Matsubara Yukiko, Kimachi Miho, Fukuma Shingo, Onishi Yoshihiro, Fukuhara Shunichi
Department of Artificial Organs, Akane-Foundation Omachi Tsuchiya Clinic, and Hiroshima Medical University, Hiroshima, Japan.
Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan.
PLoS One. 2017 Mar 8;12(3):e0173468. doi: 10.1371/journal.pone.0173468. eCollection 2017.
Cardiovascular (CV) events are the primary cause of death and becoming bedridden among hemodialysis (HD) patients. The Framingham risk score (FRS) is useful for predicting incidence of CV events in the general population, but is considerd to be unsuitable for the prediction of the incidence of CV events in HD patients, given their characteristics due to atypical relationships between conventional risk factors and outcomes. We therefore aimed to develop a new prognostic prediction model for prevention and early detection of CV events among hemodialysis patients.
We enrolled 3,601 maintenance HD patients based on their data from the Japan Dialysis Outcomes and Practice Patterns Study (J-DOPPS), phases 3 and 4. We longitudinaly assessed the association between several potential candidate predictors and composite CV events in the year after study initiation. Potential candidate predictors included the component factors of FRS and other HD-specific risk factors. We used multivariable logistic regression with backward stepwise selection to develop our new prediction model and generated a calibration plot. Additinially, we performed bootstrapping to assess the internal validity.
We observed 328 composite CV events during 1-year follow-up. The final prediction model contained six variables: age, diabetes status, history of CV events, dialysis time per session, and serum phosphorus and albumin levels. The new model showed significantly better discrimination than the FRS, in both men (c-statistics: 0.76 for new model, 0.64 for FRS) and women (c-statistics: 0.77 for new model, 0.60 for FRS). Additionally, we confirmed the consistency between the observed results and predicted results using the calibration plot. Further, we found similar discrimination and calibration to the derivation model in the bootstrapping cohort.
We developed a new risk model consisting of only six predictors. Our new model predicted CV events more accurately than the FRS.
心血管（CV）事件是血液透析（HD）患者死亡和卧床不起的主要原因。弗雷明汉风险评分（FRS）有助于预测普通人群中CV事件的发生率，但鉴于HD患者传统风险因素与预后之间的非典型关系所具有的特征，该评分被认为不适用于预测HD患者CV事件的发生率。因此，我们旨在开发一种新的预后预测模型，用于预防和早期发现血液透析患者中的CV事件。
我们根据日本透析结果与实践模式研究（J-DOPPS）第3和第4阶段的数据，纳入了3601例维持性HD患者。我们纵向评估了研究开始后一年内几个潜在候选预测因素与复合CV事件之间的关联。潜在候选预测因素包括FRS的组成因素和其他HD特异性风险因素。我们使用多变量逻辑回归和向后逐步选择来开发我们的新预测模型，并生成校准图。此外，我们进行了自助法以评估内部有效性。
在1年的随访期间，我们观察到328例复合CV事件。最终预测模型包含六个变量：年龄、糖尿病状态、CV事件史、每次透析时间以及血清磷和白蛋白水平。新模型在男性（c统计量：新模型为0.76，FRS为0.64）和女性（c统计量：新模型为0.77，FRS为0.60）中均显示出比FRS显著更好的区分能力。此外，我们使用校准图证实了观察结果与预测结果之间的一致性。此外，我们在自助法队列中发现了与推导模型相似的区分能力和校准情况。
我们开发了一种仅由六个预测因素组成的新风险模型。我们的新模型比FRS更准确地预测了CV事件。
