cox proportional hazards analysis showed that the ALI index was independently associated with an risk of in-hospital mortality (HR, 0.990 (95% CI 0.985-0.996) P<0.001), and ICU mortality (HR, 0.991 (95% CI 0.985-0.997) P=0.004). These results were further confirmed in the fully adjusted Model 3, specifically, the HR for in-hospital mortality in the highest ALI index quartile was 0.711, 95% CI: 0.615-0.822, and for ICU mortality, it was 0.730, 95% CI: 0.615-0.867, both compared with the lowest quartile. In comparison to the Q1 group, Q2, Q3 and Q4 groups exhibited significantly lower risks of in-hospital and ICU mortality, with all trend p-values below 0.05 (Table 3, Fig.3a,3b). Furthermore, the results of the restricted cubic splines analysis indicated non-linear relationship between ALI index and both hospital mortality and ICU mortality in sepsis patients (P for non-linearity=0.012 and P for non-linearity=0.025, respectively), and low levels of ALI index were associated with an increased risk of hospital mortality and ICU mortality in this population (Fig.3c and d). Subgroup analysis Furthermore, to confirm the relationship between ALI index and in-hospital mortality and ICU mortality, stratifed analyses were conducted based on age, gender, BMI, diabetes, hypertension, atrial fibrillation, heart failure, myocardial infarction, renal failure (Figs.4 and 5). Subgroup analysis showed that the association between ALI index and risk of in-hospital mortality was consistent across subgroups stratified by age, gender, BMI, diabetes, atrial fibrillation, heart failure, myocardial infarction (P for interaction > 0.05). Furthermore, two significant interactions were observed in subgroup parameter of hypertension and renal failure (P for interaction = 0.001 and 0.003, respectively; Fig. 4). In terms of stratified analyses of ICU mortality, no significant interactions were identified between the ALI index and age, gender, BMI, diabetes, atrial fibrillation, heart failure (P for interaction > 0.05; Fig.5). However hypertension, renal failure and myocardial infarction demonstrated significant interaction (P for interaction < 0.05; Fig.5). The results of the stratifed analysis consistently demonstrated a similar association of ALI index values across most sub-populations Discussion In the present study, we used the open-source MIMIC-IV database to evaluate the capacity of ALI index in predicting short-term outcomes among critically ill patients with sepsis. The results of this study indicated that a lower ALI index had associations with all-cause ICU and hospital mortality in critically ill patients with sepsis. Even after adjustment for the confounding risk factors, the ALI index was still strongly associated with all-cause ICU and hospital mortality. Our results extended the application of the ALI index to the realm of critical illness, indicating its potential value as a decision-making tool for clinicians managing patients with sepsis. Sepsis is a life-threatening medical condition that occurs when the host have an uncontrolled or abnormal immune response to overwhelming infection[ 15 ]. In sepsis, there is indeed a series of pro-inflammatory and anti-inflammatory reactions that lead to complications such as fever, cardiovascular shock, and systemic organ failure in patients [ 16 ]. The involvement of inflammatory mediators, neurotransmitters, and gene regulators results in the occurrence of local inflammatory responses[ 17 ]. According to a multitude of studies, IL-6, CRP, and the neutrophil-to-lymphocyte ratio (NLR) in patients with sepsis were closely related to prognosis [ 18 , 19 ]. On one hand, low albumin levels could lead to an increased risk of sepsis and mortality [ 20 ]. On the other hand, BMI is an independent factor of in-hospital death in sepsis patients, and sepsis patients with higher BMI had a lower mortality [ 21 ]. Consequently, Therefore, we thought that both inflammatory and nutritional status should be taken into account when comprehensively assessing the prognosis of sepsis patients. ALI index is calculated by combining serum albumin, body mass index and the inflammatory parameter NLR, and has been proven to be related to the prognosis of many types of cancer. [ 22 – 25 ]. A difference between the ALI index and previously reported indices or markers was that the ALI index includes not only NLR and albumin, but also BMI, which was used to assess nutritional status. A recent study showed that the ALI index was associated with long-term all-cause mortality in gastric cancer patients and was used as a comprehensive indicator of nutrition status and inflammation [ 26 ]. Another study showed that the ALI index was superior to the prognostic nutritional index, NLR, systemic immunoinflammatory index and for predicting and differentiating sarcopenia [ 27 ]. To date, no studies has evaluated the relationship between ALI index and all-cause of sepsis patients. Our study indicated that higher ALI index levels were associated with a reduced risk of all-cause mortality in sepsis patients. All of those demonstrated that ALI index was a very valuable prognostic predictor for sepsis patients with high robustness. Our results suggested that higher ALI index had a lower risk of hospital and ICU death. Several elements might underlie this complex relationship. Firstly, the prognosis of sepsis is closely tied to the severity of inflammatory responses. Previous studies had indicated that the NLR represented the inflammatory immune response, and a high neutrophil count was a sign of non-specific inflammation, while a low lymphocyte count suggested a relative