Temporal Trends and Prognostic Factors in Critically Ill Adult Patients with Acute Leukemia: An Individual Participant Data Meta-Analysis

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As major therapeutic advances have been made during the last decades, the aim of this study was to assess temporal trends in ICU mortality, and identify prognostic factors to inform clinician decision-making. Methods We conducted an individual participant data meta-analysis of studies including adults with acute leukemia admitted to the ICU. Patients with a history of allogeneic hematopoietic stem cell transplantation were excluded. Mixed-effects logistic regression models, accounting for center of ICU admission as a random variable, evaluated factors associated with ICU mortality, with particular focus on year of ICU admission, age (> 65 years) and invasive mechanical ventilation. Results A total of 2003 patients from 55 ICU across 19 countries were included (median age 58 years [IQR 44–67]; 72% acute myeloid leukemia [AML]; 64% admitted during induction chemotherapy). Invasive mechanical ventilation, vasopressors, and renal replacement therapy were required in 55%, 57%, and 21% of patients, respectively. Crude ICU mortality was 45% overall and 66% among ventilated patients. Age > 65 years was associated with higher ICU mortality (odds ratio (OR) 1.71 [95% CI, 1.31–2.23]). A diagnosis of AML (OR 1.65 [1.22–2.23]), admission during diagnosis or induction chemotherapy (OR 1.58 [1.16–2.13]), relapsed or refractory disease (OR 1.87 [1.25–2.79]), and the need for life-sustaining therapies (OR 6.97 [5.02–9.69]) were also associated with increased ICU mortality. Later admission period (after 2010) was associated with improved survival among ventilated patients only (OR 0.60 [0.37–0.98]). Conclusions In this large international individual participant meta-analysis, survival of critically ill patients with acute leukemia improved over time, particularly among those requiring mechanical ventilation. Age and the need for mechanical ventilation and other life-sustaining therapies remain strong, independent predictors of ICU mortality. Future work should integrate frailty and functional assessments to refine prognostic stratification and guide treatment intensity in this complex population. Trial registration The protocol was registered in PROSPERO (CRD420251046286). acute leukemia acute myeloid leukemia acute lymphoid leukemia intensive care mechanical ventilation temporal trends individual participant data meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Patients with hematological malignancies constitute a high-risk population and often require admission to the intensive care unit (ICU) due to complications from both underlying disease and treatment.( 1 , 2 ) Advances in disease prognostication, chemotherapy, targeted therapies, and hematopoietic stem cell transplantation have improved overall survival but also increased the number of patients exposed to immunosuppression and cumulative chemotherapy toxicity.( 3 – 6 ) ICU admission is necessary in up to 15% of the patients over the first year of diagnosis, mostly for sepsis, disease-specific organ involvement, and chemotherapy-related toxicities.( 1 , 7 , 8 ) Among patients with hematologic malignancies, patients with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) have the highest risk for ICU admission, occurring in up to 25% of patients with AML within the first year following diagnosis.( 7 , 9 ) Despite major advances in the management of critically ill patients and in the treatment of acute leukemias, ICU mortality remains high, with substantial heterogeneity across studies and institutions.( 10 , 11 ) In this population, decision-making regarding the benefit of ICU admission and the use of life-sustaining therapies is often a complex process. Clinicians therefore need robust, evidence-based data to guide discussions and inform patients and families about prognosis. Understanding which factors influence outcomes is essential to optimize resource allocation and deliver care aligned with the patient’s goals and values. Previous studies, mostly single-center or limited in size, have identified several predictors of mortality among patients with acute leukemia admitted to the ICU, particularly during induction chemotherapy.( 4 , 12 , 13 ) Among these, older age and the need for invasive mechanical ventilation consistently emerged as major determinants of poor outcome.( 1 , 3 , 14 ) However, these findings must be interpreted in the context of evolving clinical practice over the last decades. The introduction of new chemotherapeutic agents,( 15 , 16 ) improved antimicrobial prophylaxis,( 17 ) better organ support modalities,( 18 ) and more integrated approaches between hematology and critical care teams have substantially modified the landscape of both acute leukemia and critical illness.( 19 ) Whether these advances have translated into improved survival, especially among older patients and those requiring mechanical ventilation, remains uncertain. To address this knowledge gap, we performed a systematic review and an individual participant data meta-analysis of studies reporting outcomes of adult patients with acute leukemia admitted to the ICU. The aims of this study were to provide a contemporary, comprehensive assessment of temporal trends in outcomes and identify key prognostic factors associated with ICU mortality. Particular attention was given to the association of age and mechanical ventilation with survival, to provide clinicians with meaningful insights to guide their decisions. Material and methods Registration and approvals The protocol was registered in PROSPERO (CRD420251046286) and approved by the Société de Réanimation en Langue Française (SRLF) ethics committee (CE- SRLF 25–058). All included studies were approved by regional ethics committee or institutional review board. All results are reported according to the Preferred Reporting Items for Systematic Review and Meta- Analysis of Individual Participant Data (PRISMA-IPD) checklist ( Table S1 ). Search strategy An aggregated data meta-analysis based on the same search strategy was previously published.( 10 ) No update to the literature search was preplanned. The PubMed database was systematically searched from January 1st, 2000 to July 1st, 2023, and two reviewers (DC and DLP) independently screened the records obtained from the systematic search on title and abstract. A third independent reviewer (EA) solved any disagreements during the screening phase. Details on search strategy, exclusion criteria, and eligibility criteria are available in the previous publication. The full search strings are available in Table S2 . Quality assessment As previously reported, quality assessment of observational studies was performed by two independent reviewers (DC and DLP) using the Newcastle-Ottawa Scale (NOS) for cohort studies.( 20 ) Our search strategy resulted in including only observational studies. Quality assessment for each included study is reported in our previous publication. Data extraction and study participants After the publication of the aggregated data meta-analysis (January 16th, 2025), we contacted the corresponding authors of each included study to request access to their anonymized data. The first contact was made on March 1st, 2025. Each corresponding author was contacted twice before being considered unresponsive (reminder sent on May 1st, 2025). A standardized and prespecified spreadsheet was used. Country and center of ICU admission, patient age, gender, acute leukemia type, acute leukemia status at ICU admission, date of diagnosis, date of ICU admission, history of allogeneic hematopoietic stem cell transplant, sequential organ failure assessment (SOFA) score at ICU admission, main reason for ICU admission, white blood cell (WBC) count at ICU admission, need for invasive mechanical ventilation, need for vasopressors, need for renal replacement therapy (RRT), and ICU mortality were collected. Patients with a history of allogeneic hematopoietic stem cell transplant were excluded from the analysis. Patients with missing data for allogeneic hematopoietic stem cell transplant status were also excluded. Data consistency and completeness were assessed before analyzing the rate of missing data. No imputation of missing data was performed. Study outcomes and definitions The primary outcome was ICU mortality. Acute leukemia type was a three-level categorical variable: AML, ALL or “not specified” when the histological type was unavailable. Acute leukemia status was a three-level categorical variable according to the disease phase at ICU admission: diagnosis or induction, consolidation or remission, and relapsed or refractory disease. Neutropenia was defined by WBC count at ICU admission lower than 1 x 10 9 /L. Patients who died in the ICU without being intubated were referred to as palliative care patients. The country of ICU admission was classified according to the World Bank income classification (World Bank, 2025). Two different country types were identified: ( 1 ) high-income countries and ( 2 ) upper-middle-income countries. To better reflect the temporal changes in clinical practice, the year of admission was considered a categorical variable. Patients admitted to the ICU in 2010 or later were separated from those admitted in 2009 or earlier. We also considered patient age as a categorical variable. We chose an age cutoff of 65 years to define elderly patients based on the 2020 guidelines of the American Society of Hematology.