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Introduction: Despite widespread implementation of the Early Warning Score (EWS) in hospitals, its effect on patient outcomes remains mostly unknown. We aimed to evaluate associations between the initial EWS and in-hospital mortality, intensive care unit (ICU) admission, and hospital length of stay (LOS).  Methods: We performed a retrospective cohort study of adult patients admitted to a general hospital ward between July 1, 2014–December 31, 2017. Data were obtained from electronic health records (EHR). The primary outcome was in-hospital mortality. Secondary outcomes were ICU admission and hospital LOS. We categorized patients into three risk groups (low, medium or high risk of clinical deterioration) based on EWS. Descriptive analyses were used. Results: After applying inclusion and exclusion criteria, we included 53,180 patients for analysis. We found that the initial (low- vs high-risk) EWS was associated with an increased in-hospital mortality (1.5% vs 25.3%, P

Ineffective use of the early warning score (EWS) can compromise recognition and response to patients' deteriorating condition. This study explores nurses' and physicians' ideas on initiatives for supporting the effective use of the EWS in a hospital setting. Participatory workshops were conducted, and data were analyzed using content analysis. Ideas generated for integrating new functions into the EWS protocol to facilitate effective use are described. Also recommended was that all users receive training and an update on how to use the EWS score to support acceptance and confidence using the protocol and thereby increase adherence to the EWS. Further research is needed on the efficiency of incorporating nurses' clinical judgment in the EWS protocol within different specialties and the effect on adherence to the tool. [ABSTRACT FROM AUTHOR]

Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662–0.807) and 0.734 (95% CI: 0.688–0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830–0.835), precision of 0.764 (95% CI: 0.757–0.771), recall of 0.747 (95% CI: 0.741–0.753), and F1 score of 0.747 (95% CI: 0.741–0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156–3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management. [ABSTRACT FROM AUTHOR]

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a 21.16 % reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools. [ABSTRACT FROM AUTHOR]

Background: Severe preeclampsia presenting with maternal collapse post-cesarean section secondary to drug toxicity associated with pituitary hemorrhage is rare. Case presentation: A 24-year-old primigravida 27 weeks of gestation presented with severe preeclampsia, and underwent emergency cesarean section; postoperatively, she had a sudden maternal collapse and cardiac arrest while on labetalol and magnesium infusion. Following the return of spontaneous circulation (ROSC), the patient was found to have severe bradycardia with prolonged QTc interval and recurrent hypoglycemic episodes. Investigations had revealed low serum cortisol with the brain showing pituitary hemorrhage with features of atypical Sheehan's syndrome without lactation failure. Conclusions: Drug toxicity is an important cause of maternal collapse and cardiac arrest. Pituitary hemorrhage needs to be suspected in maternal patients with recurrent hypoglycemic episodes with or without postpartum hemorrhage (PPH). Continuous monitoring, use of maternal early warning scoring systems (MEWS), and prudence in the perioperative period/labor room will help in reducing the complications. [ABSTRACT FROM AUTHOR]

Background: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. Methods: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. Results: Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. Conclusion: The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov (NCT04951973) on June 30, 2021. [ABSTRACT FROM AUTHOR]

Background: Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning. Methods: Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment. Results: DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346). Conclusions: The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation. [ABSTRACT FROM AUTHOR]

BACKGROUND: The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. The National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases. OBJECTIVE: To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital. METHODS: We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models' performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score. RESULTS: Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient's age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score – 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage. CONCLUSION: Our model demonstrated incremental value for COVID-19 patient's status evaluation. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to assess the safety of a commonly used sedative, dexmedetomidine in neonates and infants during intensive care. Methods: A retrospective cohort study was conducted in the paediatric intensive care unit at Oulu University Hospital. The study population consisted of all children from birth up to 6 months of age who received dexmedetomidine during 2010–2016. Adverse cardiovascular outcomes were defined as abnormal heart rates or blood pressure values according to the Paediatric Early Warning Score. Results: Of the 172 infants, 56% had congenital malformation, and 48% had undergone surgery. Neonates and 1–3‐month‐olds experienced bradycardia (86% vs. 73% in 1–3‐month‐olds and 50% in 3–6‐month‐olds, p = 0.001) and severe bradycardia (17% vs. 14% in 1–3‐month‐olds and 0% in 3–6‐month‐olds, p = 0.005) more often than older patients. The median maximum rate of dexmedetomidine infusion was 0.86 μg/kg/h (IQR = 0.60–1.71 μg/kg/h). A dose‐dependent increase in bradycardia and severe hypotension was found. Adverse cardiovascular events were managed with additional fluid boluses and discontinuation of the infusion. Conclusion: Adverse cardiovascular events were common during dexmedetomidine administration in neonates and infants. Lower dexmedetomidine doses may be required in sedating neonates. [ABSTRACT FROM AUTHOR]

Background: Several early warning scores have been designed to optimize acute care by identifying patients at risk of deterioration. Methods: In this post hoc dual center study, we analyzed the performance of six clinical scores (the Goodacre score, Groarke, Worthing Physiological Score, Rapid Acute Physiology Score, Rapid Emergency Medicine Score, United Kingdom National Early Warning Score. The primary outcome is 30-day all-cause mortality after inclusion and data were obtained from previous studies performed at two different emergency departments on two continents (Denmark, Europe, and Hong Kong, Asia). Results: We included 2952 people; 1482 (50.2%) were male, mean age (standard deviation) was 65.7 (18.3) years, and 109 (3.7%) died within 30 days. Mortality rate increased steadily with increasing scores for all six scoring systems in Hong Kong while this was less obvious in Denmark. In all patients, Rapid Acute Physiology Score had the lowest discriminatory power while National Early Warning Score had the highest. National Early Warning Score performed best in Hong Kong while Worthing performed marginally better in Denmark. Discussion: Surprisingly, the performance of the scoring systems varied considerably, but were largely unaffected by location, and none of them performed close to what clinicians would normally require for predicting 30-day all-cause mortality Conclusion: All scores performed similarly across both centers, with poor prediction of 30-day all-cause mortality. Based on these findings, we believe that clinical scores must be supplemented by either biochemical values or global markers of physiological reserve to reflect reality and to be of true value. [ABSTRACT FROM AUTHOR]

