Search

Background: Clinical tools are needed in general practice to help identify children who are seriously ill. The Liverpool quick Sequential Organ Failure Assessment (LqSOFA) was validated in an emergency department and performed well. The National Paediatric Early Warning System (PEWS) has been introduced in hospitals throughout England with hopes for implementation in general practice. Aim: To validate the LqSOFA and National PEWS in general practice. Design and setting: Secondary analysis of 6703 children aged <5 years presenting to 225 general practices in England and Wales with acute illnesses, linked to hospital data. Method: Variables from the LqSOFA and National PEWS were mapped onto study data to calculate score totals. A primary outcome of admission within 2 days of GP consultation was used to calculate sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and area under the receiver operating characteristic curve (AUC). Results: A total of 104/6703 children were admitted to hospital within 2 days (pre-test probability 1.6%) of GP consultation. The sensitivity of the LqSOFA was 30.6% (95% confidence interval [CI] = 21.8% to 41.0%), with a specificity of 84.7% (95% CI = 83.7% to 85.6%), PPV of 3.0% (95% CI = 2.1% to 4.4%), NPV of 98.7% (95% CI = 98.4% to 99.0%), and AUC of 0.58 (95% CI = 0.53 to 0.63). The sensitivity of the National PEWS was 81.0% (95% CI = 71.0% to 88.1%), with a specificity of 32.5% (95% CI = 31.2% to 33.8%), PPV of 1.9% (95% CI = 1.5% to 2.5%), NPV of 99.1% (95% CI = 98.4% to 99.4%), and AUC of 0.66 (95% CI = 0.59 to 0.72). Conclusion: Although the NPVs appear useful, owing to low pre-test probabilities rather than discriminative ability, neither tool accurately identified admissions to hospital. Unconsidered use by GPs could result in unsustainable referrals. [ABSTRACT FROM AUTHOR]

Copyright of Notfall & Rettungsmedizin is the property of Springer Nature 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.)

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]

Introduction: The use of medications by emergency medical services (EMS) is increasing. Conventional scores are time‐consuming and therefore difficult to use in an emergency setting. For early decision‐making, an easy‐to‐use score based on the medications administered by the EMS may have prognostic value. The primary objective of this study was to develop the prehospital drug‐derived score (PDDS) for 2‐day mortality. Methods: A prospective, multicenter, ambulance‐based cohort study was conducted in adults with undifferentiated acute diseases treated by EMS and transferred to the emergency department. Demographic data, prehospital diagnosis data, prehospital medication and variables for the calculation of the National Early Warning Score 2 (NEWS2), Rapid Emergency Medicine Score (REMS), and Rapid Acute Physiology Score (RAPS) were collected. The PDDS was developed and validated, establishing three levels of risk of 2‐day mortality. The predictive capability of each score was determined by the area under the curve of the receiver operating characteristic curve (AUROC) and compared using the Delong's test (p‐value). Results: A total of 6401 patients were included. The PDDS included age and the use of norepinephrine, analgesics, neuromuscular blocking agents, diuretics, antihypertensive agents, tranexamic acid, and bicarbonate. The AUROC of PDDS was.86 (95% CI:.816–.903) versus NEWS2.866 (95% CI:.822–.911), p =.828; versus REMS.885 (95% CI:.845–.924), p =.311; versus RAPS.886 (95% CI:.846–.926), p =.335, respectively. Conclusion: The newly developed easy‐to‐use prehospital drug‐derived PDDS score has an excellent predictive value of early mortality. The PDDS score was comparable to the conventional risk scores and therefore might serve as an alternative score in the prehospital emergency setting. [ABSTRACT FROM AUTHOR]

