Objective: To evaluate the ability of the RIFLE classification to predict hospital mortality in adult patients who underwent cardiac surgery.
Methods: From October 1st 2006 to December 31st 2006, five hundred and nine adult patients who underwent coronary artery bypass grafting and/or valve operation were enrolled in this study. Renal function was assessed daily according to the RIFLE classification, meanwhile, APACHE II score and SOFA score were also evaluated, as well as the maximum scores were recorded.
Results: Mean duration of ventilation support was 18 (14-19) hours, the time of ICU stay was 1.4 +/- 1.0 days, and the time of postoperative hospital stay was 12.0 (10.0-15.0) days. 167 patients (32.8%) incurred postoperative ARF according to the RIFLE classification. The overall mortality was 4.3% (22/502). A significant increase (P < 0.01) was observed for mortality based on RIFLE classification. By applying the area under the receiver operating characteristic curve, the RIFLE classification had more powerful discrimination power [0.933, (95% CI 0.872-0.995), P < 0.001].
Conclusions: ARF is one of the major complications in postcardiotomy patients. Analytical data suggested the good discriminative power of the RIFLE classification for predicting inpatient mortality of adult postoperative patient with ARF, and the RIFLE classification is simple and practically performed. According to the RIFLE classification, patients with RIFLE class I or class F incur a significantly increased risk of in-hospital mortality compared with those who never develop ARF.
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PeerJ Comput Sci
October 2024
Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain.
Detecting people carrying firearms in outdoor or indoor scenes usually identifies (or avoids) potentially dangerous situations. Nevertheless, the automatic detection of these weapons can be greatly affected by the scene conditions. Commonly, in real scenes these firearms can be seen from different perspectives.
View Article and Find Full Text PDFSci Justice
November 2024
Benxi Judicial Appraisal Institution, Benxi Liaoning, China.
ce proliferation of audio sensors in surveillance, smartphones, and numerous devices has made gunshots-based event detection and forensic analysis critical for prompt police action and crime scene reconstruction. This paper initiates an analysis of the acoustic characteristics of gunshots and the variables affecting them, assessing their applicability and limitations in forensic science. It follows with a comprehensive review of existing literature on gunshots detection, identification, and classification technologies, detailing the critical components of machine learning applications, including dataset construction, feature extraction, and classifier selection.
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