Background: The emergency department at the Singapore General Hospital is an emergency department with an annual census of 140,000 and oncology-related attendances of about 4000 (2.8%). These patients are often admitted for further care. Palliative care in the emergency department for these patients is often minimal. The aim of this study was to determine the state of current management of oncology-related emergencies at the Singapore General Hospital's emergency department, hence identifying specific areas for intervention. Methods: We carried out a retrospective data review of all Singapore General Hospital's emergency department patients who had either cancer-related diagnoses or were admitted to the Medical Oncology Department in October 2018. Simple statistical analysis was then performed using IBM SPSS version 21. Results: Of 308 identified patients, there was approximately equal distribution by sex. The women were generally younger than the men (61.33 ± 13.63 years vs 67.36 ± 12.02 years, p = 0.063, confidence interval −8.94 to −3.13). Seventy-two (23.4%) of the patients arrived at emergency department by ambulance. The mean emergency department length of stay was 4.25 h. About half of the patients had either lung, colorectal, or breast as their primary site of cancer. There was no correlation between clinical severity according to the National Early Warning Scores and triage complaint-type or emergency clinical diagnosis. More than 90% were admitted, with about 32.6% dying during their inpatient stay. High National Early Warning Scores were significantly associated with mortality. Conclusion: There is large potential for interventions to improve patient well-being in the pre-hospital setting and emergency department. Given the sizable number of patients with poor outcomes, palliative care is also of paramount importance. [ABSTRACT FROM AUTHOR]

Objective: To identify the sensitivity of emergency severity index (ESI) version 4 and modified pediatric early warning score (PEWS) in identifying high urgency patients. Methods: This cross-sectional study was conducted between November, 2019 and October, 2021 in the pediatric emergency department of tertiary hospital in Delhi. 250 patients aged 0–12 years coming to pediatric emergency on pre-decided days for health-related complaints were enrolled. These were assessed with both triage systems within 30 minutes of their arrival by a single researcher. 'High urgency' patients were defined as the ones who either required admission in pediatric ICU or died or had critical value of vital parameters as per institution protocol. Results: ESI version 4 had a sensitivity of 95.5%, specificity of 80.3%, with AUC of 0.879 (95%CI 0.834–0.925) in identifying high urgency patients at levels 1 and 2. Modified PEWS had comparatively lower sensitivity of 79.1%, specificity of 97.8%, with AUC of 0.885 (95%CI 0.825–0.994) in identifying high urgency patients at score of ≥3. The ESI version 4 was found to be a better predictor of admission than the modified PEWS, with a sensitivity of 98.2%. Both the scores were able to identify patients at risk of mortality with a sensitivity of 100%. Conclusion: ESI version 4 is a better triage tool than modified PEWS in pediatric population in a tertiary care public hospital setting in this region. [ABSTRACT FROM AUTHOR]

Objective Vital sign scoring systems that alert providers of clinical deterioration prior to critical illness have been proposed as a means of reducing maternal risk. This study examined the predictive ability of established maternal early warning systems (MEWS)—as well as their component vital sign thresholds—for different types of maternal morbidity, to discern an optimal early warning system. Study Design This retrospective cohort study analyzed all patients admitted to the obstetric services of a four-hospital urban academic system in 2018. Three sets of published MEWS criteria were evaluated. Maternal morbidity was defined as a composite of hemorrhage, infection, acute cardiac disease, and acute respiratory disease ascertained from the electronic medical record data warehouse and administrative data. The test characteristics of each MEWS, as well as for heart rate, blood pressure, and oxygen saturation were compared. Results Of 14,597 obstetric admissions, 2,451 patients experienced the composite morbidity outcome (16.8%) including 980 cases of hemorrhage (6.7%), 1,337 of infection (9.2%), 362 of acute cardiac disease (2.5%), and 275 of acute respiratory disease (1.9%) (some patients had multiple types of morbidity). The sensitivities (15.3–64.8%), specificities (56.8–96.1%), and positive predictive values (22.3–44.5%) of the three MEWS criteria ranged widely for overall morbidity, as well as for each morbidity subcategory. Of patients with any morbidity, 28% met criteria for the most liberal vital sign combination, while only 2% met criteria for the most restrictive parameters, compared with 14 and 1% of patients without morbidity, respectively. Sensitivity for all combinations was low (maximum 28.2%), while specificity for all combinations was high, ranging from 86.1 to 99.3%. Conclusion Though all MEWS criteria demonstrated poor sensitivity for maternal morbidity, permutations of the most abnormal vital signs have high specificity, suggesting that MEWS may be better implemented as a trigger tool for morbidity reduction strategies in the highest risk patients, rather than a general screen. Key Points MEWS have poor sensitivity for maternal morbidity. MEWS can be optimized for high specificity using modified criteria. MEWS could be better used as a trigger tool. [ABSTRACT FROM AUTHOR]

Consensus statements have proposed the use of the National Early Warning Score 2 (NEWS2) to identify stable patients with acute pulmonary embolism (PE) and an intermediate-high risk of adverse outcomes. We aimed to externally validate NEWS2 and compare it to another predictive score (Bova). Using NEWS2 (cutoff ≥5 and ≥7) and the Bova score (cutoff >4), we classified patients as intermediate-high risk (vs. non-intermediate-high risk), and we compared the test characteristics of these risk classification tools for a complicated course within 30 days after PE diagnosis. We also assessed the validity of NEWS2 for predicting a complicated course by adding the results of echocardiography and troponin testing to the model. Of the 848 enrolled patients, the NEWS2 score ≥5 classified 471 (55.5%) and the Bova score classified 37 (4.4%) as intermediate-high risk. NEWS2 had a significantly lower specificity for a 30-day complicated course than Bova (45.4 vs. 96.3%, respectively; p < 0.001). Using the higher score threshold (≥7), the NEWS2 classified 99 (11.7%) as intermediate-high risk, and the specificity was 88.9% (difference with Bova, 7.4%; p < 0.001). The proportion of patients with intermediate-high risk PE was 2.4% for the combination of a positive troponin testing and echocardiographic right ventricle dysfunction and a positive NEWS2 (score ≥7), while the specificity was 97.8% (difference with Bova, 1.5%; p = 0.07). Bova outperforms NEWS2 for predicting a complicated course among stable patients with PE. Addition of troponin testing and echocardiography improved the specificity of NEWS2, although it was not superior to Bova. ClinicalTrials.gov number : NCT02238639. [ABSTRACT FROM AUTHOR]

Acute viral respiratory infections have proven to be a major health threat, even after the Corona Virus Disease 2019 (COVID-19) pandemic. We aimed to check whether the presence or absence of an acute respiratory infection such as COVID-19 can influence the physical activity of older hospitalised patients. We cross-sectionally studied patients aged ≥60 years, hospitalized during the pandemic in the non-COVID-19 and COVID-19 ward at the University Hospital, Kraków, Poland. Using activPAL3® technology, we assessed physical activity for 24 h upon admission and discharge. In addition, we applied the sarcopenia screening tool (SARC-F); measured the hand grip strength and calf circumference; and assessed the Modified Early Warning Score (MEWS), age-adjusted Charlson Index, SpO2%, and length of stay (LoS). Data were analysed using SAS 9.4. The mean (min, max) age of the 31 (58% women, eight with COVID-19) consecutive patients was 79.0 (62, 101, respectively) years. The daily time (activPAL3®, median [p5, p95], in hours) spent sitting or reclining was 23.7 [17.2, 24] upon admission and 23.5 [17.8, 24] at discharge. The time spent standing was 0.23 [0.0, 5.0] upon admission and 0.4 [0.0, 4.6] at discharge. The corresponding values for walking were 0.0 [0.0, 0.4] and 0.1 [0.0, 0.5]. SARC-F, admission hand grip strength, calf circumference, and LoS were correlated with physical activity upon admission and discharge (all p < 0.04). For every unit increase in SARC-F, there was a 0.07 h shorter walking time upon discharge. None of the above results differed between patients with and without COVID-19. The level of physical activity in older patients hospitalised during the pandemic was low, and was dependent on muscular function upon admission but not on COVID-19 status. This has ramifications for scenarios other than pandemic clinical scenarios. [ABSTRACT FROM AUTHOR]

