Timing of antimicrobial stewardship intervention and mortality among patients admitted to intensive care unit.

Atrial fibrillation (AF) is a common arrhythmia associated with increased morbidity and mortality among intensive care unit (ICU) patients. This study aimed to evaluate heart rate fluctuation (HRF) and its association with in-hospital mortality among ICU patients with AF. Atrial fibrillation (AF) is a common arrhythmia associated with increased morbidity and mortality among intensive care unit (ICU) patients. This study aimed to evaluate heart rate fluctuation (HRF) and its association with in-hospital mortality among ICU patients with AF. Atrial fibrillation (AF) is a common arrhythmia associated with increased morbidity and mortality among intensive care unit (ICU) patients. This study aimed to evaluate heart rate fluctuation (HRF) and its association with in-hospital mortality among ICU patients with AF. This study utilized the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV databases. Patients with recorded AF at ICU admission were included. The primary exposure variables were heart rate change, measured using the initial heart rate at ICU admission and the maximum heart rate within the first six hours, and HRF, assessed by the median absolute deviation (MAD) of all heart rate measurements within the first 24h. Logistic regression was used to assess the association with in-hospital mortality. A total of 13,475 patients were included in the analysis. For heart rate change, compared to the high to high group (initial and maximum heart rate > 110), the low to high group (initial heart rate ≤ 110 and maximum heart rate > 110) showed a higher mortality risk (Odds ratio [OR]: 1.30, 95% confidence interval [CI] 1.09-1.54; p = 0.003). Higher HRF was associated with increased mortality risk (p for trend = 0.002). Patients in the highest HRF quintile had a higher risk of in-hospital mortality (OR: 1.25, 95% CI 1.07-1.46; p = 0.004). Subgroup analyses revealed significant interactions between HRF and both rate control and initial heart rate. However, in patients receiving rate control therapy, HRF was not associated with mortality. Changes in heart rate and HRF were associated with in-hospital mortality in ICU patients with AF. However, in patients receiving rate control therapy, the impact of HRF on mortality is less significant and may not warrant as much clinical attention.

Predicting mortality in ICU patients with acute leukemias: A new risk score

The influenza virus exerts a substantial impact on morbidity, mortality, and overall disease burden among the population, particularly among intensive care unit (ICU) patients. Although multiple biomarkers have been studied for mortality risk stratification in this population, the prognostic relationship of the lactate-to-albumin ratio (LAR) remains incompletely defined in critically ill patients with influenza. This study aims to investigate whether baseline ICU admission LAR levels are independently associated with mortality in critically ill patients with influenza cases and assess its incremental predictive value beyond established scoring systems. Utilizing data from the Medical Information Mart for Intensive Care IV database (MIMIC-IV, version 3.1), this retrospective cohort study enrolled adult patients diagnosed with influenza who required ICU admission. LAR was calculated based on lactate and albumin levels measured within 24h of ICU admission. The primary outcome was 30-day mortality, with secondary outcomes including in-hospital, 60-day, 90-day, and 1-year mortality. The association between LAR and mortality risk was assessed using univariate and multivariate Cox proportional hazards regression models, with robustness evaluated through subgroup analyses and sensitivity analyses. Survival differences across LAR levels were compared via Kaplan-Meier curves and log-rank tests. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and the C-index. A total of 142 critically ill patients with influenza were included, with an overall 30-day mortality of 22.53%. Univariate Cox regression analysis demonstrated that a higher LAR was associated with an increased risk of 30-day mortality in ICU patients with influenza (HR 2.43, 95% CI 1.80-3.29; P < 0.001). After adjusting for confounding factors, multivariable Cox proportional hazards analysis confirmed this association, showing a similarly elevated risk (HR 2.93, 95% CI 1.95-4.39; P < 0.001). Kaplan-Meier analysis indicated a substantially lower 30-day survival for the high-LAR group (Log-rank P < 0.0001). The AUC of LAR for predicting mortality was 0.783, comparable to APS III (AUC 0.785, 95% CI 0.696-0.874; P = 0.140), SAPS II (AUC 0.717, 95% CI 0.617-0.817; P = 0.157), OASIS (AUC 0.643, 95% CI 0.531-0.756; P = 0.168), and SOFA (AUC 0.687, 95% CI 0.580-0.794; P = 0.160). Notably, the AUC for albumin alone was 0.791, compared with 0.783 for LAR. This study found that early LAR upon ICU admission is independently associated with the 30-day mortality rate in critically ill patients with influenza. Its predictive ability seems to be comparable to established clinical scoring systems. However, LAR did not outperform albumin alone, and its added prognostic value beyond albumin remains uncertain, and further validation in prospective studies is still needed.

