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Background. The aim of the study was to assess the effect of baricitinib on 28-day all-cause mortality and the progression of respiratory failure in patients needing transfer to the intensive care unit (ICU) with COVID-19 pneumonia treated with high-flow oxygen therapy. Methods. This retrospective study included hospitalized patients with COVID-19 pneumonia treated with high-flow oxygen non-invasive ventilation receiving standard of care (SOC) or SOC in addition to baricitinib. Data on patients' characteristics, pro-inflammatory markers, D dimer, and National Early Warning Score 2 (NEWS2) values were collected and compared between groups. The primary endpoint was 28-day all-cause in-hospital mortality and the secondary outcome was transfer to the ICU. Results. The study included 125 patients. The primary outcome was observed in 44.8% of them: 27% in the baricitinib group vs. 62% in the SOC group, p < 0.001. Transfer to the ICU ward was significantly lower in the baricitinib group: 29% vs. 81%, p < 0.001. A significant improvement was observed when the baricitinib group was compared to SOC in procalcitonin, CRP, D-dimer, neutrophil-to-lymphocyte ratio values, and NEWS2. Conclusion. Treatment with baricitinib in addition to SOC was associated with reduced mortality and a lower prevalence of transfer to the ICU in hospitalized patients with COVID-19 pneumonia treated with high-flow oxygen non-invasive therapy. [ABSTRACT FROM AUTHOR]

Context: Goals-of-care discussions are important for patient-centered care among hospitalized patients with serious illness. However, there are little data on the occurrence, predictors, and timing of these discussions.Objectives: To examine the occurrence, predictors, and timing of electronic health record (EHR)-documented goals-of-care discussions for hospitalized patients.Methods: This retrospective cohort study used natural language processing (NLP) to examine EHR-documented goals-of-care discussions for adults with chronic life-limiting illness or age ≥80 hospitalized 2015-2019. The primary outcome was NLP-identified documentation of a goals-of-care discussion during the index hospitalization. We used multivariable logistic regression to evaluate associations with baseline characteristics.Results: Of 16,262 consecutive, eligible patients without missing data, 5,918 (36.4%) had a documented goals-of-care discussion during hospitalization; approximately 57% of these discussions occurred within 24 hours of admission. In multivariable analysis, documented goals-of-care discussions were more common for women (OR=1.26, 95%CI 1.18-1.36), older patients (OR=1.04 per year, 95%CI 1.03-1.04), and patients with more comorbidities (OR=1.11 per Deyo-Charlson point, 95%CI 1.10-1.13), cancer (OR=1.88, 95%CI 1.72-2.06), dementia (OR=2.60, 95%CI 2.29-2.94), higher acute illness severity (OR=1.12 per National Early Warning Score point, 95%CI 1.11-1.14), or prior advance care planning documents (OR=1.18, 95%CI 1.08-1.30). Documentation of these discussions was less common for racially or ethnically minoritized patients (OR=0.823, 95%CI 0.75-0.90).Conclusion: Among hospitalized patients with serious illness, documented goals-of-care discussions identified by NLP were more common among patients with older age and increased burden of acute or chronic illness, and less common among racially or ethnically minoritized patients. This suggests important disparities in goals-of-care discussions. [ABSTRACT FROM AUTHOR]

Remote patient monitoring (RPM) allows monitoring of patients both inside and outside healthcare facilities, can enable expedited discharge to their homes and provide early detection of complications as they are still being monitored by healthcare professionals. The Patient Status Engine (PSE) is a wireless RPM that continuously collects physiological data and notifies medical staff when the data is outside the predefined limits for vital signs. The aim of this study was to assess the feasibility of RPM, regarding patient's acceptance, external interference and limitations. Twenty healthy volunteers were recruited and monitored for the non-invasive blood pressure, pulse oximetry, skin temperature, respiratory rate, heart rate and continuous electrocardiography. The most reliable sensor was the heart monitoring and the least reliable monitoring was the LifeTemp sensor for skin temperature. Most of the patients would be willing to use the PSE for up to two days and most of the volunteers would allow for the data to be transferred to their Hospital records and even would be willing to have a video chat in case of need. The majority of the volunteers would feel safer being monitored after surgery. To conclude, this study shows that most people are willing to contribute with home monitoring and even feel safer using this technology. RPM's technology will continue to evolve and when used properly can provide a high level of quality data. For selected patients, this technology can be of great help and allow earlier detection of complications in the postoperative period. [ABSTRACT FROM AUTHOR]

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Coronavirus disease 2019 (COVID-19) remains a global pandemic. Early warning scores (EWS) are used to identify potential clinical deterioration, and this study evaluated the ability of the Rapid Emergency Medicine score (REMS), National Early Warning Score (NEWS), and Modified EWS (MEWS) to predict in-hospital mortality in COVID-19 patients. This study retrospectively analyzed data from COVID-19 patients who presented to the emergency department and were hospitalized between 1 May and 31 July 2021. The area under curve (AUC) was calculated to compare predictive performance of the three EWS. Data from 306 COVID-19 patients (61 ± 15 years, 53% male) were included for analysis. REMS had the highest AUC for in-hospital mortality (AUC: 0.773, 95% CI: 0.69–0.85), followed by NEWS (AUC: 0.730, 95% CI: 0.64–0.82) and MEWS (AUC: 0.695, 95% CI: 0.60–0.79). The optimal cut-off value for REMS was 6.5 (sensitivity: 71.4%; specificity: 76.3%), with positive and negative predictive values of 27.9% and 95.4%, respectively. Computing REMS for COVID-19 patients who present to the emergency department can help identify those at risk of in-hospital mortality and facilitate early intervention, which can lead to better patient outcomes. [ABSTRACT FROM AUTHOR]

Abstract Background The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24 h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4 h (CSD). Methods Clinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust. The timing of D/ICU was extracted for all episodes. 1100 in-patient episodes were annotated manually to record the timing of CSD, defined as a specific event requiring a change in treatment. Time series features were entered into logistic regression models to derive DEWS for each of the clinical outcomes. Area under the receiver operating characteristic curve (AUROC) was the primary measure of model accuracy. Results AUROC (95% confidence interval) for predicting D/ICU was 0.857 (0.852–0.862) for NEWS-2 and 0.906 (0.899–0.914) for DEWS in the validation data. AUROC for predicting CSD was 0.829 (0.817–0.842) for NEWS-2 and 0.877 (0.862–0.892) for DEWS. NEWS-2 ≥ 5 had sensitivity of 88.2% and specificity of 54.2% for predicting CSD, while DEWS ≥ 0.021 had higher sensitivity of 93.6% and approximately the same specificity of 54.3% for the same outcome. Using these cut-offs, 315 out of 347 (90.8%) CSD events were detected by both NEWS-2 and DEWS, at the time of the event or within the previous 4 h; 12 (3.5%) were detected by DEWS but not by NEWS-2, while 4 (1.2%) were detected by NEWS-2 but not by DEWS; 16 (4.6%) were not detected by either scoring system. Conclusion We have developed DEWS that display greater accuracy than NEWS-2 for predicting clinical deterioration events in patients with respiratory disease. Prospective validation studies are required to assess whether DEWS can be used to reduce missed deteriorations and false alarms in real-life clinical settings.