Background: Theophylline is commonly used to control respiratory diseases, especially in developing countries. Theophylline has a narrowed therapeutic index, and its toxicity is associated with morbidity and mortality. Physicians should be aware of the early prediction of the need for intensive care unit admission (ICU) and mechanical ventilation (MV). Aim: This study aimed to assess the power of the Rapid Emergency Medicine Score (REMS), Modified Early Warning Score (MEWS) and Simple Clinical Score (SCS) in predicting the need for ICU admission and/or MV in acute theophylline-poisoned patients. Patients and methods: This cross-sectional study included 58 patients with acute theophylline poisoning who were admitted to our Poison Control Center from the 1st of July 2022 to the 31st of January 2023. The REMS, MEWS and SCS were calculated for all patients on arrival at the hospital. The area under the curve (AUC) and receiver operating characteristics were tested to compare scores. Results: The median values of all studied scores were significantly high among patients who needed MV and/or ICU admission. The AUC of SCS was >0.9, with a sensitivity of 92.9% and specificity of 90.9% for the prediction of ICU admission. Meanwhile, MEWS was an excellent predictor of the need for MV (AUC = 0.996, 95% CI = 0.983–1.000). Conclusions: We recommend using SCS as an early predictor for ICU admission in acute theophylline-poisoned patients. However, MEWS could effectively predict MV requirements in acute theophylline-poisoned patients. [ABSTRACT FROM AUTHOR]

The objective was to evaluate the Modified Early Warning Score in patients hospitalized for COVID-19 plus chronic disease. Methods: Retrospective observational study, 430 hospitalized patients with COVID-19 and chronic disease. Instrument, Modified Early Warning Score (MEWS). Data analysis, with Cox and logistic regression, to predict survival and risk. Results: Of 430 patients, 58.6% survived, and 41.4% did not. The risk was: low 53.5%, medium 23.7%, and high 22.8%. The MEWS score was similar between survivors 3.02, p 0.373 (95% CI: −0.225–0.597) and non-survivors 3.20 (95% CI: −0.224–0.597). There is a linear relationship between MEWS and mortality risk R 0.920, ANOVA 0.000, constant 4.713, and coefficient 4.406. The Cox Regression p 0.011, with a risk of deterioration of 0.325, with a positive coefficient, the higher the risk, the higher the mortality, while the invasive mechanical ventilation coefficient was negative −0.757. By providing oxygen and ventilation, mortality is lower. Conclusions: The predictive value of the modified early warning score in patients hospitalized for COVID-19 and chronic disease is not predictive with the MEWS scale. Additional assessment is required to prevent complications, especially when patients are assessed as low-risk. [ABSTRACT FROM AUTHOR]

Background: Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems. Methods: We apply the Software Development Lifecycle Framework—conceived by Winston Boyce in 1970—to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building. Results: We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time. Conclusions: Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient. [ABSTRACT FROM AUTHOR]

Objective: To investigate the value of video electroencephalogram (VEEG) combined with pediatric early warning score (PEWS), neurospecific enolase (NSE) and CD4+ /CD8+ ratio in evaluating the condition and predicting the prognosis of children with viral encephalitis. Methods: 152 children with viral encephalitis who were admitted to the Children's Hospital of Nanjing Medical University from March 2020 to March 2022 were selected, and they were divided into severe group (67 cases) and mild group (85 cases) according to the severity of the disease, another 72 hospitalized children without nervous system injury were selected as the control group. 2 weeks after treatment, they were divided into good prognosis group (4~5 grades, 89 cases) and poor prognosis group (1~3 grades, 63 cases) according to the Glasgow Outcome Scale for children (CGOS). All subjects received PEWS evaluation and VEEG examination, serum NSE level was detected, and CD4+ /CD8+ ratio was calculated. The factors affecting the prognosis of children with viral encephalitis were analyzed by univariate and multivariate logistic regression. The ROC curve was used to analyze the prognostic value of PEWS, VEEG, serum NSE, CD4+ /CD8+ ratio in children with viral encephalitis. PEWS, the proportion of VEEG severe abnormality and the serum NSE level in the severe group were higher than those in the mild group and control group, and the CD4+ /CD8+ ratio was lower than that in the mild group and control group (P＜0.05). Results: PEWS, the proportion of VEEG severe abnormality and the serum NSE level in the mild group were higher than those in the control group, and the CD4+ /CD8+ ratio was lower than that in the control group (P＜0.05). Conclusion: PEWS, the proportion of VEEG severe abnormality and serum NSE level in the poor prognosis group were higher than those in the good prognosis group, and the CD4+ /CD8+ ratio was lower than that in the good prognosis group (P＜0.05). Multivariate Logistic regression analysis showed that continuous convulsion, high PEWS, VEEG severe abnormality and increased serum NSE level were risk factors for poor prognosis in children with viral encephalitis, and increased CD4+ /CD8+ ratio was protective factor (P＜0.05). Combined with PEWS, VEEG, serum NSE and CD4+ /CD8+ ratio, the prognostic area under curve of viral encephalitis was 0.859, which was higher than that predicted by each indicator alone. High PEWS, VEEG severe abnormality, increased serum NSE level, and decreased CD4+ /CD8+ ratio in children with viral encephalitis are associated with aggravation of the disease and poor prognosis. Combining the above four indicators is of high value to assist in predicting the prognosis of children with viral encephalitis. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To recognise that children with complex care needs are at increased risk of developing serious illness which could lead to sepsis • To be aware of a bespoke virtual learning education programme on the care of the acutely unwell child for community palliative healthcare professionals • To identify the need for hospices and community palliative care services to share resources and ensure evidence-based practice for acutely unwell children Children with complex care needs are at an increased risk of developing serious illness which could lead to sepsis. Many children receiving palliative care do not have a completed advance care plan identifying their wishes for ongoing care, which means they would require active intervention if they were to become acutely unwell. Sepsis is the immune system's overreaction to an infection or injury and about 25,000 children are admitted to hospital with sepsis in the UK each year. Early recognition of the acutely unwell child and escalation of concerns from the community to an acute care setting is required to manage and treat a child who is deteriorating. This article describes a project of three phases which aimed to explore the assessment tools available to support recognition and screening of children who became acutely unwell while accessing community palliative care services. It also identifies the importance of working together in partnership with external organisations and acknowledges the achievements of all of the partners involved. [ABSTRACT FROM AUTHOR]

