Acute Therapy for Traumatic Head Injury

Miyagi, Tomoya; Maeda, M.; Inoue, Yasuo; Kondou, Daisuke; Yoshimura, F; Ookura, Akio; Morioka, Motohiro

doi:10.2490/jjrmc.50.557

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In addition, brain injury guidelines indicate that blood pressure and heart rate need to be monitored [29]. In medical environments, SpO2 is also monitored [30]. Based on medical knowledge, a domain model was created assuming that latent factors affect ABP, HR, and SpO2.…”

Section: B Estimating Risk Degrees By Sem and Hmmmentioning

confidence: 99%

Detection of Health Abnormality Considering Latent Factors Inducing a Disease

2020

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Underlying latent factors may cause a person to feel unwell. As the influence of the latent factors increases, the person will become sick. It is difficult to directly measure the influence of latent factors on risk degrees. However, early symptoms of a disease may affect vital signs such as body temperature and blood pressure, which may be a result of the influence of the latent factors. Deep learning is often used to predict the onset of a disease owing to its high accuracy. However, the reliability of this method is limited because of its characteristics of a black-box model. In this study, we propose a new approach to detect health abnormality. We regard the degree of influence of latent factors as the risk of disease and detect health abnormality before the onset of the disease. In our approach, we used a combination of Structural Equation Modeling (SEM) and Hidden Markov Model (HMM). First, in SEM, a domain model was created, and the factor score was estimated based on the relationship between latent factors and the explicit variables influenced by the factors. Thereafter, risk degrees were quantified with HMM using the estimated factor scores, and abnormality was identified in terms of risk degree. Finally, our proposed method was compared with three baselines: PCA (principal component analysis)-based approach, deep learning, and no-degree estimation methods. The average recall of our method was 98.75%, almost the same as the baselines, and false positive rate (FPR) was 0.186%, lower than the baselines. In the five-fold cross-validation comparing with no-degree estimation method, the average accuracy and recall of our method were 99.7% and 98.3% respectively, and FPR was 0.045%, all much better than the baseline. Moreover, our approach can make the process of obtaining the result visible and help detect abnormality sensitively by setting the threshold according to the risk degree, which can contribute to early detection of a disease and improve the reliability of abnormality detection as well.

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Section: B Estimating Risk Degrees By Sem and Hmmmentioning

confidence: 99%

Detection of Health Abnormality Considering Latent Factors Inducing a Disease

2020

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Serum D-dimer level as a predictor of neurological functional prognosis in cases of head injuries caused by road traffic accidents

et al. 2022

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Background The number of traffic fatalities is declining in Japan; however, a large proportion of head injuries are still attributable to traffic accidents. Severe head trauma may cause progressive and devastating coagulopathy owing to exacerbated coagulation and fibrinolysis, which results in massive bleeding and poor patient outcomes. D-dimer is a fibrinolytic marker, which remarkably increases in severe coagulopathy due to the exacerbated fibrinolytic system. Because the degree of coagulopathy is associated with patient outcomes, the D-dimer level is a useful prognostic predictor in patients with head trauma. However, the usefulness of D-dimer in cases of head trauma caused by road traffic accidents remains inadequately explored. In this study, we investigated the relationship between D-dimer levels and outcomes in head injuries caused by traffic accidents. Methods We extracted data on traffic injuries from Japan Neuro-Trauma Data Bank Project 2015, which is a prospective multicenter registry of head injuries. The analysis included 335 individuals with no missing data. The outcome variable was the score of the Glasgow Outcome Scale (GOS), a neurological outcome index. The participants were categorized into the favorable outcome (GOS score ≥ 4) and poor outcome (GOS score ≤ 3) groups. The serum D-dimer levels at the time of admission were divided into four categories at the quartiles, and the reference category was less than the first quartile (< 17.4 µg/mL). We performed a logistic regression analysis with GOS as the dependent variable and D-dimer as a predictor and performed a multivariate analysis that was adjusted for 10 physiological parameters. Results In the univariate analysis, all groups with serum D-dimer values ≥ 17.4 μg/dL showed significantly poorer outcomes than those of the reference group. In the multivariate analysis, after adjusting for other factors, D-dimer levels ≥ 89.3 μg/dL were an independent predictor of poor outcome. Conclusion After adjusting for physiological parameters, high serum D-dimer levels can be an independent factor for predicting neurological prognosis in head trauma caused by road traffic accidents.

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Acute Therapy for Traumatic Head Injury

Cited by 2 publications

References 26 publications

Detection of Health Abnormality Considering Latent Factors Inducing a Disease

Detection of Health Abnormality Considering Latent Factors Inducing a Disease

Serum D-dimer level as a predictor of neurological functional prognosis in cases of head injuries caused by road traffic accidents

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