Acute and Critical Care最新文献

Intensive care unit (ICU)-acquired muscle atrophy and weakness are key contributors to post-intensive care syndrome (PICS), which can lead to long-term functional impairments. Although the ICU survival rate has improved, many patients continue to experience persistent functional impairments that hinder their reintegration into society. This review summarizes a series of observational and interventional studies conducted as part of the Muscle Atrophy Zero Project, focusing on the etiology, assessment, and prevention of ICU-acquired muscle atrophy and weakness. The project findings highlight the critical role of inflammation, particularly neutrophil infiltration, in the pathogenesis of muscle atrophy. Muscle damage can be assessed using ultrasound, bioelectrical impedance analysis, and urinary titin. Among them, ultrasound demonstrates high diagnostic accuracy for detecting low muscularity, and urinary titin has emerged as a promising biomarker of muscle degradation. Preventive strategies include early rehabilitation, neuromuscular electrical stimulation, vibration therapy, and nutritional support, especially protein supplementation. These multimodal interventions have shown efficacy in mitigating ICU-acquired muscle atrophy and weakness. However, follow-up systems for PICS remain underdeveloped. A continual multimodal intervention approach that combines physical rehabilitation with nutritional therapy is essential. The development of structured follow-up programs is vitally needed to confront the long-term challenges posed by PICS.

Delirium is an acute disorder characterized by changes in the patient's cognitive function, which another neurocognitive or pre-existing disease cannot explain. It produces adverse outcomes for critically ill patients and their families related to adverse events associated with the accidental removal of medical devices that increase the risk of the patient and the length of stay at the hospital, manifested by agitation and confusion behaviors. Five reviewers conducted An Umbrella Review from May to August 2023 through research in the databases Medline, CINAHL, Scopus, Web of Science, Cochrane Database of Systematic Reviews and articles obtained through research in other sources. After verifying their eligibility, we obtained 22 systematic reviews and meta-analyses for data extraction and analysis. From the results obtained, the importance of the implementation of surveillance interventions and systematic evaluation of the presence of delirium is highlighted, with particular emphasis on the use of the scale, Confusion Assessment Method, followed by the implementation of multicomponent interventions, pharmacological or not, highlighting the use of dexmedetomidine and family as support, as well early mobilization for the management of delirium. Managing delirium in critically ill patients based on Meyer and Lavin's theory, is an area sensitive to nursing care with an impact on the prevention of complications and consequent promotion of the safety of these patients, which also translates into positive results for the family and health organizations, reducing morbidity, mortality, length of stay and health costs.

Background: Intracranial pressure (ICP) waveform analysis provides critical insights into brain compliance and can aid in the early detection of neurological deterioration. Deep learning (DL) has recently emerged as an effective approach for analyzing complex medical signals and imaging data. The aim of the present research was to develop a DL-based model for detecting ICP waveforms indicative of poor brain compliance.

Methods: A retrospective cohort study was conducted using ICP wave images collected from postoperative hydrocephalus (HCP) patients who underwent ventriculostomy. The images were categorized into normal and poor compliance waveforms. Precision, recall, mean average precision at the 0.5 intersection over union (mAP_0.5), and the area under the receiver operating characteristic curve (AUC) were used to test.

Results: The dataset consisted of 2,744 ICP wave images from 21 HCP patients. The best-performing model achieved a precision of 0.97, a recall of 0.96, and a mAP_0.5 of 0.989. The confusion matrix for poor brain compliance waveform detection using the test dataset also demonstrated a high classification accuracy, with true positive and true negative rates of 48.5% and 47.8%, respectively. Additionally, the model demonstrated high accuracy, achieving a mAP_0.5 of 0.994, sensitivity of 0.956, specificity of 0.970, and an AUC of 0.96 in the detection of poor compliance waveforms.

Conclusions: The DL-based model successfully detected pathological ICP waveforms, thereby enhancing clinical decision-making. As DL advances, its significance in neurocritical care will help to pave the way for more individualized and data-driven approaches to brain monitoring and management.

Background: To explore the value of the diaphragm thickness fraction (TF) and diaphragm mobility (DM) measured by ultrasound for predicting ventilator withdrawal success in patients with acute respiratory distress syndrome (ARDS) after cardiac surgery.

Methods: This study included 246 patients undergoing the spontaneous breathing trial. Diaphragmatic function was evaluated by ultrasound, including the diaphragm thickness at the end of calm breathing (thickness of the diaphragm at functional residual capacity [TdiFRC]) and the maximum diaphragm thickness at the end of inspiration (thickness of the diaphragm at full vital capacity [TdiFVC]); TF=(TdiFVC-TdiFRC)/TdiFRC×100%. DM, the oxygenation index (the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen), and the rapid shallow breathing index (RSBI) were measured.

