Consensus for the Development of a New Early Warning Score for Predicting Patients' Clinical Deterioration in Angola: A Delphi Study.

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

Early warning model to detect anastomotic leakage following colon surgery: a clinical observational study.

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<0.05). From postoperative days 1 to 5, the mean EWS among patients with leakage exceeded 2, while scores were below 2 among those without leakage (P<0.05). Receiver operating characteristic curve analysis identified postoperative day 3 as the most predictive of early leakage, with cutoff values of 2.4 for EWS and 180 mg/L for CRP; this yielded an area under the curve of 0.87 (sensitivity, 90%; specificity, 70%). Conclusion: We propose using an EWS of 2.4 and a CRP level of 180 mg/L on postoperative day 3 following colon surgery with anastomosis as threshold values to prompt investigation and treatment of AL.

FI-lab in the emergency department and adverse outcomes among acutely hospitalized older adults.

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

Physiological deterioration prior to in-hospital cardiac arrest: What does the National Early Warning Score-2 miss?

Aim: To determine the frequency with which the National Early Warning Score-2 (NEWS-2) fails to detect physiological deterioration preceding in-hospital cardiac arrest (IHCA). Methods: We conducted a retrospective observational study of all adult patients (age ≥ 18) who had suffered an IHCA between 1st July 2019 and 31st December 2021 in two large acute hospitals located in an urban centre (Nottingham, UK). Clinical observations and case notes were examined for the period leading up to IHCA events to determine if there was evidence of physiological deterioration which warranted an urgent patient assessment, whether NEWS-2 was triggered, and whether an urgent assessment actually took place. Results: Urgent assessment was indicated in the lead-up to 126/374 (33.7 %) IHCA cases, and NEWS-2 failed to trigger in 20 of these cases (15.9 %). An urgent assessment took place in 89/106 (84.0 %) cases where NEWS-2 was triggered, and 13/20 (65.0 %) cases where NEWS-2 was not triggered, with the difference in proportions being statistically significant (p = 0.048). Half of cases in which NEWS-2 missed a physiological deterioration were related to a new or rising oxygen requirement. Conclusions: A significant proportion of IHCA events are preceded by clinically important abnormalities in vital signs which are not detected by NEWS-2. This may be a causative factor in some failure-to-rescue events.

Variation in nurses' compliance with an Early Warning Score protocol: A retrospective cohort study.

Introduction: Early Warning Score (EWS) protocols are based on intermittent vital sign measurements, and aim to detect clinical deterioration in a timely manner. Despite its predictive value, its effectiveness remains suboptimal. An important limitation appears to be poor compliance with the EWS protocol and its variation between general wards. The current research does not yet provide an understanding of EWS compliance and variation in different nursing wards. Aim: To explore the variation in nurses' compliance with the EWS protocol among patients with and without complications and between different nursing wards. Methods: In a retrospective single-center cohort study, all patient files from three nursing wards of a tertiary teaching hospital in the Netherlands were reviewed over a 1-month period. Compliance was divided into three categories:1) calculation accuracy, 2) monitoring frequency end 3) clinical response. Results: The cohort of 210 patients contained 5864 measurements, of which 4125 (70.6 %) included EWS. Significant differences in the measured vital signs within incomplete measurements were found among nursing wards. Compliance to monitoring frequency was higher within EWSs of 0-1 (78.4 %) than within EWSs of ≥2 (26.1 %). The proportion of correct follow-up was significantly higher in patients with complications, as was the correct clinical response to an EWS of ≥3 (84.8 % vs. 55.0; p = .011). Conclusion: Our results suggest suboptimal compliance with the EWS protocol, with large variations between patients with and without complications and between different general care wards. Nurses tended to be more compliant with the EWS protocol for patients with complications.

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

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.

Comparison and combined use of NEWS2 and GCS scores in predicting mortality in stroke and traumatic brain injury: a multicenter retrospective study.

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

Evaluation of Pediatric Early Warning Score (PEWS) in Unintentional Childhood Injuries Admitted to the Critical Care Unit.

