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Nikouline A, Feng J, Rudzicz F, Nathens A, Nolan B. Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept. Eur J Trauma Emerg Surg 2024:10.1007/s00068-023-02423-5. [PMID: 38265444 DOI: 10.1007/s00068-023-02423-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/04/2023] [Indexed: 01/25/2024]
Abstract
PURPOSE Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data. METHODS Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequently balanced our dataset and used the Boruta algorithm to determine feature selection. Massive transfusion was defined as five units at 4 h and ten units at 24 h. Six machine learning models were trained on the balanced dataset and tested on the original. RESULTS A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the receiver-operating characteristic curve of 0.83. Extreme gradient boost modeling slightly outperformed and demonstrated adequate predictive performance with pre-hospital data only, as well as 4-h transfusion thresholds. CONCLUSIONS We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential utility of artificial intelligence as a clinical decision support tool.
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Affiliation(s)
- Anton Nikouline
- Department of Emergency Medicine, London Health Sciences Centre, 800 Commissioners Road E, London, ON, N6A 5W9, Canada.
- Division of Critical Care and Emergency Medicine, Department of Medicine, Western University, London, ON, Canada.
| | - Jinyue Feng
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Frank Rudzicz
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Avery Nathens
- Department of Surgery, Sunnybrook Health Sciences Center, Toronto, ON, Canada
- American College of Surgeons, Chicago, IL, USA
| | - Brodie Nolan
- Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada
- International Centre for Surgical Safety, St. Michael's Hospital, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
- Department of Emergency Medicine, St. Michael's Hospital, Toronto, ON, Canada
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Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, Stanworth SJ. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64:162-184. [PMID: 37950535 DOI: 10.1111/trf.17582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Suzanne Maynard
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Joseph Farrington
- Institute of Health Informatics, University College London, London, UK
| | - Samah Alimam
- Haematology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Hayley Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK
| | - Wai Keong Wong
- Director of Digital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Simon J Stanworth
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Krösbacher A, Fries D, Thaler M. Unkontrollierbare Blutungen prähospital – Retten Blutprodukte Leben? NOTARZT 2023. [DOI: 10.1055/a-1910-4518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Affiliation(s)
- Armin Krösbacher
- Univ. Klinik für Anästhesie und Intensivmedizin, Medizinische Universität Innsbruck, Innsbruck, Österreich
| | - Dietmar Fries
- Univ. Klinik für Anästhesie und Intensivmedizin, Medizinische Universität Innsbruck, Innsbruck, Österreich
| | - Markus Thaler
- Univ. Klinik für Anästhesie und Intensivmedizin, Medizinische Universität Innsbruck, Innsbruck, Österreich
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Yin G, Radulovic N, O'Neill M, Lightfoot D, Nolan B. Predictors of transfusion in trauma and their utility in the prehospital environment: a scoping review. PREHOSP EMERG CARE 2022:1-11. [PMID: 36066217 DOI: 10.1080/10903127.2022.2120935] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Background: Hemorrhage is a leading cause of preventable mortality from trauma, necessitating resuscitation through blood product transfusions. Early and accurate identification of patients requiring transfusions in the prehospital setting may reduce delays in time to transfusion upon arrival to hospital, reducing mortality. The purpose of this study is to characterize existing literature on predictors of transfusion and analyze their utility in the prehospital context.Objectives: The objectives of this study are to characterize the existing quantity and quality of literature regarding predictor scores for transfusion in injured patients, and to analyse the utility of predictor scores for massive transfusions in the prehospital setting and identify prehospital predictor scores for future research.Methods: A search strategy was developed in consultation with information specialists. A literature search of OVID MEDLINE from 1946 to present was conducted for primary studies evaluating the predictive ability of scoring systems or single variables in predicting transfusion in all trauma settings.Results: Of the 5824 studies were identified, 5784 studies underwent title and abstract screening, 94 studies underwent full text review, and 72 studies were included in the final review. We identified 16 single variables and 52 scoring systems for predicting transfusion. Amongst single predictor variables, fluids administered and systolic blood pressure had the highest reported sensitivity (100%) and specificity (89%) for massive transfusion protocol (MTP) activation respectively. Amongst scoring systems for transfusion, the Shock Index and Modified Shock Index had the highest reported sensitivity (96%), while the Pre-arrival Model had the highest reported specificity (95%) for MTP activation. Overall, 20 scores were identified as being applicable to the prehospital setting, 25 scores were identified as being potentially applicable, and seven scores were identified as being not applicable.Conclusions: We identified an extensive list of predictive single variables, validated scoring systems, and derived models for massive transfusion, presented their properties, and identified those with potential utility in the prehospital setting. By further validating applicable scoring tools in the prehospital setting, we may begin to administer more timely transfusions in the trauma population.
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Affiliation(s)
- Grace Yin
- School of Medicine, Faculty of Health Sciences, Queen's University, Kingston, Canada
| | - Nada Radulovic
- Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Canada
| | - Melissa O'Neill
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - David Lightfoot
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Brodie Nolan
- Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Canada.,Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.,Department of Emergency Medicine, St. Michael's Hospital, Toronto, Canada
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Feng YN, Xu ZH, Liu JT, Sun XL, Wang DQ, Yu Y. Intelligent prediction of RBC demand in trauma patients using decision tree methods. Mil Med Res 2021; 8:33. [PMID: 34024283 PMCID: PMC8142481 DOI: 10.1186/s40779-021-00326-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 05/11/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. METHODS A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC). RESULTS For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657-0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633-0.751) and the XGBoost (AUC 0.71, 95% CI 0.654-0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744-0.850) and the CRT (AUC 0.82, 95% CI 0.779-0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter. CONCLUSIONS The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment.
