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      A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis

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          Abstract

          Background

          Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings.

          Methods

          A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score.

          Results

          A PLT count <100×10 9/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model’s performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542.

          Conclusion

          Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.

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          Most cited references51

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          The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).

          Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.
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            Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study

            Summary Background Sepsis is life-threatening organ dysfunction due to a dysregulated host response to infection. It is considered a major cause of health loss, but data for the global burden of sepsis are limited. As a syndrome caused by underlying infection, sepsis is not part of standard Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimates. Accurate estimates are important to inform and monitor health policy interventions, allocation of resources, and clinical treatment initiatives. We estimated the global, regional, and national incidence of sepsis and mortality from this disorder using data from GBD 2017. Methods We used multiple cause-of-death data from 109 million individual death records to calculate mortality related to sepsis among each of the 282 underlying causes of death in GBD 2017. The percentage of sepsis-related deaths by underlying GBD cause in each location worldwide was modelled using mixed-effects linear regression. Sepsis-related mortality for each age group, sex, location, GBD cause, and year (1990–2017) was estimated by applying modelled cause-specific fractions to GBD 2017 cause-of-death estimates. We used data for 8·7 million individual hospital records to calculate in-hospital sepsis-associated case-fatality, stratified by underlying GBD cause. In-hospital sepsis-associated case-fatality was modelled for each location using linear regression, and sepsis incidence was estimated by applying modelled case-fatality to sepsis-related mortality estimates. Findings In 2017, an estimated 48·9 million (95% uncertainty interval [UI] 38·9–62·9) incident cases of sepsis were recorded worldwide and 11·0 million (10·1–12·0) sepsis-related deaths were reported, representing 19·7% (18·2–21·4) of all global deaths. Age-standardised sepsis incidence fell by 37·0% (95% UI 11·8–54·5) and mortality decreased by 52·8% (47·7–57·5) from 1990 to 2017. Sepsis incidence and mortality varied substantially across regions, with the highest burden in sub-Saharan Africa, Oceania, south Asia, east Asia, and southeast Asia. Interpretation Despite declining age-standardised incidence and mortality, sepsis remains a major cause of health loss worldwide and has an especially high health-related burden in sub-Saharan Africa. Funding The Bill & Melinda Gates Foundation, the National Institutes of Health, the University of Pittsburgh, the British Columbia Children's Hospital Foundation, the Wellcome Trust, and the Fleming Fund.
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              Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations.

              Reducing the global burden of sepsis, a recognized global health challenge, requires comprehensive data on the incidence and mortality on a global scale.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1298037Role: Role:
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                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                20 November 2023
                2023
                : 14
                : 1286203
                Affiliations
                [1] National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University , Shenyang, China
                Author notes

                Edited by: Bernd Rolauffs, University of Freiburg Medical Center, Germany

                Reviewed by: Chu Xianming, The Affiliated Hospital of Qingdao University, China; Ting Wang, Florida International University, United States

                *Correspondence: Xiaoxu Han, xxhan@ 123456yeah.net
                Article
                10.3389/fimmu.2023.1286203
                10694245
                38054005
                1b144c45-0eba-40be-8d00-9f56b51c5251
                Copyright © 2023 Diao, Zhao, Li, Li, Huo and Han

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 August 2023
                : 03 November 2023
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 51, Pages: 12, Words: 5966
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Chinese Academy of Medical Sciences Innovation Fund (2019RU017).
                Categories
                Immunology
                Original Research
                Custom metadata
                Inflammation

                Immunology
                machine learning,random forest,sepsis,prognosis,platelet-related genes
                Immunology
                machine learning, random forest, sepsis, prognosis, platelet-related genes

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