Early recognition of clinical deterioration plays a pivotal role in a Rapid Response System (RRS), and it has been a crucial step in reducing inpatient morbidity and mortality. Traditional Early Warning Scores (EWS) and Deep Early Warning Scores (DEWS) for identifying patients at risk are still limited because of the challenges of imbalanced multivariate temporal data. Typical issues leading to their limitations are low sensitivity, high late alarm rate, and lack of interpretability; this hinders the system's deployment in clinical settings. This study develops an EWS based on Temporal Variational Autoencoder (TVAE) and a window interval learning framework, which uses the latent space features generated from the input multivariate temporal features, to learn the temporal dependence distribution between the target labels (clinical deterioration probability). Implementing the target information in the Fully Connected Network (FCN) with a loss function assists in addressing the imbalance problem and improving the performance of the time series classification task. The proposed method is validated on an in-house clinical dataset collected from Chonnam National University Hospitals (CNUH) and a public dataset from the University of Virginia (UV). Extensive trials with diverse validation methods and study cases demonstrate that our system outperforms existing methods, showcasing remarkable performance across usual criteria for classification models, typically as the Area Under The Receiver Operating Characteristic Curve (AUROC) and the Area Under The Precision-Recall Curve (AUPRC). TVAE exhibits a notable decrease in late alarm issues across two datasets, presenting stable performance even with a limited number of data samples. [Display omitted] • Predicting a patient's deterioration is an urgent problem for healthcare system. • Medical data characteristics pose challenges for deep learning applications. • Proposed approach boosts early recognition, enhancing patient care. • Extensive trials confirm superior performance over other methods. • Ongoing integration into hospital systems for practical clinical application and enhanced care. [ABSTRACT FROM AUTHOR]

• The frailty Index-laboratory (FI-lab) has been used as a simple assessment tool for frailty, but there is limited evidence about the use of FI-lab in the emergency care setting. • A higher FI-lab was associated with in-hospital death and prolonged length of hospital stay, similarly to the national early warning score and the modified JTAS, and was independently associated with the likelihood of discharge to home. • FI-lab can be used to evaluate the severity of illness, and was a useful indicator for predicting adverse outcomes in acute care. The emergency department is treating a growing number of older patients with frailty, which has been linked to poorer outcomes. Urgency is generally emphasized in the emergency department based on indicators such as triage scores and early warning scores for decision-making. However, this approach may not be sufficient for frail older people. The Frailty Index-laboratory (FI-lab) has been used as a simple assessment tool for frailty, but it may also reflect disease severity and predict adverse outcomes in the emergency care setting. Therefore, we aimed to evaluate the association between FI-lab in the emergency room and adverse outcomes during hospitalization through comparison with assessments using triage and early warning scores. This was a retrospective cohort study conducted in a tertiary hospital. The study included patients aged 65 years or older who were admitted to the general internal medicine ward after being initially evaluated in the emergency department. FI-lab was calculated using 24 laboratory parameters from blood tests. The National Early Warning Score (NEWS), the Japan Triage and Acuity Scale (JTAS), and the modified JTAS were also used as prognostic indicators, and their association with adverse outcomes was compared with that of FI-lab. In total, 872 patients (mean age, 80.9 years; male, 52.6 %) were analyzed. Patients who died during hospitalization had a higher FI-lab than those who survived. In multiple regression analysis, FI-lab, NEWS, and the modified JTAS were significantly associated with in-hospital death and prolonged length of hospital stay. In contrast, none of these indices were associated with in-hospital falls. The FI-lab was independently associated with the likelihood of discharge to home. FI-lab evaluated in the emergency department reflected the severity of illness in acutely hospitalized older adults, similarly to NEWS and JTAS, and was a useful indicator for predicting adverse outcomes. These results may indicate the value of FI-lab for older adults in the acute care setting. [ABSTRACT FROM AUTHOR]

Extensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients. A retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of Changhai Hospital, the First Affiliated Hospital of Naval Medical University. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling. The results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, inhalation tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923(95 % CI 0.892–0.953), compared to NEWS which had a C-index of 0.699 (95 % CI 0.628–0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group. This study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems. • Age, full-thickness burn, creatinine, inhalation tracheotomy, and the NEWS are independent prognostic factors for the survival of patients with severe burns. • The results showed that the nomogram based on the NEWS had a better goodness-of-fit and accuracy for predicting survival and prognosis in patients with severe burns. • The nomogram based on the NEWS was better than the NEWS alone in predicting the mortality of patients with severe burns; • The nomogram based on the NEWS successfully stratified patients with severe burns into high- and low-risk groups, with the high-risk subgroup exhibiting a poor survival prognosis. [ABSTRACT FROM AUTHOR]

• A novel unsupervised learning method for SHM by two-level clustering-based anomaly detection. • A novel manifold learning-aided clustering technique for preparing local training subsets. • Regularized Gaussian mixture modeling supported by nearest neighbor graphs for developing the proposed clustering. • A multi-fidelity hyperparameter optimization for tuning unknown and key elements of the proposed clustering technique. • A non-parametric probabilistic anomaly detector based on a reverse Gaussian mixture function for early damage warning. Unsupervised learning is an effective and practical methodology for structural health monitoring when the preparation of labeled training data regarding damaged states is intractable, unnecessary, and expensive. Despite several studies on this field, some challenging issues need further evaluations. The main objective of this research is to overcome the challenges concerning different variability patterns in unlabeled vibration data caused by single and multiple environmental and/or operational variations, non-generality of unsupervised learners in handling training data of different sizes, and impacts of false positive and false negative errors on early damage warning. A novel integrated unsupervised learning method is proposed that emanates from manifold learning-aided data clustering and non-parametric probabilistic anomaly detection. Data clustering is based on regularized Gaussian mixture modeling supported by nearest neighbor graphs, for which training data can be modeled on a manifold structure. The primary purpose of this step is to generate local subsets of the entire training data in an effort to minimize the effects of environmental and/or operational variations. A multi-fidelity hyperparameter optimization is also designed to set the main hyperparameters of the proposed clustering algorithm, namely the number of components (clusters) and a regularization value. Using the aforementioned local subsets, a non-parametric probabilistic anomaly detector is developed from a reverse Gaussian mixture function to compute anomaly scores for early damage warning. Modal frequencies regarding two large-scale bridges are used to validate the proposed method and compare it with some state-of-the-art techniques. Results confirm the effectiveness and reliability of this method with negligible errors under different environmental variability. [ABSTRACT FROM AUTHOR]

A correction notice was issued for the article "Early warning score validation methodologies and performance metrics: a systematic review" published in BMC Medical Informatics & Decision Making. The correction addressed an incorrect citation in the "Performance metrics" section, which was rectified by referencing the appropriate source. The email address of the Corresponding Author was also updated to andrew.fang@doctoranywhere.com. The original article has been corrected, and Springer Nature maintains neutrality regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

