Browsing by Author "Jerez Mantilla, Dayron Alberto"
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- Usefulness of the Respiratory Status Scale as a Prognostic Tool to Identify the Need for Care in People With Acute Respiratory Infection
Institución: Universidad de Pamplona
Revista: Revista Ciencias Básicas en Salud
Autores: Jerez Mantilla, Dayron Alberto; Velásquez Daza, Juan David; Echavez Becerra, José Luis
Fecha de publicación en la Revista: 2025-03-21
Fecha de cosecha en Ciencia Nacional: 2026-02-05
Acute respiratory infections are disorders of the upper or lower respiratory tract that can cause a variety of illnesses, ranging from asymptomatic or mild infections to severe and fatal diseases. Therefore, a mechanism is required to identify the care needs to provide better care in the clinical stages that require it. Objective: To determine the utility of the outcome measure "Respiratory Status" as a prognostic tool in identifying the need for care in individuals with acute respiratory infection. Materials and methods: This was a quantitative, observational, and analytical study conducted retrospectively through the analysis of medical records from the emergency department to the end of hospitalization of patients with acute respiratory infection. Results: It was observed that the Respiratory Status outcome measure allows for the prediction of patient admission to the Intensive Care Unit (ICU) from the emergency department, with a range of 49% to 70% according to the Cox and Snell and Nagelkerke R-squared values. It was determined that patients with scores ranging from 5 to 10 predict their admission to the ICU with a P-value <0.05. No statistical significance was observed in the model for predicting admission to hospitalization and outpatient care (P-value >0.05). Conclusions: The Respiratory Status outcome measure, through its evaluated indicators, improves the identification of patients who will be admitted to the ICU and require higher levels of care from the emergency department by nursing professionals. However, a prospective study is recommended in the future to specify the indicators and avoid confounding variables such as data quality, among others.
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