BACKGROUND: Ventilation with low tidal volume is recommended for patients with acute lung injury. Current guidelines suggest limiting plateau pressure (P plat ) to < 30 cm H 2 O for septic patients needing mechanical ventilation. The aim of this study was to determine whether P plat within the first 24 h of ICU admission is predictive of outcome and whether P plat < 30 cm H 2 O is associated with lower mortality rates. METHODS: This study was a retrospective analysis of prospectively acquired clinical data from an ICU of a tertiary referral hospital in central Taiwan. Subjects were included if they were admitted due to sepsis and respiratory failure requiring mechanical ventilation from April 2008 to November 2009. RESULTS: There were 220 subjects (188 males, 32 females) with a median age of 76 y and a mean Acute Physiology and Chronic Health Evaluation II score of 25.0 ؎ 6.5. Pneumonia was the major cause of sepsis (85.5%). The hospital mortality rate was 39.1%. P plat was higher throughout the first 24 h of ICU admission in nonsurvivors. Higher P plat was associated with higher mortality rates regardless of acute lung injury. In multivariate regression analysis, P plat > 25 cm H 2 O at 24 h after admission was an independent risk factor for mortality (adjusted odds ratio of 2.33, 95% CI 1.10 -4.91, P ؍ .03 for hospital mortality). CONCLUSIONS: P plat within the first 24 h of ICU admission is predictive of outcome, with lower P plat associated with lower mortality rates. There is no safety margin for P plat . Limiting P plat should be considered even at < 30 cm H 2 O in septic patients with acute respiratory failure.
This study revealed that the major challenge in clinical cases of medical futility is for physicians, nurses, and patients to communicate effectively together during times of rapid and unanticipated change in patient condition. Thus, events of medical futility may be preventable. Past cases of medical futility involving critically ill patients may serve as references for guiding clinical care, education, and related policy formulation.
ObjectivePain assessment based on facial expressions is an essential issue in critically ill patients, but an automated assessment tool is still lacking. We conducted this prospective study to establish the deep learning-based pain classifier based on facial expressions.MethodsWe enrolled critically ill patients during 2020–2021 at a tertiary hospital in central Taiwan and recorded video clips with labeled pain scores based on facial expressions, such as relaxed (0), tense (1), and grimacing (2). We established both image- and video-based pain classifiers through using convolutional neural network (CNN) models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks (BiLSTM). The performance of classifiers in the test dataset was determined by accuracy, sensitivity, and F1-score.ResultsA total of 63 participants with 746 video clips were eligible for analysis. The accuracy of using Resnet34 in the polychromous image-based classifier for pain scores 0, 1, 2 was merely 0.5589, and the accuracy of dichotomous pain classifiers between 0 vs. 1/2 and 0 vs. 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high.ConclusionsThe present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.
The National Early Warning Score (NEWS) is an early warning system that predicts clinical deterioration. The impact of the NEWS on the outcome of healthcare remains controversial. This study was conducted to evaluate the effectiveness of implementing an electronic version of the NEWS (E-NEWS), to reduce unexpected clinical deterioration. We developed the E-NEWS as a part of the Health Information System (HIS) and Nurse Information System (NIS). All adult patients admitted to general wards were enrolled into the current study. The “adverse event” (AE) group consisted of patients who received cardiopulmonary resuscitation (CPR), were transferred to an intensive care unit (ICU) due to unexpected deterioration, or died. Patients without AE were allocated to the control group. The development of the E-NEWS was separated into a baseline (October 2018 to February 2019), implementation (March to August 2019), and intensive period (September. to December 2019). A total of 39,161 patients with 73,674 hospitalization courses were collected. The percentage of overall AEs was 6.06%. Implementation of E-NEWS was associated with a significant decrease in the percentage of AEs from 6.06% to 5.51% (p = 0.001). CPRs at wards were significantly reduced (0.52% to 0.34%, p = 0.012). The number of patients transferred to the ICU also decreased significantly (3.63% to 3.49%, p = 0.035). Using multivariate analysis, the intensive period was associated with reducing AEs (p = 0.019). In conclusion, we constructed an E-NEWS system, updating the NEWS every hour automatically. Implementing the E-NEWS was associated with a reduction in AEs, especially CPRs at wards and transfers to ICU from ordinary wards.
Protocol-driven therapy for sepsis was put into clinical practice. Early resuscitation and ICU bed availability were key process indicators in managing sepsis, to reduce mortality.
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