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Background: This study aimed to evaluate the predictive ability of two widely used early warning scoring systems, the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), for predicting stroke occurrence in hospitalized patients. Methods: The study enrolled 5,474 patients admitted to the intensive care unit from the general ward using data from the Smart Clinical Data Warehouse (CDW). MEWS and NEWS were calculated based on vital signs and clinical parameters within four hours of stroke onset. Stroke occurrence was categorized as ischemic or hemorrhagic. Logistic regression and receiver operating characteristic curve analyses were performed to assess the predictive abilities of the scoring systems. Results: Of the enrolled patients, 33.9% (n = 1853) experienced stroke, comprising 783 cases of ischemic stroke and 1,070 cases of hemorrhagic stroke. Both the MEWS and the NEWS were found to significantly predict overall stroke occurrence with a cutoff value of 4 (MEWS>4; OR [95% CI]: 13.90 [11.51–16.79], p<0.001; NEWS>4; OR [95% CI]: 6.71 [5.75–7.83], p<0.001). Parameters, such as prior malignancy, atrial fibrillation, AVPU response, heart rate, respiratory rate, and oxygen saturation, are also associated with stroke occurrence. The predictive ability of MEWS and NEWS was good for overall stroke occurrence. (AUC of MEWS: 0.92, 95% CI [0.91–0.93], p<0.001; AUC of NEWS: 0.85, 95% CI [0.84–0.86], p<0.001). The predictive ability was considered fair for ischemic stroke but good for hemorrhagic stroke. Conclusion: MEWS and NEWS demonstrated significant predictive abilities for overall stroke occurrence among hospitalized patients, with MEWS slightly outperforming NEWS. [ABSTRACT FROM AUTHOR]

Identifying and managing pediatric sepsis is a major research focus, yet early detection and risk assessment remain challenging. In its early stages, sepsis symptoms often mimic those of mild infections or chronic conditions, complicating timely diagnosis. Although various early warning scores exist, their effectiveness is limited, particularly in prehospital settings where accurate, rapid assessment is crucial. This review examines the roles of clinical prediction tools and biomarkers in pediatric sepsis. Traditional biomarkers, like procalcitonin (PCT), have improved diagnostic accuracy but are insufficient alone, often resulting in overprescription of antibiotics or delayed treatment. Combining multiple biomarkers has shown promise for early screening, though this approach can be resource-intensive and less feasible outside hospitals. Predicting sepsis outcomes to tailor therapy remains underexplored. While serial measurements of traditional biomarkers offer some prognostic insight, their reliability is limited, with therapeutic decisions often relying on clinical judgment. Novel biomarkers, particularly those identifying early organ dysfunction, hold potential for improved prognostic accuracy, but significant barriers remain. Many are only available in hospitals, require further validation, or need specialized assays not commonly available, limiting broader clinical use. Further research is needed to establish reliable protocols and enhance the clinical applicability of these tools. Meanwhile, a multifaceted approach that combines clinical judgment with existing tools and biomarkers remains essential to optimize pediatric sepsis management, improving outcomes and minimizing risks. [ABSTRACT FROM AUTHOR]

Background and Objectives: The interplay of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and Clostridioides difficile infection (CDI) poses a critical clinical challenge. The resultant inflammatory milieu and its impact on outcomes remain incompletely understood, especially among vulnerable subgroups such as elderly patients, those with diabetes, and individuals with cancer. This study aimed to characterize inflammatory markers and composite inflammatory severity scores—such as Acute Physiology and Chronic Health Evaluation II (APACHE II), Confusion, Urea, Respiratory rate, Blood pressure, and age ≥ 65 years (CURB-65), National Early Warning Score (NEWS), and the Systemic Immune-Inflammation Index (SII)—in hospitalized Coronavirus Disease 2019 (COVID-19) patients with and without CDI, and to evaluate their prognostic implications across key clinical subgroups. Methods: We conducted a retrospective, single-center study of 240 hospitalized adults with Reverse Transcription Polymerase Chain Reaction (RT-PCR)-confirmed COVID-19 between February 2021 and March 2023. Of these, 98 had concurrent CDI. We collected baseline demographics, comorbidities, and laboratory parameters including C-reactive protein (CRP), Interleukin-6 (IL-6), ferritin, neutrophil and lymphocyte counts, albumin, platelet counts, and calculated indices (C-reactive protein to Albumin Ratio (CAR), Neutrophil-to-Lymphocyte Ratio (NLR), Prognostic Nutritional Index (PNI), SII). Patients were stratified by CDI status and analyzed for inflammatory marker distributions, severity scores (APACHE II, CURB-65, NEWS), and outcomes (Intensive Care Unit (ICU) admission, mechanical ventilation, mortality). Subgroup analyses included diabetes, elderly (≥65 years), and cancer patients. Statistical comparisons employed t-tests, chi-square tests, and logistic regression models. Results: Patients with CDI demonstrated significantly higher CRP, IL-6, SII, and CAR, coupled with lower albumin and PNI (p < 0.05). They also had elevated APACHE II, CURB-65, and NEWS scores. CDI-positive patients experienced increased ICU admission (38.8% vs. 20.5%), mechanical ventilation (24.5% vs. 12.9%), and mortality (22.4% vs. 10.6%, all p < 0.05). Subgroup analyses revealed more pronounced inflammatory derangements and worse outcomes in elderly, diabetic, and cancer patients with CDI. Conclusions: Concurrent CDI intensifies systemic inflammation and adverse clinical trajectories in hospitalized COVID-19 patients. Elevations in inflammatory markers and severity scores predict worse outcomes, especially in high-risk subgroups. Early recognition and targeted interventions, including infection control and supportive measures, may attenuate disease severity and improve patient survival. [ABSTRACT FROM AUTHOR]

Background: Pneumonia is a potentially life-threatening respiratory tract infection. Many Early Warning Scores (EWS) were developed to detect patients with high risk for adverse clinical outcomes, but few have explored the utility of these EWS for pneumonia patients in the Emergency Department (ED) setting. We aimed to compare the prognostic utility of A-DROP, NEWS2, and REMS in predicting in-hospital mortality and the requirement for mechanical ventilation among ED patients with pneumonia. Methods: A retrospective study was conducted at the ED of Siriraj Hospital, Thailand. Adult patients diagnosed with non-COVID-19 pneumonia between June 1, 2021, and May 31, 2022, were included. We calculated and analyzed their EWS at ED arrival. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation. Results: We enrolled 735 patients; 272 (37%) died at hospital discharge, and 75 (10.2%) required mechanical ventilation. A-DROP had the highest discrimination capacity for in-hospital mortality (AUROC: 0.698, 95% CI 0.659–0.737) compared to NEWS2 (AUROC 0.657; 95%CI 0.617, 0.698) and REMS (AUROC 0.637; 95%CI 0.596, 0.678). A-DROP also had superior performances than NEWS2 and REMS in terms of calibration, overall model performance, and balanced diagnostic accuracy indices at its optimal cut point (A-DROP ≥ 2). No EWS could perform well in predicting mechanical ventilation. Conclusion: A-DROP had the highest prognostic utility for predicting in-hospital mortality in non-COVID-19 pneumonia patients in the ED compared to NEWS2 and REMS. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Status epilepticus (SE) is a neurological emergency in childhood, often leading to neuronal damage and long‐term outcomes. The study aims to identify barriers in the pre‐hospital and in‐hospital management of community‐onset pediatric SE and to evaluate the effectiveness of pediatric scores on outcomes prediction.This monocentric observational retrospective cohort study included patients treated for community‐onset pediatric SE in a tertiary care hospital between 2010 and 2021. Data were extracted following Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Inclusion criteria were community‐onset SE (according to the International League Against Epilepsy [ILAE] Task Force on SE Classification), admission to the pediatric emergency department (PED), age: 1 month to 18 years. Pre‐hospital, in‐hospital management and outcomes were analyzed. Pediatric scores for prediction of clinical worsening (Pediatric Early Warning Score ‐ PEWS) and SE outcome (Status Epilepticus in Pediatric patients Severity Score ‐ STEPSS; Pre‐status Epilepticus PCPCS, background Electroencephalographic abnormalities, Drug refractoriness, Semiology and critical Sickness Score ‐ PEDSS) were retrospectively assessed for their accuracy in predicting short‐term and long‐term outcomes.A total of 103 consecutive episodes of SE were included. Out‐of‐hospital rescue medications administration occurred in 54.4% of cases and was associated with higher SE resolution rate before PED admission (48.2% vs 27.6%, p = .033). Longer in‐PED time to treatment was observed in case of delay to PED referral (r = 0.268, p = .048) or non‐red triage labels (12 vs 5 min, p = 0.032), and was associated with longer in‐PED duration of SE (r = 0.645, p < .001). Longer SE duration was observed in episodes leading to hospitalization compared to those discharged (50 vs 16 min, p < .001). In‐PED electroencephalography (EEG) recordings were available in 39.8% of events. Predictive scores varied in accuracy, with PEWS ≥5 showing high sensitivity for intensive care unit (ICU) admission but low specificity. No patients died, 6.3% of SE was refractory.Effective pre‐hospital administration of rescue medications and prompt PED management are crucial to reduce SE duration and improve outcomes. Predictive scores can aid in assessment of the severity and prognosis of SE; their utility is still not defined. Identifying and addressing actionable care barriers in SE management pathways is essential to enhance patient outcomes in pediatric SE. [ABSTRACT FROM AUTHOR]

