Critical Care最新文献

Background: The relationship between fluid responsiveness and right-and left-sided congestion remains underexplored. This study aimed to delineate the dynamic behaviour of venous congestion and extravascular lung water index (EVLWI) during a fluid challenge depending on the concomitant changes in cardiac index (CI).

Methods: In patients from three intensive care units, for whom a 500-mL fluid challenge was administered, we retrospectively analysed CI, central venous pressure (CVP), venous excess ultrasound (VExUS), and EVLWI, which had been prospectively recorded. A VExUS congestion point was calculated from 0 to 7 by assigning 1 point to each degree of abnormality for the 4 investigated veins. A subgroup analysis was planned in patients with acute respiratory distress syndrome (ARDS).

Results: We analysed 64 patients, of whom 42 (66%) were fluid responders (FR+) defined by a CI increase ≥ 15% with fluid infusion. Before the fluid challenge, CVP was lower in FR + than in fluid non-responders (FR-) (7.3 [2.9-10.3] vs. 10.6 [8.2-13.0] mmHg,respectively, p = 0.002). VExUS grades and congestion points were not different between FR + and FR- (Grade 0: 62% vs. 55%, respectively, p = 0.601; congestion points: 1.0 [0.0-2.3] vs. 2.0 [1.0-3.0], respectively, p = 0.053). EVLWI was also similar between groups. Following fluid administration, VExUS grade deterioration occurred in 5% of FR + versus 73% of FR- (p < 0.001). After the fluid challenge, abnormal VExUS grades were more prevalent in FR - than in FR+ (91% vs. 43%, respectively, p < 0.001), and congestion points were higher (4.0 [3.0-5.0] vs. 1.5 [1.0-3.0], respectively, p < 0.001). CVP increased by 1.4 [0.4-2.4] mmHg in FR + and 2.0 [1.1-3.5] mmHg in FR- (p = 0.064). Among 25 patients with ARDS, EVLWI increased more than in patients without ARDS, in both FR- (by 1.7 [0.9-3.3] vs. 0.7 [0-1.4] mL/kg, respectively, p = 0.046) and FR+ (by 1.0 [-0.6-2.5] vs. 0 [-0.7-0.4] mL/kg, respectively, p = 0.009).

Conclusion: A fluid challenge worsened venous congestion, assessed by VExUS as well as CVP, in fluid non-responders, while it was not in fluid responders.

Background: Carnitine deficiency affects mitochondrial and muscle function, but its relevance during critical illness remains unknown. Our aim was to investigate the relationship between plasma free carnitine concentrations and outcome in prolonged critical illness.

Methods: In this secondary analysis of the EPaNIC randomised controlled trial, abnormal plasma free carnitine concentrations, measured on ICU-day-6 (N = 1600), were defined by their association with a lower likelihood of an earlier alive ICU discharge (the primary endpoint) in a Cox proportional hazards model. Subsequently, they were binned based on their distribution and partial residuals in the Cox-model. Adjusted multivariable Cox-model and logistic regression analysed both association of abnormal carnitinemia with acute and long-term morbidity and mortality, and predictive risk factors.

Results: The median plasma free carnitine concentration on ICU-day-6 was 34.8 (IQR 24.4-49.8 µmol/L). Surprisingly, higher concentrations associated with a lower likelihood of an earlier alive ICU discharge (HR [95% CI] (per 10 µmol/L increase): 0.97 [0.95-0.99]). Yet, the partial residuals plot revealed this likelihood to be lower for patients with concentrations corresponding to the lowest (< 24 µmol/L; N = 374) and highest quartiles (> 50 µmol/L; N = 395) as compared to intermediate quartiles (24-50 µmol/L; N = 831). Both low and high carnitine concentrations were associated with a prolonged ICU and hospital dependency, a prolonged need for life-supporting therapies, and increased mortality at 90-days. Carnitine concentrations above 50 µmol/L also associated with muscle weakness and increased two and five year-mortality.

Conclusion: On ICU-day-6, both low and high free carnitine concentrations associated with delayed ICU-recovery, and excess morbidity and mortality, suggesting a U-shaped relationship.

