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.

RESCUE: imputing dropout events in single-cell RNA-sequencing data.

BACKGROUND: Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, despite numerous efforts from the community, this challenge has yet to be solved. RESULTS: Here we present a computational method, called RESCUE, to mitigate the dropout problem by imputing gene expression levels using information from other cells with similar patterns. Unlike existing methods, we use an ensemble-based approach to minimize the feature selection bias on imputation. By comparative analysis of simulated and real single-cell RNA-seq datasets, we show that RESCUE outperforms existing methods in terms of imputation accuracy which leads to more precise cell-type identification. CONCLUSIONS: Taken together, these results suggest that RESCUE is a useful tool for mitigating dropouts in single-cell RNA-seq data. RESCUE is implemented in R and available at https://github.com/seasamgo/rescue .