Background: Continuous vital sign monitoring detects far more severe vital sign deviations (SVDs) than intermittent clinical rounds, and deviations are to some extent related to subsequent serious adverse events (SAEs). Early detection of SAEs is pivotal to allow for effective interventions but the time relationship between detection of SAEs by continuous vital sign monitoring versus clinical practice is not well‐described at the general ward. Aim: To quantify the time difference between detection of SAEs by continuous vital sign monitoring and clinical suspicion of deterioration (CSD) in major abdominal surgery patients. Methods: Five hundred and five patients had their vital signs continuously monitored in combination with usual clinical practice consisting of National Early Warning Score assessments at least every 8'th hour, assessments during rounds, and other kinds of staff‐patient interactions. The primary outcome was the time difference between the first chart note of CSD versus the first SVD, detected by continuous vital sign monitoring, in patients with a subsequent confirmed SAE during or up to 48 h after end of continuous vital sign monitoring. Results: Out of the 505 continuously monitored patients, 142 patients had a combination of both postoperative SAE, CSD and SVD, and thus were included in the primary analysis. The median time from the first SVD to SAE was 42.8 h (interquartile range 19.8–72.1 h) compared to 13 minutes (interquartile range − 4.8 to 3.5 h) for CSD with a median difference of 48.1 h (95% confidence interval 43.0–54.8 h), p‐value <.001. Conclusion: Continuous vital sign monitoring detects signs of oncoming SAEs in the form of SVD hours before CSD, potentially allowing for earlier and more effective treatments to reduce the extent of SAEs. [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]

ABSTRACT Aim Design Methods Results Conclusion Implications for the Profession and Patient Care Impact Reporting Method Patient or Public Contribution To explore Registered Nurses' experiences and perceptions of using the National Early Warning Score in the U.K. as part of the recognition and management of acute adult patient deterioration.Hermeneutic Phenomenological design.Sixteen Registered Nurses from a U.K. NHS hospital were interviewed using an interpretative phenomenological approach (2019–2020).Registered Nurses' use of NEWS highlighted 3 risk areas: delegation of vital sign monitoring to unregistered staff leading to uncertainty and delayed escalation, junior nurses' over‐reliance on NEWS and deference to expertise, and senior nurses' self‐management of deteriorating patients. The workplace culture revealed frequent compromises and limited learning opportunities.When using NEWS, failure to recognise associated risks threatens patient safety. Wrong decisions at the three ‘pinch points’ may lead to missed chances in preventing deterioration. Incorrect judgements may lead to unrecognised patient deterioration or inappropriate management leading to preventable adverse events.The way in which NEWS is used by healthcare professionals brings inherent patient safety risks. Addressing education gaps and fostering a supportive culture in nursing, valuing and enhancing nurses' clinical judgement, is crucial for mitigating these risks and ensuring patient safety.The study deepens understanding of nurses' use of NEWS and identifies components affecting the recognition of patient deterioration.Adherence to the EQUATOR guidelines SRQR confirmed.Service user involvement was included within the design of the study and ethical approval. [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: Nurses play a key role in the recognition and response to clinical deterioration. Aim: The aim of this scoping review was to explore, map and synthesise existing research related to the ward nurses' role in recognising and responding to clinical deterioration. Methods: A scoping review was undertaken to identify English only studies focused on the ward nurse's role in recognition and response to clinical deterioration of the hospitalised adult. Search terms included 'clinical deterioration', 'nurses', 'wards', 'general', 'hospital, units' and 'hospitals'. The Cumulative Index to Nursing and Allied Health Literature, EMBASE, Ovid MEDLINE, PubMed, ProQuest and Science Direct databases were searched for eligible studies. Results: Forty-six studies met the inclusion criteria and three major themes were synthesised: (i) recognition of deterioration; (ii) nursing assessment; and, (iii) challenges responding to patient deterioration. Conclusion: The review highlighted significant variability in the ward nurses' role, activities, and skills in assessing, monitoring, managing and escalating care for clinical deterioration. [ABSTRACT FROM AUTHOR]

