Impact of cancer on mortality in critically ill patients with sepsis: A propensity score-matched analysis

Impact of cancer on mortality in critically ill patients with sepsis: A propensity score-matched analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of cancer on mortality in critically ill patients with sepsis: A propensity score-matched analysis Lingyu Jiang, Xiangjie Duan, Jing Pang, Yonglong Zhong, Haiyan Yin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9200112/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Purpose: Given that cancer is a significant burden to healthcare systems globally, this study aimed to evaluate the influence of cancer on the mortality rate of sepsis patients admitted to the intensive care unit (ICU). Methods: This retrospective cohort study utilized the Medical Information Mart for Intensive Care IV version 3.0 database, focusing on adult ICU patients with sepsis and underlying cancer as the exposure variable. The primary outcome was 28-day all-cause mortality, analyzed using 1:1 propensity score matching (PSM). Results: The original cohort included 10,657 patients with sepsis but not cancer and 2,674 with sepsis and cancer. After PSM, both groups were balanced with 2,673 patients. The cancer group had a greater 28-day all-cause mortality rate as compared to the non-cancer group (35.84% vs. 18.97%, respectively), with a hazard ratio (HR) of 2.09 (95% confidence interval [CI]: 1.877–2.329, p < 0.001). Sensitivity analysis confirmed the persistent elevated risk (HR = 1.36, 95% CI: 1.191–1.550, p < 0.001). Furthermore, cancer was associated with significantly increased in-hospital and 90-day mortality rates among sepsis patients. Subgroup analyses revealed elevated mortality risk for sepsis patients with pre-existing cancer relative to non-cancer patients regardless of stratification by age, sex, Charlson Comorbidity Index score (CCI), Sequential Organ Failure Assessment score, lymphocyte count, mechanical ventilation requirement, vasoactive agent administration, or acute kidney injury within 48 h of ICU admission. Conclusion: Among critically ill patients with sepsis, underlying cancer is associated with a higher 28-day all-cause mortality, warranting further prospective studies to validate this finding. Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors sepsis cancer critical ill mortality propensity score matching Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction As established in the clinical literature, cancer patients exhibit a significantly elevated risk for sepsis attributable to an immunocompromised state and therapeutic interventions with chemotherapy, radiotherapy, surgical procedures, hematopoietic stem cell transplantation, and blood product administration [ 1 – 3 ] . Epidemiological data reveal both a higher sepsis incidence versus the general population and disease-specific variation across cancer subtypes [ 4 ] . Most critically ill cancer patients with sepsis demonstrate markedly increased in-hospital mortality as compared to patients with non-oncological sepsis [ 5 – 7 ] , thereby establishing sepsis as a secondary determinant of mortality in this vulnerable population, second only to primary malignancy progression [ 8 ] . The pathophysiological interplay involves multifactorial mechanisms, whereby malignancy potentiates sepsis severity via (1) tumor-induced immunosuppression [ 9 ] , (2) dysregulation of cytokine cascades [ 10 ] , (3) cancer-associated coagulopathy [ 11 ] , and (4) tumor microenvironment-mediated systemic effects. This synergistic pathophysiology exacerbates both disease complexity and fatal outcomes. Notably, prognostic disparities emerge from the molecular heterogeneity of malignancies, manifesting as divergent clinical outcomes in overall survival, therapeutic responsiveness, and disease recurrence patterns. Comparative analyses indicate greater sepsis-related mortality for hematological malignancies relative to solid tumors [ 7 ] , while treatment-induced immunosuppression from cytotoxic therapy or radiation further compounds the mortality risk. The escalating global cancer burden has precipitated a parallel increase in sepsis incidence, creating a significant comorbidity challenge [ 12 ] . Epidemiological data from healthcare systems in the United States demonstrate that malignancies contribute to an annual increase in hospitalizations for sepsis by > 20%, accounting for more than 1 million cases. Concurrently, the oncology field has undergone revolutionary therapeutic advancements during this period, as molecular diagnostics, incorporating histogenesis characterization and next-generation genomic profiling, have facilitated truly personalized treatment algorithms [ 13 ] . Modern hematopoietic stem cell transplantation protocols now demonstrate enhanced safety profiles and superior clinical outcomes. The advent of molecularly targeted agents has redefined therapeutic standards across cancer subtypes. Emerging immunotherapeutic strategies, particularly chimeric antigen receptor T-cell therapies and genetically engineered oncolytic viruses, represent paradigm-shifting interventions [ 14 ] . This rapidly evolving therapeutic landscape necessitates a critical reappraisal of the prognostic influence of cancer on sepsis outcomes, an endeavor essential to optimize multidisciplinary care protocols and improve long-term survival of such high-risk patients. Although previous studies have preliminarily suggested an association between cancer status and mortality risk of sepsis patients, the clinical translatability of existing evidence remains uncertain due to limitations, including inadequate sample sizes, insufficient adjustment for baseline confounders, and the evolving landscape of cancer therapeutics. To address these knowledge gaps, the aim of this study was to investigate the epidemiology and outcomes of cancer-associated sepsis in the contemporary era of oncological care. We hypothesize that the proportion of sepsis hospitalizations associated with cancer is higher than previously estimated, given improved cancer survivorship, and sepsis prognosis differs significantly between cancer-associated and non-cancer-associated hospitalizations. Leveraging large-scale electronic health records, we conducted a propensity score-matched analysis to establish balanced cohorts of sepsis patients with and without malignancy, while meticulously adjusting for critical confounders, including baseline disease severity, comorbidities, and demographic factors. This investigation systematically determined the independent mortality risk attributable to cancer status in ICU-managed sepsis patients, characterized outcome heterogeneity across different subgroups via age-stratified and immunocompetence-based analyses, and verified the robustness of the results by extensive sensitivity testing of model specifications. These analyses yielded high-grade evidence of sepsis mortality patterns among critically ill oncological patients, directly informing tailored ICU management approaches for this high-risk population. While extant studies have identified preliminary correlations between malignancy status and sepsis-related mortality, the clinical applicability of these findings remains indeterminate due to three fundamental limitations: (1) statistically underpowered cohorts, (2) incomplete adjustment for critical baseline confounders, and (3) the rapid evolution of oncological therapies that has fundamentally altered patient outcomes. Patients and methods Data source This study utilized data from the Medical Information Mart for Intensive Care IV version 3.0 (MIMIC-IV v3.0), a large critical care database from the United States. The MIMIC-IV v3.0 database contains comprehensive clinical information encompassing approximately 546,028 hospital admissions and 94,458 ICU stays at Beth Israel Deaconess Medical Center (Boston, MA, USA), between 2008 and 2022. Data extraction was performed using Structured Query Language, with script codes obtained from the official GitHub repository ( https://github.com/MIT-LCP/mimic-iv ). The MIMIC-IV v3.0 database has received Institutional Review Board approval from both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Patients The study population comprised critically ill adult patients with sepsis who were admitted to the ICU for the first time. Key exclusion criteria included: (1) age < 18 years, (2) ICU length of stay < 24 h, (3) missing lymphocyte count data, and (4) non-first-time ICU admissions. All enrolled sepsis patients were diagnosed in accordance with the updated Third International Consensus Definitions for Sepsis ("Sepsis-3") [ 15 ] . As defined by the Sepsis-3 criteria, sepsis is a life-threatening clinical syndrome characterized by organ dysfunction resulting from a dysregulated host response to infection. The diagnostic framework incorporates three essential components: (1) clinical or microbiological evidence of infection, (2) manifestation of a dysregulated host response, and (3) presence of organ dysfunction, which collectively distinguish sepsis from uncomplicated infections. Specifically, the diagnosis requires confirmed or suspected infection accompanied by an increase in the Sequential Organ Failure Assessment (SOFA) score of ≥ 2 points. Exposure and outcome The exposure variable was defined as the presence of an underlying malignancy during ICU hospitalization. Cancer status was determined based on either: (1) documented oncological history in the patient's medical records or (2) active cancer diagnosis identified during the index hospitalization. The primary outcome was 28-day all-cause mortality. Secondary outcomes included ICU mortality, in-hospital mortality, 90-day mortality, ICU length of stay (LOS), and total hospital LOS. Baseline data We systematically collected baseline patient characteristics including age, sex, and ethnicity. Clinical data during ICU admission for sepsis encompassed: (1) presence of acute kidney injury (AKI), (2) administration of vasoactive agents, (3) requirement for invasive mechanical ventilation, and (4) utilization of continuous renal replacement therapy (CRRT). Comorbidity profiles were ascertained using International Classification of Diseases codes, incorporating both cancer-related conditions and other systemic comorbidities, including cerebrovascular disease, hypertension, coronary artery disease, congestive heart failure, myocardial infarction, peripheral vascular disease, chronic pulmonary disease, diabetes mellitus, renal disease, hepatic disease, rheumatic disease, and human immunodeficiency virus/acquired immunodeficiency syndrome. For adult hospitalizations, the weighted Charlson Comorbidity Index (CCI) was employed to quantify the overall comorbidity burden, with modification to exclude points attributed to cancer or metastatic disease [ 16 ] . This adjustment was implemented since malignancy status served as the primary exposure variable, ensuring the CCI score specifically reflected the burden of non-oncological comorbidity for both study groups. We extracted comprehensive baseline data recorded at sepsis onset, including (1) disease severity scores (LODS, acute physiology score III [APSIII], SOFA, and Overall Anxiety Severity and Impairment Scale), (2) vital signs: heart rate, mean arterial pressure, respiratory rate, and temperature, and (3) laboratory parameters encompassing complete blood count (white blood cell count, neutrophil count, lymphocyte count, platelet count, hemoglobin), acid-base status (base excess), metabolic panel (glucose, potassium, sodium, calcium, and chloride levels), renal function tests (creatinine, blood urea nitrogen), hepatic function markers (alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, indirect bilirubin), coagulation profile (prothrombin time, activated partial thromboplastin time), and serum lactate levels. Statistical analysis The samples size for this retrospective analysis was determined based on the available data in the database, thus no formal sample size calculation was performed. The study cohort was divided into two groups based on the presence or absence of cancer: the sepsis with cancer group (cancer group) and the sepsis without cancer group (noncancer group). The proportion of missing data for each variable is detailed in Supplementary Fig. 1. To ensure data completeness and analytical reliability, multiple imputation was applied to handle missing values for each variable. Normality of continuous variables was assessed using the Shapiro–Wilk test. Based on the results, continuous variables are presented as the mean ± standard deviation (if normally distributed) or median (interquartile range, IQR) (if non-normally distributed) and compared using the Student’s t -test or Mann–Whitney U test, as appropriate. Categorical variables were analyzed using the chi-square test or Fisher’s exact test, depending on expected