Intensive care unit admission after endovascular aortic aneurysm repair is primarily determined by hospital factors, adds significant cost, and is often unnecessary

FRI0434 Patients with inflammatory myopathies admitted in icu are characterised by recent onset and untreated active disease as well as older age and high comorbidities

To analyze the influence of using mortality 1, 3, and 6 months after intensive care unit (ICU) admission instead of in-hospital mortality on the quality indicator standardized mortality ratio (SMR). A cohort study of 77,616 patients admitted to 44 Dutch mixed ICUs between 1 January 2008 and 1 July 2011. Four Acute Physiology and Chronic Health Evaluation (APACHE) IV models were customized to predict in-hospital mortality and mortality 1, 3, and 6 months after ICU admission. Models' performance, the SMR and associated SMR rank position of the ICUs were assessed by bootstrapping. The customized APACHE IV models can be used for prediction of in-hospital mortality as well as for mortality 1, 3, and 6 months after ICU admission. When SMR based on mortality 1, 3 or 6 months after ICU admission was used instead of in-hospital SMR, 23, 36, and 30% of the ICUs, respectively, received a significantly different SMR. The percentages of patients discharged from ICU to another medical facility outside the hospital or to home had a significant influence on the difference in SMR rank position if mortality 1 month after ICU admission was used instead of in-hospital mortality. The SMR and SMR rank position of ICUs were significantly influenced by the chosen endpoint of follow-up. Case-mix-adjusted in-hospital mortality is still influenced by discharge policies, therefore SMR based on mortality at a fixed time point after ICU admission should preferably be used as a quality indicator for benchmarking purposes.

To determine predictors of intensive care unit (ICU) admission and to assess health care utilization (HCU) post-ICU admission among persons with inflammatory bowel disease (IBD). We matched a population-based database of Manitobans with IBD to a general population cohort on age, sex, and region of residence and linked these cohorts to a population-based ICU database. We compared the incidence rates of ICU admission among prevalent IBD cases according to HCU in the year before admission using generalized linear models adjusting for age, sex, socioeconomic status, region, and comorbidity. Among incident cases of IBD who survived their first ICU admission, we compared HCU with matched controls who survived ICU admission. Risk factors for ICU admission from the year before admission included cumulative corticosteroid use (incidence rate ratio, 1.006 per 100 mg of prednisone; 95% confidence interval, 1.004-1.008) and IBD-related surgery (incidence rate ratio, 2.79; 95% confidence interval, 1.99-3.92). Use of immunomodulatory therapies within 1 year, or surgery for IBD beyond 1 year prior, were not associated with ICU admission. In those who used corticosteroids and immunomodulatory medications in the year before ICU admission, the use of immunomodulatory medications conferred a 30% risk reduction in ICU admission (incidence rate ratio, 0.70; 95% confidence interval, 0.50-0.97). Persons with IBD who survived ICU admission had higher HCU in the year following ICU discharge than controls. Corticosteroid use and surgery within the year are associated with ICU admission in IBD while immunomodulatory therapy is not. Surviving ICU admission is associated with high HCU in the year post-ICU discharge.

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BackgroundCritical COVID-19 is associated with high mortality, and acute kidney injury (AKI) is common. Endostatin has emerged as a promising prognostic biomarker for predicting AKI and mortality in intensive care. This study aimed to investigate plasma endostatin at intensive care unit (ICU) admission as a biomarker for AKI, renal replacement therapy (RRT), and 90-day mortality in COVID-19.MethodsA pre-planned retrospective analysis of a prospectively collected cohort of admissions with a primary SARS-CoV-2 infection to six ICUs in southern Sweden between May 2020 and May 2021 was undertaken. Endostatin at ICU admission was evaluated with multivariable logistic regression analyses adjusted for age, sex, C-reactive protein, and creatinine. Net reclassification index analyses were also performed.ResultsFour hundred eighty-four patients were included. Endostatin showed a non-linear association with AKI, RRT, and 90-day mortality. Endostatin levels of 100–200 ng/mL were associated with AKI on ICU day 1 (OR 5.1, 95% CI 1.5–18, p = 0.0097), RRT during the ICU stay (OR 3.5, 95% CI 1.1–12, p = 0.039), and 90-day mortality (OR 4.2, 95% CI 1.6–11, p = 0.0037). Adding endostatin to creatinine improved prediction of AKI on ICU day 1, while adding it to a model containing age, sex, CRP, and creatinine improved prediction of both AKI on ICU day 1 and 90-day mortality, but not RRT.ConclusionsEndostatin at ICU admission was independently associated with AKI, RRT, and 90-day mortality in ICU patients with COVID-19. In addition, endostatin improved the prediction of AKI and 90-day mortality, highlighting its potential as a biomarker for early risk stratification in intensive care.