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Affiliation(s)
- Yan-Nan Feng
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, No. 28, Fuxing Rd., Beijing, 100853 China
| | - Zhen-Hua Xu
- Beijing Hexing Chuanglian Health Technology Co., Ltd., Beijing, 100176 China
| | - Jun-Ting Liu
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, No. 28, Fuxing Rd., Beijing, 100853 China
| | - Xiao-Lin Sun
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, No. 28, Fuxing Rd., Beijing, 100853 China
| | - De-Qing Wang
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, No. 28, Fuxing Rd., Beijing, 100853 China
| | - Yang Yu
- Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, No. 28, Fuxing Rd., Beijing, 100853 China
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If not now, when? The value of the MTP in managing massive bleeding. BLOOD TRANSFUSION = TRASFUSIONE DEL SANGUE 2020; 18:415-418. [PMID: 32955418 DOI: 10.2450/2020.0275-20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Moss M, White SD, Warner H, Dvorkin D, Fink D, Gomez-Taborda S, Higgins C, Krisciunas GP, Levitt JE, McKeehan J, McNally E, Rubio A, Scheel R, Siner JM, Vojnik R, Langmore SE. Development of an Accurate Bedside Swallowing Evaluation Decision Tree Algorithm for Detecting Aspiration in Acute Respiratory Failure Survivors. Chest 2020; 158:1923-1933. [PMID: 32721404 DOI: 10.1016/j.chest.2020.07.051] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 07/06/2020] [Accepted: 07/12/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The bedside swallowing evaluation (BSE) is an assessment of swallowing function and airway safety during swallowing. After extubation, the BSE often is used to identify the risk of aspiration in acute respiratory failure (ARF) survivors. RESEARCH QUESTION We conducted a multicenter prospective study of ARF survivors to determine the accuracy of the BSE and to develop a decision tree algorithm to identify aspiration risk. STUDY DESIGN AND METHODS Patients extubated after ≥ 48 hours of mechanical ventilation were eligible. Study procedures included the BSE followed by a gold standard evaluation, the flexible endoscopic evaluation of swallowing (FEES). RESULTS Overall, 213 patients were included in the final analysis. Median time from extubation to BSE was 25 hours (interquartile range, 21-45 hours). The FEES was completed 1 hour after the BSE (interquartile range, 0.5-2 hours). A total of 33% (70/213; 95% CI, 26.6%-39.2%) of patients aspirated on at least one FEES bolus consistency test. Thin liquids were the most commonly aspirated consistency: 27% (54/197; 95% CI, 21%-34%). The BSE detected any aspiration with an accuracy of 52% (95% CI, 45%-58%), a sensitivity of 83% (95% CI, 74%-92%), and negative predictive value (NPV) of 81% (95% CI, 72%-91%). Using recursive partitioning analyses, a five-variable BSE-based decision tree algorithm was developed that improved the detection of aspiration with an accuracy of 81% (95% CI, 75%-87%), sensitivity of 95% (95% CI, 90%-98%), and NPV of 97% (95% CI, 95%-99%). INTERPRETATION The BSE demonstrates variable accuracy to identify patients at high risk for aspiration. Our decision tree algorithm may enhance the BSE and may be used to identify patients at high risk for aspiration, yet requires further validation. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT02363686; URL: www.clinicaltrials.gov.
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Affiliation(s)
- Marc Moss
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO.
| | - S David White
- University of Colorado Denver Rehabilitation Therapy Services, University of Colorado Hospital, Aurora, CO
| | - Heather Warner
- Section of Otolaryngology, Department of Surgery, Yale School of Medicine, New Haven, CT; Department of Communication Disorders, Southern Connecticut State University, New Haven, CT
| | - Daniel Dvorkin
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO; The Bioinformatics CRO, Inc, Niceville, FL
| | - Daniel Fink
- Department of Otolaryngology, University of Colorado School of Medicine, Aurora, CO
| | | | - Carrie Higgins
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO
| | - Gintas P Krisciunas
- Department of Otolaryngology, Boston Medical Center, Boston, MA; Department of Otolaryngology, Boston University School of Medicine, Boston, MA
| | - Joseph E Levitt
- Division of Pulmonary and Critical Care, Stanford University, Stanford, CA
| | - Jeffrey McKeehan
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO
| | - Edel McNally
- Department of Otolaryngology, Boston Medical Center, Boston, MA; Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA
| | - Alix Rubio
- Department of Otolaryngology, Boston Medical Center, Boston, MA
| | - Rebecca Scheel
- Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA; Division of Speech Language Pathology, Massachusetts General Hospital, Boston, MA
| | - Jonathan M Siner
- Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT
| | - Rosemary Vojnik
- Division of Pulmonary and Critical Care, Stanford University, Stanford, CA
| | - Susan E Langmore
- Department of Otolaryngology, Boston Medical Center, Boston, MA; Department of Otolaryngology, Boston University School of Medicine, Boston, MA; Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA
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