Critical care explorations最新文献

Objectives: To determine if common antimicrobials (n = 11) are sequestered or degraded during a pediatric extracorporeal membrane oxygenation (ECMO) simulation.

Design: An ex vivo model of a closed ECMO circuit was established to simulate the treatment of a 3 kg infant. A control was used to quantify spontaneous antimicrobial degradation.

Setting: University research laboratory.

Participants: None.

Main outcomes and measures: The ECMO circuit was primed and maintained at physiologic pH and temperature for 7 hours. After baseline sampling, the antimicrobials were administered as a single bolus into the circuit. Eight plasma samples were taken from the controls and ECMO circuits over 7 hours. Antimicrobial concentrations were measured using validated high-performance liquid chromatography-tandem mass spectrometry methodology. The antimicrobial recovery was compared with baseline. Each simulation was performed in triplicate to assess simulation variability.

Results: The recovery mean (%) in ECMO at 7 hours for ampicillin 78%, cefotaxime 92%, flucloxacillin 72%, meropenem 81%, micafungin 72%, piperacillin 84%, and voriconazole 42% was significantly different from the baseline (p < 0.05). The recovery in the control at 7 hours for ampicillin 83%, cefotaxime 76%, flucloxacillin 90%, gentamicin 85%, meropenem 76%, piperacillin 92%, and tazobactam 93% was also significantly different from the baseline (p < 0.05). A significant relationship was identified in the ECMO model between the antimicrobial recovery (%) and the log partition coefficient (log p) of the studied antimicrobials (R2 = 0.52; p = 0.01). No significant relationship was identified between the protein binding and antimicrobial recovery (R2 = 0.23; p = 0.13).

Conclusions and relevance: The lipophilicity of an antimicrobial is a predictor of antimicrobial recovery in ECMO. Concentrations were significantly reduced at 7 hours for greater than 60% of the study antimicrobials in the ECMO models. Clinical studies are required for children receiving ECMO to determine if the current dosing regimens for antimicrobials provide therapeutic concentrations.

ICU readmissions remain a critical concern, carrying increased morbidity, mortality, and cost. We examined the epidemiology of unplanned ICU readmissions across 19 hospitals in the Common Longitudinal ICU data Format (CLIF) Consortium from January 2020 to December 2021 and the MIMIC-IV database. The cohort included 185,241 adult ICU admissions, excluding postoperative or post-interventional procedure readmissions. Overall, 8.6% of ICU discharges were readmitted during the same hospitalization. Unplanned readmissions occurred in 1.9% of cases within 24 hours, 3.4% within 48 hours, and 4.5% within 72 hours of discharge. Readmitted patients experienced markedly higher in-hospital mortality (20.6% vs. 2.1%; p < 0.001). Compared with initial ICU stays, readmissions were associated with greater use of respiratory support (42.3% vs. 35.3%) and vasopressors (26.1% vs. 23.1%). Hospitals with stepdown units demonstrated comparable unplanned ICU readmission rates. These findings demonstrate that ICU readmissions remain common, are associated with poor outcomes, and require greater organ support. Improved characterization of high-risk subphenotypes is needed to inform safer discharge processes.

Importance: The alternative complement pathway is a key component of host defense against bacteremia and other infections. However, dysregulated activation can contribute to excessive inflammation and worse clinical outcomes during bacteremia and infectious syndromes such as sepsis.

Objectives: We aim to identify variants in alternative pathway (AP) genes that influence the risk for bacteremia and sepsis.

Design, setting, and participants: We used summary statistics from a Veterans Affairs Million Veteran Program (MVP) genome-wide by phenome-wide association study of more than 600,000 individuals.

Main outcomes and measures: Using seven electronic health record-derived Phecodes for bacteremia or sepsis, we investigated associations with single-nucleotide polymorphisms (SNPs) in genes encoding multiple AP components. We also investigated potential regulatory SNPs near candidate genes based on expression quantitative trait loci (eQTL) data or in silico modeling (Combined Annotation Dependent Depletion and RegulomeDB scores).

Results: In the MVP trans-ancestral meta-analysis, we identified 25 unique lead genic SNPs with a minor allele frequency of greater than 1% that were significantly associated with incidence of sepsis or bacteremia Phecodes. Most were intronic (n = 21), with four exonic variants, including one in C5AR1 (rs4804049) that has novel associations with multiple Phecodes. Outside of AP gene loci, we also identified significant associations in 14 unique SNPs with predicted regulatory effects by in silico modeling and 11 unique SNPs with eQTL data suggesting an impact on AP gene expression. Variants in complement factor B (CFB), complement factor I (CFI), and C5a receptors (C5AR1/C5AR2) accounted for most of the significant genic SNPs, while noncoding functional variants primarily affected CFB, CFD, and the C5a receptor 1 (C5AR1).

