Yang Cheng-Hong, Moi Sin-Hua, Chuang Li-Yeh, Chen Jin-Bor
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung.
Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung.
Ther Adv Chronic Dis. 2020 Sep 29;11:2040622320949060. doi: 10.1177/2040622320949060. eCollection 2020.
In Taiwan, approximately 90% of patients with end-stage renal disease receive maintenance hemodialysis. Although studies have reported the survival predictability of multiclinical factors, the higher-order interactions among these factors have rarely been discussed. Conventional statistical approaches such as regression analysis are inadequate for detecting higher-order interactions. Therefore, this study integrated receiver operating characteristic, logistic regression, and balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction (MDR-ER) analyses to examine the impact of interaction effects between multiclinical factors on overall mortality in patients on maintenance hemodialysis.
In total, 781 patients who received outpatient hemodialysis dialysis three times per week before 1 January 2009 were included; their baseline clinical factor and mortality outcome data were retrospectively collected using an approved data protocol (201800595B0).
Consistent with conventional statistical approaches, the higher-order interaction model could indicate the impact of potential risk combination unique to patients on maintenance hemodialysis on the survival outcome, as described previously. Moreover, the MDR-based higher-order interaction model facilitated higher-order interaction effect detection among multiclinical factors and could determine more detailed mortality risk characteristics combinations.
Therefore, higher-order clinical risk interaction analysis is a reasonable strategy for detecting non-traditional risk factor interaction effects on survival outcome unique to patients on maintenance hemodialysis and thus clinically achieving whole-scale patient care.
在台湾，约90%的终末期肾病患者接受维持性血液透析。尽管已有研究报道了多种临床因素对生存的预测能力，但这些因素之间的高阶相互作用却鲜有讨论。诸如回归分析等传统统计方法不足以检测高阶相互作用。因此，本研究整合了受试者工作特征曲线、逻辑回归和平衡函数，通过多因素降维分析（MDR-ER）调整风险类别中的比例以及不均衡病例与对照的分类错误，以检验多种临床因素之间的相互作用对维持性血液透析患者总体死亡率的影响。
共纳入2009年1月1日前每周接受三次门诊血液透析的781例患者；使用批准的数据方案（201800595B0）回顾性收集其基线临床因素和死亡率结局数据。
与传统统计方法一致，高阶相互作用模型能够如前所述，表明维持性血液透析患者特有的潜在风险组合对生存结局的影响。此外，基于MDR的高阶相互作用模型有助于检测多种临床因素之间的高阶相互作用效应，并能确定更详细的死亡风险特征组合。
因此，高阶临床风险相互作用分析是一种合理的策略，可用于检测对维持性血液透析患者生存结局有独特影响的非传统风险因素相互作用效应，从而在临床上实现全面的患者护理。
