Empresa Pública de Emergencias Sanitarias

Background: Artificial intelligence (AI) has been proposed as a transformative tool in suicide prevention, yet most evidence remains observational. To provide a rigorous benchmark, we systematically reviewed randomized controlled trials (RCTs) evaluating AI-based interventions targeting suicidal thoughts, behaviours, or help-seeking. Methods: Following PRISMA 2020 guidelines, MEDLINE, Web of Science, and Scopus were searched to 31 May 2025. Eligible studies were RCTs in humans that incorporated AI or machine learning for risk prediction, automated intervention, or treatment allocation. Methodological quality was assessed with the PEDro scale and certainty of evidence with GRADE. Results: From 1101 screened records, six RCTs (n = 793) met all criteria. Three studies tested machine learning risk prediction, two evaluated fully automated interventions (a transformer-based recommender and a digital nudge), and one examined AI-assisted treatment allocation. Risk-prediction models stratified short-term suicidal outcomes with accuracies of up to 0.67 and AUC values around 0.70. Digital interventions reduced counsellor response latency or increased crisis-service uptake by 23%. Algorithm-guided allocation reduced the occurrence of suicidal events when randomisation aligned with model recommendations. Methodological quality was moderate to high (median PEDro = 8/10), but GRADE certainty was low due to small samples and imprecision. Conclusions: AI can enhance discrete processes in suicide prevention, including risk stratification, help-seeking, and personalized treatment. However, the current evidence is limited, and larger multisite RCTs with longer follow-up, CONSORT-AI compliance, and equity-focused design are urgently required.

Abstract Introduction: Neurostimulation of the diaphragm through an indwelling catheter placed in the internal jugular vein and stimulating the diaphragm via the bilateral phrenic nerves can help to ameliorate these negative effects of inactivity of the diaphragm during invasive mechanical ventilation (IMV). There is considerable interest in identifying which patients are at risk for ongoing passive ventilation without diaphragm activity. The objective of this study was to use a machine learning model to develop a clinical score that predicts risk of passive ventilation at 48 hours after initiation of IMV for patients with acute hypoxemic respiratory failure (AHRF). Methods: A database of patients who underwent IMV was retrospectively queried to identify subjects with recorded occluded inspiratory airway pressure (Pocc), a measure of passive versus spontaneous breathing during IMV. Passive ventilation at 48 hours was defined as Pocc = 0 at 24 and 48 hours; patients with Pocc < 0 at 24 or 48 hours were deemed to not be breathing passively. A k-folds cross-validated best subsets supervised machine learning approach was used to identify clinical covariates best predicting passive ventilation at 48 hours. These covariates were used to create a clinical score to predict passive ventilation at 48 hours; the performance of the clinical score was assessed with a leave-one-out cross validation (LOOCV) area under the curve (AUC) approach. All analyses were performed in Rstudio Statistical Software. Results: There were 1186 patients in the database. 539 patients had Pocc value < 0 at 24 hours or 48 hours, while 549 patients had Pocc value = 0 at 24 and 48 hours. The k-folds cross-validated best subsets supervised machine learning model identified baseline values of prone positioning status at baseline, positive end-expiratory pressure (PEEP) setting, airway occlusion pressure (P0.1), and fraction of inspiratory oxygen (FiO2) as the covariates that predicted passive ventilation status at 48 hours after initiation of IMV. A clinical prediction score including these four covariates had an AUC of 0.836 for predicting the outcome of passive ventilation at 48 hours after initiation of IMV. Conclusion: In patients with AHRF undergoing IMV, a machine learning model identified baseline prone positioning, PEEP, P0.1, and FiO2 as best able to predict passive ventilation at 48 hours after initiation of IMV. A clinical prediction score using these 4 variables had AUC of 0.832 for predicting the outcome of interest. This prediction score may help identify which patients might benefit from diaphragmatic neurostimulation.

Objetivo principal: Implantar un sistema de clasificación telefónica en el Centro Coordinador de Urgencias y Emergencias (CCUE) del SUMMA112, para las enfermeras de coordinación, que permita estandarizar la respuesta a la demanda sanitaria. Metodología: En junio de 2020 la Dirección del SUMMA112 exploró la viabilidad de implantar un sistema de triaje para las enfermeras, optando por el sistema de Triaje Telefónico Mánchester (TTM). Se iniciaron reuniones con el Grupo de Triaje Mánchester español, portugués e inglés. Se realizó la formación de instructores, las adaptaciones en los procesos, se desarrollaron las herramientas informáticas y la implementación de un sistema de auditoría. Resultados: - Entre enero de 2022 a agosto de 2023 fueron recibidas un total de 238.496 llamadas gestionadas por médicos (sistema tradicional de regulación (STR) o enfermeras (TTM). - El 50,61% de los incidentes se gestionaron mediante el sistema de TTM y el 49,39% con el STR. - El 98,17% de los incidentes del TTM se agruparon en siete motivos de consulta. - Con relación a la resolución de la llamada y el método de gestión empleado, en un 62,66% de los incidentes de TTM se propuso un recurso móvil asistencial, hallándose las mayores diferencias entre métodos en el consejo sanitario y en la derivación a Atención Primaria. - De los incidentes de emergencia el 31,54% se gestionó mediante el TTM y el 37,34% mediante el STR. Conclusiones: El sistema de TTM ha permitido fortalecer la seguridad clínica y proporcionar un respaldo en la toma de decisiones de las enfermeras del CCUE del SUMMA112, fundamentado en la asignación de prioridades y no diagnósticos. El sistema propuesto es óptimo para dar una respuesta adecuada a la demanda telefónica en emergencias.