Conclusions and relevance: We identified potentially clinically relevant genetic variation in the alternative complement pathway that may contribute to individual susceptibility to bacteremia and sepsis syndromes. Further study is required to understand the mechanisms behind these associations and their clinical impacts.

Importance: Although survival rates in ICUs have improved, ICU length of stay is increasing, particularly among critically ill patients requiring prolonged mechanical ventilation. These patients often face challenges in early rehabilitation. Despite the importance of early mobilization, its implementation is often hindered by the severity of patient conditions. The details of rehabilitation interventions, particularly the element of intervention time, have not been fully examined.

Objectives: This study evaluated the time, content, and effects of rehabilitation during ICU stays.

Design, setting, and participants: This study is a secondary analysis of data from the Japanese Rehabilitation and Risk Factors on the Post-Intensive Care Syndrome (J-RELIFE) prospective multicenter registry, which enrolled critically ill patients across 22 institutions in Japan. From this registry, we identified 423 patients who underwent mechanical ventilation in the ICU for greater than 48 hours. Patients were categorized into three groups based on ICU stay length: short-, medium-, and long-term (199, 157, and 67 patients, respectively).

Main outcomes and measures: The primary variable was rehabilitation intervention time in the ICU, analyzed relative to ICU stay length. Secondary measures included the association between rehabilitation time and the highest Sequential Organ Failure Assessment (SOFA) score, as well as functional outcomes at ICU and hospital discharge.

Results: The long-term group had significantly greater rehabilitation time (p < 0.05). A significant correlation was observed between rehabilitation time and highest SOFA score (r = 0.354; p < 0.001). In the long-term ICU group, even with high illness severity and delayed mobilization, patients' activities of daily living were restored by discharge.

Conclusions and relevance: Our registry-based analysis shows that severely ill ICU patients require extended rehabilitation interventions. This highlights the need for staffing and implementation systems that can ensure sufficient rehabilitation time for severely ill patients.

Objectives: Multimodality monitoring based prognostication in pediatric traumatic brain injury (TBI) relies heavily on the evaluation of instantaneous and easily interpretable monitoring values. Entropy quantifies the level of disorder within a system reflecting overall activity of sensitive closed-loop feedback homeostatic mechanisms. Multiscale entropy (MSE) assesses entropy across different time scales to examine various physiologic systems and processes that operate across different time scales. The current understanding of MSE suggests that low entropy reflects increased rigidity of the various homeostatic control systems, reflecting underperformance of mechanisms such as cerebral autoregulation. This hypothesis-generating retrospective study explores the value of MSE for prognostication after pediatric TBI.

Design: Retrospective analysis of data from an observational multicenter database.

Setting: Ten PICUs across the United Kingdom.

Patients: One hundred thirty-five children with severe pediatric TBI receiving invasive neuromonitoring between 2018 and 2022.

Interventions: None.

Measurements and main results: MSE was calculated based on 10-second time trends of different biosignals (incl. blood pressure, heart rate, intracranial pressure [ICP]). MSE metrics were assessed using univariable and multivariable (logistic regression with backward stepwise elimination and sliding dichotomy) methods. Last, correlation coefficients between MSE and clinical or monitoring metrics were assessed. Decreased MSE of physiologic biosignals were associated with worse outcomes and remained associated with outcomes when added to multivariable analyses. Within multivariable logistic regression analyses (covariates: Injury Severity Score [ISS], Rotterdam score, ICP, pressure reactivity index [PRx]), the resulting odds ratios (ORs) were: MSE arterial blood pressure (abp: OR, 0.83; p = 0.014), MSE cerebral perfusion pressure (cpp: OR, 0.86; p = 0.024), and MSE icp (OR, 0.87; p = 0.029). MSE displayed weak associations with clinical parameters reflecting higher TBI severity (i.e., ISS, Abbreviated Injury Scale, Glasgow Coma Scale, etc.) but moderate correlations with PRx (correlation coefficients: MSE abp, -0.47; MSE cpp, -0.55) and ICP (MSE abp, -0.3).

Conclusions: Biosignal complexity is a promising tool for improving individualized prognostication after pediatric TBI. Our results further underpin the association between MSE and the function of physiologic autoregulatory mechanisms.

Objectives: Sepsis trials likely include patients who vary in response to therapeutic interventions. The optimal approach to identify such differences in treatment response remains unclear. Estimating individualized absolute risk differences (iARDs) to model treatment response at an individual patient level using supervised effect models applied to randomized trial data may be informative. We explored the relationship between two subgrouping approaches and a recently published iARD model for the effect of early goal-directed therapy (EGDT) resuscitation in sepsis.

