Early Warning Score Research Articles

Effect of sepsis protocol in inpatient departments triggered by Ramathibodi Early Warning Score (REWS) on treatment processes

Background: Sepsis needs to be more focused on the effect of patient management at the ward level. We aimed to evaluate the effect of implementing the sepsis protocol triggered by the Ramathibodi Early Warning Score (REWS) on treatment processes in inpatients with new-onset sepsis. Methods: We conducted a prospective observational study among adult medical patients admitted to the general wards. A 25-month pre-protocol period was assigned as a control, and a 14-month protocol period was allocated to a protocol group. An inpatient sepsis protocol comprised a nurse-initiated sepsis protocol with REWS ≥2 plus suspected infection, prompt antibiotic, lactate measurement, and fluid resuscitation. Primary outcomes were the achievement of sepsis treatment processes, including the resuscitation and management bundle, namely: 1) the percentage of patients who were taken for the initial laboratory workup for sepsis, especially lactate and blood culture taking before antibiotics; 2) the percentage of patients who received appropriate antibiotics; 3) the percentage of patients who received optimal fluid resuscitation and management; 4) the percentage of patients who performed inferior vena cava ultrasound; 5) the percentage of patients who received steroid and vasopressor drugs; 6) "time-to-antibiotic," the duration from diagnosis of sepsis to receiving antibiotic treatment; 7) "time-to-optimal intravenous fluid management;" 8) "time-to-transfer to ICU. Results: 282 patients were evaluated (141 pre-implementation, 141 post-implementation); 94.7% of patients with sepsis had REWS ≥2. More patients in the protocol period had a lactate measurement and fluid management (89 [63.1%] vs. 44 patients [31.2%], p<0.001 and (50 [35.4%] vs. 22 patients [15.6%], p<0.001, respectively). More patients in the protocol period received antibiotics within 1 hour than in the pre-protocol period (80 [56.7%] vs. 53 patients [37.6%], p=0.001). The time to antibiotic treatment (mean, SD) in the protocol period was shorter than that in the pre-protocol period (81.7 [77.86] vs. 138.22 [145.17], p=0.007). The length of the intensive care unit (ICU) stay was shorter in the protocol period (8 d [3, 16.5] vs. 10 d [5, 20.5], p=0.011). The two groups did not differ in in-hospital mortality, length of hospital stay, or time-to-transfer to the ICU. Conclusions: Implementing an in-hospital sepsis protocol was associated with significant improvement in sepsis treatment processes, namely lactate measurement, starting antibiotic treatment within 1 hour, fluid management, and a shorter length of ICU stay.

SUPER Score Contributes to Warning and Management in Early-Stage COVID-19

BackgroundSome COVID-19 patients deteriorate to severe cases with relatively higher case-fatality rates, which increases the medical burden. This necessitates identification of patients at risk of severe disease. Early assessment plays a crucial role in identifying patients at risk of severe disease. This study is to assess the effectiveness of SUPER score as a predictor of severe COVID-19 cases. MethodsWe consecutively enrolled COVID-19 patients admitted to a comprehensive medical center in Wuhan, China, and recorded clinical characteristics and laboratory indexes. The SUPER score was calculated using parameters including oxygen saturation, urine volume, pulse, emotional state, and respiratory rate. In addition, the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity of the SUPER score for the diagnosis of severe COVID-19 were calculated and compared with the National Early Warning Score 2 (NEWS2). ResultsThe SUPER score at admission, with a threshold of 4, exhibited good predictive performance for early identification of severe COVID-19 cases, yielding an AUC of 0.985 (95% confidence interval [CI] 0.897–1.000), sensitivity of 1.00 (95% CI 0.715–1.000), and specificity of 0.92 (95% CI 0.775–0.982), similar to NEWS2 (AUC 0.984; 95% CI 0.895–1.000, sensitivity 0.91; 95% CI 0.587–0.998, specificity 0.97; 95% CI 0.858–0.999). Compared with patients with a SUPER score<4, patients in the high-risk group exhibited lower lymphocyte counts, interleukin-2, interleukin-4 and higher fibrinogen, C-reactive protein, aspartate aminotransferase, and lactate dehydrogenase levels. ConclusionsIn conclusion, the SUPER score demonstrated equivalent accuracy to the NEWS2 score in predicting severe COVID-19. Its application in prognostic assessment therefore offers an effective early warning system for critical management and facilitating efficient allocation of health resources.

