Journal of Anesthesia, Analgesia and Critical Care (Online)最新文献

Background: Spinal anesthesia-induced hypotension is a common complication during cesarean delivery, often requiring vasopressor support, and is associated with maternal discomfort. Caffeine, a central nervous system stimulant with well-documented cardiovascular effects, may provide a simple adjunct to enhance hemodynamic stability. We aimed to evaluate the efficacy of a single preoperative 200 mg oral caffeine dose in reducing the incidence and severity of hypotension following spinal anesthesia in healthy patients undergoing elective cesarean delivery.

Methods: In this randomized controlled trial, 90 patients classified as ASA II and scheduled for elective cesarean delivery under spinal anesthesia were assigned to receive either 200 mg oral caffeine or a placebo 30 min before the procedure. Hemodynamic parameters, the incidence and severity of hypotension, baseline and 60 min post-administration serum caffeine levels, ephedrine requirements, incidence of postoperative nausea and vomiting, and post-dural puncture headache were recorded and analyzed.

Results: Caffeine administration significantly reduced the incidence of hypotension (9% vs. 33%, p < 0.05). Severe hypotension was not observed in the caffeine group. Patients in the caffeine group demonstrated greater hemodynamic stability, with a delayed onset of hypotension and reduced ephedrine requirements. No significant differences were observed in the incidence of bradycardia, tachycardia, or reactive hypertension. Neonatal outcomes were comparable between the groups. Additionally, caffeine was associated with lower rates of postoperative nausea and vomiting (2% vs. 20%) and post-dural puncture headache (2% vs. 16%) at 24 h.

Conclusion: Preoperative administration of 200 mg oral caffeine is a cost-effective strategy for reducing spinal anesthesia-induced hypotension, the incidence of postoperative nausea and vomiting, and post-dural puncture headache in healthy patients undergoing elective cesarean delivery. These findings support further investigation of the role of caffeine as an adjunct in obstetric anesthesia.

Trial registration: The study was registered by the principal investigator (M. Helmy) at ClinicalTrials.gov under the identifier NCT07076654 on July 11, 2025.

Background: Robotic liver transplantation (LT) is a recent advancement in minimally invasive surgery; however, perioperative and anaesthetic management have not yet been described in detail. This study aimed to characterise the anaesthetic course of fully robotic LT and compare the perioperative outcomes with those of propensity score-matched open LT.

Methods: We conducted an observational, retrospective, single-centre study at Modena University Hospital. Fully robotic LTs were compared with matched open LTs. Matching was performed in a 1:1 ratio according to age, sex, BMI, year of transplantation, indication, presence of hepatocellular carcinoma, and MELD score.

Results: Eighteen robotic and 457 open LTs were initially identified; after matching, 11 robotic and 11 open cases were included in the study. In the robotic group, the operative time was longer (p < 0.05) and associated (p < 0.05) with higher lactate levels, greater norepinephrine requirement, and larger blood loss, requiring a larger use of blood components compared to standard open surgery. Postoperatively, extubation timing, cardiovascular, respiratory, and liver function recovery, and renal complications were comparable between the groups. Pain control required less opioids (p < 0.05) and ICU (p > 0.05) and hospital stay (p < 0.05) were lower in robotic than in open surgery.

Conclusions: Fully robotic LT is associated with longer operative times, greater blood loss, and increased haemodynamic demands. Despite these challenges, postoperative recovery, particularly hospital stay, appears to be favourable. Larger multicentre studies are needed to validate these findings and refine the anaesthetic strategies.

Background: Ventilator-associated pneumonia (VAP) is a preventable complication of invasive mechanical ventilation (IMV) with a significant healthcare impact. Early risk prediction is crucial, but current models lack real-time adaptability. This study develops a real-time VAP prediction model using machine learning and high-resolution EHR data from the MIMIC-III database.

Methods: We analyzed 3523 ICU stays (3204 patients) from MIMIC-III, including adults who received IMV for at least 48 h. VAP was labeled based on microbiological cultures and antibiotic initiation. A real-time ensemble model of XGBoost regressors was developed to predict time to VAP onset, incorporating vital signs, ventilator data, and lab results. Two static classifiers (24 h and 48 h) were also compared.

Results: VAP occurred in 595 ICU stays (16.89%), with an incidence rate of 23.77 per 1000 IMV-days. Median VAP onset was 113.5 h post-IMV. The real-time model outperformed static models with a C-index of 0.68, AUROC of 0.71, and AUPRC of 0.36. It provided a median lead time of 53 h before VAP onset, with key predictors including temperature, respiratory rate, and minute ventilation.

Conclusion: We present a real-time VAP prediction model that outperforms static classifiers, providing actionable lead time for proactive microbiological surveillance. The model enables risk stratification for enhanced monitoring and, when clinically indicated, timely targeted antimicrobial therapy. Future work will focus on multicenter prospective validation and integration into ICU workflows to assess clinical utility and impact on patient outcomes.