( 21 ) Statistical analysis Continuous variables are described as median and interquartile range (IQR) and compared using Wilcoxon rank sum test. Categorical variables are described as counts and percentages and compared using Fisher's exact test. We used a mixed-effects logistic regression model to explore variables associated with ICU mortality in acute leukemia patients. The center of ICU admission was included in the model as a random variable with a random intercept. Other variables were included in the model according to the forced entry method. These variables were prespecified and chosen based on their clinical relevance and included acute leukemia type, acute leukemia status, age as a categorical variable (≤ 65 years and > 65 years), year of ICU admission as a categorical variable (before 2010, and after 2010), and need for at least one life-sustaining therapy. We performed a subgroup analysis in patients who required invasive mechanical ventilation using the same model. Need for at least one life-sustaining therapy was replaced by need for vasopressors or RRT in this model. Sensitivity analyses were performed in which age and year of admission were included in the model as continuous variables. Each model was assessed for multicollinearity by calculating the variance inflation factor (VIF) for all variables. The Akaike information criterion (AIC), and Bayesian information criterion (BIC) were calculated to evaluate the goodness of fit of each model. Receiver operating characteristic (ROC) curves and Hosmer-Lemeshow tests were also performed to evaluate the performance of each model. Results from the mixed-effects logistic regression models are expressed as odds ratios (OR) with 95% confidence intervals (95% CI). Spline regression models were computed to visualize the association between ICU mortality, age, and year of admission. These models integrated the same explanatory variables as the mixed-effects logistic regression models, with center of ICU admission as a random variable with a random intercept. Age and year of admission were modeled using a natural cubic spline with two degrees of freedom (placement of the knot was determined automatically) and an interaction term for age and year of ICU admission. All tests were two-sided, with p < 0.05 considered statistically significant. All analyses were performed using R statistical software (version 4.5.0, R Foundation for Statistical Computing, Vienna, Austria). Tables were created using the gtsummary package. The mixed-effects logistic regression model was computed using the lme4 package, spline regression curves were computed with the splines and ggplot2 packages. Results Characteristics of the included studies The search strategy identified 3044 records, of which 1331 were screened for title and abstract. We reviewed the full-texts of 215 studies and finally included 136 studies in the aggregated data meta-analysis. We collected the individual participant data of 2613 patients across 29 studies, among whom 610 allogeneic hematopoietic stem cell transplant recipients or patients with unknown allogeneic transplant status were excluded. A total of 2003 acute leukemia patients admitted to 55 ICU in 19 countries were included in the meta-analysis. All steps from systematic search to constitution of the final database are summarized in Fig. 1 . All patients were included in observational studies. The main characteristics of the studies are summarized in Table S3 . The individual participant data were checked for completeness, consistency, and plausibility. No issues were identified during the checking. Results of the quality assessment using NOS for cohort studies are presented in Table S4 . Patient characteristics at ICU admission The main patient characteristics are presented in Table 1 . Median age was 58 years (IQR 44–67) and 29% of patients were older than 65 years. Included patients had AML in 72% of cases and 64% were admitted during the earliest phases of the disease, at the time of leukemia diagnosis or during induction chemotherapy. Median time between acute leukemia diagnosis and ICU admission was 25 days (2-104). Invasive mechanical ventilation, vasopressors, and RRT were required in 55%, 57%, and 21% of patients admitted to the ICU, respectively. The median SOFA score at ICU admission was 8 ( 6 – 12 ). The main reasons for ICU admission were acute respiratory failure (52%) and shock (30%). Table 1 Characteristics and outcomes of patients admitted to the intensive care unit (ICU) with acute leukemia. Missing values 1 Overall (n = 2003) 1 Age, years 7 (0.3%) 58 (44–67) Age > 65 years 7 (0.3%) 588 (29%) Male patients 5 (0.2%) 1171 (59%) Acute leukemia type 0 (0%) ALL 283 (14%) AML 1450 (72%) Not specified 270 (13%) Acute leukemia status 218 (11%) Diagnosis or Induction 1147 (64%) Consolidation or Remission 391 (22%) Relapse or Refractory 247 (14%) ICU admission period 249 (12%) Before 2010 572 (33%) After 2010 1182 (67%) Time from diagnosis to ICU admission, days 899 (45%) 25 (2–104) SOFA score at ICU admission 372 (19%) 8 ( 6 – 12 ) Main reason of ICU admission 131 (6.5%) Respiratory 971 (52%) Shock 570 (30%) Monitoring 125 (6.7%) Neurologic 77 (4.1%) Renal or Metabolic 68 (3.6%) Other 61 (3.3%) WBC count at ICU admission 338 (17%) 2 (0–18) Neutropenia at ICU admission 4 (0.2%) 834 (42%) Need for invasive mechanical ventilation 9 (0.4%) 1090 (55%) Need for vasopressors 71 (3.5%) 1098 (57%) Need for RRT 46 (2.3%) 415 (21%) ICU mortality 5 (0.2%) 904 (45%) 1 Median (Q1 - Q3); n (%) ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; ICU, intensive care unit; SOFA, sequential organ failure assessment; RRT, renal replacement therapy; WBC, white blood cell Main characteristics of patients who needed invasive mechanical ventilation are summarized in Table S5 . Temporal trends in patient characteristics The year of ICU admission ranged from 1991 to 2020 with 67% of patients admitted after 2010. The proportion of elderly patients (aged > 65 years) admitted to the ICU was higher after (30%) than before 2010 (17%) ( Figure S1 ). Median age was 51 years (38–62) before 2010, and 59 years (49–68) after 2010 (p < 0.01) ( Figure S2 ). The difference in age also applied to patients undergoing invasive mechanical ventilation, among whom median age was 51 years (38–62) before 2010, and 58 years (47–67) after 2010 (p < 0.01) ( Figure S3 and Figure S4 ). The proportion of palliative care patients who died in the ICU without receiving invasive mechanical ventilation increased from 5% before 2010 to 13% after 2010 (p < 0.01) ( Figure S5 ). The proportion of patient undergoing mechanical ventilation was not significantly different before and after 2010 (56% versus 53%, p = 0.24) ( Figure S6 ). Risk factors for ICU mortality The crude ICU mortality was 45% (904/2003) in the overall population (Table 1 ) and was lower in high-income countries (42%) compared to upper-middle-income countries (64%) (p < 0.01) ( Figure S7 ). The results of the mixed-effects logistic regression model with the ICU considered as a random variable are presented in Fig. 2 A and Table S6 . As shown, a diagnosis of AML (as opposed to ALL or not specified acute leukemia) (OR 1.65 [1.22–2.23]), admission to the ICU during diagnosis or induction chemotherapy (OR 1.58 [1.16–2.13]), patients with relapsed or refractory disease (OR 1.87 [1.25–2.79]), and the need for mechanical ventilation and/or vasopressors and/or RRT were associated with ICU mortality (OR 6.97 [5.02–9.69]). Age > 65 years was associated with increased ICU mortality (OR 1.71 [1.31–2.23]). Year of admission was not associated with ICU mortality in the global population of critically ill patients with acute leukemia (OR 0.83 [0.55–1.24]). Figure S9 is a visual representation using a spline regression model of the association between ICU mortality, age, and year of admission in all included patients. Risk factors for ICU mortality in acute leukemia patients requiring invasive mechanical ventilation The crude ICU mortality was 66% (714/1090) in patients undergoing mechanical ventilation ( Table S5 ) and was lower in high-income countries (62%) compared to upper-middle-income countries (83%) (p < 0.01) ( Figure S8 ). Figure 3 summarizes the crude mortality rates stratified by the number of life-sustaining therapies and the ICU admission period (before or after 2010). The results of the mixed-effects logistic regression model for the subgroup of patients who required invasive mechanical ventilation are presented in Fig. 2 B and Table S7 . In this subgroup, age > 65 years (OR 1.78 [1.21–2.61]), admission to the ICU during diagnosis or induction chemotherapy (OR 1.54 [1.04–2.27]), patients with relapsed or refractory disease (OR 2.93 [1.60–5.37]), and the need for vasopressors and/or RRT (OR 2.87 [1.87–4.40]) were associated with ICU mortality. Admission to the ICU after 2010 (OR 0.60 [0.37–0.98]) was significantly associated with lower ICU mortality rates. The diagnosis of AML (as opposed to ALL or not specified acute leukemia) was not associated with ICU mortality (OR 1.20 [0.80–1.80]). The visual relation between ICU mortality, age, and year of admission in patients who required invasive mechanical ventilation is presented in Fig. 4 . The variations of predicted ICU mortality in ventilated patients over time according to a spline regression model are presented in Figure S10 . Sensitivity analysis and model performances Results from the sensitivity analysis using age and year of ICU admission as continuous variables in the mixed-effects logistic regression are presented in Figure S11 . AIC and BIC for each model are presented in Table S8 . VIF calculation for each model showed low collinearity between the included explanatory variables ( Table S9 ). ROC curves and results from Homer-Lemeshow tests for each model are presented in Figure S12 . Discussion This individual participant data meta-analysis includes 2003 patients with acute leukemia who were admitted to 55 different ICU across 19 countries over a three-decade period, and provides a comprehensive overview of contemporary outcomes across diverse healthcare systems. More than half the patients required invasive mechanical ventilation, and the crude ICU mortality rate was 45% in the overall population. Survival of critically ill patients with acute leukemia improved over time, especially among those requiring mechanical ventilation. Other factors, such as age or the need for life-sustaining therapies, are associated with ICU mortality. In line with previous reports,( 7 ) patients with AML accounted for the majority of admissions, and predominantly required ICU during the earliest phases of the disease.