Nursing Training for Early Clinical Deterioration Risk Assessment: Protocol for an Implementation Study.

During the hospitalization period, it is possible to observe considerable changes in the vital parameters of patients, which may require emergency interventions or intensive treatment. The alteration of signs and symptoms that lead to physiological instability that can worsen the clinical picture with progression to shock, respiratory failure, or cardiorespiratory arrest is currently defined as clinical deterioration. Identifying signs of clinical deterioration at an early stage can lead to substantial decreases in mortality rates, the need for emergency interventions, and unscheduled treatments in intensive care units. Identifying and appropriately referring patients who show signs of clinical deterioration can be facilitated by applying early warning systems that provide rapid responses. The nursing team is usually the first to identify clinical changes in patients. Although the literature demonstrates that early recognition of clinical deterioration is the key to early intervention and leads to better outcomes, we only sometimes pursue the most appropriate intervention. This study aims to implement and evaluate an evidence-based professional training program designed for nurses and coordinated by a nurse using the "just-in-time" methodology and the National Early Warning Score 2 (NEWS2) to assess the risk of early clinical deterioration and appropriate referral in inpatient units of a public university hospital in southeastern Brazil. This intervention protocol is structured according to the recommendations of the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Declaration 2013. The type of training to be offered, "Just-in-Time Training," consists of a teaching modality that facilitates the delivery of a time-based and work-based education, with greater emphasis on providing on-the-job learning as needed. A qualitative stage will also be conducted through focus groups and interviews with nurses to verify the factors that influence the professional practice related to the early evaluation of the clinic. A script of previously tested questions will guide and standardize the different groups. The data will define the intervention's elements: the strategy, the type of training, the location, the teaching methodology, and the teaching material. The study has received authorization from the ethics committee, and participants will be recruited in July 2023. Data collection should be completed in October of the same year. The results obtained at the end of this research will be shared with the participating nursing team through the presentation of reports. In addition, the research results will be submitted to scientific journals and presented at international scientific conferences. This study will support nurses and possibly other clinicians to improve their approach to early recognition of clinical deterioration in patients. Brazilian Registry of Clinical Trials RBR-5hq9y3k; https://ensaiosclinicos.gov.br/rg/RBR-5hq9y3k. PRR1-10.2196/47293.

Predictive Nursing Under the MEWS Combined Shenmai Injection Enhances the Success Rate and Life Cycle of Emergency Care for Patients in Septic Shock

This study aimed to analyze the effect of predictive nursing using the Modified Early Warning Score combined with Shenmai injection on the resuscitation success and life cycle of patients with septic shock. First, 98 septic shock patients were randomly divided into: the treatment group (n = 49), which received predictive nursing according to the MEWS, and the control group (n = 49), which received standard care. After analysis, we found that the treatment group had a higher success rate of resuscitation than the control group. The mechanical ventilation, antibiotic use, and duration of stay in the intensive care unit were all lower in the treatment group compared to the control group (P &lt; 0.05). The treatment group exhibited greater compliance with medication, diet, and fluid intake throughout treatment and reported fewer adverse effects than the control group (P &lt; 0.05). Prognostic follow-up revealed that overall survival was higher in the treatment group than in the control group (P &lt; 0.05). The post-care nutritional status was better in the treatment group than in the control group (P &lt; 0.05). These results suggested that predictive nursing under the MEWS combined with Shenmai injection can improve the resuscitation success rate and nutritional status of septic shock patients.