Background: There has been an increasing interest in the development and prevention of sports injuries from a complex dynamic systems perspective. From this perspective, injuries may occur following critical fluctuations in the psychophysiological state of an athlete. Our objective was to quantify these so-called Early Warning Signals (EWS) as a proof of concept to determine their explanatory performance for injuries. The sample consisted of 23 professional youth football (soccer) players. Self-reports of psychological and physiological factors as well as data from heart rate and GPS sensors were gathered on every training and match day over two competitive seasons, which resulted in an average of 339 observations per player (range = 155–430). We calculated the Dynamic Complexity (DC) index of these data, representing a metric of critical fluctuations. Next, we used this EWS to predict injuries (traumatic and overuse). Results: Results showed a significant peak of DC in 30% of the incurred injuries, in the six data points (roughly one and a half weeks) before the injury. The warning signal exhibited a specificity of 95%, that is, correctly classifying non-injury instances. We followed up on this promising result with additional calculations to account for the naturally imbalanced data (fewer injuries than non-injuries). The relatively low F1 we obtained (0.08) suggests that the model's overall ability to discriminate between injuries and non-injuries is rather poor, due to the high false positive rate. Conclusion: By detecting critical fluctuations preceding one-third of the injuries, this study provided support for the complex systems theory of injuries. Furthermore, it suggests that increasing critical fluctuations may be seen as an EWS on which practitioners can intervene. Yet, the relatively high false positive rate on the entire data set, including periods without injuries, suggests critical fluctuations may also precede transitions to other (e.g., stronger) states. Future research should therefore dig deeper into the meaning of critical fluctuations in the psychophysiological states of athletes. Key Points: Complex Systems Theory suggests that sports injuries may be preceded by a warning signal characterized by a short window of increased critical fluctuations. Results of the current study showed such increased critical fluctuations before 30% of the injuries. Across the entire data set, we also found a considerable number of critical fluctuations that were not followed by an injury, suggesting that the warning signal may also precede transitions to other (e.g., healthier) states. Increased critical fluctuations may be interpreted as a window of opportunity for the practitioner to launch timely and targeted interventions, and researchers should dig deeper into the meaning of such fluctuations. [ABSTRACT FROM AUTHOR]

Background: Viral sepsis has been suggested as a precise term to define multisystem dysregulated immune response and clinical sequelae in severe and critical COVID-19 cases. This work aimed to determine predictors of sepsis in those patients and to report their outcomes. Methodology: This prospective observational clinical study took place on 120 patients aged more than 18 years old, both genders, and hospitalized patients with laboratory-verified SARS CoV-2 infection. Enrolled patients were allocated into two groups: sepsis group (n = 49) and no sepsis group (n = 71). Results: COVID-19 severity, National Early Warning Score (NEWS) risk, and Quick Sepsis-related Organ Failure Assessment (qSOFA) score were significantly elevated in sepsis group. Glasgow Coma Scale was significantly reduced in sepsis group (P < 0.05). Mortality, length of hospital stay, ICU admission, and ventilatory support were significantly higher in sepsis group (P < 0.05). Serum procalcitonin was significantly elevated in sepsis group (P < 0.05). Serum procalcitonin can significantly predict sepsis at cutoff > 0.5 µg/L with 89.8% sensitivity and 90% specificity. Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pH (decreased in sepsis group), heart rate (HR), platelets count, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), qSOFA score, serum procalcitonin, and duration of hospitalization (increased in sepsis group) were independent predictors of sepsis. Conclusions: In COVID-19 patients, decreased SBP, DBP, MAP, and pH while increased HR, respiratory rate, platelets, LDH, ESR, qSOFA score, serum procalcitonin, and duration of hospitalization were found to be independent predictors of sepsis. [ABSTRACT FROM AUTHOR]

Background/Objectives: Charlson Comorbidity Index (CCI) is one of the most reliable indicators to assess the impact of multimorbidity on COVID-19-related outcomes. Moreover, the patient's clinical conditions are associated with SARS-CoV-2 outcomes. This study aimed to analyze the association between multimorbidity and COVID-19-related outcomes, evaluating whether the National Early Warning Score 2 (NEWS2) mediated these associations. Methods: Data were obtained through the platform "EPICLIN". We analyzed all patients who tested positive for COVID-19 after accessing the emergency department (ED) of San Luigi Gonzaga (Orbassano) and Molinette (Turin) hospitals from 1 March to 30 June 2020. Different outcomes were assessed: non-discharge from the ED, 30-day mortality, ICU admission/death among hospitalized patients, and length of hospitalization among surviving patients. Two subgroups of patients (<65 and 65+ years old) were analyzed using logistic regressions, Cox models, and mediation analyses. Results: There was a greater risk of not being discharged or dying among those who were younger and with CCI ≥ 2. Moreover, the higher the CCI, the longer the length of hospitalization. Considering older subjects, a greater CCI was associated with a higher risk of death. Regarding the mediation analyses, multimorbidity significantly impacted the hospitalization length and not being discharged in the younger population. Instead, in the older population, the NEWS2 played a mediation role. Conclusions: This research showed that multimorbidity is a risk factor for a worse prognosis of COVID-19. Moreover, there was a strong direct effect of CCI on not being discharged, and the NEWS2 was found to act as mediator in the association between multimorbidity and COVID-19-related outcomes. [ABSTRACT FROM AUTHOR]

We explored whether modified early warning scores (MEWS) could be used as a tool for triage in pre-hospital settings by comparing MEWS with patient triage on arrival to the emergency department (ED) and prognosis. Adult patients (=20 years old) admitted to EDs between 2016 and 2018 were enrolled from National Emergency Department Information System data in this retrospective study. A total of 8,609,955 participants were included in the analysis. EDMEWS of the dead (4.74 ± 2.51) was higher than that of admitted (1.86 ± 1.72) and discharged patients (1.18 ± 1.15) (p < 0.001). In admitted patients, non-survivors had higher EDMEWS than survivors (p<0.001), and as the level of the Korean Triage and Acquisition Scale was severe, EDMEWS increased (p<0.001) accordingly in these patients. EDMEWS had an adjusted hazard ratio (HR) of 1.164 (95% confidence interval: 1.135±1.194) for mortality (p<0.001). When an EDMEWS of 0 was used as a reference value, the HR increased with an increase in the EDMEWS. As EDMEWS increased from 1 to 7+, HR also increased from 1.115 to 2.508. EDMEWS has a positive correlation with mortality and admission rates in EDs. Moreover, admitted patients with higher EDMEWS had a longer duration of hospitalization and they had a higher mortality rate compared to patients with lower EDMEWS. MEWS can be a useful tool to provide evidence to support decision-making processes involving transportation to the ED and selection of the appropriate level of ED for pre-hospital EMS and longterm care facilities. [ABSTRACT FROM AUTHOR]

Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), Voting Classifier ensembles, and sequential models (LSTM, GRU) were trained on the MIMIC-III and IV datasets. A preprocessing pipeline addressed missing data, class imbalances, and synthetic data flagged with masks. Models were evaluated using a 5-min prediction window with minute-level interpolations for timely interventions. Results: TBMs outperformed sequential models in speed, interpretability, and reliability, making them better suited for real-time decision-making. Feature importance analysis identified six key physiological variables from the enhanced NEWS2+ score and emphasized the value of mask and score features for transparency. Voting Classifier ensembles showed slight metric gains but did not outperform individually optimized models, facing a precision-sensitivity tradeoff and slightly lower F1-scores for key severity levels. Conclusions: TBMs were effective for real-time hypoxemia prediction, while sequential models, though better at temporal handling, were computationally costly. This study highlights ML's potential to improve triage systems and reduce alarm fatigue, with future plans to incorporate multi-hospital datasets for broader applicability. [ABSTRACT FROM AUTHOR]

