A New Method to Evaluate Lower Esophageal Distension Capacity in Eosinophilic Esophagitis by Using Functional Lumen Imaging Probe (EndoFLIP™).

Endoluminal functional lumen impedance planimetry (EndoFLIPTM) has become the gold standard to evaluate esophageal distensibility, although the study itself and its analysis present challenges. We propose here a new method to assess lower esophageal distension capacity that overcomes several limitations of prior approaches, including incomplete and corrupted EndoFLIPTM recordings. Esophageal distension capacity was evaluated with a 16-channel EndoFLIPTM in 10 controls and 14 patients with eosinophilic esophagitis (EoE). Controls were evaluated once. EoE patients were evaluated at baseline and after at least six weeks of treatment with orodispersible budesonide tablets, 1 mg bd. Balloon volumes were increased by 5 mL stepwise, either reaching a maximum volume of 60 mL or a maximum balloon pressure of 60 mmHg. Recordings were analyzed with a homemade R script. The mean esophageal diameter at 60 mL, D (60 mL), was calculated or extrapolated depending on whether the 60 mL volume was reached. By fitting a Michaelis-Menten curve across all measured diameters throughout all constant volume steps, the mean D (60 mL) was estimated. For control subjects, the mean ± SD value of D (60 mL) was 17.08 ± 1.69 mm, and for EoE patients at baseline, D (60 mL) was 14.51 ± 2.68 mm. After six weeks of treatment of EoE patients, D (60 mL) significantly increased to 16.22 ± 1.86 mm (paired Wilcoxon signed test: p = 0.0052), although the values for control subjects were not reached. The estimated mean esophageal diameter at 60 mL is a good proxy for esophageal distension capacity, which correlates with clinical outcomes in EoE. The method presented in this study overcomes difficulties encountered during the standard measurement protocol, allowing the analysis of recordings from incomplete and corrupted registries.

Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013-2022: Data from the European Registry on H. pylori Management (Hp-EuReg).

The segmentation of patients into homogeneous groups could help to improve eradication therapy effectiveness. Our aim was to determine the most important treatment strategies used in Europe, to evaluate first-line treatment effectiveness according to year and country. Data collection: All first-line empirical treatments registered at AEGREDCap in the European Registry on Helicobacter pylori management (Hp-EuReg) from June 2013 to November 2022. A Boruta method determined the "most important" variables related to treatment effectiveness. Data clustering was performed through multi-correspondence analysis of the resulting six most important variables for every year in the 2013-2022 period. Based on 35,852 patients, the average overall treatment effectiveness increased from 87% in 2013 to 93% in 2022. The lowest effectiveness (80%) was obtained in 2016 in cluster #3 encompassing Slovenia, Lithuania, Latvia, and Russia, treated with 7-day triple therapy with amoxicillin-clarithromycin (92% of cases). The highest effectiveness (95%) was achieved in 2022, mostly in Spain (81%), with the bismuth-quadruple therapy, including the single-capsule (64%) and the concomitant treatment with clarithromycin-amoxicillin-metronidazole/tinidazole (34%) with 10 (69%) and 14 (32%) days. Cluster analysis allowed for the identification of patients in homogeneous treatment groups assessing the effectiveness of different first-line treatments depending on therapy scheme, adherence, country, and prescription year.

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.

Tracking the National Early Warning Score 2 from Prehospital Care to the Emergency Department: A Prospective, Ambulance-Based, Observational Study.