frequencies. For the primary outcome (28-day all-cause mortality), a Cox proportional hazards model was constructed to estimate the hazard ratio (HR) with the 95% confidence interval (CI). Additionally, Kaplan–Meier survival curves were generated to assess the cumulative incidence of 28-day mortality, with between-group differences evaluated using the log-rank test. For binary secondary outcomes, logistic regression was used to compute the odds ratio (OR) with the corresponding 95% CI. A two-sided probability ( p ) value < 0.05 was considered statistically significant. All analyses were performed using R software (version 4.2.3; R Foundation for Statistical Computing, Vienna, Austria). This analytical approach ensures robust statistical comparisons while accounting for potential confounders and missing data, providing reliable estimates of the association between cancer status and sepsis outcomes. Propensity score matching (PSM) In the matched cohort, preliminary analyses were conducted to assess the association between cancer status and outcomes, including the primary outcome (28-day all-cause mortality) and secondary endpoints. Variables for the PSM model were selected based on published consensus guidelines [55] and clinical relevance, incorporating baseline characteristics with significant between-group differences and high prognostic value (i.e., age, sex, congestive heart failure, cerebrovascular disease, SOFA score, and LODS score). Also, a 1:1 nearest-neighbor matching algorithm was applied with a caliper width of 0.05. Post-matching balance was evaluated using standardized mean differences (SMDs), where SMD < 0.10 indicated adequate covariate balance. For the matched dataset, univariate analyses were first performed, and significant variables ( p < 0.01) were included in subsequent multivariable models to adjust for residual confounding. The final adjusted covariates included in the multivariable analyses were age, invasive mechanical ventilation status, maximum respiratory rate, maximum heart rate, minimum mean arterial pressure, maximum prothrombin time, maximum anion gap, maximum blood urea nitrogen, minimum lymphocyte count, and minimum chloride/potassium levels (Supplementary Tables 1 and 2) . These analyses aimed to isolate the independent effect of cancer on sepsis outcomes and enhance clinical interpretability. Subgroup analyses Prespecified subgroup analyses of the matched cohort were stratified by age (< 65 vs. ≥65 years), sex, CCI score (< 5 vs. ≥5), SOFA score (< 6 vs. ≥6), lymphocyte count (< 1.0×10⁹/L vs. ≥1.0×10⁹/L), use of invasive mechanical ventilation, vasoactive drug administration, and AKI within 48 h of ICU admission. Interaction tests were employed to assess heterogeneity across subgroups, with significance set at p < 0.05 for interaction terms. Results The initial cohort comprised 94,458 patients. After applying the inclusion and exclusion criteria, the final study population included 18,731 patients, of whom 2,674 (14.28%) were sepsis patients with underlying malignancies admitted to the ICU. Following PSM, the matched cohort consisted of 5,346 patients (2,673 per group). The complete screening and enrollment process is detailed in the study flowchart ( Fig. 1 ). Comparative mortality analysis between sepsis with cancer and other clinical subgroups Our comprehensive analysis of all eligible sepsis cases (including patients with missing lymphocyte data) revealed significant outcome disparities among the four clinical subgroups. Most notably, cancer-associated sepsis patients demonstrated substantially worse survival outcomes at all evaluated timepoints as compared to other groups, while non-cancer sepsis patients maintained the most favorable prognosis. The cancer-sepsis cohort exhibited particularly striking mortality patterns, with an in-hospital mortality rate of 15.54% that escalated by three-fold as compared to non-cancer sepsis patients by 90 days, the most pronounced mortality differential observed across all comparative groups. Although the one-year survival rates remained significantly compromised in cancer-sepsis patients, longitudinal analysis revealed a progressive narrowing of this survival disadvantage relative to ICU-treated cancer patients without concurrent sepsis ( Table 1 ) . Table 1 Comparative Mortality Analysis Between Sepsis Patients with Cancer and Other Clinical Subgroups ICU mortality Cancer-sepsis (n = 3777) Noncancer-sepsis (n = 23796) Noncancer-infection (n = 3155) ICU-cancer (n = 14334) 15.54% 10.88% 10.46% 9.83% in-hospital mortality 25.39% 14.86% 17.46% 16.70% 28-day mortality 32.54% 17.23% 26.15% 23.63% 90-day mortality 45.09% 22.89% 41.20% 36.73% 1-year mortality 59.15% 29.88% 58.86% 52.62% Assessment of covariate balance before and after PSM PSM demonstrated significant baseline disparities between cohorts prior to adjustment. As detailed in Supplementary Table 1, cancer-associated sepsis patients were younger (median age 63 [IQR, 55–71] vs. 68 [58–77] years, p < 0.001) with lower comorbidity burden (median CCI 4 [3–6] vs. 6 [4–8]) and less severe illness acuity (median SOFA 5 [3–7] vs. 7 [4–9]; APS III 52 [40–68] vs. 64 [48–82]). These patients also required fewer critical interventions (vasopressor use: 38.2% vs. 51.7%; mechanical ventilation: 28.9% vs. 42.6%; CRRT: 7.8% vs. 13.9%; all, p < 0.01). Following 1:1 matching (n = 2,673), optimal balance was achieved for all included covariates (absolute SMD < 0.1). Visual inspection of the propensity score distributions confirmed excellent overlap between the matched groups ( Fig. 2 ). Impact of underlying cancer on sepsis outcomes Primary outcome analysis Analysis of the matched cohorts revealed that patients with cancer-associated sepsis demonstrated significantly higher 28-day all-cause mortality as compared to their non-cancer counterparts (35.8% vs. 19.0%, p < 0.001). Kaplan–Meier survival curves ( Fig. 3 ) visually confirmed this mortality disparity, with early separation of survival trajectories. Cox regression revealed a pronounced association between cancer status and mortality (HR = 2.09, 95% CI: 1.878–2.33; p < 0.001), which remained statistically significant after multivariable adjustment for disease severity and interventions (adjusted HR = 1.36, 95% CI: 1.192–1.551; p < 0.001). Consistent findings were observed in the pre-matched cohort, where both univariate and multivariate analyses similarly identified cancer as an independent predictor of 28-day mortality ( p < 0.001). Subgroup analysis Subgroup analyses of the matched cohort revealed consistent associations between cancer status and elevated 28-day all-cause mortality across diverse patient strata. Notably, younger patients (< 65 years) and those with lower comorbidity burden (CCI < 5) demonstrated particularly pronounced mortality risks, with HRs of 2.74 (95% CI: 2.21–3.39) and 3.59 (95% CI: 2.89–4.46), respectively ( p < 0.001). Significant associations persisted even among patients with normal lymphocyte counts (HR = 2.15, 95% CI: 1.81–2.56, p < 0.001). Importantly, the disadvantage of cancer-related mortality remained evident in clinically stable subgroups without invasive mechanical ventilation (HR = 2.67, 95% CI: 2.25–3.18) or AKI (HR = 2.73, 95% CI: 2.15–3.49) ( Fig. 4 ) . Secondary outcomes analysis The cancer cohort exhibited higher ICU mortality (17.4% vs. 11.1%, unadjusted OR = 1.66, 95% CI 1.441–1.972; p < 0.001), though this association was attenuated after multivariable adjustment (adjusted OR = 1.06, 95% CI: 0.863–1.299; p = 0.590). Similarly, in-hospital mortality was markedly elevated in cancer patients (28.06% vs. 14.37%), with an unadjusted OR of 2.33 (95% CI: 2.028–2.668; p < 0.001), and adjusted analyses (OR = 1.47, 95% CI: 1.233–1.755; p < 0.001) confirming this association. The 90-day mortality disparity was most pronounced, with cancer patients facing nearly double the risk (49.27% vs. 24.39%), consistent across univariate (OR = 2.36, 95% CI: 2.149–2.592; p < 0.001) and multivariate models (OR = 1.65, 95% CI: 1.467–1.848; p < 0.001). Complete data are presented in Table 2 . Table 2 Association Between Underlying Malignancy and Clinical Outcomes in the Propensity Score-Matched Sepsis Cohort Outcome Cancer-sepsis (n = 2673) Noncancer-sepsis(n = 2673) Univariate Analysis Multivariable Analysis* HR/OR(95% CI) p value HR/OR(95% CI) p value Primary Outcome 28-day all-cause mortality, n (%) † 958(35.84) 507(18.97) 2.09(1.877 ~ 2.329) < 0.001 1.36(1.191 ~ 1.550) < 0.001 Secondary Outcomes ICU all-cause mortality, n (%) # 465(17.40) 297(11.11) 1.66(1.441 ~ 1.972) < 0.001 1.06(0.863 ~ 1.299) 0.590 In-hospital all-cause mortality, n (%) # 750(28.06) 384(14.37) 2.33(2.028 ~ 2.668) < 0.001 1.47(1.233 ~ 1.755) < 0.001 90-day all-cause mortality, n (%) † 1317(49.27) 652(24.39) 2.36(2.149 ~ 2.592) < 0.001 1.65(1.467 ~ 1.848) < 0.001 *Analyses were adjusted for the following covariates: Age, invasive mechanical ventilation status, RR max , HR max , MAP min , minimum lymphocyte count, PT max , AG max , BUN max , minimum chloride, and minimum potassium levels. † Hazard ratios (HRs) with 95% confidence intervals (CIs) were derived from Cox proportional hazards models. # logistic regression models were used to compute odds ratios (ORs) with corresponding 95% CIs. Discussion This in-depth analysis of heterogeneity within the sepsis population employed innovative PSM to construct balanced cohorts (cancer vs. non-cancer groups) for precise evaluation of comorbidity-specific prognostic interactions. The findings demonstrate that cancer comorbidity elevates 28-day all-cause mortality to 35.84% in sepsis patients (HR = 2.09, 95% CI: 1.877–2.329), with this mortality-doubling effect remaining statistically significant across both pre-matched and matched cohorts. Multidimensional subgroup analyses stratified by sex, age, ICU organ support status, CCI burden, SOFA score-defined disease severity, and immune status consistently validated the robustness of this cancer-associated prognostic disadvantage. This independent association persisted after comprehensive adjustment for clinical confounders (adjusted HR = 1.36, 95% CI: 1.191–1.550), underscoring biological significance. This work represents the first quantification of the independent prognostic value of cancer for sepsis populations, providing critical evidence to optimize prognostic prediction algorithms and stratify management strategies for clinical decision-support systems. This study demonstrated significantly lower overall mortality rates as compared to previous investigations, including the work by Rosolem et al [ 17 ] . A large-scale U.S. cross-sectional study of over one million sepsis patients [ 6 ] corroborated this finding. By extending the observation period from 2008 to 2021 and encompassing a broader sepsis population, our research confirmed that cancer-associated sepsis patients still exhibited markedly higher in-hospital mortality than their non-cancer counterparts (25.39% vs. 14.85%, respectively). These collective findings reveal a critical clinical phenomenon: sepsis patients with underlying malignancies face substantially worse in-hospital outcomes as compared to non-cancer sepsis patients, underscoring that cancer is a complex and high-risk comorbidity that significantly worsens sepsis prognosis. Notably, historical data from the 1990s [ 6 ] reported a staggering 37.8% mortality rate among cancer patients with severe sepsis. However, our comparative analysis with contemporary population studies conducted in the United States [ 6 ] suggests an encouraging trend. The mortality gap between cancer and non-cancer sepsis patients may be narrowing by approximately 3%–4%, with current mortality differences being less pronounced than those documented in studies from the 1990s [ 18 ] . The present study revealed a notable decline in both absolute mortality rates and the magnitude of outcome disparities between cancer-associated and non-cancer sepsis patients. This temporal improvement likely reflects advances in both oncological and critical care management over the past two decades. First, the deepened understanding of sepsis pathophysiology has enabled more precise therapeutic strategies, leading to comprehensive improvements in critical care delivery. Earlier recognition of sepsis symptoms, particularly in high-risk oncology patients, and prompt initiation of treatment may have contributed to these outcomes [ 19 ] . Second, breakthroughs in cancer therapeutics and improved survival rates have enhanced both clinician engagement and treatment aggressiveness in managing sepsis within this vulnerable population [ 20 ] . Notably, the survival disadvantage among cancer patients demonstrated a time-dependent progression, with ICU mortality reaching 17.4%, escalating to 28.6% during hospitalization, and further increasing to 49.27% at 90-day follow-up, a mortality trajectory that aligns with the findings reported