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BACKGROUND: The early stages of the COVID-19 pandemic overwhelmed general hospitals in Spain. In response, a dedicated hospital for COVID-19 care, the Hospital de Emergencias Enfermera Isabel Zendal (HEEIZ), was established. This study aimed to compare clinical outcomes of COVID-19 patients treated at the specialized HEEIZ with those at conventional general hospitals (CGHs) in Madrid, Spain. METHODS: The study was a prospective, observational cohort study including COVID-19 patients admitted to the HEEIZ and 14 CGHs (December 2020 to August 2021). Patients were assigned based on hospital preference. Clinical data were collected and analyzed using multivariate regression to assess primary and secondary outcomes, including hospital mortality, need of invasive mechanical ventilation (IMV), and pharmacological treatments. RESULTS: The HEEIZ cohort (n = 2997) was younger and had lower Charlson comorbidity scores than the CGH cohort (n = 1526). Adjusted HEEIZ hospital mortality was not significantly higher compared with CGHs (OR: 1.274; 95% CI: 0.781-2.079; p = 0.332). CONCLUSIONS: During the study period, patients admitted to the HEEIZ showed no significant differences in clinical outcomes, compared with patients admitted at CGHs. These results might support the use of specialized centers in managing pandemic surges, allowing CGHs to handle other needs.
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Objetivo: Comparar la capacidad de tres escalas de fragilidad, Clinical Frailty Scale (CFS), Functional Index eMergency (FIM) e Identification Senior at Risk (ISAR), para predecir resultados adversos (RA) a 30 días en los pacientes mayores dados de alta desde el servicio de urgencias hospitalario (SUH). Método: Análisis secundario del registro FRAIL-Madrid que incluyó pacientes $ 75 años dados de alta de 10 SUH de Madrid durante un periodo de 3 meses entre 2018 y 2019. Se definió fragilidad como CFS $ 4, FIM $ 2 e ISAR $ 3. Las variables de resultado fueron revisita en urgencias, hospitalización, deterioro funcional, muerte y la variable compuesta por algún RA de los anteriores en los 30 días posteriores al alta del SUH. Resultados: Se incluyeron 619 pacientes, la edad media fue de 84 años (DE 7), 59,1% eran mujeres. Hubo 339 pacientes (54,8%) identificados como frágiles en el SUH según CFS $ 4, 386 (62,4%) según FIM $ 2 y 301 (48,6%) según ISAR $ 3. Hubo 226 pacientes (36,5%) que presentaron algún RA a los 30 días tras el alta (21,5% revisita, 12,6% hospitalización, 18,4% deterioro funcional y 3,6% muerte). El área bajo la curva (ABC) de la escala CFS fue de 0,66 (0,62-0,70; p = 0,022), de FIM 0,67 (0,62-0,71; p = 0,021) y de ISAR 0,64 (0,60-0,69; p = 0,023). La presencia de fragilidad fue un factor independiente de presentar algún RA a los 30 días tras el alta (CFS $ 4 ORa 3,18 [IC 95% 2,02-5,01, p < 0,001], FIM $ 2 ORa 3,49 [IC 95% 2,15-5,66, p < 0,001] e ISAR $ 3 ORa 2,46 [IC 95% 1,60-3,79, p < 0,001]). Conclusiones: Las tres escalas estudiadas CFS, FIM, ISAR son útiles y tienen una capacidad similar para identificar al paciente mayor frágil dado de alta del SUH con alto riesgo de presentar RA (muerte, deterioro funcional, hospitalización o revisita al SUH) a los 30 días. (AU)
Objective: To compare the ability of 3 frailty scales (the Clinical Frailty Scale [CFS], the Functional Index eMergency [FIM], and the Identification of Seniors at Risk [ISAR] scale) to predict adverse outcomes at 30 days in older patients discharged from hospital emergency departments (EDs). Methods: Secondary analysis of data from the FRAIL-Madrid registry of patients aged 75 years or older who were discharged from Madrid EDs over a period of 3 months in 2018 and 2019. Frailty was defined by a CFS score over 4, a FIM score over 2, or an ISAR score over 3. The outcome variables were revisits to an