Unveiling the Hidden Burden: Mapping the Landscape of Post-Intensive Care Syndrome Research. A Bibliometric Study and Visualization Analysis.

BACKGROUND Post-intensive care syndrome (PICS) has become a major concern for patients and their families due to the rising number of ICU admissions. We conducted a bibliometric analysis to identify hotspots and trends in PICS research. MATERIAL AND METHODS We searched for PICS-related publications in the Web of Science Core Collection up to May 1, 2022. We used CiteSpace, VOSviewer, and Scimago Graphica to analyze collaboration among countries, institutions, and authors, and to identify research hotspots and frontiers. RESULTS Our analysis included 294 research papers on PICS, with the United States leading the field with 146 published papers. Collaboration among institutions and authors was active mainly in the Americas, Europe, and Australia. Highly cited researchers were members of the Outcomes After Critical Illness and Surgery (OACIS) Group, with Ramona O Hopkins as the most published author. Research topics focused on septic shock, COVID-19, qualitative research, and rehabilitation, with publications primarily in critical care medicine journals. Keyword analysis revealed that the main research focus included stress disorders, quality of life, mechanical ventilation, acute lung injury, risk factors, and descriptive studies during hospitalization. CONCLUSIONS PICS research is limited, focusing primarily on short-term clinical effects and lacking long-term prognostic observations and multinational studies. Increased collaboration among countries and regions is necessary to advance research in this field. Hotspots in research focus on prognosis and an integrated approach to management.

A bibliometric analysis of chronic obstructive pulmonary disease and COVID-19.

The coronavirus disease 2019 (COVID-19) outbreak became the worst epidemic in decades. Since its inception, COVID-19 has had a dramatic impact on chronic obstructive pulmonary disease (COPD) patients. This study explores explore the current status, hot spots, and research frontiers of COVID-19 and COPD based on a bibliometric approach. The Web of Science Core Collection was used to search the literature related to COPD and COVID-19, and VOSviewer and CiteSpace software were applied to analyze the distribution characteristics, research hotspots, and research frontiers of literature in related fields and to map the scientific knowledge domains. A total of 816 valid publications were included, among which USA, China, and England are the core countries/regions publishing related literature, and the research institutions are concentrated in Huazhong University of Science and Technology (18 papers), University College London (17 papers), and Imperial College London (16 papers). Guan WJ is the most prolific author with the most articles. The journals with the most publications are PLOS ONE, JOURNAL OF CLINICAL MEDICINE, and FRONTIERS IN MEDICINE. The main research hotspots in this field are clinical features, disease management, and mechanism research. By constructing COPD and COVID-19 research network diagrams, we reveal the hot spots, frontiers, and development trends of relevant research fields, which provide a reference for subsequent researchers to quickly grasp the current status of related research fields.

The risk factors and predictive modeling of mortality in patients with mental disorders combined with severe pneumonia.

Background: We explored clinical characteristics and risk factors for mortality in patients with mental disorders combined with severe pneumonia and developed predictive models. Methods: We retrospectively analyzed the data of 161 patients with mental disorders combined with severe pneumonia in the intensive care unit (ICU) of a psychiatric hospital from May 2020 to February 2023, and divided them into two groups according to whether they died or not, and analyzed their basic characteristics, laboratory results and treatments, etc. We analyzed the risk factors of patients' deaths using logistics regression, established a prediction model, and drew a dynamic nomogram based on the results of the regression analysis. Based on the results of regression analysis, a prediction model was established and a dynamic nomogram was drawn. Results: The non-survivor group and the survivor group of patients with mental disorders combined with severe pneumonia were statistically different in terms of age, type of primary mental illness, whether or not they were intubated, whether or not they had been bedridden for a long period in the past, and the Montreal Cognitive Assessment (MoCA) scale, procalcitonin (PCT), albumin (ALB), hemoglobin (Hb), etc. Logistics regression analysis revealed the following: MoCA scale (OR = 0.932, 95% CI:0.872-0.997), age (OR = 1.077, 95%CI:1.029-1.128), PCT (OR = 1.078, 95% CI:10.006-10.155), ALB (OR = 0.971, 95%CI:0.893-1.056), Hb (OR = 0.971, 95% CI: 0.942-0.986) were statistically significant. The ROC curve showed that the model predicted patient death with an area under the curve (AUC) of 0.827 with a sensitivity of 73.4% and a specificity of 80.4%. Conclusion: Low MoCA score, age, PCT, and low Hb are independent risk factors for death in patients with mental disorders with severe pneumonia, and the prediction model constructed using these factors showed good predictive efficacy.