Background: The emergence of new SARS-CoV- 2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to- calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr. The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. [ABSTRACT FROM AUTHOR]

Objectives: Hyperlactatemia is associated with increased risk of mortality. Point-of-care (POC) lactate levels have been used worldwide in the emergency department (ED) and critical care units for risk stratification. The aim of this study was to determine whether POC lactate levels are predictive of outcomes in our low-resource setting. Materials and Methods: This was a non-interventional prospective cohort study carried out at the ED Critical Care wards in Kenyatta National Hospital between June 12, 2021, and July 25, 2021. The study included consecutively consenting adults above 18 years at the ED who were critically ill as indicated by a Modified Early Warning Score of equal to or above 5. Lactate was measured using a handheld Statstrip lactate meter. Patients were followed up for 7 days, and outcomes were documented. Results: Two hundred and eighty-one patients were enrolled in the study. The mean lactate level was 4.0 mmoL/L with a median interquartile range of 3.0. Hyperlactatemia was evident in 161 (57%) using a cutoff of 2.5 mmoL/L. The allcause mortality rate was 33.8%. Type A hyperlactatemia was the most common at 84.3% and 17.7% in Type B. Type A hyperlactatemia mortality rate was 37.1% and Type B 15.9% (odds ratio [OR]: 3.1, 95% confidence interval [CI]: 1.3-7.3, P = 0.006). In the high lactate group, the 7-day mortality rate was 42.2% compared to a mortality rate of 22.5% in the normal lactate group (OR: 2.5, 95% CI: 1.5-4.3, P = 0.001). Hyperlactatemia was associated with an increased need for intensive care unit admission (OR: 2.2, 95% CI: 1.3-3.7, P = 0.002) and organ support mainly mechanical ventilation (OR: 2.6, 95% CI: 1.5-4.4, P = 0.001) and inotropic/vasopressors support (OR: 2.7, 95% CI: 1.2-6.2, P = 0.016). Conclusion: POC lactate is an independent predictor of 7-day mortality with a 2-a fold increased risk of mortality and was associated with increased need for organ support. [ABSTRACT FROM AUTHOR]

The aims of this study were to assess whether completion of the emergency department (ED) Big 6 interventions (provision of pain relief, screening for delirium, early warning score (EWS) system, full blood investigation and electrocardiogram, intravenous fluids therapy, and pressure area care) in those presenting with an acute hip fracture were associated with mortality risk and length of acute hospital stay. A retrospective cohort study was undertaken. All patients aged ≥50 years that were admitted with a hip fracture via the ED at a single centre during a 42-month period were included. A total of 3613 patients (mean age 80.9; 71% female) were included. The mean follow up was 607 (range 240 to 1542) days. A total of 1180 (32.7%) patients had all six components completed. Pain relief (90.8%) was the most frequently completed component and pressure area assessment (57.6%) was the least. Completion of each of the individual Big 6 components, except for pressure areas assessment, were associated with a significantly (p ≤ 0.041) lower mortality risk at the 90-days, one-year and final follow-up. The completion of all components of the Big 6 was associated with a significantly (2.4 hours, p = 0.002) shorter time to theatre. Increasing number of Big 6 components completed were independently associated with a lower mortality risk: when all six were completed, the hazard ratio was 0.64 (95% CI 0.52 to 0.78, p < 0.001). Completion of an increasing number of Big 6 components was independently associated with shorter length of hospital stay and completion of all six was associated with a 2.3 (95% CI 0.9 to 3.8)-day shorter acute stay. The findings provide an evidence base to support the ongoing use of the Big 6 in the ED. [ABSTRACT FROM AUTHOR]

Aim: To explore general ward nurses' attitudes and perceptions towards recognising and responding to clinical deterioration in a hospital with automated rapid response system activation. Background: There is growing interest in deploying automated clinical deterioration notification systems to reduce delayed or failed recognition and response to clinical deterioration of ward patients. However, little is known about its impact on ward nurses' perspectives and work patterns. Design: A mixed‐methods study. Methods: Online survey of 168 registered nurses and individual interviews with 10 registered nurses in one acute hospital in Singapore. The study adhered to the STROBE checklist for cross‐sectional studies and the COREQ guidelines for qualitative studies. Results: Many nurses (38.1%) rarely performed patient assessments or observations other than vital signs assessment to assess for early signs of clinical deterioration. About 30% were worried about being criticised for calling the primary team doctors. Four themes emerged from the qualitative analysis: automated rapid response system activation as a safety net, being more cautious with vital signs monitoring, the NEWS2 alone is inadequate, and ward nurses as the 'middleman' between the intensive care unit outreach nurse and primary team doctors. Conclusions: Although nurses value the automated rapid response system activation as a safety net to minimise delays in accessing urgent critical care resources, it does not address the sociocultural barriers inherent in escalation of care. Although the automated system led nurses to be more cautious with vital signs monitoring, it does not encourage them to perform comprehensive patient assessments to detect early signs of deterioration. Relevance to clinical practice: Nurse education on assessing for clinical deterioration should focus on the use of broader patient assessment skills other than vital signs. Sociocultural barriers to escalation of care remain a key issue that needs to be addressed by hospital management. No patient or public contribution: No patients, service users, care‐givers or members of the public were involved in the study. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients' future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work's CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients. [ABSTRACT FROM AUTHOR]

This paper presents two cases of urosepsis which illustrate the difficult-to-predict course of sepsis. In addition to antimicrobial treatment, an adequate level of vital functions monitoring is critical for survival, which allows a quick response to sudden deterioration in patient's condition. The Early Warning Score monitoring system (NEWS scale) should be used in all patients admitted to the hospital as an emergency. Treatment of sepsis in the ICU involves antibiotic therapy, controlling the source of infection, maintaining organ function and adjuvant therapies, which include extracorporeal therapies. The cost of the ICU treatment of advanced sepsis is very high, therefore, it is reasonable to take all measures aimed at an early diagnosis of sepsis and early implementation of treatment. The mainstay of effective treatment of sepsis is early implementation of antibiotic therapy which should be based on prompt microbiological diagnostics available 24 hours a day. Sepsis is not only a clinical issue, but to a large extent also an organizational challenge. Organizational measures are vital in the rational management of sepsis and may contribute to improving the outcomes of sepsis treatment. At a minimum, the abovementioned actions should include the introduction of hospital-based programs to improve sepsis management and a system for monitoring patients using early warning scores. Furthermore, examples from other countries indicate that institutional support at the national level constitutes an essential element of the implementation of these programs. [ABSTRACT FROM AUTHOR]

