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Durston A, Chapman J, Marshall D, Mason L. Patterns of major trauma admissions to a level 1 trauma centre: A five year database analysis. Injury 2024; 55:111237. [PMID: 38096747 DOI: 10.1016/j.injury.2023.111237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 11/15/2023] [Accepted: 11/24/2023] [Indexed: 01/29/2024]
Abstract
INTRODUCTION It is only in recent years that major trauma systems and networks have been operating in the UK. High-quality data is available from the Trauma Audit and Research Network (TARN) database, enabling regional analysis. Our aim was to analyse Trauma Team Activations within the Cheshire and Merseyside major trauma network and discuss the implications of these data on resource allocation, training and trauma prevention. METHODS A retrospective analysis was performed for all patients requiring Trauma Team Activation (TTA) at a category one adult Major Trauma Centre (MTC) who were submitted to the TARN database from the 1st January 2015 to the 1st January 2020. Data collected included the date and time of arrival, location of injury and Injury Severity Score (ISS) in addition to routine demographic data. Dates of major sporting events and school holidays were obtained. RESULTS 4811 patients were identified. The median age was 57 years; 65.8 % were male. The mean frequency of TTAs was 18.5 per week. Patterns identified include annual peaks during the summer months, October and December, weekly peaks on Thursdays and Sundays and daily peaks between 16:00 and 23:59 with 45.0 % of TTAs occurring between these hours. There were 5.9 additional TTAs per week during the Isle of Man TT races. The median ISS increased from 14 to 23 for TT race TTAs and from 14 to 36 for Manx Grand Prix TTAs. Those injured during the TT races were twice as likely to require surgery and those injured during the MGP required five additional days in intensive care. School holidays did not independently affect major trauma volumes. CONCLUSIONS Major trauma in Cheshire and Merseyside did follow distinct patterns according to calendar month, day and time. Major motorsport increased trauma volumes and severity; school holidays did not. Such analysis could enable Major Trauma Centres to tailor the supply of trauma services to meet a predictable local demand for the benefit of our staff and patients.
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Affiliation(s)
- Abigail Durston
- Department of Trauma and Orthopaedics, Aneurin Bevan University Health Board, Royal Gwent Hospital, Cardiff Rd, Newport NP20 2UB, United Kingdom.
| | - James Chapman
- Department of Trauma and Orthopaedics, Liverpool University Hospitals NHS Foundation Trust, Prescot Street, Liverpool, Merseyside L7 8XP, United Kingdom; School of Medicine, Faculty of Health and Life Sciences, University of Liverpool, Cedar House, Ashton Street, Liverpool, L69 3GE, United Kingdom
| | - Daniel Marshall
- Department of Trauma and Orthopaedics, Liverpool University Hospitals NHS Foundation Trust, Prescot Street, Liverpool, Merseyside L7 8XP, United Kingdom
| | - Lyndon Mason
- Department of Trauma and Orthopaedics, Liverpool University Hospitals NHS Foundation Trust, Prescot Street, Liverpool, Merseyside L7 8XP, United Kingdom; School of Medicine, Faculty of Health and Life Sciences, University of Liverpool, Cedar House, Ashton Street, Liverpool, L69 3GE, United Kingdom
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Beiriger J, Lu L, Silver D, Brown JB. Impact of patient, system, and environmental factors on utilization of air medical transport after trauma. J Trauma Acute Care Surg 2024; 96:62-69. [PMID: 37789517 PMCID: PMC10841710 DOI: 10.1097/ta.0000000000004153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
BACKGROUND Air medical transport (AMT) improves outcomes for severely injured patients. The decision to fly patients is complex and must consider multiple factors. Our objective was to evaluate the interaction between geography, patient and environmental factors, and emergency medical services (EMS) system resources on AMT after trauma. We hypothesize that significant geographic variation in AMT utilization will be associated with varying levels of patient, environmental, and EMS resources. METHODS Patients transported by EMS in the Pennsylvania state trauma registry 2000 to 2017 were included. We used our previously developed Air Medical Prehospital Triage (AMPT; ≥2 points triage to AMT) score and Geographic Emergency Medical Services Index (GEMSI; higher indicates more system resources) as measures for patient factors and EMS resources, respectively. A mixed-effects logistic regression model determined the association of AMT utilization with patient, system, and environmental variables. RESULTS There were 195,354 patients included. Fifty-five percent of variation in AMT utilization was attributed to geographic differences. Triage to AMT by the AMPT score was associated with nearly twice the odds of AMT utilization (adjusted odds ratio, 1.894; 95% confidence interval, 1.765-2.032; p < 0.001). Each 1-point increase in GEMSI was associated with a 6.1% reduction in odds of AMT (0.939; 0.922-0.957; p < 0.001). Younger age, rural location, and more severe injuries were also associated with increased odds of AMT ( p < 0.05). When categorized by GEMSI level, the AMPT score and patient factors were more important for predicting AMT utilization in the middle tercile (moderate EMS resources) compared with the lower (low EMS resources) and higher tercile (high EMS resources). Weather, season, time-of-day, and traffic were all associated with AMT utilization ( p < 0.05). CONCLUSION Patient, system, and environmental factors are associated with AMT utilization, which varies geographically and by EMS/trauma system resource availability. A more comprehensive approach to AMT triage could reduce variation and allow more tailored efforts toward optimizing resource allocation and outcomes. LEVEL OF EVIDENCE Prognostic and Epidemiological; Level III.
