[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124823-en":3,"doc-seo-124823-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124823,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms","Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. The study investigates the role of protein biomarkers in predicting outcomes in ICH patients by measuring 22 proteins using targeted proteomics in serum samples (N=150). Poor outcomes were defined as modified Rankin scale (mRS) 3–6, incorporating clinical variables and protein biomarkers in regression and random forest machine learning models. Internal validation used five-fold cross-validation and bootstrapping.","PLOS ONE  \nOPEN ACCESS  \nCitation: Misra S, Kawamura Y, Singh P, Sengupta S, Nath M, Rahman Z, et al. (2024) Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms. PLoS ONE 19(6): e0296616 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0296616](doi.org/10.1371/journal.pone.0296616)  \nEditor: Shashank Shekhar, Duke University Medical Center: Duke University Hospital, UNITED STATES  \nReceived: December 20, 2023  \nAccepted: May 16, 2024  \nPublished: June 3, 2024  \nCopyright: © 2024 Misra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [15] partner repository with the dataset identifier PXD032917. The dataset and the machine learning analysis codes can be accessed in the Supporting Information files.  \nFunding: This study was supported in part by the AIIMS Intramural Research Grant (F. No. 8-762/A- 762/2019/RS) . The funders had no role in study  \nRESEARCH ARTICLE  \nPrognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms  \nShubham Misra1,2, Yuki Kawamura2,3, Praveen Singh4, Shantanu Sengupta4, Manabesh Nath1, Zuhaibur Rahman4, Pradeep Kumar1, Amit Kumar1,5, PraveenAggarwal6, Achal K. Srivastava1, Awadh K. Pandit1, Dheeraj Mohania7, Kameshwar Prasad1, Nishant K. Mishra2 *, Deepti Vibha1 *  \n1 Department of Neurology, All India Institute of Medical Sciences, New Delhi, India, 2 Department of Neurology, Yale University School of Medicine, New Haven, CT, United States of America, 3 School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom, 4 CSIR-Institute of Genomics and Integrative Biology, New Delhi, India, 5 Department of Laboratory Medicine, Rajendra Institute of Medical Sciences, Ranchi, India, 6 Department of Emergency Medicine, All India Institute of Medical Sciences, New Delhi, India, 7 Department of Dr. RP Centre, All India Institute of Medical Sciences, New Delhi, India  \n* [deeptivibha@gmail.com](deeptivibha@gmail.com) (DV); [nishant.mishra@yale.edu](nishant.mishra@yale.edu) (NKM)  \nAbstract  \nEarly prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers’ role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150) . We defined poor outcomes as modified Rankin scale score of 3–6 . We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI) . We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9 .23; 95%CI 2.41–35.33), alpha-2-macroglobulin (aOR 5 .57; 95%CI 1.26–24.59), and SerpinA11 (aOR 9 .33; 95%CI 1.09–79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6 .32; 95%CI 1.82–21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2 .08; 95%CI 1.24–3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19–3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomesand UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0 .8","cbCaijswOGXcE9N5","https://ap.wps.com/l/cbCaijswOGXcE9N5","pdf",1788541,1,17,"English","en",105,"# Abstract\n## Objectives and study design\n## Biomarker measurement and modeling\n## Key predictors and performance\n# Introduction\n## Burden of ICH and clinical need","[{\"question\":\"What was the main goal of the study on intracerebral hemorrhage?\",\"answer\":\"To investigate how protein biomarkers can prognosticate patient outcomes in intracerebral hemorrhage using targeted proteomics and machine learning.\"},{\"question\":\"How were poor outcomes defined for analysis?\",\"answer\":\"Poor outcomes were defined as a modified Rankin scale (mRS) score of 3–6.\"},{\"question\":\"Which modeling approaches were used to predict outcomes?\",\"answer\":\"Multivariable regression models and random forest-based machine learning algorithms, with five-fold cross-validation and bootstrapping for internal validation.\"}]","Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms | 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