Aim of the study: To assess the prognostic ability of the National Early Warning Score 2 (NEWS2) at three time points of care -at the emergency scene (NEWS2-1), just before starting the transfer by ambulance to the hospital (NEWS2- 2), and at the hospital triage box (NEWS2-3)- to estimate in-hospital mortality after two days since the index event.Methods: Prospective, multicenter, ambulance-based, cohort ongoing study in adults (>18 years) consecutively attended by advanced life support (ALS) and evacuated with high-priority to the emergency departments (ED) between October 2018 and May 2021. Vital sign measures were used to calculate the NEWS2 score at each time point, then this score was entered in a logistic regression model as the single predictor. Two outcomes were considered: first, all-cause mortality of the patients within 2 days of presentation to EMS, and second, unplanned ICU admission. The calibration and scores comparison was performed by representing the predicted vs the observed risk curves according to NEWS score value.Results: 4943 patients were enrolled. Median age was 69 years (interquartile range 53- 81). The NEWS2-3 presented the better performance for all-cause two-day in-hospital mortality with an AUC of 0.941 (95% CI: 0.917-0.964), showing statistical differences with both the NEWS2-1 (0.872 (95% CI: 0.833-0.911); p < 0.003) and with the NEWS2- 2 (0.895 (95% CI: 0.866-0.925; p < 0.05). The calibration and scores comparison results showed that the NEWS2-3 was the best predictive score followed by the NEWS2-2 and the NEWS2-1, respectively.Conclusions: The NEWS2 has an excellent predictive performance. The score showed a very consistent response over time with the difference between "at the emergency scene" and "pre-evacuation" presenting the sharpest change with decreased threshold values, thus displaying a drop in the risk of acute clinical impairment.

Development of a prehospital lactic acidosis score for early-mortality. A prospective, multicenter, ambulance-based, cohort study.

BACKGROUND: Lactic acidosis is a clinical status related to clinical worsening. Actually, higher levels of lactate is a well-established trigger of emergency situations. The aim of this work is to build-up a prehospital early warning score to predict 2-day mortality and intensive care unit (ICU) admission, constructed with other components of the lactic acidosis besides the lactate. METHODS: Prospective, multicenter, observational, derivation-validation cohort study of adults evacuated by ambulance and admitted to emergency department with acute diseases, between January 1st, 2020 and December 31st, 2021. Including six advanced life support, thirty-eight basic life support units, referring to four hospitals (Spain). The primary and secondary outcome of the study were 2-day all-cause mortality and ICU-admission. The prehospital lactic acidosis (PLA) score was derived from the analysis of prehospital blood parameters associated with the outcome using a logistic regression. The calibration, clinical utility, and discrimination of PLA were determined and compared to the performance of each component of the score alone. RESULTS: A total of 3334 patients were enrolled. The final PLA score included: lactate, pCO2, and pH. For 2-day mortality, the PLA showed an AUC of 0.941 (95%CI: 0.914-0.967), a better performance in calibration, and a higher net benefit as compared to the other score components alone. For the ICU admission, the PLA only showed a better performance for AUC: 0.75 (95%CI: 0.706-0.794). CONCLUSIONS: Our results showed that PLA predicts 2-day mortality better than other lactic acidosis components alone. Including PLA score in prehospital setting could improve emergency services decision-making.

Derivation and validation of new prehospital phenotypes for adults with COVID-19. / Derivación y validación de nuevos fenotipos prehospitalarios en pacientes adultos con enfermedad por COVID-19.

OBJECTIVES: To characterize phenotypes of prehospital patients with COVID-19 to facilitate early identification of at-risk groups. MATERIAL AND 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. CONCLUSION: 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.

OBJETIVO: Desarrollar un fenotipado prehospitalario de pacientes con COVID-19 que permita una identificación temprana de los grupos de riesgo. METODO: 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.

Derivación y validación de nuevos fenotipos prehospitalarios en pacientes adultos con enfermedad por COVID-19 / Derivation and validation of new prehospital phenotypes for adults with COVID-19

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. (AU)

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. (AU)

Role of prehospital point-of-care N-terminal pro-brain natriuretic peptide in acute life-threatening cardiovascular disease.