A delay in detecting acute hypertensive crisis in postpartum mothers can exacerbate complications in the mother. Neonatal nurses are uniquely qualified to identify postpartum warning signs in mothers while they are in the NICU with their infants. Few research studies have explored the use of neonatal nurse screenings for acute hypertensive crisis in postpartum mothers. NICU nurses screening mothers for postpartum depression has yielded success in improving outcomes, and this model could be translated into screening for acute hypertensive crisis. Further education should be implemented for NICU nurses that include a review of adult blood pressure monitoring, early warning signs, and symptoms of preeclampsia that the mother should report. This article discusses the importance of the neonatal nurse's role in identifying early warning signs of maternal postpartum hypertensive crisis. [ABSTRACT FROM AUTHOR]

Objective: Acute exacerbations of COPD (AECOPD) are common causes of hospitalization. Various scoring systems have been proposed to classify the risk of clinical deterioration or mortality in hospitalized patients with AECOPD. We sought to investigate whether clinical deterioration and mortality scores at admission can predict adverse events occurring during hospitalization and after discharge of patients with AECOPD. Methods: We performed a retrospective study of patients admitted with AECOPD. The National Early Warning Score 2 (NEWS2), the NEWS288-92%, the Dyspnea, Eosinopenia, Consolidation, Acidemia, and atrial Fibrillation (DECAF) score, and the modified DECAF (mDECAF) score were calculated at admission. We assessed the sensitivity, specificity, and overall performance of the scores for the following outcomes: in-hospital mortality; need for invasive mechanical ventilation or noninvasive ventilation (NIV); long hospital stays; hospital readmissions; and future AECOPD. Results: We included 119 patients admitted with AECOPD. The median age was 75 years, and 87.9% were male. The NEWS288-92% was associated with an 8.9% reduction in the number of individuals classified as requiring close, continuous observation, without an increased risk of death in the group of individuals classified as being low-risk patients. The NEWS288-92% and NEWS2 scores were found to be adequate in predicting the need for acute NIV and longer hospital stays. The DECAF and mDECAF scores were found to be better at predicting in-hospital mortality than the NEWS2 and NEWS288-92%. Conclusions: The NEWS288-92% safely reduces the need for clinical monitoring in patients with AECOPD when compared with the NEWS2. The NEWS2 and NEWS288-92% appear to be good predictors of the length of hospital stay and need for NIV, but they do not replace the DECAF and mDECAF scores as predictors of mortality. [ABSTRACT FROM AUTHOR]

Introduction: The Emergency Department (ED) serves as the primary point of access for a vast majority of patients seeking medical care. This seems to be a worldwide problem which results in waiting times for patients with serious diseases. Serious illnesses, or life-threatening conditions, need to be identified quickly, minutes after patients' arrival. In response to this challenge, pediatric triage systems have been developed and used in order to ensure the rapid identification of patients with severe conditions and their immediate transfer to the definitive treatment site. Aim: The purpose of our study was to compare the patient triage method we use in our hospital with a corresponding international tool and to investigate the possibilities of complementing each other of the two systems. Results: A total of 261 children who presented at the Emergency Department of our Hospital with symptoms compatible with pediatric diseases were included. At arrival of the children at the triage station they were evaluated, by a nurse and a medical doctor, using both the currentsystem and the system "PEWS". Conclusions: Our analysis reveals that both PEWS scores and the "current situation" wield significant influence over the variations in "incident outcome". [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Clinical Nursing in Practice is the property of Journal of Clinical Nursing in Practice (Editorial Board, Shanghai Jiao Tong University Press) 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.)

INTRODUCTION: Upper gastrointestinal bleeding (UGIB) is a life-threatening presentation to the emergency department (ED). In a busy emergency department, emergency department, proper risk stratification is critical to better patients management for patients with variceal or nonvariceal bleeding. This study study was designed to the scoring systems (Modified Early Warning Score (MEW), Pre-endoscopic Rockall (PER), and Glasgow Blatchford Score (GBS) in predicting 15-day mortality, requirement of blood transfusion, probability of rebleeding, and patient outcome within 15-day period in ED. MATERIAL AND METHODS: This study was carried out in with 51 acute UGIB at the emergency department, Department (ED) and department of Medical gastroenterology (MGE) of Narayana Medical College and Hospital, Nellore, India, from February 2020 to June 2022. Clinical The clinical history, presenting signs and symptoms, comorbidities, vitals, laboratory variables, endoscopic diagnosis and treatment history of all patients were recorded and they were followed for 15 days to evaluate rebleeding and its outcome. The chi-square test was applied to qualitative variables. RESULTS: The study enroled 51 patients, of which 82.4% were male, with the majority between 51 and 60 years of age. The majority of cases were presented to the emergency department with haematemesis (60.8%). Non-vasriceal bleeding observed in 94.1% cases. In the 15-day follow-up, 7 patients (7.84%) died and 7 patients (13.7%) rebleed. Totally 26 (50.9%) had a MEWS score of 1, 13 (25.4%) had a score of 2, 9 (17.64%) had a score of >3, and 3 (5.88%) had a score of 0. A PER score of 1 was found in 17 (33.3%), 2 in 4 (7.84%), 3 in 3 (5.88%), 4 in 6 (11.76), 5 in 10 (19.6%) and 6 in 5 (9.80%) patients. 9 (17.6%) cases had a low-risk GBS score of 0-5. Compared to MEWS and GBS, the ROC curve for mortality calculated at 15 days for PERS was 0.96; 95% CI of 0.9 - 1.0, indicating good accuracy. The AUCROC curve for predicting rebleed by PERS score showed area under curve - 0.79, which is also better than the other 2 scoring systems. CONCLUSIONS: PER had a higher negative predictive value (90%) than GBS (80.7%) and MEWS (88.1%) for rebleed measurement. GBS had a higher negative predictive value (96.15%) than PER (52.5%) and MEWS (42.8%) to predict admission of a patient with UGIB. The GBS score >8.5, MEWS score >1.5, and the PER score 4.5 predicted rebleeding. The GBS predicted the need for packed red blood cell transfusions better than the MEWS score and the pre-endoscopic Rockall score. The MEWS score is better at predicting admission and type of bleeding. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the performance of early warning scoring systems regarding adverse events of unanticipated clinical deterioration in complementary and alternative medicine hospitals. Methods: A medical record review of 500 patients from 5-year patient data in two traditional Korean medicine hospitals was conducted. Unanticipated clinical deterioration events included unexpected in-hospital mortality, cardiac arrest, and unplanned transfers to acute-care conventional medicine hospitals. Scores of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and National Early Warning Score 2 (NEWS2) were calculated. Their performance was evaluated by calculating areas under the receiver-operating characteristic curve for the event occurrence. Multiple logistic regression analyses were performed to determine the factors associated with event occurrence. Results: The incidence of unanticipated clinical deterioration events was 1.1% (225/21101). The area under the curve of MEWS, NEWS, and NEWS2 was .68, .72, and .72 at 24 hours before the events, respectively. NEWS and NEWS2, with almost the same performance, were superior to MEWS (p = .009). After adjusting for other variables, patients at low-medium risk (OR = 3.28; 95% CI = 1.02–10.55) and those at medium and high risk (OR = 25.03; 95% CI = 2.78–225.46) on NEWS2 scores were more likely to experience unanticipated clinical deterioration than those at low risk. Other factors associated with the event occurrence included frailty risk scores, clinical worry scores, primary medical diagnosis, prescribed medicine administration, acupuncture treatment, and clinical department. Conclusions: The three early warning scores demonstrated moderate-to-fair performance for clinical deterioration events. NEWS2 can be used for early identification of patients at high risk of deterioration in complementary and alternative medicine hospitals. Additionally, patient, care, and system factors need to be considered to improve patient safety.

