Can data from an electronic medical record identify which patients with pneumonia have Pneumocystis carinii infection?

Journal Name: 
Int J Med Inf
Authors: 
Diero,L.,Stiffler,T.,Einterz,R.M.,Tierney,W.M.
Abstract: 
BACKGROUND: Pneumocystis carinii is the leading opportunistic pulmonary infection in HIV-infected patients. Invasive diagnostic procedures might be avoided if available electronic data can accurately identify patients with Pneumocystis pneumonia (PCP). METHODS: We extracted data from electronic hospital records, emergency department records, and a pathology database for 299 HIV-infected patients with pneumonia who underwent bronchoscopy. We identified independent indicators of confirmed PCP using logistic regression analysis on a random half of the patients and validated the predictive power of the resulting model on the other half. RESULTS: Bronchoscopy confirmed pneumocystis carinii in 111 patients (37%). Five of the seven significant independent predictors of PCP came from patients' electronic medical records: infiltrate on chest radiograph, male gender, lower red cell distribution width, lower serum creatinine, and a prior positive HIV test. The other two (duration of illness and presence of dyspnea) came from the emergency department record. A simple index found 43% of patients at low risk (18% with pneumocystis), 37% at moderate risk (36% with pneumocystis), and 20% at high risk (74% with pneumocystis). CONCLUSIONS: Data from electronic medical records can help quantify the risk of PCP among HIV-infected patients. However, the model failed to identify 18% of patients with PCP in the low risk group, and empiric therapy would erroneously treat 26% of patients classified as high risk. Bronchoscopy is needed to accurately diagnose PCP among HIV-infected patients with pneumonia. However, if bronchoscopy is not available, the model can help with initial decisions about antibiotic therapy.
2004
Volume: 
73
Pages: 
743-750
Keywords: 
analysis, Bronchoscopy, decision, diagnostic, Dyspnea, electronic, Emergencies, hospital, Hospital Records, Male, medical, Medical Records, Methods, pathology, patient, Patients, Record, Records, regression, Regression Analysis, ResNet, Risk, therapy