by Rosolem et al [ 17 ] . This temporal pattern highlights the critical need for differentiated and longitudinal management strategies specifically designed for cancer-associated sepsis patients, addressing not only acute critical illness but also long-term survivorship concerns. From a long-term perspective, cancer-associated sepsis patients continue to face substantial survival challenges. Historical data (2001–2012) indicate that ICU-admitted cancer patients had a one-year survival rate of only 53.3% (2,579/4,836) regardless of sepsis status, which plummeted to < 20% among those with septic shock or elevated lactate levels [ 21 ] . Courtright et al. [ 22 ] identified cancer as the strongest predictor of one-year mortality of sepsis survivors receiving home healthcare (OR = 3.66, 95% CI: 3.50–3.83), exceeding the risks associated with severe sepsis (OR = 1.30) or septic shock (OR = 1.14). Our findings corroborate this temporal divergence. One-year survival of cancer-associated sepsis patients declined to 40%, while non-cancer sepsis patients maintained significantly higher survival rates (70%), highlighting the persistent mortality gap that widens over time. This enduring disparity underscores the need for specialized long-term follow-up protocols addressing both oncological and critical care sequelae in this high-risk population. Stratified analyses revealed consistently elevated sepsis-related mortality among patients with underlying malignancies in all predefined subgroups. The heightened mortality risk in lower CCI subgroups suggests that the prognostic impact of cancer becomes more pronounced with fewer competing comorbidities. Interestingly, this study revealed that the magnitude of the difference in mortality between cancer-associated and non-cancer-associated sepsis varied significantly with age. Among patients aged < 65 years, those with cancer-associated sepsis exhibited a mortality rate as high as 32.2%, with a mortality risk ratio of 2.74, —a finding consistent with the observations by Hensley et al. [ 6 ] , who reported that this mortality gap progressively narrowed with advancing age and became negligible in patients aged ≥ 85 years. This phenomenon may be attributed to immunosenescence, wherein the natural aging process impairs immune function to a degree comparable to the immunosuppressive effects of cancer and related treatments [ 23 , 24 ] . The convergence of mortality rates in older adults likely reflects similarly severe immune dysfunction in both cancer-associated and non-cancer-associated sepsis at advanced ages. Additionally, in patients with lower CCI scores, pre-existing cancer still contributed to increased sepsis-related mortality, aligning with the established association between greater comorbidity burden and worse clinical outcomes. Similar patterns emerged across other clinically relevant strata (lower SOFA scores, non-mechanical ventilation, absence of AKI), indicating differential effects of malignancy depending on baseline physiological reserve. As anticipated, worsening organ dysfunction (higher SOFA scores) correlated with more dysregulated inflammatory/immune responses [ 25 – 27 ] and poorer outcomes [ 28 ] , while hypotension reflected tissue hypoperfusion and increased mortality [ 29 ] . Interestingly, norepinephrine (NE) administration, though indicative of shock, did not independently worsen cancer patient outcomes, which may be related to several factors: (1) Controversies in blood pressure targets, particularly for patients with chronic hypertension/diabetes/renal impairment, (2) the potential immunomodulatory effects of NE when used as a first-line vasopressor, (3) timing benefits: animal studies show immediate NE improves microcirculation better than delayed use after fluid resuscitation [ 29 ] , with clinical data linking early NE administration to lower mortality [ 30 ] , and (4) dose-dependent effects while high NE equivalents correlate with mortality [ 31 ] , combination strategies (e.g., with vasopressin) may mitigate organ injury through synergistic mechanisms]. Current evidence underscores the need to investigate cancer subtype-specific responses to vasoactive agents [ 32 ] , suggesting our neutral findings may reflect complex interactions between drug selection, dose-time optimization, and individualized treatment approaches. Peripheral lymphocyte subsets serve dual purposes as prognostic biomarkers for cancer and predictors of therapeutic response, constituting a major focus in contemporary cancer immunology research [ 33 ] . While cancer inherently induces immunosuppression, sepsis exacerbates lymphocyte apoptosis and functional exhaustion [ 34 ] , creating a compounded immunocompromised state. Our study systematically excluded cancer-sepsis patients with missing lymphocyte counts to enable precise evaluation of lymphocytic influences. Subgroup analyses revealed differential effects: the prognostic impact of cancer was less pronounced at lymphocyte counts < 1.0×10 9 /L as compared to ≥ 1.0×10 9 /L, suggesting that profound lymphopenia reflects end-stage immunosuppression (from sepsis-induced apoptosis or bone marrow suppression), where immune dysfunction becomes equally severe regardless of cancer status [ 35 ] . At this threshold, extreme lymphocyte depletion directly impairs infection control and promotes organ failure, emerging as the dominant mortality driver [ 36 ] , potentially overshadowing the baseline immunosuppressive effects of cancer through more severe sepsis-associated immunoparalysis [ 37 ] . These findings underscore the necessity for lymphocyte-guided risk stratification in clinical practice, as prognostic assessments should integrate both quantitative lymphocyte thresholds and cancer-specific immunological profiles. Several key limitations should be acknowledged in interpreting these findings: (1) The retrospective observational design, despite employing PSM and multivariable adjustment, remains susceptible to residual confounding and unmeasured variables, (2) the inherent nature of the study precludes causal inferences, (3) the lack of cancer staging and treatment details prevents disentanglement of the biological effects of malignancy from therapy-related impacts and (4) temporal advancements in sepsis management over the extended study period were not accounted for in the analysis. Conclusions Notwithstanding these constraints, this investigation robustly demonstrates that underlying malignancy significantly elevates 28-day all-cause mortality of critically ill sepsis patients. The particularly dismal long-term survival outcomes (evidenced by progressive mortality divergence at 90 days) mandate heightened clinical vigilance for this vulnerable population. These retrospective findings warrant validation through prospective, ideally multi-center studies incorporating detailed oncological characterization and standardized sepsis protocols to better elucidate the cancer-sepsis pathophysiological interplay. Abbreviations PSM Propensity Score Matching SOFA Sequential Organ Failure Assessment LODS Logistic Organ Dysfunction Score ICU Intensive Care Unit APACHE II Acute Physiology and Chronic Health Evaluation II MIMIC Medical Information Mart for Intensive Care CCI Charlson Comorbidity Index APSIII Acute Physiology and Chronic Health Evaluation III GCS Glasgow Coma Scale RR Respiratory Rate T Temperature SBP Systolic Blood Pressure DBP Diastolic Blood Pressure MBP Mean Arterial Pressure IQRs Interquartile Ranges WBC White Blood Cells PLT Platelet HGB Hemoglobin ANC Absolute Neutrophil Count ANC Lymphocyte Count BE Base Excess AG Anion Gap BUN Blood Urea Nitrogen Cr Creatinine PT Prothrombin Time PTT Partial Thromboplastin Time AKI Acute Kidney Injury CRRT C ontinuous Renal Replacement Therapy HR Hard Ratio OR Odds Ratio SMD Standardized Mean Differences CI confidence interval NE Norepinephrine Declarations Ethics approval and consent to participate The collection of patient information and creation of the research resource was reviewed by the Institutional Review Board at the Beth Israel Deaconess Medical Center, who granted a waiver of informed consent and approved the data sharing initiative. All procedures performed in studies involving human participants were performed in accordance with the ethical standards of the Ethics Committee of the People’s Hospital of Guangxi Zhuang Autonomous Region (KY-IIT-2024-135) and the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from the patient. Consent for publication Not Applicable. Competing Interests The authors declare that they have no conflict of interest. Funding This study received financial support from the Youth Science Foundation Project of Guangxi (No. 2023GXNSFBA 026096) and the National Natural Science Foundation of China (No.82072232), the Science and Technology Program of Guangzhou, China (No.202201020028), the Science and Technology Projects in Guangzhou (No.2025A03J4248), the Science and Technology Projects in Guangzhou (No.2025A03J3472), the Special Projects in Key Areas of General Colleges and Universities in Guangdong Province (No.2022ZDZX2003), the 2021 Annual Medical Teaching and Education Management Reform Research Project of Jinan University (No.2021YXJG029), the Fundamental Research Funds for The Central Universities (No. 21624318), the Medical Scientific Research Foundation of Guangdong Province, China(No. A2024458), the Science and Technology Projects in Guangzhou (No. 2025A04J3478), the Fundamental Research Funds for the Central Universities (No. 21623302 ), the Science and Technology Projects in Guangzhou (No. 2024A04J3706), Guangdong Basic and Applied Basic Research Foundation(No. 2024A1515220120). Authors' contributions All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Lingyu Jiang: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft. Xiangjie Duan: drafting, Software, Validation, Visualization, Formal analysis Jing Pang: Conceptualization, Methodology, Project administration, Supervision Zhong, Yong Long: Acquisition of data, Data curation, Resources, Haiyan Yin: Project administration, Methodology, Supervision, Validation, Writing – review & editing. Lin Han: Acquisition of data, Investigation, Writing – review & editing. Shulin Xiang: Conceptualization, Supervision,Validation, Writing – review & editing. Acknowledgements We thank International Science Editing ( http://www.internationalscienceediting.com ) for editing this manuscript. Clinical trial number This study was registered with the Chinese Clinical Trial Registry on April 22, 2025 (registration number: ChiCTR-PID-270259) Data Availability Statement The data supporting the findings of this study are available from two publicly accessible repositories. The MIMIC-IV database is available at: https://physionet.org/content/mimiciv/3.0/. The eICU Collaborative Research Database (eICU-CRD) access information can be found at: https://eicu-crd.mit.edu/gettingstarted/access/. Access to both databases requires completion of the CITI "Data or Specimens Only Research" course and formal request approval via PhysioNet, in accordance with their data use agreements. References DANAI P A, MOSS M, MANNINO D M, et al. The epidemiology of sepsis in patients with malignancy [J]. Chest, 2006, 129(6): 1432-40. BABU A, NOEL ALEXANDER F H, MUZUMDER S, et al. Sepsis surveillance in patients with head-and-neck cancer undergoing chemo-radiation [J]. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024, 32(11): 724. HEWAMANA S, SKANDARAJAH T, JAYASINGHE C, et al. Successful Management of Neutropenic Sepsis Is Key to Better Survival of Patients With Blood Cancer in Sri Lanka: Real-World Data From the Resource-Limited Setting [J]. JCO Global Oncology, 2024, (10). LIU M A, BAKOW B R, HSU T-C, et al. Temporal Trends in Sepsis Incidence and Mortality in Patients With Cancer in the US Population [J]. American Journal of Critical Care, 2021, 30(4): e71-e9. TORRES V B, AZEVEDO L C, SILVA U V, et al. Sepsis-Associated Outcomes in Critically Ill Patients with Malignancies [J]. Annals of the American Thoracic Society, 2015, 12(8): 1185-92. HENSLEY M K, DONNELLY J P, CARLTON E F, et al. Epidemiology and Outcomes of Cancer-Related Versus Non-Cancer-Related Sepsis Hospitalizations [J]. Crit Care Med, 2019, 47(10): 1310-6. LEMIALE V, PONS S, MIROUSE A, et al. Sepsis and Septic Shock in Patients With Malignancies: A Groupe de Recherche Respiratoire en Réanimation Onco-Hématologique Study [J]. Crit Care Med, 2020, 48(6): 822-9. PAVON A, BINQUET C, KARA F, et al. Profile of the risk of death after septic shock in the present era: an epidemiologic study [J]. Crit Care Med, 2013, 41(11): 2600-9. SLAVIN M A, WORTH L J, SEYMOUR J F, et al. Better Sepsis Management Rather Than Fluoroquinolone Prophylaxis for Patients With Cancer-Related Immunosuppression [J]. