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Jakob DA, Müller M, Kolitsas A, Exadaktylos AK, Demetriades D. Surgical Repair vs Splenectomy in Patients With Severe Traumatic Spleen Injuries. JAMA Netw Open 2024; 7:e2425300. [PMID: 39093564 PMCID: PMC11297384 DOI: 10.1001/jamanetworkopen.2024.25300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/02/2024] [Indexed: 08/04/2024] Open
Abstract
Importance The spleen is often removed in laparotomy after traumatic abdominal injury, with little effort made to preserve the spleen. Objective To explore the association of surgical management (splenic repair vs splenectomy) with outcomes in patients with traumatic splenic injuries undergoing laparotomy and to determine whether splenic repair is associated with lower mortality compared with splenectomy. Design, Setting, and Participants This is a trauma registry-based cohort study using the American College of Surgeons Trauma Quality Improvement Program database from January 2013 to December 2019. Participants included adult patients with severe splenic injuries (Abbreviated Injury Scale [AIS] grades 3-5) undergoing laparotomy after traumatic injury within 6 hours of admission. Data analysis was performed from April to August 2023. Exposures Splenic repair vs splenectomy in patients with severe traumatic splenic injury. Main Outcomes and Measures The primary outcome was in-hospital mortality. Outcomes were compared using different statistical approaches, including 1:1 exact matching with consecutive conditional logistic regression analysis as the primary analysis and multivariable logistic regression, propensity score matching, and inverse-probability weighting as sensitivity analyses. Results A total of 11 247 patients (median [IQR] age, 35 [24-52] years; 8179 men [72.7%]) with a severe traumatic splenic injury undergoing laparotomy were identified. Of these, 10 820 patients (96.2%) underwent splenectomy, and 427 (3.8%) underwent splenic repair. Among patients who underwent an initial splenic salvage procedure, 23 (5.3%) required a splenectomy during the subsequent hospital stay; 400 patients with splenic preservation were matched with 400 patients who underwent splenectomy (matched for age, sex, hypotension, trauma mechanism, AIS spleen grade, and AIS groups [0-2, 3, and 4-5] for head, face, neck, thorax, spine, and lower and upper extremity). Mortality was significantly lower in the splenic repair group vs the splenectomy group (26 patients [6.5%] vs 51 patients [12.8%]). The association of splenic repair with lower mortality was subsequently verified by conditional regression analysis (adjusted odds ratio, 0.4; 95% CI, 0.2-0.9; P = .03). Multivariable logistic regression, propensity score matching, and inverse-probability weighting confirmed this association. Conclusions and Relevance In this retrospective cohort study, splenic repair was independently associated with lower mortality compared with splenectomy during laparotomy after traumatic splenic injury. These findings suggest that efforts to preserve the spleen might be indicated in selected cases of severe splenic injuries.
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Affiliation(s)
- Dominik A. Jakob
- Division of Trauma and Surgical Critical Care, Department of Surgery, Los Angeles General Medical Center, University of Southern California, Los Angeles
- Department of Emergency Medicine, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Visceral Surgery, Lindenhofspital, Bern, Switzerland
| | - Martin Müller
- Department of Emergency Medicine, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
| | - Apostolos Kolitsas
- Division of Trauma and Surgical Critical Care, Department of Surgery, Los Angeles General Medical Center, University of Southern California, Los Angeles
| | - Aristomenis K. Exadaktylos
- Department of Emergency Medicine, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
| | - Demetrios Demetriades
- Division of Trauma and Surgical Critical Care, Department of Surgery, Los Angeles General Medical Center, University of Southern California, Los Angeles
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Hamghalam M, Moreland R, Gomez D, Simpson A, Lin HM, Jandaghi AB, Tafur M, Vlachou PA, Wu M, Brassil M, Crivellaro P, Mathur S, Hosseinpour S, Colak E. Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography. Can Assoc Radiol J 2024; 75:534-541. [PMID: 38189316 DOI: 10.1177/08465371231221052] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study interpretation, which can be a challenge on busy emergency radiology services. A machine learning system has the potential to automate the process, potentially leading to a faster clinical response. This study aimed to create such a system. METHOD Using the American Association for the Surgery of Trauma (AAST), spleen injuries were classified into 3 classes: normal, low-grade (AAST grade I-III) injuries, and high-grade (AAST grade IV and V) injuries. Employing a 2-stage machine learning strategy, spleens were initially segmented from input CT images and subsequently underwent classification via a 3D dense convolutional neural network (DenseNet). RESULTS This single-centre retrospective study involved trauma protocol CT scans performed between January 1, 2005, and July 31, 2021, totaling 608 scans with splenic injuries and 608 without. Five board-certified fellowship-trained abdominal radiologists utilizing the AAST injury scoring scale established ground truth labels. The model achieved AUC values of 0.84, 0.69, and 0.90 for normal, low-grade injuries, and high-grade splenic injuries, respectively. CONCLUSIONS Our findings demonstrate the feasibility of automating spleen injury detection using our method with potential applications in improving patient care through radiologist worklist prioritization and injury stratification. Future endeavours should concentrate on further enhancing and optimizing our approach and testing its use in a real-world clinical environment.