Purpose: Sepsis is a leading cause of morbidity and mortality globally. The lack of specific prognostic markers necessitates tools for early risk identification in patients with suspected infections in emergency department (ED). This study evaluates the prognostic accuracy of various Early Warning Scores (EWS)—MEWS, NEWS, NEWS-2, and qSOFA—for in-hospital mortality, 30-day mortality, and ICU admission, considering the site of infection. Methods: A retrospective analysis was conducted using data from the Acutelines cohort, which included data collected from patients admitted to the University Medical Centre Groningen ED between September 2020 and July 2023. Patients were included if they had an ICD-10 code for infection. EWS were calculated using clinical data within 8 h post-admission. Predictive performance was assessed using AUC-ROC, calibration by the Hosmer-Lemeshow test and calibration curves, and operative characteristics like sensitivity and specificity. Results: A total of 1661 patients were analyzed, with infections distributed as follows: lower respiratory tract (32.9%), urinary tract (30.7%), abdominal (12.5%), skin and soft tissue (9.5%), and others (8.2%). The overall in-hospital mortality was 6.7%, and ICU admission was 7.1%. The highest AUC-ROC for in-hospital mortality prediction was observed with NEWS and NEWS-2 in abdominal infections (0.86), while the lowest was for qSOFA in skin and soft tissue infections (0.57). Predictive performance varied by infection site. Conclusions: The study highlights the variability in EWS performance based on infection site, emphasizing the need to consider the source of infection in EWS development for sepsis prognosis. Tailored or hybrid models may enhance predictive accuracy, balancing simplicity and specificity. [ABSTRACT FROM AUTHOR]

The article in the journal "Transplant Infectious Disease" discusses the high incidence of Clostridioides difficile infection (CDI) in patients undergoing CAR-T-cell therapy for B-cell malignancies. The study emphasizes the need for enhanced antimicrobial stewardship to balance the use of antibiotics and prevent CDI. Various studies and trials are summarized to highlight the incidence of CDI in different cohorts, and the challenges of managing febrile neutropenia with broad-spectrum antibiotics are addressed. The authors call for individualized antimicrobial strategies and rapid diagnostics to optimize outcomes for CAR-T-cell therapy recipients. [Extracted from the article]

In information infrastructures at hospitals, various stakeholders are responsible for specific information and communications technology (ICT) portfolios. Each portfolio represents a unique digital trajectory with a past, present, and future. This study investigated how stakeholders (in this study, software developers, ICT operations organizations, and users) collaborate to facilitate the convergence of different digital trajectories, thus contributing to the successful evolution of information infrastructures. Empirically, we focused on the preparatory work involved in implementing an app that would enable nurses to register and calculate National Early Warning Scores at Nordland Hospital in northern Norway. Specifically, we examined the collaboration between three stakeholders to align their respective ICT portfolios and prepare for the new solution. These stakeholders were the Finnish software developer Medanets, the Norwegian Electronic Health Record developer DIPS ASA, and the Northern Norway Regional Health Authority, which governed the regional health ICT infrastructure. These stakeholders governed three distinct portfolios that had been developed over many years and, in this sense, represented digital trajectories with a past, a present, and a possible future. This study is positioned within the computer-supported cooperative work field, and the analysis draws upon the theoretical concepts of information infrastructure and trajectories. [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]