INTRODUCTION: The evidence about the use of natriuretic peptides (NP) to predict mortality in the pre-hospital setting is limited. The main objective of this study is to assess the ability of point-of-care testing (POCT) N-terminal portion of B-type natriuretic peptide (NT-proBNP) to predict 2-day in-hospital mortality of acute cardiovascular diseases (ACVD). METHODS: We conducted a multicentric, prospective, observational study in adults with ACVD transferred by ambulance to emergency departments (ED). The primary outcome was 2-day in-hospital mortality. The discrimination capacity of the NT-proBNP was performed through a prediction model trained using a derivation cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic on a validation cohort. RESULTS: A total of 1006 patients were recruited. The median age was 75 (IQR 63-84) years and 421 (41.85%) were females. The 2-day in-hospital mortality was 5.8% (58 cases). The predictive validity of NT-proBNP, for 2-day mortality reached the following AUC: 0.823 (95%CI: 0.758-0.889, p < 0.001), and the optimal specificity and sensitivity were 73.1 and 82.7. Predictive power of NT-proBNP obtained an AUC 0.549 (95%CI: 0.432-0.865, p 0.215) for acute heart failure, AUC 0.893 (95%CI: 0.617-0.97, p < 0.001) for ischemic heart disease, AUC 0.714 (95%CI: 0.55-0.87, p = 0.0069) for arrhythmia and AUC 0.927 (95%CI: 0.877-0.978, p < 0,001) for syncope. CONCLUSION: POCT NT-proBNP has proven to be a strong predictor of early mortality in ACVD, showing an excellent predictive capacity in cases of syncope. However, this biomarker does not appear to be useful for predicting outcome in patients with acute heart failure.

Combination of Prehospital NT-proBNP with qSOFA and NEWS to Predict Sepsis and Sepsis-Related Mortality.

BACKGROUND: The aim of this study was to assess the role of prehospital point-of-care N-terminal probrain natriuretic peptide to predict sepsis, septic shock, or in-hospital sepsis-related mortality. METHODS: A prospective, emergency medical service-delivered, prognostic, cohort study of adults evacuated by ambulance and admitted to emergency department between January 2020 and May 2021. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve of the receiver operating characteristic on the validation cohort. RESULTS: A total of 1,360 patients were enrolled with medical disease in the study. The occurrence of sepsis, septic shock, and in-hospital sepsis-related mortality was 6.4% (67 cases), 4.2% (44 cases), and 6.1% (64 cases). Prehospital National Early Warning Score 2 had superior predictive validity than quick Sequential Organ Failure Assessment and N-terminal probrain natriuretic peptide for detecting sepsis and septic shock, but N-terminal probrain natriuretic peptide outperformed both scores in in-hospital sepsis-related mortality estimation. Application of N-terminal probrain natriuretic peptide to subgroups of the other two scores improved the identification of sepsis, septic shock, and sepsis-related mortality in the group of patients with low-risk scoring. CONCLUSIONS: The incorporation of N-terminal probrain natriuretic peptide in prehospital care combined with already existing scores could improve the identification of sepsis, septic shock, and sepsis-related mortality.

Spectral and network characterization of focal seizure types and phases.

BACKGROUND AND OBJECTIVE: Currently, epileptic seizure characterization relies on several clinical features that allow their classification into different types. The present work aims to characterize both seizure types and phases based exclusively on electrophysiological characteristics. METHODS: Based on the analysis of intracranial EEG recordings of 129 seizures from 22 patients obtained from the European Epilepsy Database, network and spectral measures were calculated in five-second temporal windows. Statistically significant differences between each window of the seizure phases (preictal, ictal, and postictal) and the interictal phase were used to identify/classify seizure types and their phases. A support vector machine (SVM) working on a multidimensional feature space of network and spectral measures was implemented for the classification of each seizure type; a traditional statistical approach was also conducted to highlight the underlying patterns to each seizure type or phase. RESULTS: The percentage of correct classification of seizure types, corrected by chance, provided by the SVM exceeded 70%, considering all measures and the entire seizure (preictal + ictal + postictal). This percentage increased to more than 80% when all the measures during the ictal period for the depth electrodes or during the postictal for subdural electrodes were considered. Regarding the statistical approach, several measures presented a monotonic ascending and descending behavior with respect to seizure severity; these changes were observed during the ictal and postictal periods. Some measures were specific of each seizure type. CONCLUSIONS: Our results provide a new framework to seizure characterization and reveal the possibility of an exclusively intracranial EEG-based classification. This could be used to build an automatic seizure classification system and provides new evidence of the network-related physiopathology of epilepsies. Thus, the novelty of this work is the possibility of differentiating seizure types based exclusively on the EEG recordings, providing evidence of the underlying patterns or characteristics to each seizure type and/or phase that would allow their optimal classification.