INTRODUCTION: Pediatric Early Warning Score (PEWS), also known as Brighton PEWS or Monaghan PEWS, was developed to identify children at risk for clinical deterioration in hospitals. We designed this study to describe the epidemiology of unintentional injuries in children admitted to the critical care unit in a tertiary healthcare setting, and to determine the predictive properties of PEWS in these injuries. METHODS: This is a cross-sectional study. Injury-related data were based on Haddon's matrix of agent, host, and environment factors. Each child was evaluated using PEWS on admission. We noted the following outcomes: duration of stay in the intensive unit; major intervention required; and death. RESULTS: We analyzed data from 157 children. Most of the children were in the age group of one to five years (57.7%), followed by more than five to 12 years old (37.6%). The most common injuries were bites (35.7%), falls (24.2%), and poisoning (21.7%). These injuries occurred at home (52.7%) and in the presence of a caretaker (40.0%). On admission, 11% of children were classified as green, 40% as yellow, 36% as orange, and 13% as red by PEWS. Classification of red versus the rest (orange/yellow/green) had a high sensitivity (100%), specificity (88.3%), and negative predictive value (100%) for "death" as an outcome. CONCLUSION: Most of the injuries occurred at home and at a time when the caretaker was around. Thus, it will be useful to develop "safe home interventions" and train parents in first aid to take care of these injuries on-site. Baseline PEWS was a good predictor of "poor" as well as "positive" outcomes. It may be worthwhile to implement this score regularly in the management of childhood injuries in hospitals.

Unveiling the performance of the prehospital Rapid Emergency Medicine Score (pREMS): How the predictive score impacts in-hospital outcomes in traumatic brain injury (TBI): A retrospective observational cohort study.

INTRODUCTION: This study aimed to evaluate the predictive accuracy of the prehospital rapid emergency medicine score (pREMS) for predicting the outcomes of hospitalized patients with traumatic brain injury (TBI) who died, were discharged, were admitted to the intensive care unit (ICU), or were admitted to the operating room (OR) within 72 h. METHODS: A retrospective cohort analysis was performed on a sample of 513 TBI patients admitted to the emergency department (ED) of Besat Hospital in 2023. Only patients of both sexes aged 18 years or older who were not pregnant and had adequate documentation of vital signs were included in the analysis. Patients who died during transport and patients who were transferred from other hospitals were excluded. The predictive power of the pREMS for each outcome was assessed by calculating the sensitivity and specificity curves and by analyzing the area under the receiver operating characteristic curve (AUROC). RESULTS: The mean pREMS scores for hospital discharge, death, ICU admission and OR admission were 11.97 ± 3.84, 6.32 ± 3.15, 8.24 ± 5.17 and 9.88 ± 2.02, respectively. pREMS accurately predicted hospital discharge and death (AOR = 1.62, P < 0.001) but was not a good predictor of ICU or OR admission (AOR = 1.085, P = 0.603). The AUROCs for the ability of the pREMS to predict outcomes in hospitalized TBI patients were 0.618 (optimal cutoff point = 7) for ICU admission and OR and 0.877 (optimal cutoff point = 9.5) for hospital discharge and death at 72 h. CONCLUSION: The results indicate that the pREMS, a new preclinical trauma score for traumatic brain injury, is a useful tool for prehospital risk stratification (RST) in TBI patients. The pREMS showed good discriminatory power for predicting in-hospital mortality within 72 h in patients with traumatic brain injury.

The combined use of the Charlson Comorbidity Index and National Early Warning Score 2 helps predict the prognosis of candidemia.

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

A novel deterioration prediction system for mild COVID-19 patients in Korea: a retrospective study.

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

Comparison of Critical Illness Score in Patients Admitted to Intermediate Care Units of a Tertiary Care Hospital: A Comparative Cross-Sectional Study from Karachi, Pakistan.