Introduction: During the COVID-19 pandemic, there were difficulties in diagnostic applications in patients who applied to the emergency department with dyspnea. We aimed to compare the Oxygen, Predisposing factors, Effusion, Radiology, Age (OPERA) scoring that we determined to be fast in diagnosis and treatment, with the Modified Early Warning Score (MEWS) scoring and imaging findings. We investigated the effectiveness of scoring in predicting prognosis and mortality. Methods: Our retrospective cross-sectional study included 271 patients who presented to a university emergency department between 07 April and 31 July 2020 with dyspnea. MEWS and OPERA scores, demographic characteristics, vital signs, serological tests and detailed findings of computed tomography (CT) of the patients included in the study were scanned. Patients were analyzed in terms of diagnosis, need for intensive care, and two-month mortality. Result: A total of 271 patients (149 (55%) women, mean age 60.6 ± 18.1 years old) who presented to the emergency department with dyspnea were included in our study. While 43 (15.9%) patients died in the last two months, 69 (25.5%) patients needed intensive care. When the value of 4 was determined as the limit for the MEWS score, 21 (14.1%) patients admitted to the intensive care unit were found to be <4, while 48 (39.3%) patients were ≥4. While 9 (6.0%) of the patients with MEWS score <4 were mortal, 34 (27.9%) patients with MEWS score ≥4 were found to be mortal. OPERA score cutoff value of 6 was calculated. While 27 patients (12.8%) were admitted to the intensive care unit with a score of <6, 52 patients (37.7%) were hospitalized with a score of ≥6. While 4 (3.0%) patients with OPERA score <6 were mortal, 39 (28.3%) patients with ≥6 scores. While the sensitivity of the MEWS score was 69.6% and specificity 63.4% in the need for intensive care, the sensitivity was 79.1% and the specificity was 61.4% in mortality. In the OPERA scoring, the sensitivity for the need for intensive care was 75.4%, the specificity was 57.4%, while the sensitivity for mortality was 90.7% and the specificity was 56.6%. All results are similar between both scores and there is no statistically significant difference (p<0.001). Conclusion: While OPERA scoring is based on the patient's history and imaging, MEWS is calculated based on vital signs. However, no statistically significant difference was found in all results in terms of predicting both mortality and intensive care hospitalization in both scorings (p<0.00). [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.

ABSTRACT Aims Design Methods Results Conclusion Implications for Patient Care Impact Reporting Method To investigate the prevalence of rapid response team delays, survival distribution of admission to rapid response team delay and its prognostic factors.A retrospective single‐centre study.Data on rapid response team activations from 1 January 2018 to 31 December 2022 were retrieved from electronic medical records at a tertiary hospital in Hangzhou, China. All patients who met the eligibility criteria were included. Multivariable Cox regression analysis was conducted to analyse the data.Out of 636 patients included, 18.4% (117) experienced a delay, with a median (interquartile range) of 8.5 (12) days from admission to rapid response team activation. Six significant prognostic factors were found to be associated with the higher hazard ratio of rapid response team delay, including call time (05:01 PM and 7:59 AM), emergency admission, a higher Modified Early Warning Score, an admission diagnosis of infection, a comorbidity of respiratory failure/Acute Respiratory Distress Syndrome and the absence of lung infection.The prevalence of rapid response team delays was lower, and the days from admission to rapid response team delay was longer than in previous studies. Healthcare providers are suggested to prioritise the care of high‐risk patient groups and provide proactive monitoring to ensure timely identification and management.Implementing artificial intelligence in continuous monitoring systems for high‐risk patients is recommended. The findings help nurses anticipate potential delays in rapid response team activation, enabling better preparedness.The study highlights the prevalence of rapid response team delays, timing from admission to rapid response team activation and six prognostic factors influencing delays. It could shape patient care and inform future research. Hospital administrators should review staffing, especially during night shifts, to minimise delays. Further qualitative research is needed to explore why nurses may delay rapid response team activation.The STROBE checklist was adhered to when reporting this study.‘No patient or public contribution’. [ABSTRACT FROM AUTHOR]

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: Vital signs assessment is critical for patient surveillance and safety. Research has found, however, that this assessment is often neglected in clinical practice. The reasons for this are unclear as few studies have explored this issue. Those studies that have are small, single site studies and found that culture and poor understanding are contributing factors. Aim: The aim was to explore the link between the clinical neglect of vital signs assessment and patient mortality and provide a better understanding of factors influencing vital signs assessment in the context of acute patient care. Coroners' reports represent an untapped source of information regarding shortfalls in vital signs assessment. Using a framework analysis, an audit was conducted of the Australian National Coronial Information System for cases where vital signs' assessment was mentioned in coronial reports. Results: Fifty‐eight cases met the eligibility criteria, with deceased patients aged from 7 days to 93 years. Key themes related to absence of reassessment of vital signs, inappropriate delegation, passing responsibility to another staff member and not following policy. Conclusions: The findings reflect a combination of individual and institutional failings and suggest that vital signs assessment was not considered a priority aspect of care. Relevance to Clinical Practice: Vital signs assessment must be considered an essential aspect of clinical care in all patients. This important aspect of care should be emphasized across all domains of patient care. [ABSTRACT FROM AUTHOR]