Design: Secondary analysis of the Protocolized Care for Early Septic Shock (ProCESS) and Australasian Resuscitation in Sepsis Evaluation (ARISE) trials. We applied clinical subtypes (α, β, γ, δ) to 829 ProCESS and 1588 ARISE patients and biologic "hyperinflammatory" and "nonhyperinflammatory" subphenotypes to 363 ProCESS patients with biomarker data using established methods. We predicted iARDs with supervised learning using clinical variables as predictors and 90-day mortality as the primary outcome. We evaluated iARD variability within subgroups.

Setting: Eighty-one sites worldwide.

Patients/subjects: Adults with septic shock.

Interventions: EGDT or usual care.

Measurements and main results: The average treatment effect of EGDT appeared to vary within both clinical and biologic subphenotypes. EGDT appeared potentially beneficial in the β and nonhyperinflammatory subphenotypes but harmful in the γ and hyperinflammatory subphenotypes. However, the predicted iARDs within each subgroup ranged from considerable harm to considerable benefit. For example, for the β-subtype, the average mortality reduction from EGDT was 8.5% (95% CI, -0.4 to 17.5), but the iARDs ranged from a 29% increase to a 16% reduction in mortality, with 39% of patients predicted to be harmed.

Conclusions: Although both clinical and biologic phenotyping may identify subgroups whose average treatment effect is beneficial or harmful, individual risks and benefits within subgroups still vary dramatically, raising concern that phenotyping may not reliably or safely personalize sepsis care.

Context: To evaluate and compare sublingual microscopy (SLM) to urinary glycosaminoglycan (GAG) assays, dimethylmethylene blue (DMMB) assay, and liquid chromatography tandem mass spectrometry (LC-MS/MS) quantification of GAGs as biomarkers of microvascular dysfunction in patients with sepsis and septic shock.

Hypothesis: Indicators of microvascular dysfunction and markers of endothelial glycocalyx (eGC) degradation would be associated with sepsis.

Methods and models: Prospective, observational case-control study.

Setting: Denver Health Medical Center, a safety-net hospital in Denver, CO.

Subjects: Forty-four adult patients with sepsis or septic shock and 24 healthy control patients undergoing elective orthopedic procedures. Exclusion criteria included pregnancy and incarceration.

Results: Sublingual microvascular parameters (De Backer Density, proportion of perfused vessels) were measured using darkfield sidestream microscopy, and urinary GAGs were measured via DMMB colorimetric assay and LC-MS/MS targeting heparan sulfate (HS), dermatan sulfate, and keratan sulfate. Validation of HS quantification was performed on a subset using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS). LC-MS/MS HS was significantly higher in sepsis vs. controls (Area under the curve 0.85; 95% CI, 0.76-0.95), demonstrating higher diagnostic performance than SLM (De Backer Density AUC 0.71) and DMMB GAGs (AUC 0.62). LC-MS/MS and HILIC-MS HS levels were strongly correlated (R² = 0.97, p < 0.001). DMMB GAGs were associated with HS subtypes (p = 0.05) and SLM density (p = 0.03). No significant associations with in-hospital mortality or acute kidney injury were observed.

Interpretation and conclusions: Among the evaluated modalities, LC-MS/MS quantification of HS showed the greatest discriminative ability for identifying sepsis and correlated strongly with established mass spectrometric methods. SLM exhibited moderate diagnostic utility and significant associations with GAG levels, reinforcing its biologic relevance. However, its bedside application may be limited by challenges in image acquisition and analysis. The concordance across SLM, DMMB, and LC-MS/MS supports eGC degradation as a key feature of sepsis pathophysiology. These findings highlight the promise of LC-MS/MS as a scalable, rapid, and mechanistically informed platform for biomarker-driven enrichment in future sepsis trials and clinical care.

Background: Mortality prediction in ICU adults is only marginally improved when medication regimen complexity (MRC) data is incorporated into traditional regression models. Machine learning (ML) may improve this prediction.

Objective: To compare the performance of different ML approaches incorporating MRC data to both traditional and advanced regression approaches, with and without MRC data, to predict hospital mortality in ICU adults.

Derivation cohort: Nine hundred ninety-one ICU adults at the University of North Carolina (UNC) Health System.

Validation cohort: A temporally distinct cohort of 4,878 ICU adults at UNC and an external cohort of 12,290 ICU adults at the Oregon Health and Science University.

Prediction model: Supervised, classification-based ML models (e.g., Random Forest, Support Vector Machine [SVM], and XGBoost) were developed. Twenty-seven variables at ICU baseline (age, sex, service, diagnosis) and 24 hours (illness severity, supportive care use, fluid balance, laboratory values, MRC-ICU, vasopressor use) associated with mortality, and 14 missingness indicator variables, were included in each ML model. Traditional and advanced (equipped with linear predictors, predictors in nature cubic splines, predictors in smoothing cubic splines, and local linear predictors) regression models were optimized using stepwise selection by Bayesian Information Criterion. Area under the receiver operating characteristic (AUROC) was compared among models.