Background: Idiopathic systemic capillary leak syndrome (SCLS), or Clarkson syndrome, is a rare disorder characterized by increased capillary permeability causing fluid leakage into interstitial spaces. SCLS remains underdiagnosed in both adults and children due to its resemblance to septic shock, with particularly limited pediatric data available.

Methods: We conducted a retrospective analysis of six pediatric patients with seven SCLS episodes diagnosed in two Italian PICUs over a ten-year period.

Results: Patients aged 4-10 years presented with hypovolemic shock and the characteristic triad of hypovolemia, hemoconcentration, and hypoalbuminemia without albuminuria. No monoclonal gammopathy was observed. Viral infections (75%, predominantly influenza) were identified as triggers. Common complications included rhabdomyolysis (100%), compartment syndrome (100%), acute kidney injury (71.4%), and cardiac involvement (71.4%). Cardiothoracic index measurements showed significant elevation during acute phases (mean 58% ± 1%) with reduction after treatment (mean 51% ± 1%). Mean hospitalization was 20 days (12 in PICU). One patient died from refractory ventricular fibrillation, another experienced recurrence.

Conclusions: Pediatric SCLS represents a severe condition with distinctive characteristics compared to adult cases, including absence of monoclonal gammopathy, frequent viral triggers, and universal complications of rhabdomyolysis and compartment syndrome. Early recognition using our proposed diagnostic red flags can facilitate prompt intervention and improve outcomes.

Transesophageal echocardiography (TEE) has emerged as a transformative tool in the management of cardiac arrest, offering significant advantages over traditional transthoracic echocardiography (TTE) by enabling continuous, high-resolution cardiac imaging during resuscitation. Initially used in operating rooms before 2000, TEE expanded into emergency departments (EDs), intensive care units (ICUs), and even prehospital settings. Its superior imaging capability during cardiopulmonary resuscitation (CPR) supports rapid diagnosis, optimization of compression quality, and more accurate rhythm assessment, including the differentiation between asystole and fine ventricular fibrillation. TEE has been shown to influence intra-arrest clinical decision-making in up to 78% of cases, often revealing pathologies-such as aortic dissection or cardiac rupture-not detected by TTE. Importantly, TEE aids in identifying reversible causes of cardiac arrest, guiding high-quality CPR by assessing left ventricular outflow tract (LVOT) obstruction, and shortening the duration of pulse checks. It also may play a role in extracorporeal CPR (ECPR) and organ donation procedures, particularly in cannulation and monitoring during extracorporeal membrane oxygenation (ECMO) and normothermic regional perfusion (NRP). TEE use is feasible and safe during cardiac arrest, with high insertion success rates and minimal complications when performed by trained personnel. While cardiologists, anesthesiologists, and intensivists traditionally perform TEE, simplified training protocols now enable emergency physicians to safely and effectively deploy TEE in critical settings. Protocols such as "POCUS-TEE" may promote rapid acquisition of essential views, facilitating broader implementation. Despite the potential benefits, several barriers to widespread adoption remain. These include the availability of equipment, limitations in training, and concerns regarding the safety of the probe during defibrillation. Resuscitation guidelines recommend removing the transesophageal echocardiogram (TEE) probe, or at least insulating it, as a precaution during defibrillation. TEE is particularly valuable when TTE proves inadequate, such as in obese or mechanically ventilated patients. In conclusion, TEE might enhance the quality and precision of resuscitation in cardiac arrest and has the potential to improve survival and neurological outcomes in selected cases. Its broader adoption hinges on institutional support, streamlined protocols, operator training, and further research to validate its impact on patient-centered outcomes. As technology advances and clinical integration improves, TEE may become a cornerstone of advanced life support in both in-hospital and out-of-hospital settings.

The ongoing revolution in artificial intelligence (AI) is reshaping perioperative care, including obstetric anesthesia. This narrative review synthesizes major AI applications in cesarean delivery, the world's most common inpatient surgery. Integrating history, obstetric factors, physiological variables, and imaging, AI tools enhance preoperative evaluation (estimation of risks of difficult airway), prediction of adverse events, ultrasound spine evaluation for neuraxial procedure, and postpartum hemorrhage. Language models can bridge consent and education gaps, while improving detection and treatment of postoperative pain. Machine learning models improve hemodynamic management with prediction of spinal-induced hypotension, assisted fluid management, and vasopressor requirements, with reduction of hypotensive burden. Yet cesarean-specific evidence remains limited and heterogeneous, with uncertain effects on maternal-neonatal outcomes. While promising, AI cannot replace the expertise and clinical judgment of a trained obstetric anesthesiologist. It should, instead, be viewed as a valuable tool to facilitate and support our practice, and multicenter prospective trials are needed to guide implementation.