Institutional-related Related Factors Associated with the Management Outcome of Patients Triaged During 48 Hours of Interventions at Accident and Emergency Department, Kenyatta National Hospital

Objective: The study sought to evaluate institutional-related factors associated with the management outcome of patients triaged during 48 hours of interventions at the accident and emergency department, Kenyatta National Hospital.&#x0D; Methodology: The study was a crosssectional study involving the triaged and coded patients flagged by scores using the Triage Early Warning Score (TEWS) whereby structured questionnaires were used as well as an observation checklist. The study site was accident and emergency department, Kenyatta National Hospital. The target population were all coded patients in the accident and emergency unit, a sample of 385 patients were used during this study, data collection was through structured questionnaires and checklist to assess the healthcare provider and institutional-related factors. A pilot study was done at Kakamega County Referral Hospital. Data analysis was done using Statistical Package for Social Sciences (SPSS) version 25, descriptive and inferential statistics were used to test the associated factors in relation to the outcomes. The odds ratio was used to test the strength between the provider and health facility factors associated with the management outcome of triaged and coded patients, and a one-way analysis of variance was used to the differences in mean scores in the institutional and provider factors. Data was presented in tables and bar graphs.&#x0D; Findings: The study results indicated that there was a positive and significant relationship between institutional-related factors and management outcomes of triaged patients. This is depicted by a Pearson correlation coefficient r=0.452 pvalue =0.008&lt; 0.05 which was significant at a 0.05 level of significance. This implies that improved institutional-related factors result in an increase in the management outcome of triaged patients.&#x0D; Conclusion and Recommendations: It's therefore important to note that institutional-related factors have an impact on the outcome of triaged and coded patients in the accident and emergency department, at Kenyatta National Hospital. Thus it's important for the institution to invest in human resource capacity, procurement of equipment and drugs to be used as well as improve on infrastructure.

An Evaluation of the National Early Warning Score 2 and the Laboratory Data Decision Tree Early Warning Score in Predicting Mortality in Geriatric Patients

BackgroundDue to the high rate of geriatric patient visits, scoring systems are needed to predict increasing mortality rates. ObjectiveIn this study, we aimed to investigate the in-hospital mortality prediction power of the National Early Warning Score 2 (NEWS2) and the Laboratory Data Decision Tree Early Warning Score (LDT-EWS), which consists of frequently performed laboratory parameters. MethodsWe retrospectively analyzed 651 geriatric patients who visited the emergency department (ED), were not discharged on the same day from ED, and were hospitalized. The patients were categorized according to their in-hospital mortality status. The NEWS2 and LDT-EWS values of these patients were calculated and compared on the basis of deceased and living patients. ResultsMedian (interquartile range [IQR]) NEWS2 and LDT-EWS values of the 127 patients who died were found to be statistically significantly higher than those of the patients who survived (NEWS2: 5 [3–8] vs. 3 [1–5]; p < 0.001; LDT-EWS: 8 [7–10] vs. 6 [5–8]; p < 0.001). In the receiver operating characteristic curve analysis, the NEWS2, LDT-EWS, and NEWS2+LDT-EWS—formed by the sum of the two scoring systems—resulted in 0.717, 0.705, and 0.775 area under curve values, respectively. ConclusionsThe NEWS2 and LDT-EWS were found to be valuable for predicting in-hospital mortality in geriatric patients. The power of the NEWS2 to predict in-hospital mortality increased when used with the LDT-EWS.

Association between SARS-CoV-2 viral kinetics and clinical score evolution in hospitalized patients.

The role of antiviral treatment in coronavirus disease 2019 hospitalized patients is controversial. To address this question, we analyzed simultaneously nasopharyngeal viral load and the National Early Warning Score 2 (NEWS-2) using an effect compartment model to relate viral dynamics and the evolution of clinical severity. The model is applied to 664 hospitalized patients included in the DisCoVeRy trial (NCT04315948; EudraCT 2020-000936-23) randomly assigned to either standard of care (SoC) or SoC + remdesivir. Then we use the model to simulate the impact of antiviral treatments on the time to clinical improvement, defined by a NEWS-2 score lower than 3 (in patients with NEWS-2 <7 at hospitalization) or 5 (in patients with NEWS-2 ≥7 at hospitalization), distinguishing between patients with low or high viral load at hospitalization. The model can fit well the different observed patients trajectories, showing that clinical evolution is associated with viral dynamics, albeit with large interindividual variability. Remdesivir antiviral activity was 22% and 78% in patients with low or high viral loads, respectively, which is not sufficient to generate a meaningful effect on NEWS-2. However, simulations predicted that antiviral activity greater than 99% could reduce by 2 days the time to clinical improvement in patients with high viral load, irrespective of the NEWS-2 score at hospitalization, whereas no meaningful effect was predicted in patients with low viral loads. Our results demonstrate that time to clinical improvement is associated with time to viral clearance and that highly effective antiviral drugs could hasten clinical improvement in hospitalized patients with high viral loads.