Gezy Weita Giwangkancana,1 Yani Gezy Setiasih,2 Anisa Hasanah,2 Yunita Persiyawati,2 Wawan2 1Department of Anesthesia and Intensive Care, Faculty of Medicine Universitas Padjadjaran / Dr. Hasan Sadikin National Referral and Teaching Hospital, Bandung, Indonesia; 2Department of Nursing Dr. Hasan Sadikin National Referral and Teaching Hospital, Bandung, IndonesiaCorrespondence: Gezy Weita Giwangkancana, Department of Anesthesia and Intensive Care, Faculty of Medicine Universitas Padjadjaran / Dr. Hasan Sadikin National Referral and Teaching Hospital Bandung, Bandung, 40161, Indonesia, Tel +628122005952, Email gezy.weita@unpad.ac.idBackground: In-hospital cardiac arrest (IHCA) is a critical emergency, occurring at rates of 1– 6 events per 1000 hospital admissions, necessitating immediate and efficient resuscitation efforts. This study aims to determine the frequency, demographic characteristics, and outcomes of Code Blue activations in a tertiary teaching hospital in a low-middle-income country.Methods: This retrospective observational study was conducted at in National Referral and Teaching Hospital in a middle income country in Asia, covering data from January 1, 2017, to December 31, 2023. The study included 2184 Code Blue activations, with data on Early Warning Scores (EWS) and palliative scores available from 2021 onwards. Statistical analyses were performed to evaluate the relationship between these scores and patient outcomes.Results: Out of 2184 Code Blue activations, 713 cases included both EWS and palliative scores. The highest number of activations was recorded in 2019 (535 cases), and the lowest in 2021 (152 cases). Calculated incidence where 5.46 per 1000 visits. The return of spontaneous circulation (ROSC) rates ranged from 11% to 27.6%, with an average of 17.7% per year. The mean EWS and palliative scores for Code Blue activations were 9.2 (SD ± 2.3) and 7.8 (SD ± 1.9), respectively.Discussion: The findings highlight trends in IHCA incidence, causes, and outcomes, emphasizing the importance of early identification and management of patients at risk. The study underscores the need for continuous monitoring and early intervention, particularly for patients with high EWS. Additionally, the integration of palliative care considerations into hospital protocols is crucial for improving patient outcomes and resource allocation.Conclusion: Early warning system and palliative care scoring may predict code blue activation and if managed can reduce its number.Keywords: In-hospital cardiac arrest, code blue, early warning score, palliative care, emergency response, resuscitation

Purpose We aimed to develop a predictive tool for anastomotic leakage (AL) following colon cancer surgery by combining a clinical early warning score (EWS) with the C-reactive protein (CRP) level. Methods The records of 1,855 patients who underwent colon cancer surgery at the Oxford University Hospitals NHS Foundation Trust between January 2013 and December 2018, with or without AL, were retrospectively reviewed. EWS and CRP levels were assessed daily from the first postoperative day until discharge. AL was defined as an anastomotic defect observed at reoperation, the presence of feculent fluid in a pelvic drain, or evidence of AL on computed tomography. The tool incorporated postoperative EWS and CRP levels for the accurate early detection of AL. Results From postoperative days 3 to 7, the mean CRP level exceeded 200 mg/L in patients with AL and was under 200 mg/L in those without AL (P

Abstract Background Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. Methods Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. Results Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. Conclusions Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Background: Timely identification of deteriorating patients is crucial to prevent the progression to cardiac arrest. However, current methods predicting emergency department cardiac arrest are primarily static, rule-based with limited precision and cannot accommodate time-series data. Deep learning has the potential to continuously update data and provide more precise predictions throughout the emergency department stay. Methods: We developed and internally validated a deep learning-based scoring system, the Deep EDICAS for early prediction of cardiac arrest and a subset of arrest, cardiopulmonary resuscitation (CPR), in the emergency department. Our proposed model effectively integrates tabular and time series data to enhance predictive accuracy. To address data imbalance and bolster early prediction capabilities, we implemented data augmentation techniques. Results: Our system achieved an AUPRC of 0.5178 and an AUROC of 0.9388 on on data from the National Taiwan University Hospital. For early prediction, our system achieved an AUPRC of 0.2798 and an AUROC of 0.9046, demonstrating superiority over other early warning scores. Moerover, Deep EDICAS offers interpretability through feature importance analysis. Conclusion: Our study demonstrates the effectiveness of deep learning in predicting cardiac arrest in emergency department. Despite the higher clinical value associated with detecting patients requiring CPR, there is a scarcity of literature utilizing deep learning in CPR detection tasks. Therefore, this study embarks on an initial exploration into the task of CPR detection. [ABSTRACT FROM AUTHOR]

Objectives: The national early warning score (NEWS) was introduced to improve the detection of, and standardise the assessment of, the severity of acute illness in the National Health Service (NHS). We assessed whether the recommended threshold trigger score of 5 or more in a Critical Care Outreach Team (CCOT) review could accurately predict patients at risk of deterioration following cardiac surgery and patient outcomes. Methods: We investigated adult cardiac surgery patients between October 2019 and December 2021. NEWS 2 parameters triggering CCOT referrals and NEWS 2 parameters < 5 versus ≥5 were compared, and the resulting patient outcomes were evaluated. Results: Over this period, 3710 patients underwent surgery, of whom 162 (4.4%) initiated 193 calls to the CCOT. The mean number of NEWS 2 parameters on CCOT activation was 6.14 ± 2.43 (NEWS 0–16); 34 (20.98%) activations were from patients with NEWS 2 < 5. Low oxygen saturation (SpO2) (59.3%) and oxygen therapy (83.3%) were the most common physiological parameters raising the score. CCOT activations led to 38 transfers from the ward to the high-dependency unit (HDU) and 18 transfers to the intensive therapy unit (ITU). Cardiac arrest calls were initiated in 12 (7.40%) patients and two culminated in death. Fourteen (8.64%) had emergency resternotomy. The in-hospital mortality rate was 10.5% (17/162) in patients referred to CCOT versus 3.9% (139/3548) in patients who were not (p < 0.001). The in-hospital mortality in patients with NEWS 2 < 5 vs. NEWS ≥ 5 was 17.6% (6/34) versus 8.6% (11/128) (p = 0.126). Conclusions: There was no difference in in-hospital mortality in patients below or above a NEWS 2 of 5, but there was a significant difference in in-hospital mortality in patients reviewed by the CCOT (p < 0.001). Tailoring the threshold score specifically for the cardiac surgical cohort, in conjunction with clinician involvement, may improve outcomes. [ABSTRACT FROM AUTHOR]

Breathing patterns (respiratory kinematics) contain vital prognostic information. This dimension of physiology is not captured by conventional vital signs. We sought to determine the feasibility and utility of quantifying respiratory kinematics. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We extracted 34 features based on an explainable correspondence with well-established breathing patterns. K-means clustering revealed that respiratory kinematics had three dimensions apart from the respiratory rate. We represented these dimensions using respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of the kinematic measures revealed two profiles consistent with the established clinical constructs of labored and unlabored breathing. In logistic regression, the labored breathing profile improved model discrimination for critical illness beyond the Sequential Organ Failure Assessment (SOFA) score (AUROC 0.77 v/s 0.72; p = 0.02). These findings quantitatively confirm the prior understanding that the respiratory rate alone does not adequately represent the complexity of respiratory kinematics; they demonstrate that high-dimensional signatures of labored breathing can be quantified in routine practice settings, and they can improve predictions of clinical deterioration. [ABSTRACT FROM AUTHOR]