The National Early Warning Score 2 (NEWS2) standardizes assessment and response to acute illnesses using vital signs. Whether NEWS2 is useful in predicting the prognosis of candidemia remains to be determined. Our study, conducted as a rigorous and retrospective analysis, examined patients with candidemia who were hospitalized between January 2014 and December 2023. We assessed candidemia severity using the Pitt Bacteremia Score (PBS) and NEWS2, while the Charlson Comorbidity Index (CCI) was used to assess underlying medical conditions. The endpoint was all-cause mortality within 30 days of candidemia onset, ensuring comprehensive evaluation of the patient's prognosis. Overall, 93 patients with candidemia were included. The 30-day all-cause mortality rate was 29.0 %. The area under the receiver operating characteristic curve (AUC) for CCI, PBS, and NEWS2 were 0.87 (95 % confidence interval [CI]: 0.80–0.95), 0.75 (95 % CI: 0.66–0.85), and 0.92 (95 % CI: 0.87–0.97), respectively, for predicting the 30-day mortality in patients with candidemia. The AUC values for CCI combined with PBS and NEWS2 were 0.89 (95 % CI: 0.83–0.96) and 0.96 (95 % CI: 0.93–1.00) for predicting the 30-day mortality in candidemia. Among the items that were significant in the univariate analysis, multivariate analysis showed that the combination of NEWS2 ≥ 10 and CCI ≥4 was the helpful prognostic factor for 30-day mortality. The combination of NEWS2 ≥ 10 and CCI ≥4 scores may be useful in predicting the risk of 30-day mortality in patients with candidemia. [ABSTRACT FROM AUTHOR]

• Heart rate variability (HRV) provides non-invasive and continuous monitoring for early detection of deterioration in septic patients by reflecting the sympathovagal imbalance. • We developed HRV based machine learning models to predict sepsis and septic shock 120 mins in advance for patients with suspected sepsis. • Our model outperformed the conventional scoring systems (delta SOFA, NEWS, MEWS) in predicting clinical deterioration in patients with suspected sepsis, and was externally validated on the Medical Information Mart for Intensive Care III (MIMIC-III) database. To develop and compare heart rate variability (HRV) based machine learning algorithms for septic shock and sepsis prediction against established metrics validated on the Medical Information Mart for Intensive Care III (MIMIC-III) database for early detection of septic patient deterioration. We developed HRV-based prediction models in a prospective cohort study in an emergency department (ED). Adult patients with a clinical diagnosis of infection and suspected sepsis were recruited in our ED between April 2016 and September 2017. Different machine learning algorithms were trained to continuously predict septic shock onset 120 min ahead on the included patients using HRV obtained in five-minute intervals. We further applied transfer learning among patients with Sepsis-3 in the MIMIC- III database. We recruited and included 38 and 755 patients with a total recording time of 360 and 19,321 patient hours from our ED and the MIMIC-III database, respectively. The extreme gradient boosting (XGBoost) model outperformed other machine learning models and conventional models including the delta SOFA score, National Early Warning Score (NEWS), and Modified Early Warning Score (MEWS) in predicting septic shock (AUROC: 0.94; 95% CI: 0.91–0.96) during internal validation. After transfer learning, the XGBoost model had an AUROC of 0.91 (95% CI: 0.90–0.91) to predict septic shock in patients with Sepsis-3 in the MIMIC-III database. HRV-based XGBoost models provide a promising non-invasive tool for early septic shock prediction and patient deterioration due to infection, potentially enhancing patient management strategies. [ABSTRACT FROM AUTHOR]

Abstract Background Administration of antibiotics to septic patients within 1 h was recommended in 2018 by the Surviving Sepsis Campaign (SSC) as a strategy to improve survival outcomes. The use of sepsis screening tools in emergency departments (EDs) is important for early diagnosis and initiation of sepsis care. This study aimed to assess the impact of the Ramathibodi early warning score (REWs) on the administration of antibiotics within 1 h of presentation. Methods This was an observational retrospective cohort study with propensity score matching between the sepsis-3 criteria (pre-period) and the REWs (post-period) as screening tools in adult patients with sepsis in EDs. The primary outcome was the proportion of receiving antibiotics within 1 h of presentation in the pre- and post-periods. Results A total of 476 patients were analyzed without propensity matching. The proportion of antibiotic administration within 1 h was higher in patients screened using the REWs compared with standard of care in the total study population (79.5% vs. 61.4%, p < 0.001). After propensity score matching, 153 patients were included in both groups. The proportion of antibiotic administration within 1 h was similar in patients screened using the REWs and those receiving standard of care (79.7% vs. 80.4%, p = 0.886). However, time to intensive care unit (ICU) admission was faster in patients screened using the REWs. Delays in receiving antibiotics of longer than 3 h were associated with increased mortality (adjusted hazard ratio 7.04, 95% confidence interval 1.45 to 34.11, p = 0.015). Conclusions Implementing the REWs as a tool in sepsis screening protocols in EDs did not improve rates of antibiotic administration within 1 h as recommended by the SSC. However, time to ICU admission was improved after implementation of the REWs.

Abstract Background In Iran, the emergency departments (EDs) have largely adopted the emergency severity index (ESI) to prioritize the emergency patients, however emergency medical services (EMS) mainly triage the patients based on the paramedics’ gestalt. The National Early Warning Score (NEWS) is a recommended prehospital triage in the UK. We aimed to compare prehospital NEWS and ED ESI for predicting severe outcomes in emergency patients. Methods An observational study was conducted in a university-affiliated ED between January and April 2021. Adult patients who arrived in the ED by EMS were included. EMS providers calculated the patients' NEWS upon arriving on the scene using an Android NEWS application. In the ED, triage nurses utilized the ESI algorithm to prioritize patients with higher clinical risk. Then, Research nurses recorded patients' 30-day severe outcomes (death or ICU admission). Finally, The prognostic properties of ESI and NEWS were evaluated. Results One thousand forty-eight cases were included in the final analysis, of which 29 (2.7%) patients experienced severe outcomes. The difference between the prehospital NEWS and ED ESI in predicting severe outcomes was not statistically significant (AUC = 0.825, 95% CI: 0.74–0.91 and 0.897, 95% CI, 0.83–0.95, for prehospital NEWS and ESI, respectively). Conclusion Our findings indicated that prehospital NEWS compares favorably with ED ESI in predicting 30-day severe outcomes in emergency patients.

ObjectivesGlobally, pediatric hospitals have implemented Pediatric Early Warning Scores (PEWS) to standardize escalation of care and improve detection of clinical deterioration in pediatric patients. This study aims to utilize qualitative methodology to understand barriers and facilitators of PEWS implementation at Philippine Children's Medical Center (PCMC), a tertiary care hospital in Manila, Philippines.MethodsSemi-structured interviews querying current processes for clinical monitoring, Pediatric Intensive Care Unit (PICU) transfer, and clinician attitudes towards PEWS implementation were audio recorded. In-person hospital observations served to triangulate interview findings. The Systems Engineering Initiative for Patient Safety (SEIPS) framework guided content coding of interviews to characterize work systems, processes, and outcomes related to patient monitoring and care escalation. Thematic coding was performed using Dedoose software. This model allowed identification of barriers and facilitators to PEWS implementation.ResultsBarriers within PCMC workflow included: limited bed capacity, delay in referral, patient overflow, limited monitoring equipment, and high patient to staff ratio. Facilitators of PEWS implementation included support for PEWS adaptation and existence of systems for vital sign monitoring. Observations by study personnel confirmed validity of themes.ConclusionUtilizing qualitative methodology to understand barriers and facilitators to PEWS in specific contexts can guide implementation at resource-limited hospitals.