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.

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.

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.

Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72-0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.

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.

Time for a prehospital-modified sequential organ failure assessment score: An ambulance-Based cohort study.

BACKGROUND: To adapt the Sequential Organ Failure Assessment (SOFA) score to fit the prehospital care needs; to do that, the SOFA was modified by replacing platelets and bilirubin, by lactate, and tested this modified SOFA (mSOFA) score in its prognostic capacity to assess the mortality-risk at 2 days since the first Emergency Medical Service (EMS) contact. METHODS: Prospective, multicentric, EMS-delivery, ambulance-based, pragmatic cohort study of adults with acute diseases, referred to two tertiary care hospitals (Spain), between January 1st and December 31st, 2020. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) on the validation cohort. RESULTS: A total of 1114 participants comprised two separated cohorts recruited from 15 ambulance stations. The 2-day mortality rate (from any cause) was 5.9% (66 cases). The predictive validity of the mSOFA score was assessed by the calculation of the AUC of ROC in the validation cohort, resulting in an AUC of 0.946 (95% CI, 0.913-0.978, p < .001), with a positive likelihood ratio was 23.3 (95% CI, 0.32-46.2). CONCLUSIONS: Scoring systems are now a reality in prehospital care, and the mSOFA score assesses multiorgan dysfunction in a simple and agile manner either bedside or en route. Patients with acute disease and an mSOFA score greater than 6 points transferred with high priority by EMS represent a high early mortality group. TRIAL REGISTRATION: ISRCTN48326533, Registered Octuber 312,019, Prospectively registered (doi:https://doi.org/10.1186/ISRCTN48326533).

Association of Prehospital Oxygen Saturation to Inspired Oxygen Ratio With 1-, 2-, and 7-Day Mortality.

Importance: The early identification of patients at high risk of clinical deterioration represents one of the greatest challenges for emergency medical services (EMS). Objective: To assess whether use of the ratio of prehospital oxygen saturation measured by pulse oximetry (Spo2) to fraction of inspired oxygen (Fio2) measured during initial contact by EMS with the patient (ie, the first Spo2 to Fio2 ratio) and 5 minutes before the patient's arrival at the hospital (ie, the second Spo2 to Fio2 ratio) can predict the risk of early in-hospital deterioration. Design, Setting, and Participants: A prospective, derivation-validation prognostic cohort study of 3606 adults with acute diseases referred to 5 tertiary care hospitals in Spain was conducted between October 26, 2018, and June 30, 2020. Eligible patients were recruited from among all telephone requests for EMS assistance for adults who were later evacuated with priority in advanced life support units to the referral hospitals during the study period. Main Outcomes and Measures: The primary outcome was hospital mortality from any cause within the first, second, third, or seventh day after EMS transport to the hospital. The main measure was the Spo2 to Fio2 ratio. Results: A total of 3606 participants comprised 2 separate cohorts: the derivation cohort (3081 patients) and the validation cohort (525 patients). The median age was 69 years (interquartile range, 54-81 years), and 2122 patients (58.8%) were men. The overall mortality rate of the patients in the study cohort ranged from 3.6% for 1-day mortality (131 patients) to 7.1% for 7-day mortality (256 patients). The best model performance was for 2-day mortality with the second Spo2 to Fio2 ratio with an area under the curve of 0.890 (95% CI, 0.829-0.950; P < .001), although the other outcomes also presented good results. In addition, a risk-stratification model was generated. The optimal cutoff resulted in the following ranges of Spo2 to Fio2 ratios: 50 to 100 for high risk of mortality, 101 to 426 for intermediate risk, and 427 to 476 for low risk. Conclusions and Relevance: This study suggests that use of the prehospital Spo2 to Fio2 ratio was associated with improved management of patients with acute disease because it accurately predicts short-term mortality.