Acute and Critical Care最新文献

Antimicrobial resistance has emerged as a critical global health challenge. Significant variability in antibiotic prescribing practices underscores the urgent need for high-quality evidence to inform optimal antibiotic prescribing policies. The ideal duration of antimicrobial therapy remains uncertain, and a one-size-fits-all approach is far from ideal. In this review, we examine bacterial growth kinetics and antibiotic pharmacodynamics and explore various strategies for determining the duration of antibiotic therapy: fixed duration, biomarker-guided, clinical course-based, and the more recent double-trigger approach.

Nurse-led glycemic management in critical care settings has been demonstrated to reduce the incidence of dysglycemia, including hyperglycemia and hypoglycemia, while stabilizing glycemic variability, contributing to enhanced patient outcomes. This scoping review aimed to identify nurse-led glycemic management protocols in intensive care units, analyze their components (e.g., target glucose range, monitoring frequency, and implementation methods), and evaluate their effectiveness. Seven databases, including PubMed and CINAHL, were searched for studies published between January 2015 and April 2025. Studies were selected using predefined inclusion criteria, and two independent reviewers evaluated methodological quality using the JBI critical appraisal tool. Ultimately, seven quasi-experimental studies were included. Most protocols employed continuous intravenous insulin infusions (n=5), whereas others focused on hypoglycemia management (n=2). The target glucose levels ranged from 100-180 mg/dl, and the monitoring intervals varied from 15 minutes to 4 hours depending on the protocol type. All protocols excluded patients on oral diets and those receiving intermittent enteral nutrition. Four studies used printed guidelines with manual adjustments, whereas three employed computerized decision-support systems. The studies indicated that nurse-led glycemic control management was associated with reductions in both glycemic variability and in the incidence of hyper- and hypoglycemia. These findings highlight the need for evidence-based updates to nurse-led glycemic control protocols in critical care for safe and effective management through a multidisciplinary approach.

Acute brain injuries-including traumatic brain injury, subarachnoid hemorrhage, and intracerebral hemorrhage-exhibit profound pathophysiological heterogeneity, yet are often managed using standardized treatment protocols. While evidence-based guidelines have improved outcomes at a population level, they frequently overlook patient-specific variations in cerebral compliance, autoregulation, and metabolic reserve. This review explores the evolving paradigm of personalized neurocritical care, which integrates dynamic multimodal monitoring, individualized intracranial pressure management strategies, and real-time physiological indices such as pressure reactivity index, cerebral perfusion pressure optimization, and waveform analytics. We highlight the role of noninvasive modalities including quantitative pupillometry, transcranial Doppler, optic nerve sheath diameter ultrasound, near-infrared spectroscopy, and electroencephalography as adjuncts when invasive monitoring is limited or contraindicated. Furthermore, we examine tissue-level monitoring using brain oxygen tension and cerebral microdialysis and emerging blood-based biomarkers such as glial fibrillary acidic protein and neurofilament light. These tools provide granular insight into evolving secondary injury processes. In parallel, advances in artificial intelligence (AI) and machine learning enable deep phenotyping, predictive modeling, and integration of high-dimensional data including imaging, physiology, and omics-based profiles. The development of digital twin models further supports individualized simulation and therapeutic planning. While challenges remain in implementation, data harmonization, and resource availability, the convergence of physiologic monitoring, molecular profiling, and computational modeling offers a transformative pathway toward precision medicine in neurocritical care.

Acute care settings, including emergency medicine and intensive care units, comprise a substantial portion of healthcare and are essential in the prompt management of conditions that can prove fatal. Critical care conditions require timely management that can be delayed by high patient volumes and the need for complex clinical decision making. Artificial intelligence (AI) tools have been created to enhance diagnostic accuracy and optimize workflow to improve patient care. This narrative review discusses the current status of AI in acute care, with a focus on its applications in triaging and diagnosis. AI-enhanced electrocardiogram analysis, identification of myocardial infarction and acute coronary syndrome, and heart failure risk stratification led to better patient-specific management and improved results. AI models successfully determined and aided in the timely management of various acute conditions, including pneumonia, pulmonary embolism, and respiratory failure. The AI algorithms used accurately determined sepsis onset and course, superseding traditionally used clinical tools and leading to early diagnosis and reduced sepsis mortality. These models showed high sensitivity and specificity in diagnosing and triaging neurological conditions, including altered levels of consciousness, seizures, and intracranial hemorrhages. AI that involved advanced machine learning imaging software led to faster and more accurate stroke diagnosis. Diagnostic tools assisted by AI improved the detection and classification of acute pancreatitis, appendicitis, and gastrointestinal bleeding. AI has shown promising results in optimizing management in acute care settings. However, critical issues in data standardization, ethical considerations, and clinical workflow integration need to be addressed to enable clinical implementation.

Background: Critically ill patients with muscle wasting experience prolonged intensive care unit (ICU) stays, delayed weaning, and higher mortality. Trauma-induced stress disrupts protein metabolism, leading to immunosuppression and muscle loss. This study evaluates whether high-protein intake through enteral nutrition preserves muscle mass and improves clinical outcomes compared to standard protein intake.

Methods: In our multicenter research, 102 critically ill, mechanically ventilated patients (age, 39±7; female, 52; body mass index, 23.8±2.7 kg/m2) were assigned randomly to receive either a high-protein (2.2 g/kg BW/day) or standard (1.5 g/kg BW/day) diet. Enteral nutrition was individualized based on energy expenditure. Ultrasound measured whether the rectus femoris muscle cross-sectional area (RFM-9 CSA) and pennation angle correlated with dietary intake. The data are presented as mean±standard deviation.