Coronary heart disease (CHD) is one of the major cardiovascular diseases, a common chronic disease in the elderly and a major cause of disability and death in the world. Currently, intensive care unit (ICU) patients have a high probability of concomitant coronary artery disease, and the mortality of this category of patients in the ICU is receiving increasing attention. Therefore, the aim of this study was to verify whether the composite inflammatory indicators are significantly associated with ICU mortality in ICU patients with CHD and to develop a simple personalized prediction model. 7115 patients from the Multi-Parameter Intelligent Monitoring in Intensive Care Database IV were randomly assigned to the training cohort (n = 5692) and internal validation cohort (n = 1423), and 701 patients from the eICU Collaborative Research Database served as the external validation cohort. The association between various inflammatory indicators and ICU mortality was determined by multivariate Logistic regression analysis and Cox proportional hazards model. Subsequently, a novel predictive model for mortality in ICU patients with CHD was developed in the training cohort and performance was evaluated in the internal and external validation cohorts. Various inflammatory indicators were demonstrated to be significantly associated with ICU mortality, 30-day ICU mortality, and 90-day ICU mortality in ICU patients with CHD by Logistic regression analysis and Cox proportional hazards model. The area under the curve of the novel predictive model for ICU mortality in ICU patients with CHD was 0.885 for the internal validation cohort and 0.726 for the external validation cohort. The calibration curve showed that the predicted probabilities of the model matched the actual observed probabilities. Furthermore, the decision curve analysis showed that the novel prediction model had a high net clinical benefit. In ICU patients with CHD, various inflammatory indicators were independent risk factors for ICU mortality. We constructed a novel predictive model of ICU mortality risk in ICU patients with CHD that had great potential to guide clinical decision-making.

DEVELOPMENT AND VALIDATION OF A CLINICAL DECISION-SUPPORT TOOL FOR PREDICTION OF WEANING OUTCOME ON ADMISSION TO THE ICU

Critical care is an expensive and limited resource in the United States. Estimates from more than a decade ago suggest that over $100 billion a year is spent on critical care services.1 Over the past two decades, the number of patients presenting to the Emergency Department (ED) requiring critical care services has increased at a much higher rate than the growth in overall ED volume.2,3 The proportion of ED patients requiring Intensive Care Unit (ICU) admission has increased 75% over the first decade of the twenty-first century. In addition to the increase in the absolute number of patients requiring critical care admission, the ED length of stay for critically ill patients increased by 60 minutes. This resulted in a total nationwide increase in critical care provided in the ED by more than threefold. This disproportionate increase in critical care time reflects both the increase in critical care volume and the increase in ED boarding of critically ill patients. Data from 2008 reported the median boarding time for a patient waiting in the ED for an ICU bed was more than 5 hours, and 30% of patients waited more than 6 hours for an ICU bed.2,3 This article is protected by copyright. All rights reserved.

Background/aimPatients with hematological malignancies (HM) often require admission to the intensive care unit (ICU) due to organ failure, disease progression or treatment-related complications, and they generally have a poor prognosis. Therefore, understanding the factors affecting ICU mortality in HM patients is important. In this study, we aimed to identify the risk factors for ICU mortality in our critically ill HM patients.Materials and methodsWe retrospectively reviewed the medical records of HM patients who were hospitalized in our medical ICU between January 1, 2010 and December 31, 2018. We recorded some parameters of these patients and compared these parameters by statistically between survivors and nonsurvivors to determine the risk factors for ICU mortality.ResultsThe study included 368 critically ill HM patients who were admitted to our medical ICU during a 9-year period. The median age was 58 (49–67) years and 63.3% of the patients were male. Most of the patients (43.2%) had acute leukemia. Hematopoietic stem cell transplantation (HSCT) was performed in 153 (41.6%) patients. The ICU mortality rate was 51.4%. According to univariable analyses, a lot of parameters (e.g., admission APACHE II and SOFA scores, length of ICU stay, some laboratory parameters at the ICU admission, the reason for ICU admission, comorbidities, type of HM, type of HSCT, infections on ICU admission and during ICU stay, etc.) were significantly different between survivors and nonsurvivors. However, only high SOFA scores at ICU admission (OR:1.281, p = 0.004), presence of septic shock (OR:17.123, p = 0.0001), acute kidney injury (OR:48.284, p = 0.0001), and requirement of invasive mechanical ventilation support during ICU stay (OR:23.118, p = 0.0001) were independent risk factors for ICU mortality.ConclusionIn our cohort, critically ill HM patients had high ICU mortality. We found four independent predictors for ICU mortality. Yet, there is still a need for further research to better understand poor outcome predictors in critically ill HM patients.

