Neurologic Manifestations of Long COVID Differ Based on Acute COVID-19 Severity.

OBJECTIVE: To characterize neurologic manifestations in post-hospitalization Neuro-PASC (PNP) and non-hospitalized Neuro-PASC (NNP) patients. METHODS: Prospective study of the first 100 consecutive PNP and 500 NNP patients evaluated at a Neuro-COVID-19 clinic between 5/2020 and 8/2021. RESULTS: PNP were older than NNP patients (mean 53.9 vs 44.9 y; p < 0.0001) with a higher prevalence of pre-existing comorbidities. An average 6.8 months from onset, the main neurologic symptoms were "brain fog" (81.2%), headache (70.3%), and dizziness (49.5%) with only anosmia, dysgeusia and myalgias being more frequent in the NNP compared to the PNP group (59 vs 39%, 57.6 vs 39% and 50.4 vs 33%, all p < 0.003). Moreover, 85.8% of patients experienced fatigue. PNP more frequently had an abnormal neurologic exam than NNP patients (62.2 vs 37%, p < 0.0001). Both groups had impaired quality of life in cognitive, fatigue, sleep, anxiety, and depression domains. PNP patients performed worse on processing speed, attention, and working memory tasks than NNP patients (T-score 41.5 vs 55, 42.5 vs 47 and 45.5 vs 49, all p < 0.001) and a US normative population. NNP patients had lower results in attention task only. Subjective impression of cognitive ability correlated with cognitive test results in NNP but not in PNP patients. INTERPRETATION: PNP and NNP patients both experience persistent neurologic symptoms affecting their quality of life. However, they harbor significant differences in demographics, comorbidities, neurologic symptoms and findings, as well as pattern of cognitive dysfunction. Such differences suggest distinct etiologies of Neuro-PASC in these populations warranting targeted interventions. ANN NEUROL 2023;94:146-159.

Non-Classical Swine Leukocyte Antigens SLA-6, -7, and -8, Are Xenoantigens for Some Waitlisted Patients.

Attack of donor tissues by pre-formed anti-pig antibodies is well known to cause graft failure in xenotransplantation. Genetic engineering of porcine donors to eliminate targets of these pre-formed antibodies coupled with advances in immunosuppressive medicines have now made it possible to achieve extended survival in the pre-clinical pig-to-non-human primate model. Despite these improvements, antibodies remain a risk over the lifetime of the transplant, and many patients continue to have pre-formed donor-specific antibodies even to highly engineered pigs. While therapeutics exist that can help mitigate the detrimental effects of antibodies, they act broadly potentially dampening beneficial immunity. Identifying additional xenoantigens may enable more targeted approaches, such as gene editing, to overcome these challenges by further eliminating antibody targets on donor tissue. Because we have found that classical class I swine leukocyte antigens are targets of human antibodies, we now examine whether related pig proteins may also be targeted by human antibodies. We show here that non-classical class I swine leukocyte proteins (SLA-6, -7, -8) can be expressed at the surface of mammalian cells and act as antibody targets.

Implementation of a novel computer assisted telephone follow-up model for older patients after emergency department discharge in an Asian population.

BACKGROUND: While the impact of telephone follow-up (TFU) for older emergency department (ED) patients is controversial, its effects on the Asian population remain uncertain. In this study, we evaluated the effectiveness of a novel computer assisted TFU model specifically for this demographic. METHODS: At a Taiwanese tertiary medical center, we developed a TFU protocol that included a referral and case management system within the ED hospital information system. We provided TFU to older discharged patients between April 1, 2021, and May 31, 2021. We compared this cohort with a non-TFU cohort of older ED patients and analyzed demographic characteristics and post-ED discharge outcomes. RESULTS: The TFU model was successfully implemented, with 395 patients receiving TFU and 191 without TFU. TFU patients (median age: 76 years, male proportion: 48.9%) differed from non-TFU patients (median age: 74 years, male proportion: 43.5%). Compared with the non-TFU cohort, the multivariate logistic regression analysis revealed that the TFU cohort had a lower total medical expenditure < 1 month (adjusted odds ratio [AOR]: 0.32; 95% CI: 0.21 - 0.47 for amounts exceeding 5,000 New Taiwan Dollars), and higher satisfaction (AOR: 2.80; 95% CI: 1.46 - 5.36 for scores > 3 on a five-point Likert Scale). However, the TFU cohort also had a higher risk of hospitalization < 1 month (AOR: 2.50; 95% CI: 1.31 - 4.77) compared to the non-TFU cohort. CONCLUSION: Computer-assisted TFU appears promising. Further research involving a larger number of patients and validation in other hospitals is necessary to bolster the evidence and extend the findings to a broader context.