Validation of NEWS2, SIRS, and qSOFA in Postoperative Cardiac Patients: A Retrospective Cohort Study

IntroductionThe ‘quick Sepsis Related Organ Failure Assessment’ (qSOFA), ‘Systemic Inflammatory Response Syndrome’ (SIRS), and ‘National Early Warning Score’ 2 (NEWS2) scores are yet to be comparatively validated in ward-based cardiac surgical patients despite widespread routine use in clinical practice. We sought to assess the predictive validity of NEWS, SIRS, and qSOFA in identifying postoperative, ward-level cardiac surgical patients at risk of poor short-term mortality. MethodsAll adult patients who underwent cardiac surgery at a single tertiary center between November 2014 and October 2017 were identified. Data for bedside observations, hematological results, and microbiology requests were obtained from electronic health records. Survival data were acquired from a national registry. The primary outcome was the discriminatory ability, measured by the area under the receiver operating characteristic (AUROC), of each score for in-hospital mortality. ResultsOne thousand five hundred forty three (male n = 1101, 71%) patients were included. Overall in-hospital mortality was 2.4%. There was no significant difference in discriminatory ability of NEWS (AUROC 0.5060), SIRS (AUROC 0.4874), and qSOFA (AUROC 0.5139) for in-hospital mortality (P = 0.881). Sensitivity for this outcome was ubiquitously low (13.51-40.54%). ConclusionsCurrent illness-severity scores show a low discriminatory ability for in-hospital mortality in ward-based cardiac surgical patients. Caution should be used in the application of these prognostic screening tools for early detection of poor outcomes in this population.

A quality improvement project to establish accuracy in completing Early Warning Score chart and appropriate clinical response depending on the score in acute care wards at Asiri Surgical Hospital

Failure or delay in recognition of acutely deteriorating patients in acute care ward setting leads to preventable morbidity, cardiac arrest, and death. Assessment and responding to Early Warning Score (EWS) adapted from National Early Warning Score 2 is implemented at Asiri Surgical Hospital to capture these patients early and escalate their care appropriately. Though a colour-coded EWS chart and a hospital policy are available, gaps in accurate recording, monitoring, and triggering were reported. Thus, a quality improvement project was conducted with the aim of establishing more than 80% accuracy in completing the EWS chart and appropriate clinical response to EWS score in acute care wards at Asiri Surgical Hospital by August 2022.A multidisciplinary team performed a series of Plan-Do-Study-Act cycles focusing on correct documentation and calculation of EWS score and appropriate monitoring and escalation of care. Key drivers for hindering the pathway were identified and addressed. Over the course of the project, correct recording improved from 81.25% to 99.4%, accurate calculation of total EWS score had improved from 79% to 88%, patients who received recommended monitoring as triggered by EWS score improved from 78.8% to 84.6% and escalation of care when EWS score is 5 or more improved from 70.9% to 91.7%.A step improvement in the clinical response to a deteriorating patient in the acute care ward setting at Asiri Surgical Hospital was attained by the successful quality improvement project involving the frontline care providers.

Comparison of four early warning scores in predicting the prognosis of critically ill patients in secondary hospitals