Background/Objectives: In March 2018, a tertiary teaching hospital launched a medical hospitalist team. This study presents the clinical characteristics and outcomes of medical hospitalist care and reveals the relationship between them. Methods: This study included 4003 patients first admitted to the hospitalist team via emergency room and then discharged from the hospitalist team between March 2018 and November 2022. The patients were admitted either to the teaching admitter hospitalist team or the hospitalist-led acute medical unit (AMU). Afterward, the patients were either discharged, if possible, within a few days or transferred to ward hospitalists if assigned wards for hospitalist care were available. Results: The patients' mean Charlson Comorbidity Index score was 3.5 and the mean National Early Warning Score was 3.4. Of the admissions, 44.2% of the patients were admitted to the AMU, and 26.8% received an early consultation with a subspecialist. Each hospitalist managed 12.8 patients per month on average. The patients' mean LOS was 14.52 days, 10.5% of patients died during hospitalization, and 13.0% of patients had unscheduled readmission within 1 month. The patients' mean total cost per hospital stay was 572,836 won per day. Admission to the AMU was associated with a lower total cost per hospital stay, but the relationships with mortality, readmission, and LOS were not significant. Conclusions: The study reports on the outcomes of implementing a medical hospitalist care system that combines short-term admission wards with integrated care models to manage complex cases. These findings provide insights into optimizing hospitalist systems for improved patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Early detection of critical events in hospitalized patients improves clinical outcomes and reduces mortality rates. Traditional early warning score systems, such as the National Early Warning Score 2 (NEWS2), effectively identify at-risk patients. Integrating artificial intelligence (AI) could enhance the predictive accuracy and operational efficiency of such systems. The study describes the development and implementation of an AI-enhanced early warning system based on a modified NEWS2 scale with laboratory parameters (mNEWS2-Lab) and evaluates its ability to improve patient safety in hospital wards. Methods: For this retrospective cohort study of 3,790 adults admitted to hospital wards, data were collected before and after implementing the mNEWS2-Lab protocol with and without AI enhancement. The study used a multivariate prediction model with statistical analyses such as Fisher's chi-square test, relative risk (RR), RR reduction, and various AI models (logistic regression, decision trees, neural networks). The economic cost of the intervention was also analyzed. Results: The mNEWS2-Lab reduced critical events from 6.15% to 2.15% (RR, 0.35; P<0.001), representing a 65% risk reduction. AI integration further reduced events to 1.59% (RR, 0.26; P<0.001) indicating a 10% additional risk reduction and enhancing early warning accuracy by 15%. The intervention was cost-effective, resulting in substantial savings by reducing critical events in hospitalized patients. Conclusions: The mNEWS2-Lab scale, particularly when integrated with AI models, is a powerful and cost-effective tool for the early detection and prevention of critical events in hospitalized patients. [ABSTRACT FROM AUTHOR]

Objective: To establish a model that can predict the risk of requiring mechanical ventilation within 48 h after admission in patients with sepsis. Methods: Data for patients with sepsis admitted to Dongyang People's Hospital from October 2011 to October 2023 were collected and divided into a modeling group and a validation group. Independent risk factors in the modeling group were analyzed, and a corresponding predictive nomogram was established. The model was evaluated for discriminative power (the area under the curve of the receiver operating characteristic curve, AUC), calibration degree (Hosmer-Lemeshow test), and clinical benefit (decision curve analysis, DCA). Models based on the Sequential Organ Failure Assessment (SOFA) scores, the National Early Warning Score (NEWS) scores and multiple machine learning methods were also established. Results: The independent factors related to the risk of requiring mechanical ventilation in patients with sepsis within 48 h included lactic acid, pro-brain natriuretic peptide (PRO-BNP), and albumin levels, as well as prothrombin time, the presence of lung infection, and D-dimer levels. The AUC values of nomogram model in the modeling group and validation group were 0.820 and 0.837, respectively. The nomogram model had a good fit and clinical value. The AUC values of the models constructed using SOFA scores and NEWSs were significantly lower than those of the nomogram (P < 0.01). The AUC value of the integrated machine-learning model for the validation group was 0.849, comparable to that of the nomogram model (P = 0.791). Conclusion: The established nomogram could effectively predict the risk of requiring mechanical ventilation within 48 h of admission by patients with sepsis. Thus, the model can be used for the treatment and management of sepsis. [ABSTRACT FROM AUTHOR]

Background: Adverse events in psychiatric settings present ongoing challenges for both patients and staff. Despite advances in psychiatric interventions and treatments, research on early warning scores and tools to predict patient deterioration is limited. This review provides a summary of the few tools that have been developed in a psychiatric setting, comparing machine learning (ML) and nonmachine learning/traditional methodologies. The outcomes of interest include the selected key variables that contribute to adverse events and the performance and validation measures of the predictive models. Methods: Three databases, Ovid MEDLINE, PsycINFO, and Embase, were searched between February 2023 and April 2023 to identify all relevant studies that included a combination of (and were not limited to) the following search terms: "Early warning," "Alerting tool," and "Psychiatry". Peer-reviewed primary research publications were included without imposing any date restrictions. A total of 1,193 studies were screened. A total of 9 studies met the inclusion and exclusion criteria and were included in this review. The PICOS model, the Joanna Briggs Institute (JBI) Reviewer's Manual, and PRISMA guidelines were applied. Results: This review identified nine studies that developed predictive models for adverse events in psychiatric settings. Encompassing 41,566 participants across studies that used both ML and non-ML algorithmic approaches, performance metrics, primarily AUC ROC, varied among studies between 0.62 and 0.95. The best performing model that had also been validated was the random forest (RF) ML model, with a score of 0.87 and a high sensitivity of 74% and a specificity of 88%. Conclusion: Currently, few predictive models have been developed for adverse events and patient deterioration in psychiatric settings. The findings of this review suggest that the use of ML and non-ML algorithms show moderate to good performance in predicting adverse events at the hospitals/units where the tool was developed. Understanding these models and the methodology of the studies is crucial for enhancing patient care as well as staff and patient safety research. Further research on the development and implementation of predictive tools in psychiatry should be carried out to assess the feasibility and efficacy of the tool in psychiatric patients. [ABSTRACT FROM AUTHOR]

Background: Early recognition of sepsis by the EMS (Emergency Medical Services), along with communicating this concern to the emergency department, could improve patient prognosis and outcome. Knowledge is limited about the performance of sepsis identification screening tools in the EMS setting. Research is also limited on the effectiveness of prehospital use of blood tests for sepsis identification. Integrating blood analyses with screening tools could improve sepsis identification, leading to prompt interventions and improved patient outcomes. Aim: The aim of the present study is firstly to evaluate the performance of various screening tools for sepsis identification in the EMS setting and secondly to assess the potential improvement in accuracy by incorporating blood analyses. Methods: This is a retrospective observational cohort study. The data were collected from prehospital and hospital medical records in Region Halland. Data on demographics, vital signs, blood tests, treatment, and outcomes were collected from patients suspected by EMS personnel of having infection. The data were analysed using Student's t-test. Sensitivity, specificity, positive predictive value, negative predictive value and odds ratio were used to indicate accuracy and predictive value. Results: In total, 5,405 EMS missions concerning 3,225 unique patients were included. The incidence of sepsis was 9.8%. None of the eleven tools included had both high sensitivity and specificity for sepsis identification. White blood cell (WBC) count was the blood analysis with the highest sensitivity but the lowest specificity for identifying sepsis. Adding WBC, C-reactive protein (CRP) or lactate to the National Early Warning Score (NEWS) increased the specificity to > 80% but substantially lowered the sensitivity. Conclusions: Identifying sepsis in EMS settings remains challenging, with existing screening tools offering limited accuracy. CRP, WBC, and lactate blood tests add minimal predictive value in distinguishing sepsis or determining non-conveyance eligibility. [ABSTRACT FROM AUTHOR]

Background: Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. Methods: We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland–Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. Results: Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high. Conclusions: In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods. Trial registration: January 26, 2023, NCT05699967. [ABSTRACT FROM AUTHOR]

Background: Identifying high-risk COVID-19 patients is critical for emergency department decision-making. Our study's primary objective was to identify new independent predictors of mortality and their predictive utility in combination with traditional pneumonia risk assessment scores and new risk scores for COVID-19 developed during the pandemic. Methods: A retrospective study was performed in two Italian University Hospitals. A multivariable logistic model was used to locate independent parameters associated with mortality. Results: Age, PaO2/FiO2, and SpO2/FiO2 ratios were found to be independent parameters associated with mortality. This study found that the Pneumonia Severity Index (PSI) was superior to many of the risk scores developed during the pandemic, for example, the International Severe Acute Respiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC 4C) (AUC 0.845 vs. 0.687, p < 0.001), and to many of the risk scores already in use, for example, the National Early Warning Score 2 (NEWS2) (AUC 0.845 vs. 0.589, p < 0.001). Furthermore, our study found that the Pneumonia Severity Index had a similar performance to other risk scores, such as CRB-65 (AUC 0.845 vs. 0.823, p = 0.294). Combining the PaO2/FiO2 or SpO2/FiO2 ratios with the risk scores analyzed improved the prognostic accuracy. Conclusions: Adding the SpO2/FiO2 ratio to the traditional, validated, and already internationally known pre-pandemic prognostic scores seems to be a valid and rapid alternative to the need for developing new prognostic scores. Future research should focus on integrating these markers into existing pneumonia scores to improve their prognostic accuracy. [ABSTRACT FROM AUTHOR]

