Critical care explorations最新文献

Objectives: To test whether urine olfactomedin 4 (uOLFM4) can predict furosemide responsiveness in patients at high risk for acute kidney injury (AKI) early in the PICU course. A secondary outcome was prediction of kidney replacement therapy (KRT) initiation in this cohort.

Design: Prospective observational cohort study.

Setting: Two quaternary care PICUs.

Patients: Two hundred forty PICU patients with a renal angina index greater than or equal to 8 and a urine sample collected on PICU days 0-1. Fifty-six patients received a furosemide dose on PICU days 1-4 and 44 received KRT.

Interventions: None.

Measurements and main results: uOLFM4 was measured via enzyme-linked immunosorbent assay. Urine neutrophil gelatinase-associated lipocalin (uNGAL) was measured via particle-enhanced turbidimetric immunoassay by the clinical laboratory. We compared groups using Mann-Whitney U tests or Kruskal-Wallis tests and calculated area under the receiver operating characteristic curve for performance of uOLFM4 and uNGAL to predict furosemide responsiveness on PICU days 1-4 and KRT receipt. Median (interquartile range) uOLFM4 and uNGAL concentrations were higher in patients who were furosemide nonresponsive (uOLFM4 694 ng/mL [214-1478 ng/mL] vs. 139 ng/mL [46-529 ng/mL]; p = 0.0004 and uNGAL 1149 ng/mL [204-2284 ng/mL] vs. 53 ng/mL [50-1533 ng/mL]; p = 0.0076) and higher in patients who received KRT. uOLFM4 and uNGAL had similar moderate discriminatory ability to predict furosemide responsiveness (area under the curve, 0.77 [95% CI, 0.65-0.90]; p = 0.0005 and 0.71 [95% CI, 0.57-0.85]; p = 0.0088, respectively). uOLFM4 of 156 ng/mL had 59% sensitivity, 96% specificity, a positive predictive value of 64%, and negative predictive value (NPV) of 95% to predict furosemide responsiveness.

Conclusions: In critically ill children at high risk for AKI, both uOLFM4 and uNGAL have moderate discriminatory ability to predict furosemide responsiveness and KRT receipt on the first day of PICU stay. The NPV greater than or equal to 95% for uOLFM4 for both outcomes make it a promising candidate for implementation into clinical decision support to facilitate early KRT initiation decision-making.

Importance: IV fluids are the cornerstone for management of acute kidney injury (AKI) after sepsis but can cause fluid overload. A restrictive fluid strategy may benefit some patients; however, identifying them is challenging. Novel causal machine learning (ML) techniques can estimate heterogenous treatment effects (HTEs) of IV fluids among these patients.

Objectives: To develop and validate a causal-ML framework to identify patients who benefit from restrictive fluids (< 500 mL fluids within 24 hr after AKI).

Design setting and participants: We conducted a retrospective study among patients with sepsis who developed acute kidney injury (AKI) within 48 hours of ICU admission. We developed a causal-ML approach to estimate individualized treatment effects and guide fluid therapy. We developed the model in Medical Information Mart for Intensive Care IV and externally validated it in Salzburg Intensive Care database.

Main outcomes and measures: Our primary outcome was early AKI reversal at 24 hours. Secondary outcomes included sustained AKI reversal and major adverse kidney events by 30 days (MAKE30). Model performance to identify HTE of restrictive IV fluids was assessed using the area under the targeting operator characteristic curve (AUTOC), which quantifies how well a model captures HTE, and compared with a random forest model.

Results: Causal forest model outperformed random forest in identifying HTE of restrictive IV fluids with AUTOC 0.15 vs. -0.02 in external validation cohort. Among 1931 patients in external validation cohort, the model recommended restrictive fluids for 68.9%. Among these, patients who received restrictive fluids demonstrated significantly higher rates of early AKI reversal (53.9% vs. 33.2%, p < 0.001), sustained AKI reversal (34.2% vs. 18.0%, p < 0.001), and lower rates of MAKE30 (17.1% vs. 34.6%, p = 0.003). Results were consistent in the adjusted analysis.

Conclusions and relevance: Causal-ML framework outperformed random forest model in identifying patients with AKI and sepsis who benefit from restrictive fluid therapy. This provides a data-driven approach for personalized fluid management and merits prospective evaluation in clinical trials.

Objectives: To identify the prevalence of over-assistance from mechanical ventilation (MV) and to assess whether reducing MV support could be done safely in neurosurgical ICU patients in terms of risk of under-assistance and brain's oxygenation.

Design: Prospective observation study.

Setting: Neurosurgical trauma ICU, Toronto, ON, Canada.

