Ben-Assuli Ofir, Ramon-Gonen Roni, Heart Tsipi, Jacobi Arie, Klempfner Robert
Faculty of Business Administration, Ono Academic College, 104 Zahal Street, Kiryat Ono 55000, Israel.
The Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan, Israel.
J Biomed Inform. 2023 Apr;140:104340. doi: 10.1016/j.jbi.2023.104340. Epub 2023 Mar 17.
Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.
了解患者的生存概率以及影响生存概率的因素是研究人员和从业者，尤其是患有严重慢性疾病（如充血性心力衰竭，CHF）的患者所关注的重要问题。CHF是一种以合并症和不良医疗事件为特征的临床综合征。进行风险分层以识别出院后不久最有可能死亡的患者，可以通过更好地分配组织资源和个性化干预措施来提高护理质量。概率评估可改善临床决策，有助于个性化护理并节省成本。尽管最具信息量的指标之一是每个患者发生不良事件的时间，通常使用生存分析方法进行分析，但由于医学数据的复杂性，这些方法实施起来往往具有挑战性。许多研究使用Cox比例风险（PH）回归方法来生成生存分布模式和影响生存的因素。该模型虽然在生存分析方面具有优势，但假设患者之间的风险比具有同质性，并且观察结果在生存时间方面具有独立性。这些假设在实际数据中经常被违反，特别是当数据集由慢性病患者的再入院数据组成时，因为这些重复观察本质上是相关的。本研究对从丰富的医院数据集中通过机器学习算法选择的特征集进行了Cox PH回归分析。这里建模的事件是患者出院后90天内的死亡率。样本由以色列谢巴医疗中心多次住院的患者的病历组成，以CHF作为主要诊断。我们使用Cox PH回归对CHF患者的生存情况进行建模，分别采用了有和没有解决Cox模型缺点的共享脆弱性校正方法。比较了Cox PH回归的两种模型（有和没有共享脆弱性校正）的结果。结果表明，在我们的分析中具有统计学意义的共享脆弱性校正提高了基本Cox PH模型的性能。虽然这是主要贡献，但我们还表明，该模型在预测CHF患者早期死亡率方面优于两种常用的测量方法（ADHERE和EFFECT）。因此，结果说明了应用高级分析如何优于传统方法。另一个贡献是使用机器学习方法选择的特征集，它与现有文献中使用的特征集不同。