Higher red blood cell distribution width (RDW) levels have gained attention in the prognostication of many chronic metabolic and malignant diseases, as well as coronavirus disease 2019 (COVID-19). We aimed to evaluate whether accounting for RDW might contribute to risk stratification when added to commonly used risk scoring systems in adult COVID-19 patients. We retrospectively analyzed a cohort of 3212 non-critical COVID-19 patients hospitalized in a tertiary-level institution from March 2020 to June 2021. Admission RDW values were considered normal if they were ≤14.5% in males or ≤16.1% in females. The Modified Early Warning Score (MEWS), International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium score (ISARIC 4C), and Veterans Health Administration COVID-19 (VACO) index were evaluated as prognostic scores. RDW exceeded the upper limit in 628 (19.6%) of the patients. When RDW was accounted for, risks of the predicted outcomes were considerably different within the same MEWS, 4C score, and VACO index levels. The same patterns applied equally to patients who started, and those who did not start, remdesivir before deterioration. RDW may be a useful tool for stratifying risk when considered on top of commonly used prognostic scores in non-critical COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Sepsis, a major global health concern, leads to millions of deaths annually, hence the need for early and reliable prognostic tools to assess patient risk and guide clinical decision making becomes crucial. This cross-sectional study evaluated the prognostic accuracy of integrating blood lactate and serum procalcitonin (PCT) levels with the National Early Warning Score (NEWS) for predicting mortality in sepsis patients. The objective was to assess whether this lactate and procalcitonin integrated with NEWS score (LP NEWS) could serve as a more effective early prognostic tool compared to established severity scores. Methods: Spanning 12 months, the study enrolled adult patients meeting the criteria of sepsis in the ICU and medicine ward of a tertiary care hospital in North India. Data collection included demographics, clinical characteristics, and blood samples for lactate and PCT at admission. NEWS, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and LP-NEWS scores were calculated with treatment administered per Surviving Sepsis-3 guidelines. Results: The research included 200 participants, uncovering significant correlations between blood lactate, PCT levels, and mortality. Survivors had a mean lactate of 2.12 ± 0.70 and PCT of 11.27 ± 11.75, while nonsurvivors had 3.30 ± 1.17 and 30 ± 18.48, respectively (P < 0.001). LP-NEWS significantly differentiated survivors from nonsurvivors (8.23 ± 2.02 vs. 14.12 ± 2.23), with a cutoff of 11 showing 96.9% sensitivity and 88.5% specificity for predicting mortality. LP-NEWS had the highest odds ratio = 3.12, P < 0.001, and area under the receiver operating characteristic curve value (0.966), outperforming APACHE II and SOFA scores. Conclusion: The LP-NEWS score which integrates blood lactate and serum PCT levels could serve as an effective standalone bedside score, particularly in the initial risk stratification of sepsis. [ABSTRACT FROM AUTHOR]

Background: Improving the quality of medical care in hospitals is a major priority for all departments. The early warning score (EWS) trend is an effective early risk stratification tool that reflects the changes in patient condition and allows better assessment of deterioration risk. Objective: The aim of this study was to investigate whether utilizing the trend of the modified early warning score (MEWS) level within 4 h of a patient's arrival in the emergency department (ED) could identify patients at risk of clinical deterioration at 8 h after arrival in the ED. Methods: We conducted a retrospective observational study of non‐trauma patients who had at least two vital sign measurements (Glasgow Coma Scale score, heart rate, blood pressure, respiratory rate, and body temperature) within 8 h of arriving in the ED. The primary outcome was patients who had MEWS ≥ 4 at 8 h after arrival in the ED. We performed multivariate logistic regression analysis using age, sex, MEWS level at arrival in the ED, MEWS level within 4 h after arrival in the ED, and MEWS level trend over time. Results: Among the 5825 patients, 680 (11.7%) were at risk of deterioration at 8 h after arrival in the ED. To predict the risk of deteriorating conditions (MEWS ≥ 4), utilizing the MEWS level trend within 4 h of arrival in the ED was more effective in identifying patients at risk of deterioration after 8 h of arrival in the ED compared to using a single MEWS value during the ED stay. The corresponding areas under the receiver operating characteristic curve were 0.756 (95% confidence interval (CI) 0.734–0.778) and 0.846 (95% CI 0.827–0.865), respectively (p < 0.01). Conclusions: The proposed trend‐based predictive model for MEWS levels can alert healthcare personnel regarding patients at increased risk of deterioration (MEWS ≥ 4), potentially reducing mortality rates during ED stays. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to summarize the results of coronavirus disease 2019 (COVID-19) risk assessments and to examine the associations between risk levels and various indicators, including COVID-19 incidence, risk perception, community mobility, and government policy. Methods: The results of the risk assessment and the indicators utilized were summarized. From November 2021 to May 2022, the COVID-19 risk level was evaluated on a weekly basis, and its correlation with these indicators was analyzed. Data were obtained from press releases by the Korea Disease Control and Prevention Agency, regular surveys conducted by Hankook Research, and information available on the Google and Oxford websites. Results: Weekly risk assessments were conducted for 30 weeks, using different indices depending on the phases. Correlation analysis revealed the strongest positive correlation between risk level and risk perception (r =0.841). The risk level from "1-week lead" demonstrated a strong positive correlation with the time-varying reproduction number (Rt). Similarly, the risk level from "week lagged value" showed a strong positive correlation with the number of severe cases in the hospital. Conclusion: At the time of risk assessment, the Rt precedes the risk level, while severe cases in hospitals follow. Therefore, the assessed risk level functioned as an early warning system. Risk perception demonstrated the strongest correlation with the risk level, suggesting consistency throughout the assessment period. Contextual indicators (e.g., risk perception) that consider time lags and implementation scales, could improve the evaluation of future risk assessment results, particularly when there are challenges in reflecting specific situations in coordinated emergency response. [ABSTRACT FROM AUTHOR]

The association of anosmia/ageusia with a positive severe respiratory syndrome coronavirus 2 (SARS-CoV-2) test is well-established, suggesting these symptoms are reliable indicators of coronavirus disease 2019 (COVID-19) infection. This study investigates the clinical characteristics and systemic inflammatory markers in hospitalized COVID-19 patients in Taiwan, focusing on those with anosmia/ageusia. We conducted a retrospective observational study on 231 hospitalized COVID-19 patients (alpha variant) from April to July 2021. Clinical symptoms, dyspnea grading, and laboratory investigations, including neutrophil-lymphocyte ratios (NLRs), platelet-lymphocyte ratios (PLRs), and ANDC scores (an early warning score), were analyzed. Cough (64.1%), fever (58.9%), and dyspnea (56.3%) were the most common symptoms, while anosmia/ageusia affected 9% of patients. Those with anosmia/ageusia were younger, had lower BMI, lower systemic inflammatory markers, and better ANDC scores than those without these symptoms. Female patients exhibited lower NLR values and ANDC scores compared to male patients (all p < 0.05). Multivariable regression analysis demonstrated significant associations between NLR and CRP and ferritin levels (all p < 0.01), and between PLR and ESR and ferritin levels (p < 0.01). Categorized ANDC scores significantly correlated with the total hospital length of stay (all p < 0.05). Despite ethnic differences in the prevalence of anosmia/ageusia, our study highlights similar clinical presentations and inflammatory profiles to those observed in Western countries. The ANDC score effectively predicted hospital stay duration. These findings suggest that anosmia/ageusia may be associated with less severe disease and a lower inflammatory response, particularly in younger and female patients. The ANDC score can serve as a valuable prognostic tool in assessing the severity and expected hospital stay of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Nearly 30 years since its inception, the early warning scores (EWSs) remain pivotal, yet variations have emerged for hospital and prehospital use. Aggregated scores, reflecting multiple physiological parameters, outperform single‐parameter systems in assessing acute illness severity, though consensus on optimal approaches is lacking. Resource‐limited countries, including Angola, lack adapted EWSs, emphasizing the need for cost‐effective and adaptable solutions to enhance patient care. Objective: To explore the perspectives of Angolan experts to identify physiological parameters suitable for incorporation into existing EWSs, allowing the development of a new tool adjusted to the healthcare context in Angola. Methods: We conducted a three‐round Delphi survey, engaging a national expert panel comprising twenty‐five physicians and nurses with expertise in internal medicine, surgery, emergency rooms, intensive care units, and/or teachers at universities or at teaching courses in these fields. Participants were asked to rate items using a five‐point Likert scale. Consensus was achieved if the items received a rating ≥ 80% from the panel. Results: Consensus was evident for the inclusion of standard physiological parameters, such as systolic blood pressure, heart rate, respiratory rate, temperature, oxygen saturation, neurological status, and the presence or absence of supplemental oxygen. Furthermore, there was consensus for the consideration of specific items, namely, seizures, jaundice, cyanosis, capillary refill time, and pain—typically not included in the current EWSs. Consensus was reached regarding the exclusion of both oxygen saturation and temperature measurements in healthcare settings where oximeters and thermometers might not be readily available. Conclusion: Angolan experts were able to identify the physiological parameters suitable for incorporation into the basic EWSs. Further study must be conducted to test and validate the impact of the newly suggested vital parameters on the discriminant and predictive capability of a new aggregated model specifically adjusted to the Angolan healthcare setting. [ABSTRACT FROM AUTHOR]