Patients: Twenty-seven brain-injured patients on MV having indication of a spontaneous breathing trial (SBT).

Interventions: Level of pressure support ventilation (PSV).

Measurements and main results: In neurosurgical patients, regional ventilation distribution using electrical impedance tomography, patient's respiratory drive (airway occlusion at 100 ms [P0.1]), respiratory muscle pressure (Pmus), diaphragm and parasternal intercostal (PI) thickening fraction, brain oximetry, and electroencephalogram were assessed at clinical PSV (ClinPS), low PSV (LowPS, pressure support [PS] 5 cm H₂O, positive end-expiratory pressure [PEEP] 5 cm H₂O), SBT, PS 0 cm H₂O, and PEEP 0 cm H₂O. Over-assistance was defined by pressure muscle index less than 0 cm H₂O; under-assistance was defined as Pmus greater than or equal to 15 cm H₂O. Mixed effects models were used for analysis. Imbalanced dorsal/ventral distribution of ventilation improved by reducing assistance while respiratory effort increased. Over-assistance was present in ten cases (37%) during ClinPS and in none at LowPS and SBT; under-assistance was present in two, four, and seven cases at ClinPS, LowPS, and SBT. During SBT, compliance and end-expiratory lung volume decreased (p < 0.0001). Brain activity did not vary. P0.1 greater than or equal to 4 cm H₂O was associated with Pmus greater than or equal to 15 cm H₂O with 80% sensitivity and 91% specificity during SBT.

Conclusions: Neurosurgical patients seem to frequently be overassisted under PSV. Reducing the ventilatory support is often feasible and Pmus and P0.1 can help with detecting under-assistance.

Importance: Long-term functional outcomes and health-related quality of life (HRQoL) in survivors of cardiogenic shock treated with venoarterial extracorporeal membrane oxygenation (ECMO) remain poorly understood.

Objectives: This study aimed to evaluate these outcomes in a cohort of venoarterial ECMO survivors.

Design, setting, and participants: This single-center observational study was conducted in the ICU of a French academic hospital and included consecutive adult patients treated with venoarterial ECMO who were discharged alive between February 2016 and December 2021.

Main outcomes and measures: The primary endpoint was a favorable functional outcome at least one year after ICU discharge, defined as a score on the modified Rankin Scale of 0 or 1, indicating no functional limitations affecting usual activities. Secondary endpoints included HRQoL, assessed using the EuroQol 5D five levels (EQ-5D-5L) and 36-item short-form health survey (SF-36) questionnaires. Of 79 hospital survivors, 65 patients were evaluated after a median follow-up of 2.8 years (1.2-4.2 yr). A favorable functional outcome was observed in 35 of 65 patients (54%). No association was found between ICU admission characteristics, serum neurobiomarkers (neuron-specific enolase, S100B), electroencephalogram findings during venoarterial ECMO, and functional outcome. Male sex was the only parameter associated with higher odds of favorable functional outcome (adjusted odds ratio, 4.19; 95% CI, 1.35-14.5). HRQoL assessments showed moderate-to-severe issues in 15% of patients, mainly affecting mobility, pain/discomfort, and mental health. Patients with favorable outcomes reported better scores across all domains of the EQ-5D-5L and higher scores on both the physical and mental components of the SF-36.

Conclusions and relevance: Approximately half of venoarterial ECMO survivors achieved excellent long-term functional outcomes. Nonetheless, a subset experienced ongoing limitations, particularly related to physical function and mental health, underscoring the need for targeted long-term follow-up and support.

Background: A process evaluation systematically examines how an intervention is delivered, including activities, procedures, and contextual factors influencing implementation. Existing process evaluation frameworks were primarily developed for education or public health settings, and do not reflect the complexity of critical care trials, which often involve medical technologies, high-acuity patients, and multidisciplinary care in dynamic environments. This study aimed to develop a framework (the POETIC (PrOcess Evaluation of Trials In Critical care) framework) to guide researchers in designing and conducting process evaluations that capture delivery quality and contextual understanding specific to critical care settings.

Methods: Framework development began in 2015 and followed an iterative, multi-phase process. Phase 1 included structured literature reviews to identify a) existing process evaluation frameworks and dimensions, and b) critical care trials with embedded process evaluations. Both reviews were updated in 2025 to reflect POETIC's usage and ensure continued relevance. Phase 2 involved expert consultations with trialists, clinicians, and methodologists to refine framework dimensions.

