Journal of Intensive Care Medicine最新文献

ObjectivesHistorically, patients with hematologic malignancies were often declined ICU admission due to anticipated poor outcomes. However, recent publications describe significant improvements in ICU and in-hospital mortality for critically ill patients with hematologic malignancies. It is unclear whether clinicians' perceptions of outcomes in this patient population have changed, or whether there is consensus on management. This study evaluated intensivist and hematologist perceptions of prognosis in critically ill patients with hematologic malignancies and identified factors that inform their decision-making.DesignWe conducted an electronic cross-sectional survey of Canadian intensivists and hematologists. The survey included 19 questions and a case-based scenario with variations in clinical factors. The survey data were summarized using frequency with percent. Data was compared between intensivists and hematologists using χ² tests for categorical data. A post-hoc analysis of secondary variables was also conducted using χ² tests.ResultsA total of 180 clinicians responded to the survey - 63% were intensivists, 36% hematologists and 1% dually trained. Most clinicians reported using a variety of cancer-, patient- and critical illness-related factors for prognostication, and most demonstrated awareness of factors associated with worse prognosis in this patient population. When presented with a hypothetical case, survey results revealed consensus on admitting the patient to ICU but variability in limitations to treatment and goals of care. Additionally, we found wide variability in predicted patient outcomes. There was significant variability in decision-making around withdrawal of life sustaining therapies, but minimal between-group differences between intensivist and hematologist responses.ConclusionsThis study found significant variation among clinicians in predicting prognosis for critically ill patients with hematologic malignancies, although concordance between intensivists and hematologists overall. Further study examining factors affecting prognosis and long-term outcomes for this patient population will help guide clinicians and better inform decisions about medical care.

PurposeDespite the need for advanced hemodynamic monitoring, the role of the pulmonary artery catheter (PAC) in cardiogenic shock (CS) remains controversial due to conflicting evidence from previous studies.Material and MethodsThis single-center retrospective study utilized the MIMIC-IV database to assess the impact of PAC use on 30-day in-hospital mortality and clinical outcomes in CS patients admitted between 2008 and 2019. Propensity score matching (PS) and inverse propensity treatment weighting (IPTW) were employed to adjust for baseline differences. The primary outcome was 30-day in-hospital mortality; secondary outcomes included hospital and ICU length of stay and complications. Cox proportional hazard ratio analyses were performed to evaluate the association between PAC use and mortality outcomes.ResultsThe final cohort consisted of 1940 adult CS patients, with 134 receiving PAC and 1806 not. PAC use significantly reduced 30-day in-hospital mortality (PS-matched HR 0.57, 95% CI: 0.39-0.83; IPTW HR 0.58, 95% CI: 0.35-0.96) but was associated with longer hospital stays (16.47 vs 12.37 days) and ICU stays (9.26 vs 7.52 days).ConclusionPAC use in CS patients was associated with improved short-term survival but also with longer hospitalization and potential complications, underscoring the need for careful patient selection and further research.

BackgroundTo investigate the association between Charlson comorbidity index (CCI) and Intensive care unit (ICU) admission in subgroup aortic aneurysm (AA) patients with different comorbidities.MethodsPatient data (N = 996) was collected from the MIMIC-IV database. The relationship between CCI and ICU admission was analyzed by logistic regression analysis. The receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to analyze the prediction efficacy and clinical benefits of CCI. CCI-based models were also established to assess the improvement.ResultsThere were significant differences in age, AA types, rupture, surgery, obesity, and smoking between patients with and without admitting to ICU (all P < 0.05). Among 18 comorbidities, CCI was independently associated with ICU admission mainly reflected in patients with comorbidities of hypertension, coronary heart disease, hyperlipidemia, and congestive heart failure (all P < 0.05). However, singe CCI had limited prediction performance (AUC all less than 0.7) and clinical net benefit in any comorbidities. Combining with other independent factors of ICU admission in 4 key comorbidities specifically, CCI-based models significantly improved the prediction performance and increased clinical net benefit than single CCI. Especially, CCI-based model had the best predictive performance in patients with comorbidity of hypertension (AUC = 0.752).ConclusionsCCI is independently associated with ICU admission in AA patients, with enhanced predictive value when combined with other clinical factors, particularly in those with hypertension.