ED, hospitalization, functionaldecline, death, and a composite variable of finding any of the previously named variables within 30 days of discharge. Results: A total of 619 patients were studied. The mean (SD) age was 84 (7) years, and 59.1% were women. The CFS identified as frail a total of 339 patients (54.8%), the FIM 386 (62.4%), and the ISAR 301 (48.6%). An adverse outcome occurred within 30 days in 226 patients (36.5%): 21.5% revisited, 12.6% were hospitalized, 18.4% experienced functional decline, and 3.6% died. The areas under the receiver operating characteristic curves were as follows: CFS, 0.66 (95% CI, 0.62-0.70; P = .022); FIM, 0.67 (95% CI, 0.62-0.71; P = .021), and ISAR, 0.64 (95% CI, 0.60-0.69; P = .023). Adjusted odds ratios (aOR) showed that frailty was an independent risk factor for presenting anyof the named adverse outcomes: aOR for CFS >4, 3.18 (95% CI, 2.02-5.01), P < .001; aOR for FIM > 2, 3.49 (95% CI, 2.15-5.66), P < .001; and aOR for ISAR >3, 2.46 (95% CI, 1.60-3.79), P < .001. Conclusions: All 3 scales studied the CFS, the FIM and the ISAR are useful for identifying frail older patients at high risk of developing an adverse outcome (death, functional decline, hospitalization, or revisiting the ED) within 30 days after discharge. (AU)
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Humanos , Masculino , Femenino , Anciano , Anciano de 80 o más Años , Servicios Médicos de Urgencia , Fragilidad , Estudios Prospectivos , España , Alta del PacienteRESUMEN
Antecedentes y objetivo: Diagnosticar la tromboembolia pulmonar (TEP) es difícil. Existen escalas de probabilidad clínica (EPC) que ayudan. Se quiere averiguar cuál es mejor.Pacientes y métodosSe realizó un estudio unicéntrico transversal retrospectivo que evaluó la validez de 4 EPC en 200 pacientes en los que se realizó una tomografía computarizada de arterias pulmonares: criterios Pulmonary Embolism Rule out Criteria (PERC), Wells, Ginebra y YEARS. Se estimó su grado de uso, y la posible correlación entre su puntuación y el dímero D (DD).ResultadosLas 4 EPC tienen una alta sensibilidad, cercana a uno y sin diferencias entre ellas. La escala YEARS resultó ser entre 2 y 4 veces más específica que las otras 3. El grado de uso de las escalas se estimó en un 14% (IC 95%: 9,19-18,81). Se halló una débil correlación positiva entre la puntuación de las escalas Wells y Ginebra y el DD.ConclusionesLa escala YEARS se muestra mejor que las demás por su potencial mayor número de pruebas de imagen evitadas y el grado de aplicación de las EPC es mejorable. (AU)
Background and objective: Diagnosing pulmonary thromboembolism (PTE) is difficult. Clinical probability scales (CPS) can help. The aim is to find out which one is the best.Patients and methodsA retrospective cross-sectional single-center study was conducted. It evaluated four CPS (Pulmonary Embolism Rule Out Criteria [PERC], Wells, Geneva, and YEARS criteria) validity in 200 patients who underwent computerized tomography angiography of the pulmonary arteries. Their degree of use was estimated, as well as the possible correlation between them and DD (D dimer).ResultsThe four CPS have a high sensitivity, close to 1 and without differences between them. The YEARS scale is between 2 and 4 times more specific than the others. The degree of use of the scales was estimated at 14% (95% CI: 9.19-18.81). A weak positive correlation was found between the scores on the Wells and Geneva scales and the DD.ConclusionsThe YEARS scale is shown to be better than the others due to its potential greater number of imaging tests avoided and the degree of application of the CPS could be improved. (AU)
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Humanos , Angiografía por Tomografía Computarizada , Hospitales , Embolia Pulmonar/diagnóstico por imagen , Estudios Transversales , Estudios RetrospectivosRESUMEN