Background: Improving the uptake of relevant and reliable research is an important priority in long-term care to achieve sustainable and high-quality services for the increasingly older population. Aim: The purpose was to assess the effectiveness of a tailored, adaptive and a multifaceted KT capacity program, relative to usual practice, on the implementation of National Early Warning Score 2 (NEWS2). Methods: This study was carried out as a pragmatic cluster-randomized controlled trial. The capacity program consisted of an educational part to address implementation capacity gaps and a facilitation-upon-implementation part to address a relevant knowledge gap in nursing homes. A collective decision was made to address the challenge of early detection of clinical deterioration among nursing home residents, by implementing the (NEWS2) as clinical innovation. Public nursing homes in a Norwegian municipality (n = 21) with a total of 1 466 beds were eligible for inclusion. The study-period spanned over a 22-month period, including a 12-month follow-up. Data was extracted from the Electronic Patient Journal system and analyzed using multilevel growth model analysis. Results: The intervention had a large effect on the use of NEWS2 among care staff in intervention nursing homes, compared to the control group (standardized mean difference, d = 2.42). During the final month of the implementation period, residents in the intervention group was assessed with NEWS2 1.44 times (95% CI: 1.23, 1.64) per month, which is almost four times more often than in the control group (mean = 0.38, 95% CI: 0.19, 0.57). During the follow-up period, the effect of the intervention was not only sustained in the intervention group but there was a substantial increase in the use of NEWS2 in both the intervention (mean = 1.75, 95% CI: 1.55, 1.96) and control groups (mean = 1.45, 95% CI: 1.27, 1.65). Conclusions: This tailored implementation strategy had a large effect on the use of NEWS2 among care staff, demonstrating that integrated knowledge translation strategies can be a promising strategy to achieve evidence-based care in the nursing home sector. Trial registration: ISRCTN12437773. Registered 19/3 2020, retrospectively. [ABSTRACT FROM AUTHOR]

Background: In the recent years, National Early Warning Score2 (NEWS2) is utilized to predict early on, the worsening of clinical status in patients. To this date the predictive accuracy of National Early Warning Score (NEWS2), Revised Trauma Score (RTS), and Trauma and injury severity score (TRISS) regarding the trauma patients' mortality rate have not been compared. Therefore, the objective of this study is comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set. Methods: This cross-sectional retrospective diagnostic study performed on 6905 trauma patients, of which 4191 were found eligible, referred to the largest trauma center in southern Iran, Shiraz, during 2022–2023 based on their prehospital data set in order to compare the prognostic power of NEWS2, RTS, and TRISS in predicting in-hospital mortality rate. Patients are divided into deceased and survived groups. Demographic data, vital signs, and GCS were obtained from the patients and scoring systems were calculated and compared between the two groups. TRISS and ISS are calculated with in-hospital data set; others are based on prehospital data set. Results: A total of 129 patients have deceased. Age, cause of injury, length of hospital stay, SBP, RR, HR, temperature, SpO2, and GCS were associated with mortality (p-value < 0.001). TRISS and RTS had the highest sensitivity and specificity respectively (77.52, CI 95% [69.3–84.4] and 93.99, CI 95% [93.2–94.7]). TRISS had the highest area under the ROC curve (0.934) followed by NEWS2 (0.879), GCS (0.815), RTS (0.812), and ISS (0.774). TRISS and NEWS were superior to RTS, GCS, and ISS (p-value < 0.0001). Conclusion: This novel study compares the accuracy of NEWS2, TRISS, and RTS scoring systems in predicting mortality rate based on prehospital data. The findings suggest that all the scoring systems can predict mortality, with TRISS being the most accurate of them, followed by NEWS2. Considering the time consumption and ease of use, NEWS2 seems to be accurate and quick in predicting mortality based on prehospital data set. [ABSTRACT FROM AUTHOR]

Background: Solid-organ transplant recipients (SOTRs) receiving immunosuppressive therapy are expected to have worse clinical outcomes from coronavirus disease 2019 (COVID-19). However, published studies have shown mixed results, depending on adjustment for important confounders such as age, variants, and vaccination status. Materials and Methods: We retrospectively collected the data on 7,327 patients hospitalized with COVID-19 from two tertiary hospitals with government-designated COVID-19 regional centers. We compared clinical outcomes between SOTRs and non-SOTRs by a propensity score-matched analysis (1:2) based on age, gender, and the date of COVID-19 diagnosis. We also performed a multivariate logistic regression analysis to adjust other important confounders such as vaccination status and the Charlson comorbidity index. Results: After matching, SOTRs (n=83) had a significantly higher risk of high-flow nasal cannula use, mechanical ventilation, acute kidney injury, and a composite of COVID-19 severity outcomes than non-SOTRs (n=160) (all P <0.05). The National Early Warning Score was significantly higher in SOTRs than in non-SOTRs from day 1 to 7 of hospitalization (P for interaction=0.008 by generalized estimating equation). In multivariate logistic regression analysis, SOTRs (odds ratio [OR], 2.14; 95% confidence interval [Cl],1.12-4.11) and male gender (OR, 2.62; 95% Cl, 1.26-5.45) were associated with worse outcomes, and receiving two to three doses of COVID-19 vaccine (OR, 0.43; 95% Cl, 0.24-0.79) was associated with better outcomes. Conclusion: Hospitalized SOTRs with COVID-19 had a worse prognosis than non-SOTRs. COVID-19 vaccination should be implemented appropriately to prevent severe COVID-19 progression in this population. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) pandemic presents serious public health threats. Omicron, the current most prevalent strain of COVID-19, has a low fatality rate and very high transmissibility, so the number of patients with mild symptoms of COVID-19 is rapidly increasing. This change of pandemic challenges medical systems worldwide in many aspects, including sharp increases in demands for hospital infrastructure, critical shortages in medical equipment, and medical staff. Predicting deterioration in mild patients could alleviate these problems. A novel scoring system was proposed for predicting the deterioration of patients whose condition may worsen rapidly and those who all still mild or asymptomatic. Retrospective cohorts of 954 and 2,035 patients that quarantined in the Residential Treatment Center were assembled for derivation and external validation of mild COVID-19, respectively. Deterioration was defined as transfer to a local hospital due to worsening condition of the patients during the 2-week isolation period. A total of 15 variables: sex, age, seven pre-existing conditions (diabetes, hypertension, cardiovascular disease, respiratory disease, liver disease, kidney disease, and organ transplant), and five vital signs (systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), body temperature, and oxygen saturation (SpO2)) were collected. A scoring system was developed using seven variables (age, pulse rate, SpO2, SBP, DBP, temperature, and hypertension) with significant differences between the transfer and not transfer groups in logistic regression. The proposed system was compared with existing scoring systems that assess the severity of patient conditions. The performance of the proposed scoring system to predict deterioration in patients with mild COVID-19 showed an area under the receiver operating characteristic (AUC) of 0.868. This is a statistically significant improvement compared to the performance of the previous patient condition assessment scoring systems. During external validation, the proposed system showed the best and most robust predictive performance (AUC = 0.768; accuracy = 0.899). In conclusion, we proposed a novel scoring system for predicting patients with mild COVID-19 who will experience deterioration which could predict the deterioration of the patient's condition early with high predictive performance. Furthermore, because the scoring system does not require special calculations, it can be easily measured to predict the deterioration of a patients' condition. This system can be used as effective tool for early detection of deterioration in mild COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. Methods: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. Results: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. Conclusions: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking. Highlights: • This article explored decision-making processes of clinicians using a clinical prediction model for deteriorating patients, also known as an early warning score. • Our study identified that the clinical utility of deterioration models may lie in their assistance in generating, evaluating, and selecting diagnostic hypotheses, an important part of clinical decision making that is underrepresented in the prediction modelling literature. • Nurses in particular stressed the need for models that encourage critical thinking and further investigation rather than prescribe strict care protocols. [ABSTRACT FROM AUTHOR]