Background: In recent years, some studies classified patients with sepsis and predicted their mortality by using some evaluation scales. Several studies reported significant differences in the predictive values of several tools, and the non‐uniformity of the cut‐off value. Aim: To determine and compare the prognostic accuracy of Sequential Organ Failure Assessment (SOFA) score, Modified Early Warning Score (MEWS), and Systemic Inflammatory Response Syndrome (SIRS) criteria in predicting the mortality of patients with sepsis. Methods: This study comprised of systematic literature review and meta‐analysis according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. We searched PubMed, Embase, Web of Science and Cochrane Library databases from their establishment to July 31, 2022. The research articles published in the index journals provide sufficient data (true positive, false positive, true negative, and false negative results) for patients with sepsis. The combined sensitivity and specificity of the 95% confidence interval (CI) were calculated using the bivariate random effect model (BRM). The hierarchical overall subject working characteristics (HSROC) curve was drawn to evaluate the accuracy of the overall prognosis. Results: Data of 55 088 patients from 32 studies were included in this meta‐analysis. SOFA had an intermediate sensitivity of 0.73 (95% CI: 0.67–0.78) and a specificity of 0.70 (0.63–0.76). SIRS criteria had the highest sensitivity of 0.75 (0.66–0.82) and the lowest specificity of 0.40 (0.29–0.52). MEWS had the lowest sensitivity of 0.49 (0.40–0.59) and the highest specificity of 0.82 (0.78–0.86). Conclusions: Among SOFA, MEWS, and SIRS criteria, SOFA showed moderate sensitivity and specificity for predicting mortality in patients with sepsis, the highest sensitivity of SIRS and the strongest specificity of MEWS for predicting mortality in patients with sepsis. The future research direction is to combine the relevant indicators of MEWS and SIRS to develop a measurement tool with high reliability and validity. Relevance to clinical practice: The review provides useful insights into the prognostic accuracy of different assessment tools in predicting mortality in sepsis patients, which will help clinicians choose the most appropriate tool for early identification and treatment of sepsis. The findings may also contribute to the development of more accurate and reliable prognostic models for sepsis. [ABSTRACT FROM AUTHOR]

Early identification of sepsis is vital as prompt treatment leads to improved patient outcomes. The purpose of this evidence-based practice project was to determine the most appropriate tool for early sepsis identification. [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]

Early warning scores serve as valuable tools for predicting adverse events in patients. This study aimed to compare the diagnostic performance of National Early Warning Score, Hamilton Early Warning Score, Standardized Early Warning Score, and Triage Early Warning Score in forecasting intubation and mortality among patients with coronavirus disease 2019. This predictive correlation study included 370 patients admitted to the emergency department of 22 Bahman Hospital in Neyshabur, Iran, from December 2021 to March 2022. The aforementioned scores were assessed daily upon patient admission and throughout a 1-month hospitalization period, alongside intubation and mortality occurrences. Data analysis used SPSS 26 and MEDCALC 20.0.13 software. We adhered to the Standards for Reporting of Diagnostic Accuracy Studies guidelines to ensure the accurate reporting of our study. The patients' mean age was 65.03 ± 18.47 years, with 209 (56.5%) being male. Both Standardized Early Warning Score and Hamilton Early Warning Score demonstrated high diagnostic performance, with area under the curve values of 0.92 and 0.95, respectively. For Standardized Early Warning Score, the positive likelihood ratio was 10.81 for intubation and 17.90 for mortality, whereas for Hamilton Early Warning Score, the positive likelihood ratio was 7.88 for intubation and 10.40 for mortality. The negative likelihood ratio values were 0.23 and 0.17 for Standardized Early Warning Score and 0.21 and 0.18 for Hamilton Early Warning Score, respectively, for the 24-hour period preceding intubation events and mortality. Findings suggest that Standardized Early Warning Score, followed by Hamilton Early Warning Score, has superior diagnostic performance in predicting intubation and mortality in patients with coronavirus disease 2019 within 24 hours before these outcomes. Therefore, serial assessments of Hamilton Early Warning Score or Standardized Early Warning Score may be valuable tools for health care providers in identifying high-risk patients with coronavirus disease 2019 who require intubation or are at increased risk of mortality. [ABSTRACT FROM AUTHOR]