BackgroundPatients with malignant neoplasms exhibit an elevated risk of sepsis and associated mortality. For septic patients with hemodynamic instability, early albumin administration is recommended, yet its specific impact in cancer-related sepsis remains unclear. This study aims to explore the relationship between early albumin administration and prognostic outcomes in patients with solid malignant neoplasms complicated by sepsis.MethodsThis study employed a retrospective cohort analysis, utilizing data obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV v3.1) database. Patients were categorized into two groups: no-albumin and albumin. Within the albumin group, patients were further subclassified into early-albumin (infusion within 24 h of ICU admission) and late-albumin (infusion more than 24 h after ICU admission but before discharge). The primary endpoint was 28-day in-hospital mortality, while secondary endpoints including in-hospital mortality, length of hospital stay (Los_hospital), and length of ICU stay (Los_ICU).ResultsAmong 3700 eligible patients (2596 no-albumin; 1104 albumin), further subclassification within the albumin group revealed 736 early-albumin and 368 late-albumin patients. After propensity score matching (PSM), 312 pairs (early vs late) were analyzed. Cox regression models showed that early albumin administration significantly improved 28-day survival prospects. Compared to both no-albumin and late-albumin groups, the early-albumin group exhibited a pronounced survival advantage. Additionally, early albumin administration was associated with a shorter ICU stay. Subgroup analyses confirmed benefits across various demographics and clinical characteristics in the early-albumin group.ConclusionsEarly albumin administration within 24 h of ICU admission significantly decreases 28-day and in-hospital mortality and shortens ICU stay in septic patients with solid malignant neoplasms. Our findings suggest that early albumin administration should be integrated into personalized resuscitation strategies for this high-risk population and merit further prospective validation.

IntroductionGlucocorticoids are commonly used in pediatric critical illness and may lead to subsequent adrenal insufficiency, causing morbidity among pediatric intensive care unit (PICU) survivors. We aimed to determine the prevalence of and risk factors for adrenal insufficiency among children who received glucocorticoids during PICU admission.MethodsWe conducted a retrospective cohort study using structured medical record review to determine the prevalence of adrenal insufficiency and clinical characteristics of PICU patients 0-18 years old who received enteral and/or parenteral glucocorticoids. Patients were consecutively admitted to an academic tertiary referral PICU over 2 years.ResultsAmong 530 patients who received glucocorticoids, 12 (2.3%) were diagnosed with adrenal insufficiency at a median of 55 (IQR 8-156) days after initial glucocorticoid exposure. Unadjusted analyses showed that patients with adrenal insufficiency were younger (median 0.5 vs 2 years, p = .020), had a longer PICU stay (79 vs 4 days, p < .001) and hospital stay (96 vs 6 days, p < .001), and had a lower survival rate at 1 year after PICU discharge (75% vs 94%, p = .033). There were no significant differences in sex, race/ethnicity, illness severity, or diagnostic categories. Patients with adrenal insufficiency were more likely to have received glucocorticoids for hyperinflammation (21% vs 8%) and less likely for reactive airway disease (10% vs 26%) (p = .036), had a higher median total hydrocortisone equivalent dose (2508 vs 480 mg, p = .007), and were more likely to have had a steroid taper (48% vs 24%, p = .003). Multivariable logistic regression showed no significant associations between clinical characteristics and the diagnosis of adrenal insufficiency.ConclusionsAmong PICU patients who received glucocorticoids, 2.3% were subsequently diagnosed with adrenal insufficiency. We identified potential risk factors for adrenal insufficiency after glucocorticoid use in the PICU, which warrant future study to better delineate and mitigate adrenal insufficiency's contribution to morbidity and mortality among critically ill children.

PurposeThe aim of this study was to describe seasonal variation in the incidence and outcomes of sepsis in the United States.MethodsThis is a retrospective study using National Inpatient Sample database from 2017-2019. Adult sepsis patients were identified based on the CMS SEP-1 measure codes. Monthly sepsis incidence, in-hospital mortality, and organ failure patterns were analyzed. Multivariable logistic regression was used to assess in-hospital mortality by month. We also analyzed the monthly variation in each type of organ failure to uncover patterns that could account for the observed differences in sepsis incidence and outcomes.Main ResultsThere were 57,019,369 hospitalizations due to sepsis during the study period. The incidence of sepsis hospitalizations was highest in January. January also had the highest in-hospital mortality rate (10.66%), while July had the lowest (8.66%). A multivariable logistic regression analysis showed that January had a significantly higher mortality rate compared to July (odds ratio of 1.22, p < 0.001). The relationship between month and in-hospital mortality for sepsis followed a U-shaped pattern (from January to December), both in raw and adjusted analysis. Respiratory failure similarly followed the U-shaped pattern, with January having the highest incidence, and July and August the lowest. Other organ failures showed consistent patterns throughout the year. The relationship between sepsis due to pneumonia was also U-shaped, especially in the Southern region.ConclusionsThis study revealed a U-shaped relationship between both incidence and in-hospital mortality of sepsis, and month throughout the year, with a peak during winter months. Respiratory failure significantly increased in winter, while other organ failures remained constant throughout the year. These data suggest that respiratory infection and respiratory failure appear to mediate the seasonal variation observed in sepsis incidence and mortality, respectively.