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2019, 37(13): 1139-40. SHAH D, SOPER B, SHOPLAND L. Cytokine release syndrome and cancer immunotherapies - historical challenges and promising futures [J]. Frontiers in immunology, 2023, 14: 1190379. KVOLIK S, JUKIC M, MATIJEVIC M, et al. An overview of coagulation disorders in cancer patients [J]. Surgical oncology, 2010, 19(1): e33-46. SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA: a cancer journal for clinicians, 2021, 71(3): 209-49. ROLDAN-VALADEZ E A-O, SALAZAR-RUIZ S Y, IBARRA-CONTRERAS R, et al. Current concepts on bibliometrics: a brief review about impact factor, Eigenfactor score, CiteScore, SCImago Journal Rank, Source-Normalised Impact per Paper, H-index, and alternative metrics [J]. Ir J Med Sci, 2019 Aug, 188(3): 939-51. MANJAPPACHAR N K, CUENCA J A, RAMíREZ C M, et al. Outcomes and Predictors of 28-Day Mortality in Patients With Hematologic Malignancies and Septic Shock Defined by Sepsis-3 Criteria [J]. Journal of the National Comprehensive Cancer Network, 2022, 20(1): 45-53. SINGER M, DEUTSCHMAN C S, SEYMOUR C W, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) [J]. Jama, 2016, 315(8). CHARLSON M E, POMPEI P, ALES K L, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation [J]. Journal of chronic diseases, 1987, 40(5): 373-83. ROSOLEM M M, RABELLO L S C F, LISBOA T, et al. Critically ill patients with cancer and sepsis: Clinical course and prognostic factors [J]. Journal of Critical Care, 2012, 27(3): 301-7. WILLIAMS M D, BRAUN L, COOPER L M, et al. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care [J]. Critical Care, 2004, 8(5). LARCHé J, AZOULAY E, FIEUX F, et al. Improved survival of critically ill cancer patients with septic shock [J]. Intensive Care Med, 2003, 29(10): 1688-95. WEIR H K, THOMPSON T D, STEWART S L, et al. Cancer Incidence Projections in the United States Between 2015 and 2050 [J]. Preventing Chronic Disease, 2021, 18. CUENCA J A, NATES J L, LASERNA A, et al. Long-Term Survival of Patients With Cancer, Sepsis, and Vasopressor Requirements Based on Lactate Levels [J]. Critical Care Explorations, 2024, 6(4). COURTRIGHT K R, JORDAN L, MURTAUGH C M, et al. Risk Factors for Long-term Mortality and Patterns of End-of-Life Care Among Medicare Sepsis Survivors Discharged to Home Health Care [J]. JAMA Network Open, 2020, 3(2). FRASCA D, BLOMBERG B B. Inflammaging decreases adaptive and innate immune responses in mice and humans [J]. Biogerontology, 2016, 17(1): 7-19. FUENTES E, FUENTES M, ALARCóN M, et al. Immune System Dysfunction in the Elderly [J]. Anais da Academia Brasileira de Ciencias, 2017, 89(1): 285-99. JEKARL D W, KIM J Y, HA J H, et al. Diagnosis and Prognosis of Sepsis Based on Use of Cytokines, Chemokines, and Growth Factors [J]. Disease markers, 2019, 2019: 1089107. LI N, REN P, WANG J, et al. Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis [J]. Int J Mol Sci, 2024, 25(2). LIU Y, WANG X, YU L. Th17, rather than Th1 cell proportion, is closely correlated with elevated disease severity, higher inflammation level, and worse prognosis in sepsis patients [J]. Journal of clinical laboratory analysis, 2021, 35(5): e23753. KARAKIKE E, KYRIAZOPOULOU E, TSANGARIS I, et al. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort [J]. Critical care (London, England), 2019, 23(1): 387. KHANNA A K, KINOSHITA T, NATARAJAN A, et al. Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study [J]. Annals of intensive care, 2023, 13(1): 9. AHN C, YU G, SHIN T G, et al. Comparison of Early and Late Norepinephrine Administration in Patients With Septic Shock: A Systematic Review and Meta-Analysis [J]. Chest, 2024, 166(6): 1417-30. SACHA G L, LAM S W, WANG L, et al. Association of Catecholamine Dose, Lactate, and Shock Duration at Vasopressin Initiation With Mortality in Patients With Septic Shock [J]. Crit Care Med, 2022, 50(4): 614-23. COOPER A J, KELLER S P, CHAN C, et al. Improvements in Sepsis-associated Mortality in Hospitalized Patients with Cancer versus Those without Cancer. A 12-Year Analysis Using Clinical Data [J]. Annals of the American Thoracic Society, 2020, 17(4): 466-73. WANG H, HUANG H, LIU T, et al. Peripheral blood lymphocyte subsets predict the efficacy of TACE with or without PD-1 inhibitors in patients with hepatocellular carcinoma: a prospective clinical study [J]. Frontiers in immunology, 2024, 15: 1325330. SANTACROCE E, D'ANGERIO M, CIOBANU A L, et al. Advances and Challenges in Sepsis Management: Modern Tools and Future Directions [J]. Cells, 2024, 13(5). LI D, ZHANG J, CHENG W, et al. Dynamic changes in peripheral blood lymphocyte trajectory predict the clinical outcomes of sepsis [J]. Frontiers in immunology, 2025, 16: 1431066. DE ROP L, BOS D A, STEGEMAN I, et al. Accuracy of routine laboratory tests to predict mortality and deterioration to severe or critical COVID-19 in people with SARS-CoV-2 [J]. The Cochrane database of systematic reviews, 2024, 8(8): Cd015050. MIROUSE A, VIGNERON C, LLITJOS J-F, et al. Sepsis and Cancer: An Interplay of Friends and Foes [J]. American Journal of Respiratory and Critical Care Medicine, 2020, 202(12): 1625-35. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 26 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9200112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":629313282,"identity":"fc699566-6a1f-49b7-b7cb-4942594d921d","order_by":0,"name":"Lingyu Jiang","email":"","orcid":"","institution":"The People's Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Lingyu","middleName":"","lastName":"Jiang","suffix":""},{"id":629313284,"identity":"a9b446e5-62de-4b62-b503-d1fbaf42ae9c","order_by":1,"name":"Xiangjie Duan","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan 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11:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9200112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9200112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107916793,"identity":"50cbef5c-bd7a-40fc-bcbd-b954e7739f67","added_by":"auto","created_at":"2026-04-27 14:19:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":998750,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of sepsis cohort selection for the study.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/bc95498ab17352bf7b8b8c1c.png"},{"id":107916794,"identity":"04be197a-e569-4c66-b1b4-21aa9c04da2a","added_by":"auto","created_at":"2026-04-27 14:19:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1008230,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of covariate balance before and after propensity score matching.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/9d58903e3760aa54116a456b.png"},{"id":108006502,"identity":"63993ea0-a4f8-437b-9eee-712d6537b141","added_by":"auto","created_at":"2026-04-28 12:55:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1696782,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for 28-day all-cause mortality in both matched and pre-matched cohorts. \u003cstrong\u003eA-1:\u003c/strong\u003e\u0026nbsp;Unadjusted analysis (matched cohort). \u003cstrong\u003eA-2:\u003c/strong\u003e\u0026nbsp;Adjusted analysis (matched cohort). \u003cstrong\u003eB-1:\u003c/strong\u003e\u0026nbsp;Unadjusted analysis (pre-matched cohort). \u003cstrong\u003eB-2:\u003c/strong\u003e\u0026nbsp;Adjusted analysis (pre-matched cohort). Multivariable adjustment covariates\u003cstrong\u003e:\u003c/strong\u003e\u0026nbsp;Age, invasive mechanical ventilation status, RR\u003csub\u003e max\u003c/sub\u003e, HR\u003csub\u003e max\u003c/sub\u003e, MAP\u003csub\u003emin\u003c/sub\u003e, minimum lymphocyte count, PT\u003csub\u003emax\u003c/sub\u003e, AG\u003csub\u003e max\u003c/sub\u003e, BUN\u003csub\u003e max\u003c/sub\u003e, minimum chloride, and minimum potassium levels.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/7801f5cc4d12636acfe384bd.png"},{"id":108007403,"identity":"64d73541-9315-4bab-9ff4-927c8e21af80","added_by":"auto","created_at":"2026-04-28 12:59:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2581579,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of 28-Day All-Cause Mortality in the Matched Cohort.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/3a8ed40fcec8616c8026be23.png"},{"id":107916797,"identity":"c592daa6-e3cf-4134-878d-b9d2e91ff188","added_by":"auto","created_at":"2026-04-27 14:19:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1567012,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/cd1dad61da5746bea5b1c4d6.png"},{"id":108181395,"identity":"459a8aa9-8a3e-47f6-b142-fd6e555536d9","added_by":"auto","created_at":"2026-04-30 08:58:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7996013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/5e322c05-d9cd-4954-ace1-b1e6493dad29.pdf"},{"id":108006208,"identity":"0a2921b7-8450-420e-aae6-7d33076056b8","added_by":"auto","created_at":"2026-04-28 12:54:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26335,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9200112/v1/7324399096d51e1ab203dbe6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eImpact of cancer on mortality in critically ill patients with sepsis: A propensity score-matched analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs established in the clinical literature, cancer patients exhibit a significantly elevated risk for sepsis attributable to an immunocompromised state and therapeutic interventions with chemotherapy, radiotherapy, surgical procedures, hematopoietic stem cell transplantation, and blood product administration\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Epidemiological data reveal both a higher sepsis incidence versus the general population and disease-specific variation across cancer subtypes\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Most critically ill cancer patients with sepsis demonstrate markedly increased in-hospital mortality as compared to patients with non-oncological sepsis\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, thereby establishing sepsis as a secondary determinant of mortality in this vulnerable population, second only to primary malignancy progression\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The pathophysiological interplay involves multifactorial mechanisms, whereby malignancy potentiates sepsis severity via (1) tumor-induced immunosuppression\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, (2) dysregulation of cytokine cascades\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, (3) cancer-associated coagulopathy\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and (4) tumor microenvironment-mediated systemic effects. This synergistic pathophysiology exacerbates both disease complexity and fatal outcomes. Notably, prognostic disparities emerge from the molecular heterogeneity of malignancies, manifesting as divergent clinical outcomes in overall survival, therapeutic responsiveness, and disease recurrence patterns. Comparative analyses indicate greater sepsis-related mortality for hematological malignancies relative to solid tumors\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, while treatment-induced immunosuppression from cytotoxic therapy or radiation further compounds the mortality risk.\u003c/p\u003e \u003cp\u003eThe escalating global cancer burden has precipitated a parallel increase in sepsis incidence, creating a significant comorbidity challenge\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Epidemiological data from healthcare systems in the United States demonstrate that malignancies contribute to an annual increase in hospitalizations for sepsis by \u0026gt;\u0026thinsp;20%, accounting for more than 1\u0026nbsp;million cases. Concurrently, the oncology field has undergone revolutionary therapeutic advancements during this period, as molecular diagnostics, incorporating histogenesis characterization and next-generation genomic profiling, have facilitated truly personalized treatment algorithms\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Modern hematopoietic stem cell transplantation protocols now demonstrate enhanced safety profiles and superior clinical outcomes. The advent of molecularly targeted agents has redefined therapeutic standards across cancer subtypes. Emerging immunotherapeutic strategies, particularly chimeric antigen receptor T-cell therapies and genetically engineered oncolytic viruses, represent paradigm-shifting interventions\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This rapidly evolving therapeutic landscape necessitates a critical reappraisal of the prognostic influence of cancer on sepsis outcomes, an endeavor essential to optimize multidisciplinary care protocols and improve long-term survival of such high-risk patients.