( 22 ) This population is at high risk for sepsis due to the immunosuppression caused by induction chemotherapy, and for leukemia-specific organ involvement such as leukostasis, leukemic pulmonary infiltration, and acute lysis pneumopathy.( 23 ) Our findings reinforce the need for preventive and early intervention strategies, such as infection prophylaxis,( 17 ) early sepsis recognition,( 24 ) and optimized management of chemotherapy-related toxicities and leukemia-specific organ involvement. Furthermore, these findings underscore the heterogeneity of acute leukemia patients and the paramount importance of understanding the course of the disease when deciding to admit these patients to the ICU. Our cohort confirms the considerable severity of illness among acute leukemia patients, as reflected by high SOFA scores and frequent requirements for life-support interventions, especially invasive mechanical ventilation and vasopressors. Despite this substantial burden of organ failure, survival now occurs more often than death. Another finding was a possible change in admission policy with older patients being admitted to the ICU during the last decade. These changes are the result of mounting evidence over the last decades in favor of admitting onco-hematological patients to the ICU.( 25 ) Despite these changes in patient case-mix, ICU mortality among intubated patients decreased over time. This encouraging finding may reflect the improvements in critical care management, especially for the most severe patients,( 10 , 19 ) and better patient selection. Patients treated with mechanical ventilation are those who are also eligible for intensive leukemia treatment, while patients who are not intubated often represent a palliative population with a very poor prognosis.( 26 ) In the overall population, the changes in patient case-mix may have mitigated the effect of time over ICU mortality. ICU mortality remains strongly associated with the need for mechanical ventilation and other life-sustaining therapies.( 27 ) Age consistently emerged as an independent risk factor of ICU mortality. However, our dataset did not allow assessment of performance status or frailty, which are increasingly recognized as crucial determinants of outcomes in older adults with acute leukemia.( 28 ) This limitation underscores the need for prospective studies integrating geriatric and functional assessments to refine prognostic evaluation, and guide admission policy in this population.( 29 , 30 ) Notably, we found geographical disparities with lower ICU mortality in high-income countries. This finding suggests that differences in healthcare system organization, ICU resource allocation, and clinical practice may influence patient survival, highlighting the need for management standardization. This study is one of the largest focusing on individual-level outcomes of critically ill acute leukemia patients over the last decades. However, it has several limitations. First, retrospective individual participant data meta-analysis cannot control for all potential confounders. Although the center of ICU admission was integrated as a random variable in our models, we could not account for heterogeneity in ICU organization, admission policies, and local expertise associated with high-volume centers. These institutional factors may play a crucial role in the patient prognostic. Prior studies suggest that collaborative care models, such as daily joint rounds by hematologists and intensivists, are associated with improved survival.( 31 ) Second, we did not include a significant sample from each geographical region. This might have arisen from our language restriction during the systematic search. Therefore, our results may not be generalizable worldwide and may better reflect outcomes in high-income countries, as most of the included patients were treated at tertiary care centers in Europe and North America. ICU with specific training or experience in managing hematologic malignancies have reported better outcomes.( 31 ) Third, although there were few missing data, the incompleteness of some databases may have affected the quality of our models. However, we decided not to perform imputation for missing data to avoid misleading conclusions. Notably, we had no missing data on the primary outcome. Fourth, our choice to analyze age and year of ICU admission as categorical variables may have limited our ability to model and represent any gradual temporal trends. However, the age stratification was based on recent literature and the definition of elderly acute leukemia patients, and may enable a simple yet clinically meaningful message to guide physicians in their admission decisions. Similar reasoning guided our choice to dichotomize year of ICU admission, as considering year per decade may better measure the changes in clinical practice that may occur unevenly across the years. Moreover, our findings were consistent across sensitivity analyses in which both variables were modeled as continuous variables. Fifth, we limited our data collection to ICU-related outcomes and variables. We did not collect any data regarding cytogenetic or molecular features. No inferences could be made regarding longer-term or hematological outcomes of critically ill acute leukemia patients, despite recent findings that suggested an important functional burden caused by the ICU stay.( 32 ) Finally, no treatment recommendation can be formulated, as no intervention was included in the data collection. Further prospective studies or target trial emulations are warranted to assess the effect of specific treatments on ICU mortality, such as different cytoreduction strategies in leukostasis or the use of dexamethasone to avoid pulmonary lesions. In this large international individual participant data meta-analysis, we demonstrated that despite substantial severity and a shift toward admission of older patients to the ICU, survival among mechanically ventilated patients improved over time. Patient age and the need for mechanical ventilation and other life-sustaining therapies remain the strongest predictors of mortality. However, the absence of frailty measures limits the ability to distinguish fit elderly patients from those unlikely to benefit from intensive treatment. Understanding the organizational determinants of mortality and integrating a multidisciplinary, individualized approach are examples of key priorities for future research to improve survival of this complex population. Abbreviations AIC Akaike information criterion ALL Acute lymphoblastic leukemia AML Acute myeloid leukemia BIC Bayesian information criterion CI Confidence interval ICU Intensive care unit IPD Individual participant data IQR Interquartile range MV Mechanical ventilation NOS Newcastle-Ottawa Scale OR Odds ratio PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses ROC Receiver operating characteristic RRT Renal replacement therapy SOFA Sequential organ failure assessment SRLF Société de Réanimation de Langue Française V Vasopressors VIF Variance inflation factor WBC White blood cell Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding The authors did not receive support from any organization for the submitted work. Authors' contributions DC and DLP independently performed the study selection and extracted the data from full-text reading. EA solved the disagreements during the screening phase. The statistical analysis was performed by DC and TD. DC and EA wrote the manuscript. All authors discussed the results and commented on the manuscript. Acknowledgements Not applicable. Availability of data and materials Not applicable. Competing interests On behalf of all authors, the corresponding author states that there is no conflict of interest. References Azoulay E, Mokart D, Pène F, Lambert J, Kouatchet A, Mayaux J et al (2013) Outcomes of critically ill patients with hematologic malignancies: prospective multicenter data from France and Belgium–a groupe de recherche respiratoire en réanimation onco-hématologique study. J Clin Oncol 31(22):2810–2818 Asdahl PH, Christensen S, Kjærsgaard A, Christiansen CF, Kamper P (2020) One-year mortality among non-surgical patients with hematological malignancies admitted to the intensive care unit: a Danish nationwide population-based cohort study. 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Intensive Care Med 50(4):561–572 Supplementary Files AcuteLeukemiaIPDEquatorChecklistV1.docx AcuteLeukemiaIPDSAV1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 30 Jan, 2026 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 10 Nov, 2025 First submitted to journal 07 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8036406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543536311,"identity":"0822ac7f-d355-469d-aab7-5cf8cbfdbe43","order_by":0,"name":"Dara 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1","display":"","copyAsset":false,"role":"figure","size":85384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of systematic search, screening process, and individual participant data retrieval. \u003c/strong\u003eICU stands for intensive care unit.