Nursing documentation is an integral component of a nurse's day as it is a professional requirement. A recent improvement project carried out in NHS Greater Glasgow and Clyde (NHSGGC) revealed that this documentation was ritualistic and not person-centred. Up to 100% of what was documented in the nursing continuation notes was either documented already or not relevant, and the time dedicated to the 'writing up' took up to 8% of a nurse's working week, was the cause of nurses being late off duty and nurses feeling overwhelmed and took nurses away from hands-on care. NHSGGC also identified that despite this time being dedicated to record keeping, there was room for improvement regarding the quality, volume and relevance of record keeping. This article describes the improvement journey that NHSGGC went through to develop a framework for nursing documentation resulting in better clinical outcomes, time freed up for direct nursing care and positive impact on staff wellbeing. [ABSTRACT FROM AUTHOR]

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. [ABSTRACT FROM AUTHOR]

Abstract Background Pulmonary embolism (PE) is an important cause of mortality and morbidity in the geriatric population. We aimed to compare the ability of the pulmonary embolism severity index (PESI), rapid emergency medicine score (REMS), and hypotension, oxygen saturation, low temperature, electrocardiogram change, and loss of independence (HOTEL) to predict prognosis and intensive care requirement in geriatric patient with PE. Results The median age of 132 patients was 77 (71–82) years. PESI was higher in the non-survivor group [132 (113–172)] (P =0.001). The median REMS was 8 (7–10), and it was higher in the non-survivor group [10 (7.5–12.0)] (p = 0.005). The median HOTEL score was 1 (0–2) in the whole cohort and 2 (1–3) in the non-survivor group, indicating significant difference compared to the survivor group (P = 0.001). The area under the curve (AUC) values of HOTEL, REMS, and PESI were determined as 0.72, 0.65, and 0.71, respectively. For the prediction of intensive care requirement, the AUC values of HOTEL, REMS, and PESI were 0.76, 0.75, and 0.76, respectively, with no significant difference in pairwise comparisons (PESI vs. REMS: p = 0.520, HOTEL vs. PESI: P = 0.526, REMS vs. HOTEL: P = 0.669, overall test: P = 0.96, DeLong’s test). The risk ratios of HOTEL and PESI were parallel to each other [5.31 (95% confidence interval (CI): 2.53–11.13) and 5.34 (95% CI: 2.36–12.08), respectively]. Conclusion HOTEL and REMS were as successful as PESI in predicting short-term mortality and intensive care requirement in geriatric patients with PE. These scores are also more practical since they have fewer parameters than PESI.

Abstract Introduction Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient’s disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission. Methods A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients’ need for hospital admission. Results In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p

Abstract Introduction Early and adequate preliminary diagnosis reduce emergency department (ED) and hospital stay and may reduce mortality. Several studies demonstrated adequate preliminary diagnosis as stated by emergency medical services (EMS) ranging between 61 and 77%. Dutch EMS are highly trained, but performance of stating adequate preliminary diagnosis remains unknown. Methods This prospective observational study included 781 patients (> 18years), who arrived in the emergency department (ED) by ambulance in two academic hospitals. For each patient, the diagnosis as stated by EMS and the ED physician was obtained and compared. Diagnosis was categorized based on the International Classification of Diseases, 11th Revision. Results The overall diagnostic agreement was 79% [95%-CI: 76–82%]. Agreement was high for traumatic injuries (94%), neurological emergencies (90%), infectious diseases (84%), cardiovascular (78%), moderate for mental and drug related (71%), gastrointestinal (70%), and low for endocrine and metabolic (50%), and acute internal emergencies (41%). There is no correlation between 28-day mortality, the need for ICU admission or the need for hospital admission with an adequate preliminary diagnosis. Conclusion In the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should use these findings to improve patient outcome.

Background: This study was performed to determine the relationship between chest X-ray findings and the severity of bronchiolitis using the modified Tal score scale (MTS). Methods: This retrospective study was conducted among 999 children aged 2-24 months admitted to a referral teaching hospital in Isfahan, Iran. The severity of bronchiolitis was determined by MTS criteria, with scores ranging from 0 to 12. We considered scores 0-5 mild, 6-9 moderate, and 10-12 severe bronchiolitis. The patient's CXRs were also extracted from the hospital's picture archiving and communication system (PACS) and reported by an expert Pediatric radiologist. The radiologic findings were compared with the MTS criteria.Results: The mean (SD) of the MTS score in the patients was 4.58 ± 1.92. Overall, 757 patients (75.78%) had normal radiographies. The frequency of normal radiography was 75.3% in the group of mild bronchiolitis and 77.3% in the group of moderate bronchiolitis. Reports of 9 patients with severe disease showed that 6 of them had normal CXRs (66.7%), 2 had hyperinflation, and 1 had atelectasis. There was no statistically significant relationship between radiographic results and the severity of bronchiolitis, according to MTS criteria (P = 0.23). The agreement between radiographic results and the severity of bronchiolitis was very weak (0.004) without statistical significance (P = 0.632). Conclusion: Considering that 99.3% of children with bronchiolitis do not have significant findings in chest X-rays, routine chest X-ray is not recommended in these patients.

Introduction: The WHO has now declared COVID-19 as endemic and it may be prudent to identify high-risk patients requiring reverse transcriptase-polymerase chain reaction (RT-PCR). During the pandemic, RT-PCR testing and reporting were running around-the-clock. We attempted to validate this scoring system and predict high probable in the obstetric subpopulation based on a modified early warning scoring system (EWSS) score. Materials and Methods: Data were collected from case sheets of pregnant women who were admitted to the obstetrics and gynecology department, according to the EWSS protocol. All the criteria mentioned in the EWSS score were tabulated in an Excel sheet. A modified list of signs, symptoms, and investigations was correlated with RT-PCR. This model obtained was validated with appropriate statistical tests. Patients who had undergone lower-segment cesarean section had hypertension (HTN), and diabetes as comorbidities and were included in the scoring system. Results: The odds ratio of the patient being positive was 3.56 with diabetes mellitus and 0.93 with HTN, most presented with minor symptoms such as expectoration, sore throat, dyspnea, and anosmia and these had odds ratio of 7.03, 12.68, 4.24, and 2.45, respectively, but the confidence interval was very wide. In laboratory investigations, C-reactive protein (CRP) and lactate dehydrogenase (LDH) elevations were found commonly and the odds ratio was 16.09 and 8.78, respectively. Conclusion: We concluded that if a pregnant woman presents with a sore throat, expectoration, dyspnea, or anosmia and presents with raised CRP and LDH with diabetes as a comorbidity there is a very high probability that the patient may have COVID-19 and needs to be tested or isolated.