\u003c/p\u003e \u003cp\u003eAlthough previous studies have preliminarily suggested an association between cancer status and mortality risk of sepsis patients, the clinical translatability of existing evidence remains uncertain due to limitations, including inadequate sample sizes, insufficient adjustment for baseline confounders, and the evolving landscape of cancer therapeutics. To address these knowledge gaps, the aim of this study was to investigate the epidemiology and outcomes of cancer-associated sepsis in the contemporary era of oncological care. We hypothesize that the proportion of sepsis hospitalizations associated with cancer is higher than previously estimated, given improved cancer survivorship, and sepsis prognosis differs significantly between cancer-associated and non-cancer-associated hospitalizations. Leveraging large-scale electronic health records, we conducted a propensity score-matched analysis to establish balanced cohorts of sepsis patients with and without malignancy, while meticulously adjusting for critical confounders, including baseline disease severity, comorbidities, and demographic factors. This investigation systematically determined the independent mortality risk attributable to cancer status in ICU-managed sepsis patients, characterized outcome heterogeneity across different subgroups via age-stratified and immunocompetence-based analyses, and verified the robustness of the results by extensive sensitivity testing of model specifications. These analyses yielded high-grade evidence of sepsis mortality patterns among critically ill oncological patients, directly informing tailored ICU management approaches for this high-risk population.\u003c/p\u003e \u003cp\u003eWhile extant studies have identified preliminary correlations between malignancy status and sepsis-related mortality, the clinical applicability of these findings remains indeterminate due to three fundamental limitations: (1) statistically underpowered cohorts, (2) incomplete adjustment for critical baseline confounders, and (3) the rapid evolution of oncological therapies that has fundamentally altered patient outcomes.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis study utilized data from the Medical Information Mart for Intensive Care IV version 3.0 (MIMIC-IV v3.0), a large critical care database from the United States. The MIMIC-IV v3.0 database contains comprehensive clinical information encompassing approximately 546,028 hospital admissions and 94,458 ICU stays at Beth Israel Deaconess Medical Center (Boston, MA, USA), between 2008 and 2022. Data extraction was performed using Structured Query Language, with script codes obtained from the official GitHub repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MIT-LCP/mimic-iv\u003c/span\u003e\u003cspan address=\"https://github.com/MIT-LCP/mimic-iv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The MIMIC-IV v3.0 database has received Institutional Review Board approval from both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatients\u003c/h3\u003e\n\u003cp\u003eThe study population comprised critically ill adult patients with sepsis who were admitted to the ICU for the first time. Key exclusion criteria included: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years, (2) ICU length of stay\u0026thinsp;\u0026lt;\u0026thinsp;24 h, (3) missing lymphocyte count data, and (4) non-first-time ICU admissions.\u003c/p\u003e \u003cp\u003eAll enrolled sepsis patients were diagnosed in accordance with the updated Third International Consensus Definitions for Sepsis (\"Sepsis-3\")\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. As defined by the Sepsis-3 criteria, sepsis is a life-threatening clinical syndrome characterized by organ dysfunction resulting from a dysregulated host response to infection. The diagnostic framework incorporates three essential components: (1) clinical or microbiological evidence of infection, (2) manifestation of a dysregulated host response, and (3) presence of organ dysfunction, which collectively distinguish sepsis from uncomplicated infections. Specifically, the diagnosis requires confirmed or suspected infection accompanied by an increase in the Sequential Organ Failure Assessment (SOFA) score of \u0026ge;\u0026thinsp;2 points.\u003c/p\u003e\n\u003ch3\u003eExposure and outcome\u003c/h3\u003e\n\u003cp\u003eThe exposure variable was defined as the presence of an underlying malignancy during ICU hospitalization. Cancer status was determined based on either: (1) documented oncological history in the patient's medical records or (2) active cancer diagnosis identified during the index hospitalization. The primary outcome was 28-day all-cause mortality. Secondary outcomes included ICU mortality, in-hospital mortality, 90-day mortality, ICU length of stay (LOS), and total hospital LOS.\u003c/p\u003e\n\u003ch3\u003eBaseline data\u003c/h3\u003e\n\u003cp\u003eWe systematically collected baseline patient characteristics including age, sex, and ethnicity. Clinical data during ICU admission for sepsis encompassed: (1) presence of acute kidney injury (AKI), (2) administration of vasoactive agents, (3) requirement for invasive mechanical ventilation, and (4) utilization of continuous renal replacement therapy (CRRT). Comorbidity profiles were ascertained using International Classification of Diseases codes, incorporating both cancer-related conditions and other systemic comorbidities, including cerebrovascular disease, hypertension, coronary artery disease, congestive heart failure, myocardial infarction, peripheral vascular disease, chronic pulmonary disease, diabetes mellitus, renal disease, hepatic disease, rheumatic disease, and human immunodeficiency virus/acquired immunodeficiency syndrome.\u003c/p\u003e \u003cp\u003eFor adult hospitalizations, the weighted Charlson Comorbidity Index (CCI) was employed to quantify the overall comorbidity burden, with modification to exclude points attributed to cancer or metastatic disease\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This adjustment was implemented since malignancy status served as the primary exposure variable, ensuring the CCI score specifically reflected the burden of non-oncological comorbidity for both study groups.\u003c/p\u003e \u003cp\u003eWe extracted comprehensive baseline data recorded at sepsis onset, including (1) disease severity scores (LODS, acute physiology score III [APSIII], SOFA, and Overall Anxiety Severity and Impairment Scale), (2) vital signs: heart rate, mean arterial pressure, respiratory rate, and temperature, and (3) laboratory parameters encompassing complete blood count (white blood cell count, neutrophil count, lymphocyte count, platelet count, hemoglobin), acid-base status (base excess), metabolic panel (glucose, potassium, sodium, calcium, and chloride levels), renal function tests (creatinine, blood urea nitrogen), hepatic function markers (alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, indirect bilirubin), coagulation profile (prothrombin time, activated partial thromboplastin time), and serum lactate levels.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe samples size for this retrospective analysis was determined based on the available data in the database, thus no formal sample size calculation was performed. The study cohort was divided into two groups based on the presence or absence of cancer: the sepsis with cancer group (cancer group) and the sepsis without cancer group (noncancer group). The proportion of missing data for each variable is detailed in \u003cb\u003eSupplementary Fig.\u0026nbsp;1.\u003c/b\u003e To ensure data completeness and analytical reliability, multiple imputation was applied to handle missing values for each variable.\u003c/p\u003e \u003cp\u003eNormality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. Based on the results, continuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (if normally distributed) or median (interquartile range, IQR) (if non-normally distributed) and compared using the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney U test, as appropriate. Categorical variables were analyzed using the chi-square test or Fisher\u0026rsquo;s exact test, depending on expected frequencies. For the primary outcome (28-day all-cause mortality), a Cox proportional hazards model was constructed to estimate the hazard ratio (HR) with the 95% confidence interval (CI). Additionally, Kaplan\u0026ndash;Meier survival curves were generated to assess the cumulative incidence of 28-day mortality, with between-group differences evaluated using the log-rank test. For binary secondary outcomes, logistic regression was used to compute the odds ratio (OR) with the corresponding 95% CI. A two-sided probability (\u003cem\u003ep\u003c/em\u003e) value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using R software (version 4.2.3; R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003cp\u003eThis analytical approach ensures robust statistical comparisons while accounting for potential confounders and missing data, providing reliable estimates of the association between cancer status and sepsis outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePropensity score matching (PSM)\u003c/h2\u003e \u003cp\u003eIn the matched cohort, preliminary analyses were conducted to assess the association between cancer status and outcomes, including the primary outcome (28-day all-cause mortality) and secondary endpoints. Variables for the PSM model were selected based on published consensus guidelines [55] and clinical relevance, incorporating baseline characteristics with significant between-group differences and high prognostic value (i.e., age, sex, congestive heart failure, cerebrovascular disease, SOFA score, and LODS score). Also, a 1:1 nearest-neighbor matching algorithm was applied with a caliper width of 0.05. Post-matching balance was evaluated using standardized mean differences (SMDs), where SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.10 indicated adequate covariate balance. For the matched dataset, univariate analyses were first performed, and significant variables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were included in subsequent multivariable models to adjust for residual confounding. The final adjusted covariates included in the multivariable analyses were age, invasive mechanical ventilation status, maximum respiratory rate, maximum heart rate, minimum mean arterial pressure, maximum prothrombin time, maximum anion gap, maximum blood urea nitrogen, minimum lymphocyte count, and minimum chloride/potassium levels \u003cb\u003e(Supplementary Tables\u0026nbsp;1 and 2)\u003c/b\u003e. These analyses aimed to isolate the independent effect of cancer on sepsis outcomes and enhance clinical interpretability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubgroup analyses\u003c/h3\u003e\n\u003cp\u003ePrespecified subgroup analyses of the matched cohort were stratified by age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), sex, CCI score (\u0026lt;\u0026thinsp;5 vs. \u0026ge;5), SOFA score (\u0026lt;\u0026thinsp;6 vs. \u0026ge;6), lymphocyte count (\u0026lt;\u0026thinsp;1.0\u0026times;10⁹/L vs. \u0026ge;1.0\u0026times;10⁹/L), use of invasive mechanical ventilation, vasoactive drug administration, and AKI within 48 h of ICU admission. Interaction tests were employed to assess heterogeneity across subgroups, with significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for interaction terms.