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Affiliation(s)
- Jamison Beiriger
- Division of Trauma and General Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
| | - Liling Lu
- Division of Trauma and General Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
| | - David Silver
- Division of Trauma and General Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
| | - Joshua B. Brown
- Division of Trauma and General Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
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田 楚, 陈 翔, 朱 桓, 秦 晟, 石 柳, 芮 云. [Application and prospect of machine learning in orthopaedic trauma]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2023; 37:1562-1568. [PMID: 38130202 PMCID: PMC10739668 DOI: 10.7507/1002-1892.202308064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
Objective To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. Methods A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. Results The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. Conclusion The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
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Affiliation(s)
- 楚伟 田
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 翔溆 陈
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 桓毅 朱
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 晟博 秦
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 柳 石
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 云峰 芮
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
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Stonko DP, Hicks CW. Mature artificial intelligence- and machine learning-enabled medical tools impacting vascular surgical care: A scoping review of late-stage, US Food and Drug Administration-approved or cleared technologies relevant to vascular surgeons. Semin Vasc Surg 2023; 36:460-470. [PMID: 37863621 PMCID: PMC10589449 DOI: 10.1053/j.semvascsurg.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/14/2023] [Accepted: 06/20/2023] [Indexed: 10/22/2023]
Abstract
Artificial intelligence and machine learning (AI/ML)-enabled tools are shifting from theoretical or research-only applications to mature, clinically useful tools. The goal of this article was to provide a scoping review of the most mature AI/ML-enabled technologies reviewed and cleared by the US Food and Drug Administration relevant to the field of vascular surgery. Despite decades of slow progress, this landscape is now evolving rapidly, with more than 100 AI/ML-powered tools being approved by the US Food and Drug Administration each year. Within the field of vascular surgery specifically, this review identified 17 companies with mature technologies that have at least one US Food and Drug Administration clearance, all occurring between 2016 and 2022. The maturation of these technologies appears to be accelerating, with improving regulatory clarity and clinical uptake. The early AI/ML-powered devices extend or amplify clinically entrenched platform technologies and tend to be focused on the diagnosis or evaluation of time-sensitive, clinically important pathologies (eg, reading Digital Imaging and Communications in Medicine-compliant computed tomography images to identify pulmonary embolism), or when physician efficiency or time savings is improved (eg, preoperative planning and intraoperative guidance). The majority (>75%) of these technologies are at the intersection of radiology and vascular surgery. It is becoming increasingly important that the contemporary vascular surgeon understands this shifting paradigm, as these once-nascent technologies are finally maturing and will be encountered with increasingly regularity in daily clinical practice.
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Affiliation(s)
- David P Stonko
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Halsted 668, Baltimore, MD 21287
| | - Caitlin W Hicks
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Halsted 668, Baltimore, MD 21287.
