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Extubation failure (EF) is a significant concern in mechanically ventilated newborns, and predicting its occurrence is an ongoing area of research. To investigate the predictors of EF in newborns undergoing planned extubation, we conducted a systematic review and meta-analysis. A systematic literature search was conducted in PubMed, Web of Science, Embase, and Cochrane Library for studies published in English from the inception of each database to March 2023. The PRISMA guidelines were followed in all phases of this systematic review. The Risk of Bias Assessment for Nonrandomized Studies tool was used to assess methodological quality. Thirty-four studies were included, 10 of which were overall low risk of bias, 15 of moderate risk of bias, and 9 of high risk of bias. The studies reported 43 possible predictors in six broad categories (intrinsic factors; maternal factors; diseases and adverse conditions of the newborn; treatment of the newborn; characteristics before and after extubation; and clinical scores and composite indicators). Through a qualitative synthesis of 43 predictors and a quantitative meta-analysis of 19 factors, we identified five definite factors, eight possible factors, and 22 unclear factors related to EF. Definite factors included gestational age, sepsis, pre-extubation pH, pre-extubation FiO, and respiratory severity score. Possible factors included age at extubation, anemia, inotropic use, mean airway pressure, pre-extubation PCO, mechanical ventilation duration, Apgar score, and spontaneous breathing trial. With only a few high-quality studies currently available, well-designed and more extensive prospective studies investigating the predictors affecting EF are still needed. In the future, it will be important to explore the possibility of combining multiple predictors or assessment tools to enhance the accuracy of predicting extubation outcomes in clinical practice.
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http://dx.doi.org/10.1186/s13052-023-01538-0 | DOI Listing |
Eur Heart J Open
September 2025
Calderdale and Huddersfield NHS Foundation Trust, Acre St, Lindley, Huddersfield HD3 3EA, UK.
Aims: Cardiogenic shock remains a significant cause of mortality despite multiple advancements in medical interventions. Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) provides crucial circulatory support but also increases left ventricular (LV) after-load, potentially worsening outcomes. Effective LV unloading strategies can enhance patient survival during VA-ECMO treatment.
View Article and Find Full Text PDFRisk Manag Healthc Policy
September 2025
Department of Orthopedics, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, Jiangsu, 225300, People's Republic of China.
Objective: This study aimed to investigate the status of negative information attentional bias, self-management, and health-promoting behaviors among patients with chronic refractory wounds receiving orthopedic care. Additionally, the study sought to construct a structural equation model (SEM) to evaluate the influence of negative attentional bias on self-management and health-promoting behaviors, with the intent of informing the clinical implementation of evidence-based health behavior education programs.
Methods: A total of 226 patients with chronic refractory wounds under orthopedic treatment at a single institution between January 2020 and December 2022 were enrolled in this study.
Front Med (Lausanne)
August 2025
Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.
The asymmetrical nature of the relationship between social workers and their clients may lead to abuse of power due to a human trait or corruption. A high level of power sensitivity is thus crucial to counteract power abuse. Ideally, this topic should be covered during studies, as the risk of corruption rises with everyday working life.
View Article and Find Full Text PDFRev Cardiovasc Med
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Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, LE3 9QP Leicester, UK.
Adult congenital heart disease (ACHD) constitutes a heterogeneous and expanding patient cohort with distinctive diagnostic and management challenges. Conventional detection methods are ineffective at reflecting lesion heterogeneity and the variability in risk profiles. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, has revolutionized the potential for improving diagnosis, risk stratification, and personalized care across the ACHD spectrum.
View Article and Find Full Text PDFRev Cardiovasc Med
August 2025
Department of Emergency Medicine, General Practice Medical Center, National Clinical Research Center for Geriatrics, West China Hospital, 610041 Chengdu, Sichuan, China.
Background: Compared to patients with controllable hypertension, those with resistant hypertension (RH) have a higher incidence of cardiovascular complications, including stroke, left ventricular hypertrophy, and congestive heart failure. Therefore, an urgent need exists for improved management and control, along with more effective medications. Aldosterone synthase inhibitors (ASIs) are newly emerging drugs that have gradually attracted an increasing amount of attention.
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