Acute and Critical Care最新文献

Background: Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality.

Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing.

Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700-1.000).

Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Background: This meta-analysis aims to evaluate the effects of ketamine in critically ill intensive care unit (ICU) patients.

Methods: We searched for randomized controlled trials (RCTs) in PubMed, Scopus, and the Cochrane Library; the search was performed initially in January but was repeated in December of 2023. We focused on ICU patients of any age. We included studies that compared ketamine with other traditional agents used in the ICU. We synthesized evidence using RevMan v5.4 and presented the results as forest plots. We also used trial sequential analysis (TSA) software v. 0.9.5.10 Beta and presented results as TSA plots. For synthesizing results, we used a random-effects model and reported differences in outcomes of two groups in terms of mean difference (MD), standardized MD, and risk ratio with 95% confidence interval. We assessed the risk of bias using the Cochrane RoB tool for RCTs. Our outcomes were mortality, pain, opioid and midazolam requirements, delirium rates, and ICU length of stay.

Results: Twelve RCTs involving 805 ICU patients (ketamine group, n=398; control group, n=407) were included in the meta-analysis. The ketamine group was not superior to the control group in terms of mortality (in five studies with 318 patients), pain (two studies with 129 patients), mean and cumulative opioid consumption (six studies with 494 patients), midazolam consumption (six studies with 304 patients), and ICU length of stay (three studies with 270 patients). However, the model favored the ketamine group over the control group in delirium rate (four studies with 358 patients). This result is significant in terms of conventional boundaries (alpha=5%) but is not robust in sequential analysis. The applicability of the findings is limited by the small number of patients pooled for each outcome.

Conclusions: Our meta-analysis did not demonstrate differences between ketamine and control groups regarding any outcome except delirium rate, where the model favored the ketamine group over the control group. However, this result is not robust as sensitivity analysis and trial sequential analysis suggest that more RCTs should be conducted in the future.

Background: Successful liberation from mechanical ventilation is one of the most crucial processes in critical care because it is the first step by which a respiratory failure patient begins to transition out of the intensive care unit and return to their own life. Therefore, when devising appropriate strategies for removing mechanical ventilation, it is essential to consider not only the individual experiences of healthcare professionals, but also scientific and systematic approaches. Recently, numerous studies have investigated methods and tools for identifying when mechanically ventilated patients are ready to breathe on their own. The Korean Society of Critical Care Medicine therefore provides these recommendations to clinicians about liberation from the ventilator.

Methods: Meta-analyses and comprehensive syntheses were used to thoroughly review, compile, and summarize the complete body of relevant evidence. All studies were meticulously assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method, and the outcomes were presented succinctly as evidence profiles. Those evidence syntheses were discussed by a multidisciplinary committee of experts in mechanical ventilation, who then developed and approved recommendations.

Results: Recommendations for nine PICO (population, intervention, comparator, and outcome) questions about ventilator liberation are presented in this document. This guideline includes seven conditional recommendations, one expert consensus recommendation, and one conditional deferred recommendation.

Conclusions: We developed these clinical guidelines for mechanical ventilation liberation to provide meaningful recommendations. These guidelines reflect the best treatment for patients seeking liberation from mechanical ventilation.

This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.

Background: Although preoxygenation is an essential procedure for safe endotracheal intubation, in some cases securing sufficient time for tracheal intubation may not be possible. Patients with head and neck cancer might have a difficult airway and need a longer time for endotracheal intubation. We hypothesized that the extended apneic period with preoxygenation via a high-flow nasal cannula (HFNC) is beneficial to patients who undergo head and neck surgery compared with preoxygenation with a simple mask.

Methods: The study was conducted as a single-center, single-blinded, prospective, randomized controlled trial. Patients were divided into groups based on one of the two preoxygenation.

Methods: HFNC group or simple facemask (mask group). Preoxygenation was performed for 5 minutes with each method, and endotracheal intubation for all patients was performed using a video laryngoscope. Oxygen partial pressures of the arterial blood were compared at the predefined time points.

Results: For the primary outcome, the mean arterial oxygen partial pressure (PaO2 ) immediately after intubation was 454.2 mm Hg (95% confidence interval [CI], 416.9-491.5 mm Hg) in the HFNC group and 370.7 mm Hg (95% CI, 333.7-407.4 mm Hg) in the mask group (P=0.002). The peak PaO2 at 5 minutes after preoxygenation was not statistically different between the groups (P=0.355).

Conclusions: Preoxygenation with a HFNC extending to the apneic period before endotracheal intubation may be beneficial in patients with head and neck cancer.