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Beyene RT, Stonko DP, Gondek SP, Morrison JJ, Dennis BM. Identifying temporal variations in burn admissions. PLoS One 2023; 18:e0286154. [PMID: 37289792 PMCID: PMC10249893 DOI: 10.1371/journal.pone.0286154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/10/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Variations in admission patterns have been previously identified in non-elective surgical services, but minimal data on the subject exists with respect to burn admissions. Improved understanding of the temporal pattern of burn admissions could inform resource utilization and clinical staffing. We hypothesize that burn admissions have a predictable temporal distribution with regard to the time of day, day of week, and season of year in which they present. STUDY DESIGN A retrospective, cohort observational study of a single burn center from 7/1/2016 to 3/31/2021 was performed on all admissions to the burn surgery service. Demographics, burn characteristics, and temporal data of burn admissions were collected. Bivariate absolute and relative frequency data was captured and plotted for all patients who met inclusion criteria. Heat-maps were created to visually represent the relative admission frequency by time of day and day of week. Frequency analysis grouped by total body surface area against time of day and relative encounters against day of year was performed. RESULTS 2213 burn patient encounters were analyzed, averaging 1.28 burns per day. The nadir of burn admissions was from 07:00 and 08:00, with progressive increase in the rate of admissions over the day. Admissions peaked in the 15:00 hour and then plateaued until midnight (p<0.001). There was no association between day of week in the burn admission distribution (p>0.05), though weekend admissions skewed slightly later (p = 0.025). No annual, cyclical trend in burn admissions was identified, suggesting that there is no predictable seasonality to burn admissions, though individual holidays were not assessed. CONCLUSION Temporal variations in burn admissions exist, including a peak admission window late in the day. Furthermore, we did not find a predictable annual pattern to use in guiding staffing and resource allocation. This differs from findings in trauma, which identified admission peaks on the weekends and an annual cycle that peaks in spring and summer.
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Affiliation(s)
- Robel T. Beyene
- Division of Acute Care Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - David P. Stonko
- Department of Surgery, The Johns Hopkins Hospital, Baltimore, Maryland, United States of America
| | - Stephen P. Gondek
- Division of Acute Care Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jonathan J. Morrison
- Mayo Clinic Division of Vascular and Endovascular Surgery, Rochester, MN, United States of America
| | - Bradley M. Dennis
- Division of Acute Care Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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Stonko DP, Weller JH, Gonzalez Salazar AJ, Abdou H, Edwards J, Hinson J, Levin S, Byrne JP, Sakran JV, Hicks CW, Haut ER, Morrison JJ, Kent AJ. A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay. Surg Innov 2023; 30:356-365. [PMID: 36397721 PMCID: PMC10188661 DOI: 10.1177/15533506221139965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS). METHODS Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019. Trauma patients with one or fewer days LOS were excluded. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized on area under the receiver operator characteristic curve (AUROC). The predictive performance of the model was assessed on a separate test set using binary classification measures of accuracy, precision, and error. RESULTS 2953 admitted trauma patients with more than one-day LOS were included in this study. They were 70% male, 60% white, and averaged 47 years-old (SD: 21). 28% were penetrating trauma. Median length of stay was 5 days (IQR 3-9). For prediction of prolonged LOS, the deep neural network achieved an AUROC of 0.80 (95% CI: 0.786-0.814) specificity was 0.95, sensitivity was 0.32, with an overall accuracy of 0.79. CONCLUSION Machine learning can predict, with excellent specificity, trauma patients who will have prolonged length of stay with only physiologic and demographic data available at the time of admission. These patients may benefit from additional resources with respect to disposition planning at the time of admission.
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Affiliation(s)
- David P. Stonko
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
- R. Adams Cowley Shock Trauma Center, Baltimore, MD, USA
| | - Jennine H. Weller
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Andres J. Gonzalez Salazar
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Hossam Abdou
- R. Adams Cowley Shock Trauma Center, Baltimore, MD, USA
| | | | - Jeremiah Hinson
- Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Scott Levin
- Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James P. Byrne
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Joseph V. Sakran
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Caitlin W. Hicks
- Division of Vascular and Endovascular Therapy, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Elliott R. Haut
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Armstrong Institute for Patient Safety and Quality, Johns Hopkins Medicine, Baltimore, MD, USA
- Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins Baltimore, MD, USA
| | | | - Alistair J. Kent
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
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Hunter OF, Perry F, Salehi M, Bandurski H, Hubbard A, Ball CG, Morad Hameed S. Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg 2023; 18:16. [PMID: 36879293 PMCID: PMC9987401 DOI: 10.1186/s13017-022-00469-1] [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: 10/29/2022] [Accepted: 12/12/2022] [Indexed: 03/08/2023] Open
Abstract
Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within within trauma care. Our paper overviews the current uses of AI along the continuum of trauma care, including injury prediction, triage, emergency department volume, assessment, and outcomes. Starting at the point of injury, algorithms are being used to predict severity of motor vehicle crashes, which can help inform emergency responses. Once on the scene, AI can be used to help emergency services triage patients remotely in order to inform transfer location and urgency. For the receiving hospital, these tools can be used to predict trauma volumes in the emergency department to help allocate appropriate staffing. After patient arrival to hospital, these algorithms not only can help to predict injury severity, which can inform decision-making, but also predict patient outcomes to help trauma teams anticipate patient trajectory. Overall, these tools have the capability to transform trauma care. AI is still nascent within the trauma surgery sphere, but this body of the literature shows that this technology has vast potential. AI-based predictive tools in trauma need to be explored further through prospective trials and clinical validation of algorithms.