We are hearing more and more about robots taking over the world, and the existential threat artificial intelligence (AI) could pose to humanity. [ABSTRACT FROM AUTHOR]

Development and internal validation of a novel risk adjustment model for adult patients undergoing emergency laparotomy surgery: the National Emergency Laparotomy Audit risk model. In patients with multiple comorbidities to the extent that the clinical team wouldn't countenance an elective laparotomy or when the patient is reluctant to proceed, the shared decision-making process can be challenging. Keywords: emergency laparotomy; risk scoring EN emergency laparotomy risk scoring 1055 1057 3 08/07/23 20230901 NES 230901 More than 22,000 people underwent an emergency laparotomy in England and Wales in the 12 months from December 2020 to November 2021, according to the 8th report of the National Emergency Laparotomy Audit (NELA) [[1]]. [Extracted from the article]

Objective: To study the potential mechanism of Lactobacillus crispatus inhibiting cervical squamous intraepithelial lesion (SIL) and screen the early warning factors of SIL. Methods: The effects of Lactobacillus crispatus on the proliferation, apoptosis, cross pore migration and invasion and cytokines of cervical precancerous cells Ect1/E6E7 were detected respectively. The effect of Lactobacillus crispatus on the expression of differential proteins screened in Ect1/E6E7 cells were detected by Western blot. Results: Lactobacillus crispatus significantly inhibited the proliferation, induced apoptosis and inhibited cell migration of Ect1/E6E7 cells in a time-dependent manner (P < 0.05), but had no significant effect on cell invasion. Lactobacillus crispatus significantly promoted the secretion of Th1 cytokines and inhibited the secretion of Th2 cytokines by Ect1/E6E7 cells (P < 0.05). In addition, compared with SiHa cells in the control group, the expression of differential proteins PCNA, ATM, LIG1 and HMGB1 in Ect1/E6E7cells decreased significantly, while the expression of TDG and OGG1 proteins increased significantly (P < 0.05). ABCG2 protein in Ect1/E6E7 cells was slightly higher than that in SiHa cells, but the difference was not statistically significant. What is interesting is that Lactobacillus crispatus significantly inhibited the expression of ABCG2, PCNA, ATM, LIG1, OGG1 and HMGB1 proteins in Ect1/E6E7 cells, and promoted the expression of TDG protein. Conclusions: Lactobacillus crispatus may inhibit the function of Ect1/E6E7 cells through multiple pathways and exert the potential to reverse the progression of SIL. [ABSTRACT FROM AUTHOR]

Early warning scores reliably identify patients at risk of imminent death, but do not provide insight into what may be wrong with the patient or what to do about it. Our aim was to explore whether the Shock Index (SI), pulse pressure (PP), and ROX Index can place acutely ill medical patients in pathophysiologic categories that could indicate the interventions required. A retrospective post-hoc analysis of previously obtained and reported clinical data for 45,784 acutely ill medical patients admitted to a major regional referral Canadian hospital between 2005 and 2010 and validated on 107,546 emergency admissions to four Dutch hospitals between 2017 and 2022. SI, PP, and ROX values divided patients into eight mutually exclusive physiologic categories. Mortality was highest in patient categories that included ROX Index value < 22, and a ROX Index value < 22 multiplied the risk of any other abnormality. Patients with a ROX Index value < 22, PP < 42 mm Hg, and SI > 0.7 had the highest mortality and accounted for 40% of deaths within 24 h of admission, whereas patients with a PP ≥ 42 mm Hg, SI ≤ 0.7, and ROX Index value ≥ 22 had the lowest risk of death. These results were the same in both the Canadian and Dutch patient cohorts. SI, PP, and ROX Index values can place acutely ill medical patients into eight mutually exclusive pathophysiologic categories with different mortality rates. Future studies will assess the interventions needed by these categories and their value in guiding treatment and disposition decisions. [ABSTRACT FROM AUTHOR]

Aim: To explore and describe how the National Early Warning Score (NEWS) and Individual Early Warning Score (I‐EWS) are used and how they support nurses' patient risk assessment practice. Design: A qualitative observational fieldwork study drawing on ethnographical principles was performed in six hospitals in two regions of Denmark in 2019. Methods: Data were generated from participant observations and informal interviews with 32 nurses across 15 different wards in the hospitals. A total of 180 h of participant observation was performed. The observations lasted between 1.5 and 8 h and were conducted during day or evening shifts. Results: NEWS and I‐EWS supported nurses' observations of patients, providing useful knowledge for planning patient care, and prompting critical thinking. However, the risk assessment task was sometimes delegated to less experienced staff members, such as nursing students and healthcare assistants. The Early Warning Score (EWS) systems were often adapted by nurses according to contextual aspects, such as the culture of the speciality in which the nurses worked and their levels of competency. In some situations, I‐EWS had the effect of enhancing nurse autonomy and responsibility for decision‐making in relation to patient care. Conclusions: EWS systems support nurses' patient risk assessment practice, providing useful information. I‐EWS makes it easier to factor the heterogeneity of patients and the clinical situation into the risk assessments. The delegation of risk assessment to other, less experienced staff members pose a risk to patient safety, which needs to be addressed in the ongoing debate regarding the shortage of nurses. Impact The findings of this study can help ward nurses, hospital managers and policymakers to develop and improve strategies for improved person‐centred nursing care. [ABSTRACT FROM AUTHOR]

Rapid response (RR) systems are associated with decreased hospital mortality. Systemic biases and inequities can negatively impact RR outcomes. Language barriers between patients and providers are associated with worse outcomes, but it is unknown if language barriers are associated with RR outcomes. We analyzed all adult hospitalized patients who experienced a RR over one year (January 2020 to December 2020) at a tertiary care academic medical center. We used an objective scoring system to establish disease severity at the time of the event. We then compared disease severity and outcomes for patients who are primary language Spanish (PLS) and primary language English (PLE) using both univariable and multivariable analyses. Of 1133 patients, 42 identified as PLS and 1091 as PLE. In multivariable analyses, PLS patients had significantly higher disease severity scores, as measured by deterioration index score (8.2, p = 0.021) at the time of their rapid responses. PLS patients also had 18.5% increase in length of stay (LOS) after RRs and this disparity was not mitigated when controlling for disease severity at the time of RRs. PLS was not a significant predictor for hospital mortality after RRs. Our study found that PLS patients had increased disease severity at the time of RRs and increased LOS after RRs. However, the disparity in LOS was not mitigated when controlling for disease severity at the time of RRs. These findings suggest that language barriers may cause both delays in activation of RR systems, as well as the care provided during and after RRs. [ABSTRACT FROM AUTHOR]

Background: Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support. Methods: Multicentre, prospective, emergency medical services (EMS)‐delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non‐invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS). Results: Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918–0.958), a calibration‐in‐large of 0.026 and a higher net benefit as compared with the other scores. Conclusions: Our results showed that REWS is a remarkably aid for the decision‐making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management. [ABSTRACT FROM AUTHOR]

