[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125399-en":3,"doc-seo-125399-105":30,"detail-sidebar-cat-0-en-105":83},{"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":20,"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},125399,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine Learning Models for Predicting the Need for Early Packed Red Blood Cell Transfusion in Multiple Trauma Patients","Trauma mortality can be reduced through earlier recognition and immediate treatment of hemorrhagic shock, including timely packed red blood cell (PRBC) transfusion. This retrospective longitudinal study developed and optimized machine learning models to predict whether at least one PRBC unit would be required within 24 hours after injury in multiple trauma patients. SHAP-based feature selection identified key predictors, and models were assessed using AUC, F1, sensitivity, specificity, PPV, and NPV. Random Forest achieved the highest performance, with strong discrimination and sensitivity/specificity balance, while further multicenter validation is needed.","Archives of Academic Emergency Medicine. 2026; 14(1): e1  \n| \u003Cbr>ORIGINAL RESEARCH |  |\n| --- | --- |\n| Machine Learning Models for Predicting the Need for Early Packed Red Blood Cell Transfusion in Multiple Trauma Patients\u003Cbr>Saeed Safari1,2 , Hamed Zarei3,4 ∗ , Kiarash Zare3 , Seyed HadiAghili1,5,6 , Narges Saadatipour6 , Mohammadhossein Vazirizadeh-Mahabadi6 , Mahmoud Yousefifard7 , Ali Sharifi8†\u003Cbr>1. Research Center for Trauma in Police Operations, Directorate of Health, Rescue & Treatment, Police Headquarter, Tehran, Iran\u003Cbr>2. Men’s Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran\u003Cbr>3. Emergency Care Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran\u003Cbr>[4. InoVision.ae](4. InoVision.ae), Dubai, United Arab Emirates\u003Cbr>5. Neurosurgery Department, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran\u003Cbr>6. Department of Neurosurgery, Valiasr Hospital, Tehran, Iran\u003Cbr>7. Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran\u003Cbr>8. Hepatopancreaticobiliary & Organ Transplantation Surgery Department, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran\u003Cbr>Received: July 2025; Accepted: August 2025; Published online: 1 October 2025 |  |\n| Abstract: Introduction: One of the preventable contributors to trauma mortality is hemorrhagic shock, which requires early recognition and immediate intervention. In this retrospective analysis, we aimed to develop and optimize machine learning (ML) algorithms to predict the need for packed red blood cell (PRBC) transfusion within 24 hours of injury in multiple trauma patients Methods: This retrospective longitudinal study analyzed consecutive multiple trauma pa |  |\n| tients admitted to the emergency department. The outcome was transfusion of at least one unit ofPRBC within the first\u003Cbr>24 hours of traumatic injury. SHAP analysis was employed for feature selection, and the five key predictors were identified and entered in the models: Glasgow Coma Scale (GCS), hemoglobin (Hb), pulse rate (PR), systolic blood pressure (SBP), and pulse pressure. The dataset was split 80:20 for training/testing, and multiple machine learning algorithms were evaluated based on area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, specificity, positive predictive value (PPV ), and negative predictive value (NPV ) . Results: The study cohort consisted of 908 patients, with a median age of 34 years. PRBC transfusions were more common in older adults with lower GCS scores, higher PR, lower SBP, lower pulse pressure, and lower Hb levels on admission. Among the machine learning models, Random Forest performed best (AUC: 0.997, sensitivity: 0.938, specificity: 0.994), followed by K-Nearest Neighbors and Logistic Regression, both of which showed perfect specificity but lower sensitivity. Conclusion: Random Forest outperformed other ML algorithms, achieving high discriminative ability, sensitivity, and specificity. PR, GCS, Hb, SBP, and pulse pressure were the most influential predictors of the need for early transfusion. Despite promising results, further multicenter validation studies are needed to confirm the real-world applicability of these models.\u003Cbr>Keywords: Mathematical model; Machine learning; Wounds and injuries; Glasgow coma scale |  |\n|  Cite this article as:  Safari S, Zarei H, Zare K, et al. Machine Learning Models for Predicting the Need for Early Packed Red Blood Cell Transfu- |  |\n| sion in Multiple Trauma Patients. Arch Acad Emerg Med. 2026; 14(1): e1. [https://doi.org/10.22037/aaem.v14i1.2820](https://doi.org/10.22037/aaem.v14i1.2820) . |  |\n| 1. Introduction\u003Cbr>Globally, trauma is the sixth leading cause of death, accounting for 9% of mortalities worldwide, and it is considered the\u003Cbr>∗ Corresponding Author: Hamed Zarei; Emergency Care Promotion Research Center, Shahid Beheshti University of Medical S","cbCaij4Nmz3lsSG0","https://ap.wps.com/l/cbCaij4Nmz3lsSG0","pdf",369080,1,9,"English","en",105,"# Introduction\n## Predicting hemorrhagic shock and early PRBC transfusion\n# Methods\n## Study design and cohort\n## Outcome definition\n## Feature selection and modeling\n## Evaluation metrics\n# Results\n## Cohort characteristics\n## Feature importance and trends\n## Model comparison performance\n# Conclusion\n## Clinical implications and need for validation","[{\"question\":\"Do the authors conclude the models are ready for real-world use?\",\"answer\":\"Despite strong results, the paper concludes that further multicenter validation studies are required to confirm real-world applicability.\"}]","Machine Learning Models for Predicting the Need for Early Packed Red Blood Cell Transfusion in Multiple Trauma Patients | PDF",1785898675,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-models-for-predicting-the-need-for-early-packed-red-blood-cell-transfusion-in-multiple-trauma-patients","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-for-predicting-the-need-for-early-packed-red-blood-cell-transfusion-in-multiple-trauma-patients/125399/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Do the authors conclude the models are ready for real-world use?","Question",{"text":75,"@type":76},"Despite strong results, the paper concludes that further multicenter validation studies are required to confirm real-world applicability.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]