Major Causes of Preventable Death in Trauma Patients

Park, Young-Eun; Lee, Gil Jae; Lee, Min A; Choi, Kang Kook; Gwak, Jihun; Hyun, Sung Youl; Jeon, Yang Bin; Yoon, Yong-Cheol; Lee, Jungnam; Yu, Byungchul

doi:10.20408/jti.2020.0074

Cited by 25 publications

(16 citation statements)

References 15 publications

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“…It is well known that head injury is the leading cause of death in trauma patients. [28][29][30][31][32][33] In addition, our results support the assumption that head injury in combination with thoracic injury could be even worse for a patient and increase the risk for mortality. Also, we found differences in mortality with regards to comorbidity (ASA class 1-2 compared to ASA 3-4).…”

Section: Discussionsupporting

confidence: 79%

Thoracic injuries in trauma patients Epidemiology and its influence on mortality

Lundin

Akram

Berg

et al. 2022

Preprint

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BackgroundThoracic injuries are common among trauma patients. Studies on trauma patients with thoracic injuries have reported considerable differences in morbidity and mortality, and there is limited research on comparison between trauma patients with and without thoracic injuries, particularly in the Scandinavian population. Thoracic injuries in trauma patients should be identi ed early and need special attention since the differences in injury patterns among patient population are important as they entail different treatment regimens and in uence patient outcomes.The aim of the study was to describe the epidemiology of trauma patients with and without thoracic injuries and its in uence on 30-day mortality. MethodsPatients were identi ed through the Karolinska Trauma Register. The Abbreviated Injury Scale (AIS) system was used to nd patients with thoracic injuries. Logistic regression analysis was performed to evaluate factors (age, gender, ASA class, GCS (Glasgow Coma Scale), NISS (New Injury Severity Score) and thoracic injury) associated with 30-day mortality. ResultsA total of 2397 patients were included. Of those, 768 patients (32%) had a thoracic injury. The mean (± SD, range) age of all patients (n=2397) was 46 (20, 18-98) years, and the majority (n=1709, 71%) of the patients were males. There was a greater proportion of patients with rib fractures among older (≥ 60 years) patients, whereas younger patients had a higher proportion of injuries to the internal thoracic organs. The 30-day mortality was 11% (n=87) in patients with thoracic injury and 4.3% (n=71) in patients without. After multivariable adjustment, a thoracic injury was found to be associated with an increased risk of 30-day mortality (OR 1.9, 95% CI 1.3-3.0); as was age ≥ 60 years (OR 3.7, 95% CI 2.3-6.0), ASA class 3-4 (OR 2.3, 95% CI 1.4-3.6), GCS 1-8 (OR 21, 95% CI 13-33) and NISS >15 (OR 4.2, 2.4-7.3). ConclusionThoracic injury was an independent predictor of 30-day mortality after adjustment for relevant key variables. We also found a difference in injury patterns with older patients having a higher proportion of rib fractures, whilst younger patients suffered more internal thoracic organ injuries.

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Section: Discussionsupporting

confidence: 79%

Thoracic injuries in trauma patients Epidemiology and its influence on mortality

Lundin

Akram

Berg

et al. 2022

Preprint

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“…Deep learning is generally considered a “black box,” hence the feature importance analysis based on a machine learning algorithm provides meaningful insight to clinicians and researchers. Finally, we aspire for the global application of our model and have produced a publicly available web application for hospitals to utilize for the benefit of the entire trauma system [ 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea

Lee¹,

Kang²,

Seo³

et al. 2022

J Med Internet Res

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Background Physical trauma–related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. Objective We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. Methods We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. Results Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). Conclusions Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

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“…Although emergency care and surgical care improvement led to a better outcome, up to 8.0% of all trauma patients’ death are considered preventable or potentially preventable ( 3 ). Management errors arise because of time pressure, inexperience, reliance on memory, multitasking, information flow analysis, and failures in trauma team coordination, particularly during the initial minutes of patient reception and resuscitation in emergency rooms.…”

Section: Introductionmentioning

confidence: 99%

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Baur

Gehlen²,

Scherer³

et al. 2022

Front. Surg.

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IntroductionTreating severely injured patients requires numerous critical decisions within short intervals in a highly complex situation. The coordination of a trauma team in this setting has been shown to be associated with multiple procedural errors, even of experienced care teams. Machine learning (ML) is an approach that estimates outcomes based on past experiences and data patterns using a computer-generated algorithm. This systematic review aimed to summarize the existing literature on the value of ML for the initial management of severely injured patients.MethodsWe conducted a systematic review of the literature with the goal of finding all articles describing the use of ML systems in the context of acute management of severely injured patients. MESH search of Pubmed/Medline and Web of Science was conducted. Studies including fewer than 10 patients were excluded. Studies were divided into the following main prediction groups: (1) injury pattern, (2) hemorrhage/need for transfusion, (3) emergency intervention, (4) ICU/length of hospital stay, and (5) mortality.ResultsThirty-six articles met the inclusion criteria; among these were two prospective and thirty-four retrospective case series. Publication dates ranged from 2000 to 2020 and included 32 different first authors. A total of 18,586,929 patients were included in the prediction models. Mortality was the most represented main prediction group (n = 19). ML models used were artificial neural network ( n = 15), singular vector machine (n = 3), Bayesian network (n = 7), random forest (n = 6), natural language processing (n = 2), stacked ensemble classifier [SuperLearner (SL), n = 3], k-nearest neighbor (n = 1), belief system (n = 1), and sequential minimal optimization (n = 2) models. Thirty articles assessed results as positive, five showed moderate results, and one article described negative results to their implementation of the respective prediction model.ConclusionsWhile the majority of articles show a generally positive result with high accuracy and precision, there are several requirements that need to be met to make the implementation of such models in daily clinical work possible. Furthermore, experience in dealing with on-site implementation and more clinical trials are necessary before the implementation of ML techniques in clinical care can become a reality.

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Major Causes of Preventable Death in Trauma Patients

Cited by 25 publications

References 15 publications

Thoracic injuries in trauma patients Epidemiology and its influence on mortality

Thoracic injuries in trauma patients Epidemiology and its influence on mortality

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

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