Background: Various prognosticative approaches to assist in recognizing clinical deterioration have been proposed. To date, early warning scores (EWSs) have been evaluated in hospital with limited research investigating their suitability in the prehospital setting. This study evaluated the predictive ability of established EWSs and other clinical factors for prehospital clinical deterioration. Methods: A retrospective cohort study investigating adult patients of all etiologies attended by Queensland Ambulance Service paramedics between January 1, 2018, and December 31, 2020, was conducted. With logistic regression, several models were developed to predict adverse event outcomes. The National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Queensland Adult Deterioration Detection System (Q‐ADDS), and shock index were calculated from vital signs taken by paramedics. Results: A total of 1,422,046 incidents met the inclusion criteria. NEWS, MEWS, and Q‐ADDS were found to have comparably high predictive ability with area under the receiver operating characteristic curve (AUC‐ROC) between 70% and 90%, whereas shock index had relatively low AUC‐ROC. Sensitivity was lower than specificity for all models. Although established EWSs performed well when predicting adverse events, these scores require complex calculations requiring multiple vital signs that may not be suitable for the prehospital setting. Conclusions: This study found NEWS, MEWS, and Q‐ADDS all performed well in the prehospital setting. Although a simple shock index is easier for paramedics to use in the prehospital environment, it did not perform comparably to established EWSs. Further research is required to develop suitably performing parsimonious solutions until established EWSs are integrated into technological solutions to be used by prehospital clinicians in real time. [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]

Objective Methods Results Conclusions Improved understanding of the deteriorating patient in the pre‐hospital setting may result in earlier recognition and response. Considering the effects of undetected deterioration are profound, it is fundamental to report the prevalence of pre‐hospital clinical deterioration to advance our understanding. The present study investigated the prevalence of pre‐hospital clinical deterioration and adverse events (AEs) within 3 days of the pre‐hospital episode of care.This retrospective cohort study was based on pre‐hospital incidents involving adult patients attended by Queensland Ambulance Service between 1 January 2018 and 31 December 2020. Due to lacking a standardised definition of pre‐hospital clinical deterioration, established early warning scores (NEWS, MEWS and Q‐ADDS) were calculated from pre‐hospital vital signs to identify clinical deterioration. Linked hospital data were used to identify the occurrence of an AE.Some degree of physiological derangement was initially observed in over half of the patients, and pre‐hospital clinical deterioration was seen in 2.7%–4% of patients. The prevalence of AEs was 3.2%. Patients that experienced an AE were more likely to be male, elderly, suffering from a medical (non‐trauma) condition, and had a greater burden of disease. Concerningly, almost 50% of patients that suffered an AE did not meet escalation thresholds of NEWS, MEWS or Q‐ADDS.The present study found the prevalence of pre‐hospital clinical deterioration and AEs subsequent to pre‐hospital episodes of care to be low. Future research should prioritise using standardised criteria to define pre‐hospital clinical deterioration and evaluate the performance of early warning scores. [ABSTRACT FROM AUTHOR]