Background: Annually in England, over 1.5 million children and young people (CYP) are admitted to hospital. However, a proportion of these CYP will experience failure to rescue (FtR), a failure to recognize, respond and escalate clinical deterioration, which can result in significant harm or death. Aim: To identify and quantify FtR episodes from emergency events at a 110‐bedded tertiary children's hospital located within a University Teaching Hospital and evaluate the impact of targeted interventions on reducing FtR. Methods: A quality improvement approach was adopted. From 170 446 patients admitted between 2011 and 2019, all emergency event calls were systematically reviewed to identify FtR episodes. Root–cause analysis was performed to identify practice deficiencies. The Plan‐Do‐Study‐Act fundamentals were used. Results: A total of 520 emergency events were reviewed over the 9‐year period. One hundred and thirty‐two (n = 132; 25%) were cardiac arrest events, with the majority occurring within the PCCU setting. Three hundred and twelve (60%) of the events were in children who had been inpatient for more than 48 hours. FtR trend declined over the study period from 23.6% in 2011 when the project commenced to 2.5% or less over the following 8 years. Conclusions: Identifying rates of FtR events from routinely collected emergency events data can be used as a patient safety measure to identify emergency concerns. This enables dynamic problem solving through delivery of strategic and targeted interventions. The proposed interventions outlined in this quality improvement study have application to critical care nursing as mechanisms for reducing unplanned admissions to paediatric critical care unit (PCCU), patient mortality, and PCCU and non‐PCCU cardiac arrests. Relevance to Clinical Practice: This study emphasises the importance in understanding the antecedence of emergency events for paediatric inpatient populations. This intelligence can be used to direct targeted interventions to significantly reduce failure to rescue rates. [ABSTRACT FROM AUTHOR]

Background/aim: In low-income or underdeveloped countries with conflict and internal unrest, healthcare facilities and staff are limited. For these reasons, it is necessary to use the most straightforward scoring systems to ensure that health facilities and staff are used effectively and to expedite processes through early and effective interventions for patients. In this study, we evaluate and compare the scoring systems used to predict patient prognosis for Emergency Department (ED) patients in northern Syria, which is an area marred by conflict and internal unrest. Materials and methods: In this study, patients hospitalized in the Afrin, Azez Vatan, Jarablus, Tel Abyad, Rasulayn, El Bab, and Çobanbey hospitals in northern Syria were investigated. Only patients that were hospitalized in the emergency departments of these hospitals, including wards and intensive care units, were included in the study. Patients that were hospitalized from 03/01/2021 to 08/31/2021, the study period, were prospectively analyzed. Vital signs, medical histories and demographic data of the patients were recorded by calculating National Early Warning Score 2 (NEWS2), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and HOTEL Score (hypotension, oxygen saturation, low temperature, electrocardiogram, loss of independence). Acceptance parameters and scores were analyzed using statistical methods and by comparing groups. Results: All four scoring systems were found to be effective in predicting mortality regarding ROC curve analysis. However, the statistical significance of the RAPS was slightly stronger than that of the other scores and REMS had the highest sensitivity and specificity amongst the four systems, at 86.2% and 84.1%, respectively. Regarding the risk of hospitalization in the ICU (p < 0.05), the sensitivity values of the cut-off values offered by the scoring systems remained below 0.70 regarding ROC curve analysis. RAPS had the highest sensitivity (65.2%) of the four systems with a cut-off value of 1.5. Conclusion: This study in northern Syria has shown that although RAPS had stronger statistical power, REMS had better sensitivity and specificity for the prediction of mortality. Additionally, RAPS had better sensitivity for ICU risk. This study will contribute to the evaluation of healthcare in similar regions and to cost-effective healthcare delivery by using scoring systems for ED patients’ admission. [ABSTRACT FROM AUTHOR]

Several studies attest to the long-term consequences of COVID-19 infection on survivors' mental illness, especially in terms of high prevalence of post-traumatic stress disorder (PTSD) 1–3 months after hospitalization. Aims of the present study were (1) to jointly evaluate PTSD and positive mental health among COVID-19 survivors and family members after hospital discharge, and (2) to investigate the relationship between perceived healthcare staff's relational empathy during hospitalization and survivors' post-traumatic stress levels. In this cross-sectional study, 60 survivors (Mage = 60.45; 63.3% men) and 40 family members (Mage = 52.33; 60% women) participated in an online survey 3–7 months after hospital discharge. In addition to providing socio-demographic data, they completed PTSD Checklist for DSM-5 and Mental Health Continuum Short Form. Survivors also completed the Consultation and Relational Empathy measure. Percentages of participants meeting a provisional PTSD and mental health diagnosis (flourishing, moderate, languishing) were calculated. A hierarchical regression analysis was performed on survivors' data, with perceived staff's empathy as predictor and post-traumatic stress symptoms (PTSS) as outcome. One-fifth of the participants received a provisional PTSD diagnosis, about half were diagnosed with flourishing or moderate mental health, and only 5% were languishing, with no significant between-group differences. Among survivors, a negative association was detected between perceived healthcare staff's empathy and PTSS, explaining 10.5% of the model variance over and above demographic and clinical variables. Findings highlighted the coexistence of PTSD and positive mental health among survivors and family members, suggesting the usefulness of assessing both negative and positive dimensions of mental health, in order to promote psycho-social adaptation once returning to everyday life. In addition, the role of compassionate care in clinical practice emerged as a potential means to mitigate severe traumatic reactions among survivors. [ABSTRACT FROM AUTHOR]

Objective: to evaluate the association of the risk classification categories with the Modified Early Warning Score and the outcomes of COVID-19 patients in the emergency service. Method: a cross-sectional study carried out with 372 patients hospitalized with a COVID-19 diagnosis and treated at the Risk Classification Welcoming area from the Emergency Room. In this study, the patients' Modified Early Warning Score was categorized into without and with clinical deterioration, from 0 to 4 and from 5 to 9, respectively. Clinical deterioration was considered to be acute respiratory failure, shock and cardiopulmonary arrest. Results: the mean Modified Early Warning Score was 3.34. In relation to the patients' clinical deterioration, it was observed that, in 43%, the time for deterioration was less than 24 hours and that 65.9% occurred in the Emergency Room. The most frequent deterioration was acute respiratory failure (69.9%) and the outcome was hospital discharge (70.3%). Conclusion: COVID-19 patients who had a Modified Early Warning Scores > 4 were associated with the urgent, very urgent and emergency risk classification categories, had more clinical deterioration, such as respiratory failure and shock, and evolved more to death, which shows that the Risk Classification Protocol correctly prioritized patients at risk of life. [ABSTRACT FROM AUTHOR]

Safe delivery of systemic anti-cancer treatment for skin cancers during the COVID-19 pandemic However, patients undergoing targeted treatment were already being seen less often, in comparison with other SACT patients, before the start of the COVID-19 pandemic. The COVID-19 pandemic has had a profound impact on healthcare services worldwide due to the redeployment of staff and suspension of many scheduled medical and surgical activities.[1] In the context of skin malignancies, treatment regimens have been modified to decrease the need for hospital visits. [Extracted from the article]

Objectives: Vitals are the most important parameters for assessing a patient's status and the National Early Warning Score helps to assess vitals to predict how urgent a patient's condition is. The perfusion index is the ratio of the pulsatile blood flow to the non-pulsatile/static blood flow in a patient's peripheral tissue. We aimed to investigate the relationship between National Early Warning Score and perfusion index in the courses and evaluate whether NEWS and PI are useful in predicting outcomes of older patients in an Emergency Department. Method: In this prospective cross-sectional study, we evaluated non-traumatized older patients admitted to an Emergency Department. National Early Warning Score, perfusion index and patients' Emergency Department outcomes were recorded. Results: A total of 967 patients (55% female, mean age=74.8) were enrolled. The initial National Early Warning Score of the patients had a significant, negative correlation with perfusion index (p <0.001). Patients admitted to the hospital had higher National Early Warning Score and lower perfusion index values than those discharged (both p <0.001). Patients admitted to an ICU had significantly higher National Early Warning Score and lower perfusion index values than those admitted to the medical floor (both p <0.001). Conclusion: The National Early Warning Score and the perfusion index are useful instruments to distinguish critically ill elderly patients in an Emergency Department. [ABSTRACT FROM AUTHOR]