Despite the refinement in guidelines and improving outcomes, a subset of patients with septic shock fails to respond to treatment and progresses into refractory septic shock with an associated high morbidity and mortality. This population remains underrepresented in clinical trials due to their heterogeneity, in addition to time and ethical constraints. As a large specialist referral centre, we propose an updated, pragmatic, and largely inexpensive approach based on our current clinical practice, which focuses on early multimodal therapy, aiming to reduce the detrimental effects associated with high-dose vasopressors.

Background: Chronic postsurgical pain affects surgical patients with a mean incidence of approximately 20%, posing a major public health concern with substantial implications for patients and healthcare systems. Despite increasing knowledge of risk factors, the incidence of chronic postsurgical pain remains high. Hence, there is growing interest in developing individualised pain management strategies using predictive risk. A novel chronic postsurgical pain risk assessment system using machine learning is under development in Western Norway. As a first step in implementing the risk assessment system, this study explored how in-hospital healthcare professionals perceive the potential utility of access to individualised chronic postsurgical pain risk profiles for clinical practice.

Methods: This qualitative study included seven focus groups with 39 healthcare professionals from postanaesthesia care units, surgical units and orthopaedic wards across two hospitals in Norway. Data were analysed inductively using reflexive thematic analysis.

Results: Our analyses yielded two overarching themes: (1) Lack of fit of risk predictions and (2) potentials of knowing risk profiles. Participants questioned the applicability of chronic postsurgical pain predictions in the in-hospital settings, highlighting role boundaries, time constraints, and limited influence over long-term outcomes. However, they also identified the benefits of risk awareness, including improved clinical reflection, more cautious decision-making, and an enhanced potential for individualised treatment and care.

Conclusion: Healthcare professionals expressed a balanced view of chronic postsurgical pain risk profiles, recognising both scepticism about them and their potential benefits. Effective implementation will require predictive validity, clear guidance, and cross-disciplinary collaboration. Education and training will be essential to support clinicians in interpreting and acting on risk information.

This retrospective study explores the prognostic significance of the frequency and persistence of Neurological Pupil index (NPi) differentials (NPi-d) in patients with acute brain injury admitted to the intensive care unit. A total of 57 patients with traumatic brain injury, subarachnoid hemorrhage, or intracerebral hemorrhage were included, all of whom underwent serial quantitative pupillometry. An NPi-d was defined as a difference of ≥ 0.7 between the NPi of the two pupils, while the persistence of the differential (pNPi-d) captured sustained asymmetry over time, regardless of whether the threshold was reached. The analysis revealed that both the number of NPi-d events and the pNPi-d were significantly associated with unfavorable neurological outcomes and mortality at intensive care unit (ICU) discharge. Multinomial logistic regression showed that an increased number of NPi-d was associated with a more than threefold increased risk of poor outcome and death. Similarly, higher pNPi-d values correlated with greater odds of poor outcome, death, elevated need for therapeutic interventions to manage intracranial hypertension, decompressive craniectomy, and longer ICU stay. These findings suggest that, beyond static NPi thresholds, dynamic measures such as the frequency and persistence of NPi asymmetries provide valuable prognostic information. Incorporating these pupillometry-based metrics into routine neuromonitoring could enhance early risk stratification and guide treatment strategies in critically ill neurological patients.

Carbon dioxide (CO₂) is a byproduct of cellular metabolism, with the human body storing approximately 120 liters in various chemical forms across different compartments. Through gas exchange and tidal ventilation, arterial CO₂ and blood pH are tightly regulated within the narrow ranges required for cellular function. Not all tidal ventilation, however, contributes to CO₂ elimination: the portion of each breath that does not participate in gas exchange is defined as dead space. First described in 1891 by Christian Bohr in his seminal work "Über die Lungenatmung", the concept gained practical applicability in 1938 when Enghoff proposed replacing the unmeasurable alveolar partial pressure CO₂ (PACO₂) with the arterial partial pressure of CO₂ (PaCO₂) in the calculation. Since then, dead space has become a cornerstone parameter for quantifying the severity of respiratory failure. Recent advances in lung imaging have expanded the possibilities for assessing dead space distribution by integrating anatomical and functional information. Techniques such as contrast-enhanced computed tomography (CT), dual-energy CT (DECT), magnetic resonance imaging (MRI), and, increasingly, electrical impedance tomography (EIT) now offer novel opportunities to visualize and quantify regional ventilation-perfusion (V/Q) mismatch. In this narrative review, we outline the mathematical foundations of dead space computation and examine the role of each variable in the calculation. We then explore derived indices such as the ventilatory ratio and standardized minute ventilation. Finally, we discuss recent technological innovations, including EIT, MRI, and CT, and present clinical cases to illustrate the practical application of dead space assessment in daily clinical practice.