Results: Random Forest, SVM, and XGBoost achieved AUROCs of 0.83, 0.85, and 0.82, respectively, on the test set. Traditional regression models based on Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation (APACHE) II, MRC-ICU + Sequential Organ Failure Assessment + APACHE II with and without an interaction term, and a full model including all 27 available variables demonstrated AUROCs of 0.81, 0.72, 0.82, 0.83, and 0.86, respectively. Advanced regression models yielded AUROCs of 0.85, 0.86, 0.85, and 0.84, respectively. The MRC-ICU exhibited a moderate level of feature importance in both XGBoost and Random Forest models. Models demonstrated lower performance in the validation cohorts.

Conclusions: Use of ML, compared with traditional and advanced regression methods, did not improve hospital mortality prediction despite medication data inclusion. The MRC-ICU demonstrates moderate feature importance in select ML models.

Objective: Severe infectious diseases are a leading cause of morbidity and mortality worldwide, particularly in sub-Saharan Africa (SSA), where young and middle-aged adults are disproportionately affected. Although age-related immune changes such as inflammaging and immunosenescence have been well characterized in high-income countries, their relevance to host responses during infection in SSA remains poorly understood. We aimed to characterize age-associated differences in immune, metabolic, and endothelial responses to severe infection in a prospective, multicenter cohort of adults in Uganda.

Design: Prospective cohort study.

Setting: Two public referral hospitals in Uganda.

Patients: Non-pregnant adults (18 yr old or older) hospitalized with severe, undifferentiated infection.

Interventions: None.

Measurements and main results: We analyzed clinical data and serum Olink proteomic profiles from 434 participants (median age: 45 yr old, interquartile range : 31-57). Clinically, illness severity and mortality were highest and comparable among adults 35-44, 45-59, and 60 years old or older, relative to younger adults. HIV prevalence peaked in the 35-44 and 45-59 age groups. Although most host responses were conserved across age groups after adjustment for sex and high-burden co-infections, patients 60 years old or older exhibited distinct immune dysregulation characterized by signs of Th1-predominant innate immune activation (increased CXCL9, CCL18, MCP1, and MCP4 expression, reduced interleukin-13 expression), dysregulated adaptive immunity (increased soluble CD27 and CD70 expression, reduced CD21 [CR2] expression), and increased cellular turnover and endothelial remodeling.

Conclusions: Older age (60 yr old or older) is associated with distinct host responses to severe infection in SSA. These findings may inform development of age-stratified, host-directed treatment strategies for severe infectious diseases.

Importance: Excessive cognitive load impairs task performance and contributes to burnout, but studies of cognitive load in pediatric critical care medicine (PCCM) settings are limited.

Objectives: To better understand cognitive load in an academic PCCM setting and how cognitive load differs based on experience, role, task type, and task frequency.

Design, settings, and participants: Prospective two-part survey at a quaternary children's hospital PCCM department. Part 1 (February to March 2022) assessed routine role-specific tasks; part 2 (June to August 2022) evaluated acute resuscitation. Participants were registered nurses (RNs), respiratory therapists (RTs), and physicians + advanced practice providers (APPs).

Main outcomes and measures: Raw cognitive load (1-9 Paas scale), net cognitive load (Paas × task frequency), and NASA-Task Load Index (NASA-TLX) subdomain scores (0-100) for acute resuscitation. Role was the primary exposure; between-group differences were analyzed using analysis of variance with pairwise comparisons.

Results: There were 109-part 1 and 79-part 2 survey respondents. Across all tasks, mean raw Paas scores were highest for physicians + APPs (5.2 ± 1.1), followed by RNs (4.8 ± 1.0) and RTs (4.0 ± 1.4; p = 0.004). In the three highest-load shared tasks-acute resuscitation, rescuing a decompensating patient, and managing advanced life-support devices-RNs reported significantly higher raw load than physicians + APPs and RTs. For bedside patient assessment, RNs had higher net cognitive load (25.0 ± 8.7) than physicians + APPs (20.3 ± 7.0; p = 0.01) and RTs (18.9 ± 8.9; p = 0.01). Nursing experience correlated with overall net cognitive load (r = 0.30; p = 0.02). During resuscitation, RNs reported higher NASA-TLX scores than other providers in all but two subdomains.

Conclusions and relevance: Cognitive load in PCCM varies significantly by role and task type. Nurses experience high raw cognitive load from critical events and net cognitive load from bedside patient assessment, suggesting opportunities for role-specific workflow redesign and cognitive load reduction strategies to benefit staff and patients.