Abstract Background Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes of COVID-19 in the Emergency Department (ED), including the quick Sequential Organ Failure Assessment (qSOFA), the Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has not been widely validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of qSOFA, MEWS, and NEWS for predicting mortality in emergency COVID-19 patients. Methods We conducted a multi-center retrospective study at five EDs of various levels of care in Thailand. Adult patients visiting the ED who tested positive for COVID-19 prior to ED arrival or within the index hospital visit between January and December 2021 were included. Their EWSs at ED arrival were calculated and analysed. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation. Results A total of 978 patients were included in the study; 254 (26%) died at hospital discharge, and 155 (15.8%) were intubated. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.771 (95% confidence interval (CI) 0.738, 0.804)), which was significantly higher than qSOFA (AUROC 0.620 (95%CI 0.589, 0.651); p

Background China owns a huge number of asthma patients and an increasing incidence of asthma, but low rate of overall control and high rate of acute asthma exacerbation. Moreover, there is a lack of methods for systematically screening the risks of acute exacerbation of asthma. Objective To develop a risk prediction questionnaire for acute exacerbation of asthma after screening the associated risk factors using the modified Delphi technique, providing a reference for building a multidimensional risk prediction model of asthma acute exacerbation. Methods Based on literature reports and our clinical pre-investigation results, an expert consultation questionnaire on risk predictors of acute exacerbation of asthma was developed, and was distributed through E-mail to 30 experts for review through two rounds of online consultation survey using the modified Delphi technique conducted from April to October 2021. SPSS 25.0 was used to assign weight and analyze the risk predictors. Then according to the screening criteria developed by the experts, the risk predictors were finally determined. Results Both two rounds of consultation achieved a 100% response rate. The familiarity, judgment, and authority coefficients as well as Kendall's W were 0.894, 0.963, 0.929, and 0.331, respectively, for the first round of expert consultation (P

Background. Early warning scores (EWSs) can be easily calculated from physiological indices; however, the extent to which intraoperative EWSs and the corresponding changes are associated with patient prognosis is unknown. In this study, we investigated whether EWS and the corresponding time-related changes are associated with patient outcomes during the anesthetic management of lower gastrointestinal perforation. Methods. This was a single-center, retrospective cohort study conducted at a tertiary emergency care center. Adult patients who underwent surgery for spontaneous lower gastrointestinal perforations between September 1, 2012, and December 31, 2019, were included. The National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) were calculated based on the intraoperative physiological indices, and the associations with in-hospital death and length of hospital stay were investigated. Results. A total of 101 patients were analyzed. The median age was 70 years, and there were 11 cases of in-hospital death (mortality rate: 10.9%). There was a significant association between the intraoperative maximum NEWS and in-hospital death (odds ratio (OR): 1.60, 95% confidence interval (CI): 1.10–2.32, p = 0.013) and change from initial to maximum NEWS (OR: 1.60, 95% CI: 1.07–2.40, p = 0.023) in the crude analysis. However, when adjustments were made for confounding factors, no statistically significant associations were found. Other intraoperative EWS values and changes were not significantly associated with the investigated outcomes. The preoperative sepsis-related organ failure assessment score and the intraoperative base excess value were significantly associated with in-hospital death. Conclusions. No clear association was observed between EWSs and corresponding changes and in-hospital death in cases of lower gastrointestinal perforation. The preoperative sepsis-related organ failure assessment score and intraoperative base excess value were significantly associated with in-hospital death. [ABSTRACT FROM AUTHOR]

Introduction: Risk stratification in patients with COVID-19 is a challenging task. Early warning scores (EWSs) are commonly used tools in the initial assessment of critical patients. However, their utility in patients with COVID-19 is still undetermined. Aim: This study aimed to discover the most valuable predictive model among existing EWSs for ICU admissions and mortality in COVID-19 patients. Materials and methods: This was a single-center cohort study that included 3608 COVID-19 patients admitted to the University Clinical Hospital Center Bezanijska Kosa, Belgrade, Serbia, between 23 June 2020, and 14 April 2021. Various demographic, laboratory, and clinical data were collected to calculate several EWSs and determine their efficacy. For all 3608 patients, five EWSs were calculated (MEWS, NEWS, NEWS2, REMS, and qSOFA). Model discrimination performance was tested using sensitivity, specificity, and positive and negative predictive values. C statistic, representing the area under the receiver operating characteristic (ROC) curve, was used for the overall assessment of the predictive model. Results: Among the evaluated prediction scores for 3068 patients with COVID-19, REMS demonstrated the highest diagnostic performance with the sensitivity, PPV, specificity, and NPV of 72.1%, 20.6%, 74.9%, and 96.8%, respectively. In the multivariate logistic regression analysis, aside from REMS, age (p < 0.001), higher CT score (p < 0.001), higher values of urea (p < 0.001), and the presence of bacterial superinfection (p < 0.001) were significant predictors of mortality. Conclusions: Among all evaluated EWSs to predict mortality and ICU admission in COVID-19 patients, the REMS score demonstrated the highest efficacy. [ABSTRACT FROM AUTHOR]

Introduction: Sickle cell disease (SCD) in pregnancy is associated with worse maternal and neonatal outcomes. There is limited available data describing the burden and outcomes of critically ill obstetric patients affected by SCD in low-income settings. Objectives: We aimed to define SCD burden and impact on mortality in critically-ill obstetric patients admitted to an urban referral hospital in Sierra Leone. We hypothesized that SCD burden is high and independently associated with increased mortality. Methods: We performed a registry-based cross-sectional study from March 2020 to December 2021 in the high-dependency unit (HDU) of Princess Christian Maternity Hospital PCMH, Freetown. Primary endpoints were the proportion of patients identified in the SCD group and HDU mortality. Secondary endpoints included frequency of maternal direct obstetric complications (MDOCs) and the maternal early obstetric warning score (MEOWS). Results: Out of a total of 497 patients, 25 (5.5%) qualified to be included in the SCD group. MEOWS on admission was not different between patients with and without SCD and SCD patients had also less frequently reported MDOCs. Yet, crude HDU mortality in the SCD group was 36%, compared to 9.5% in the non SCD group (P < 0.01), with an independent association between SCD group exposure and mortality when accounting for severity on admission (hazard ratio 3.40; 95%CI 1.57—7.39; P = 0.002). Patients with SCD had a tendency to longer HDU length of stay. Conclusions: One out of twenty patients accessing a HDU in Sierra Leone fulfilled criteria for SCD. Despite comparable severity on admission, mortality in SCD patients was four times higher than patients without SCD. Optimization of intermediate and intensive care for this group of patients should be prioritized in low-resource settings with high maternal mortality. [ABSTRACT FROM AUTHOR]

