Critical Care Medicine最新文献

Objectives: We sought to determine trends in use of IV vitamin C for hospitalized patients with sepsis in the context of evolving evidence, including a single-center before-after study in late 2016 and several trials in 2019-2021.

Design: Retrospective cohort study.

Setting: One thousand one hundred fifteen U.S. hospitals contributing to the Premier Healthcare Database, 2008-2021.

Patients: Eleven million three hundred seventy-five thousand three hundred twenty-six adult inpatients with sepsis.

Interventions: IV vitamin C, at any point of the hospital stay.

Measurements and main results: Patients had a median (interquartile range [IQR]) age of 71 years (59-81 yr) and a median (IQR) of 5 comorbidities (4-7 comorbidities); 53.0% were female; on hospital day 1, 6.9% were mechanically ventilated and 7.5% received a vasopressor. Overall, 32,131 patients (0.3%) received IV vitamin C at any point during hospitalization. During the study period, administration fell from 2008, quarter 1 (0.5%) through 2017, quarter 1 (< 0.1%), then rose and peaked in 2020, quarter 1 (0.6%), and fell through 2021, quarter 4 (0.1%). Examining three time periods defined by predetermined cutpoints (2015 quarter 4, when International Classification of Diseases coding for sepsis changed, and 2020 quarter 1, when the COVID-19 pandemic began), vitamin C use also varied ( p < 0.001): 0.2% (2008 quarter 1 to 2015 quarter 3); 0.3% (2015 quarter 4 to 2019 quarter 4); and 0.3% (2020-2021). Temporal trends were similar in sicker subcohorts defined by early mechanical ventilation, early vasopressor use, and diagnosis of COVID-19 (2020-2021). A multilevel logistic regression model with data from 91 hospitals that contributed at least 1 sepsis case per quarter showed a similar utilization pattern, with substantial between-hospital variability (median odds ratio, 7.78; 95% CI, 5.45-11.58).

Conclusions: IV vitamin C prescription for hospitalized patients with sepsis in the United States was overall infrequent over the 14-year study period, rising after the publication of a before-after study and declining in the COVID-19 pandemic as clinical trial results emerged.

Objectives: To identify factors associated with short-term mortality among patients receiving microaxial flow pump (mAFP) therapy for acute myocardial infarction-related cardiogenic shock (AMI-CS).

Data sources: We searched four databases (MEDLINE, Embase, CENTRAL, and Scopus) from January 1, 2004, to January 1, 2025.

Study selection: We selected English-language studies that included adults with AMI-CS receiving mAFP and evaluated factors associated with short-term mortality. We excluded patients receiving concurrent venoarterial extracorporeal membrane oxygenation, as well as studies that solely included patients presenting with out-of-hospital cardiac arrest.

Data extraction: Two authors performed citation screening and data extraction. For each factor evaluated in at least two studies, we performed meta-analyses of adjusted odds ratios (aORs) using a random-effects model. Risk of bias was evaluated using the Quality in Prognosis Studies tool, and the certainty of evidence was evaluated using Grading of Recommendations, Assessment, Development, and Evaluations methodology.

Data synthesis: Our primary analysis included 18 studies, encompassing 20,617 patients. Median short-term mortality across studies was 50.7% (interquartile range 38.4-55.3%). Factors associated with short-term mortality based on high-certainty evidence included: increased age (aOR, 1.04 per year [95% CI, 1.03-1.05 per year] or ≥ 65 yr (aOR, 2.42 yr [95% CI, 0.77-7.64 yr]), female sex (aOR, 1.26 [95% CI, 1.09-1.45]), higher body mass index (aOR, 1.05 per point [95% CI, 1.04-1.07 per point]), higher heart rate (aOR, 1.02 per beats/min [95% CI, 1.01-1.02 per beats/min]), higher serum creatinine (aOR, 1.35 per mg/dL [95% CI, 1.08-1.70 per mg/dL]), mechanical ventilation (aOR, 2.53 [95% CI, 1.82-3.53]), vasopressors (aOR, 1.52 [95% CI, 1.11-2.08] for any vasopressors and aOR, 1.37 [95% CI, 1.18-1.58] per each vasopressor), presentation with ST-elevation myocardial infarction (aOR, 1.59 [95% CI, 1.11-2.26]), cardiac arrest (aOR, 2.85 [95% CI, 2.22-3.64]), and hypoxic-ischemic brain injury (aOR, 5.36 [95% CI, 3.03-9.47]).

