The etiology of severe pneumonia requiring mechanical ventilation in the very elderly has been imprecise because of lack of comprehensive studies and low yield of diagnostic approach. Overall, 104 patients 75 yr of age and older with severe pneumonia were studied prospectively at two university-affiliated hospitals. Microbial investigation included blood culture, serology, pleural fluid, and bronchoalveolar secretions. Streptococcus pneumoniae (14%), gram-negative enteric bacilli (14%), Legionella sp. (9%), Hemophilus influenzae (7%), and Staphylococcus aureus (7%) were the predominant pathogens in community-acquired pneumonia (CAP). Staphylococcus aureus (29%), gram-negative enteric bacilli (15%), Streptococcus pneumoniae (9%), and Pseudomonas aeruginosa (4%) accounted for most isolates of nursing home-acquired pneumonia (NHAP). The case fatality rate was 55% (53% for CAP and 57% for NHAP; p > 0.5). Activity of Daily Living (ADL) Index, pulmonary, endocrine and central nervous system (CNS) comorbidities were associated with distinct microbial etiology. By multivariate analysis, hospital mortality was associated independently with 24-h urine output (odds ratio [OR], 5.6; 95% confidence interval [CI], 2.5 to 7.9; p < 0.001), septic shock (OR, 4.3; 95% CI, 1.9 to 8.9; p = 0.0059), radiographic multilobar involvement (OR, 3.7; 95% CI, 1.8 to 15.6; p = 0.02), and inadequate antimicrobial therapy (OR, 2.6; 95% CI, 1.4 to 23.9; p = 0.034). Further studies should focus on identifying effective antimicrobial regimens in randomized trials.

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Although the three severity of illness scoring systems (APACHE II, MPM II and SAPS II) demonstrated average discrimination when applied to estimate hospital mortality in the elderly patients with severe pneumonia, MPM II had the closest fit to our database. Alternative modeling approaches might be needed to customize the model coefficients to the elderly population for more accurate probabilities or to develop specialized models targeted to the designed population.

The classification tree model demonstrated a superior predictive accuracy to that of logistic regression and APACHE II. If validated prospectively, the classification tree can be used as a tool to assess the outcome of older patients with severe pneumonia requiring mechanical ventilation on admission to the ICU. In addition, the classification tree can be used to assist healthcare workers in providing a concise summary of local outcome experience and prognostic information to patients and their surrogates.

EUS-FNA as a first test (after CT) has high diagnostic yield and accuracy for detecting lung cancer metastases to the mediastinum and distant sites. Metastasis to the CLNs is associated with poor prognosis. EUS-FNA is able to detect occult metastasis to the CLNs and thus avoids thoracotomy.

OBJECTIVES: To develop a prognostic model to predict outcome of older patients with severe pneumonia requiring mechanical ventilation. DESIGN: A nonconcurrent prospective study. SETTING: A 24‐bed intensive care unit (ICU) within two university‐affiliated tertiary care hospitals. PARTICIPANTS: All patients age 75 and older with severe pneumonia between June 1996 and September 1999 were included. Demographic data including activities of daily living (ADL) index score before admission, and clinical and laboratory data were collected in the first 24 hours of admission to the ICU. One hundred four patients (mean age ± standard deviation (SD) 82.3 ± 5.5 years) met the inclusion criteria. MEASUREMENTS: A classification tree was developed using binary recursive partitioning to predict hospital discharge. The model was compared with a logistic regression model using variables selected by the tree analysis and with the Acute Physiologic and Chronic Health Evaluation (APACHE) II. RESULTS: Outcome predictors for the classification tree were use of vasopressors, presence of multilobar pneumonia on chest radiograph, ratio of blood urea nitrogen to creatinine, Glasgow Coma Scale, urine output, and ADL score before admission. The tree achieved a sensitivity of 83.8% (95% confidence interval (CI) 69.2–92.4) and a specificity of 93.3% (95% CI 83–98.1). The predictive accuracy as assessed by the area under the curve (c‐index ± standard error) was significantly higher with the classification tree (0.932 ± 0.03) than with logistic regression and APACHE II, (0.801 ± 0.028 and 0.711 ± 0.049, respectively (P < .05). CONCLUSIONS: The classification tree model demonstrated a superior predictive accuracy to that of logistic regression and APACHE II. If validated prospectively, the classification tree can be used as a tool to assess the outcome of older patients with severe pneumonia requiring mechanical ventilation on admission to the ICU. In addition, the classification tree can be used to assist healthcare workers in providing a concise summary of local outcome experience and prognostic information to patients and their surrogates.

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