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Affiliation(s)
- Olivia F Hunter
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Frances Perry
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Mina Salehi
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | | | - Alan Hubbard
- University of California, Berkeley School of Public Health, Berkeley, USA
| | - Chad G Ball
- Department of Surgery, University of Calgary, Calgary, Canada
| | - S Morad Hameed
- Department of Surgery, University of British Columbia, Vancouver, Canada. .,T6 Health Systems, Boston, USA.
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Peng HT, Siddiqui MM, Rhind SG, Zhang J, da Luz LT, Beckett A. Artificial intelligence and machine learning for hemorrhagic trauma care. Mil Med Res 2023; 10:6. [PMID: 36793066 PMCID: PMC9933281 DOI: 10.1186/s40779-023-00444-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 02/01/2023] [Indexed: 02/17/2023] Open
Abstract
Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.
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Affiliation(s)
- Henry T Peng
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, M3K 2C9, Canada.
| | - M Musaab Siddiqui
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, M3K 2C9, Canada
| | - Shawn G Rhind
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, M3K 2C9, Canada
| | - Jing Zhang
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, M3K 2C9, Canada
| | | | - Andrew Beckett
- St. Michael's Hospital, Toronto, ON, M5B 1W8, Canada
- Royal Canadian Medical Services, Ottawa, K1A 0K2, Canada
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Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circ Res 2021; 128:1833-1850. [PMID: 34110911 PMCID: PMC8285054 DOI: 10.1161/circresaha.121.318224] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.
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Affiliation(s)
- Alyssa M Flores
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
| | - Falen Demsas
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
| | - Nicholas J Leeper
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
- Department of Medicine, Division of Cardiovascular Medicine (N.J.L.), Stanford University School of Medicine, CA
- Stanford Cardiovascular Institute, CA (N.J.L., E.G.R.)
| | - Elsie Gyang Ross
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, CA. (E.G.R.)
- Stanford Cardiovascular Institute, CA (N.J.L., E.G.R.)
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Rublee C, Bills C, Sorensen C, Lemery J, Calvello Hynes E. At Ground Zero—Emergency Units in Low‐ and Middle‐Income Countries Building Resilience for Climate Change and Human Health. WORLD MEDICAL & HEALTH POLICY 2021. [DOI: 10.1002/wmh3.417] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Yeates EO, Juillard C, Grigorian A, Schellenberg M, Owattanapanich N, Barmparas G, Margulies D, Garber K, Cryer H, Tillou A, Burruss S, Penaloza-Villalobos L, Lin A, Figueras RA, Brenner M, Firek C, Costantini T, Santorelli J, Curry T, Wintz D, Biffl WL, Schaffer KB, Duncan TK, Barbaro C, Diaz G, Johnson A, Chinn J, Naaseh A, Leung A, Grabar C, Yeates TO, Nahmias J. The coronavirus disease 2019 (COVID-19) stay-at-home order's unequal effects on trauma volume by insurance status in Southern California. Surgery 2021; 170:962-968. [PMID: 33849732 DOI: 10.1016/j.surg.2021.02.060] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/02/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND The rapid spread of coronavirus disease 2019 in the United States led to a variety of mandates intended to decrease population movement and "flatten the curve." However, there is evidence some are not able to stay-at-home due to certain disadvantages, thus remaining exposed to both coronavirus disease 2019 and trauma. We therefore sought to identify any unequal effects of the California stay-at-home orders between races and insurance statuses in a multicenter study utilizing trauma volume data. METHODS A posthoc multicenter retrospective analysis of trauma patients presenting to 11 centers in Southern California between the dates of January 1, 2020, and June 30, 2020, and January 1, 2019, and June 30, 2019, was performed. The number of trauma patients of each race/insurance status was tabulated per day. We then calculated the changes in trauma volume related to stay-at-home orders for each race/insurance status and compared the magnitude of these changes using statistical resampling. RESULTS Compared to baseline, there was a 40.1% drop in total trauma volume, which occurred 20 days after stay-at-home orders. During stay-at-home orders, the average daily trauma volume of patients with Medicaid increased by 13.7 ± 5.3%, whereas the volume of those with Medicare, private insurance, and no insurance decreased. The average daily trauma volume decreased for White, Black, Asian, and Latino patients with the volume of Black and Latino patients dropping to a similar degree compared to White patients. CONCLUSION This retrospective multicenter study demonstrated that patients with Medicaid had a paradoxical increase in trauma volume during stay-at-home orders, suggesting that the most impoverished groups remain disproportionately exposed to trauma during a pandemic, further exacerbating existing health disparities.
