Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison.

BACKGROUND: Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support. METHODS: Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS). RESULTS: Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores. CONCLUSIONS: Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Derivación y validación de nuevos fenotipos prehospitalarios en pacientes adultos con enfermedad por COVID-19

Objective. To characterize phenotypes of prehospital patients with COVID-19 to facilitate early identification of at-risk groups. Methods. Multicenter observational noninterventional study of a retrospective cohort of 3789 patients, analyzing 52 prehospital variables. The main outcomes were 4 clusters of prehospital variables describing the phenotypes. Secondary outcomes were hospitalization, mechanical ventilation, admission to an intensive care unit, and cumulative mortality inside or outside the hospital on days 1, 2, 3, 7, 14, 21, and 28 after hospitalization and after start of prehospital care. Results. We used a principal components multiple correspondence analysis (factor analysis) followed by decomposition into 4 clusters as follows: cluster 1, 1090 patients (28.7%);cluster 2, 1420 (37.4%);cluster 3, 250 (6.6%), and cluster 4, 1029 (27.1%). Cluster 4 was comprised of the oldest patients and had the highest frequencies of residence in group facilities and low arterial oxygen saturation. This group also had the highest mortality (44.8% at 28 days). Cluster 1 was comprised of the youngest patients and had the highest frequencies of smoking, fever, and requirement for mechanical ventilation. This group had the most favorable prognosis and the lowest mortality. Conclusions. Patients with COVID-19 evaluated by emergency medical responders and transferred to hospital emergency departments can be classified into 4 phenotypes with different clinical, therapeutic, and prognostic characteristics. The phenotypes can help health care professionals to quickly assess a patient's future risk, thus informing clinical decisions. Objetivos. Desarrollar un fenotipado prehospitalario de pacientes con COVID-19 que permita una identificación temprana de los grupos de riesgo. Método. Estudio observacional de cohorte retrospectivo multicéntrico, sin intervención con 3.789 pacientes y 52 variables prehospitalarias. Las variables de resultado principal fueron las cuatro agrupaciones prehospitalarios obtenidos, #1, #2, #3 y #4. Los resultados secundarios fueron: ingreso hospitalario, ventilación mecánica, ingreso en unidad de cuidados intensivos y mortalidad acumulada a los 1, 2, 3, 7, 14, 21 y 28 días desde el ingreso hospitalario (hospitalaria y extrahospitalaria). Resultados. Por medio de una descomposición en componentes principales/correspondencia múltiple de datos mixtos (continuos y categóricos), seguido de una descomposición en agrupaciones, se obtuvo cuatro agrupaciones/fenotipos #1, #2, #3 y #4 de 1.090 (28,7%), 1.420 (37,4%), 250 (6,6%) y 1.029 (27,1%) pacientes, respectivamente. El grupo #4, compuesto por los pacientes de mayor edad, baja saturación de oxígeno e institucionalización es el que presenta la mayor mortalidad (44,8% de mortalidad a 28 días). El grupo #1, compuesto de pacientes de menor edad, con mayor porcentaje de tabaquismo, fiebre y necesidades de ventilación mecánica, es el de pronóstico más favorable con la menor tasa de mortalidad. Conclusiones. Los pacientes con COVID-19 valorados por los servicios médicos de emergencias y transferidos al servicio de urgencias hospitalario se pueden clasificar en 4 fenotipos con diferentes consideraciones clínicas, terapéuticas y de pronóstico, y permite a los profesionales sanitarios discriminar rápidamente el nivel de riesgo futuro del paciente y ayuda por lo tanto en el proceso de toma de decisiones.

One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19.

OBJECTIVE: To compare the predictive value of the quick COVID-19 Severity Index (qCSI) and the National Early Warning Score (NEWS) for 90-day mortality amongst COVID-19 patients. METHODS: Multicenter retrospective cohort study conducted in adult patients transferred by ambulance to an emergency department (ED) with suspected COVID-19 infection subsequently confirmed by a SARS-CoV-2 test (polymerase chain reaction). We collected epidemiological data, clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and use of supplemental oxygen) and hospital variables. The primary outcome was cumulative all-cause mortality during a 90-day follow-up, with mortality assessment monitoring time points at 1, 2, 7, 14, 30 and 90 days from ED attendance. Comparison of performances for 90-day mortality between both scores was carried out by univariate analysis. RESULTS: From March to November 2020, we included 2,961 SARS-CoV-2 positive patients (median age 79 years, IQR 66-88), with 49.2% females. The qCSI score provided an AUC ranging from 0.769 (1-day mortality) to 0.749 (90-day mortality), whereas AUCs for NEWS ranging from 0.825 for 1-day mortality to 0.777 for 90-day mortality. At all-time points studied, differences between both scores were statistically significant (p < .001). CONCLUSION: Patients with SARS-CoV-2 can rapidly develop bilateral pneumonias with multiorgan disease; in these cases, in which an evacuation by the EMS is required, reliable scores for an early identification of patients with risk of clinical deterioration are critical. The NEWS score provides not only better prognostic results than those offered by qCSI at all the analyzed time points, but it is also better suited for COVID-19 patients.KEY MESSAGESThis work aims to determine whether NEWS is the best score for mortality risk assessment in patients with COVID-19.AUCs for NEWS ranged from 0.825 for 1-day mortality to 0.777 for 90-day mortality and were significantly higher than those for qCSI in these same outcomes.NEWS provides a better prognostic capacity than the qCSI score and allows for long-term (90 days) mortality risk assessment of COVID-19 patients.

