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.

Prognostic value of plasma brain natriuretic peptide value for patientswith sepsis: A meta-analysis.

PURPOSE: The aim of this meta-analysis was to clarify the diagnostic role of plasma BNP and NT-proBNP in predicting mortality for septic patients. METHODS: A systematic review was conducted prior to January 2018. Summary sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) of the prognostic value of plasma BNP and NT-proBNP for septic patients. The area under the receiver operating curves (AUROC) were used to summarize overall test performance. RESULTS: Twenty-two studies with 3417 septic patients were selected in the analysis. The summary sensitivity, specificity, PLR, NLR, DOR and the AUROC of the overall analysis of BNP were: 0.84, 0.73, 3.1, 0.22, 14, 0.85; and these values of NT-proBNP were: 0.71, 0.73, 2.6, 0.39, 7 and 0.7 respectively; Subgroup analysis and meta-regression analyses showed that the tested method and observation endpoint influenced the summary sensitivity, specificity of BNP, but the tested day, tested method or observation endpoint did not influence the summary sensitivity, specificity of NT-proBNP. CONCLUSIONS: This meta-analysis indicates that both elevated plasma BNP and NT-proBNP have moderate predicts value for the mortality of septic patients, and the tested method and observation endpoint influence the results of BNP.