deficiency in immune regulation [ 28 ]. Furthermore, a correlation between elevated NLR and poorer prognoses in sepsis patients was found in prior studies [ 29 ]. The findings in Tables 1 revealed that, spanning from group Q1 to Q4, there was a significant decrease in neutrophils and a significant increase in lymphocytes, with a corresponding decrease in NLR, paralleled by a substantial decline in the risk of all-cause mortality. Therefore, we proposed a consistent trend: a decrease in NLR correlated with a concurrent reduction in mortality risk in sepsis patients. Secondly, serum albumin was a frequently utilized marker for assessing nutritional status. Prior studies indicated a negative correlation between albumin levels and the incidence of sepsis [ 30 ]. Owing to its anti-inflammatory effects, albumin served an essential role in sepsis therapy. Sepsis patients with higher albumin levels had a better prognosis compared to those with lower levels. This evidence suggested that albumin levels were closely related to the occurrence of sepsis, the progression of complications, and prognosis. In this study, we noticed that from group Q1 to Q4, albumin levels gradually increased, and all-cause mortality significantly decreased. Therefore, we believed that the elevated albumin levels mainly contributed to consistently decrease the all-cause mortality risks for sepsis patients. Finally, the impact of BMI on the mortality of sepsis patients. Obesity was often a high-risk factor for a variety of diseases. However, the relationship between BMI and the prognosis of sepsis patients was controversial [ 31 ]. Previous studies had shown that sepsis patients with higher BMI had a lower mortality rate, a paradox that might be explained by the obesity paradox [ 32 ]. In other words, obesity was associated with a lower mortality rate in sepsis. The underlying mechanism might be that patients with higher BMI had stronger anti-inflammatory capabilities [ 33 ]. This study indicated that as BMI levels increased from Q1 to Q4, the risk of all-cause mortality in sepsis patients significantly decreased. Our study further analyzed the risk stratification of various subgroups. Our subgroup analysis suggests that the predictive value of the ALI index for hospital mortality and ICU mortality is consistent among sepsis patients, regardless of age, gender, obesity, atrial fibrillation, and heart failure. We did not find any link between the ALI index and in-hospital all-cause mortality in included patients with diabetes, myocardial infarction at baseline. The reason may be that sepsis patients who have been diagnosed with diabetes and myocardial infarction have a poorer prognosis [ 34 , 35 ]. Moreover, the current study revealed that the predictive value of the ALI index significantly differs between sepsis patients with and without atrial fibrillation and between those with and without renal failure. This was because sepsis patients with renal failure had a higher mortality rate, and hypertension could reduce the mortality rate in sepsis patients [ 36 , 37 ]. In this study, we also found a significant linear relationship between the ALI index and in-hospital mortality, indicating that the ALI index may be a reliable tool for detecting high mortality risk in sepsis patients. This study has several strengths. Firstly, our study, based on a a large public database that was nationally representative, verified that ALI index was an important independent risk factor in critically ill patients with sepsis in a US cohort. Secondly, we considered a multitude of confounding factors, utilized multivariable-adjusted Cox analysis, stratified analysis, and interaction analysis. Lastly, ALI index was an easily calculable and derivable comprehensive index, proving highly convenient and practical for clinical usage. The current study has some limitations. First, given that this was an observational research, it was not possible to definitively establish a causal link between ALI index and the mortality associated with sepsis patients. Second, we collected data from the first-time measurements. and did not dynamically monitor the data during the follow-up period. Therefore, we plan to continue expanding the sample size to clarify their causality, and apply various statistical methods to reduce bias. Conclusions In conclusion, our results extended the utility of the ALI index to critically ill patients with sepsis and demonstrated that the ALI index could be used as a potential index for risk stratification of in-hospital and ICU mortality among these patients. Therefore, enhancing risk assessment and directing subsequent interventions. However, additional prospective studies are required to validate these findings. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no confict of interest. Funding None. Author Contribution Lei Zhang designed the study. Lei Zhang extracted, collected and analyzed data. Minye Li, Jianfei Liu prepared tables and figures. Zhanwei Zhao, Lijun Zhou reviewed the results, interpreted data, and wrote the manuscript. All authors have made an intellectual contribution to the manuscript and approved the submission. Acknowledgements None. Data Availability The data utilized in this study were sourced from the MIMIC-IV database. For more information about the database, please visit: https://mimic.physionet.org/. The datasets extracted and analyzed during this study can be made available by the corresponding author upon reasonable request. References Fleischmann, C. et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. Am. J. Respir. Crit Care Med. 193 (3), 259–272 (2016). Huang, S. et al. Effectiveness of sodium bicarbonate infusion on mortality for elderly septic patients with acute metabolic acidosis. Front. Pharmacol. 