Usefulness of the CONUT index upon hospital admission as a potential prognostic indicator of COVID-19 health outcomes.

BACKGROUND: In-hospital mortality in patients with coronavirus disease 2019 (COVID-19) is high. Simple prognostic indices are needed to identify patients at high-risk of COVID-19 health outcomes. We aimed to determine the usefulness of the CONtrolling NUTritional status (CONUT) index as a potential prognostic indicator of mortality in COVID-19 patients upon hospital admission. METHODS: Our study design is of a retrospective observational study in a large cohort of COVID-19 patients. In addition to descriptive statistics, a Kaplan-Meier mortality analysis and a Cox regression were performed, as well as receiver operating curve (ROC). RESULTS: From February 5, 2020 to January 21, 2021, there was a total of 2969 admissions for COVID-19 at our hospital, corresponding to 2844 patients. Overall, baseline (within 4 days of admission) CONUT index could be scored for 1627 (57.2%) patients. Patients' age was 67.3 ±â€Š16.5 years and 44.9% were women. The CONUT severity distribution was: 194 (11.9%) normal (0-1); 769 (47.2%) light (2-4); 585 (35.9%) moderate (5-8); and 79 (4.9%) severe (9-12). Mortality of 30 days after admission was 3.1% in patients with normal risk CONUT, 9.0% light, 22.7% moderate, and 40.5% in those with severe CONUT (P < 0.05). An increased risk of death associated with a greater baseline CONUT stage was sustained in a multivariable Cox regression model (P < 0.05). An increasing baseline CONUT stage was associated with a longer duration of admission, a greater requirement for the use of non-invasive and invasive mechanical ventilation, and other clinical outcomes (all P < 0.05). The ROC of CONUT for mortality had an area under the curve (AUC) and 95% confidence interval of 0.711 (0.676-0746). CONCLUSION: The CONUT index upon admission is potentially a reliable and independent prognostic indicator of mortality and length of hospitalization in COVID-19 patients.

Comorbidity-adjusted NEWS predicts mortality in suspected patients with COVID-19 from nursing homes: Multicentre retrospective cohort study.

AIMS: To assess the prognostic accuracy of comorbidity-adjusted National Early Warning Score in suspected Coronavirus disease 2019 patients transferred from nursing homes by the Emergency Department. DESIGN: Multicentre retrospective cohort study. METHODS: Patients transferred by high-priority ambulances from nursing homes to Emergency Departments with suspected severe acute respiratory syndrome coronavirus 2 infection, from March 12 to July 31 2020, were considered. Included variables were: clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and supplemental oxygen use), the presence of comorbidities and confirmatory analytical diagnosis of severe acute respiratory syndrome coronavirus 2 infection. The primary outcome was a 2-day mortality rate. The discriminatory capability of the National Early Warning Score was assessed by the area under the receiver operating characteristic curve in two different cohorts, the validation and the revalidation, which were randomly selected from the main cohort. RESULTS: A total of 337 nursing homes, 10 advanced life support units, 51 basic life support units and 8 hospitals in Spain entailing 1,324 patients (median age 87 years) was involved in this study. Two-day mortality was 11.5% (152 cases), with a positivity rate of severe acute respiratory syndrome coronavirus 2 of 51.2%, 77.7% of hospitalization from whom 1% was of intensive care unit admission. The National Early Warning Score results for the revalidation cohort presented an AUC of 0.771, and of 0.885, 0.778 and 0.730 for the low-, medium- and high-level groups of comorbidities. CONCLUSION: The comorbidity-adjusted National Early Warning Score provides a good short-term prognostic criterion, information that can help in the decision-making process to guide the best strategy for each older adult, under the current pandemic. IMPACT: What problem did the study address? Under the current coronavirus disease 2019 pandemic, targeting older adults at high risk of deterioration in nursing homes remains challenging. What were the main findings? Comorbidity-adjusted National Early Warning Score helps to forecast the risk of clinical deterioration more accurately. Where and on whom will the research have impact? A high NEWS, with a low level of comorbidity is associated with optimal predictive performance, making these older adults likely to benefit from continued follow up and potentially hospital referral under the current coronavirus disease 2019 pandemic.

Predicting Health Care Workers' Tolerance of Personal Protective Equipment: An Observational Simulation Study.

BACKGROUND: More recently, due to the coronavirus disease 2019 pandemic, health care workers have to deal with clinical situations wearing personal protective equipment (PPE); however, there is a question of whether everybody will tolerate PPE equally. The main objective of this study was to develop a risk model to predict whether health care workers will tolerate wearing PPE, C category, 4B/5B/6B type, during a 30-minute simulation. METHODS: A nonexperimental simulation study was conducted at the Advanced Simulation Center, Faculty of Medicine, Valladolid University (Spain) from April 3rd to 28th, 2017. Health care students and professionals were equipped with PPE and performed a 30-minute simulation. Anthropometric, physiological, and analytical variables and anxiety levels were measured before and after simulation. A scoring model was constructed. RESULTS: Ninety-six volunteers participated in the study. Half the sample presented metabolic fatigue in the 20 minutes after finishing the simulation. The predictive model included female sex, height, muscle and bone mass, and moderate level of physical activity. The validity of the main model using all the variables presented an area under the curve of 0.86 (95% confidence interval: 0.786-0.935), and the validity of the model had an area under the curve of 0.725 (95% confidence interval: 0.559-0.89). CONCLUSIONS: Decision-making in biohazard incidents is a challenge for emergency team leaders. Knowledge of health care workers' physiological tolerance of PPE could improve their performance.