This paper implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in the multiple experimental rat animal protocols. A total of 61 animals were studied across multiple HS experiments, which encompassed two different HS protocols and two resuscitation protocols using blood stored for short periods using five different techniques. Twenty-seven different systemic hemodynamics, cardiac function, and blood gas parameters were measured in each experiment, of which feature selection deemed only 25% of the them as relevant. The reduced feature set was used to train a final logistic regression model. A final test set accuracy is 84% compared to 74% for a baseline classifier using only MAP and HR measurements. Receiver operating characteristics (ROC) curve analysis and Cohens kappa statistics were also used as measures of performance, with the final reduced model outperforming the model, including all parameters. Our results suggest that LRMs trained with a combination of systemic hemodynamics, cardiac function, and blood gas parameters measured at multiple timepoints during HS can successfully classify HS recovery groups. Our results show the predictive ability of traditional and novel hemodynamic and cardiac function features and their combinations, many of which had not previously been taken into consideration, for monitoring HS. Furthermore, we have devised an effective methodology for feature selection and shown ways in which the performance of such predictive models should be assessed in future studies.

中文翻译：

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使用逻辑回归预测严重失血性休克的恢复


本文实现了逻辑回归模型 (LRM) 和特征选择，以创建在多个实验大鼠动物方案中使用血液进行复苏的失血性休克 (HS) 恢复的预测模型。共有 61 只动物在多个 HS 实验中进行了研究，其中包括两种不同的 HS 方案和两种使用五种不同技术短期储存血液的复苏方案。每个实验中测量了 27 种不同的全身血流动力学、心功能和血气参数，其中特征选择仅认为其中 25% 相关。减少的特征集用于训练最终的逻辑回归模型。最终测试集的准确度为 84%，而仅使用 MAP 和 HR 测量的基线分类器的准确度为 74%。受试者工作特征 (ROC) 曲线分析和 Cohens kappa 统计也被用作性能衡量指标，最终的简化模型（包括所有参数）均优于模型。我们的结果表明，结合全身血流动力学、心功能和 HS 期间多个时间点测量的血气参数进行训练的 LRM 可以成功对 HS 恢复组进行分类。我们的结果显示了传统和新颖的血流动力学和心脏功能特征及其组合的预测能力，其中许多特征以前没有被考虑用于监测 HS。此外，我们设计了一种有效的特征选择方法，并展示了在未来的研究中评估此类预测模型性能的方法。