To explore the safety and effectiveness of electric cardioversion to treat atrial fibrillation in a hospital emergency department (ED). Retrospective observational study in a hospital ED. We reviewed episodes of atrial fibrillation in patients aged 18 years orolder treated with cardioversion in our ED or referred for scheduling of cardioversion. Clinical outcome measures were conversion to sinus rhythm, immediate adverse effects (hypotension, arrythmia, or bronchial aspiration), revisiting within 90 days for atrial fibrillation, and complications (stroke, major bleeding, heart failure, or death). We studied factors associated with recurrence and adverse effects according to sex. Cardioversion was used in 365 episodes (median patient age, 67 years); 38.6% were women. Cardioversion was applied in the ED in 75.1% of the episodes, and 24.9% were referred for scheduled cardioversion. Sinus rhythm was restored in 90.7% of the episodes. Emergency cardioversion was more effective than a scheduled procedure (odds ratio [OR], 4.258; 95% CI, 2.046-8.859; P < .001). No serious immediate adverse effects were reported, but 16.7% of the patients revisited for atrial fibrillation within 90 days. Factors associated with revisits were heart failure (hazard ratio [HR], 2.603; 95% CI, 1.298-5.222; P = .007), sleep apnea (HR, 2.598; 95% CI, 1.163-5.803; P = .020), and, in women, hypertension (HR, 3.706; 95% CI, 1.051-13.068; P = .042). Eleven patients developed late adverse events, including stroke (n = 2), major bleeding (n = 1), heart failure (n = 5), and death (n = 3). Cardioversion is a useful, effective, and safe treatment for atrial fibrillation in the ED, although there are frequent recurrences. Factors associated with recurrence differ according to sex.

INTRODUCCIÓN. La transfusión precoz de concentrados de hematíes (CH) podría mejorar la supervivencia en shock hemorrágico, pero se requiere más evidencia sobre su seguridad y eficacia en el ámbito extrahospitalario. OBJETIVO. Describir la experiencia inicial con la transfusión extrahospitalaria de CH en pacientes con shock hemorrágico severo secundario a traumatismo, atendidos por el primer helicóptero medicalizado andaluz con capacidad transfusional. MATERIAL Y MÉTODOS. Estudio observacional retrospectivo de pacientes transfundidos con concentrados de hematíes 0– (CH0–) por el helicóptero medicalizado 061 de Málaga (HELMA) del Centro de Emergencias Sanitarias 061 (CES-061) de Andalucía, entre marzo de 2022 y marzo de 2024. Se registraron variables demográficas, clínicas, escalas de gravedad –Índice de shock (IS), Taruma Score y Assessment of Blood Consumption (ABC)– y mortalidad global. RESULTADOS. Se registraron 13 casos, con una media de 43 años y un 92,3% varones. El mecanismo lesional predominante fueron los accidentes de tráfico (69,2%), presentando shock hemorrágico grave (IS inicial 1,56) secundario a politraumatismos con afectación toracoabdominal y traumatismo craneoencefálico (TCE). Se transfundió una mediana de 1 CH0– (RIC 1-2). Hubo una mejoría significativa del índice de shock tras la transfusión prehospitalaria (IS 1,02 al ingreso hospitalario; p = 0,0027). No se registraron reacciones adversas postransfusionales. La mortalidad global fue del 46,1%. El Trauma score corregido fue significativamente menor en pacientes fallecidos (p = 0,0143). El 100% de los CH0– no transfundidos por HELMA fueron reintroducidos en el circuito asistencial hospitalario. CONCLUSIONES. La transfusión precoz de CH0– en el ámbito extrahospitalario en pacientes con shock hemorrágico secundario a traumatismo grave es segura y potencialmente efectiva para la estabilización inicial, mejorando los parámetros fisiológicos. Se requieren estudios con mayor tamaño muestral para confirmar estos hallazgos preliminares.

Anais do VI Congresso Internacional de Saúde Única e II Simpósio Internacional Pluriprofissional de Saúde (978-85-5722-750-7) - Importância Do Enfermeiro Obstétrico Em Um Pronto Atendimento De Saúde Da Mulher