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/f598719341d3fdebbd6dc95d.png"},{"id":96555876,"identity":"a9697f2a-5b46-467c-aed3-486d8f7abd5d","added_by":"auto","created_at":"2025-11-23 11:40:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of results from mixed-effects logistic regression models.\u003c/strong\u003e Center of intensive care unit (ICU) admission was modeled as a random variable with random intercept. Results are represented as odds ratios (OR) with 95% confidence intervals (95% CI) using a log scale in the forest plot. OR with their 95% CI are presented on the right-side part of each panel. \u003cstrong\u003ePanel A\u003c/strong\u003e presents the results for all included patients. \u003cstrong\u003ePanel B\u003c/strong\u003e presents the results for patients who required invasive mechanical ventilation. P-value \u0026lt; 0.05 indicates that the explanatory variable is associated with ICU mortality. RRT stands for renal replacement therapy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/8feb6edaccf959240dc36915.png"},{"id":96555878,"identity":"4e4600c1-0831-462e-b640-3097357a69fe","added_by":"auto","created_at":"2025-11-23 11:40:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThree-dimensional surface plot summarizing the crude intensive care unit (ICU) mortality (y-axis) in our cohort, stratified on the need for life-sustaining therapies (x-axis), and year of admission (z-axis).\u003c/strong\u003eThe x-axis represents different combinations of organ support: mechanical ventilation alone (MV), mechanical ventilation and vasopressors (MV + V), mechanical ventilation + vasopressors + renal replacement therapy (MV + V + RRT).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/8820fbcc4c62822475d6260a.png"},{"id":96604859,"identity":"2d06d8aa-ebb4-4731-baf9-368437f7ddb6","added_by":"auto","created_at":"2025-11-24 09:15:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisual representation of the association between intensive care unit (ICU) mortality, age, and year of admission, in patients who required invasive mechanical ventilation.\u003c/strong\u003e This representation was modeled using a spline regression model. Patients admitted before 2010 and patients admitted after 2010 are modeled in blue and orange with confidence intervals, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/85fe9c9f34ffdd57e88b9deb.png"},{"id":96708689,"identity":"6364479c-156d-4ad7-aa2d-3a6bfa36ffcc","added_by":"auto","created_at":"2025-11-25 10:05:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1765998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/6bba50c0-e7f2-4a44-87ab-9f83aae7666d.pdf"},{"id":96555882,"identity":"e88a8c11-e14f-441b-b4d2-1a55de268139","added_by":"auto","created_at":"2025-11-23 11:40:51","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":155704,"visible":true,"origin":"","legend":"","description":"","filename":"AcuteLeukemiaIPDEquatorChecklistV1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/16f93ed539419be7cfcadf57.docx"},{"id":96605572,"identity":"a281a42e-9490-44ee-9f46-be3b5ed05742","added_by":"auto","created_at":"2025-11-24 09:23:31","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":862173,"visible":true,"origin":"","legend":"","description":"","filename":"AcuteLeukemiaIPDSAV1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8036406/v1/0e092ce22c9ac5334af26f70.docx"}],"financialInterests":"","formattedTitle":"Temporal Trends and Prognostic Factors in Critically Ill Adult Patients with Acute Leukemia: An Individual Participant Data Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatients with hematological malignancies constitute a high-risk population and often require admission to the intensive care unit (ICU) due to complications from both underlying disease and treatment.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Advances in disease prognostication, chemotherapy, targeted therapies, and hematopoietic stem cell transplantation have improved overall survival but also increased the number of patients exposed to immunosuppression and cumulative chemotherapy toxicity.(\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) ICU admission is necessary in up to 15% of the patients over the first year of diagnosis, mostly for sepsis, disease-specific organ involvement, and chemotherapy-related toxicities.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAmong patients with hematologic malignancies, patients with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) have the highest risk for ICU admission, occurring in up to 25% of patients with AML within the first year following diagnosis.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Despite major advances in the management of critically ill patients and in the treatment of acute leukemias, ICU mortality remains high, with substantial heterogeneity across studies and institutions.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) In this population, decision-making regarding the benefit of ICU admission and the use of life-sustaining therapies is often a complex process. Clinicians therefore need robust, evidence-based data to guide discussions and inform patients and families about prognosis. Understanding which factors influence outcomes is essential to optimize resource allocation and deliver care aligned with the patient\u0026rsquo;s goals and values.\u003c/p\u003e\u003cp\u003ePrevious studies, mostly single-center or limited in size, have identified several predictors of mortality among patients with acute leukemia admitted to the ICU, particularly during induction chemotherapy.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Among these, older age and the need for invasive mechanical ventilation consistently emerged as major determinants of poor outcome.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) However, these findings must be interpreted in the context of evolving clinical practice over the last decades. The introduction of new chemotherapeutic agents,(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) improved antimicrobial prophylaxis,(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) better organ support modalities,(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and more integrated approaches between hematology and critical care teams have substantially modified the landscape of both acute leukemia and critical illness.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Whether these advances have translated into improved survival, especially among older patients and those requiring mechanical ventilation, remains uncertain.\u003c/p\u003e\u003cp\u003eTo address this knowledge gap, we performed a systematic review and an individual participant data meta-analysis of studies reporting outcomes of adult patients with acute leukemia admitted to the ICU. The aims of this study were to provide a contemporary, comprehensive assessment of temporal trends in outcomes and identify key prognostic factors associated with ICU mortality. Particular attention was given to the association of age and mechanical ventilation with survival, to provide clinicians with meaningful insights to guide their decisions.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eRegistration and approvals\u003c/h2\u003e\u003cp\u003eThe protocol was registered in PROSPERO (CRD420251046286) and approved by the Soci\u0026eacute;t\u0026eacute; de R\u0026eacute;animation en Langue Fran\u0026ccedil;aise (SRLF) ethics committee (CE- SRLF 25\u0026ndash;058). All included studies were approved by regional ethics committee or institutional review board. All results are reported according to the Preferred Reporting Items for Systematic Review and Meta- Analysis of Individual Participant Data (PRISMA-IPD) checklist (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eAn aggregated data meta-analysis based on the same search strategy was previously published.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) No update to the literature search was preplanned. The PubMed database was systematically searched from January 1st, 2000 to July 1st, 2023, and two reviewers (DC and DLP) independently screened the records obtained from the systematic search on title and abstract. A third independent reviewer (EA) solved any disagreements during the screening phase. Details on search strategy, exclusion criteria, and eligibility criteria are available in the previous publication. The full search strings are available in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eQuality assessment\u003c/h3\u003e\n\u003cp\u003eAs previously reported, quality assessment of observational studies was performed by two independent reviewers (DC and DLP) using the Newcastle-Ottawa Scale (NOS) for cohort studies.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Our search strategy resulted in including only observational studies. Quality assessment for each included study is reported in our previous publication.\u003c/p\u003e\n\u003ch3\u003eData extraction and study participants\u003c/h3\u003e\n\u003cp\u003eAfter the publication of the aggregated data meta-analysis (January 16th, 2025), we contacted the corresponding authors of each included study to request access to their anonymized data. The first contact was made on March 1st, 2025. Each corresponding author was contacted twice before being considered unresponsive (reminder sent on May 1st, 2025).\u003c/p\u003e\u003cp\u003eA standardized and prespecified spreadsheet was used. Country and center of ICU admission, patient age, gender, acute leukemia type, acute leukemia status at ICU admission, date of diagnosis, date of ICU admission, history of allogeneic hematopoietic stem cell transplant, sequential organ failure assessment (SOFA) score at ICU admission, main reason for ICU admission, white blood cell (WBC) count at ICU admission, need for invasive mechanical ventilation, need for vasopressors, need for renal replacement therapy (RRT), and ICU mortality were collected.