A predictive model for the treatment approach to community-acquired pneumonia in patients needing ICU admission

Abstract

Objective: To create a predictive model for the treatment approach to community-acquired pneumonia (CAP) in patients needing Intensive Care Unit (ICU) admission.

Design: Multicenter prospective study.

Setting: Twenty-six Spanish ICUs.

Patients: One hundred seven patients with CAP, all of them with accurate etiological diagnosis, divided in three groups according to their etiology in typical (bacterial pneumonia), Legionella and other atypical (Mycoplasma, Chlamydia spp. and virus). For the multivariate analysis we grouped Legionella and other atypical etiologies in the same category.

Methods: We recorded 34 variables including clinical characteristics, risk factors and radiographic pattern. We used a multivariate logistic regression analysis to find out a predictive model.

Results: We have the complete data in 70 patients. Four variables: APACHE II, (categorized as a dummy variable) serum sodium and phosphorus and "length of symptoms" gave an accurate predictive model (c = 0.856). From the model we created a score that predicts typical pneumonia with a sensitivity of 90.2% and specificity 72.4%.

Conclusion: Our model is an attempt to help in the treatment approach to CAP in ICU patients based on a predictive model of basic clinical and laboratory information. Further studies, including larger numbers of patients, should validate and investigate the utility of this model in different clinical settings.