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Affiliation(s)
- Eric O Yeates
- University of California, Irvine (UCI), Department of Surgery, Orange, CA
| | - Catherine Juillard
- University of California, Los Angeles (UCLA), Department of Surgery, Los Angeles, CA
| | - Areg Grigorian
- University of California, Irvine (UCI), Department of Surgery, Orange, CA; University of Southern California (USC), Department of Surgery, Los Angeles, CA
| | - Morgan Schellenberg
- University of Southern California (USC), Department of Surgery, Los Angeles, CA
| | | | | | - Daniel Margulies
- Cedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA
| | - Kent Garber
- University of California, Los Angeles (UCLA), Department of Surgery, Los Angeles, CA
| | - Henry Cryer
- University of California, Los Angeles (UCLA), Department of Surgery, Los Angeles, CA
| | - Areti Tillou
- University of California, Los Angeles (UCLA), Department of Surgery, Los Angeles, CA
| | - Sigrid Burruss
- Loma Linda University, Department of Surgery, Loma Linda, CA
| | | | - Ann Lin
- Loma Linda University, Department of Surgery, Loma Linda, CA
| | | | - Megan Brenner
- University of California, Riverside/Riverside University Health System Department of Surgery, Moreno Valley, CA
| | - Christopher Firek
- Comparative Effectiveness and Clinical Outcomes Research Center (CECORC), Riverside University Health System, Moreno Valley, CA
| | - Todd Costantini
- University of California, San Diego (UCSD), Department of Surgery, San Diego, CA
| | - Jarrett Santorelli
- University of California, San Diego (UCSD), Department of Surgery, San Diego, CA
| | - Terry Curry
- University of California, San Diego (UCSD), Department of Surgery, San Diego, CA
| | - Diane Wintz
- Sharp Memorial Hospital, Department of Surgery, San Diego, CA
| | - Walter L Biffl
- Scripps Memorial Hospital La Jolla, Trauma Department, La Jolla, CA
| | | | - Thomas K Duncan
- Ventura County Medical Center, Department of Surgery, Ventura, CA
| | - Casey Barbaro
- Ventura County Medical Center, Department of Surgery, Ventura, CA
| | - Graal Diaz
- Ventura County Medical Center, Department of Surgery, Ventura, CA
| | - Arianne Johnson
- Santa Barbara Cottage Hospital, Cottage Health Research Institute, Santa Barbara, CA
| | - Justine Chinn
- University of California, Irvine (UCI), Department of Surgery, Orange, CA
| | - Ariana Naaseh
- University of California, Irvine (UCI), Department of Surgery, Orange, CA
| | - Amanda Leung
- University of California, Irvine (UCI), Department of Surgery, Orange, CA
| | - Christina Grabar
- University of California, Irvine (UCI), Department of Surgery, Orange, CA
| | - Todd O Yeates
- University of California, Los Angeles (UCLA), Department of Chemistry and Biochemistry, Los Angeles, CA
| | - Jeffry Nahmias
- University of California, Irvine (UCI), Department of Surgery, Orange, CA.