13 , 974271 (2022). Zheng, R. et al. Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database. Cardiovasc. Diabetol. 22 (1), 307 (2023). Yao, J., Chen, X., Meng, F., Cao, H. & Shu, X. 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Prognostic performance of GRACE and TIMI risk scores in critically ill patients with sepsis and a concomitant myocardial infarction. Arch. Cardiovasc. Dis. 115 (6–7), 359–368 (2022). Kim, H., Hur, M., Struck, J., Bergmann, A. & Di Somma, S. Proenkephalin Predicts Organ Failure, Renal Replacement Therapy, and Mortality in Patients With Sepsis. Annals Lab. Med. 40 (6), 466–473 (2020). Nunes, J. P. Arterial hypertension and sepsis. Revista portuguesa de cardiologia: orgao oficial da Sociedade Portuguesa de Cardiologia = Portuguese . J. Cardiol. : official J. Portuguese Soc. Cardiol. 22 (11), 1375–1379 (2003). Tables Table 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table 1 Characteristics and outcomes of participants categorized by ALI index Table2.docx Table 2 Baseline characteristics of the survivors and non-survivors groups Table3.docx Table 3 Cox proportional hazard ratios (HR) for all-cause mortality TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Apr, 2025 Reviews received at journal 11 Feb, 2025 Reviewers agreed at journal 11 Feb, 2025 Reviews received at journal 10 Feb, 2025 Reviewers agreed at journal 10 Feb, 2025 Reviewers invited by journal 03 Feb, 2025 Editor assigned by journal 03 Feb, 2025 Editor invited by journal 15 Jan, 2025 Submission checks completed at journal 10 Jan, 2025 First submitted to journal 06 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5772539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400494868,"identity":"dc125910-7fed-4b50-b862-1cf2e457f4be","order_by":0,"name":"Lei Zhang","email":"","orcid":"","institution":"The Sixth Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":400494869,"identity":"15632a8a-34c4-4f39-b451-b1895e5fe1a8","order_by":1,"name":"Minye Li","email":"","orcid":"","institution":"Nursing Department of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Minye","middleName":"","lastName":"Li","suffix":""},{"id":400494870,"identity":"1953d4d9-711b-4665-96de-0a3e4d6649ed","order_by":2,"name":"Jianfei Liu","email":"","orcid":"","institution":"The Sixth Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianfei","middleName":"","lastName":"Liu","suffix":""},{"id":400494871,"identity":"b1fc5aa4-273c-4862-ab28-c61d51cd2cd6","order_by":3,"name":"Zhanwei Zhao","email":"","orcid":"","institution":"The General Surgery Department of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhanwei","middleName":"","lastName":"Zhao","suffix":""},{"id":400494872,"identity":"5394a291-01bf-4732-80af-4224d2f89fe6","order_by":4,"name":"Lijun Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACAwYGNiBlwczPzHzwASlaJJgl29mSDUjSwmBwnsdMgCgt5vzHnz342CbBbnyYwYyBocYmmqAWy4YD6YYz2ySYzQ4zpD1gOJaW20DQYQcbjknzQrQcN2BsOEyElsOMbWAtxs2MbRLEaTnGzAbWYsDMzEakljNsbJIzzkkwSxxmYzZIIMov548/k/hQZpPM33/+44MPNTaEtcBAMphMIFY5CNiRongUjIJRMApGGAAAXBI2n8bIbGYAAAAASUVORK5CYII=","orcid":"","institution":"The Sixth Medical Center of Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-01-06 09:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5772539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5772539/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08713-9","type":"published","date":"2025-07-01T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73788194,"identity":"4270f505-e1f1-4942-9402-c4138e56728c","added_by":"auto","created_at":"2025-01-14 16:33:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38241,"visible":true,"origin":"","legend":"\u003cp\u003eInclusion/exclusion criteria. MIMIC: Medical Information Mart for Intensive Care\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/4a446c281f7d2349ff90d463.png"},{"id":73788195,"identity":"4659b413-c061-441f-9a61-10bbd89c8d17","added_by":"auto","created_at":"2025-01-14 16:33:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74590,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis curves for all-cause mortality according to groups at 30 days (a), and 90 days (b)\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/51139b2df83d2eaa7316eea5.png"},{"id":73788199,"identity":"f04fcca1-4ea9-421a-b5a6-d4c895ba4f2f","added_by":"auto","created_at":"2025-01-14 16:33:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48960,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship for the levels of ALI index with in-hospital mortality and ICU mortality. a–b Hazard ratios (95% CIs) for in-hospital and in-ICU mortality according to ALI index quartiles after adjusting for sex, age, race, atrial fibrillation, diabetes, heart failure, hypertension, myocardial infarction, renal failure, Sofa, Platelets, WBC, Alp, Ptt, Ast, Inr, Pt, Hemoglobin, Sodium, Alt. Error bars indicate 95% CIs. The first quartile is the reference. (c) Restricted cubic spline for hospital mortality. (d) Restricted cubic spline for ICU mortality. HR, hazard ratio; CI, confidence interval; ICU, intensive care unit; ALI, advanced lung cancer inflammation\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/5f718b1d5c46bd8de73d8426.png"},{"id":73789456,"identity":"1de23643-11b2-4cf0-a06a-6d1c7e370da1","added_by":"auto","created_at":"2025-01-14 16:41:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27224,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses for the association of ALI index with in-hospital