The purpose of this study was to evaluate the effects of botulinum toxin A (BTX-A, Botox) dilution volume and post-injection exercise with electrical stimulation on muscle paralysis. We injected 10 units of BTX-A diluted with 0.1 ml (B1, n=8) or 0.5 ml (B5, n=8) normal saline into both gastrocnemius muscles of 16 New Zealand white rabbits; two controls received no BTX-A. After BTX-A injection, all rabbits received calf muscle stretching exercise and electrical stimulation for 2 hours on the left leg. The compound muscle action potential (CMAP) decrease was most pronounced at 1 week and progressive recovery was observed (i.e. recovery from paralysis, increase of CMAP). There was a significant decrease of CMAP amplitudes in the B5 group compared with the B1 group at week 1 and week 4 (p<0.001). Left limbs with stretching exercise and electrical stimulation showed lower CMAP amplitudes compared with control right limbs of all rabbits. To maximize the muscle paralysis effect of BTX-A, increasing dilution volume and performing post-injection stretching exercise with electrical stimulation may be a promising strategy for increasing the beneficial effect of BTX-A treatment. Future studies are needed to investigate the clinical application of this finding.
Background Within the trauma system, the emergency department (ED) is the hospital’s first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED. Objective The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED. Methods We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED. Results Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320). Conclusions Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.
Background and Objectives: Angioembolization has emerged as an effective therapeutic approach for pelvic hemorrhages; however, its exact effect size remains uncertain. Therefore, we conducted this systematic review and meta-analysis to investigate the effect size of embolization-related pelvic complications after nonselective angioembolization compared to that after selective angioembolization in patients with pelvic injury accompanying hemorrhage. Materials and Methods Relevant articles were collected by searching the PubMed, EMBASE, and Cochrane databases until June 24, 2023. Meta-analyses were conducted using odds ratios (ORs) for binary outcomes. Quality assessment was conducted using the risk of bias tool in non-randomized studies of interventions. Results: Five studies examining 357 patients were included in the meta-analysis. Embolization-related pelvic complications did not significantly differ between patients with nonselective and selective angioembolization (OR 1.581, 95% confidence interval [CI] 0.592 to 4.225, I2 = 0%). However, in-hospital mortality was more likely to be higher in the nonselective group (OR 2.232, 95% CI 1.014 to 4.913, I2 = 0%) than in the selective group. In the quality assessment, two studies were found to have a moderate risk of bias, whereas two studies exhibited a serious risk of bias. Conclusions: Despite the favorable outcomes observed with nonselective angioembolization concerning embolization-related pelvic complications, determining the exact effect sizes was limited owing to the significant risk of bias and heterogeneity. Nonetheless, the low incidence of ischemic pelvic complications appears to be a promising result.
BACKGROUND Within the trauma system, the emergency department (ED) is the hospital’s first contact and is vital for controlling and providing medical resources. However, ED mortality patients may have limited information. Therefore, we aimed to develop an artificial intelligence (AI) model to predict trauma mortality for all patients visiting the ED. Additionally, we aimed to analyze what information from trauma patients had a significant impact on mortality using that AI model. OBJECTIVE We aim to develop an artificial intelligent (AI) model to predict trauma mortality among emergency department patients using the international classification of disease (ICD)-10, triage scale, and other clinical features. METHODS We used the Korean National Emergency Department Information System (NEDIS) dataset (n=6,536,306), incorporating over 400 hospitals between 2016 to 2019. Next, we included the International Classification of Disease 10th Revision (ICD-10) and selected the following input features to predict ED mortality: patient’s age, sex, intentionality, injury mechanism, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale; Korean Triage and Acuity Scale (KTAS); and vital signs. For the AI input information, we compared three different feature set performances: all features (n=921), ICD-10 features (n=878), and features excluding the ICD-10 (n=43). We presented various machine learning models with Ensemble via five-fold cross-validation and compared each with traditional prediction models. Lastly, we investigated feature effects for explainable AI and deployed our final AI model on a public website (http://ai-wm.khu.ac.kr/mortality_visiting_ED), allowing access to the mortality prediction results among visiting ED patients RESULTS Our proposed AI model with the all feature set provided the highest area under the receiver operating curve (AUROC) of 0.9974 (AdaBoost, AdaBoost + LightGBM: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction model AUROCs: XGBoost, 0.9972; LightGBM, 0.9973; ICD-based injury severity scores, 0.9328 (inclusive model) and 0.9567 (exclusive model); and KTAS, 0.9405. In addition, we compared our model’s prediction performance to the state-of-the-art AI model designed for in-hospital mortality prediction. The results indicated that our proposed AI model outperformed the in-hospital mortality AI model (0.7675 AUROC) for all ED visitors. Finally, from the AI model, we also found that age and unresponsiveness (coma) were the top two contributors in predicting mortality among visiting ED patients. Next were oxygen saturation, S224 (multiple rib fractures), painful response (stupor, semi-coma) and S320 (lumbar vertebra fracture). CONCLUSIONS Our proposed AI model for predicting ED mortality exhibits remarkable accuracy. Despite the necessity for external validation, a large nationwide dataset would provide a more accurate model and minimize overfitting.
The purpose of this study was to evaluate the effects of botulinum toxin A (BTX‐A, Botox) dilution volume and post‐injection exercise with electrical stimulation on muscle paralysis. We injected 10 units of BTX‐A diluted with 0.1 ml (B1, n=8) or 0.5 ml (B5, n=8) normal saline into both gastrocnemius muscles of 16 New Zealand white rabbits; two controls received no BTX‐A. After BTX‐A injection, all rabbits received calf muscle stretching exercise and electrical stimulation for 2 hours on the left leg. The compound muscle action potential (CMAP) decrease was most pronounced at 1 week and progressive recovery was observed (i.e. recovery from paralysis, increase of CMAP). There was a significant decrease of CMAP amplitudes in the B5 group compared with the B1 group at week 1 and week 4 (p<0.001). Left limbs with stretching exercise and electrical stimulation showed lower CMAP amplitudes compared with control right limbs of all rabbits. To maximize the muscle paralysis effect of BTX‐A, increasing dilution volume and performing post‐injection stretching exercise with electrical stimulation may be a promising strategy for increasing the beneficial effect of BTX‐A treatment. Future studies are needed to investigate the clinical application of this finding.
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