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Affiliation(s)
- Mohammad Hamghalam
- School of Computing and Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Robert Moreland
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - David Gomez
- Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
- Department of Surgery, Temetry Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Amber Simpson
- School of Computing and Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Hui Ming Lin
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Ali Babaei Jandaghi
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Monica Tafur
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Paraskevi A Vlachou
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Matthew Wu
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Michael Brassil
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Priscila Crivellaro
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Shobhit Mathur
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Shahob Hosseinpour
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Errol Colak
- Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
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Nann S, Clark M, Kovoor J, Jog S, Aromataris E. Prophylactic embolization versus observation for high-grade blunt trauma splenic injury: a systematic review with meta-analysis. JBI Evid Synth 2024:02174543-990000000-00331. [PMID: 39028141 DOI: 10.11124/jbies-24-00110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
OBJECTIVE The objective of this systematic review was to compare the effectiveness of prophylactic angioembolization with observation as primary management strategies for patients with high-grade (grades 3-5) blunt trauma splenic injury. INTRODUCTION The spleen is frequently injured in abdominal trauma. Historical management practices involved splenectomy, but more recent evidence suggests an increased risk of severe infections and sepsis associated with this approach. Accordingly, non-operative management strategies, including prophylactic splenic artery embolization and clinical observation, have gained prominence. This systematic review with meta-analysis directly compares angioembolization with clinical observation for high-grade splenic injuries only, aiming to provide clarity on this matter amid ongoing debates and variations in clinical practice. INCLUSION CRITERIA This review included adult patients aged 15 years or older with high-grade splenic injuries (grade 3-5) due to blunt trauma. Outcomes of interest include the need for further intervention (failure of management), mortality, complications, red blood cell transfusion requirements, hospital length of stay, and intensive care unit length of stay. METHODS A comprehensive search of PubMed, Embase, and CINAHL (EBSCOhost), was performed with no restrictions on language or publication date. Gray literature was searched, including trial registries and relevant conference proceedings. After deduplication, 2 reviewers independently assessed titles and abstracts, and, subsequently, full-text articles for eligibility. Methodological quality of the included studies was assessed using standardized instruments from JBI. Data was extracted using predefined templates, and statistical meta-analysis was performed, where possible, using a random effects model. Heterogeneity was assessed using statistical methods, and potential publication bias was tested with a funnel plot. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach was used to evaluate the certainty of evidence. RESULTS Sixteen studies were included in this review. Methodological quality assessment indicated some risk of bias in most studies, with concerns primarily related to differences in injury severity and potential confounding factors. Meta-analysis revealed that prophylactic angioembolization significantly reduced risk of management failure by 57% (OR 0.43, 95% CI 0.28-0.68, I2=53%, 15 studies) and decreased patient mortality by 37% (OR 0.63, 95% CI 0.43-0.93, I2=0%, 9 studies) compared with clinical observation alone. There was a 47% reduction in risk of complications associated with prophylactic embolization compared with clinical observation (OR 0.53, 95% CI 0.29-0.95, I2=0%, 4 studies). Some statistical heterogeneity was observed, with I2 ranging from 0% to 53%. No significant differences were observed between the 2 management strategies for red blood cell transfusion requirements and hospital length of stay. CONCLUSIONS The results of this study support the use of prophylactic embolization for high-grade blunt trauma splenic injuries, indicated by lower failure of management rates, reduced need for additional interventions, lower mortality, and fewer complications. REVIEW REGISTRATION PROSPERO CRD42023420220.
