Purpose Studies have demonstrated that red blood cell distribution width (RDW) is closely associated with the prognosis of patients with chronic obstructive pulmonary disease (COPD). In addition, the dynamic changes in RDW appear to play an important role. Thus, we aimed to investigate the relationship between dynamic changes in RDW and 30-day all-cause readmission of patients with acute exacerbation of COPD (AECOPD). Methods In this retrospective cohort study, we enrolled patients with AECOPD hospitalized in the Department of Respiratory Medicine in Liyuan Hospital (Wuhan China), a tertiary, university-affiliated, public hospital. Patients with AECOPD were divided into three groups based on their RDW values after the first and fourth days of admission. The normal range for RDW is 10–15%. Patients with normal RDW values were included in the normal group. Patients with an RDW value >15% on the first day, which subsequently decreased by >2% on the fourth day was included in the decreased group. The increased group was comprised of patients with an RDW value >15% on the first day which continued to increase, or those with a normal RDW value on the first day which increased >15% on the fourth day. Results A total of 239 patients (age: 72 years [range: 64–81 years]; male: n=199 [83.3%]) were included. There were 108, 72, and 59 patients in the RDW normal, decreased, and increased groups, respectively; the 30-day all-cause readmission rate was 9.3%, 9.7%, 27.1%, respectively; (p=0.003), being noticeably higher in the RDW increased group. Dynamic increase of RDW (OR:3.45, 95% CI: 1.39–8.58, p= 0.008) was independently correlated with 30-day all-cause readmission of patients with AECOPD. Conclusion The dynamic increase of RDW is an independent prognostic factor of 30-day all-cause readmission of patients with AECOPD.
Chronic obstructive pulmonary disease (COPD) is still a constant threat to people's health. We aimed to identify the relationship between increased red cell distribution width (RDW) on admission and length of hospitalization in acute exacerbation of chronic obstructive pulmonary disease patients (AECOPD). Patients with AECOPD were recruited and divided into 3 groups based on RDW tertiles. Two hundred eighty six patients with AECOPD admitted to our department during January 1, 2017 and June 30, 2019 were enrolled in the study. According to the RDW tertiles (≤12.8%, 12.9% to 13.6%, >13.6%), the patients were divided into 3 groups. Length of stay was significantly related to RDW ( P < .001) in AECOPD patients. Correlation analysis indicated that RDW was negatively associated with FEV1% predicted ( r = −0.142, P = .016). However, RDW was positively associated with prolonged of stay ( r = 0.298, P < .001) in AECOPD patients. Multivariate regression analysis discovered that RDW was independently associated with the length of hospitalization ( P = .001). Receiver operating characteristic (ROC) curve showed that RDW was a good predictor of prolonged hospital stay in AECOPD patients, and the area under the curve (AUC) was 0.818 (95% CI: 0.769–0.868). The highest sensitivity to predict prolonged hospital stay was 83.8% and the specificity was 71.6% with the cut-off 13.35%. In conclusion, prolonged hospital stay in AECOPD patients was closely associated with increased RDW. Elevated RDW may be an independent predictor for prolonged hospitalization in AECOPD patients.
BACKGROUND The application of artificial intelligence (AI) has increasingly been used in various medical fields, including metabolic dysfunction-associated fatty liver disease (MAFLD). This study endeavors to undertake a bibliometric analysis to predict the research hotspots and current advancements in AI permeating the field of MAFLD, to provide valuable information that could serve for further precision medicine. OBJECTIVE Using bibliometric analysis to review the research hotspots and collaborative networks of the application of artificial intelligence in the field of MAFLD in the past years. METHODS Publications were on the application of AI within the MAFLD domain from the Web of Science Core Collection (WoSCC), encompassing the period between 2009 and 2022. After undergoing a manual selection process, the target variables were analyzed by using Microsoft Excel 2019. Bibliometrix, VOSviewer, and CiteSpace knowledge mapping tools were used to analyze the number of publications, countries, institutions, authors, journals, references, and keywords in this field while yielding the visualization results in the form of a map. RESULTS From 2009 to 2022, a total of 164 publications related to the application of AI in the field of MAFLD were published, which covered 1,237 authors from 42 different countries/regions, across 475 institutions. Specifically, the United States emerged as the leading contributor with 62 publications, followed by China (n=43) and the United Kingdom (n=26). PLoS ONE was the most published (n=6) and most cited journal (n=148). The most effective institution and authors in this field were the University of California, San Diego, and Loomba Rohit. Keywords analysis showed that AI, machine learning, and deep learning were hotspots in the field of MAFLD and non-alcoholic steatohepatitis (NAS). CONCLUSIONS The application of AI in the field of MAFLD has held significant potential and received increasing interest from scholars. It can be anticipated that non-invasive diagnosis and accurate minimally invasive treatment through AI, in particular, deep learning technologies, will still be the focus of research in the future.
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