Background: For the early recognition of deteriorating patients several Pediatric Early Warning Score (PEWS) systems have been developed with the assumption that early detection can prevent further deterioration. Although PEWS are widely being used in hospitals in the Netherlands, there is no national consensus on which score to use and how to embed the score into a PEWS system. This resulted in a substantial heterogeneity of PEWS systems, of which many are unvalidated or self-designed. The primary objective of this study was to develop a pragmatic consensus-based PEWS system that can be utilized in all Dutch hospitals (University Medical Centers, teaching hospitals, and general hospitals). Methods: This study is an iterative mixed-methods study. The methods from the Core Outcome Measures in Effectiveness Trials (COMET) initiative were used and consisted of two Delphi rounds, two inventories set out to all Dutch hospitals and a focus group session with parents. The study was guided by five expert meetings with different stakeholders and a final consensus meeting that resulted in a core PEWS set. Results: The first Delphi round was completed by 292 healthcare professionals, consisting of pediatric nurses and physicians. In the second Delphi round 217 healthcare professionals participated. Eventually, the core PEWS set was been developed comprising of the parameters work of breathing, respiratory rate, oxygen therapy, heart rate and capillary refill time, and AVPU (Alert, Verbal, Pain, and Unresponsive). In addition, risk stratification was added to the core set with standardized risk factors consisting of [1] worried signs from healthcare professionals and parents and [2] high-risk treatment, with the option to add applicable local defined risk factors. Lastly, the three categories of risk stratification were defined (standard, medium, and high risk) in combination with standardized actions of the professionals for each category. Conclusion: This study demonstrates a way to end a country's struggle with PEWS heterogeneity by co-designing a national Dutch PEWS system. Currently, the power of the system is being investigated in a large multi-center study in the Netherlands. [ABSTRACT FROM AUTHOR]

Children with single ventricle physiology (SV) are at high risk of in-hospital morbidity and mortality. Identifying children at risk for deterioration may allow for earlier escalation of care and subsequently decreased mortality. We conducted a retrospective chart review of all admissions to the pediatric cardiology non-ICU service from 2014 to 2018 for children < 18 years old. We defined clinical deterioration as unplanned transfer to the ICU or inpatient mortality. We selected children with SV by diagnosis codes and defined infants as children < 1 year old. We compared demographic, vital sign, and lab values between infants with and without a deterioration event. We evaluated vital sign and medical therapy changes before deterioration events. Among infants with SV (129 deterioration events over 225 admissions, overall 25% with hypoplastic left heart syndrome), those who deteriorated were younger (p = 0.001), had lower baseline oxygen saturation (p = 0.022), and higher baseline respiratory rate (p = 0.022), heart rate (p = 0.023), and hematocrit (p = 0.008). Median Duke Pediatric Early Warning Score increased prior to deterioration (p < 0.001). Deterioration was associated with administration of additional oxygen support (p = 0.012), a fluid bolus (p < 0.001), antibiotics (p < 0.001), vasopressor support (p = 0.009), and red blood cell transfusion (p < 0.001). Infants with SV are at high risk for deterioration. Integrating baseline and dynamic patient data from the electronic health record to identify the highest risk patients may allow for earlier detection and intervention to prevent clinical deterioration. [ABSTRACT FROM AUTHOR]

Background: The clinical profile of hospitalized moderate-category COVID-19 patients has been understudied globally and in India. Aim: The present study was conducted to study the clinical profile and assess the proportions of patients who progressed to severe disease and its predictors among moderate COVID-19 patients. Materials and Methods: In this single-center observational study, 100 moderate-category COVID-19 patients as per Ministry of Health and Family Welfare (MoHFW) criteria of age ≥18 years of either sex, excluding pregnant females from February to November 2021, were studied by analyzing their clinical profiles and assessing Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score 2 (NEWS-2), and chest computed-tomography severity score (CTSS) to predict progression to severe disease. Severe disease was defined as per MoHFW criteria. Results: Out of 100 moderate-category COVID-19 patients, progression to severe disease was seen in 11 patients (11%), among which eight patients had expired, three patients were discharged, and the rest of the 89 patients (89%) who did not progress to severe disease were discharged. A higher age (62.2± 19.5 vs 54.8 ± 14.6 years), along with multivariate analysis revealing male sex (1.25 times), chronic kidney disease (2.86 times), leukocytosis (6.10 times), thrombocytopenia (1.04 times), anemia (9.3 times), a higher qSOFA score (3.6 times), and a higher NEWS-2 score on admission (1.56 times) had higher odds of progression to severe disease. A significant correlation (P < .05) of qSOFA score with serum LDH, ferritin, and hs-CRP levels; CT severity score with the serum ferritin, IL-6, and LDH levels; and NEWS-2 with serum LDH, hs-CRP, and ferritin levels were found. Moreover, the NEWS-2 score was found slightly better than qSOFA on receiver operating characteristic (ROC) curve analysis, with an area under the curve of 85.8% and 83.2%, respectively, predicting progression to severe disease. Conclusion: Our study revealed male gender, chronic kidney disease, leukocytosis, anemia, thrombocytopenia, a higher qSOFA and NEWS-2 score on admission, and further, NEWS-2 score better than qSOFA on ROC curve analysis, with an area under the curve of 85.8% and 83.2%, respectively, in predicting severe disease among hospitalized moderate COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Necrotizing enterocolitis (NEC) risk has been shown to arise from multiple sources and risk awareness may be supported using bedside tools. Purpose: The purpose of this research was to examine the extent to which GutCheckNEC was associated with scores for clinical deterioration, severity of illness, and clinical outcome, and further to examine how scores might improve NEC prediction. Methods: A retrospective, correlational case-control study with infant data from 3 affiliated neonatal intensive care units was conducted. Results: Of 132 infants (44 cases, 88 controls), most were 28 weeks of gestation at birth and less (74%). Median age at NEC onset was 18 days (range: 6-34 days), with two-thirds diagnosed before 21 days. At 68 hours of life, higher GutCheckNEC scores were associated with NEC requiring surgery or resulting in death (relative risk ratio [RRR] = 1.06, P =.036), associations that persisted at 24 hours prior to diagnosis (RRR = 1.05, P =.046), and at the time of diagnosis (RRR = 1.05, P =.022) but showed no associations for medical NEC. GutCheckNEC scores were significantly correlated with pediatric early warning scores (PEWS) (r > 0.30; P <.005) and SNAPPE-II scores (r > 0.44, P <.0001). Increasing numbers of clinical signs and symptoms were positively associated with GutCheckNEC and PEWS at the time of diagnosis (r = 0.19, P =.026; and r = 0.25, P =.005, respectively). Implications for Practice and Research: GutCheckNEC provides structure to streamline assessment and communication about NEC risk. Yet, it is not intended to be diagnostic. Research is needed on how GutCheckNEC impacts timely recognition and treatment. [ABSTRACT FROM AUTHOR]