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe initial cohort comprised 94,458 patients. After applying the inclusion and exclusion criteria, the final study population included 18,731 patients, of whom 2,674 (14.28%) were sepsis patients with underlying malignancies admitted to the ICU. Following PSM, the matched cohort consisted of 5,346 patients (2,673 per group). The complete screening and enrollment process is detailed in the study flowchart \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparative mortality analysis between sepsis with cancer and other clinical subgroups\u003c/h2\u003e \u003cp\u003eOur comprehensive analysis of all eligible sepsis cases (including patients with missing lymphocyte data) revealed significant outcome disparities among the four clinical subgroups. Most notably, cancer-associated sepsis patients demonstrated substantially worse survival outcomes at all evaluated timepoints as compared to other groups, while non-cancer sepsis patients maintained the most favorable prognosis. The cancer-sepsis cohort exhibited particularly striking mortality patterns, with an in-hospital mortality rate of 15.54% that escalated by three-fold as compared to non-cancer sepsis patients by 90 days, the most pronounced mortality differential observed across all comparative groups. Although the one-year survival rates remained significantly compromised in cancer-sepsis patients, longitudinal analysis revealed a progressive narrowing of this survival disadvantage relative to ICU-treated cancer patients without concurrent sepsis \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Mortality Analysis Between Sepsis Patients with Cancer and Other Clinical Subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eICU mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer-sepsis (n\u0026thinsp;=\u0026thinsp;3777)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNoncancer-sepsis (n\u0026thinsp;=\u0026thinsp;23796)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNoncancer-infection (n\u0026thinsp;=\u0026thinsp;3155)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICU-cancer (n\u0026thinsp;=\u0026thinsp;14334)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.54%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.88%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.46%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.83%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ein-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of covariate balance before and after PSM\u003c/h2\u003e \u003cp\u003ePSM demonstrated significant baseline disparities between cohorts prior to adjustment. As detailed in Supplementary Table\u0026nbsp;1, cancer-associated sepsis patients were younger (median age 63 [IQR, 55\u0026ndash;71] vs. 68 [58\u0026ndash;77] years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with lower comorbidity burden (median CCI 4 [3\u0026ndash;6] vs. 6 [4\u0026ndash;8]) and less severe illness acuity (median SOFA 5 [3\u0026ndash;7] vs. 7 [4\u0026ndash;9]; APS III 52 [40\u0026ndash;68] vs. 64 [48\u0026ndash;82]). These patients also required fewer critical interventions (vasopressor use: 38.2% vs. 51.7%; mechanical ventilation: 28.9% vs. 42.6%; CRRT: 7.8% vs. 13.9%; all, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Following 1:1 matching (n\u0026thinsp;=\u0026thinsp;2,673), optimal balance was achieved for all included covariates (absolute SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Visual inspection of the propensity score distributions confirmed excellent overlap between the matched groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImpact of underlying cancer on sepsis outcomes\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003ePrimary outcome analysis\u003c/h2\u003e \u003cp\u003eAnalysis of the matched cohorts revealed that patients with cancer-associated sepsis demonstrated significantly higher 28-day all-cause mortality as compared to their non-cancer counterparts (35.8% vs. 19.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Kaplan\u0026ndash;Meier survival curves \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e visually confirmed this mortality disparity, with early separation of survival trajectories. Cox regression revealed a pronounced association between cancer status and mortality (HR\u0026thinsp;=\u0026thinsp;2.09, 95% CI: 1.878\u0026ndash;2.33; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which remained statistically significant after multivariable adjustment for disease severity and interventions (adjusted HR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.192\u0026ndash;1.551; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Consistent findings were observed in the pre-matched cohort, where both univariate and multivariate analyses similarly identified cancer as an independent predictor of 28-day mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses of the matched cohort revealed consistent associations between cancer status and elevated 28-day all-cause mortality across diverse patient strata. Notably, younger patients (\u0026lt;\u0026thinsp;65 years) and those with lower comorbidity burden (CCI\u0026thinsp;\u0026lt;\u0026thinsp;5) demonstrated particularly pronounced mortality risks, with HRs of 2.74 (95% CI: 2.21\u0026ndash;3.39) and 3.59 (95% CI: 2.89\u0026ndash;4.46), respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant associations persisted even among patients with normal lymphocyte counts (HR\u0026thinsp;=\u0026thinsp;2.15, 95% CI: 1.81\u0026ndash;2.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Importantly, the disadvantage of cancer-related mortality remained evident in clinically stable subgroups without invasive mechanical ventilation (HR\u0026thinsp;=\u0026thinsp;2.67, 95% CI: 2.25\u0026ndash;3.18) or AKI (HR\u0026thinsp;=\u0026thinsp;2.73, 95% CI: 2.15\u0026ndash;3.49) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSecondary outcomes analysis\u003c/h2\u003e \u003cp\u003eThe cancer cohort exhibited higher ICU mortality (17.4% vs. 11.1%, unadjusted OR\u0026thinsp;=\u0026thinsp;1.66, 95% CI 1.441\u0026ndash;1.972; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), though this association was attenuated after multivariable adjustment (adjusted OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 0.863\u0026ndash;1.299; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.590). Similarly, in-hospital mortality was markedly elevated in cancer patients (28.06% vs. 14.37%), with an unadjusted OR of 2.33 (95% CI: 2.028\u0026ndash;2.668; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and adjusted analyses (OR\u0026thinsp;=\u0026thinsp;1.47, 95% CI: 1.233\u0026ndash;1.755; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) confirming this association. The 90-day mortality disparity was most pronounced, with cancer patients facing nearly double the risk (49.27% vs. 24.39%), consistent across univariate (OR\u0026thinsp;=\u0026thinsp;2.36, 95% CI: 2.149\u0026ndash;2.592; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and multivariate models (OR\u0026thinsp;=\u0026thinsp;1.65, 95% CI: 1.467\u0026ndash;1.848; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Complete data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Underlying Malignancy and Clinical Outcomes in the Propensity Score-Matched Sepsis Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCancer-sepsis (n\u0026thinsp;=\u0026thinsp;2673)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNoncancer-sepsis(n\u0026thinsp;=\u0026thinsp;2673)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariable Analysis*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR/OR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR/OR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePrimary Outcome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day all-cause mortality, n (%) \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e958(35.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e507(18.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09(1.877\u0026thinsp;~\u0026thinsp;2.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36(1.191\u0026thinsp;~\u0026thinsp;1.550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSecondary Outcomes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU all-cause mortality, n (%)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465(17.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66(1.441\u0026thinsp;~\u0026thinsp;1.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(0.863\u0026thinsp;~\u0026thinsp;1.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital all-cause mortality, n (%)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750(28.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384(14.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33(2.028\u0026thinsp;~\u0026thinsp;2.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47(1.233\u0026thinsp;~\u0026thinsp;1.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day all-cause mortality, n (%) \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1317(49.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e652(24.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36(2.149\u0026thinsp;~\u0026thinsp;2.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65(1.467\u0026thinsp;~\u0026thinsp;1.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Analyses were adjusted for the following covariates: Age, invasive mechanical ventilation status, RR \u003csub\u003emax\u003c/sub\u003e, HR \u003csub\u003emax\u003c/sub\u003e, MAP\u003csub\u003emin\u003c/sub\u003e, minimum lymphocyte count, PT\u003csub\u003emax\u003c/sub\u003e, AG \u003csub\u003emax\u003c/sub\u003e, BUN \u003csub\u003emax\u003c/sub\u003e, minimum chloride, and minimum potassium levels. \u0026dagger; Hazard ratios (HRs) with 95% confidence intervals (CIs) were derived from Cox proportional hazards models. # logistic regression models were used to compute odds ratios (ORs) with corresponding 95% CIs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis in-depth analysis of heterogeneity within the sepsis population employed innovative PSM to construct balanced cohorts (cancer vs. non-cancer groups) for precise evaluation of comorbidity-specific prognostic interactions. The findings demonstrate that cancer comorbidity elevates 28-day all-cause mortality to 35.84% in sepsis patients (HR\u0026thinsp;=\u0026thinsp;2.09, 95% CI: 1.877\u0026ndash;2.329), with this mortality-doubling effect remaining statistically significant across both pre-matched and matched cohorts. Multidimensional subgroup analyses stratified by sex, age, ICU organ support status, CCI burden, SOFA score-defined disease severity, and immune status consistently validated the robustness of this cancer-associated prognostic disadvantage. This independent association persisted after comprehensive adjustment for clinical confounders (adjusted HR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.191\u0026ndash;1.550), underscoring biological significance. This work represents the first quantification of the independent prognostic value of cancer for sepsis populations, providing critical evidence to optimize prognostic prediction algorithms and stratify management strategies for clinical decision-support systems.\u003c/p\u003e \u003cp\u003eThis study demonstrated significantly lower overall mortality rates as compared to previous investigations, including the work by Rosolem et al\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. A large-scale U.S. cross-sectional study of over one million sepsis patients\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e corroborated this finding. By extending the observation period from 2008 to 2021 and encompassing a broader sepsis population, our research confirmed that cancer-associated sepsis patients still exhibited markedly higher in-hospital mortality than their non-cancer counterparts (25.39% vs. 14.85%, respectively). These collective findings reveal a critical clinical phenomenon: sepsis patients with underlying malignancies face substantially worse in-hospital outcomes as compared to non-cancer sepsis patients, underscoring that cancer is a complex and high-risk comorbidity that significantly worsens sepsis prognosis. Notably, historical data from the 1990s\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e reported a staggering 37.8% mortality rate among cancer patients with severe sepsis. However, our comparative analysis with contemporary population studies conducted in the United States\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e suggests an encouraging trend. The mortality gap between cancer and non-cancer sepsis patients may be narrowing by approximately 3%\u0026ndash;4%, with current mortality differences being less pronounced than those documented in studies from the 1990s\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The present study revealed a notable decline in both absolute mortality rates and the magnitude of outcome disparities between cancer-associated and non-cancer sepsis patients. This temporal improvement likely reflects advances in both oncological and critical care management over the past two decades. First, the deepened understanding of sepsis pathophysiology has enabled more precise therapeutic strategies, leading to comprehensive improvements in critical care delivery. Earlier recognition of sepsis symptoms, particularly in high-risk oncology patients, and prompt initiation of treatment may have contributed to these outcomes\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Second, breakthroughs in cancer therapeutics and improved survival rates have enhanced both clinician engagement and treatment aggressiveness in managing sepsis within this vulnerable population\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Notably, the survival disadvantage among cancer patients demonstrated a time-dependent progression, with ICU mortality reaching 17.4%, escalating to 28.6% during hospitalization, and further increasing to 49.27% at 90-day follow-up, a mortality trajectory that aligns with the findings reported by Rosolem et al\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This temporal pattern highlights the critical need for differentiated and longitudinal management strategies specifically designed for cancer-associated sepsis patients, addressing not only acute critical illness but also long-term survivorship concerns.