To explore the predictive value of acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), quick sequential organ failure assessment (qSOFA) and modified early warning score (MEWS) in evaluating the prognosis of patients in intensive care unit (ICU) of secondary hospitals, and to provide guidance for clinical application. The clinical data of adult critical patients admitted to the ICU of Wanzhou District First People's Hospital from October 2022 to April 2023 were retrospectively analyzed. According to the clinical outcome of ICU, the patients were divided into improvement group and death group. The general information, blood routine, heart, liver and kidney function indicators, coagulation indicators, blood gas analysis, APACHE II score, SOFA score, qSOFA score, MEWS score at the time of admission to the ICU, the number of cases of invasive mechanical ventilation (IMV) and continuous blood purification (CBP) were compared between the two groups. Univariate analysis was performed, and multivariate Logistic regression analysis was used to analyze the related factors of death. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive value of the four scores in ICU patients. A total of 126 patients were included, of which 45 patients died in the ICU and 81 patients improved and transferred out. Univariate analysis of death-related critically ill patients showed that procalcitonin (PCT), serum creatinine (SCr), blood urea nitrogen (BUN), albumin (ALB), prothrombin time (PT), activated partial prothrombin time (APTT), D-dimer, pH value, HCO3-, blood lactic acid (Lac), number of patients treated with IMV and CBP, APACHE II score, SOFA score, qSOFA score and MEWS score were significantly different between the two groups (all P < 0.05). Multivariate Logistic regression analysis showed that the APACHE II score [odds ratio (OR) = 1.115, 95% confidence interval (95%CI) was 1.025-1.213, P = 0.011], SOFA score (OR = 1.204, 95%CI was 1.037-1.398, P = 0.015), MEWS score (OR = 1.464, 95%CI was 1.102-1.946, P = 0.009), and APTT (OR = 1.081, 95%CI was 1.015-1.152, P = 0.016) were independent risk factors affecting the mortality of critically ill patients in the ICU. ROC curve analysis showed that APACHE II, SOFA, qSOFA, and MEWS scores could predict the prognosis of critically ill ICU patients, among which SOFA score had the strongest predictive effect, and the area under the curve (AUC) was 0.808. There was a statistically significant difference in the time required for the four scores (F = 117.333, P < 0.001), among which the MEWS scoring required the shortest time [(1.03±0.39) minutes], and the APACHE II scoring required the longest time [(2.81±1.04) minutes]. APACHE II, SOFA, qSOFA, and MEWS scores can be used to assess the severity of critically ill patients and predict in-hospital mortality. The SOFA score is superior to other scores in predicting severity. The MEWS is preferred because its assessment time is shortest. Early warning score can help secondary hospitals to detect potentially critical patients early and provide help for clinical rapid urgent emergency decision-making.

Applying Early Warning Score (EWS) in hospitals for adult mortality risk factors

The mortality rate among adult hospital patients is a significant indicator in assessing the quality of care and health services. High mortality rates can affect a hospital's reputation, reduce the credibility of medical personnel, and trigger public anxiety. Failure to recognize the worsening of a patient's condition can delay appropriate life support management. This scoping review aims to identify and analyze the Early Warning Score (EWS) application for adults with risk factors for death in regional general hospitals (RSUD). The scoping review was conducted by searching literature using electronic databases from January 2018-May 2023, such as PubMed, ScienceDirect, LibMed UGM, and hand searching. Relevant articles were selected based on inclusion criteria. The results contained two main themes: factors related to mortality in adult patients and the benefits of implementing EWS on the clinical outcomes of adult patients. Implementing EWS in adult patients with high-risk factors for mortality at Regional General Hospitals can help identify the worsening of the patient's condition early, reducing the risk of death. Increasing awareness of the importance of EWS, EWS training, and consistent use of EWS according to hospital protocols can help reduce mortality and improve overall patient clinical outcomes.

Performance of risk scores in patients with acute exacerbations of COPD.

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

Ursodeoxycholic acid does not affect the clinical outcome of SARS-CoV-2 infection: A retrospective study of propensity score-matched cohorts.

Ursodeoxycholic acid (UDCA) has been recently proposed as a modulator of angiotensin-converting enzyme 2 (ACE2) receptor expression, with potential effects on COVID-19. We retrospectively evaluated the clinical course and outcome of subjects taking UDCA admitted to the hospital for COVID-19 compared with matched infected subjects. Differences regarding the severity and outcome of the disease between treated and non-treated subjects were assessed. The Kaplan-Meier survival analysis and log-rank test were used to evaluate the effect of UDCA on all-cause intra-hospital mortality. Among 6444 subjects with confirmed COVID-19 admitted to the emergency department (ED) from 1 March 2020 to 31 December 2022, 109 subjects were taking UDCA. After matching 629 subjects were included in the study: 521 in the no UDCA group and 108 in the UDCA group. In our matched cohort, 144 subjects (22.9%) died, 118 (22.6%) in the no-UDCA group and 26 (24.1%) in the UDCA group. The Kaplan-Meier analysis showed no significant difference in survival between groups. In univariate regression analysis, the presence of pneumonia, National Early Warning Score (NEWS) score, and Charlson Comorbidity Index (CCI) were significant independent predictors of death. At multivariate Cox regression analysis, age, NEWS, pneumonia and CCI index were confirmed significant independent predictors of death. UDCA treatment was not a predictor of survival both in univariate and multivariate regressions. UDCA treatment does not appear to have significant effects on the outcome of COVID-19. Specially designed prospective studies are needed to evaluate efficacy in preventing infection and severe disease.

