Ischemic stroke is a leading cause of mortality and disability. The relationships of heart rate variability (HRV) and stroke-related factors with mortality and functional outcome are complex and not fully understood. Understanding these relationships is crucial for providing better insights regarding ischemic stroke prognosis. The objective of this study is to examine the relationship between HRV, neurological function, and clinical factors with mortality and 3-month behavioral functional outcome in ischemic stroke. We prospectively collected the HRV data and monitored the behavioral functional outcome of patients with ischemic stroke. The behavioral functional outcome was represented by a modified Rankin Scale (mRS) score. This study population consisted of 58 ischemic stroke patients (56.9% male; mean age 70) with favorable (mRS score ≤ 2) and unfavorable (mRS score ≥ 3) outcome. The analysis indicated that the median of the mean RR interval (RR mean) showed no statistical difference between mortality groups. Conversely, the median of the RR mean had significant association with unfavorable outcome (OR = 0.989, p = 0.007). Lower hemoglobin levels had significant association with unfavorable outcome (OR = 0.411, p = 0.010). Higher National Institute of Health Stroke Scale (NIHSS) score at admission had significant association with unfavorable outcome (OR = 1.396, p = 0.002). In contrast, age, stroke history, NIHSS score at admission, and hemoglobin showed no significant association with mortality in ischemic stroke. These results imply that HRV, as indicated by the median of RR mean, alongside specific clinical factors and neurological function at admission (measured by NIHSS score), may serve as potential prognostic indicators for 3-month behavioral functional outcome in ischemic stroke.

Background: Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. Objective: The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. Methods: We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. Results: A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). Conclusions: This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

BACKGROUND Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Background: Difficult peripheral intravenous cannulation affects a substantial proportion of hospitalized patients, yet factors associated with cannulation failure among cases assisted by a dedicated intravenous therapy team on general wards remain poorly characterized. Aim: To identify patient‐related, operator‐related, and procedural factors independently associated with non‐first‐attempt success in peripheral intravenous cannulation performed by an intravenous therapy team on general medical and surgical wards. Design: Prospective observational study and secondary analysis of prospectively collected clinical data. Methods: Cannulation events recorded by an intravenous therapy team over a 1‐year period were analysed. The primary outcome was non‐first‐attempt success, defined as requiring two or more insertion attempts. A generalized linear mixed model accounted for repeated events within hospitalizations. Three sensitivity analyses assessed robustness. Results: Approximately one in five events required two or more attempts. Five factors were independently associated with non‐first‐attempt success: lower nurse experience, reduced patient mobility, smaller catheter gauge, replacement prompted by difficult access, and lower‐limb or trunk insertion sites. Patient age, sex, comorbidity burden, and body mass index were not independently associated. Findings were robust across all sensitivity analyses. Conclusion: Nurse experience, catheter gauge, and insertion site are modifiable factors associated with first‐attempt success. Patient mobility emerged as a novel, easily assessable risk indicator. Implications for the Profession and/or Patient Care: Findings can inform intravenous therapy team training priorities, catheter gauge and insertion‐site selection protocols, and prospective bedside risk screening to reduce repeated cannulation attempts. Impact: This study clarified which factors drive cannulation failure on general wards, identifying five independently associated factors—three of them modifiable—and highlighting patient mobility as a novel indicator relevant to intravenous therapy teams, ward nurses, and hospitalized adults internationally. Reporting Method: This study adhered to the STROBE reporting guideline for observational studies. Patient or Public Contribution: No patient or public contribution.