Conclusions: We identified several prognostic factors associated with short-term mortality in AMI-CS patients receiving mAFP support. This work may help inform clinicians, patients, and families regarding utilization of mAFP in AMI-CS.

Objectives: Trauma-induced coagulopathy biomarkers may be influenced by injury mechanism. We sought to identify differences in patterns of coagulopathy with and without severe traumatic brain injury (TBI).

Design: Retrospective cohort study.

Setting: Harmonized database composed of six major hemorrhagic shock trials: Control of Major Bleeding After Trauma (COMBAT), Cold-stored Platelet Early Intervention in Hemorrhagic Shock (CriSP-HS), Prehospital Air Medical Plasma (PAMPer), Prehospital Whole Blood in Emergency Resuscitation (PPOWER), Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR), and Study of Tranexamic Acid During Air Medical and Ground Prehospital Transport (STAAMP).

Patients: All subjects randomized to placebo or standard-of-care groups with complete data for international normalized ratio (INR), thromboelastography values (alpha angle [AA], K time, maximum amplitude [MA], and lysis in 30 min), and Abbreviated Injury Scores (AISs). Subjects from COMBAT and CriSP-HS were screened and ultimately excluded from the final analysis as they did not meet eligibility criteria.

Interventions: None.

Measurements and main results: Stratified principal component (PC) analysis was performed for INR and thromboelastography values. Strata were defined based on AIS scores as: 1) isolated severe TBI (iTBI); 2) severe polytrauma (POLY), those with both major head injury and torso/extremity trauma; and 3) isolated severe torso/extremity trauma (iTRUNK). We identified 506 subjects with complete data: 51 iTBI, 115 POLY, and 340 iTRUNK. For each stratum, two PCs were identified that accounted for more than 65% of the variance. Associations between PC scores and outcomes, including need for blood product transfusion within 24 hours as a surrogate marker for early coagulopathy and bleeding, were examined with logistic regression. For both iTBI and POLY, PC1 included INR, AA, K time, and MA, and was associated with greater odds of early transfusion (odds ratio [OR], 3.57; 95% CI, 1.50-8.45; p = 0.004 for iTBI and OR, 2.29; 95% CI, 1.11-4.75; p = 0.026 for POLY). For iTRUNK, PC1 included INR, AA, and MA and was protective with reduced odds of early transfusion (OR, 0.51; 95% CI, 0.37-0.70; p < 0.001).

Conclusions: PC analysis demonstrated a unique pattern of coagulation biomarkers common to patients with severe TBI, irrespective of other injuries.

Objectives: To explore the association between a prolonged emergency department (ED) length of stay and the deployment of process of care measures (e.g., low tidal volume ventilation) for critically ill patients.

Design: Retrospective cohort study.

Setting: Eight academic ICUs in Toronto.

Patients: Mechanically ventilated adult patients who were directly admitted to the ICU from the ED from June 2014 to February 2023.

Interventions: None.

Measurements and main results: The cohort was divided into a short ED stay group (i.e., < 6 hr) and a prolonged ED stay group (i.e., from 6 to 24 hr). We used propensity score methods and multivariable logistic regression models to estimate the association between a prolonged ED stay and the receipt of process of care measures on day 2 after ICU admission, adjusting for baseline characteristics. Associations were reported as odds ratios (ORs) and 95% CIs. We included 7072 patients, of whom 1462 (21%) had a prolonged ED stay. Both groups had comparable severity of illness at baseline. There was no difference in the deployment of processes of care measures on day 2 after ICU admission between the two groups. The adjusted OR for the prolonged ED stay group compared with a short ED stay group for each measure were as follows: low tidal volume ventilation 0.92 (95% CI, 0.68-1.22), spontaneous breathing trial 1.12 (95% CI, 0.92-1.35), extubation among those eligible 0.78 (95% CI, 0.55-1.12), deep vein thrombosis prophylaxis 1.13 (95% CI, 0.95-1.34), and continuous sedation 0.92 (95% CI, 0.81-1.06).