Introduction: Stroke-associated pneumonia (SAP) frequently complicates stroke and is associated with significant mortality. Clinicians often use physiological variables within the National Early Warning Score (NEWS) when diagnosing and prescribing antibiotics for SAP, but little is known of its association with mortality. We investigated the relationship of the NEWS 2 score and its components (respiratory rate, heart rate, temperature, oxygen requirement, oxygen saturation, and alertness level) prior to antibiotic initiation, with time-to-mortality in SAP. Methods: We included patients with SAP (n = 389) from a single hyperacute stroke unit. Diagnosis of SAP was made if pneumonia occurred within 7 days of hospital admission. Kaplan-Meier survival curves were generated to assess NEWS 2 parameters influencing survival at pre-defined time periods (1 year and 5 years). The association of these parameters on time-to-mortality were analysed using multivariable Cox-regression models to account for a set of pre-specified potential confounders. Results: The median age was 80 years (71–87 years) and median NIHSS was 7 (IQR 4–17). Mortality within 1 year was 52.4% and 65.8% within 5 years. In the multivariable analyses, time-to-mortality was independently associated with respiratory rate (heart rate [HR] 1.04, 95% confidence intervals [CI] 1.01–1.08, p = 0.009) and total NEWS 2 score (HR 1.13, 95% CI 1.06–1.21, p < 0.001). Conclusions: In patients with SAP, higher respiratory rate and total NEWS 2 score prior to antibiotic initiation were independently associated with time-to-mortality. Further studies are warranted to identify potential opportunities for intervention and ultimately guide treatment to improve outcomes in SAP patients. [ABSTRACT FROM AUTHOR]

Objective: To assess the efficacy of continuous contactless vital signs monitoring with an automated Early Warning System (EWS) in detecting clinical deterioration among patients in general wards. Methods: A prospective observational cohort study was conducted in the medical unit of a tertiary care hospital in India, involving 706 patients over 84,448 monitoring hours. The study used a contactless ballistocardiography system (Dozee system) to continuously monitor heart rate, respiratory rate, and blood pressure. The study assessed total, mean, and median alerts at 24, 48, 72, 96, 120 h, and length of stay (LOS) before patient deterioration or discharge. It analyzed alert sensitivity and specificity, average time from initial alert to deterioration, and healthcare practitioners (HCP) activity. Study was registered with the Clinical Trials Registry-India CTRI/2022/10/046404. Results: Out of 706 patients, 33 (5%) experienced clinical deterioration, while 673 (95%) did not. The deterioration group consistently had a higher number of alerts compared to those who were discharged normally, across all time-points. On average, the time between the initial alert and clinical deterioration was 16 h within the last 24 h preceding the event. The sensitivity of the Dozee-EWS varied between 67% and 94%. HCP spend 10% of their time on vital signs check and documentation. Conclusions: This study suggests that utilizing contactless continuous vital signs monitoring with Dozee-EWS in general ward holds promise for enhancing the early detection of clinical deterioration. Further research is essential to evaluate the effectiveness across a wider range of clinical settings. [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]

Aim Methods Results Conclusion As early detection of deterioration is a challenge in children, the Swedish Paediatric Early Warning Score (SwePEWS) is used to systematically assess paediatric patients' clinical state. Here, we aimed to evaluate the use and predictive ability of SwePEWS.Electronic health records of paediatric patients admitted due to respiratory syncytial virus infection or COVID‐19 were reviewed retrospectively. Registered vital signs were compared to the assigned SwePEWS score and monitored vital sign values to identify discrepancies. Additionally, SwePEWS's ability to predict transfer to the paediatric intensive care unit (PICU) was assessed.Among 1374 SwePEWS assessments, one‐third were either incomplete or contained errors. Incomplete SwePEWS assessments were more frequent during night‐time. Single measurements of oxygen saturation presented lower values compared to average saturation from continuous monitoring. SwePEWS's ability to predict PICU transfer was low.There was a surprisingly high occurrence of underestimated SwePEWS scores. This study provides new insights into pitfalls when developing and implementing paediatric early warning scores for systematic re‐evaluations in paediatric patients. [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]