mortality. HR, hazard ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/e8a9cd1f9833508b4415b5b6.png"},{"id":73789443,"identity":"9d6e35b9-55f2-4030-8abc-6aaed4c5543f","added_by":"auto","created_at":"2025-01-14 16:41:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":27266,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses for the association of ALI index with ICU mortality. HR, hazard ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/f15f6205306ada0513d2573e.png"},{"id":86179016,"identity":"3462dbfa-991d-4a40-bbe6-50a805df4b0c","added_by":"auto","created_at":"2025-07-07 16:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":768714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/76ae4557-e85a-4134-b93c-939d79cd48f6.pdf"},{"id":73788197,"identity":"352ab270-81a5-4cb3-b3cc-b5efb593024d","added_by":"auto","created_at":"2025-01-14 16:33:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":55250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Characteristics and outcomes of participants categorized by ALI index\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/d671a9f350300f133d8ae6b4.docx"},{"id":73788200,"identity":"5ad0b23a-4411-4a6b-ac98-8d4d02ec5e38","added_by":"auto","created_at":"2025-01-14 16:33:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Baseline characteristics of the survivors and non-survivors groups\u003c/p\u003e","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/5e5e3ae5cc0bb03f906d603c.docx"},{"id":73788201,"identity":"89cd82d3-1c0c-4ed3-90bd-c9bce88bcbf0","added_by":"auto","created_at":"2025-01-14 16:33:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Cox proportional hazard ratios (HR) for all-cause mortality\u003c/p\u003e","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/6ce47b9a475e1793d5d34868.docx"},{"id":73789917,"identity":"b7245afe-33c1-4cc0-a711-cb82878ce58d","added_by":"auto","created_at":"2025-01-14 16:49:54","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":32554,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5772539/v1/55867e8b732f94a4b61b8327.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Advanced lung cancer inflammation index and all- cause mortality in critically ill patients with sepsis: analysis of the MIMIC- IV database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a life-threatening condition resulting from a dysregulated immune response to infection, which can lead to severe organ dysfunction and, if untreated, potentially fatal organ failure[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It remains a significant contributor to morbidity and mortality in intensive care units (ICUs), with short-term mortality rates reaching up to 50%, depending on the severity of the illness[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite continuous efforts, the incidence and mortality of sepsis have seen minimal improvement over the past decades[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. there is an urgent requirement for new predictive indicators to evaluate the prognosis of sepsis.\u003c/p\u003e \u003cp\u003eThe advanced lung cancer inflammation index (ALI) is a comprehensive index developed in recent years to assess the nutritional and inflammatory status of patients, encompassing parameters such as albumin, BMI, and neutrophil to lymphocyte ratio (NLR)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This blend of nutritional and inflammatory indicators makes ALI index an effective tool for assessing the prognosis of cancer patients[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, studies have found ALI index to be associated with prognosis in a variety of inflammatory diseases, such as heart failure, coronary artery disease, hypertension, and diabetes[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the relationship between ALI index and prognosis of sepsis is currently not well understood.\u003c/p\u003e \u003cp\u003eThe aim of this study was to assess the role of the ALI index in predicting all-cause mortality in critically ill patients with sepsis by analysing the Medical Information Mart for Intensive Care IV (MIMIC-IV)\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study employed a retrospective observational design, utilizing data from the publicly available Medical Information Mart for Intensive Care IV (MIMIC-IV) database, specifically the records of ICU patients at the Beth Israel Deaconess Medical Center, located in the United States [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In order to comply with relevant regulations, the author Lei Zhang obtained both a Collaborative Institutional Training Initiative (CITI) license (Record ID 64101469), along with the necessary permissions to utilize the MIMIC-IV database. This project adhered to the regulations outlined in the Helsinki Declaration. Since the database has already undergone anonymization, the Ethical Committee of the Sixth Medical Center of Chinese PLA General Hospital, China, waived the necessity for informed consent and ethical approval.