METHODS: With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost.
RESULTS: During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction.
CONCLUSIONS: The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies.
CONCLUSIONS: This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.
方法:只有不到1%的病例导致这些结果中的至少一种,我们调查了98个特征,以确定它们在预测患者恶化中的作用,使用单变量分析。此外,采用LASSO正则化逻辑回归(LR)进行多变量分析。我们还评估了分类模型,包括支持向量机等非线性分类器,随机森林,XGBoost
结果:在评估期间,仔细注意数据不平衡,因此使用了多个评估指标,对不平衡不太敏感。这些度量包括接收器操作特性下的区域,精确召回率和卡帕曲线,和精度,灵敏度,kappa,和F1得分。将未预期的ICU入院率和死亡率结合到单一结果中,总体上改善了预测性能。然而,这导致预测个体恶化形式的准确性降低,LR显示了组合预测的最佳性能。
结论:该研究强调了特定的围手术期特征在预测患者恶化方面的重要性,特别是通过单变量分析揭示。重要的是,逻辑回归等可解释模型优于复杂分类器,表明他们的实用性。尤其是,当组合在集成模型中以预测多种形式的恶化时。这些发现主要受到数据失衡的限制,因为术后恶化很少发生。因此,未来的研究应该集中在捕获更多的恶化事件,并可能将验证扩展到多中心研究。
结论:这项工作证明了准确预测围手术期患者恶化的潜力,强调几个围手术期特征的重要性和逻辑回归等可解释模型的实用性,以及用于预测几种结果类型的集成模型。在未来的临床实践中,这些数据驱动的预测模型可能会通过提供基于证据的风险评估来形成术后风险分层的基础。