Long-term risk of death in patients with infection attended by prehospital emergency services

To develop and validate a risk model for 1-year mortality based on variables available from early prehospital emergency attendance of patients with infection. Prospective, observational, noninterventional multicenter study in adults with suspected infection transferred to 4 Spanish hospitals by adva...

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Published inEmergencias : revista de la Sociedad Espanola de Medicina de Emergencias Vol. 36; no. 2; p. 88
Main Authors Melero Guijarro, Laura, Martín-Rodríguez, Francisco, Álvarez Manzanares, Jesús, Del Pozo Vegas, Carlos, Sanz García, Ancor, Castro Villamor, Miguel Ángel, López-Izquierdo, Raúl
Format Journal Article
LanguageEnglish
Spanish
Published Spain 01.04.2024
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Summary:To develop and validate a risk model for 1-year mortality based on variables available from early prehospital emergency attendance of patients with infection. Prospective, observational, noninterventional multicenter study in adults with suspected infection transferred to 4 Spanish hospitals by advanced life-support ambulances from June 1, 2020, through June 30, 2022. We collected demographic, physiological, clinical, and analytical data. Cox regression analysis was used to develop and validate a risk model for 1-year mortality. Four hundred ten patients were enrolled (development cohort, 287; validation cohort, 123). Cumulative mortality was 49% overall. Sepsis (infection plus a Sepsis-related Organ Failure Assessment score of 2 or higher) was diagnosed in 29.2% of survivors vs 56.7% of nonsurvivors. The risk model achieved an area under the receiver operating characteristic curve of 0.89 for 1-year mortality. The following predictors were included in the model: age; institutionalization; age-adjusted Charlson comorbidity index; PaCO2; potassium, lactate, urea nitrogen, and creatinine levels; fraction of inspired oxygen; and diagnosed sepsis. The model showed excellent ability to predict 1-year mortality based on epidemiological, analytical, and clinical variables, identifying patients at high risk of death soon after their first contact with the health care system.
ISSN:2386-5857
DOI:10.55633/s3me/023.2023