\u003c/p\u003e\u003cp\u003ePatients with a history of allogeneic hematopoietic stem cell transplant were excluded from the analysis. Patients with missing data for allogeneic hematopoietic stem cell transplant status were also excluded.\u003c/p\u003e\u003cp\u003eData consistency and completeness were assessed before analyzing the rate of missing data. No imputation of missing data was performed.\u003c/p\u003e\n\u003ch3\u003eStudy outcomes and definitions\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was ICU mortality.\u003c/p\u003e\u003cp\u003eAcute leukemia type was a three-level categorical variable: AML, ALL or \u0026ldquo;not specified\u0026rdquo; when the histological type was unavailable. Acute leukemia status was a three-level categorical variable according to the disease phase at ICU admission: diagnosis or induction, consolidation or remission, and relapsed or refractory disease. Neutropenia was defined by WBC count at ICU admission lower than 1 x 10\u003csup\u003e9\u003c/sup\u003e/L. Patients who died in the ICU without being intubated were referred to as palliative care patients. The country of ICU admission was classified according to the World Bank income classification (World Bank, 2025). Two different country types were identified: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) high-income countries and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) upper-middle-income countries.\u003c/p\u003e\u003cp\u003eTo better reflect the temporal changes in clinical practice, the year of admission was considered a categorical variable. Patients admitted to the ICU in 2010 or later were separated from those admitted in 2009 or earlier. We also considered patient age as a categorical variable. We chose an age cutoff of 65 years to define elderly patients based on the 2020 guidelines of the American Society of Hematology.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are described as median and interquartile range (IQR) and compared using Wilcoxon rank sum test. Categorical variables are described as counts and percentages and compared using Fisher's exact test.\u003c/p\u003e\u003cp\u003eWe used a mixed-effects logistic regression model to explore variables associated with ICU mortality in acute leukemia patients. The center of ICU admission was included in the model as a random variable with a random intercept. Other variables were included in the model according to the forced entry method. These variables were prespecified and chosen based on their clinical relevance and included acute leukemia type, acute leukemia status, age as a categorical variable (\u0026le;\u0026thinsp;65 years and \u0026gt;\u0026thinsp;65 years), year of ICU admission as a categorical variable (before 2010, and after 2010), and need for at least one life-sustaining therapy.\u003c/p\u003e\u003cp\u003eWe performed a subgroup analysis in patients who required invasive mechanical ventilation using the same model. Need for at least one life-sustaining therapy was replaced by need for vasopressors or RRT in this model.\u003c/p\u003e\u003cp\u003eSensitivity analyses were performed in which age and year of admission were included in the model as continuous variables. Each model was assessed for multicollinearity by calculating the variance inflation factor (VIF) for all variables. The Akaike information criterion (AIC), and Bayesian information criterion (BIC) were calculated to evaluate the goodness of fit of each model. Receiver operating characteristic (ROC) curves and Hosmer-Lemeshow tests were also performed to evaluate the performance of each model.\u003c/p\u003e\u003cp\u003eResults from the mixed-effects logistic regression models are expressed as odds ratios (OR) with 95% confidence intervals (95% CI).\u003c/p\u003e\u003cp\u003eSpline regression models were computed to visualize the association between ICU mortality, age, and year of admission. These models integrated the same explanatory variables as the mixed-effects logistic regression models, with center of ICU admission as a random variable with a random intercept. Age and year of admission were modeled using a natural cubic spline with two degrees of freedom (placement of the knot was determined automatically) and an interaction term for age and year of ICU admission.\u003c/p\u003e\u003cp\u003eAll tests were two-sided, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. All analyses were performed using R statistical software (version 4.5.0, R Foundation for Statistical Computing, Vienna, Austria). Tables were created using the \u003cem\u003egtsummary\u003c/em\u003e package. The mixed-effects logistic regression model was computed using the \u003cem\u003elme4\u003c/em\u003e package, spline regression curves were computed with the \u003cem\u003esplines\u003c/em\u003e and \u003cem\u003eggplot2\u003c/em\u003e packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of the included studies\u003c/h2\u003e\u003cp\u003eThe search strategy identified 3044 records, of which 1331 were screened for title and abstract. We reviewed the full-texts of 215 studies and finally included 136 studies in the aggregated data meta-analysis. We collected the individual participant data of 2613 patients across 29 studies, among whom 610 allogeneic hematopoietic stem cell transplant recipients or patients with unknown allogeneic transplant status were excluded. A total of 2003 acute leukemia patients admitted to 55 ICU in 19 countries were included in the meta-analysis. All steps from systematic search to constitution of the final database are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll patients were included in observational studies. The main characteristics of the studies are summarized in \u003cb\u003eTable S3\u003c/b\u003e. The individual participant data were checked for completeness, consistency, and plausibility. No issues were identified during the checking. Results of the quality assessment using NOS for cohort studies are presented in \u003cb\u003eTable S4\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics at ICU admission\u003c/h2\u003e\u003cp\u003eThe main patient characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Median age was 58 years (IQR 44\u0026ndash;67) and 29% of patients were older than 65 years. Included patients had AML in 72% of cases and 64% were admitted during the earliest phases of the disease, at the time of leukemia diagnosis or during induction chemotherapy. Median time between acute leukemia diagnosis and ICU admission was 25 days (2-104). Invasive mechanical ventilation, vasopressors, and RRT were required in 55%, 57%, and 21% of patients admitted to the ICU, respectively. The median SOFA score at ICU admission was 8 (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The main reasons for ICU admission were acute respiratory failure (52%) and shock (30%).\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\u003eCharacteristics and outcomes of patients admitted to the intensive care unit (ICU) with acute leukemia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing values\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2003)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (44\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65 years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e588 (29%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale patients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1171 (59%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcute leukemia type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e283 (14%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1450 (72%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot specified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270 (13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcute leukemia status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis or Induction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1147 (64%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsolidation or Remission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e391 (22%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelapse or Refractory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247 (14%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU admission period\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore 2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e572 (33%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter 2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1182 (67%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime from diagnosis to ICU admission, days\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e899 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (2\u0026ndash;104)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSOFA score at ICU admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e372 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMain reason of ICU admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e971 (52%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e570 (30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurologic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal or Metabolic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC count at ICU admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e338 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0\u0026ndash;18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutropenia at ICU admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e834 (42%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeed for invasive mechanical ventilation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1090 (55%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeed for vasopressors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1098 (57%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeed for RRT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415 (21%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU mortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e904 (45%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003eMedian (Q1 - Q3); n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; ICU, intensive care unit; SOFA, sequential organ failure assessment; RRT, renal replacement therapy; WBC, white blood cell\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMain characteristics of patients who needed invasive mechanical ventilation are summarized in \u003cb\u003eTable S5\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTemporal trends in patient characteristics\u003c/h2\u003e\u003cp\u003eThe year of ICU admission ranged from 1991 to 2020 with 67% of patients admitted after 2010.