Trends in Costs of Care and Utilization for Medicaid Patients With Diabetes in Accountable Care Communities.

BACKGROUND/OBJECTIVES: Medicaid beneficiaries with diabetes have complex care needs. The Accountable Care Communities (ACC) Program is a practice-level intervention implemented by UnitedHealthcare to improve care for Medicaid beneficiaries. We examined changes in costs and utilization for Medicaid beneficiaries with diabetes assigned to ACC versus usual care practices. RESEARCH DESIGN: Interrupted time series with concurrent control group analysis, at the person-month level. The ACC was implemented in 14 states, and we selected comparison non-ACC practices from those states to control for state-level variation in Medicaid program. We adjusted the models for age, sex, race/ethnicity, comorbidities, seasonality, and state-by-year fixed effects. We examined the difference between ACC and non-ACC practices in changes in the time trends of expenditures and hospital and emergency room utilization, for the 4 largest categories of Medicaid eligibility [Temporary Assistance to Needy Families, Supplemental Security Income (without Medicare), Expansion, Dual-Eligible]. SUBJECTS/MEASURES: Eligibility and claims data from Medicaid adults with diabetes from 14 states between 2010 and 2016, before and after ACC implementation. RESULTS: Analyses included 1,200,460 person-months from 66,450 Medicaid patients with diabetes. ACC implementation was not associated with significant changes in outcome time trends, relative to comparators, for all Medicaid categories. CONCLUSIONS: Medicaid patients assigned to ACC practices had no changes in cost or utilization over 3 years of follow-up, compared with patients assigned to non-ACC practices. The ACC program may not reduce costs or utilization for Medicaid patients with diabetes.

Evaluation of a National Care Coordination Program to Reduce Utilization Among High-cost, High-need Medicaid Beneficiaries With Diabetes.

BACKGROUND: Medical, behavioral, and social determinants of health are each associated with high levels of emergency department (ED) visits and hospitalizations. OBJECTIVE: The objective of this study was to evaluate a care coordination program designed to provide combined "whole-person care," integrating medical, behavioral, and social support for high-cost, high-need Medicaid beneficiaries by targeting access barriers and social determinants. RESEARCH DESIGN: Individual-level interrupted time series with a comparator group, using person-month as the unit of analysis. SUBJECTS: A total of 42,214 UnitedHealthcare Medicaid beneficiaries (194,834 person-months) age 21 years or above with diabetes, with Temporary Assistance to Needy Families, Medicaid expansion, Supplemental Security Income without Medicare, or dual Medicaid/Medicare. MEASURES: Our outcome measures were any hospitalizations and any ED visits in a given month. Covariates of interest included an indicator for intervention versus comparator group and indicator and spline variables measuring changes in an outcome's time trend after program enrollment. RESULTS: Overall, 6 of the 8 examined comparisons were not statistically significant. Among Supplemental Security Income beneficiaries, we observed a larger projected decrease in ED visit risk among the intervention sample versus the comparator sample at 12 months postenrollment (difference-in-difference: -6.6%; 95% confidence interval: -11.2%, -2.1%). Among expansion beneficiaries, we observed a greater decrease in hospitalization risk among the intervention sample versus the comparator sample at 12 months postenrollment (difference-in-difference: -5.8%; 95% confidence interval: -11.4%, -0.2%). CONCLUSION: A care coordination program designed to reduce utilization among high-cost, high-need Medicaid beneficiaries was associated with fewer ED visits and hospitalizations for patients with diabetes in selected Medicaid programs but not others.

Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis.

BACKGROUND: Early hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance. AIMS: We leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors. METHODS: A national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission. RESULTS: We analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration. CONCLUSION: Our model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

Enhancing Efficacy and Stability of an Antiheroin Vaccine: Examination of Antinociception, Opioid Binding Profile, and Lethality.

In recent years, drug conjugate vaccines have shown promise as therapeutics for substance use disorder. As a means to improve the efficacy of a heroin conjugate vaccine, we systematically explored 20 vaccine formulations with varying combinations of carrier proteins and adjuvants. In regard to adjuvants, we explored a Toll-like receptor 9 (TLR9) agonist and a TLR3 agonist in the presence of alum. The TLR9 agonist was cytosine-guanine oligodeoxynucleotide 1826 (CpG ODN 1826), while the TLR3 agonist was virus-derived genomic doubled-stranded RNA (dsRNA). The vaccine formulations containing TLR3 or TLR9 agonist alone elicited strong antiheroin antibody titers and blockade of heroin-induced antinociception when formulated with alum; however, a combination of TLR3 and TLR9 adjuvants did not result in improved efficacy. Investigation of month-long stability of the two lead formulations revealed that the TLR9 but not the TLR3 formulation was stable when stored as a lyophilized solid or as a liquid over 30 days. Furthermore, mice immunized with the TLR9 + alum heroin vaccine gained significant protection from lethal heroin doses, suggesting that this vaccine formulation is suitable for mitigating the harmful effects of heroin, even following month-long storage at room temperature.