Objetivos. Comparar las características generales, estructurales y organizativas de los servicios de urgencias de hospitales públicos (SUHP) de la Comunidad de Madrid con los de Cataluña. Método. Estudio descriptivo tipo encuesta estructurada con 3 apartados: aspectos generales del hospital (18 preguntas), aspectos generales y estructurales de urgencias (14 preguntas), y aspectos organizativos y laborales de urgencias (30 preguntas). Los centros se agruparon según complejidad: niveles I-hospital comarcal y niveles II y III-hospital de alta tecnología o de referencia. Resultados. Se incluyeron los 26 SUHP de la Comunidad de Madrid (21 nivel II-III y 5 nivel I), y 55 de Cataluña (24 nivel II- III y 31 nivel I). En Madrid, comparada con Cataluña: los hospitales son de construcción mas reciente (p = 0,002); tienen mayor número de camas de hospitalización (p < 0,001) y de cuidados críticos (p < 0,001); están más frecuentemente vinculados a la universidad (p < 0,001) y cubren mayor población (p = 0,027). Los servicios de urgencias: tienen mayor superficie para la actividad clínica (p < 0,001) y la primera asistencia (p < 0,001); mayor número de puestos de primera asistencia (p < 0,001) y camas de observación (p = 0,001) y la mediana del número de atenciones urgentes es mayor (p < 0,001). De forma global, hay más facultativos contratados en Cataluña, pero el número de horas de médico y enfermera contratadas por centro es mayor en Madrid, donde los médicos suelen realizar su actividad exclusivamente en urgencias (92,5% frente a 56,8%; p < 0,001), muy pocos con contrato fijo indefinido (30,5% frente a 75,1%; p < 0,001) con relación médico residente/adjunto diferente en turnos de tarde, noche y días festivos en comparación con los SUHP catalanes (3:1 frente a 1:1). Conclusiones. La estructura física y funcional de los SUHP madrileños y catalanes difiere de forma significativa sin que pueda explicarse, exclusivamente, por los aspectos geográficos (AU)
Objective. To compare the general, structural, and organizational characteristics of public hospital emergency departments in the Spanish autonomous communities of Madrid and Catalonia. Methods. Descriptive survey-based study covering 3 areas of inquiry: general hospital features (18 questions), structural features of the emergency department (14 questions), and organizational and work-related policies of the emergency department (30 questions). Hospitals were grouped according to complexity: local hospitals (level 1), high-technology or referral hospitals (levels 2-3). Results. We studied 26 hospital departments in Madrid (21, levels 2-3; 5, level 1) and 55 in Catalonia (24, levels 2-3; 31, level 1). Hospitals in Madrid are in newer buildings (P=.002), have more beds on conventional wards and in critical care units (P<.001, both comparisons), are more often affiliated with a university (P<.001), and serve larger populations (P=.027). The emergency departments in Madrid have larger surface areas available for clinical care and more cubicles for preliminary evaluations and observation beds (P=.001, all comparisons). Hospitals in Madrid also attended a larger median number of emergencies (P<.001). More physicians were employed in Catalonia overall, but the numbers of physician- and nurse-hours per hospital were higher in Madrid, where it was more usual for physicians to work exclusively in the emergency department (92.5% in Madrid vs 56.8% in Catalonia, P<.001). However, fewer of the employed physicians had permanent contracts in Madrid (30.5% vs 75.1% in Catalonia, P<.001). The ratio of resident physicians to staff physicians differs between the 2 communities on afternoon/evening, night, and holiday shifts (3:1 in Madrid; 1:1 in Catalonia). Conclusions. The physical and functional structures of hospital emergency departments in the communities of Madrid and Catalonia differ significantly. The differences cannot be attributed exclusively to geographic location (AU)