\u003c/p\u003e \u003cp\u003eFrom a long-term perspective, cancer-associated sepsis patients continue to face substantial survival challenges. Historical data (2001\u0026ndash;2012) indicate that ICU-admitted cancer patients had a one-year survival rate of only 53.3% (2,579/4,836) regardless of sepsis status, which plummeted to \u0026lt;\u0026thinsp;20% among those with septic shock or elevated lactate levels\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Courtright et al.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e identified cancer as the strongest predictor of one-year mortality of sepsis survivors receiving home healthcare (OR\u0026thinsp;=\u0026thinsp;3.66, 95% CI: 3.50\u0026ndash;3.83), exceeding the risks associated with severe sepsis (OR\u0026thinsp;=\u0026thinsp;1.30) or septic shock (OR\u0026thinsp;=\u0026thinsp;1.14). Our findings corroborate this temporal divergence. One-year survival of cancer-associated sepsis patients declined to 40%, while non-cancer sepsis patients maintained significantly higher survival rates (70%), highlighting the persistent mortality gap that widens over time. This enduring disparity underscores the need for specialized long-term follow-up protocols addressing both oncological and critical care sequelae in this high-risk population.\u003c/p\u003e \u003cp\u003eStratified analyses revealed consistently elevated sepsis-related mortality among patients with underlying malignancies in all predefined subgroups. The heightened mortality risk in lower CCI subgroups suggests that the prognostic impact of cancer becomes more pronounced with fewer competing comorbidities. Interestingly, this study revealed that the magnitude of the difference in mortality between cancer-associated and non-cancer-associated sepsis varied significantly with age. Among patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years, those with cancer-associated sepsis exhibited a mortality rate as high as 32.2%, with a mortality risk ratio of 2.74, \u0026mdash;a finding consistent with the observations by Hensley et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, who reported that this mortality gap progressively narrowed with advancing age and became negligible in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;85 years. This phenomenon may be attributed to immunosenescence, wherein the natural aging process impairs immune function to a degree comparable to the immunosuppressive effects of cancer and related treatments\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The convergence of mortality rates in older adults likely reflects similarly severe immune dysfunction in both cancer-associated and non-cancer-associated sepsis at advanced ages. Additionally, in patients with lower CCI scores, pre-existing cancer still contributed to increased sepsis-related mortality, aligning with the established association between greater comorbidity burden and worse clinical outcomes. Similar patterns emerged across other clinically relevant strata (lower SOFA scores, non-mechanical ventilation, absence of AKI), indicating differential effects of malignancy depending on baseline physiological reserve. As anticipated, worsening organ dysfunction (higher SOFA scores) correlated with more dysregulated inflammatory/immune responses\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e and poorer outcomes\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, while hypotension reflected tissue hypoperfusion and increased mortality\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Interestingly, norepinephrine (NE) administration, though indicative of shock, did not independently worsen cancer patient outcomes, which may be related to several factors: (1) Controversies in blood pressure targets, particularly for patients with chronic hypertension/diabetes/renal impairment, (2) the potential immunomodulatory effects of NE when used as a first-line vasopressor, (3) timing benefits: animal studies show immediate NE improves microcirculation better than delayed use after fluid resuscitation\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, with clinical data linking early NE administration to lower mortality\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, and (4) dose-dependent effects while high NE equivalents correlate with mortality\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, combination strategies (e.g., with vasopressin) may mitigate organ injury through synergistic mechanisms]. Current evidence underscores the need to investigate cancer subtype-specific responses to vasoactive agents\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, suggesting our neutral findings may reflect complex interactions between drug selection, dose-time optimization, and individualized treatment approaches.\u003c/p\u003e \u003cp\u003ePeripheral lymphocyte subsets serve dual purposes as prognostic biomarkers for cancer and predictors of therapeutic response, constituting a major focus in contemporary cancer immunology research\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. While cancer inherently induces immunosuppression, sepsis exacerbates lymphocyte apoptosis and functional exhaustion\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, creating a compounded immunocompromised state. Our study systematically excluded cancer-sepsis patients with missing lymphocyte counts to enable precise evaluation of lymphocytic influences. Subgroup analyses revealed differential effects: the prognostic impact of cancer was less pronounced at lymphocyte counts\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L as compared to \u0026ge;\u0026thinsp;1.0\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, suggesting that profound lymphopenia reflects end-stage immunosuppression (from sepsis-induced apoptosis or bone marrow suppression), where immune dysfunction becomes equally severe regardless of cancer status\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. At this threshold, extreme lymphocyte depletion directly impairs infection control and promotes organ failure, emerging as the dominant mortality driver\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, potentially overshadowing the baseline immunosuppressive effects of cancer through more severe sepsis-associated immunoparalysis\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the necessity for lymphocyte-guided risk stratification in clinical practice, as prognostic assessments should integrate both quantitative lymphocyte thresholds and cancer-specific immunological profiles.\u003c/p\u003e \u003cp\u003eSeveral key limitations should be acknowledged in interpreting these findings: (1) The retrospective observational design, despite employing PSM and multivariable adjustment, remains susceptible to residual confounding and unmeasured variables, (2) the inherent nature of the study precludes causal inferences, (3) the lack of cancer staging and treatment details prevents disentanglement of the biological effects of malignancy from therapy-related impacts and (4) temporal advancements in sepsis management over the extended study period were not accounted for in the analysis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNotwithstanding these constraints, this investigation robustly demonstrates that underlying malignancy significantly elevates 28-day all-cause mortality of critically ill sepsis patients. The particularly dismal long-term survival outcomes (evidenced by progressive mortality divergence at 90 days) mandate heightened clinical vigilance for this vulnerable population. These retrospective findings warrant validation through prospective, ideally multi-center studies incorporating detailed oncological characterization and standardized sepsis protocols to better elucidate the cancer-sepsis pathophysiological interplay.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePSM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Propensity Score Matching\u003c/p\u003e\n\u003cp\u003eSOFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eLODS Logistic Organ Dysfunction Score\u003c/p\u003e\n\u003cp\u003eICU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eAPACHE II\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Acute Physiology and Chronic Health Evaluation II\u003c/p\u003e\n\u003cp\u003eMIMIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Medical Information Mart for Intensive Care\u003c/p\u003e\n\u003cp\u003eCCI Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPSIII \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute Physiology and Chronic Health Evaluation III\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGCS Glasgow Coma Scale\u003c/p\u003e\n\u003cp\u003eRR Respiratory Rate\u003c/p\u003e\n\u003cp\u003eT Temperature\u003c/p\u003e\n\u003cp\u003eSBP Systolic Blood Pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eMBP Mean Arterial Pressure\u003c/p\u003e\n\u003cp\u003eIQRs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interquartile Ranges\u003c/p\u003e\n\u003cp\u003eWBC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;White Blood Cells\u003c/p\u003e\n\u003cp\u003ePLT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Platelet\u003c/p\u003e\n\u003cp\u003eHGB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hemoglobin\u003c/p\u003e\n\u003cp\u003eANC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Absolute Neutrophil Count\u003c/p\u003e\n\u003cp\u003eANC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lymphocyte Count\u003c/p\u003e\n\u003cp\u003eBE Base Excess\u003c/p\u003e\n\u003cp\u003eAG Anion Gap\u003c/p\u003e\n\u003cp\u003eBUN Blood Urea Nitrogen\u003c/p\u003e\n\u003cp\u003eCr Creatinine\u003c/p\u003e\n\u003cp\u003ePT \u003cstrong\u003eProthrombin Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePTT \u003cstrong\u003ePartial Thromboplastin Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAKI \u003cstrong\u003eAcute Kidney Injury\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCRRT \u003cstrong\u003eC\u003c/strong\u003eontinuous Renal Replacement Therapy\u003c/p\u003e\n\u003cp\u003eHR \u003cstrong\u003eHard Ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOR \u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSMD Standardized Mean Differences\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;confidence interval\u003c/p\u003e\n\u003cp\u003eNE \u003cstrong\u003eNorepinephrine\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe collection of patient information and creation of the research resource was reviewed by the Institutional Review Board at the Beth Israel Deaconess Medical Center, who granted a waiver of informed consent and approved the data sharing initiative. All procedures performed in studies involving human participants were performed in accordance with the ethical standards of the Ethics Committee of the People\u0026rsquo;s Hospital of Guangxi Zhuang Autonomous Region (KY-IIT-2024-135) and the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from the patient.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study received financial support from\u0026nbsp;the Youth Science Foundation Project of Guangxi (No.\u0026nbsp;2023GXNSFBA 026096) and\u0026nbsp;the National Natural Science Foundation of China (No.82072232), the Science and Technology Program of Guangzhou, China (No.202201020028), the Science and Technology Projects in Guangzhou (No.2025A03J4248), the Science and Technology Projects in Guangzhou (No.2025A03J3472), the Special Projects in Key Areas of General Colleges and Universities in Guangdong Province (No.2022ZDZX2003), the 2021 Annual Medical Teaching and Education Management Reform Research Project of Jinan University (No.2021YXJG029), the Fundamental Research Funds for The Central Universities (No. 21624318), the Medical Scientific Research Foundation of Guangdong Province, China(No. A2024458), the Science and Technology Projects in Guangzhou (No. 2025A04J3478), the Fundamental Research Funds for the Central Universities (No. 21623302 ), the Science and Technology Projects in Guangzhou (No. 2024A04J3706), Guangdong Basic and Applied Basic Research Foundation(No. 2024A1515220120).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLingyu Jiang: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing \u0026ndash; original draft. \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eXiangjie Duan: drafting, Software, Validation, Visualization, Formal analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJing Pang: Conceptualization, Methodology, Project administration, Supervision\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZhong, Yong Long: Acquisition of data, Data curation, Resources,\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHaiyan Yin: Project administration, Methodology, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLin Han: Acquisition of data, Investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eShulin Xiang: Conceptualization, Supervision,Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank International Science Editing ( http://www.internationalscienceediting.com ) for editing this manuscript.