Metformin prescription for insured adults with prediabetes from 2010 to 2012: a retrospective cohort study.

BACKGROUND: Prediabetes affects 1 in 3 Americans. Both intensive lifestyle intervention and metformin can prevent or delay progression to diabetes. Over the past decade, lifestyle interventions have been translated across various settings, but little is known about the translation of evidence surrounding metformin use. OBJECTIVE: To examine metformin prescription for diabetes prevention and patient characteristics that may affect metformin prescription. DESIGN: Retrospective cohort analysis over a 3-year period. SETTING: Employer groups that purchased health plans from the nation's largest private insurer. PARTICIPANTS: A national sample of 17 352 working-age adults with prediabetes insured for 3 continuous years between 2010 and 2012. MEASUREMENTS: Percentage of health plan enrollees with prediabetes who were prescribed metformin. RESULTS: Only 3.7% of patients with prediabetes were prescribed metformin over the 3-year study window. After adjustment for age, income, and education, the predicted probability of metformin prescription was almost 2 times higher among women and obese patients and more than 1.5 times higher among patients with 2 or more comorbid conditions. LIMITATION: Missing data on lifestyle interventions, possible misclassification of prediabetes and metformin use, and inability to define eligible patients exactly as defined in the American Diabetes Association guidelines. CONCLUSION: Evidence shows that metformin is rarely prescribed for diabetes prevention in working-age adults. Future studies are needed to understand potential barriers to wider adoption of this safe, tolerable, evidence-based, and cost-effective prediabetes therapy. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention (Division of Diabetes Translation) and the National Institute of Diabetes and Digestive and Kidney Diseases.

Adherence to Metformin, Statins, and ACE/ARBs Within the Diabetes Health Plan (DHP).

BACKGROUND: Reducing patient cost-sharing and engaging patients in disease management activities have been shown to increase uptake of evidence-based care. OBJECTIVE: To evaluate the effect of employer purchase of a disease-specific plan with reduced cost-sharing and disease management (the Diabetes Health Plan/DHP) on medication adherence among eligible employees and dependents. DESIGN: Employer-level "intent to treat" cohort study, including data from eligible employees and their dependents with diabetes, regardless of whether they were enrolled in the DHP. SETTING: Employers that contracted with a large national health plan administrator in 2009, 2010, and/or 2011. PARTICIPANTS: Ten employers that purchased the DHP and 191 employers that did not (controls). Inverse probability weighting (IPW) estimation was used to adjust for inter-group differences. INTERVENTION: The DHP includes free or low-cost medications and physician visits. Enrollment strategies and specific benefit designs are determined by the employer and vary in practice. DHP participants are notified up front that they must engage in their own health care (e.g., receiving diabetes-related screening) in order to remain enrolled. MAIN OUTCOME MEASURE: Mean employee adherence to metformin, statins, and ACE/ARBs at the employer level at one year post-DHP implementation, as measured by the proportion of days covered (PDC). RESULTS: Baseline adherence to the three medications was similar across DHP and control employers, ranging from 64 to 69 %. In the first year after DHP implementation, predicted employer-level adherence for metformin (+4.9 percentage points, p = 0.017), statins (+4.8, p = 0.019), and ACE/ARBs (+4.4, p = 0.02) was higher with DHP purchase. LIMITATIONS: Non-randomized, observational study. CONCLUSIONS: The Diabetes Health Plan, an innovative health plan that combines reduced cost-sharing and disease management with an up-front requirement of enrollee participation in his or her own health care, is associated with a modest improvement in medication adherence at 12 months.

Getting value from health spending: going beyond payment reform.

It is widely held that fee-for-service (FFS) payment systems reward volume and intensity of services, contributing to overall cost inflation, while doing little to reward quality, efficiency, or care coordination. Recently, The National Commission on Physician Payment Reform (sponsored by SGIM) has recommended that payers "should largely eliminate stand-alone fee-for-service payment to medical practices because of its inherent inefficiencies and problematic financial incentives." As the current and former Chief Medical Officers of a large national insurer, we agree that payment reform is a critical component of health care modernization. But calls to transform payment simultaneously go too far, and don't go far enough. Based on our experience, we believe there are several critical ingredients that are either missing or under-emphasized in most payment reform proposals, including: health care is local so no one size fits all; upgrading performance measures; monitoring/overcoming unintended consequences; using a full toolbox to achieve transformation; and ensuring that the necessary components for successful delivery reform are in place. Thinking holistically and remembering that healthcare is a complex adaptive system are crucial to achieving better results for patients and the health system.

Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals.

BACKGROUND: The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period. METHODS: Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc., and Eur. J. Med. Res.) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm. RESULTS: Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (n = 492) than other counterparts (i.e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions. CONCLUSION: Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.

Dispersion-free inertial focusing (DIF) for high-yield polydisperse micro-particle filtration and analysis.

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, resulting from its inherent size-dependent dispersion, could turn into a drawback that challenges applications requiring consistent and uniform positioning of polydisperse particles, such as microfiltration and flow cytometry. To overcome this fundamental challenge, we introduce Dispersion-Free Inertial Focusing (DIF). This new method minimizes particle size-dependent dispersion while maintaining the high throughput and precision of standard inertial focusing, even in a highly polydisperse scenario. We demonstrate a rule-of-thumb principle to reinvent an inertial focusing system and achieve an efficient focusing of particles ranging from 6 to 30 µm in diameter onto a single plane with less than 3 µm variance and over 95% focusing efficiency at highly scalable throughput (2.4-30 mL h-1) - a stark contrast to existing technologies that struggle with polydispersity. We demonstrated that DIF could be applied in a broad range of applications, particularly enabling high-yield continuous microparticle filtration and large-scale high-resolution single-cell morphological analysis of heterogeneous cell populations. This new technique is also readily compatible with the existing inertial microfluidic design and thus could unleash more diverse systems and applications.

Enhancing nicotine vaccine immunogenicity with liposomes.

A major liability of existing nicotine vaccine candidates is the wide variation in anti-nicotine immune responses among clinical trial participants. In order to address this liability, significant emphasis has been directed at evaluating adjuvants and delivery systems that confer more robust potentiation of the anti-nicotine immune response. Toward that end, we have initiated work that seeks to exploit the adjuvant effect of liposomes, with or without Toll-like receptor agonist(s). The results of the murine immunization study described herein support the hypothesis that a liposomal nicotine vaccine formulation may provide a means for addressing the immunogenicity challenge.

Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph.

BACKGROUND: The appearance of a topic in a document stream is signaled by a burst of activity, with certain features rising sharply in frequency as the topic emerges. Although temporal bar graph (TBG) is frequently applied to present the burst spot in the bibliographical study, none of the research has combined the inflection point (IP) to interpret the burst spot feature. The aims of this study are to improve the traditional TBG and apply the TBG to understand better the evolution of a topic (e.g., publications and citations for a given author). METHODS: The EISTL model, including entity, indicator, selection of a few vital ones (named attributes) with higher values in quantity (e.g., the citation data of the top 10 entities), TBG and line-chart plots to verify the trend of interest, was proposed to demonstrate the TBG as a whole. The IP locations compared to the median point in data along with the heap map and line-chart trend were identified. The burst strength was computed. A dashboard on Google Maps was designed and launched for bibliometric analysis. Four authors in MDPI (Multidisciplinary Digital Publishing Institute) journals named to be Citation Laureates 2021 were recruited to compare their research achievements shown on the TBG, particularly displaying the burst spots and the recent developments and stages (e.g., increasing, ready to increase, slowdown, or decreasing). RESULTS: We observed that the highest burst strengths in publication and citations are earned by Barry Halliwell (8.99) and Jean-Pierre Changeux (18.01). The breakthrough of TBG using the EISTL model to display the influence of authors in academics was made with 2 parts of the primary IP point and the trend feature in the data. CONCLUSION: The dashboard-type TBG shown on Google Maps is unique and innovative and able to provide deeper insights to readers, not merely limited to the publications and citations for a given author as we did in this study.

Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis.

BACKGROUND: The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis. METHODS: A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms). RESULTS: Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%. CONCLUSIONS: No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.

Analysis of citation trends to identify articles on delirium worth reading using DDPP model with temporal heatmaps (THM): A bibliometric analysis.