Introduction: Intermediate care units (IMCUs) serve as step-up units for emergency department patients and as step-down units for critically ill patients transferred from intensive care units. This study compares four critical illness scores for assessment of acutely ill patients and their accuracy in predicting mortality in patients admitted to IMCU. Methods: A comparative cross-sectional study on patients aged ≥18 admitted to IMCU of Aga Khan University Hospital from 2017 to 2019. All patients admitted to IMCU from the emergency room were included in the study. Patient's record were reviewed for demographic data, physiological and laboratory parameters. Critical illness scores were calculated from these variables for each patient. Results: A total of 1192 patients were admitted to the IMCU, of which 923 (77.4%) medical records were finally analyzed. The mean (SD) age of participants was 62 years (± 16.5) and 469 (50.8%) were women. The overall hospital mortality rate of patients managed in IMCU was 6.4% (59/923 patients). The median scores of APACHE II, SOFA, SAPS II and MEWS were 16 (IQR 11-21), 4 (IQR 2-6), 36 (IQR 30-53) and 3 (IQR 2-4) points respectively. AUC for SAPS II was 0.763 (95% CI: 0.71-0.81), SOFA score was 0.735 (95% CI: 0.68-0.79) and MEWS score was 0.714 (95% CI: 0.66-0.77). The lowest ROC curve was 0.584 (95% CI: 0.52-0.64) for APACHE II. Conclusion: In conclusion, our study found that SAPS II, followed by SOFA and MEWS scores, provided better discrimination in stratifying critical illness in patients admitted to IMCU of a tertiary care hospital in Pakistan.

Predictive ability of the REMS and HOTEL scoring systems for mortality in geriatric patients with pulmonary embolism.

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

Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis.

This work aimed to investigate the adoption value of blood lactic acid (BLA) combined with the National Early Warning Score (NEWS) in the early screening of sepsis patients and assessing their severity. The data and materials utilized in this work were obtained from the electronic medical record system of 537 anonymized sepsis patients who received emergency rescue in the emergency rescue area of Liuzhou People's Hospital, Guangxi, from July 1, 2020, to December 26, 2020. Based on the 28-day outcomes of sepsis patients, the medical records were rolled into Group S (407 survival cases) and Group D (130 dead cases). Basic information such as the mode of hospital admission, initial management, use of emergency ventilator within 24 h of admission, NEWS score, arterial oxygen pressure/alveolar oxygen pressure ratio (PaO2/PAO2), alveolar-arterial oxygen difference (A-aDO2), serum creatinine (SCr), blood urea nitrogen (BUN), oxygenation index (OI), Glasgow Coma Scale (GCS), D-dimer, use of vasoactive drugs within 24 h of admission, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), quick Sequential Organ Failure Assessment (qSOFA) score, SOFA score, BLA level, NEWS with lactate (NEWS-L) score, SOFA score including lactate level (SOFA-L) score, Intensive Care Unit (ICU) length of stay, total hospital stay, ICU stay/total hospital stay, and septic shock condition were compared between groups. Logistic regression analysis was performed to assess the impact of various predictive factors on prognosis and to plot the receiver operating characteristic (ROC) curve. The results suggested marked differences between Group S and Group D in terms of mean age (t = -5.620; OR = -9.96, 95 % CI: -13.44∼-6.47; P < 0.001). Group S showed drastic differences in terms of mode of hospital admission (χ2 = 9.618, P < 0.01), method of initial management (χ2 = 51.766, P < 0.001), use of emergency ventilator within 24 h of admission (χ2 = 98.564, P < 0.001), incidence of septic shock (χ2 = 77.545, P < 0.001), use of vasoactive drugs within 24 h of admission (χ2 = 102.453, P < 0.001), heart rate (t = -4.063, P < 0.001), respiratory rate (t = -4.758, P < 0.001), oxygenation status (χ2 = 20.547, P < 0.001), NEWS score (t = -6.120, P < 0.001), PaO2/PAO2 ratio (t = 2.625, P < 0.01), A-aDO2 value (Z = -3.581, P < 0.001), OI value (Z = -3.106, P < 0.01), PLT value (Z = -2.305, P < 0.05), SCr value (Z = -3.510, P < 0.001), BUN value (Z = -3.170, P < 0.01), D-dimer (Z = -4.621, P < 0.001), CRP level (Z = -4.057, P < 0.001), PCT value (Z = -2.783, P < 0.01), IL-6 level (Z = -2.904, P < 0.001), length of hospital stay (Z = -4.138, P < 0.001), total hospital stay (Z = -8.488, P < 0.001), CCU/total hospital stay (Z = -9.118, P < 0.001), NEWS score (t = -6.120, P < 0.001), SOFA score (t = -6.961, P < 0.001), SOFA-L score (Z = -4.609, P < 0.001), NEWS-L score (Z = -5.845, P < 0.001), BLA level (Z = -6.557, P < 0.001), and GCS score (Z = 6.909, P < 0.001) when compared to Group D. The use of ventilators, septic shock, PCT, NEWS score, GCS score, SOFA score, SOFA-L score, NEWS-L score, and BLA level were identified as independent risk factors for predicting the prognosis of sepsis patients (P < 0.001). The areas under ROC curve (AUC) of blood lactic acid, PCT, NEWS, NEWS-L, GCS, SOFA, and SOFA-L were 0.695, 0.665, 0.692, 0.698, 0.477, 0.700, and 0.653, respectively. These findings indicate that the combination of BLA with NEWS (NEWS-L) score and SOFA score has certain advantages in assessing the prognosis of sepsis.