\u003c/p\u003e \u003cp\u003eStudy participants met the sepsis 3.0 diagnostic criteria were included in this study, which with infection and Sequential Organ Failure Assessment (SOFA) score\u0026thinsp;\u0026ge;\u0026thinsp;2[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The exclusion criteria were: (1) patients aged less than 18 years at the time of first admission; (2) length of stay in ICU was less than 48h; (3) multiple admissions to the ICU for sepsis, for whom only data from the first admission were extracted; (4) missing BMI, Albumin, neutrophil and lymphocyte counts within 24h of admission. The flowchart of this study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable extraction\u003c/h3\u003e\n\u003cp\u003eThe software PostgresSQL (version 16.1.0) and Navicate Premium (version 17.1.9) were used to extract information with a running Structured Query Language (SQL). Data extracted from the MIMIC-IV database on the frst 24h of ICU admission, which encompassed patient demographics ( age, gender, body mass index (BMI), race), vital signs (heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP)), severity at admission (measured by Simplified acute physiological score II (SAPSII), Glasgow Coma Scale (GCS) score, Acute physiology score III (APSIII), Oxford Acute Severity of Illness Score (OASIS) and the SOFA Other relevant data, including laboratory test results, clinical outcomes, and comorbidities were obtained. All laboratory parameters extracted from the MIMIC-IV database were measured on the frst time after ICU admission. Follow-up began on the date of admission and ended on the date of death. ALI index upon admission was calculated using the following formula: ALI index\u0026thinsp;=\u0026thinsp;BMI \u0026times; Alb / NLR, in which BMI represents weight in kilogrammes divided by height in metres squared, Alb stands for serum albumin in grammes perdecilitre and NLR is the ratio of absolute neutrophil count to absolute lymphocyte count.\u003c/p\u003e \u003cp\u003eTo avoid possible bias, variables were excluded if they had more than 20% missing values. Variables with missing data less than 20% were processed by multiple imputation using a random forest algorithm (trained by other non-missing variables) by the \u0026ldquo;mice\u0026rdquo; package of R software (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eClinical outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint of the present study was hospital all-cause mortality, and the second endpoint included ICU mortality. Patient mortality information for discharged patients was accessed from the US Social Security Death Index.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eALI index was divided into four groups according to quartiles. Categorical variables were evaluated using Fisher\u0026rsquo;s exact or chi-square tests and are presented as counts (percentages). For continuous variables, the Wilcoxon rank-sum test, Student\u0026rsquo;s t-test, or one-way analysis of variance was employed. Kaplan-Meier survival analysis was employed to assess the incidence rate of endpoints among groups based on different levels of the ALI index, and their differences were assessed through log-rank tests. Cox proportional hazards models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) between the ALI index and endpoints, and also adjusted for some models. Confounding variables included variables selected based on p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis. And clinically relevant and prognosis-associated variables were also enrolled in the multivariate model: model 1: unadjusted; model 2: adjusted for age,gender, race; model 3 adjusted for age, gender atrial fibrillation, diabetes, heart failure, hypertension, myocardial infarction, renal failure, sofa, Platelets, WBC, Alp, Ptt, Ast, Inr, Pt, Hemoglobin, Sodium, Alt. Further, we also analyzed the nonlinear association between baseline ALI index with hospital all-cause mortality, ICU mortality using a restricted cubic spline regression model. The receiver operating characteristic (ROC) curves were analyzed to determine the cutoff value of the ALI index. The ALI index was entered into the models as continuous variables or ordinal variables (the first quartile of the ALI index was taken as a reference group). The P values for trends were calculated using the quartile level. Subgroup analyses were conducted to explore potential differences across various subgroups based on age (\u0026lt;\u0026thinsp;65 and \u0026ge;\u0026thinsp;65 years), gender, BMI (\u0026lt;\u0026thinsp;30 and \u0026ge;\u0026thinsp;30 kg/ m2), diabetes, hypertension, atrial fibrillation, heart failure, myocardial infarction, renal failure to identify the consistency of the prognostic value of the ALI index for primary outcomes. The interactions between ALI index and variables used for stratification were examined with likelihood ratio tests. Data processing and analysis were carried out via R version 4.4.2, with statistical significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for two-tailed tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 6489 patients were included in the final data analysis. The median age of the included patients was 65.14 (IQR: 53.92\u0026ndash;76.21) years, and 3837 (59.1%) were men. Te in-hospital, ICU, 30-day, and 90-day mortality were 25.40%, 19.00%, 28.000%, and 37.00% respectively. Patients were categorized into four groups based on the quartile of ALI index as follows: Q1 (ALI \u0026lt; 4.6; n = 1623), Q2 (4.6.1\u0026ndash;8.8; n = 1622), Q3 (8.8\u0026ndash;16.3; n = 1622) and Q3 ((ALI \u0026ge;16.3; n = 1622). The baseline characteristics of these patients were shown in Table 1. Patients with the lowest ALI (Q1) exhibited lower values in BMI, temperature, SBP, DBP, Spo2, hematocrit, lymphocyte, albumin, bicarbonate, chloride, sodium, basophil, eosinophil, calcium and hemoglobin. Additionally, Q1 had higher values of age, SOFA, APS III, SAPS II, OASISl, heart rate, respiration rate, WBC, neutrophil, aniongap, creatinine, total bilirubin, alp, inr, pt, bun, in- hospital, ICU, 30-day, and 90-day mortality. More patients in Q1 had atrial fibrillation and renal failure. In Table 2, patients in the non-survivor