Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.

PURPOSE: With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI. METHODOLOGY: We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity. RESULT: A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance. CONCLUSION: According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

Hyperexcitability to electrical stimulation and accelerated muscle fatiguability of taut bands in rats.

OBJECTIVE: Myofascial trigger points contribute significantly to musculoskeletal pain and motor dysfunction and may be associated with accelerated muscle fatiguability. The aim of this study was to investigate the electrically induced force and fatigue characteristics of muscle taut bands in rats. METHODS: Muscle taut bands were dissected out and subjected to trains of electrical stimulation. The electrical threshold intensity for muscle contraction and maximum contraction force (MCF), electrical intensity dependent fatigue and electrical frequency dependent fatigue characteristics were assessed in three different sessions (n=10 each) and compared with non-taut bands in the biceps femoris muscle. RESULTS: The threshold intensity for muscle contraction and MCF at the 10th, 15th and 20th intensity dependent fatigue stimuli of taut bands were significantly lower than those of non-taut bands (all p<0.05). The MCF at the 15th and 20th intensity dependent fatigue stimuli of taut bands were significantly lower than those at the 1st and 5th stimuli (all p<0.01). The MCF in the frequency dependent fatigue test was significantly higher and the stimulus frequency that induced MCF was significantly lower for taut bands than for non-taut bands (both p<0.01). CONCLUSIONS: The present study demonstrates that the muscle taut band itself was more excitable to electrical stimulation and significantly less fatigue resistant than normal muscle fibres.