CURB-65 and Long-Term Mortality of Community-Acquired Pneumonia: A Retrospective Study on Hospitalized Patients.

Background Community-acquired pneumonia remains a significant factor in global mortality. Several clinical scoring models are used for predicting pneumonia severity and mortality, aiding in the clinical decision relative to the therapeutic approach, including the CURB-65 score. However, currently, no models exist to identify high-risk patients relative to long-term prognosis when recent evidence reveals a significantly higher mortality rate in the first year after community-acquired pneumonia (CAP) hospitalization. Purpose of the study The purpose of this study is to evaluate the application of the CURB-65 scoring model in our population and examine its potential to predict prognosis and subsequent mortality 6 months after hospitalization. Other potential factors influencing mortality during and after hospitalization were characterized: patient demographics, nosocomial infections, readmissions, and identified pathogens. Study design We conducted a retrospective observational study, enrolling 130 patients admitted with a diagnosis of CAP in the department of internal medicine of Centro Hospitalar Universitário Cova da Beira between January and December of 2018. Consultation of electronic medical records was used to calculate the CURB-65 score on admission at the first hospitalization by CAP, categorizing patients into five risk groups. Mortality and readmission were evaluated after 30, 90, and 180 days. Key results High-risk patients (CURB>2) accounted for 96.9% of our study population. Inpatient mortality of 13%, increasing to 21.5% after six months, was similar to previous studies and was correlated to the CURB-65 score on admission. A microbiologic agent was identified in 37% of cases, with 53% isolates of Streptococcus (S.) pneumoniae. Conclusions Identifying high-risk patients is important for more individualized healthcare and management. The CURB-65 score, only validated for a short-term (30 days) prediction, demonstrates a potential to also predict mortality and rehospitalization in the six-month period after hospitalization, as supported by our findings and previous studies.

Eosinopenia as predictor of infection in patients admitted to an internal medicine ward: a cross-sectional study.

BACKGROUND: The identification of infection in an internal medicine ward is crucial but not always straightforward. Eosinopenia has been proposed as a marker of infection, but specific cutoffs for prediction are not established yet. We aim to assess whether there is difference in eosinophil count between infected and noninfected patients and, if so, the best cutoffs to differentiate them. METHODS: Cross-sectional, observational study with analysis of all patients admitted to an Internal Medicine Department during 2 consecutive months. Clinical, laboratory and imaging data were analyzed. Infection at hospital admission was defined in the presence of either a microbiological isolation or suggestive clinical, laboratory, and/or imaging findings. Use of antibiotics in the 8 days before hospital admission, presence of immunosuppression, hematologic neoplasms, parasite, or fungal infections were exclusion criteria. In case of multiple hospital admissions, only the first admission was considered.Sensitivity and specificity values for eosinophils, leukocytes, neutrophils, and C-reactive protein were determined by receiver operating characteristic curve. Statistical analysis was performed with IBM SPSS Statistics® v25 and MedCalc Statistical Software® v19.2.3. RESULTS: A total of 323 hospitalization episodes were evaluated, each corresponding to a different patient. One hundred fifteen patients were excluded. A total of 208 patients were included, 62.0% (n = 129) of them infected at admission. Ten patients had multiple infections.Infected patients had fewer eosinophils than uninfected patients (15.8 ±â€Š42 vs 71.1 ±â€Š159 cell/mm3; P < .001). An eosinophil count at admission ≤69 cell/mm3 had a sensitivity of 89.1% and specificity of 54.4% (area under the curve 0.752; 95% confidence interval 0.682-0.822) for the presence of infection. Eosinophil count of >77 cells/mm3 had a negative likelihood ratio of 0.16. CONCLUSIONS: Eosinophil count was significantly lower in infected than in uninfected patients. The cutoff 69 cells/mm3 was the most accurate in predicting infection. Eosinophil count >77 cells/mm3 was a good predictor of absence of infection.