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Affiliation(s)
- Silas Nann
- JBI, School of Public Health, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
- The Gold Coast University Hospital, Southport, Qld, Australia
| | - Molly Clark
- JBI, School of Public Health, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Joshua Kovoor
- JBI, School of Public Health, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
| | - Shivangi Jog
- The Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Edoardo Aromataris
- JBI, School of Public Health, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
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Sarkar N, Kumagai M, Meyr S, Pothapragada S, Unberath M, Li G, Ahmed SR, Smith EB, Davis MA, Khatri GD, Agrawal A, Delproposto ZS, Chen H, Caballero CG, Dreizin D. An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading graphical user interface prototype. Emerg Radiol 2024; 31:167-178. [PMID: 38302827 PMCID: PMC11257379 DOI: 10.1007/s10140-024-02202-8] [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: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
PURPOSE The AAST Organ Injury Scale is widely adopted for splenic injury severity but suffers from only moderate inter-rater agreement. This work assesses SpleenPro, a prototype interactive explainable artificial intelligence/machine learning (AI/ML) diagnostic aid to support AAST grading, for effects on radiologist dwell time, agreement, clinical utility, and user acceptance. METHODS Two trauma radiology ad hoc expert panelists independently performed timed AAST grading on 76 admission CT studies with blunt splenic injury, first without AI/ML assistance, and after a 2-month washout period and randomization, with AI/ML assistance. To evaluate user acceptance, three versions of the SpleenPro user interface with increasing explainability were presented to four independent expert panelists with four example cases each. A structured interview consisting of Likert scales and free responses was conducted, with specific questions regarding dimensions of diagnostic utility (DU); mental support (MS); effort, workload, and frustration (EWF); trust and reliability (TR); and likelihood of future use (LFU). RESULTS SpleenPro significantly decreased interpretation times for both raters. Weighted Cohen's kappa increased from 0.53 to 0.70 with AI/ML assistance. During user acceptance interviews, increasing explainability was associated with improvement in Likert scores for MS, EWF, TR, and LFU. Expert panelists indicated the need for a combined early notification and grading functionality, PACS integration, and report autopopulation to improve DU. CONCLUSIONS SpleenPro was useful for improving objectivity of AAST grading and increasing mental support. Formative user research identified generalizable concepts including the need for a combined detection and grading pipeline and integration with the clinical workflow.
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Affiliation(s)
- Nathan Sarkar
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
| | - Mitsuo Kumagai
- University of Maryland College Park, 4603 Calvert Rd, College Park, MD, 20740, USA
| | - Samantha Meyr
- University of Maryland College Park, 4603 Calvert Rd, College Park, MD, 20740, USA
| | - Sriya Pothapragada
- University of Maryland College Park, 4603 Calvert Rd, College Park, MD, 20740, USA
| | - Mathias Unberath
- Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA
| | - Guang Li
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
| | - Sagheer Rauf Ahmed
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
- R Adams Cowley Shock Trauma Center, 22 S Greene St, Baltimore, MD, 21201, USA
| | - Elana Beth Smith
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA
- R Adams Cowley Shock Trauma Center, 22 S Greene St, Baltimore, MD, 21201, USA
| | | | | | - Anjali Agrawal
- Teleradiology Solutions, 22 Lianfair Road Unit 6, Ardmore, PA, 19003, USA
| | | | - Haomin Chen
- Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA
| | | | - David Dreizin
- University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA.
- R Adams Cowley Shock Trauma Center, 22 S Greene St, Baltimore, MD, 21201, USA.
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Chen H, Unberath M, Dreizin D. Toward automated interpretable AAST grading for blunt splenic injury. Emerg Radiol 2023; 30:41-50. [PMID: 36371579 DOI: 10.1007/s10140-022-02099-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The American Association for the Surgery of Trauma (AAST) splenic organ injury scale (OIS) is the most frequently used CT-based grading system for blunt splenic trauma. However, reported inter-rater agreement is modest, and an algorithm that objectively automates grading based on transparent and verifiable criteria could serve as a high-trust diagnostic aid. PURPOSE To pilot the development of an automated interpretable multi-stage deep learning-based system to predict AAST grade from admission trauma CT. METHODS Our pipeline includes 4 parts: (1) automated splenic localization, (2) Faster R-CNN-based detection of pseudoaneurysms (PSA) and active bleeds (AB), (3) nnU-Net segmentation and quantification of splenic parenchymal disruption (SPD), and (4) a directed graph that infers AAST grades from detection and segmentation results. Training and validation is performed on a dataset of adult patients (age ≥ 18) with voxelwise labeling, consensus AAST grading, and hemorrhage-related outcome data (n = 174). RESULTS AAST classification agreement (weighted κ) between automated and consensus AAST grades was substantial (0.79). High-grade (IV and V) injuries were predicted with accuracy, positive predictive value, and negative predictive value of 92%, 95%, and 89%. The area under the curve for predicting hemorrhage control intervention was comparable between expert consensus and automated AAST grading (0.83 vs 0.88). The mean combined inference time for the pipeline was 96.9 s. CONCLUSIONS The results of our method were rapid and verifiable, with high agreement between automated and expert consensus grades. Diagnosis of high-grade lesions and prediction of hemorrhage control intervention produced accurate results in adult patients.
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Affiliation(s)
- Haomin Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - David Dreizin
- Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
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