Background: Using peripheral arteries to infer central hemodynamics is common among hemodynamic monitors. Doppler ultrasound of the common carotid artery has been used in this manner with conflicting results. We investigated the relationship between changing common carotid artery Doppler measures and stroke volume (SV), hypothesizing that more consecutively-averaged cardiac cycles would improve SV-carotid Doppler correlation.

Methods: Twenty-seven healthy volunteers were recruited and studied in a physiology laboratory. Carotid artery Doppler pulse was measured with a wearable, wireless ultrasound during central hypovolemia and resuscitation induced by a stepped lower body negative pressure protocol. The change in maximum velocity time integral (VTI) and corrected flow time of the carotid artery (ccFT) were compared with changing SV using repeated measures correlation.

Results: In total, 73,431 cardiac cycles were compared across 27 subjects. There was a strong linear correlation between changing SV and carotid Doppler measures during simulated hemorrhage (repeated-measures linear correlation [Rrm ]=0.91 for VTI; 0.88 for ccFT). This relationship improved with larger numbers of consecutively-averaged cardiac cycles. For ccFT, beyond four consecutively-averaged cardiac cycles the correlation coefficient remained strong (i.e., Rrm of at least 0.80). For VTI, the correlation coefficient with SV was strong for any number of averaged cardiac cycles. For both ccFT and VTI, Rrm remained stable around 25 consecutively-averaged cardiac cycles.

Conclusions: There was a strong linear correlation between changing SV and carotid Doppler measures during central blood volume loss. The strength of this relationship was dependent upon the number of consecutively-averaged cardiac cycles.

Background: The Controlling Nutritional Status (CONUT) score and the prognostic nutritional index (PNI) have emerged as important nutritional indices because they provide an objective assessment based on data. We aimed to investigate how these nutritional indices relate to outcomes in patients with sepsis.

Methods: Data were collected retrospectively at five hospitals for patients aged ≥18 years receiving treatment for sepsis between January 1, 2017, and December 31, 2021. Serum albumin and total cholesterol concentrations, and peripheral lymphocytes were used to calculate the CONUT score and PNI. To identify predictors correlated with 30-day mortality, analyses were conducted using univariate and multivariate Cox proportional hazards models.

Results: The 30-day mortality rate among 9,763 patients was 15.8% (n=1,546). The median CONUT score was 5 (interquartile range [IQR], 3-7) and the median PNI score was 39.6 (IQR, 33.846.4). Higher 30-day mortality rates were associated with individuals with moderate (CONUT score: 5-8; PNI: 35-38) or severe (CONUT: 9-12; PNI: <35) malnutrition compared with those with no malnutrition (CONUT: 0-1; PNI: >38). With CONUT scores, the hazard ratio (HR) associated with moderate malnutrition was 1.52 (95% confidence interval [CI], 1.24-1.87; P<0.001); for severe, HR=2.42 (95% CI, 1.95-3.02; P<0.001). With PNI scores, the HR for moderate malnutrition was 1.29 (95% CI, 1.09-1.53; P=0.003); for severe, HR=1.88 (95% CI, 1.67-2.12; P<0.001).

Conclusions: The nutritional indices CONUT score and PNI showed significant associations with mortality of sepsis patients within 30 days.

Background: Point of care ultrasound (POCUS) is being explored for dynamic measurements like inferior vena cava collapsibility index (IVC-CI) and left ventricular outflow tract velocity time integral (LVOT-VTI) to guide anesthesiologists in predicting fluid responsiveness in the preoperative period and in treating post-induction hypotension (PIH) with varying accuracy.

Methods: In this prospective, observational study on included 100 adult patients undergoing elective surgery under general anesthesia, the LVOT-VTI and IVC-CI measurements were performed in the preoperative room 15 minutes prior to surgery, and PIH was measured for 20 minutes in the post-induction period.

Results: The incidence of PIH was 24%. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of the two techniques at 95% confidence interval was 0.613, 30.4%, 93.3%, 58.3%, 81.4%, 73.6% for IVC-CI and 0.853, 83.3%, 80.3%, 57.1%, 93.8%, 77.4% for LVOT-VTI, respectively. In multivariate analysis, the cutoff value for IVC-CI was >51.5 and for LVOT-VTI it was ≤17.45 for predicting PIH with odd ratio [OR] of 8.491 (P=0.025) for IVCCI and OR of 17.427 (P<0.001) for LVOT. LVOT-VTI assessment was possible in all the patients, while 10% of patients were having poor window for IVC measurements.

Conclusions: We recommend the use of POCUS using LVOT-VTI or IVC-CI to predict PIH, to decrease the morbidity of patients undergoing surgery. Out of these, we recommend LVOT-VTI measurements as it has showed a better diagnostic accuracy (77.4%) with no failure rate.