\u003c/p\u003e\u003cp\u003eThe proportion of elderly patients (aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years) admitted to the ICU was higher after (30%) than before 2010 (17%) (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Median age was 51 years (38\u0026ndash;62) before 2010, and 59 years (49\u0026ndash;68) after 2010 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). The difference in age also applied to patients undergoing invasive mechanical ventilation, among whom median age was 51 years (38\u0026ndash;62) before 2010, and 58 years (47\u0026ndash;67) after 2010 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cb\u003eFigure S3\u003c/b\u003e and \u003cb\u003eFigure S4\u003c/b\u003e). The proportion of palliative care patients who died in the ICU without receiving invasive mechanical ventilation increased from 5% before 2010 to 13% after 2010 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cb\u003eFigure S5\u003c/b\u003e). The proportion of patient undergoing mechanical ventilation was not significantly different before and after 2010 (56% versus 53%, p\u0026thinsp;=\u0026thinsp;0.24) (\u003cb\u003eFigure S6\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRisk factors for ICU mortality\u003c/h2\u003e\u003cp\u003eThe crude ICU mortality was 45% (904/2003) in the overall population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and was lower in high-income countries (42%) compared to upper-middle-income countries (64%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cb\u003eFigure S7\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe results of the mixed-effects logistic regression model with the ICU considered as a random variable are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cb\u003eTable S6\u003c/b\u003e. As shown, a diagnosis of AML (as opposed to ALL or not specified acute leukemia) (OR 1.65 [1.22\u0026ndash;2.23]), admission to the ICU during diagnosis or induction chemotherapy (OR 1.58 [1.16\u0026ndash;2.13]), patients with relapsed or refractory disease (OR 1.87 [1.25\u0026ndash;2.79]), and the need for mechanical ventilation and/or vasopressors and/or RRT were associated with ICU mortality (OR 6.97 [5.02\u0026ndash;9.69]). Age\u0026thinsp;\u0026gt;\u0026thinsp;65 years was associated with increased ICU mortality (OR 1.71 [1.31\u0026ndash;2.23]). Year of admission was not associated with ICU mortality in the global population of critically ill patients with acute leukemia (OR 0.83 [0.55\u0026ndash;1.24]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure S9\u003c/b\u003e is a visual representation using a spline regression model of the association between ICU mortality, age, and year of admission in all included patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRisk factors for ICU mortality in acute leukemia patients requiring invasive mechanical ventilation\u003c/h2\u003e\u003cp\u003eThe crude ICU mortality was 66% (714/1090) in patients undergoing mechanical ventilation (\u003cb\u003eTable S5\u003c/b\u003e) and was lower in high-income countries (62%) compared to upper-middle-income countries (83%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (\u003cb\u003eFigure S8\u003c/b\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the crude mortality rates stratified by the number of life-sustaining therapies and the ICU admission period (before or after 2010).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of the mixed-effects logistic regression model for the subgroup of patients who required invasive mechanical ventilation are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cb\u003eTable S7\u003c/b\u003e. In this subgroup, age\u0026thinsp;\u0026gt;\u0026thinsp;65 years (OR 1.78 [1.21\u0026ndash;2.61]), admission to the ICU during diagnosis or induction chemotherapy (OR 1.54 [1.04\u0026ndash;2.27]), patients with relapsed or refractory disease (OR 2.93 [1.60\u0026ndash;5.37]), and the need for vasopressors and/or RRT (OR 2.87 [1.87\u0026ndash;4.40]) were associated with ICU mortality. Admission to the ICU after 2010 (OR 0.60 [0.37\u0026ndash;0.98]) was significantly associated with lower ICU mortality rates. The diagnosis of AML (as opposed to ALL or not specified acute leukemia) was not associated with ICU mortality (OR 1.20 [0.80\u0026ndash;1.80]).\u003c/p\u003e\u003cp\u003eThe visual relation between ICU mortality, age, and year of admission in patients who required invasive mechanical ventilation is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The variations of predicted ICU mortality in ventilated patients over time according to a spline regression model are presented in \u003cb\u003eFigure S10\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis and model performances\u003c/h2\u003e\u003cp\u003eResults from the sensitivity analysis using age and year of ICU admission as continuous variables in the mixed-effects logistic regression are presented in \u003cb\u003eFigure S11\u003c/b\u003e. AIC and BIC for each model are presented in \u003cb\u003eTable S8\u003c/b\u003e. VIF calculation for each model showed low collinearity between the included explanatory variables (\u003cb\u003eTable S9\u003c/b\u003e). ROC curves and results from Homer-Lemeshow tests for each model are presented in \u003cb\u003eFigure S12\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis individual participant data meta-analysis includes 2003 patients with acute leukemia who were admitted to 55 different ICU across 19 countries over a three-decade period, and provides a comprehensive overview of contemporary outcomes across diverse healthcare systems. More than half the patients required invasive mechanical ventilation, and the crude ICU mortality rate was 45% in the overall population. Survival of critically ill patients with acute leukemia improved over time, especially among those requiring mechanical ventilation. Other factors, such as age or the need for life-sustaining therapies, are associated with ICU mortality.\u003c/p\u003e\u003cp\u003eIn line with previous reports,(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) patients with AML accounted for the majority of admissions, and predominantly required ICU during the earliest phases of the disease.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) This population is at high risk for sepsis due to the immunosuppression caused by induction chemotherapy, and for leukemia-specific organ involvement such as leukostasis, leukemic pulmonary infiltration, and acute lysis pneumopathy.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Our findings reinforce the need for preventive and early intervention strategies, such as infection prophylaxis,(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) early sepsis recognition,(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and optimized management of chemotherapy-related toxicities and leukemia-specific organ involvement. Furthermore, these findings underscore the heterogeneity of acute leukemia patients and the paramount importance of understanding the course of the disease when deciding to admit these patients to the ICU.\u003c/p\u003e\u003cp\u003eOur cohort confirms the considerable severity of illness among acute leukemia patients, as reflected by high SOFA scores and frequent requirements for life-support interventions, especially invasive mechanical ventilation and vasopressors. Despite this substantial burden of organ failure, survival now occurs more often than death. Another finding was a possible change in admission policy with older patients being admitted to the ICU during the last decade. These changes are the result of mounting evidence over the last decades in favor of admitting onco-hematological patients to the ICU.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) Despite these changes in patient case-mix, ICU mortality among intubated patients decreased over time. This encouraging finding may reflect the improvements in critical care management, especially for the most severe patients,(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and better patient selection. Patients treated with mechanical ventilation are those who are also eligible for intensive leukemia treatment, while patients who are not intubated often represent a palliative population with a very poor prognosis.(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) In the overall population, the changes in patient case-mix may have mitigated the effect of time over ICU mortality.\u003c/p\u003e\u003cp\u003eICU mortality remains strongly associated with the need for mechanical ventilation and other life-sustaining therapies.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) Age consistently emerged as an independent risk factor of ICU mortality. However, our dataset did not allow assessment of performance status or frailty, which are increasingly recognized as crucial determinants of outcomes in older adults with acute leukemia.