Prediction of clinical outcomes after percutaneous coronary intervention: Machine-learning analysis of the National Inpatient Sample

Galimzhanov, Akhmetzhan; Matetic, Andrija; Tenekecioglu, Erhan; Mamas, Mamas A.

doi:10.1016/j.ijcard.2023.131339

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…Based on laboratory findings and clinical features at the time of initial treatment, three IVIG risk prediction models have been established by logistic regression analysis and verified externally 20–22 . ML can handle nonlinear input variables and therefore sometimes outperforms traditional models 51,52 . Currently, ML is used in clinical diagnosis and outcome prediction in many medical fields 53–55 .…”

Section: Discussionmentioning

confidence: 99%

“… 20 , 21 , 22 ML can handle nonlinear input variables and therefore sometimes outperforms traditional models. 51 , 52 Currently, ML is used in clinical diagnosis and outcome prediction in many medical fields. 53 , 54 , 55 The diagnostic criteria for IVIG resistance in KD are based on clinical parameters, and while traditional predictive models can incorporate only a small number of clinical features, models computed by ML can integrate all aspects of clinical indicators, including continuous variables, without the need for categorization.…”

Section: Discussionmentioning

confidence: 99%

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Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasaki disease: A bibliometric analysis (1997−2023)

Zhang,

Huang,

et al. 2024

Immunity Inflam & Disease

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BackgroundKawasaki disease (KD) is an autoimmune disease with cardiovascular disease as its main complication, mainly affecting children under 5 years old. KD treatment has made tremendous progress in recent years, but intravenous immunoglobulin (IVIG) resistance remains a major dilemma. Bibliometric analysis had not been used previously to summarize and analyze publications related to IVIG resistance in KD. This study aimed to provide an overview of the knowledge framework and research hotspots in this field through bibliometrics, and provide references for future basic and clinical research.MethodsThrough bibliometric analysis of relevant literature published on the Web of Science Core Collection (WoSCC) database between 1997 and 2023, we investigated the cooccurrence and collaboration relationships among countries, institutions, journals, and authors and summarized key research topics and hotspots.ResultsFollowing screening, a total of 364 publications were downloaded, comprising 328 articles and 36 reviews. The number of articles on IVIG resistance increased year on year and the top three most productive countries were China, Japan, and the United States. Frontiers in Pediatrics had the most published articles, and the Journal of Pediatrics had the most citations. IVIG resistance had been studied by 1889 authors, of whom Kuo Ho Chang had published the most papers.ConclusionResearch in the field was focused on risk factors, therapy (atorvastatin, tumor necrosis factor‐alpha inhibitors), pathogenesis (gene expression), and similar diseases (multisystem inflammatory syndrome in children, MIS‐C). “Treatment,” “risk factor,” and “prediction” were important keywords, providing a valuable reference for scholars studying this field. We suggest that, in the future, more active international collaborations are carried out to study the pathogenesis of IVIG insensitivity, using high‐throughput sequencing technology. We also recommend that machine learning techniques are applied to explore the predictive variables of IVIG resistance.

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Section: Discussionmentioning

confidence: 99%