Results: Protein intake was 1.8±0.2 vs. 1.2±0.4 g/kg/day in high-protein and standard groups, respectively. In the intervention and standard groups, the baseline RFM-CSA and Pennation angle were 11.43±0.87 mm vs. 11.3±0.91 mm and 9.1±0.58 mm vs. 8.91±1.04 mm (P>0.05). Days 5, 10, and 20 showed significant variations in RFM-CSA and pennation angle (P<0.001). The intervention group experienced a shorter ICU length of stay (47±19.5 vs. 56.3±26.9 days, P=0.001) and a shorter period of mechanical ventilation (33±2.3 vs. 30±3.5 days, P=0.001).

Conclusions: Early high protein intake significantly preserves muscle mass, reducing the duration of stay in the ICU and the need for mechanical ventilation.

Background: Predicting the weaning outcomes is critical, since premature or delayed extubation is associated with an increased risk of mortality. This study aimed to compare two physiological indices, thoracic fluid content (TFC) and diaphragmatic excursion (DE), for predicting weaning success in mechanically ventilated patients.

Methods: This observational cohort study involved 100 mechanically ventilated patients with congested lungs who were eligible for weaning. Patients' TFC and DE were measured using electrical cardiometry and ultrasonography, respectively, before starting the spontaneous breathing trial. Following extubation, patients were grouped into successful and failed-weaning groups, with failure defined as reintubation or a need for non-invasive ventilation within 48 hours. Respiratory and cardiovascular variables were compared. The receiver operating characteristic (ROC) curve was used to assess the ability of TFC and DE to predict weaning success.

Results: Successful weaning occurred in 73 patients (73%) and failed weaning occurred in 27 patients (27%). The two groups' baseline characteristics were comparable; however, TFC and DE were significantly different between the failed- and successful-weaning groups (P<0.001). The area under the ROC curve (AUC) exhibited moderate predictive abilities of both the TFC and DE in predicting weaning success (AUC, 0.805, cutoff <40 kΩ-1 and AUC, 0.774, cutoff >1.45 cm). In the cardiac patient subgroup, TFC exhibited high predictive ability (AUC, 0.861), but DE did not achieve comparable results (AUC, 0.750).

Conclusions: Both TFC and DE are significant predictors for successful weaning from mechanical ventilators. In particular, a TFC of <40 kΩ-1 demonstrated an excellent ability to predict weaning success in patients with low ejection fraction.

Background: Emergency department (ED) overcrowding poses a global challenge, particularly for critically ill patients requiring intensive care unit (ICU) admission. Although delays in ICU transfer increase mortality in critically ill populations, the optimal timing for septic shock remains uncertain.

Methods: We conducted a target trial emulation using a prospective cohort of 815 septic shock patients from 19 Korean hospitals. Delayed ICU transfer was defined using restricted cubic splines. The primary outcome was in-hospital mortality. Multivariable logistic regression and inverse probability treatment weighting were used to adjust for confounders of age, sex, comorbidities, severity of illness, and mechanical ventilation use. Subgroup analyses were performed to assess the effect across patient characteristics.

Results: The median time of ED-to-ICU transfer was 6.7 hours (interquartile range, 4.7-11.4), and only 7% of patients were transferred within 3 hours. ICU transfer within 3 hours was associated with significantly lower in-hospital mortality (odds ratio, 0.48; 95% CI, 0.24-0.94) compared to later transfers. Mortality risk increased with elapsing time up to 6 hours and then plateaued. The benefit of early ICU transfer was consistent across subgroups but was particularly pronounced in patients requiring extracorporeal membrane oxygenation or continuous renal replacement therapy (P for interaction=0.02).

Conclusions: Early ICU transfer within 3 hours significantly reduces mortality in patients with septic shock, with the greatest benefit observed in those requiring advanced organ support. These findings highlight the need for system-wide strategies to reduce ED boarding time and prioritize timely ICU admission for septic shock management.

Background: Body weight can fluctuate during critical illness due to factors such as fluid shifts, nutritional status, the type of acute illness, and underlying comorbidities. We investigated the association between acute body weight variability (WV) and clinical outcomes in critically ill pediatric patients.

Methods: We retrospectively analyzed data from patients aged 1 month to 18 years who were admitted to the pediatric intensive care unit (PICU) of a university-affiliated tertiary hospital between August 2017 and July 2021. WV was defined as the percentage difference between the measured body weight at PICU admission and the usual body weight, obtained either from recent hospital records or caregiver reports. Associations between WV and clinical outcomes, including PICU mortality and ventilator-free days (VFDs), were assessed.

Results: Of the 926 patients, 74 (8.0%) died. Median WV was significantly higher in non-survivors than in survivors (8.7% vs. 0.0%; P<0.001). Increased WV was independently associated with higher mortality (hazard ratio [HR], 1.102; 95% CI, 1.073-1.131) and fewer VFDs (odds ratio [OR], 0.599; 95% CI, 0.524-0.684). Combining WV with Pediatric Index of Mortality 3 score significantly improved mortality prediction over either parameter alone (area under the curve, 0.888; P=0.047).

Conclusions: Higher WV at PICU admission is independently associated with adverse clinical outcomes, including increased mortality and fewer VFDs. WV could complement existing mortality prediction models in pediatric critical care.