Cardiopulmonary resuscitation training to improve out-of-hospital cardiac arrest survival: addressing potential health inequalities.

Repositioning a patient from the prone to supine position can delay the initiation of cardiopulmonary resuscitation (CPR). Investigators used high-fidelity simulation to assess the time to initiate chest compressions and the time during which compressions did not occur for supine and prone CPR. Sixty participants completed a knowledge assessment before and after attending an education session and completing two simulations (ie, supine, prone).

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Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study.

EClinicalMedicine

February 2025

Emergency Centre, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.

Shan Jiang Shuai Dai Yulin Li Xianlong Zhou Cheng Jiang

Background: Sepsis is a significant health burden on a global scale. Timely identification and treatment of sepsis can greatly improve patient outcomes, including survival rates. However, time-consuming laboratory results are often needed for screening sepsis.

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Spontaneous Regression of Locally Advanced Breast Cancer Following Cardiopulmonary Arrest: A Case Report.

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Surgery, Japanese Red Cross Shizuoka Hospital, Shizuoka, JPN.

Asahi Kannari Masayuki Kikuchi Hirokazu Matsushima Rika Miyabe Koji Atsuta

Spontaneous regression in breast cancer is rare but can dramatically improve patient prognosis. Although the underlying mechanism is unknown, it may be due to a biological response to external invasion. An 81-year-old woman presented to our emergency department with a 600x100mm large breast mass.

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Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.

Int J Med Inform

January 2025

Department of Emergency Medicine Qilu Hospital of Shandong University Jinan China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine Institute of Emergency and Critical Care Medicine of Shandong University Chest Pain Center Qilu Hospital of Shandong University Jinan China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging Qilu Hospital of Shandong University Jinan China. Electronic address:

Jiqing Li Shuo Wu Jianhua Gu

Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.

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