Objective: perform the predictive validity of National Early Warning Score 2 - Brazilian version (NEWS 2 - BR) in discharge and death outcomes in patients with COVID-19. Method: crosssectional study with predictive validity analysis. Social-demographical and clinical variables, outcomes and the score components were collected with an electronic health record and analyzed through descriptive and inferential statistics. Outcomes: 400 patients were included, with median age of 61 years. The score, at the moment of admission, had a median of 5 points, with a range from 0 to 21. There is an association between the highest scores and the death outcome and the lowest scores and the discharge outcome. The predictive validity of NEWS 2 - BR for death was established by the analysis of the ROC curve and the most accurate cut-off point was six points. Conclusion: The Brazilian version of NEWS 2 is a valid score to assess patients with COVID-19. [ABSTRACT FROM AUTHOR]

Background: High in-hospital mortality in sepsis patients remains challenging for clinicians worldwide. Early recognition, prognostication, and aggressive management are essential for treating septic patients. Many scores have been formulated to guide clinicians to predict the early deterioration of such patients. Our objective was to compare predictive values of quick Sequential Organ Failure Assessment (qSOFA) and National Early Warning Score 2 (NEWS2) with respect to in-hospital mortality. Methods: This prospective observational study was conducted in a tertiary care center in India. Adults with suspected infection with at least two Systemic Inflammatory Response Syndrome criteria presenting to the emergency department (ED) were enrolled. NEWS2 and qSOFA scores were calculated, and patients were followed until their primary outcome of mortality or hospital discharge. The diagnostic accuracy of qSOFA and NEWS2 for predicting mortality was analyzed. Results: Three hundred and seventy-three patients were enrolled. Overall mortality was 35.12%. A majority of patients had LOS between 2 and 6 days (43.70%). NEWS2 had higher area under curve at 0.781 (95% confidence interval [CI] (0.59, 0.97)) than qSOFA at 0.729 (95% CI [0.51, 0.94]), with P < 0.001. Sensitivity, specificity, and diagnostic efficiency to predict mortality by NEWS2 were 83.21% (95% CI [83.17%, 83.24%]); 57.44% (95% CI [57.39%, 57.49%]); and 66.48% (95% CI [66.43%, 66.53%]), respectively. qSOFA score had sensitivity, specificity, and diagnostic efficiency to predict mortality of 77.10% (95% CI [77.06%, 77.14%]); 42.98% (95% CI [42.92%, 43.03%]); and 54.95% (95% CI [54.90%, 55.00%]), respectively. Conclusion: NEWS2 is superior to qSOFA in predicting in-hospital mortality for sepsis patients presenting to the ED in India. [ABSTRACT FROM AUTHOR]

Purpose: To identify the clinical indicators of acute deterioration in residents and the factors that influence residential aged care facility staff's identification of these. Design Rapid review and narrative synthesis. Methods: The WHO and Cochrane Rapid Review Methods Group recommendations guided the review processes. CINAHL, Medline, PubMed, and the Cochrane Library were searched from 2000 to January 2022. Data related to clinical indicators of deterioration were categorized using the Airway, Breathing, Circulation, Disability, Exposure assessment framework, and factors influencing detection were grouped as consumer (resident and family), aged care workforce, and organization factors. Results: Twenty publications were included of which 14 informed clinical indicators; nine highlighted factors that influence staff's identification of these and three informed both. Included article were collectively below moderate quality. Most clinical indicators were grouped into the 'Disability' category with altered level of consciousness, behavior, and pain identified most frequently. Few studies reported more traditional indicators of deterioration used in the general population – changes in vital signs. The most common factors influencing the detection of acute deterioration were organizational and workforce‐related including resource, knowledge, and confidence deficits. Conclusion: Findings suggest subtle changes in resident's health status, rather than focusing primarily on physiologic parameters used in early warning tools for acute care settings, should be recognized and considered in the design of early warning tools for residential aged care facilities. Clinical Relevance: Early warning tools sensitive to the unique needs of residents and support for aged care facility staff are recommended to improve the capacity of aged care facility care staff to identify and manage acute deterioration early to avoid hospitalization. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to perform a narrative review of the leading pediatric triage systems in emergency departments (EDs). Data source: Articles published between 1999 and 2019 were identified by searching the MEDLINE, EMBASE, and PubMed databases using the keywords “pediatric triage”, “pediatric assessment tools”, and “emergency department triage” with an emphasis on studies that evaluated the validation and reliability of triage systems. Data synthesis: A total of 105 articles on pediatric emergency triage systems in 12 countries were evaluated. Triage systems were divided into two groups: color-stratified triage systems and alert systems. The color-stratified triage systems included in this review were the Canadian Triage and Acuity Scale (CTAS), Manchester Triage System (MTS), Emergency Severity Index (ESI), and Australasian Triage Scale (ATS), and the alert systems included were the Paediatric Observation Priority Score (POPS), Pediatric Early Warning Score (PEWS), and Pediatric Approach Triangle (PAT). Evidence corroborates the validity and reliability of MTS, PaedCTAS, ESI version 4, PEWS, POPS, and PAT in pediatric emergency services. Conclusions: These are fundamental tools for risk classification of patients seeking treatment in EDs. Not all triage systems have been assessed for validity and reliability; nor are they well suited for all regions of the world. Employing triage systems in Brazil requires cultural adaptation and rigorous training of the local health staff, in addition to validation and reliability studies in our country, since the social and cultural context of this country differs from those where these tools were developed. [ABSTRACT FROM AUTHOR]

Documentation quality of patient–physician discussion, assessment, and intervention at the time of patient-directed discharges (elopement and 'against medical advice' discharges) is found to be poor in available studies and, importantly, may be a proxy for quality of care delivered. Less is known about the patient–physician interactions and documentation at the time a patient vocalizes the desire to leave early ('early warning interaction') prior to a patient-directed discharge. This was a cohort study comprising a retrospective chart review of patients leaving 'against medical advice' from an inpatient internal medicine—infectious disease service at a tertiary medical center from 01 July 2020 to 24 September 2021. Documentation quality was assessed using 11 extractable factors detailing patient–physician conversation elements from the assess, investigate, mitigate, explain, and document framework, plus related interventions pertinent to patient safety and care optimization. Descriptive statistics were mainly utilized with inferential statistics and regression models as appropriate. Fifty-two patients left against medical advice and 49 eloped; 11% had an early warning interaction. Aggregate documentation quality scores at early warning interaction (13%), 'against medical advice' discharge (42%), and at elopement (31%) were low. Half of the suggested documentation elements were recorded in no patients. The overall documentation quality was poor, suggesting the need for further training and interventions to facilitate more thorough documentation. [ABSTRACT FROM AUTHOR]

