1. In-hospital mortality risk factors in community acquired pneumonia: evaluation of immunocompetent adult patients without comorbidities.
- Author
-
Vicco MH, Ferini F, Rodeles L, Scholtus P, Long AK, and Musacchio HM
- Subjects
- Adult, Aged, Aged, 80 and over, Brazil epidemiology, Community-Acquired Infections mortality, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, ROC Curve, Random Allocation, Risk Assessment, Severity of Illness Index, Hospital Mortality, Immunocompetence, Pneumonia mortality
- Abstract
Objective: several scores were developed in order to improve the determination of community acquired pneumonia (CAP) severity and its management, mainly CURB-65 and SACP score. However, none of them were evaluated for risk assessment of in-hospital mortality, particularly in individuals who were non-immunosuppressed and/or without any comorbidity. In this regard, the present study was carried out., Methods: we performed a cross-sectional study in 272 immunocompetent patients without comorbidities and with a diagnosis of CAP. Performance of CURB- 65 and SCAP scores in predicting in-hospital mortality was evaluated. Also, variables related to death were assessed. Furthermore, in order to design a model of in-hospital mortality prediction, sampled individuals were randomly divided in two groups. The association of the variables with mortality was weighed and, by multiple binary regression, a model was constructed in one of the subgroups. Then, it was validated in the other subgroup., Results: both scores yielded a fair strength of agreement, and CURB-65 showed a better performance in predicting in-hospital mortality. In our casuistry, age, white blood cell counts, serum urea and diastolic blood pressure were related to death. The model constructed with these variables showed a good performance in predicting in-hospital mortality; moreover, only one patient with fatal outcome was not correctly classified in the group where the model was constructed and in the group where it was validated., Conclusion: our findings suggest that a simple model that uses only 4 variables, which are easily accessible and interpretable, can identify seriously ill patients with CAP.
- Published
- 2015
- Full Text
- View/download PDF