BackgroundExtracorporeal Membrane Oxygenation (ECMO) is a critical support system for patients with acute and severe cardiac and respiratory failure. This study investigates the impact of different patient body weight categories on the mortality rates of patients undergoing ECMO support.MethodsUsing the Nationwide Sample (NIS) database and ICD-10 codes for 2016 to 2020 in adults over age 18, we evaluated total mortality based on weight categories compared to normal weights using univariate and multivariate analyses.ResultsA total population of 47 990 patients underwent ECMO insertion with a mean age of 52.6 years. Total mortality was 45.7%. Patients with cachexia, overweight, and obesity had similar mortality to normal-weight patients. (Cachexia: 43.75%, normal weight: 46.30%, p = .60, OR = 0.90, 95% CI: 0.61-1.33, overweight 42.31%, p = .69, OR = 0.85, 95% CI: 0.38-1.89, and obesity 45.73%, p = .73, OR = 0.98, 95% CI: 0.85-1.12). However, morbid obesity had the lowest mortality in the univariate analysis (41.89%, p = .01, OR = 0.84, 95% CI: 0.73-0.96) but was not significant in the multivariate analysis (p = .66, OR: 0.97, CI: 0.83-1.12). Separating peripheral veno-arterial versus veno-venous ECMO showed similar results with similar mortalities based on weight categories.ConclusionsOur data suggest that the 'obesity paradox' does not exist in ECMO-treated patients, with no effect of weight on total mortality . Further research is necessary to understand the underlying factors contributing to these outcomes.

Post-intensive care syndrome (PICS) is a clinical syndrome characterized by new or worsening changes in mental health, cognition, or physical function that persist following critical illness. The psychiatric domain of PICS encompasses new or worsened psychiatric burdens following critical illness, including post-traumatic stress disorder (PTSD), depression, and anxiety. Many of the established predisposing and precipitating factors for the psychiatric domain of PICS are commonly found in the setting of critical illness, including mechanical ventilation (MV), exposure to sedating medications, and physical restraint. Importantly, previous psychiatric history is a strong risk factor for the development of the psychiatric domain of PICS and should be considered when screening patients to diagnose psychiatric impairment and interventions. Delirium has been associated with psychiatric symptoms following ICU admission, therefore prevention warrants careful consideration. Dexmedetomidine has been shown to have the lowest risk for development of delirium when compared to other sedatives and has been the only sedative studied in relation to the psychiatric domain of PICS. Nocturnal dexmedetomidine and intensive care unit (ICU) diaries have been associated with decreased psychiatric burden after ICU discharge. Studies evaluating the impact of other intra-ICU practices on the development of the psychiatric domain of PICS, including the ABCDEF bundle, depth of sedation, and daily spontaneous awakening trials, have been limited and inconclusive. The psychiatric domain of PICS is difficult to treat and may be less responsive to multidisciplinary post-discharge programs and targeted interventions than the cognitive and physical domains of PICS. Given the high morbidity associated with the psychiatric domain of PICS, intensivists should familiarize themselves with the risk factors and intra-ICU interventions that can mitigate this important and under-recognized condition.

Background and ObjectiveHealthcare professionals may be able to anticipate more accurately a patient's timing of death and assess their possibility of recovery by implementing a real-time clinical decision support system. Using such a tool, the healthcare system can better understand a patient's condition and make more informed judgements about distributing limited resources. This scoping review aimed to analyze various death prediction AI (Artificial Intelligence) algorithms that have been used in ICU (Intensive Care Unit) patient populations.MethodsThe search strategy of this study involved keyword combinations of outcome and patient setting such as mortality, survival, ICU, terminal care. These terms were used to perform database searches in MEDLINE, Embase, and PubMed up to July 2022. The variables, characteristics, and performance of the identified predictive models were summarized. The accuracy of the models was compared using their Area Under the Curve (AUC) values.ResultsDatabases search yielded an initial pool of 8271 articles. A two-step screening process was then applied: first, titles and abstracts were reviewed for relevance, reducing the pool to 429 articles. Next, a full-text review was conducted, further narrowing down the selection to 400 key studies. Out of 400 studies on different tools or models for prediction of mortality in ICUs, 16 papers focused on AI-based models which were ultimately included in this study that have deployed different AI-based and machine learning models to make a prediction about negative patient outcome. The accuracy and performance of the different models varied depending on the patient populations and medical conditions. It was found that AI models compared with traditional tools like SAP3 or APACHE IV score were more accurate in death prediction, with some models achieving an AUC of up to 92.9%. The overall mortality rate ranged from 5% to more than 60% in different studies.ConclusionWe found that AI-based models exhibit varying performance across different patient populations. To enhance the accuracy of mortality prediction, we recommend customizing models for specific patient groups and medical contexts. By doing so, healthcare professionals may more effectively assess mortality risk and tailor treatments accordingly. Additionally, incorporating additional variables-such as genetic information-into new models can further improve their accuracy.