Development and validation of the early warning system scores ontology

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

A model study for the classification of high-risk groups for cardiac arrest in general ward patients using simulation techniques.

Currently, many hospitals use vital signs-based criteria such as modified early warning score (MEWS) and national early warning score (NEWS) to classify high-risk patients for cardiac arrest, but there are limitations in selecting high-risk patients with a possibility of cardiac arrest. The purpose of this study is to develop a cardiac arrest classification model to identify patients at high risk of cardiac arrest based on the patient family and past history, and blood test results after hospitalization, rather than vital signs. This study used electronic medical record (EMR) data from A university hospital, and patients in the high-risk group for cardiac arrest were defined as those who underwent cardio-pulmonary resuscitation (CPR) after cardiac arrest. Considering the use of the rapid response team of A university hospital, patients hospitalized in intensive care units (ICU), emergency medicine departments, psychiatric departments, pediatric departments, cardiology departments, and palliative care wards were excluded. This study included 325,534 patients, of which 3291 low-risk and 382 high-risk patients were selected for study. Data were split into training and validation data sets and univariate analysis was performed for 13 candidate risk factors. Then, multivariate analysis was performed using a bivariate logistic regression model, and an optimal model was selected using simulation analysis. In the training data set, it was calculated as sensitivity 75.25%, precision 21.59%, specificity 66.89%, accuracy 67.79%, F1 score 33.56, area under curve (AUC) 71.1 (95% confidence interval [CI] = 68.9-73.1 P value=<.001). In the validation data set, sensitivity 73.37%, precision 25.81%, specificity 75.03%, accuracy 74.86%, F1 score 38.19, AUC 74.2 (95% CI = 72.1-76.2, P value=<.001) were calculated. A model for classifying the high-risk group of cardiac arrest should be developed from various perspectives. In the future, in order to classify patients with high risk of cardiac arrest, a prospective study on the combined use of the model developed by this study and NEWS or MEWS should be conducted.

Pediatric Early Warning System in the Short Stay Unit

INTRODUCTION: Pediatric Early Warning Scores were developed as standardized clinical aides for early recognition of clinical deterioration in children. These systems improve situational awareness among professionals. A major concern is the workload increase. After implementation in the Short Stay Unit, we intended to evaluate its effectiveness in our population.METHODS: We reviewed medical records of children admitted to the Short Stay Unit between July 2017 and February 2020. We developed a statistical analysis to evaluate the test performance.RESULTS: One thousand three hundred twenty-three patients were included (median age 3.07 years-old); 5.7% were considered to have clinical deterioration (maximum score median 7.00 vs 2.00) (p&lt;0.001). The area under the ROC curve was 0.947. Defining 4 as cut-off value, the sensitivity is 97.1% (specificity 82.7%).CONCLUSION: This score proved to be a good test for early identification of clinical deterioration in our population.

Role of Hematological Parameters in the Grading of COVID-19 and a Model to Predict the Outcome in Inpatients.