group were more likely to be older, and have higher severity of illness scores, higher prevalence of atrial fibrillation, heart failure, myocardial infarction, renal failure, higher value of WBC, monocyte, neutrophil, aniongap, creatinine, potassium, total bilirubin, alt, alp, ptt, ast, inr, pt, calcium, bun. The ALI index levels in the non-survivor group were significantly lower than those in the survivor group (7.33 vs. 9.39, P＜0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier survival analysis curves were employed to analyze incidence of primary outcomes among groups, based on the ALI index quartiles are presented in Fig.2. Patients with a lower ALI index had a higher risk of hospital and ICU death. There were significant differences during the short-term of 30 days and 90 days (log-rank P all \u0026lt;0.001). We evaluated the ALI index clinical efficacy using the ROC analysis. However, the the AUC of ALI index was not good enough (in hospital death AUC:0.571, P \u0026lt; 0.001; ICU death AUC: 0.560, P \u0026lt; 0.001 ). The cutoff value of ALI index was 7.78 and 7.18 for hospital death and ICU death, respectively.\u003c/p\u003e\n\u003cp\u003eMultivariate Cox proportional hazards analysis showed that the ALI index was independently associated with an risk of in-hospital mortality (HR, 0.990 (95% CI 0.985-0.996) P\u0026lt;0.001), and ICU mortality (HR, 0.991 (95% CI 0.985-0.997) P=0.004). These results were further confirmed in the fully adjusted Model 3, specifically, the HR for in-hospital mortality in the highest ALI index quartile was 0.711, 95% CI: 0.615-0.822, and for ICU mortality, it was 0.730, 95% CI: 0.615-0.867, both compared with the lowest quartile. In comparison to the Q1 group, Q2, Q3 and Q4 groups exhibited significantly lower risks of in-hospital and ICU mortality, with all trend p-values below 0.05 (Table 3, Fig.3a,3b).\u0026nbsp;Furthermore, the results of the restricted cubic splines analysis indicated non-linear relationship between ALI index and both hospital mortality and ICU mortality in sepsis patients (P for non-linearity=0.012 and P for non-linearity=0.025, respectively), and low levels of ALI index were associated with an increased risk of hospital mortality and ICU mortality in this population (Fig.3c and d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, to confirm the relationship between ALI index and in-hospital mortality and ICU mortality, stratifed analyses were conducted based on age, gender, BMI, diabetes, hypertension, atrial fibrillation, heart failure, myocardial infarction, renal failure (Figs.4 and 5).\u0026nbsp;Subgroup analysis showed that the association between ALI index and risk of in-hospital mortality was consistent across subgroups stratified by age, gender, BMI, diabetes, atrial fibrillation, heart failure, myocardial infarction (P for interaction \u0026gt; 0.05). Furthermore, two significant interactions were observed in subgroup parameter of hypertension and renal failure (P for interaction = 0.001 and 0.003, respectively; Fig. 4).\u0026nbsp;In terms of stratified analyses of ICU mortality,\u0026nbsp;no significant interactions were identified between the ALI index and age, gender, BMI, diabetes, atrial fibrillation, heart failure (P for interaction \u0026gt; 0.05; Fig.5). However hypertension, renal failure and myocardial infarction demonstrated significant interaction (P for interaction \u0026lt; 0.05; Fig.5). The results of the stratifed analysis consistently demonstrated a similar association of ALI index values across most sub-populations\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we used the open-source MIMIC-IV database to evaluate the capacity of ALI index in predicting short-term outcomes among critically ill patients with sepsis. The results of this study indicated that a lower ALI index had associations with all-cause ICU and hospital mortality in critically ill patients with sepsis. Even after adjustment for the confounding risk factors, the ALI index was still strongly associated with all-cause ICU and hospital mortality. Our results extended the application of the ALI index to the realm of critical illness, indicating its potential value as a decision-making tool for clinicians managing patients with sepsis.\u003c/p\u003e \u003cp\u003eSepsis is a life-threatening medical condition that occurs when the host have an uncontrolled or abnormal immune response to overwhelming infection[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In sepsis, there is indeed a series of pro-inflammatory and anti-inflammatory reactions that lead to complications such as fever, cardiovascular shock, and systemic organ failure in patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The involvement of inflammatory mediators, neurotransmitters, and gene regulators results in the occurrence of local inflammatory responses[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. According to a multitude of studies, IL-6, CRP, and the neutrophil-to-lymphocyte ratio (NLR) in patients with sepsis were closely related to prognosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. On one hand, low albumin levels could lead to an increased risk of sepsis and mortality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. On the other hand, BMI is an independent factor of in-hospital death in sepsis patients, and sepsis patients with higher BMI had a lower mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, Therefore, we thought that both inflammatory and nutritional status should be taken into account when comprehensively assessing the prognosis of sepsis patients.