Francisco Martín-Rodríguez1, Ancor Sanz-García2, Lorena Ortega Moreno3, Carlos Del Pozo Vegas4, Miguel A Castro-Villamor5, José L Martín-Conty6, Raúl López-Izquierdo7, Guillermo Ortega Rabbione2. 1. Centro de Simulación Clínica Avanzada, Facultad de Medicina, Universidad de Valladolid, Valladolid, España. Unidad Móvil de Emergencias, Gerencia de Emergencias Sanitarias de Castilla y León (SACYL), Valladolid, España. 2. Unidad de Análisis de Datos (UAD) del Instituto de Investigación Sanitaria del Hospital de la Princesa (IIS-IP), Madrid, España. 3. Unidad de Enfermedad Inflamatoria Intestinal del Instituto de Investigación Sanitaria del Hospital de la Princesa (IIS-IP), Madrid, España. Departamento de Medicina, Universidad Autónoma de Madrid, España. 4. Servicio de Urgencias, Hospital Clínico Universitario (SACYL), Valladolid, España. 5. Centro de Simulación Clínica Avanzada, Facultad de Medicina, Universidad de Valladolid, Valladolid, España. 6. Facultad de Ciencias de la Salud. Universidad de Castilla la Mancha, España. 7. Centro de Simulación Clínica Avanzada, Facultad de Medicina, Universidad de Valladolid, Valladolid, España. Servicio de Urgencias, Hospital Universitario Río Hortega (SACYL), Valladolid, España.
Abstract
OBJECTIVES: To develop and validate a prehospital risk model to predict early in-hospital mortality (#48 hours) in patients aged 65 years or older. MATERIAL AND METHODS: Prospective multicenter observational study in a cohort of patients aged 65 years or older attended by advanced life support emergency services and transferred to 5 Spanish hospitals between April 2018 and July 2019. We collected demographic, clinical and laboratory variables. A risk score was constructed and validated based on the analysis of prehospital variables associated with death within 48 hours. Predictors were selected by logistic regression. RESULTS: A total of 1759 patients were recruited. The median age was 79 years (interquartile range, 72-85 years), and 766 (43.5%) were women. One hundred eight patients (6.1%) died within 48 hours. Predictors in the Prehospital Older Adults Warning Scale (POAWS) were age, systolic blood pressure, temperature, the ratio of oxygen saturation to the fraction of inspired oxygen, score on the Glasgow coma scale, and lactic acid concentration in venous blood. The area under the receiver operating characteristic curve of the model to predict early mortality was 0.853 (95% CI, 0.80-0.91; P<.001). Mortality in patients at high risk (POAWS score, >7) was 69%. CONCLUSION: The prehospital POAWS score can be used to stratify risk for death within 48 hours in patients aged 65 years or older.
OBJECTIVES: To develop and validate a prehospital risk model to predict early in-hospital mortality (#48 hours) in patients aged 65 years or older. MATERIAL AND METHODS: Prospective multicenter observational study in a cohort of patients aged 65 years or older attended by advanced life support emergency services and transferred to 5 Spanish hospitals between April 2018 and July 2019. We collected demographic, clinical and laboratory variables. A risk score was constructed and validated based on the analysis of prehospital variables associated with death within 48 hours. Predictors were selected by logistic regression. RESULTS: A total of 1759 patients were recruited. The median age was 79 years (interquartile range, 72-85 years), and 766 (43.5%) were women. One hundred eight patients (6.1%) died within 48 hours. Predictors in the Prehospital Older Adults Warning Scale (POAWS) were age, systolic blood pressure, temperature, the ratio of oxygen saturation to the fraction of inspired oxygen, score on the Glasgow coma scale, and lactic acid concentration in venous blood. The area under the receiver operating characteristic curve of the model to predict early mortality was 0.853 (95% CI, 0.80-0.91; P<.001). Mortality in patients at high risk (POAWS score, >7) was 69%. CONCLUSION: The prehospital POAWS score can be used to stratify risk for death within 48 hours in patients aged 65 years or older.
Entities:
Keywords:
Aged; Anciano; Atención prehospitalaria; Biomarcador; Biomarkers; Early death; Escalas de alerta temprana pronóstico; Mortalidad precoz; Prehospital care; Risk scores
Authors: Francisco Martín-Rodríguez; Ancor Sanz-García; Laura Melero Guijarro; Guillermo J Ortega; Marta Gómez-Escolar Pérez; Miguel A Castro Villamor; Julio C Santos Pastor; Juan F Delgado Benito; Raúl López-Izquierdo Journal: J Adv Nurs Date: 2021-09-14 Impact factor: 3.057