Background: Pleural line (PL) movement, assessed by lung ultrasound, is crucial for the detection of pneumothorax and might also indicate overdistention, but research is limited by the lack of a quantitative tool. We set out to answer two research questions: can PL movement be quantified using open-source motion tracking software, and can PL movement be used to identify overdistention? We hypothesize that motion tracking of the PL is feasible and represents an accurate estimation of lung sliding.

Methods: Lung ultrasound video clips from three patient groups were used: (1) healthy volunteers during expiratory hold maneuvers (functional residual capacity) and quiet breathing, (2) ICU patients, blindly assessed for lung sliding (absent, doubtful, evident but limited or evident and extensive) and (3) Severe COVID-19 viral pneumonia patients undergoing PEEP titration and electrical-impedance tomography. Open-source software that implements the "Channel and Spatial Reliability Tracking" tracker algorithm was used for motion tracking, identifying the PL at three points and a soft tissue reference. Each motion-time curve was subsequently smoothed and normalized to account for soft tissue displacement. The maximum lateral movement on the transversal plane among the three normalized PL landmarks defined PL movement.

Results: In 143 video clips from 7 healthy individuals, PL movement increased from functional residual capacity (1.2 ± 0.6 mm) to quiet breathing (5.4 ± 2.5 mm; p < 0.01). In 336 video clips from 40 ICU patients, PL movement increased from absent (2.7 ± 1.2 mm) to extensive lung sliding (14.7 ± 5.8 mm; p < 0.01). Ordered logistic regression predicted Absent sliding with 71% balanced accuracy, with motion tracking correctly identifying all cases and no patients without lung sliding misclassified as extensive, based on visual inspection of the pleural line. In 358 video clips from 30 patients undergoing PEEP titration, there was an association between overdistention quantified by electrical-impedance tomography and PL movement (Spearman-rho=-0.6). PL movement decreased from low to high PEEP levels (p < 0.01).

Conclusions: Pleural line motion tracking is feasible and provides quantitative insight into pleural movement based on data from healthy volunteers and visual inspection of images from ICU patients. Moreover, pleural line movement allows accurate assessment of overdistention during mechanical ventilation when compared with electrical-impedance tomography.

Objective: To assess whether skin blood flow (SBF) monitoring combined with passive leg raising (PLR) can predict microvascular fluid responsiveness in septic patients.

Design: Prospective observational study.

Setting: Single-center, 18-bed medical ICU in a tertiary university hospital in Paris, France.

Patients: Adult patients with sepsis requiring intravenous fluid administration.

Interventions: Patients underwent a standardized PLR maneuver followed by a 500 mL saline fluid administration. Peripheral SBF was continuously monitored by fingertip laser Doppler flowmetry.

Measurements and main results: Of 37 patients included, 27 (73%) were classified as fluid responders, defined by a > 15% increase in SBF after volume expansion (ΔSBF-VE). In responders, SBF increased significantly during PLR (ΔSBF-PLR 40% [21-105]), while no significant changes were observed in non-responders. SBF variations induced by PLR (ΔSBF-PLR) strongly predicted fluid responsiveness with an AUROC of 0.95 [0.86-1.00] (P < 0.001). A ΔSBF-PLR threshold of > 6% identified responders with an 96 [80-100] % sensitivity and 90 [59-100] % specificity. Positive predictive value was 96 [80-100] % and negative predictive value was 91 [59-100]. Changes in SBF did not correlate with changes in cardiac output after volume expansion (R² =0.04, P = 0.28).

Conclusions: In septic patients, PLR-induced changes in SBF reliably predict peripheral microvascular responsiveness to a subsequent volume expansion. This simple, non-invasive approach may facilitate personalized fluid strategies aimed at optimizing microvascular tissue perfusion.

Background: Mechanical power increases with positive end-expiratory pressure (PEEP). However, its injurious potential may depend on the available lung gas volume, which can be modified by alveolar recruitment. We investigated how PEEP-induced recruitment affects mechanical power.