\u003c/p\u003e \u003cp\u003eALI index is calculated by combining serum albumin, body mass index and the inflammatory parameter NLR, and has been proven to be related to the prognosis of many types of cancer. [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A difference between the ALI index and previously reported indices or markers was that the ALI index includes not only NLR and albumin, but also BMI, which was used to assess nutritional status. A recent study showed that the ALI index was associated with long-term all-cause mortality in gastric cancer patients and was used as a comprehensive indicator of nutrition status and inflammation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another study showed that the ALI index was superior to the prognostic nutritional index, NLR, systemic immunoinflammatory index and for predicting and differentiating sarcopenia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To date, no studies has evaluated the relationship between ALI index and all-cause of sepsis patients. Our study indicated that higher ALI index levels were associated with a reduced risk of all-cause mortality in sepsis patients. All of those demonstrated that ALI index was a very valuable prognostic predictor for sepsis patients with high robustness.\u003c/p\u003e \u003cp\u003eOur results suggested that higher ALI index had a lower risk of hospital and ICU death. Several elements might underlie this complex relationship. Firstly, the prognosis of sepsis is closely tied to the severity of inflammatory responses. Previous studies had indicated that the NLR represented the inflammatory immune response, and a high neutrophil count was a sign of non-specific inflammation, while a low lymphocyte count suggested a relative deficiency in immune regulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, a correlation between elevated NLR and poorer prognoses in sepsis patients was found in prior studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The findings in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed that, spanning from group Q1 to Q4, there was a significant decrease in neutrophils and a significant increase in lymphocytes, with a corresponding decrease in NLR, paralleled by a substantial decline in the risk of all-cause mortality. Therefore, we proposed a consistent trend: a decrease in NLR correlated with a concurrent reduction in mortality risk in sepsis patients. Secondly, serum albumin was a frequently utilized marker for assessing nutritional status. Prior studies indicated a negative correlation between albumin levels and the incidence of sepsis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Owing to its anti-inflammatory effects, albumin served an essential role in sepsis therapy. Sepsis patients with higher albumin levels had a better prognosis compared to those with lower levels. This evidence suggested that albumin levels were closely related to the occurrence of sepsis, the progression of complications, and prognosis. In this study, we noticed that from group Q1 to Q4, albumin levels gradually increased, and all-cause mortality significantly decreased. Therefore, we believed that the elevated albumin levels mainly contributed to consistently decrease the all-cause mortality risks for sepsis patients. Finally, the impact of BMI on the mortality of sepsis patients. Obesity was often a high-risk factor for a variety of diseases. However, the relationship between BMI and the prognosis of sepsis patients was controversial [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous studies had shown that sepsis patients with higher BMI had a lower mortality rate, a paradox that might be explained by the obesity paradox [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In other words, obesity was associated with a lower mortality rate in sepsis. The underlying mechanism might be that patients with higher BMI had stronger anti-inflammatory capabilities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This study indicated that as BMI levels increased from Q1 to Q4, the risk of all-cause mortality in sepsis patients significantly decreased.\u003c/p\u003e \u003cp\u003eOur study further analyzed the risk stratification of various subgroups. Our subgroup analysis suggests that the predictive value of the ALI index for hospital mortality and ICU mortality is consistent among sepsis patients, regardless of age, gender, obesity, atrial fibrillation, and heart failure. We did not find any link between the ALI index and in-hospital all-cause mortality in included patients with diabetes, myocardial infarction at baseline. The reason may be that sepsis patients who have been diagnosed with diabetes and myocardial infarction have a poorer prognosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, the current study revealed that the predictive value of the ALI index significantly differs between sepsis patients with and without atrial fibrillation and between those with and without renal failure. This was because sepsis patients with renal failure had a higher mortality rate, and hypertension could reduce the mortality rate in sepsis patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, we also found a significant linear relationship between the ALI index and in-hospital mortality, indicating that the ALI index may be a reliable tool for detecting high mortality risk in sepsis patients.