Results and conclusions: Four key process evaluation frameworks and two U.K.-based critical care trials informed initial development. The 2025 update identified five additional U.K. trials, four of which applied POETIC, supporting its relevance and applicability. Expert consensus identified five core dimensions:• Context (Unit Culture, Organizational Structure, Resources, Usual Practice, Attitudes and Perceptions)• Fidelity (extent to which the intervention is delivered as intended)• Dose (amount of the intended intervention delivered and received)• Reach (extent to which the target population is exposed to, or engages with, the intervention)• Quality of Delivery (integrative measure of Fidelity, Dose, and Reach)The framework includes recommended methods such as checklists, interviews, routine trial data, and observations. It was iteratively refined to enhance usability and adaptability and has since been applied in multiple U.K.-based perioperative and critical care trials, demonstrating its utility in U.K. ICU settings. The POETIC framework supports structured evaluation of delivery quality and context in critical care trials, improving trial interpretation and advancing intervention design, delivery, and real-world applicability. Distinctively, POETIC operationalizes ICU-specific Context sub-constructs and provides a prespecified composite Quality of Delivery index to link intervention delivery to outcomes.

Objectives: In the ICU, the optimal patient population for neuromuscular electrical stimulation (NMES) and the most appropriate evaluation tools remain unclear. This study aimed to assess whether combining early mobilization with NMES in older critically ill patients improves lower limb muscle strength and physical function at hospital discharge.

Design: Assessor-blinded, randomized controlled trial.

Setting: A single-center, emergency and critical care center ICU in Japan.

Patients: Patients 65 years old or older with an Acute Physiology and Chronic Health Evaluation II (APACHE II) score greater than 20 admitted to the ICU.

Interventions: The participants were randomly assigned to the NMES group (NMES in addition to early mobilization) or the control group (early mobilization alone).

Measurements and main results: The primary outcome was quadriceps isometric strength (QIS), which was measured using a hand-held dynamometer to ensure objective assessment. QIS values were normalized to body weight. Outcome assessors were blinded to group allocation. A total of 44 patients were randomized, and 32 completed the study (NMES group: 17; control group: 15). The mean age was 77.6 ± 6.5 years, and the mean APACHE II score was 29.7 ± 6.3. NMES was performed for an average of 9.6 ± 4.8 days. There were no baseline differences between groups. At hospital discharge, the mean QIS was 0.46 ± 0.13 kgf/kg in the NMES group and 0.30 ± 0.13 kgf/kg in the control group (mean difference, 0.16; 95% CI, 0.07-0.25; p = 0.002). Secondary outcomes, including the 6-minute walk distance and the Barthel Index, were also greater in the NMES group.

Conclusions: NMES combined with early mobilization improved lower limb muscle strength and functional outcomes in older ICU patients.

Objectives: Our aim was to describe trends in tracheostomy utilization in infants requiring congenital heart surgery (CHS) during their index admission with specific focus on clinical and financial outcomes.

Design: A retrospective cohort study.

Setting: Data were obtained from the Pediatric Health Information System database.

Patients: Patients admitted as neonates (≤ 28 d) undergoing CHS with the use of cardiopulmonary bypass (CPB) during admission from 2004 to 2022 were identified. The cohort was divided into patients with vs. without tracheostomy.

Interventions: None.

Measurements and main results: We identified 13,415 neonatal admissions who underwent CHS with use of CPB, of which 391 (3%) underwent tracheostomy. Tracheostomy patients, compared with those without, were more likely to be female (46.8% vs. 40.0%; p = 0.007), of Black race (17.1% vs. 10.6%), preterm (29.2% vs. 14.1%), low birthweight (29.4% vs. 14.1%), had a higher frequency of chromosomal defects (23.5% vs. 8%), congenital airway (24% vs. 3.3%), and pulmonary (19.7% vs. 1.7%) abnormalities (all p < 0.001). Tracheostomy was associated with higher in-hospital mortality (23.8% vs. 8.6%), longer length of stay (183 vs. 26 d), higher cost of hospitalization ($1.2 vs. $0.2 million), and discharge to a location other than home (35.1% vs. 6.3%; all p < 0.001). Tracheostomy rates increased from 1.9% in 2004-2010 to 3% in 2017-2022 (p = 0.002), while the in-hospital mortality in these patients was similar (p = 0.72).

Conclusions: The rate of tracheostomy placement in complex neonates and infants requiring CHS has increased in recent years. Patients with congenital airway or pulmonary abnormalities, cleft lip and/or palate, chromosomal disorders, and those requiring more than one surgery requiring CPB during admission were at greatest risk for tracheostomy placement. Tracheostomy is associated with longer ICU and hospital length of stay, six-fold increase in hospitalization cost, and higher rate of in-hospital mortality in our study population.