Background: In the prehospital setting, the incidence of seriously unwell children is low and emergency medical staff have repeatedly expressed concerns about their confidence and knowledge with regard to assessing children. The paediatric observation priority score (POPS) was designed in response to concerns about the identification of seriously unwell children. The ambulance service in this study began to implement POPS in 2018. Aim: To examine the use and documentation of POPS by ambulance staff in a Welsh health board. Methods: A retrospective cross-sectional study was carried out, covering 121 paediatric patients over 3 months with data collected from patient clinical records. Findings: Patient observations were well documented (77.7%), score accuracy was high (82%) and the majority of patients were pre-alerted appropriately (80% of POPS 7). Statistically significant associations were drawn between patient age and observations as well as between POPS and patient outcomes. However, compliance with POPS was only 32.3%. Conclusion: Although the use of POPS is relatively low, children mainly did have relevant observations recorded and when POPS was used, it was accurate. Education and training would improve compliance. Future research recommendations include investigating the education and confidence of emergency medical staff regarding paediatric patients. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective: Acute exacerbations of COPD (AECOPD) are common causes of hospitalization. Various scoring systems have been proposed to classify the risk of clinical deterioration or mortality in hospitalized patients with AECOPD. We sought to investigate whether clinical deterioration and mortality scores at admission can predict adverse events occurring during hospitalization and after discharge of patients with AECOPD. Methods: We performed a retrospective study of patients admitted with AECOPD. The National Early Warning Score 2 (NEWS2), the NEWS288-92%, the Dyspnea, Eosinopenia, Consolidation, Acidemia, and atrial Fibrillation (DECAF) score, and the modified DECAF (mDECAF) score were calculated at admission. We assessed the sensitivity, specificity, and overall performance of the scores for the following outcomes: in-hospital mortality; need for invasive mechanical ventilation or noninvasive ventilation (NIV); long hospital stays; hospital readmissions; and future AECOPD. Results: We included 119 patients admitted with AECOPD. The median age was 75 years, and 87.9% were male. The NEWS288-92% was associated with an 8.9% reduction in the number of individuals classified as requiring close, continuous observation, without an increased risk of death in the group of individuals classified as being low-risk patients. The NEWS288-92% and NEWS2 scores were found to be adequate in predicting the need for acute NIV and longer hospital stays. The DECAF and mDECAF scores were found to be better at predicting in-hospital mortality than the NEWS2 and NEWS288-92%. Conclusions: The NEWS288-92% safely reduces the need for clinical monitoring in patients with AECOPD when compared with the NEWS2. The NEWS2 and NEWS288-92% appear to be good predictors of the length of hospital stay and need for NIV, but they do not replace the DECAF and mDECAF scores as predictors of mortality.

Objective: To determine the diagnostic accuracy of a modified early warning system (MEOWS) for predicting severe maternal morbidity, taking intensive care unit admission as the reference standard. Study Design: Cross-sectional study. Setting and Duration of Study: Combined Military Hospital, Abbottabad Pakistan, from Nov 2019 to Apr 2020. Methodology: After getting approval from the Institutional Ethical Committee, pregnant women fulfilling the inclusion criteria were included from the Labour Room of Department of Obstetrics & Gynecology. A modified early obstetric warning system chart was used to assess all women, labelling them positive or negative as per the criteria. In case of a fatal outcome or development of severe morbidity warranting intensive care unit admission, the patient was labelled as positive. Women who developed severe morbidity were managed as per standard protocols. Results: The early warning chart showed sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy as 69.44%, 90.97%, 64.10%, 92.76% and 86.91%, respectively, for predicting severe obstetric morbidity considering intensive care unit admission as gold standard. Conclusion: Modified early obstetric warning system chart effectively predicts severe obstetric morbidity. It could lead to better patient care, thus preventing severe obstetric morbidity in our local population if implemented as part of routine clinical evaluation.

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model—SepsisFinder—which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care. [ABSTRACT FROM AUTHOR]

Background: A few prospective trials and case series have suggested that hyperbaric oxygen therapy (HBOT) may be efficacious for the treatment of severe COVID-19, but safety is a concern for critically ill patients. We present an interim analysis of the safety of HBOT via a randomized controlled trial (COVID-19-HBO). Methods: A randomized controlled, open-label, clinical trial was conducted in compliance with good clinical practice to explore the safety and efficacy of HBOT for severe COVID-19 in critically ill patients with moderate acute respiratory distress syndrome (ARDS). Between 3 June 2020, and 17 May 2021, 31 patients with severe COVID-19 and moderate-to-severe ARDS, a ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) < 26.7 kPa (200 mmHg), and at least two defined risk factors for intensive care unit (ICU) admission and/or mortality were enrolled in the trial and randomized 1:1 to best practice, or HBOT in addition to best practice. The subjects allocated to HBOT received a maximum of five treatments at 2.4 atmospheres absolute (ATA) for 80 min over seven days. The subjects were followed up for 30 days. The safety endpoints were analyzed. Results: Adverse events (AEs) were common. Hypoxia was the most common adverse event reported. There was no statistically significant difference between the groups. Numerically, serious adverse events (SAEs) and barotrauma were more frequent in the control group, and the differences between groups were in favor of the HBOT in PaO2/FiO2 (PFI) and the national early warning score (NEWS); statistically, however, the differences were not significant at day 7, and no difference was observed for the total oxygen burden and cumulative pulmonary oxygen toxicity dose (CPTD). Conclusion: HBOT appears to be safe as an intervention for critically ill patients with moderate-to-severe ARDS induced by COVID-19. Clinical trial registration: NCT04327505 (31 March 2020) and EudraCT 2020-001349-37 (24 April 2020). [ABSTRACT FROM AUTHOR]

Respiratory disease is ubiquitous in hospitals and community healthcare settings in the UK. Nurses, therefore, must be able to understand the physiology and pathophysiology that underpins the care they provide for people living with a respiratory disorder. This article summarises the fundamental anatomy and physiology of the respiratory system and respiration. It also explores the pathophysiological changes that occur in the four most common respiratory conditions, namely pneumonia, lung cancer, asthma and chronic obstructive pulmonary disease. Key elements of a comprehensive respiratory assessment and how nurses can determine acute deterioration are explored. The case study and reflective questions aim to enhance the reader’s understanding of respiratory assessment and nursing care. [ABSTRACT FROM AUTHOR]