Methods: We analyzed previously collected data on 20 patients with acute respiratory distress syndrome who underwent a decremental PEEP trial (15-5 cmH₂O). End-expiratory lung volume and respiratory mechanics were measured to quantify recruited volume, functional residual capacity (FRC), and the recruitment-to-inflation (R/I) ratio. Absolute power and power normalized to aerated lung volume (FRC + recruited volume) were calculated at each PEEP level. Patients were classified as having higher or lower recruitability according to the cohort median recruited volume accrued between PEEP 5 and 15 cmH₂O, expressed as a fraction of FRC (median 0.42).

Results: Absolute mechanical power increased linearly with rising PEEP (approximately + 1 J/min per cmH₂O), from 20 [16-23] J/min at 5 cmH₂O, to 31 [28-33] J/min at 15 cmH₂O, irrespective of recruitability (low recruitability: + 1.12 J/min per cmH₂O, p < 0.001; high recruitability: + 0.96 J/min per cmH₂O, p < 0.001, p for interaction = 0.12). Normalized power increased in patients with lower recruitability (+ 0.43 J/min/L per cmH₂O, p < 0.001) but decreased in those with higher recruitability (- 0.33 J/min/L per cmH₂O, p < 0.001; p for interaction < 0.001). The reduction in normalized power was strongly related to PEEP-induced recruitment, expressed as recruited volume/FRC (- 102% per unit, R² = 0.75, p < 0.001), and to R/I ratio (- 38% per unit, R² = 0.69, p < 0.001). Associations with PEEP-related changes in compliance (R² = 0.40, p = 0.003) and PaO₂/FiO₂ (R² = 0.33, p = 0.008) were weaker. In the multivariate model, PEEP-induced recruitment (p = 0.002) and compliance changes (p = 0.011) remained independent predictors of normalized power changes.

Conclusions: Absolute mechanical power increases with higher PEEP, but power per aerated lung decreases when PEEP produces substantial recruitment. PEEP-induced increases in absolute power do not necessarily imply a higher mechanical load per alveolar unit. Recruited volume and compliance changes are the main physiological determinants of this effect. Among bedside tools, the R/I ratio best identifies whether and to what extent PEEP will reduce or increase mechanical power per alveolar unit.

Background: The assessment of comatose ICU patients presents several challenges with respect to the etiology, depth and ultimate outcome. The acceptance in 1959 of the worst-outcome scenarios of coma, i.e. brain death, and the publication of the Harvard Brain Death Criteria in 1968, were key developments in the management of irreversible coma. Pharmacologic confounders often complicate coma assessments, including brain-death determination. Moreover, associated clinical factors during coma, such as organ failure, hypothermia, prolonged continuous infusions, intoxication, and extreme obesity often alter drug metabolism and clearance. Such circumstances may further complicate standard assessments, and guideline recommendations often do not account for altered pharmacokinetics and pharmacodynamics.

Main text: The assessment of comatose patients involves complex pharmacologic considerations that significantly impact diagnostic accuracy. Accurate differentiation between pharmacologic, metabolic, and structural causes of coma is essential, particularly since drug-related unconsciousness generally carries a more favorable prognosis than other etiologies. Nonetheless, for best outcomes, it is imperative that the etiology of any drug-induced coma be determined as early as possible. It is important to recognize, however, that routine toxicology screens are not comprehensive. Additionally, the interplay between hypothermia and drug metabolism poses unique challenges, as core temperature significantly affects pharmacokinetic parameters such as hepatic metabolism, leading to reduced drug clearance. Multiorgan dysfunction, common after severe neurological injury, further complicates these assessments. Overdose scenarios introduce additional complexity. While ancillary testing may aid in diagnosis of brain death, they have limitations, particularly in cases of profound intoxication. Additionally, premature use of ancillary testing could lead to misdiagnosis. This review is organized into two main sections: Part I examines general coma and its associated pharmacologic considerations, followed by Part II which focuses on brain death.

Conclusion: Accurate assessment of coma and brain death often requires a multidisciplinary approach, integrating expertise in neurology, pharmacy, critical care, and toxicology. Current brain death guidelines provide a framework but leave open critical gaps in pharmacologic and toxicologic confounders. This review article highlights the importance of multidisciplinary approach to the care of coma and brain death patients and further research to refine diagnostic accuracy and mitigate the risks of premature brain death declarations.