Development and validation of a novel prediction score for cardiac tamponade in emergency department patients with pericardial effusion.

AIMS: Determining which patients with pericardial effusion require urgent intervention can be challenging. We sought to develop a novel, simple risk prediction score for patients with pericardial effusion. METHODS AND RESULTS: Adult patients admitted through the emergency department (ED) with pericardial effusion were retrospectively evaluated. The overall cohort was divided into a derivation and validation cohort for the generation and validation of a novel risk score using logistic regression. The primary outcome was a pericardial drainage procedure or death attributed to cardiac tamponade within 24 h of ED arrival. Among 195 eligible patients, 102 (52%) experienced the primary outcome. Four variables were selected for the novel score: systolic blood pressure < 100 mmHg (1.5 points), effusion diameter [1-2 cm (0 points), 2-3 cm (1.5 points), >3 cm (2 points)], right ventricular diastolic collapse (2 points), and mitral inflow velocity variation > 25% (1 point). The need for pericardial drainage within 24 h was stratified as low (<2 points), intermediate (2-4 points), or high (≥4 points), which corresponded to risks of 8.1% [95% confidence interval (CI) 3.0-16.8%], 63.8% [95% CI 50.1-76.0%], and 93.7% [95% CI 84.5-98.2%]. The area under the curve of the simplified score was 0.94 for the derivation and 0.91 for the validation cohort. CONCLUSION: Among ED patients with pericardial effusion, a four-variable prediction score consisting of systolic blood pressure, effusion diameter, right ventricular collapse, and mitral inflow velocity variation can accurately predict the need for urgent pericardial drainage. Prospective validation of this novel score is warranted.

Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC).

Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents "consistent with cancer" to evaluate system performance in the domains of colorectal (CRC), prostate (PC), and lung (LC) cancer. Using an end-user-supplied reference set, the automated retrieval console (ARC) iteratively calculated performance of combinations of natural language processing-derived features and supervised classification algorithms. Training and testing involved 10-fold cross-validation for three sets of 500 documents each. Performance metrics included recall, precision, and F-measure. Annotation time for five physicians was also measured. Top performing algorithms had recall, precision, and F-measure values as follows: for CRC, 0.90, 0.92, and 0.89, respectively; for PC, 0.97, 0.95, and 0.94; and for LC, 0.76, 0.80, and 0.75. In all but one case, conditional random fields outperformed maximum entropy-based classifiers. Algorithms had good performance without custom code or rules development, but performance varied by specific application.