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eThis study was registered with the Chinese Clinical Trial Registry on April 22, 2025 (registration number: ChiCTR-PID-270259)\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from two publicly accessible repositories. The MIMIC-IV database is available at: https://physionet.org/content/mimiciv/3.0/. The eICU Collaborative Research Database (eICU-CRD) access information can be found at: https://eicu-crd.mit.edu/gettingstarted/access/. Access to both databases requires completion of the CITI \u0026quot;Data or Specimens Only Research\u0026quot; course and formal request approval via PhysioNet, in accordance with their data use agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDANAI P A, MOSS M, MANNINO D M, et al. The epidemiology of sepsis in patients with malignancy [J]. Chest, 2006, 129(6): 1432-40.\u003c/li\u003e\n \u003cli\u003eBABU A, NOEL ALEXANDER F H, MUZUMDER S, et al. Sepsis surveillance in patients with head-and-neck cancer undergoing chemo-radiation [J]. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024, 32(11): 724.\u003c/li\u003e\n \u003cli\u003eHEWAMANA S, SKANDARAJAH T, JAYASINGHE C, et al. Successful Management of Neutropenic Sepsis Is Key to Better Survival of Patients With Blood Cancer in Sri Lanka: Real-World Data From the Resource-Limited Setting [J]. JCO Global Oncology, 2024, (10).\u003c/li\u003e\n \u003cli\u003eLIU M A, BAKOW B R, HSU T-C, et al. Temporal Trends in Sepsis Incidence and Mortality in Patients With Cancer in the US Population [J]. American Journal of Critical Care, 2021, 30(4): e71-e9.\u003c/li\u003e\n \u003cli\u003eTORRES V B, AZEVEDO L C, SILVA U V, et al. Sepsis-Associated Outcomes in Critically Ill Patients with Malignancies [J]. Annals of the American Thoracic Society, 2015, 12(8): 1185-92.\u003c/li\u003e\n \u003cli\u003eHENSLEY M K, DONNELLY J P, CARLTON E F, et al. Epidemiology and Outcomes of Cancer-Related Versus Non-Cancer-Related Sepsis Hospitalizations [J]. Crit Care Med, 2019, 47(10): 1310-6.\u003c/li\u003e\n \u003cli\u003eLEMIALE V, PONS S, MIROUSE A, et al. Sepsis and Septic Shock in Patients With Malignancies: A Groupe de Recherche Respiratoire en R\u0026eacute;animation Onco-H\u0026eacute;matologique Study [J]. Crit Care Med, 2020, 48(6): 822-9.\u003c/li\u003e\n \u003cli\u003ePAVON A, BINQUET C, KARA F, et al. Profile of the risk of death after septic shock in the present era: an epidemiologic study [J]. Crit Care Med, 2013, 41(11): 2600-9.\u003c/li\u003e\n \u003cli\u003eSLAVIN M A, WORTH L J, SEYMOUR J F, et al. Better Sepsis Management Rather Than Fluoroquinolone Prophylaxis for Patients With Cancer-Related Immunosuppression [J]. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2019, 37(13): 1139-40.\u003c/li\u003e\n \u003cli\u003eSHAH D, SOPER B, SHOPLAND L. Cytokine release syndrome and cancer immunotherapies - historical challenges and promising futures [J]. Frontiers in immunology, 2023, 14: 1190379.\u003c/li\u003e\n \u003cli\u003eKVOLIK S, JUKIC M, MATIJEVIC M, et al. An overview of coagulation disorders in cancer patients [J]. Surgical oncology, 2010, 19(1): e33-46.\u003c/li\u003e\n \u003cli\u003eSUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA: a cancer journal for clinicians, 2021, 71(3): 209-49.\u003c/li\u003e\n \u003cli\u003eROLDAN-VALADEZ E A-O, SALAZAR-RUIZ S Y, IBARRA-CONTRERAS R, et al. Current concepts on bibliometrics: a brief review about impact factor, Eigenfactor score, CiteScore, SCImago Journal Rank, Source-Normalised Impact per Paper, H-index, and alternative metrics [J]. Ir J Med Sci, 2019 Aug, 188(3): 939-51.\u003c/li\u003e\n \u003cli\u003eMANJAPPACHAR N K, CUENCA J A, RAM\u0026iacute;REZ C M, et al. Outcomes and Predictors of 28-Day Mortality in Patients With Hematologic Malignancies and Septic Shock Defined by Sepsis-3 Criteria [J]. Journal of the National Comprehensive Cancer Network, 2022, 20(1): 45-53.\u003c/li\u003e\n \u003cli\u003eSINGER M, DEUTSCHMAN C S, SEYMOUR C W, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) [J]. Jama, 2016, 315(8).\u003c/li\u003e\n \u003cli\u003eCHARLSON M E, POMPEI P, ALES K L, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation [J]. Journal of chronic diseases, 1987, 40(5): 373-83.\u003c/li\u003e\n \u003cli\u003eROSOLEM M M, RABELLO L S C F, LISBOA T, et al. Critically ill patients with cancer and sepsis: Clinical course and prognostic factors [J]. Journal of Critical Care, 2012, 27(3): 301-7.\u003c/li\u003e\n \u003cli\u003eWILLIAMS M D, BRAUN L, COOPER L M, et al. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care [J]. Critical Care, 2004, 8(5).\u003c/li\u003e\n \u003cli\u003eLARCH\u0026eacute; J, AZOULAY E, FIEUX F, et al. Improved survival of critically ill cancer patients with septic shock [J]. Intensive Care Med, 2003, 29(10): 1688-95.\u003c/li\u003e\n \u003cli\u003eWEIR H K, THOMPSON T D, STEWART S L, et al. Cancer Incidence Projections in the United States Between 2015 and 2050 [J]. Preventing Chronic Disease, 2021, 18.\u003c/li\u003e\n \u003cli\u003eCUENCA J A, NATES J L, LASERNA A, et al. Long-Term Survival of Patients With Cancer, Sepsis, and Vasopressor Requirements Based on Lactate Levels [J]. Critical Care Explorations, 2024, 6(4).\u003c/li\u003e\n \u003cli\u003eCOURTRIGHT K R, JORDAN L, MURTAUGH C M, et al. Risk Factors for Long-term Mortality and Patterns of End-of-Life Care Among Medicare Sepsis Survivors Discharged to Home Health Care [J]. JAMA Network Open, 2020, 3(2).\u003c/li\u003e\n \u003cli\u003eFRASCA D, BLOMBERG B B. Inflammaging decreases adaptive and innate immune responses in mice and humans [J]. Biogerontology, 2016, 17(1): 7-19.\u003c/li\u003e\n \u003cli\u003eFUENTES E, FUENTES M, ALARC\u0026oacute;N M, et al. Immune System Dysfunction in the Elderly [J]. Anais da Academia Brasileira de Ciencias, 2017, 89(1): 285-99.\u003c/li\u003e\n \u003cli\u003eJEKARL D W, KIM J Y, HA J H, et al. Diagnosis and Prognosis of Sepsis Based on Use of Cytokines, Chemokines, and Growth Factors [J]. Disease markers, 2019, 2019: 1089107.\u003c/li\u003e\n \u003cli\u003eLI N, REN P, WANG J, et al. Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis [J]. Int J Mol Sci, 2024, 25(2).\u003c/li\u003e\n \u003cli\u003eLIU Y, WANG X, YU L. Th17, rather than Th1 cell proportion, is closely correlated with elevated disease severity, higher inflammation level, and worse prognosis in sepsis patients [J]. Journal of clinical laboratory analysis, 2021, 35(5): e23753.\u003c/li\u003e\n \u003cli\u003eKARAKIKE E, KYRIAZOPOULOU E, TSANGARIS I, et al. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort [J]. Critical care (London, England), 2019, 23(1): 387.\u003c/li\u003e\n \u003cli\u003eKHANNA A K, KINOSHITA T, NATARAJAN A, et al. Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study [J]. Annals of intensive care, 2023, 13(1): 9.\u003c/li\u003e\n \u003cli\u003eAHN C, YU G, SHIN T G, et al. Comparison of Early and Late Norepinephrine Administration in Patients With Septic Shock: A Systematic Review and Meta-Analysis [J]. Chest, 2024, 166(6): 1417-30.\u003c/li\u003e\n \u003cli\u003eSACHA G L, LAM S W, WANG L, et al. Association of Catecholamine Dose, Lactate, and Shock Duration at Vasopressin Initiation With Mortality in Patients With Septic Shock [J]. Crit Care Med, 2022, 50(4): 614-23.\u003c/li\u003e\n \u003cli\u003eCOOPER A J, KELLER S P, CHAN C, et al. Improvements in Sepsis-associated Mortality in Hospitalized Patients with Cancer versus Those without Cancer. A 12-Year Analysis Using Clinical Data [J]. Annals of the American Thoracic Society, 2020, 17(4): 466-73.\u003c/li\u003e\n \u003cli\u003eWANG H, HUANG H, LIU T, et al. Peripheral blood lymphocyte subsets predict the efficacy of TACE with or without PD-1 inhibitors in patients with hepatocellular carcinoma: a prospective clinical study [J]. Frontiers in immunology, 2024, 15: 1325330.\u003c/li\u003e\n \u003cli\u003eSANTACROCE E, D\u0026apos;ANGERIO M, CIOBANU A L, et al. Advances and Challenges in Sepsis Management: Modern Tools and Future Directions [J]. Cells, 2024, 13(5).\u003c/li\u003e\n \u003cli\u003eLI D, ZHANG J, CHENG W, et al. Dynamic changes in peripheral blood lymphocyte trajectory predict the clinical outcomes of sepsis [J]. Frontiers in immunology, 2025, 16: 1431066.\u003c/li\u003e\n \u003cli\u003eDE ROP L, BOS D A, STEGEMAN I, et al. Accuracy of routine laboratory tests to predict mortality and deterioration to severe or critical COVID-19 in people with SARS-CoV-2 [J]. The Cochrane database of systematic reviews, 2024, 8(8): Cd015050.\u003c/li\u003e\n \u003cli\u003eMIROUSE A, VIGNERON C, LLITJOS J-F, et al. Sepsis and Cancer: An Interplay of Friends and Foes [J]. American Journal of Respiratory and Critical Care Medicine, 2020, 202(12): 1625-35.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"sepsis, cancer, critical ill, mortality, propensity score matching","lastPublishedDoi":"10.21203/rs.3.rs-9200112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9200112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eGiven that cancer is a significant burden to healthcare systems globally, this study aimed to evaluate the influence of cancer on the mortality rate of sepsis patients admitted to the intensive care unit (ICU).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective cohort study utilized the Medical Information Mart for Intensive Care IV version 3.0 database, focusing on adult ICU patients with sepsis and underlying cancer as the exposure variable. The primary outcome was 28-day all-cause mortality, analyzed using 1:1 propensity score matching (PSM).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe original cohort included 10,657 patients with sepsis but not cancer and 2,674 with sepsis and cancer. After PSM, both groups were balanced with 2,673 patients. The cancer group had a greater 28-day all-cause mortality rate as compared to the non-cancer group (35.84% vs. 18.97%, respectively), with a hazard ratio (HR) of 2.09 (95% confidence interval [CI]: 1.877–2.329, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). Sensitivity analysis confirmed the persistent elevated risk (HR = 1.36, 95% CI: 1.191–1.550, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). Furthermore, cancer was associated with significantly increased in-hospital and 90-day mortality rates among sepsis patients. Subgroup analyses revealed elevated mortality risk for sepsis patients with pre-existing cancer relative to non-cancer patients regardless of stratification by age, sex, Charlson Comorbidity Index score (CCI), Sequential Organ Failure Assessment score, lymphocyte count, mechanical ventilation requirement, vasoactive agent administration, or acute kidney injury within 48 h of ICU admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAmong critically ill patients with sepsis, underlying cancer is associated with a higher 28-day all-cause mortality, warranting further prospective studies to validate this finding.\u003c/p\u003e","manuscriptTitle":"Impact of cancer on mortality in critically ill patients with sepsis: A propensity score-matched analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 14:19:13","doi":"10.21203/rs.3.rs-9200112/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-19T01:45:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T20:09:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31714407911387084126891154284557712641","date":"2026-04-25T05:11:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147812860999418739867810205887828663691","date":"2026-04-24T00:01:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83535148212351296693743827378503146833","date":"2026-04-23T14:10:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T21:34:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336379517410147752166297242559166000387","date":"2026-04-17T20:35:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T18:54:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T01:03:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T11:22:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T11:22:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-23T11:41:44+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"169ea03c-8d03-4c06-98d2-e51aee4354d4","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-19T01:45:08+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":67032679,"name":"Biological sciences/Cancer"},{"id":67032680,"name":"Health sciences/Diseases"},{"id":67032681,"name":"Health sciences/Medical research"},{"id":67032682,"name":"Health sciences/Oncology"},{"id":67032683,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-19T01:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 14:19:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9200112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9200112","identity":"rs-9200112","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}