To evaluate the correlation of mean arterial oxygen tension (PaO₂) during the first 24 h following intensive care unit (ICU) admission with mortality in critically ill patients with acute kidney injury (AKI) and determine the optimal PaO₂ threshold for devising oxygen therapy strategies for these patients. We collected the clinical data of ICU patients with AKI from the MIMIC-IV database. Based on the optimal first 24-h PaO₂ threshold determined by receiver operating characteristic (ROC) curve analysis and the Youden index maximization principle, we classified the patients into hyperoxia group (with PaO₂ ≥137.029 mmHg) and hypoxemia group (PaO₂<137.029 mm Hg). Multivariable logistic regression and propensity score matching were used to evaluate the correlation of first 24-h PaO₂ levels with in-hospital mortality of the patients. Among the 18 335 patients, 46.7% were in the hyperoxia group, who had an overall mortality rate of 16.9%. The optimal PaO₂ threshold (137.029 mm Hg) had a sensitivity of 78.3%, a specificity of 63.7%, and an AUC of 0.76 (95% CI: 0.74=0.78). Hyperoxia within the first 24 h after ICU admission was associated with a significantly lower in-hospital mortality (OR=0.78) and 90-day mortality (OR=0.77), particularly in stage 1 AKI patients. A non-linear relationship was identified between PaO₂ and mortality of the patients (P<0.001). Kaplan-Meier survival curves indicated a significantly increased 90-day survival rate in the patients in hyperoxia group (P<0.001), who also had shorter durations of mechanical ventilation, less vasopressor use, and shorter lengths of hospital/ICU stay. Maintenance of a PaO₂ level ≥137.029 mmHg within 24 h after ICU admission may improve clinical outcomes of critically ill AKI patients, which underscores the importance of targeted oxygen delivery in ICU care.

Chronic obstructive pulmonary disease (COPD) is a common reason for admission to the intensive care unit (ICU), where accurate risk stratification is crucial for clinical decision-making. This study aimed to develop and validate machine learning models for predicting in-hospital mortality risk in ICU patients with COPD using multicenter critical care databases, and to evaluate their incremental value and clinical utility. This was a multicenter retrospective study utilizing data from three public databases: MIMIC-IV (for model development and internal validation), MIMIC-III (for internal temporal validation), and eICU (for external validation). Patients with a first ICU admission, aged ≥ 18years, and meeting ICD diagnosis codes for COPD were included; those with an ICU length of stay < 24h were excluded. The primary outcome was in-hospital mortality. Core predictors were selected through collinearity analysis, the Boruta algorithm, and recursive feature elimination with tenfold nested cross-validation. Twelve algorithms were employed for model development, and cost-sensitive learning was applied to address class imbalance in the training set (MIMIC-IV). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves (calibration intercept and slope), Brier score, and decision curve analysis (DCA). The DeLong test was used to compare AUC between models, and the Integrated Discrimination Improvement (IDI) quantified the incremental value of the model relative to the SAPS II score. The SHAP method was used for model interpretation. A total of 7,900 patients from the MIMIC-IV cohort, 1,979 from MIMIC-III, and 8,491 from the eICU cohort were included. Thirteen core predictors were ultimately selected (e.g., SAPS II, respiratory rate, heart rate, blood urea nitrogen, lactate). The CatBoost model demonstrated the best robustness across the three independent validation sets, achieving an AUC of 0.753 (95% CI: 0.722-0.784) in internal validation, 0.731 (95% CI: 0.701-0.760) in temporal validation, and 0.735 (95% CI: 0.718-0.751) in external validation. After calibration, the model's predictive accuracy improved significantly in both MIMIC-III (Brier score decreased from 0.16 to 0.14) and eICU (Brier score decreased from 0.11 to 0.09). DCA indicated a clinical net benefit for the model within the 0.10-0.60 risk threshold range. Compared to the SAPS II score, CatBoost significantly improved discrimination and reclassification in the MIMIC-IV (ΔAUC = + 0.063, P < 0.001; IDI = 0.066, P < 0.001) and MIMIC-III (ΔAUC = + 0.044, P < 0.001; IDI = 0.058, P < 0.001) cohorts. SHAP analysis identified SAPS II, respiratory rate, and blood urea nitrogen as key drivers of risk prediction. An online risk calculator based on this model has been publicly deployed. This study successfully developed a CatBoost model for predicting in-hospital mortality in ICU patients with COPD using multicenter data. The model demonstrated good discrimination, calibration, and clinical utility across cross-institutional and cross-temporal validation, with performance superior to the traditional SAPS II score. The online tool, integrated with SHAP explanations, can provide clinicians with individualized risk prediction support.