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Kirubarajan A, Taher A, Khan S, Masood S. Artificial intelligence in emergency medicine: A scoping review. J Am Coll Emerg Physicians Open 2020; 1:1691-1702. [PMID: 33392578 PMCID: PMC7771825 DOI: 10.1002/emp2.12277] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 09/04/2020] [Accepted: 09/22/2020] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Despite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine. METHODS The scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews using Medline-OVID, EMBASE, CINAHL, and IEEE, with a double screening and extraction process. The search included articles published until February 28, 2020. Articles were excluded if they did not self-classify as studying an AI intervention, were not relevant to the emergency department (ED), or did not report outcomes or evaluation. RESULTS Of the 1483 original database citations, 395 were eligible for full-text evaluation. Of these articles, a total of 150 were included in the scoping review. The majority of included studies were retrospective in nature (n = 124, 82.7%), with only 3 (2.0%) prospective controlled trials. We found 37 (24.7%) interventions aimed at improving diagnosis within the ED. Among the 150 studies, 19 (12.7%) focused on diagnostic imaging within the ED. A total of 16 (10.7%) studies were conducted in the out-of-hospital environment (eg, emergency medical services, paramedics) with the remainder occurring either in the ED or the trauma bay. Of the 24 (16%) studies that had human comparators, there were 12 (8%) studies in which AI interventions outperformed clinicians in at least 1 measured outcome. CONCLUSION AI-related research is rapidly increasing in emergency medicine. There are several promising AI interventions that can improve emergency care, particularly for acute radiographic imaging and prediction-based diagnoses. Higher quality evidence is needed to further assess both short- and long-term clinical outcomes.
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Affiliation(s)
- Abirami Kirubarajan
- Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Institute of Health Policy Management and EvaluationUniversity of TorontoTorontoOntarioCanada
| | - Ahmed Taher
- Division of Emergency Medicine, Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Shawn Khan
- Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Sameer Masood
- Division of Emergency Medicine, Department of MedicineUniversity of TorontoTorontoOntarioCanada
- Toronto General Hospital Research InstituteUniversity Health NetworkTorontoOntarioCanada
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Ehrlich H, McKenney M, Elkbuli A. The niche of artificial intelligence in trauma and emergency medicine. Am J Emerg Med 2020; 45:669-670. [PMID: 33129644 DOI: 10.1016/j.ajem.2020.10.050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/11/2020] [Accepted: 10/20/2020] [Indexed: 12/23/2022] Open
Affiliation(s)
- Haley Ehrlich
- Department of Surgery, Division of Trauma and Surgical Critical Care, Kendall Regional Medical Center, Miami, FL, USA
| | - Mark McKenney
- Department of Surgery, Division of Trauma and Surgical Critical Care, Kendall Regional Medical Center, Miami, FL, USA; Department of Surgery, University of South FL, Tampa, FL, USA
| | - Adel Elkbuli
- Department of Surgery, Division of Trauma and Surgical Critical Care, Kendall Regional Medical Center, Miami, FL, USA.
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Stonko DP, Guillamondegui OD, Fischer PE, Dennis BM. Artificial intelligence in trauma systems. Surgery 2020; 169:1295-1299. [PMID: 32921479 DOI: 10.1016/j.surg.2020.07.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 06/30/2020] [Accepted: 07/03/2020] [Indexed: 10/23/2022]
Abstract
Local trauma care and regional trauma systems are data-rich environments that are amenable to machine learning, artificial intelligence, and big-data analysis mechanisms to improve timely access to care, to measure outcomes, and to improve quality of care. Pilot work has been done to demonstrate that these methods are useful to predict patient flow at individual centers, so that staffing models can be adapted to match workflow. Artificial intelligence has also been proven useful in the development of regional trauma systems as a tool to determine the optimal location of a new trauma center based on trauma-patient geospatial injury data and to minimize response times across the trauma network. Although the utility of artificial intelligence is apparent and proven in small pilot studies, its operationalization across the broader trauma system and trauma surgery space has been slow because of cost, stakeholder buy-in, and lack of expertise or knowledge of its utility. Nevertheless, as new trauma centers or systems are developed, or existing centers are retooled, machine learning and sophisticated analytics are likely to be important components to help facilitate decision-making in a wide range of areas, from determining bedside nursing and provider ratios to determining where to locate new trauma centers or emergency medical services teams.
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Affiliation(s)
- David P Stonko
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD
| | - Oscar D Guillamondegui
- Division of Trauma, Surgical Critical Care, and Emergency General Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Peter E Fischer
- Department of Surgery, University of Tennessee Health Science Center, Memphis, TN
| | - Bradley M Dennis
- Division of Trauma, Surgical Critical Care, and Emergency General Surgery, Vanderbilt University Medical Center, Nashville, TN.
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