Conclusions: In this multicenter study of critically ill adult patients, a prolonged ED stay was not associated with a significant difference in the deployment of evidence-based process of care measures for critically ill adult patients.

Objective: We aimed to examine how the predicted 1-year survival and its prognostic certainty evolve during the first two weeks of ICU admission.

Design: Retrospective cohort study.

Setting: Two academic medical centers in The Netherlands. External validation in the Medical Information Mart for Intensive Care-IV database from a tertiary medical center in the United States.

Patients: Patients with active hematologic malignancies admitted to the ICU.

Interventions: None.

Measurements and main results: Separate prediction models for 1-year survival were developed using data available at day 1, 7, and 14 after ICU admission for 555, 181, and 94 ICU admissions resulting in an area under the receiver operating characteristics curves of 0.71, 0.67, and 0.66, respectively. At the individual patient level, prognostic certainty quantified by entropy increased meaningfully (entropy decrease > 0.25) in 2% of patients between day 1 and day 7 (in an additional 12% certainty increased because of death) and in 14% of patients between day 7 and day 14 (in an additional 18% because of death). Among patients alive on day 1, 2% of patients with an "uncertain" and 10% with a "poor" initial prognosis had shifted to a more favorable category by day 7. Of the patients alive and still in the ICU on day 7, 31% of patients with an "uncertain" and 16% with a "poor" prognosis had shifted to a more favorable category by day 14. Results in the external validation cohort were comparable.

Conclusions: In patients with hematologic malignancies admitted to the ICU, prognostic certainty about long-term survival increased little during in the first 2 weeks of ICU admission, aside from increases in prognostic certainty due to early mortality. Despite the use of rich ICU datasets and different state-of-the-art modeling strategies, overall model performance was modest, suggesting that prognosis in this population is largely driven by disease-related and patient-specific factors beyond the ICU course.

Objective: The Pao2/Fio2 (PF) ratio is widely used as an assessment of respiratory failure in guiding ventilation strategies and prognostication in critically ill patients. However, given that it mandates invasive arterial access, the Spo2/Fio2 (SF) ratio has been suggested as a noninvasive and readily accessible alternative. What are the best ways to convert SF and PF ratios in critically ill patients, in terms of their diagnostic/prognostic accuracy and clinical utility?

Data sources: We comprehensively searched databases (MEDLINE, Embase, Web of Science, Cochrane library) to identify relevant studies.

Study selection: Any observational studies that compared the SF to PF ratio in critically ill patients. We assessed individual study risk of bias (ROB) using the revised QUADAS II tool.

Data extraction: We included 45 observational studies, ranging from 61 to 141,000 measurements.

Data synthesis: SF to PF imputation was less accurate when the Spo2 was equal to or greater than 97%. Otherwise, all studies were able to establish strong correlational relationships between SF and PF ratios, but there was no clear best equation. Based on ease of use, size, generalizability and methodology, we were able to prioritize four equations (one linear, one logarithmic linear, and two nonlinear). All four equations showed strong correlation between SF and PF ratios, with the linear equation being easiest to apply. The SF ratio also correlated well with clinical outcomes when compared with the PF ratio, both as an individual value and as part of a comprehensive score, with more discriminating performance in some cases.

Conclusions: SF and PF ratios demonstrate good correlation, and may have similar prognostic value. Although there is no clear optimal method to convert SF to PF ratios, linear equations show acceptable correlation and are most easily applied at the bedside.