P-639. Predictive Model to Identify Adult Patients with RSV at Risk of Developing Severe Acute Respiratory Infection
Jose A Castro Cordero, Juan Villalobos Vindas

TL;DR
This study develops a clinical scoring system to predict which adult RSV patients are at risk of severe respiratory infection, based on factors like age and symptoms.
Contribution
A novel clinical prediction score was developed and validated to identify RSV patients at risk of severe acute respiratory infection.
Findings
Six independent risk factors for severe acute respiratory infection (SARI) in RSV patients were identified.
The clinical scoring system demonstrated excellent discriminative capacity with an AUC of 0.82.
The model was internally validated and stratified patients into four risk categories.
Abstract
Respiratory Syncytial Virus (RSV) infection in adults can evolve into severe conditions. Identifying risk factors for Severe Acute Respiratory Infection (SARI) would optimize clinical management. To develop and validate a predictive model to identify adult patients with RSV at risk of developing SARI.Independent Risk FactorsIndependent Risk Factors for SARI in Adult RSV PatientsClinical Prediction ScoreClinical Prediction Score for SARI Risk in Adult RSV Patients Independent Risk Factors Independent Risk Factors for SARI in Adult RSV Patients Clinical Prediction Score Clinical Prediction Score for SARI Risk in Adult RSV Patients Analysis of 187 adults with PCR-confirmed RSV (Mexico Hospital, 2022-2024). Using multivariate logistic regression, independent factors associated with SARI were identified. A clinical scoring system was developed, and its diagnostic performance was…
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Taxonomy
TopicsRespiratory viral infections research · COVID-19 Clinical Research Studies · Delphi Technique in Research