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) This limitation underscores the need for prospective studies integrating geriatric and functional assessments to refine prognostic evaluation, and guide admission policy in this population.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) Notably, we found geographical disparities with lower ICU mortality in high-income countries. This finding suggests that differences in healthcare system organization, ICU resource allocation, and clinical practice may influence patient survival, highlighting the need for management standardization.\u003c/p\u003e\u003cp\u003eThis study is one of the largest focusing on individual-level outcomes of critically ill acute leukemia patients over the last decades. However, it has several limitations. First, retrospective individual participant data meta-analysis cannot control for all potential confounders. Although the center of ICU admission was integrated as a random variable in our models, we could not account for heterogeneity in ICU organization, admission policies, and local expertise associated with high-volume centers. These institutional factors may play a crucial role in the patient prognostic. Prior studies suggest that collaborative care models, such as daily joint rounds by hematologists and intensivists, are associated with improved survival.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Second, we did not include a significant sample from each geographical region. This might have arisen from our language restriction during the systematic search. Therefore, our results may not be generalizable worldwide and may better reflect outcomes in high-income countries, as most of the included patients were treated at tertiary care centers in Europe and North America. ICU with specific training or experience in managing hematologic malignancies have reported better outcomes.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Third, although there were few missing data, the incompleteness of some databases may have affected the quality of our models. However, we decided not to perform imputation for missing data to avoid misleading conclusions. Notably, we had no missing data on the primary outcome. Fourth, our choice to analyze age and year of ICU admission as categorical variables may have limited our ability to model and represent any gradual temporal trends. However, the age stratification was based on recent literature and the definition of elderly acute leukemia patients, and may enable a simple yet clinically meaningful message to guide physicians in their admission decisions. Similar reasoning guided our choice to dichotomize year of ICU admission, as considering year per decade may better measure the changes in clinical practice that may occur unevenly across the years. Moreover, our findings were consistent across sensitivity analyses in which both variables were modeled as continuous variables. Fifth, we limited our data collection to ICU-related outcomes and variables. We did not collect any data regarding cytogenetic or molecular features. No inferences could be made regarding longer-term or hematological outcomes of critically ill acute leukemia patients, despite recent findings that suggested an important functional burden caused by the ICU stay.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) Finally, no treatment recommendation can be formulated, as no intervention was included in the data collection. Further prospective studies or target trial emulations are warranted to assess the effect of specific treatments on ICU mortality, such as different cytoreduction strategies in leukostasis or the use of dexamethasone to avoid pulmonary lesions.\u003c/p\u003e\u003cp\u003eIn this large international individual participant data meta-analysis, we demonstrated that despite substantial severity and a shift toward admission of older patients to the ICU, survival among mechanically ventilated patients improved over time. Patient age and the need for mechanical ventilation and other life-sustaining therapies remain the strongest predictors of mortality. However, the absence of frailty measures limits the ability to distinguish fit elderly patients from those unlikely to benefit from intensive treatment. Understanding the organizational determinants of mortality and integrating a multidisciplinary, individualized approach are examples of key priorities for future research to improve survival of this complex population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAkaike information criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute lymphoblastic leukemia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAML\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute myeloid leukemia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian information criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntensive care unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIndividual participant data\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMechanical ventilation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNewcastle-Ottawa Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRISMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRRT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRenal replacement therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequential organ failure assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRLF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSoci\u0026eacute;t\u0026eacute; de R\u0026eacute;animation de Langue Fran\u0026ccedil;aise\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVasopressors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariance inflation factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite blood cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\u003cp\u003eDC and DLP independently performed the study selection and extracted the data from full-text reading. EA solved the disagreements during the screening phase. The statistical analysis was performed by DC and TD. DC and EA wrote the manuscript. All authors discussed the results and commented on the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003eCompeting interests\u003c/p\u003e\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAzoulay E, Mokart D, P\u0026egrave;ne F, Lambert J, Kouatchet A, Mayaux J et al (2013) Outcomes of critically ill patients with hematologic malignancies: prospective multicenter data from France and Belgium\u0026ndash;a groupe de recherche respiratoire en r\u0026eacute;animation onco-h\u0026eacute;matologique study. J Clin Oncol 31(22):2810\u0026ndash;2818\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsdahl PH, Christensen S, Kj\u0026aelig;rsgaard A, Christiansen CF, Kamper P (2020) One-year mortality among non-surgical patients with hematological malignancies admitted to the intensive care unit: a Danish nationwide population-based cohort study. Intensive Care Med 46(4):756\u0026ndash;765\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaeng CV, Christiansen CF, Liu KD, Kamper P, Christensen S, Medeiros BC et al (2022) Factors associated with risk and prognosis of intensive care unit admission in patients with acute leukemia: a Danish nationwide cohort study. Leuk Lymphoma 63(10):2290\u0026ndash;2300\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMamez AC, Raffoux E, Chevret S, Lemiale V, Boissel N, Canet E et al (2016) Pre-treatment with oral hydroxyurea prior to intensive chemotherapy improves early survival of patients with high hyperleukocytosis in acute myeloid leukemia. Leuk Lymphoma 57(10):2281\u0026ndash;2288\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePardo E, Lemiale V, Mokart D, Stoclin A, Moreau A, Kerhuel L et al (2019) Invasive pulmonary aspergillosis in critically ill patients with hematological malignancies. Intensive Care Med 45:1732\u0026ndash;1741\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkiada A, Lanternier F, Groll AH, Pagano L, Zimmerli S, Herbrecht R et al (2013) Diagnosis and treatment of mucormycosis in patients with hematological malignancies: guidelines from the 3rd European Conference on Infections in Leukemia (ECIL 3). Haematologica. ;98(4):492\u0026ndash;504\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreyro BL, Scales DC, Wunsch H, Cheung MC, Gupta V, Saskin R et al (2021) Critical illness in patients with hematologic malignancy: a population-based cohort study. Intensive Care Med 47(10):1104\u0026ndash;1114\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzoulay \u0026Eacute;, Soares M, Lenglin\u0026eacute; \u0026Eacute; (2021) Preempting critical care services for patients with hematological malignancies. Intensive Care Med 47:1140\u0026ndash;1143\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHalpern AB, Culakova E, Walter RB, Lyman GH (2017) Association of Risk Factors, Mortality, and Care Costs of Adults With Acute Myeloid Leukemia With Admission to the Intensive Care Unit. JAMA Oncol 3(3):374\u0026ndash;381\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChean D, Luque-Paz D, Poole D, Fodil S, Lenglin\u0026eacute; E, Dupont T et al (2025) Critically ill adult patients with acute leukemia: a systematic review and meta-analysis. Ann Intensive Care 15(1):9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLecuyer L, Chevret S, Guidet B, Aegerter P, Martel P, Schlemmer B et al (2008) Case volume and mortality in haematological patients with acute respiratory failure. Eur Respir J 32:748\u0026ndash;754\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCerrano M, Seegers V, Raffoux E, Rabian F, S\u0026eacute;bert M, Itzykson R et al (2020) Predictors and outcomes associated with hydroxyurea sensitivity in acute myeloid leukemia patients with