Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00246-022-03092-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Developing and validating a machine learning model to predict multidrug-resistant -related septic shock.
Front Immunol
January 2025
Department of Medical Laboratory, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Background: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.
Methods: A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024.
Effects of Implementing an Enhanced Recovery After Cardiac Surgery Protocol with On-Table Extubation on Patient Outcome and Satisfaction-A Before-After Study.
J Clin Med
January 2025
Department of Anesthesiology, University Hospital of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany.
: Enhanced recovery after surgery (ERAS) protocols aim to improve clinical outcomes, shorten hospital length of stay (LOS), and reduce costs through a multidisciplinary perioperative approach. Although introduced in colorectal surgery, they are less established in cardiac surgery, especially in combination with on-table extubation (OTE). This study evaluates the impact of a novel ERAS concept with OTE (RERACS) in elective aortic-valve-replacement and coronary bypass surgery.
View Article and Find Full Text PDFRisk factors for early periventricular intraventricular hemorrhage in extremely low birth weight infants: a retrospective study.
BMC Pediatr
January 2025
Department of Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, 510623, Guangzhou, China.
Background: This study aimed to explore the risk factors for early intraventricular hemorrhage (IVH) in extremely low birth weight infants (ELBWIs) to provide guidance for early intervention, thereby improving survival rates and quality of life for these vulnerable infants.
Methods: A retrospective study was conducted on 205 ELBWIs admitted to the Women and Children's Medical Center of Guangzhou Medical University from January 2019 to December 2023. Standard head ultrasound screening (HUS) was used to assess the presence and severity of IVH on days 1-3 and 5-7 post-birth.
[Severe malnutrition during pregnancy complicated with acute pyelonephritis causing sepsis, refractory septic shock and multiple organ failure: A case report].
Beijing Da Xue Xue Bao Yi Xue Ban
February 2025
Department of Critical Care Medicine, Peking University First Hospital, Beijing 100034, China.
This study reports the diagnosis and treatment of a 26-year-old pregnant woman with severe malnutrition combined with acute pyelonephritis causing sepsis, refractory septic shock and multiple organ failure. A female patient, 26 years old, was admitted to hospital mainly due to "menelipsis for more than 19 weeks, nausea and vomiting for 20 days, fever with fatigue for 3 days". At the end of 19 weeks of intrauterine pregnancy, the patient presented with fever accompanied by urinary tract irritation.
View Article and Find Full Text PDFMachine learning prediction of in-hospital mortality and external validation in patients with cardiogenic shock: the RESCUE score.
Rev Esp Cardiol (Engl Ed)
January 2025
Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. Electronic address:
Introduction And Objectives: Despite advances in mechanical circulatory support, mortality rates in cardiogenic shock (CS) remain high. A reliable risk stratification system could serve as a valuable guide in the clinical management of patients with CS. This study aimed to develop and externally validate a risk prediction model for in-hospital mortality in CS patients using machine learning (ML) algorithms.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!