Objective: To determine the accuracy of a custom version of the generative pretrained transformer (GPT)-4o large language model (LLM) in identifying PICU admissions with vs. without bacterial pneumonia using clinical notes.

Design: In this retrospective cohort study, the GPT-4o model was provided guidance on our institution's pneumonia diagnosis practices through a custom prompt and instructed to analyze PICU provider notes from the first 2 calendar days of PICU admission to identify bacterial pneumonia diagnoses. Diagnoses from the manually curated Virtual Pediatric Systems (VPS) Registry were used as the gold standard.

Setting: A 48-bed, academic, quaternary care PICU.

Patients: Children 3 months old to 18 years old admitted to the PICU from January 1, 2023, to December 31, 2023.

Interventions: None.

Measurements and main results: GPT-4o analyzed 10,081 notes from 3,317 PICU admissions over 5.0 minutes (mean 0.03 s per note). Of the 3317 study encounters, 481(14.5%) had a VPS admission pneumonia diagnosis. GPT-4o accurately classified 3143 of 3317 (94.8%) encounters. In a post hoc adjudication analysis, a blinded PICU attending reviewed patient charts with VPS-GPT discordant classifications. The GPT-4o classification matched that of the blinded PICU attending in 125 of 174 (71.8%) of such encounters. The most common reason for incorrect classification by GPT-4o was that a pneumonia diagnosis was listed in the initial notes but later rescinded when a different diagnosis was identified.

Conclusions: The GPT-4o LLM was able to accurately and rapidly identify critically ill children with vs. without bacterial pneumonia. This study suggests similar tools could be developed to automate and accelerate processes typically requiring manual chart review.

High-flow nasal cannula (HFNC) for acute hypoxemic respiratory failure is typically restricted to ICUs. We evaluated a ward-based, medical emergency team (MET)-supervised HFNC protocol (flow ≤ 40 L/min, Fio2 ≤ 0.40) with 2-/4-/8-hour nursing and respiratory therapist reassessments. Among 82 ward HFNC initiations (2021-2024), 38 (46%) required immediate ICU transfer (IMT) and 44 (54%) were Ward-Managed After MET (WMAM). Of WMAM patients, 18 transferred to ICU within 48 hours, and 26 remained on ward. WMAM patients accrued a median 1.46 ICU bed-days saved (interquartile range, 0.73-2.67); bootstrapped mean 1.63 (95% CI, 1.32-1.94), equivalent to 163 ICU days-saved per 100 initiations. Intubation (30% vs. 42%; p = 0.24) and 28-day mortality (32% vs. 39%; p = 0.47) were similar between WMAM and IMT; adjusted analyses were directionally consistent. Using an estimated $5,000 per ICU-day, cost avoidance was ≈$815,000 per 100 initiations. This MET-supervised model appears feasible, resource-sparing, and without apparent safety signal.

Objectives: Secondary infections are a common occurrence in critically ill COVID-19 patients. These are difficult to identify, and antibiotic usage is high in this population. Identification of biomarkers for secondary infections would help to ensure antibiotics are being utilized only for patients who require them. This study sought to identify a panel of biomarkers capable of distinguishing critically ill COVID-19 patients with and without secondary infections.

Design: A multicenter retrospective cohort study.

Setting: Three critical care units in Scotland, United Kingdom.

Patients: One hundred five patients admitted to critical care with COVID-19, and 49 healthy volunteer controls.

Interventions: None.

Measurements and main results: Serial blood samples were obtained from critically ill COVID-19 patients with and without confirmed secondary infections, and a single sample was collected from healthy volunteers to provide baseline metabolic profiles. Metabolomic analysis was performed using liquid chromatography-mass spectrometry, and metabolites that were significantly different between patients with and without secondary infections were identified. Additionally, metabolites capable of distinguishing Gram-positive from Gram-negative organisms were also investigated. Forty patients developed a secondary infection during the study period. A significant increase in metabolites creatine and 2-hydroxyisovalerylcarnitine, and a significant reduction in S-methyl-L-cysteine were detected in patients with secondary infections. This metabolite panel could identify patients with secondary infections with an area under the curve (AUC) of 0.83 (95% CI, 0.68-0.97). Metabolites differentiating Gram-positive and Gram-negative infections included betaine, N(6)-methyllysine, and phosphatidylcholines (PCs; 38:6), PC(38:4), PC(40:6), and PC(36:4) with an AUC of 0.88 (95% CI, 0.68-1.0).

Conclusions: Metabolomic profiling of critically ill COVID-19 shows promise for identification of novel biomarkers for secondary infections. Larger validation studies will help to confirm these findings.