high hyperleukocytosis. Leuk Lymphoma 61:737\u0026ndash;740\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan de Louw A, Desai RJ, Zhu J, Claxton DF (2018) Characteristics of early acute respiratory distress syndrome in newly diagnosed acute myeloid leukemia. Leuk Lymphoma 59(10):2369\u0026ndash;2376\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSlavin SD, Fenech A, Jankowski AL, Abel GA, Brunner AM, Steensma DP et al (2019) Outcomes for older adults with acute myeloid leukemia after an intensive care unit admission. Cancer 125(21):3845\u0026ndash;3852\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLitzow MR, Sun Z, Mattison RJ, Paietta EM, Roberts KG, Zhang Y et al (2024) Blinatumomab for MRD-Negative Acute Lymphoblastic Leukemia in Adults. N Engl J Med 391(4):320\u0026ndash;333\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontesinos P, Recher C, Vives S, Zarzycka E, Wang J, Bertani G et al (2022) Ivosidenib and Azacitidine in IDH1-Mutated Acute Myeloid Leukemia. N Engl J Med 386(16):1519\u0026ndash;1531\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCornely OA, Maertens J, Winston DJ, Perfect J, Ullmann AJ, Walsh TJ et al (2007) Posaconazole vs. fluconazole or itraconazole prophylaxis in patients with neutropenia. N Engl J Med 356(4):348\u0026ndash;359\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzoulay E, Zafrani L, Nates J, Maillard A, Chean D, Ferreyro B et al (2025) Advances in the critical care management for patients with hematological malignancies. Blood Rev. ;101306\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDarmon M, Bourmaud A, Georges Q, Soares M, Jeon K, Oeyen S et al (2019) Changes in critically ill cancer patients\u0026rsquo;\u0026rsquo; short-term outcome over the last decades: results of systematic review with meta-analysis on individual data. Intensive Care Med 45:977\u0026ndash;987\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWells G, Shea B, O\u0026rsquo;Connell D, Robertson J, Peterson J, Losos M et al The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomized Studies in Meta- Analysis\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSekeres MA, Guyatt G, Abel G, Alibhai S, Altman JK, Buckstein R et al (2020) American Society of Hematology 2020 guidelines for treating newly diagnosed acute myeloid leukemia in older adults. Blood Adv 4(15):3528\u0026ndash;3549\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRabbat A, Chaoui D, Montani D, Legrand O, Lefebvre A, Rio B et al (2005) Prognosis of patients with acute myeloid leukaemia admitted to intensive care. Br J Haematol 129(3):350\u0026ndash;357\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoreau AS, Lengline E, Seguin A, Lemiale V, Canet E, Raffoux E et al (2014) Respiratory events at the earliest phase of acute myeloid leukemia. Leuk Lymphoma 55(11):2556\u0026ndash;2563\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNates JL, P\u0026egrave;ne F, Darmon M, Mokart D, Castro P, David S et al (2024) Septic shock in the immunocompromised cancer patient: a narrative review. Crit Care 28(1):285\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMokart D, Pastores SM, Darmon M (2014) Has survival increased in cancer patients admitted to the ICU? Yes. Intensive Care Med 40(10):1570\u0026ndash;1572\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzoulay E, Demoule A, Jaber S, Kouatchet A, Meert A, Papazian L et al (2011) Palliative noninvasive ventilation in patients with acute respiratory failure. Intensive Care Med 37:1250\u0026ndash;1257\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGroeger JS, White P, Nierman DM, Glassman J, Shi W, Horak D et al (1999) Outcome for cancer patients requiring mechanical ventilation. J Clin Oncol 17(3):991\u0026ndash;997\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZampieri FG, Bozza FA, Moralez GM, Mazza DDS, Scotti AV, Santino MS et al (2017) The effects of performance status one week before hospital admission on the outcomes of critically ill patients. Intensive Care Med 43(1):39\u0026ndash;47\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsatnik J, Matarrese A, Leone B, Cesar G, Kleinert M, Sosa F et al (2022) Frailty and clinical outcomes in critically ill patients with cancer: A cohort study. J Geriatr Oncol 13(8):1156\u0026ndash;1161\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerrat E, Paillaud E, Caillet P, Laurent M, Tournigand C, Lagrange JL et al (2017) Performance of Four Frailty Classifications in Older Patients With Cancer: Prospective Elderly Cancer Patients Cohort Study. J Clin Oncol 35(7):766\u0026ndash;777\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoares M, Bozza FA, Azevedo LCP, Silva UVA, Corr\u0026ecirc;a TD, Colombari F et al (2016) Effects of Organizational Characteristics on Outcomes and Resource Use in Patients With Cancer Admitted to Intensive Care Units. J Clin Oncol 34(27):3315\u0026ndash;3324\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMunshi L, Dumas G, Rochwerg B, Shoukat F, Detsky M, Fergusson DA et al (2024) Long-term survival and functional outcomes of critically ill patients with hematologic malignancies: a Canadian multicenter prospective study. Intensive Care Med 50(4):561\u0026ndash;572\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"intensive-care-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"icme","sideBox":"Learn more about [Intensive Care Medicine](http://link.springer.com/journal/134)","snPcode":"134","submissionUrl":"https://www.editorialmanager.com/icme/default2.aspx","title":"Intensive Care Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"acute leukemia, acute myeloid leukemia, acute lymphoid leukemia, intensive care, mechanical ventilation, temporal trends, individual participant data meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-8036406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8036406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eCritically ill patients with acute leukemia often require intensive care unit (ICU) admission. As major therapeutic advances have been made during the last decades, the aim of this study was to assess temporal trends in ICU mortality, and identify prognostic factors to inform clinician decision-making.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted an individual participant data meta-analysis of studies including adults with acute leukemia admitted to the ICU. Patients with a history of allogeneic hematopoietic stem cell transplantation were excluded. Mixed-effects logistic regression models, accounting for center of ICU admission as a random variable, evaluated factors associated with ICU mortality, with particular focus on year of ICU admission, age (\u0026gt;\u0026thinsp;65 years) and invasive mechanical ventilation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 2003 patients from 55 ICU across 19 countries were included (median age 58 years [IQR 44\u0026ndash;67]; 72% acute myeloid leukemia [AML]; 64% admitted during induction chemotherapy). Invasive mechanical ventilation, vasopressors, and renal replacement therapy were required in 55%, 57%, and 21% of patients, respectively. Crude ICU mortality was 45% overall and 66% among ventilated patients. Age\u0026thinsp;\u0026gt;\u0026thinsp;65 years was associated with higher ICU mortality (odds ratio (OR) 1.71 [95% CI, 1.31\u0026ndash;2.23]). A diagnosis of AML (OR 1.65 [1.22\u0026ndash;2.23]), admission during diagnosis or induction chemotherapy (OR 1.58 [1.16\u0026ndash;2.13]), relapsed or refractory disease (OR 1.87 [1.25\u0026ndash;2.79]), and the need for life-sustaining therapies (OR 6.97 [5.02\u0026ndash;9.69]) were also associated with increased ICU mortality. Later admission period (after 2010) was associated with improved survival among ventilated patients only (OR 0.60 [0.37\u0026ndash;0.98]).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIn this large international individual participant meta-analysis, survival of critically ill patients with acute leukemia improved over time, particularly among those requiring mechanical ventilation. Age and the need for mechanical ventilation and other life-sustaining therapies remain strong, independent predictors of ICU mortality. Future work should integrate frailty and functional assessments to refine prognostic stratification and guide treatment intensity in this complex population.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eThe protocol was registered in PROSPERO (CRD420251046286).\u003c/p\u003e","manuscriptTitle":"Temporal Trends and Prognostic Factors in Critically Ill Adult Patients with Acute Leukemia: An Individual Participant Data Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:40:46","doi":"10.21203/rs.3.rs-8036406/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-01-30T10:05:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-11-12T14:07:54+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T20:19:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T06:07:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Intensive Care Medicine","date":"2025-11-07T09:54:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"intensive-care-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"icme","sideBox":"Learn more about [Intensive Care Medicine](http://link.springer.com/journal/134)","snPcode":"134","submissionUrl":"https://www.editorialmanager.com/icme/default2.aspx","title":"Intensive Care Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f18be5be-2e3c-4c4d-bc99-4b0334582c4f","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T16:07:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 11:40:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8036406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8036406","identity":"rs-8036406","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}