This study aims to investigate whether combining scoring systems with monocyte distribution width (MDW) improves early sepsis detection in older adults in the emergency department (ED). In this prospective observational study, we enrolled older adults aged ≥60 years who presented with confirmed infectious diseases to the ED. Three scoring systems—namely quick sepsis-related organ failure assessment (qSOFA), Modified Early Warning Score (MEWS), and National Early Warning Score (NEWS), and biomarkers including MDW, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein (CRP), were assessed in the ED. Logistic regression models were used to construct sepsis prediction models. After propensity score matching, we included 522 and 2088 patients with and without sepsis in our analysis from January 1, 2020, to September 30, 2021. NEWS ≥5 and MEWS ≥3 exhibited a moderate-to-high sensitivity and a low specificity for sepsis, whereas qSOFA score ≥2 demonstrated a low sensitivity and a high specificity. When combined with biomarkers, the NEWS-based, the MEWS-based, and the qSOFA-based models exhibited improved diagnostic accuracy for sepsis detection without CRP inclusion (c-statistics=0.842, 0.842, and 0.826, respectively). Of the three models, MEWS ≥3 with white blood cell (WBC) count ≥11 × 109/L, NLR ≥8, and MDW ≥20 demonstrated the highest diagnostic accuracy in all age subgroups (c-statistics=0.886, 0.825, and 0.822 in patients aged 60–74, 75–89, and 90–109 years, respectively). Our novel scoring system combining MEWS with WBC, NLR, and MDW effectively detected sepsis in older adults. [ABSTRACT FROM AUTHOR]

Background: Wearable devices could be used to continuously monitor vital signs in patients who are hospitalized, but they require validation. Objective: This study aimed to evaluate the clinical validity of the prototype of a semiautomated wearable wrist device (ChroniSense Polso) to measure vital signs and provide National Early Warning Scores (NEWSs). Methods: Vital signs and NEWSs measured using the wearable device were compared with standard, nurse-lead manual measurements. We enrolled adult patients (aged ≥18 years) who required vital sign measurements at least every 6 hours in a UK teaching district general hospital. Wearable device measurements were not used for clinical decision-making. The primary outcome was the agreement on the individual National Early Warning parameter scores and vital sign measurements: respiratory rate, oxygen saturation, body temperature, systolic blood pressure, and heart rate. Secondary outcomes were the agreement on the total NEWS, incidence of adverse events, and user acceptance. To compare the wearable device measurements with the standard measurements, we analyzed vital sign measurements by limits of agreement (Bland-Altman analysis) and conducted κ agreement analyses for NEWSs. A user experience survey was conducted with questions about comfort of the wrist device, safety, preference, and use. Results: We included 132 participants in the study, with a mean age of 62 (SD 15.81) years; most of them were men (102/132, 77.3%). The highest weighted κ values were found for heart rate (0.69, 95% CI 0.57-0.81 for all 385 measurements) and systolic blood pressure (0.39, 95% CI 0.30-0.47 for all 339 measurements). Weighted κ values were low for respiration rate (0.03, 95% CI −0.001 to 0.05 for all 445 measurements), temperature (0, 95% CI 0-0 for all 231 measurements), and oxygen saturation (−0.11, 95% CI −0.20 to −0.02 for all 187 measurements). Weighted κ using Cicchetti-Allison weights showed κ of 0.20 (95% CI 0.03-0.38) when using all 56 total NEWSs. The user acceptance survey found that approximately half (45/91, 49%) of the participants found it comfortable to wear the device and liked its appearance. Most (85/92, 92%) of them said that they would wear the device during their next hospital visit, and many (74/92, 80%) said that they would recommend it to others. Conclusions: This study shows the promising use of a prototype wearable device to measure vital signs in a hospital setting. Agreement between the standard measurements and wearable device measurements was acceptable for systolic blood pressure and heart rate, but needed to be improved for respiration rate, temperature, and oxygen saturation. Future studies need to improve the clinical validity of this wearable device. Large studies are required to assess clinical outcomes and cost-effectiveness of wearable devices for vital sign measurement. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-028219 [ABSTRACT FROM AUTHOR]

The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS). The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch. A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13–0.29], REWS: 0.20 95% CI: [0.13–0.29]) and specificity (MEWS: 0.96 95% CI: [0.95–0.97], REWS: 0.96 95% CI: [0.95–0.97]) were assessed. The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements. [ABSTRACT FROM AUTHOR]

Copyright of Revista Gaucha de Enfermagem is the property of Revista Gaucha de Enfermagem and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To assess the Pediatric Alert Score (EPA) accuracy, usefulness, reproducibility and applicability in identifying clinical deterioration in hospitalized children and adolescents. Methods: This is a prospective diagnostic test study, carried out between October/2018 and October/2019, to measure EPA diagnostic accuracy in a sample of 240 children, and its reproducibility and applicability in a sample of 60 children. Data were processed and analyzed on MedCalc and VassarStats.net. Results: At cut-off point ≥ 3, the score had a sensitivity of 73.6%, specificity of 95.7%, positive predictive value of 83%, negative predictive value of 92.7, area under the ROC curve of 93.6%, estimated prevalence of 19.6%, positive probability ratio of 17.1, positive post-test probability of 77.8%, simple Kappa of 0.946. Conclusion: The study provides evidence on EPA high accuracy, usefulness and reproducibility in identifying clinical deterioration in a Brazilian pediatric hospital setting, and considered the instrument applicable in the context of the research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Nursing UFPE / Revista de Enfermagem UFPE is the property of Revista de Enfermagem UFPE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heart rate and respiratory rate display circadian variation. Pediatric single-parameter rapid response system is activated when heart rate or respiratory rate deviate from age-specific criteria, though activation criteria do not differentiate between daytime and nighttime, and unnecessary activation has been reported due to nighttime bradycardia. We evaluated the relationship between rapid response system activation and the patient's clinical outcome by separately applying the criteria to daytime and nighttime in patients < 18. The observation period was divided into daytime and nighttime (8:00–20:00, and 20:00 to 8:00), according to which measured heart rate and respiratory rate were divided and rapid response system activation criteria were applied. We classified lower nighttime than daytime values into the 'decreased group', and the higher ones into the 'increased group', to analyze their effect on cardiopulmonary resuscitation occurrence or intensive care unit transfer. Nighttime heart rate and respiratory rate were lower than the daytime ones in both groups (both p values < 0.001), with no significant association with cardiopulmonary resuscitation occurrence or intensive care unit transfer in either group. Heart rate and respiratory rate tend to be lower at nighttime; however, their effect on the patient's clinical outcome is not significant. [ABSTRACT FROM AUTHOR]