Results: Successful liberation from mechanical ventilation was observed in 209 patients. There were no significant differences in the TdiFRC (0.3±0.1 cm vs. 0.3±0.1 cm) or TdiFVC (0.3±0.1 cm vs. 0.2±0.1 cm) between the ventilator withdrawal success group and the ventilator withdrawal failure group (P>0.05). The TF was greater in the ventilator withdrawal success group than in the ventilator withdrawal failure group (40.8%±15.8% vs. 37.7%±9.2%, P<0.01). DM in the ventilator withdrawal success group was greater than that in the ventilator withdrawal failure group (1.5±0.5 cm vs. 1.2±0.4 cm, P=0.040). The RSBI was lower in the ventilator withdrawal success group than in the ventilator withdrawal failure group (74.3±25.6 breaths·min-1·L -1 vs. 89.9±34.5 breaths·min-1·L -1, P<0.01).

Conclusions: Diaphragmatic ultrasound can be used to predict the success of ventilator withdrawal in patients with ARDS.

Background: Delirium is an acute condition marked by disturbances in cognition, awareness, and attention, commonly observed in hospitalized patients due to factors such as illness severity and medication. It is particularly prevalent in intensive care unit settings, affecting up to 80% of ventilated patients. This study investigates whether coronavirus disease 2019 (COVID-19) delirium aligns with expectations of non-COVID-19 delirium incidence in other hospitalized patients and identifies unique or common factors contributing to delirium in these groups.

Methods: An observational cross-sectional study was conducted on 107 hospitalized patients diagnosed with delirium, comprising 56 COVID-19 patients and 51 non-COVID-19 patients. Data were collected through detailed medical record reviews and structured interviews with patients and their caregivers to evaluate factors associated with delirium.

Results: The findings revealed a higher prevalence of medication-related stressors in COVID-19 delirium compared to non-COVID-19 delirium. This suggests that overmedication may play a critical role in the development of delirium, regardless of the underlying critical illness condition.

Conclusions: This study highlights the significant association between medication stressors and COVID-19 delirium. These findings emphasize the importance of minimizing unnecessary medications and closely monitoring pharmacological treatments to reduce delirium incidence and improve outcomes in hospitalized populations.

Background: The administration of high-dose vitamins has been focused on in critically ill patients as adjunctive therapy for life-threatening conditions. We evaluated the association between serum vitamin C concentrations and patient prognosis.

Methods: We retrospectively reviewed and collected clinical and biochemical data, including thiamine and vitamin C levels, of patients admitted to the pediatric intensive care unit (PICU).

Results: In total, 177 patients were admitted to the PICU during the study period, and 63 children were enrolled in this study. The most common reason for PICU admission was sepsis (33.3%). The median thiamine and vitamin C levels were 3.6 µg/dl (interquartile range [IQR], 2.9-4.5 µg/dl) and 2.84 µg/ml (IQR, 1.61-4.55 µg/ml), respectively. Thiamine deficiency was observed in 10 patients (15.9%), and 17 (27.0%) had vitamin C deficiency. There were no differences in the vitamin levels according to the reason for PICU admission. Vitamin C levels were affected by nutritional status. The length of stay in the PICU and duration of mechanical ventilation were longer in patients with vitamin C deficiency than in those without (P=0.035 and P=0.010, respectively). The serum delta neutrophil index and C-reactive protein and lactate levels increased in the vitamin C-deficient group (P=0.028 and P=0.039, respectively). There was a significant difference in Pediatric Index of Mortality 3 scores according to vitamin C levels but not in mortality directly.

Conclusions: Vitamin C deficiency was associated with elevated inflammatory marker levels, increased mechanical ventilation durations, and PICU admission. Our results support the potential benefits of vitamin C administration in critically ill children.

Background: Arterial pH reflects both metabolic and respiratory distress in cardiac arrest and has prognostic implications. However, it was excluded from the 2024 update of the Utstein out-of-hospital cardiac arrest (OHCA) registry template. We investigated the rationale for including arterial pH into models predicting clinical outcomes.

Methods: Data were sourced from the Korean Cardiac Arrest Research Consortium, a nationwide OHCA registry (NCT03222999). Prediction models were constructed using logistic regression, random forest, and eXtreme Gradient Boosting frameworks. Each framework included three model types: pH, low-flow time, and combined models. Then the area under the receiver operating characteristic curve (AUROC) of each predicting model was compared. The primary outcome was 30- day death or neurologically unfavorable status (cerebral performance category ≥3).

Results: Among the 15,765 patients analyzed, 92.2% experienced death or unfavorable neurological outcomes. The predicting performance of the models including pH (AUROC, 0.92-0.94) were comparable to the models including low-flow time in all frameworks (0.93-0.94) (all P>0.05). Inclusion of pH into low-flow time models consistently showed higher AUROCs than individual models in all frameworks (AUROC, 0.93-0.95; all P<0.05).