Objective: To determine patients' perspectives on home monitoring at emergency department (ED) presentation and shortly after admission and compare these with their physicians' perspectives. Methods: Forty Dutch hospitals participated in this prospective flash mob study. Adult patients with acute medical conditions, treated by internal medicine specialties, presenting at the ED or admitted at the admission ward within the previous 24 h were included. The primary outcome was the proportion of patients who were able and willing to undergo home monitoring. Secondary outcomes included identifying barriers to home monitoring, patient's prerequisites, and assessing the agreement between the perspectives of patients and treating physicians. Results: On February 2, 2023, in total 665 patients [median age 69 (interquartile range: 55–78) years; 95.5% community dwelling; 29.3% Modified Early Warning Score ≥3; 29.5% clinical frailty score ≥5] were included. In total, 19.6% of ED patients were admitted and 26% of ward patients preferred home monitoring as continuation of care. Guaranteed readmission (87.8%), ability to contact the hospital 24/7 (77.3%), and a family caregiver at home (55.7%) were the most often reported prerequisites. Barriers for home monitoring were feeling too severely ill (78.8%) and inability to receive the required treatment at home (64.4%). The agreement between patients and physicians was fair (Cohens kappa coefficient 0.26). Conclusions: A substantial proportion of acutely ill patients stated that they were willing and able to be monitored at home. Guaranteed readmission, availability of a treatment team (24/7), and a home support system are needed for successful implementation of home monitoring in acute care. [ABSTRACT FROM AUTHOR]

The national early warning score (NEWS) is an appropriate tool for assessing patients experiencing catastrophic deterioration and enabling prompt intervention. The present study was carried out to assess the applicability of the NEWS in emergency departments and follow-up patients in the ICU in an Indian scenario. Methods: The study was conducted involving 270 patients of either sex and age greater than 16 years selected by simple random sampling. The data, which includes respiration rate, pulse rate, temperature, oxygen saturations, systolic blood pressure, and degree of consciousness, was gathered using the National Early Warning Score. Results: The mean age was 56.4 ± 16.9 years. The gender distribution was almost equal, with 53.7% male and 46.3% female. NEWS was 1-4 in 25.9% of subjects. 5-6 in 28.1% subjects and >/=7 in 45.9% subjects. The mean NEWS was 6.5 ± 2.9. Mortality was 22.6%, 75.9% of subjects were discharged, and 1.5% were referred. Apart from blood pressure and urine output, none of the parameters were found to be significantly different, including NEWS, between subjects with outcomes of death and discharge. The NEWS cut-off of 7.5 was found to predict mortality with 63.4% sensitivity and 49.2% specificity. Conclusion: NEWS effectively identifies subjects in need of immediate medical attention and paves the way for the development of a nationally validated scoring system to assess and convey the condition of subjects at intra- and inter-hospital facilities. [ABSTRACT FROM AUTHOR]

Febrile neutropenia (FN) is a common consequence of intensive chemotherapy in hematological patients. More than 90% of the patients with acute myeloid leukemia (AML) develop FN, and 5%–10% of them die from subsequent sepsis. FN is very common also in autologous stem cell transplant recipients, but the risk of death is lower than in AML patients. In this review, we discuss biomarkers that have been evaluated for diagnostic and prognostic purposes in hematological patients with FN. In general, novel biomarkers have provided little benefit over traditional inflammatory biomarkers, such as C‐reactive protein and procalcitonin. The utility of most biomarkers in hematological patients with FN has been evaluated in only a few small studies. Although some of them appear promising, much more data is needed before they can be implemented in the clinical evaluation of FN patients. Currently, close patient follow‐up is key to detect complicated course of FN and the need for further interventions such as intensive care unit admission. Scoring systems such as q‐SOFA (Quick Sequential Organ Failure Assessment) or NEWS (National Early Warning Sign) combined with traditional and/or novel biomarkers may provide added value in the clinical evaluation of FN patients. [ABSTRACT FROM AUTHOR]