The accuracy of predicting hospital admission by emergency medical service and emergency department personnel compared to the prehospital MEWS: a prospective multicenter study.

INTRODUCTION: Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient's disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission. METHODS: A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients' need for hospital admission. RESULTS: In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p < 0.001). Specificity ranged from 56.4 to 67.0%. All healthcare providers outperformed MEWS ≥ 3 score on predicting hospital admission (sensitivity 80.0-91.9% versus 44.0%; all p < 0.001). Predictions for ward admissions specifically were significantly more accurate than MEWS (specificity 94.7-95.9% versus 60.6%, all p < 0.001). CONCLUSIONS: Healthcare providers can accurately predict the need for hospital admission, and all providers outperformed the MEWS score.

Comparison of Performance Characteristics in Early Warning Scoring Tools for Diagnosis of Intubation and Mortality Among COVID-19 Patients.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION: 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.

Diagnostic agreement between emergency medical service and emergency department physicians, a prospective multicentre study.

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

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal-external validation.

BACKGROUND: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. METHODS: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal-external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. RESULTS: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. CONCLUSION: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

Exploring the potential impact of adding upper limit single trigger MET thresholds to a paediatric early warning scoring tool at a tertiary children's hospital: a retrospective review.

Objective: This study aims to determine the impact of incorporating upper threshold vital sign triggers into the digital Children's Early Warning Tool (CEWT) on the number of medical emergency team (MET) alerts. Methods: De-identified vital set data from the Cerner Millennium Integrated Electronic Medical Records were obtained for all paediatric patients aged ≤16 years at a tertiary children's hospital in Brisbane over a 12-month period in 2022. Patients in the paediatric intensive care unit, post-anaesthetic care unit, or the emergency department were excluded as they would not trigger MET alerts in these locations. Microsoft Excel scripts were used to tabulate and graph the data to compare the number of MET alerts in the current system vs. the system with proposed upper thresholds for heart rate, respiratory rate, systolic blood pressure, and severe respiratory distress. Results: A total of 389,352 vital sets were used for analysis after exclusions. Total cumulative MET alerts increased by 229% from 1,707 to 5,623. The number of increased alerts was inversely proportional to the age group. Respiratory rate and systolic blood pressure were the vital signs most associated with increased alerts. The largest number of new alerts came from patients with lower CEWT scores, while the largest proportional increase in alerts came from those with higher CEWT scores. Conclusions: Incorporating upper threshold vital sign triggers into the digital CEWT leads to a substantial increase in MET alerts. The consequent workload is not justified, given the lack of evidence suggesting a failure of the current CEWT system in recognising deteriorating patients.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department.

BACKGROUND: This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores (EWSs) and three shock indices in early sepsis prediction in the emergency department (ED). METHODS: We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong. The primary outcome was sepsis (Sepsis-3 definition) within 48 h of ED presentation. Using c-statistics and the DeLong test, we compared 11 EWSs, including the National Early Warning Score 2 (NEWS2), Modified Early Warning Score, and Worthing Physiological Scoring System (WPS), etc., and three shock indices (the shock index [SI], modified shock index [MSI], and diastolic shock index [DSI]), with Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the primary outcome, intensive care unit admission, and mortality at different time points. RESULTS: We analyzed 601 patients, of whom 166 (27.6%) developed sepsis. NEWS2 had the highest point estimate (area under the receiver operating characteristic curve [AUROC] 0.75, 95%CI 0.70-0.79) and was significantly better than SIRS, qSOFA, other EWSs and shock indices, except WPS, at predicting the primary outcome. However, the pooled sensitivity and specificity of NEWS2 ≥ 5 for the prediction of sepsis were 0.45 (95%CI 0.37-0.52) and 0.88 (95%CI 0.85-0.91), respectively. The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point. CONCLUSION: NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.