Conclusions: The predicting performance of models including arterial pH was comparable to models including low-flow time, and addition of arterial pH into low-flow time models could increase the performance of the models. Key Words: blood pH; hydrogen-ion con.

Background: Resting energy expenditure (REE) estimation is crucial in critically ill patients. While indirect calorimetry (IC) is the gold standard, its limited availability often necessitates alternative methods. In this exploratory study, we compared the accuracy of the stress factor-corrected Harris-Benedict (cREEHB) and weight-based (REEWB) equations with the Weir equation (REEW) using oxygen consumption (VO₂) and carbon dioxide production (VCO₂) estimated via the Fick principle.

Methods: We included patients admitted to the intensive care unit (ICU) between January and August 2024, and computed cREEHB, REEWB (22.5 kcal/kg/day), and REEW. Agreement between methods was assessed through Bland-Altman analysis. Sensitivity and correlation analyses identified bias determinants. Multiple linear regression explored associations of REEW with VO₂, VCO₂, and cardiac output (CO).

Results: The sample size consisted of 30 patients. No correlation was found between REEW and cREEHB (r=0.177, P=0.349) or REEWB (r=-0.006, P=0.975). Compared to REEW, cREEHB underestimated REE (mean bias, -47.9 kcal), while REEWB overestimated it (mean bias, +9.7 kcal). CREEHB bias was associated with sex, height, body surface area (BSA), VO2, and respiratory quotient (RQ); REEWB bias was influenced by actual body weight, body mass index, BSA, VO2, and RQ (all P<0.05). Multiple linear regression analysis showed that REEW was influenced by VO2 (P<0.001) and VCO2 (P<0.001) but not by CO (P=0.164).

Conclusions: Predictive equations may not be interchangeable in ICU settings, leading to inaccurate metabolic assessments. Studies incorporating IC as a reference are needed to determine the most reliable approach for estimating REE and optimizing nutritional support in critical patients.

Background: The objective of the study was to investigate the role of the Shock Index (SI), the Reverse Shock Index (RSI) along with oxygen saturation in predicting mortality in coronavirus disease 2019 (COVID-19).

Methods: This cross-sectional study was conducted at an academic hospital over a period of 6 months and involved patients over the age of 18 who had been diagnosed with COVID-19 via positive polymerase chain reaction tests. The data were recorded anonymously using a checklist. The study focused on indices such as the SI and RSI, both alone and in conjunction with age and oxygen saturation, to predict hospital mortality. Statistical analysis was conducted using SPSS software.

Results: The study involved 500 COVID-19 patients with a 14.4% mortality rate. Key differences were found between survival and mortality groups in terms of age, vital signs except diastolic blood pressure, length of stay, and a series of laboratory tests. Logistic regression showed gender, oxygen saturation, hemoglobin, direct bilirubin, lactate dehydrogenase, D-dimer, and Age SI/oxygen saturation (SpO2) and RSI×SpO2/Age indices significantly associated with hospital mortality. Receiver operating characteristic analysis indicated Age SI/SpO2 and RSI×SpO2/Age as effective mortality predictors, exhibiting an area under the curve of 0.80 and achieving a sensitivity, specificity, and accuracy of over 70%.

Conclusions: The combination of SI, the RSI along with hypoxia, and age has been identified as a potentially more significant role in ruling out hospital mortality in COVID-19 patients than vital signs alone, given the established role of hypoxia as a major risk factor in such cases.

Background: Survivors of critical illness often face significant physical and psychological challenges, including post-traumatic stress disorder (PTSD), anxiety, and depression, which can severely impact their quality of life. This study aimed to evaluate the prevalence and associated factors of PTSD, anxiety, and depression among intensive care unit (ICU) survivors 3 months after discharge, and investigate the prevalence and determinants of post-intensive care syndrome-related neuropsychiatric disorders (PICS-ND).

Methods: This is a prospective observational analytical study carried out in a medical ICU. Three months after discharge from the ICU, survivors were contacted by telephone to complete the Impact Event Scale-Revised and Hospital Anxiety and Depression Scale questionnaires. Univariate and multivariate analyses were performed to identify variables that were independently and significantly associated with outcomes.

Results: A total of 114 survivors was enrolled. At 3 months, PTSD, anxiety, and depression were prevalent in 21.9%, 21.0%, and 9.6% of patients, respectively. Associated factors were younger age, female, physical restraint, and critical illness polyneuropathy and myopathy (CIPNM) for PTSD; unmarried, low Charlson index, and physical restraint for anxiety; and younger age and CIPNM for depression. PICS-ND, a composite measure of neuropsychiatric morbidity, was present in 28.9% of patients, with younger age, female, and physical restraint identified as associated factors.

Conclusions: PTSD, anxiety, depression, and PICS-ND were common among ICU survivors at 3 months. Various factors, including younger age, female, unmarried, lower Charlson index, physical restraint, and CIPNM, were associated with these psychological outcomes.