BACKGROUND: Delirium is one of the most common geriatric syndromes in older patients, accounting for 25% of hospitalized older patients, 31 to 35% of patients in the intensive care unit, and 8% to 17% of older patients in the emergency department (ED). A number of articles have been published in the literature regarding delirium. However, it is unclear about article citations evolving in the field. This study proposed a temporal heatmap (THM) that can be applied to all bibliographical studies for a better understanding of cited articles worth reading. METHODS: As of November 25, 2022, 11,668 abstracts published on delirium since 2013 were retrieved from the Web of Science core collection. Research achievements were measured using the CJAL score. Social network analysis was applied to examine clusters of keywords associated with core concepts of research. A THM was proposed to detect articles worth reading based on recent citations that are increasing. The 100 top-cited articles related to delirium were displayed on an impact beam plot (IBP). RESULTS: The results indicate that the US (12474), Vanderbilt University (US) (634), Anesthesiology (2168), and Alessandro Morandi (Italy) (116) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. Articles worthy of reading were highlighted on a THM and an IBP when an increasing trend of citations over the last 4 years was observed. CONCLUSION: The THM and IBP were proposed to highlight articles worth reading, and we recommend that more future bibliographical studies utilize the 2 visualizations and not restrict them solely to delirium-related articles in the future.

Analyzing the number of articles with network meta-analyses using chord diagrams and temporal heatmaps over the past 10 years: Bibliometric analysis.

BACKGROUND: Network meta-analyses (NMAs) are statistical techniques used to synthesize data from multiple studies and compare the effectiveness of different interventions for a particular disease or condition. They have gained popularity in recent years as a tool for evidence-based decision making in healthcare. Whether publications in NMAs have an increasing trend is still unclear. This study aimed to investigate the trends in the number of NMA articles over the past 10 years when compared to non-NMA articles. METHODS: The study utilized data from the Web of Science database, specifically searching for articles containing the term "meta-analysis" published between 2013 and 2022. The analysis examined the annual number of articles, as well as the countries, institutions, departments, and authors associated with the articles and the journals in which they were published. Ten different visualization techniques, including line charts, choropleth maps, chord diagrams, circle packing charts, forest plots, temporal heatmaps, impact beam plots, pyramid plots, 4-quadrant radar plots, and scatter plots, were employed to support the hypothesis that the number of NMA-related articles has increased (or declined) over the past decade when compared to non-NMA articles. RESULTS: Our findings indicate that there was no difference in mean citations or publication trends between NMA and non-NMA; the United States, McMaster University (Canada), medical schools, Dan Jackson from the United Kingdom, and the Journal of Medicine (Baltimore) were among the leading entities; NMA ranked highest on the coword analysis, followed by heterogeneity, quality, and protocol, with weighted centrality degrees of 32.51, 30.84, 29.43, and 24.26, respectively; and the number of NMA-related articles had increased prior to 2020 but experienced a decline in the past 3 years, potentially due to being overshadowed by the intense academic focus on COVID-19. CONCLUSION: It is evident that the number of NMA articles increased rapidly between 2013 and 2019 before leveling off in the years following. For researchers, policymakers, and healthcare professionals who are interested in evidence-based decision making, the visualizations used in this study may be useful.

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis.

BACKGROUND: The field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale. Network charts have traditionally been used to highlight author collaborations and coword phenomena (ACCP). It is necessary to determine whether chord network charts (CNCs) can provide a better understanding of ACCP, thus requiring clarification. This study aimed to achieve 2 objectives: evaluate global research trends in AI in intensive care medicine on publication outputs, coauthorships between nations, citations, and co-occurrences of keywords; and demonstrate the use of CNCs for ACCP in bibliometric analysis. METHODS: The web of science database was searched for a total of 1992 documents published between 2013 and 2022. The document type was limited to articles and article reviews, and titles and abstracts were screened for eligibility. The characteristics of the publications, including preferred journals, leading research countries, international collaborations, top institutions, and major keywords, were analyzed using the category-journal rank-authorship-L-index score and trend analysis. The 100 most highly cited articles are also listed in detail. RESULTS: Between 2018 and 2022, there was a sharp increase in publications, which accounted for 92.8% (1849/1992) of all papers included in the study. The United States and China were responsible for nearly 50% (936/1992) of the total publications. The leading countries, institutes, departments, authors, and journals in terms of publications were the US, Massachusetts Gen Hosp (US), Medical School, Zhongheng Zhang (China), and Science Reports. The top 3 primary keywords denoting research hotspots for AI in critically ill patients were mortality, model, and intensive care unit, with mortality having the highest burst strength (4.49). The keywords risk and system showed the highest growth trend (0.98) in counts over the past 4 years. CONCLUSIONS: This study provides valuable insights into the potential for ACCP and future research opportunities. For AI-based clinical research to become widely accepted in critical care practice, collaborative research efforts are necessary to strengthen the maturity and robustness of AI-driven models using CNCs for display.