Community-acquired pneumonia: development of a bedside predictive model and scoring system to identify the aetiology

Although initial presentation has been commonly used to select empirical th erapy in patients with community-acquired pneumonia (CAP), few studies have provided a quantitative estimation of its value. The objective of this stu dy was to analyse whether a combination of basic clinical and laboratory in formation performed at bedside can accurately predict the aetiology of pneu monia. A prospective study was developed among patients admitted to the Emergency Department University Hospital Arnau de Vilanova, Lleida, Spain, with CAP. Informed consent was obtained from patients in the study. At entry, basic c linical (age, comorbidity, symptoms and physical findings) and laboratory ( white blood cell count) information commonly used by clinicians in the mana gement of respiratory infections, was recorded. According to microbiologica l results, patients were assigned to the following categories: bacterial (S treptococcus pneumoniae and other pyogenic bacteria), virus-like (Mycoplasm a pneumoniae, Chlamydia spp and virus) and unknown pneumonia. A scoring sys tem to identify the aetiology was derived from the odds ratio (OR) assigned to independent variables, adjusted by a logistic regression model. The acc uracy of the prediction rule was tested by using receiver operating charact eristic curves. One hundred and three consecutive patients were classified as having virus- like (48), bacterial (37) and unknown (18) pneumonia, respectively. Indepen dent predictors related to bacterial pneumonia were an acute onset of sympt oms (OR 31; 95% Cl, 6-150), age greater than 65 or comorbidity (OR 6.9; 95% Cl, 2-23), and leukocytosis or leukopenia (OR 2; 95% Cl, 0.6-7). The sensi tivity and specificity of the scoring system to identify patients with bact erial pneumonia were 89% and 94%, respectively. The prediction rule develop ed from these three variables classified the aetiology of pneumonia with a ROC curve area of 0.84. Proper use of basic clinical and laboratory information is useful to identi fy the aetiology of CAP. The prediction rule may help clinicians to choose initial antibiotic therapy.