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these "silver" labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes mellitus (DM) and hyperglycemia are important risk factors for poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19). The aim of the present study was to analyze the factors associated with the composite outcome of the necessity of invasive mechanical ventilation (IMV) or admission to the intensive care unit (ICU) in subjects with severe COVID-19 infection treated with dexamethasone comparing patients with DM vs. patients without DM. Research design and methods: An observational retrospective cohort study was performed, including hospitalized subjects with a diagnosis of SARS-CoV-2 pneumonia. Inclusion criteria were: age ≥18 years old with severe COVID-19 disease requiring daily intravenous 6mg dexamethasone treatment for 10 days. Exclusion criteria were: <18 years old, non-severe illness and/or patients in charge of ICU. Variables related to clinical and analytical parameters, glycemic control, acquired-hospital superinfections, mortality, IMV requirement, ICU admission and length of stay were included. Results: Two hundred and nine individuals with COVID-19 disease treated with dexamethasone were included. One hundred twenty-five out of these subjects (59.8%) were patients with DM. Overall, from the 209 subjects, 66 (31.6%) required IMV or were admitted to the ICU, with significant differences between patients with DM (n=50) vs. patients without DM (n=16) (76% vs. 24%, p=0.002). Among the group of subjects with DM (n=125), those who required IMV or were admitted to the ICU showed higher serum concentrations of C-reactive protein, interleukin-6, D-dimer, ferritin and pro-calcitonin and significantly lower serum concentrations of albumin compared to those who did not require IMV or were not admitted to the ICU. Besides, between these two groups of patients with DM, we observed no differences in glycemic parameters, including median capillary blood glucose values, glycosylated hemoglobin, coefficient of variability and hypoglycemic episodes. In the multinomial analysis, factors independently associated with the composite outcome of IMV or admission to the ICU in the insulin-treated group were the National Early Warning Score (NEWS) 2 score (OR 1.55 [1.17-2.17], p=0.005) and the presence of hospital-acquired superinfections (OR 35.21 [5.11-386.99], p=0.001). Conclusions: In our study, parameters related to glycemic control were not associated with IMV requirement nor admission to the ICU in patients with DM and severe COVID-19 disease receiving daily 6 mg of dexamethasone for 10 days. However, hospital-acquired superinfections and disease severity at admission were independent factors associated with this composite outcome. [ABSTRACT FROM AUTHOR]

Background: Emphysematous pyelonephritis (EPN) is a rare but severe necrotizing infection causing there to be gas in the pelvicalyceal system, renal parenchyma, and perirenal or pararenal space. Physicians should attend to EPN because of its life-threatening septic complications. The overall mortality rate has been reported to be as high as 20–40%. In addition, most patients had diabetes mellitus (DM) and obstructive uropathy. The most common isolated microorganism is Escherichia coli. This study aims to analyze the risk factors and performance of scoring systems in predicting the clinical outcomes of patients with EPN. Materials and Methods: We collected the data of patients with EPN in this single hospital-based retrospective study from the electronic medical records of Taichung Veterans General Hospital between January 2007 and December 2020. Radiological investigations of abdominal computed tomography (CT) confirmed the diagnosis of EPN. In addition, we analyzed demographics, clinical characteristics, and laboratory data. Finally, we used various scoring systems to predict clinical outcomes. Results: A total of fifty patients with EPN, whose diagnoses were confirmed through CT, were enrolled in the study. There were 18 males (36%) and 32 females (64%), with a mean age of 64.3 ± 11.3 years. The in-hospital mortality rate was 16%. A DM of 34 (68%) patients was the most common comorbidity. Fever was the most common symptom, found in 25 (50%) patients. The Mortality in Emergency Department Sepsis (MEDS) score was 4.64 ± 3.67 for survivors and 14.25 ± 5.34 for non-survivors (p < 0.001). The National Early Warning Score (NEWS) was 3.64 ± 2.33 for survivors and 7.13 ± 4.85 for non-survivors (p = 0.046). The Rapid Emergency Medicine Score (REMS) was 5.81 ± 1.97 for survivors and 9.13 ± 3.87 for non-survivors (p = 0.024). Regarding performance of mortality risk prediction, the AUC of ROC was 0.932 for MEDS, 0.747 for REMS, and 0.72 for NEWS. Conclusions: MEDS, REMS, and NEWS could be prognostic tools for the prediction of the clinical outcomes of patients with EPN. MEDS showed the best sound performance. In those with higher scores in MEDS (≥12), REMS (≥10), and NEWS (≥8), we recommended aggressive management and appropriate antimicrobial therapy as soon as possible to reduce mortality. Further large-scale studies are required to gain a deep understanding of this disease and to ensure patient safety. [ABSTRACT FROM AUTHOR]

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients. [ABSTRACT FROM AUTHOR]

Background: Maternal mortality is still a major challenge for health systems, while severe maternal complications are the primary causes of maternal death. Our study aimed to determine whether severe maternal morbidity is effectively predicted by a newly proposed Modified Obstetric Early Warning Score (MOEWS) in the setting of an obstetric intensive care unit (ICU).Methods: A retrospective study of pregnant women admitted in the ICU from August 2019 to August 2020 was conducted. MOEWS was calculated 24 h before and 24 h after admission in the ICU, and the highest score was taken as the final value. For women directly admitted from the emergency department, the worst value before admission was collected. The aggregate performance of MOEWS in predicting critical illness in pregnant women was evaluated and finally compared with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) score.Results: A total of 352 pregnant women were enrolled; 290 women (82.4%) with severe maternal morbidity were identified and two of them died (0.6%). The MOEWSs of women with serious obstetric complications were significantly higher than those of women without serious obstetric complications [8(6, 10) vs. 4(2, 4.25), z = -10.347, P < 0.001]. MOEWSs of 24 h after ICU admission had higher sensitivity, specificity and AUROC than MOEWSs of 24 h before ICU admission. When combining the two MOEWSs, sensitivity of MOEWS was 99.3% (95% CI: 98-100), specificity 75.8% (95% CI: 63-86), positive predictive value (PPV) 95.1% (95% CI: 92-97) and negative predictive value (NPV) 95.9% (95% CI: 86-100). The areas under the receiver operator characteristic (ROC) curves of MOEWS were 0.92 (95% CI: 0.88-0.96) and 0.70 (95% CI: 0.63-0.76) of the APACHE II score.Conclusion: The newly proposed MOEWS has an excellent ability to identify critically ill women early and is more effective than APACHE II. It will be a valuable tool for discriminating severe maternal morbidity and ultimately improve maternal health. [ABSTRACT FROM AUTHOR]

Physicians tend to overestimate patients' pretest probability of having bacteremia. The low yield of blood cultures and contaminants is associated with significant financial cost, as well as increased length of stay and unnecessary antibiotic treatment. This study examined the abilities of the National Early Warning Score (NEWS), the Quick Sequential Organ Failure Assessment (qSOFA), the Modified Sequential Organ Failure Assessment (mSOFA), and two versions of the causal probabilistic network, SepsisFinder™ (SF) to predict bacteremia in adult emergency department (ED) patients. This cohort study included adult ED patients from a large urban, academic tertiary hospital, with blood cultures obtained within 24 h of admission between 2016 and 2017. The outcome measure was true bacteremia. NEWS, qSOFA, mSOFA, and the two versions of SF score were calculated for all patients based on the first available full set of vital signs within 2 h and laboratory values within 6 h after drawing the blood cultures. Area under the receiver operating characteristic curve (AUROC) was calculated for each scoring system. The study included 3106 ED patients, of which 199 (6.4%) patients had true bacteremia. The AUROCs for prediction of bacteremia were: NEWS = 0.65, qSOFA = 0.60, SF I = 0.65, mSOFA = 0.71, and SF II = 0.80. Scoring systems using only vital signs, NEWS, and SF I showed moderate abilities in predicting bacteremia, whereas qSOFA performed poorly. Scoring systems using both vital signs and laboratory values, mSOFA and especially SF II, showed good abilities in predicting bacteremia. [ABSTRACT FROM AUTHOR]

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