\u003c/p\u003e \u003cp\u003eThis study has several strengths. Firstly, our study, based on a a large public database that was nationally representative, verified that ALI index was an important independent risk factor in critically ill patients with sepsis in a US cohort. Secondly, we considered a multitude of confounding factors, utilized multivariable-adjusted Cox analysis, stratified analysis, and interaction analysis. Lastly, ALI index was an easily calculable and derivable comprehensive index, proving highly convenient and practical for clinical usage.\u003c/p\u003e \u003cp\u003eThe current study has some limitations. First, given that this was an observational research, it was not possible to definitively establish a causal link between ALI index and the mortality associated with sepsis patients. Second, we collected data from the first-time measurements. and did not dynamically monitor the data during the follow-up period. Therefore, we plan to continue expanding the sample size to clarify their causality, and apply various statistical methods to reduce bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our results extended the utility of the ALI index to critically ill patients with sepsis and demonstrated that the ALI index could be used as a potential index for risk stratification of in-hospital and ICU mortality among these patients. Therefore, enhancing risk assessment and directing subsequent interventions. However, additional prospective studies are required to validate these findings.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no confict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eLei Zhang designed the study. Lei Zhang extracted, collected and analyzed data. Minye Li, Jianfei Liu prepared tables and figures. Zhanwei Zhao, Lijun Zhou reviewed the results, interpreted data, and wrote the manuscript. All authors have made an intellectual contribution to the manuscript and approved the submission.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data utilized in this study were sourced from the MIMIC-IV database. For more information about the database, please visit: https://mimic.physionet.org/. The datasets extracted and analyzed during this study can be made available by the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFleischmann, C. et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. \u003cem\u003eAm. J. Respir. Crit Care Med.\u003c/em\u003e \u003cb\u003e193\u003c/b\u003e (3), 259\u0026ndash;272 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, S. et al. 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A. \u0026amp; Delano, M. J. Obesity and type 2 diabetes mellitus drive immune dysfunction, infection development, and sepsis mortality. \u003cem\u003eJ. Leukoc. Biol.\u003c/em\u003e \u003cb\u003e104\u003c/b\u003e (3), 525\u0026ndash;534 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesnos, C. et al. Prognostic performance of GRACE and TIMI risk scores in critically ill patients with sepsis and a concomitant myocardial infarction. \u003cem\u003eArch. Cardiovasc. Dis.\u003c/em\u003e \u003cb\u003e115\u003c/b\u003e (6\u0026ndash;7), 359\u0026ndash;368 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H., Hur, M., Struck, J., Bergmann, A. \u0026amp; Di Somma, S. Proenkephalin Predicts Organ Failure, Renal Replacement Therapy, and Mortality in Patients With Sepsis. \u003cem\u003eAnnals Lab. Med.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (6), 466\u0026ndash;473 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunes, J. P. Arterial hypertension and sepsis. \u003cem\u003eRevista portuguesa de cardiologia: orgao oficial da Sociedade Portuguesa de Cardiologia\u0026thinsp;=\u0026thinsp;Portuguese\u003c/em\u003e. \u003cem\u003eJ. Cardiol. : official J. Portuguese Soc. Cardiol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (11), 1375\u0026ndash;1379 (2003).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Advanced lung cancer inflammation index, All-cause mortality, Sepsis, MIMIC-IV database","lastPublishedDoi":"10.21203/rs.3.rs-5772539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5772539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to explore the association between the advanced lung cancer inflammation (ALI) index and the risk of mortality in critically ill patients with sepsis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 6489 critically ill patients with sepsis from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The participants were grouped into four groups according to the ALI index quartiles. The outcome was in-hospital mortality and ICU mortality. Cox proportional hazards regression analysis and restricted cubic spline regression was used to evaluate the association between the ALI index and clinical outcomes in critically ill patients with sepsis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 6489 patients (59.1% male) were included in the study. The in-hospital and intensive care unit (ICU) mortality were 25.4% and 19.0%, respectively. Multivariate Cox proportional hazards analysis showed that the ALI index was independently associated with to all-cause mortality. After confounders adjusting, patients with an elevated ALI index had a significant association with hospital mortality (adjusted hazard ratio, 0.990; 95% confidence interval, 0.985\u0026ndash;0.996; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ICU mortality (adjusted hazard ratio, 0.991; 95% confidence interval, 0.985\u0026ndash;0.997; P\u0026thinsp;=\u0026thinsp;0.004). Restricted cubic splines revealed a non-linear association between ALI and all-cause mortality in sepsis patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study indicates that the ALI index has a significant association with hospital and ICU all-cause mortality in critically ill sepsis patients. 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