Introduction Human coronaviruses, identified in the 1960s, are known culprits of respiratory infections. Classified into alpha, beta, gamma, and delta subgroups, these viruses have the capacity to transition from animal reservoirs to causing severe respiratory ailments in humans. Notable outbreaks like the 2003 severe acute respiratory distress syndrome (SARS) epidemic and the ongoing coronavirus disease 2019 (COVID-19) pandemic underscore the recurring emergence of novel coronaviruses with severe human infection potential. COVID-19, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly become a leading global cause of severe acute respiratory syndrome. Immune system disruptions and cytokine imbalances contribute to severe cases, necessitating early diagnosis and precise severity assessment. Methodology This retrospective cross-sectional study encompassed 211 COVID-19 patients admitted to AIIMS Patna from May to July 2020. Clinical and hematological parameters, including neutrophils, eosinophils, basophils, lymphocytes, monocytes, red and white blood cell counts, platelet count, C-reactive protein (CRP), serum ferritin, and d-dimer, were meticulously recorded. Patients were categorized into non-severe and severe groups using the National Early Warning Score (NEWS) 2. Results Our findings underscore the pivotal role of hematological markers in gauging COVID-19 severity. Notably, markers such as neutrophil-to-lymphocyte ratio (NLR), derived NLR, lymphocyte monocyte ratio, platelet lymphocyte ratio, d-dimer, CRP, and serum ferritin exhibited notable elevation in severe cases. Survival analysis further established the predictive potential of these markers in assessing disease progression and mortality risk. We advocate for the integration of these markers into existing severity assessment frameworks to foster objective clinical evaluations. Conclusion In conclusion, our study unravels the intricate connection between COVID-19 severity and hematological parameters. We emphasize the early warning capabilities of NLR, derived NLR, platelet lymphocyte ratio, and other markers in predicting disease progression. This research underscores the imperative need to incorporate hematological markers into the evaluation of COVID-19 severity, thereby providing invaluable insights for enhancing clinical practice and patient outcomes.

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity

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

Evaluation of a modified paediatric early warning score for children with congenital heart disease.

Paediatric early warning score systems are used for early detection of clinical deterioration of patients in paediatric wards. Several paediatric early warning scores have been developed, but most of them are not suitable for children with cyanotic CHD who are adapted to lower arterial oxygen saturation. The present study compared the original paediatric early warning system of the Royal College of Physicians of Ireland with a modification for children with cyanotic CHD. Retrospective single-centre study in a paediatric cardiology intermediate care unit at a German university hospital. The distribution of recorded values showed a significant shift towards higher score values in patients with cyanotic CHD (p < 0.001) using the original score, but not with the modification. An analysis of sensitivity and specificity for the factor "requirement of action" showed an area under the receiver operating characteristic for non-cyanotic patients of 0.908 (95% CI 0.862-0.954). For patients with cyanotic CHD, using the original score, the area under the receiver operating characteristic was reduced to 0.731 (95% CI 0.637-0.824, p = 0.001) compared to 0.862 (95% CI 0.809-0.915, p = 0.207), when the modified score was used. Using the critical threshold of scores ≥ 4 in patients with cyanotic CHD, sensitivity and specificity for the modified score was higher than for the original (sensitivity 78.8 versus 72.7%, specificity 78.2 versus 58.4%). The modified score is a uniform scoring system for identifying clinical deterioration, which can be used in children with and without cyanotic CHD.

Comparison of National Early Warning Score with shock index in patients with necrotizing fasciitis.

Shock index (SI) and national early warning score (NEWS) are more frequently used as assessment tools in acute illnesses, patient disposition and early identification of critical condition. Both they are consisted of common vital signs and parameters including heart rate, systolic blood pressure, respiratory rate, oxygen saturation and level of conscious, which made it easy to evaluate in medical facilities. Its ability to predict mortality in patients with necrotizing fasciitis (NF) in the emergency department remains unclear. This study was conducted to compare the predictive capability of the risk scores among NF patients. A retrospective cohort study of hospitalized patients with NF was conducted in 2 tertiary teaching hospitals in Taiwan between January 2013 and March 2015. We investigated the association of NEWS and SI with mortality in NF patients. Of the 395 NF patients, 32 (8.1%) died in the hospital. For mortality, the area under the receiver curve value of NEWS (0.81, 95% confidence interval 0.76-0.86) was significantly higher than SI (0.76, 95% confidence interval 0.73-0.79, P = .016). The sensitivities of NEWS of 3, 4, and 5 for mortality were 98.1%, 95.6%, and 92.3%. On the contrast, the sensitivities of SI of 0.5, 0.6, and 0.7 for mortality were 87.8%, 84.7%, and 81.5%. NEWS had advantage in better discriminative performance of mortality in NF patients. The NEWS may be used to identify relative low risk patients among NF patients.