Objectives: Limited research exists on COVID-19 associated brain fog, and on the long-term cognitive and psychiatric sequelae in racially and ethnically diverse patients. We characterize the neuropsychological sequelae of post-acute COVID-19 in a diverse cohort and investigate whether COVID-19 clinical severity remains associated with brain fog and cognitive deficits approximately 2 years post infection. Methods: A cross-sectional study of patients with a history of COVID-19 hospitalization (March-September 2020). COVID-19 clinical severity was indexed using the National Early Warning Score 2 and a comprehensive neuropsychological tele-battery was administered 2 years post discharge. Pearson's r correlations assessed association, while independent sample t-tests examined group differences. Significant outcomes were further analyzed using multiple regression and ANCOVAs, adjusting for key covariates. Results: In 41 adult patients (19 female, 30 Hispanic, 13 Black, mean age of 65 (SD = 15), COVID-19 level of severity was associated with greater number of endorsed brain fog symptoms (Pearson's r =.34, 95% CI [.04,.59]), worse overall cognitive functioning (global cognition: r = -.36, 95% CI [-.61, -.05]) and reduced performance on an attention and working memory task (digit span backwards: r = -.41, 95% CI [-.66, -.09]) at 2-year follow-up. Brain fog symptoms most associated with COVID-19 severity included difficulty focusing (r =.46, 95% CI [.18,.67]), detached (r =.41, 95% CI [.12,.64]) and feeling sleepy (r =.40, 95% CI [.11,.63]). Patients' cognitive performance was generally below average (global cognition z-score: M = -.96, SD =.66), with group differences based on sex and ethnicity evidenced on individual cognitive tests. Discussion: This study emphasizes the importance of continued research on the long-term effects of COVID-19 infection on neuropsychological outcomes, particularly among underrepresented, health-disparate groups. Greater understanding of these associations could improve detection and treatment of those at increased risk of cognitive decline or impairment. [ABSTRACT FROM AUTHOR]

Objective: This study aims to assess the effectiveness of the National Early Warning Score 2 (NEWS2) versus Glasgow Coma Scale (GCS) in predicting hospital mortality among patients with stroke and traumatic brain injury (TBI). Location: This multicenter study was conducted at two anonymized tertiary care hospitals in distinct climatic regions of China, with a combined annual emergency admission exceeding 10,000 patients. Patients: The study included 2,276 adult emergency admissions diagnosed with stroke (n = 1,088) or TBI (n = 1,188) from January 2021 to December 2023, excluding those with chronic pulmonary disease, severe cardiac conditions, or a history of brain surgery. Measuring and main outcomes: The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were utilized to analyze the predictive accuracy of NEWS2 and GCS for hospital mortality at 24, 48, and 72 h postadmission and at discharge. Results: Out of 2,276 patients (mean age 61.4, 65.6% male), 1855 survived while 421 succumbed. NEWS2 demonstrated superior predictive accuracy (AUC = 0.962) over GCS (AUC = 0.854) for overall hospital mortality. Specifically, NEWS2 outperformed GCS in predicting mortality at 24 h (0.917 vs. 0.843), 48 h (0.893 vs. 0.803), and 72 h (0.902 vs. 0.763). Notably, despite a higher AUC for NEWS2 at predicting 24-h hospital mortality, the sensitivity and specificity of GCS were considerably lower (12 and 31%, respectively) compared to NEWS2 (sensitivity of 95% and specificity of 81%). Subgroup analysis showed NEWS2 outperforming GCS in predicting in-hospital mortality for TBI and stroke patients. For TBI patients (n = 260), NEWS2 had an AUC of 0.960 (95% CI: 0.948-0.973) vs. GCS's AUC of 0.811 (95% CI: 0.781-0.840). For stroke patients (n = 161), NEWS2 had an AUC of 0.930 (95% CI: 0.908-0.952) vs. GCS's AUC of 0.858 (95% CI, 0.823-0.892). NEWS2 showed greater sensitivity in both groups, highlighting its effectiveness in identifying high-risk neurological patients. Conclusion: NEWS2 scores are more precise and effective in predicting hospital mortality in stroke and TBI patients compared to GCS scores, although slightly less so within the first 24 h. Combining NEWS2 with GCS and clinical findings within the initial 24 h is recommended for a comprehensive prognosis evaluation. [ABSTRACT FROM AUTHOR]

Background: The respiratory adjusted shock index (RASI) is a risk score whose usefulness in patients with sepsis and trauma has previously been reported. However, its relevance in elderly emergency patients with medical diseases is yet to be clarified. This study assessed the usefulness of the RASI, which can be evaluated without requiring special equipment, to provide objective and rapid emergency responses. Methods: In this retrospective study, we recruited patients with medical diseases, aged 65 years or older, who were transported to the emergency room from Tokushima Prefectural Kaifu Hospital and underwent arterial blood gas testing from 1 January 2022 to 31 December 2023. We investigated the association of the RASI with other indices, including the lactate level, National Early Warning Score 2 (NEWS2), Shock Index (SI), Sequential Organ Failure Assessment (SOFA) score, quick SOFA (qSOFA) score, and systemic inflammatory response syndrome (SIRS). Results: In this study, we included 260 patients (mean age, 86 years), of whom 234 were admitted to the hospital; 27 and 49 patients died within 7 and 30 days of admission, respectively. The RASI was positively correlated with the lactate level, NEWS2, SI, and increase in the SOFA score (p < 0.001). The RASI was higher in patients with a SIRS or qSOFA score ≥ 2 than in those without (p < 0.001). It predicted death within 7 and 30 days of admission with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.73–0.87), sensitivity of 96.3%, and specificity of 53.6% when the cutoff value was set to 1.58 and with an AUC of 0.73 (95% CI: 0.66–0.81), sensitivity of 69.4%, and specificity of 70.6% when the cutoff value was set to 1.83, respectively. Conclusions: The RASI is a simple indicator that can be used for predicting in-hospital outcomes in elderly emergency patients with medical diseases. Larger prospective studies based on this study are needed. [ABSTRACT FROM AUTHOR]

Background: Pulmonary embolism (PE) is an important cause of mortality and morbidity in the geriatric population. We aimed to compare the ability of the pulmonary embolism severity index (PESI), rapid emergency medicine score (REMS), and hypotension, oxygen saturation, low temperature, electrocardiogram change, and loss of independence (HOTEL) to predict prognosis and intensive care requirement in geriatric patient with PE. Results: The median age of 132 patients was 77 (71–82) years. PESI was higher in the non-survivor group [132 (113–172)] (P =0.001). The median REMS was 8 (7–10), and it was higher in the non-survivor group [10 (7.5–12.0)] (p = 0.005). The median HOTEL score was 1 (0–2) in the whole cohort and 2 (1–3) in the non-survivor group, indicating significant difference compared to the survivor group (P = 0.001). The area under the curve (AUC) values of HOTEL, REMS, and PESI were determined as 0.72, 0.65, and 0.71, respectively. For the prediction of intensive care requirement, the AUC values of HOTEL, REMS, and PESI were 0.76, 0.75, and 0.76, respectively, with no significant difference in pairwise comparisons (PESI vs. REMS: p = 0.520, HOTEL vs. PESI: P = 0.526, REMS vs. HOTEL: P = 0.669, overall test: P = 0.96, DeLong's test). The risk ratios of HOTEL and PESI were parallel to each other [5.31 (95% confidence interval (CI): 2.53–11.13) and 5.34 (95% CI: 2.36–12.08), respectively]. Conclusion: HOTEL and REMS were as successful as PESI in predicting short-term mortality and intensive care requirement in geriatric patients with PE. These scores are also more practical since they have fewer parameters than PESI. [ABSTRACT FROM AUTHOR]

Various scoring systems are available for COVID-19 risk stratification. This study aimed to validate their performance in predicting severe COVID-19 course in a large, heterogeneous Swiss cohort. Scores like the National Early Warning Score (NEWS), CURB-65, 4C mortality score (4C), Spanish Society of Infectious Diseases and Clinical Microbiology score (COVID-SEIMC), and COVID Intubation Risk Score (COVID-IRS) were assessed in patients hospitalized for COVID-19 in 2020 and 2021. Predictive accuracy for severe course (defined as all-cause in-hospital death or invasive mechanical ventilation (IMV)) was evaluated using receiver operating characteristic curves and the area under the curve (AUC). The new 'COVID-COMBI' score, combining parameters from the top two scores, was also validated. This study included 1,051 patients (mean age 65 years, 60% male), with 162 (15%) experiencing severe course. Among the established scores, 4C had the best accuracy for predicting severe course (AUC 0.76), followed by COVID-IRS (AUC 0.72). COVID-COMBI showed significantly higher accuracy than all established scores (AUC 0.79, p = 0.001). For predicting in-hospital death, 4C performed best (AUC 0.83), and, for IMV, COVID-IRS performed best (AUC 0.78). The 4C and COVID-IRS scores were robust predictors of severe COVID-19 course, while the new COVID-COMBI showed significantly improved accuracy but requires further validation. [ABSTRACT FROM AUTHOR]

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED. [ABSTRACT FROM AUTHOR]