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列线图预测腹膜透析患者心血管死亡率:一项观察性研究。

Nomogram for Predicting Cardiovascular Mortality in Incident Peritoneal Dialysis Patients: An Observational Study.

机构信息

The First Affiliated Hospital of Sun Yat-sen University, Department of Nephrology, Guangzhou, 510080, People's Republic of China.

Ministry of Health and Guangdong Province, Key Laboratory of Nephrology, Guangzhou, 510080, People's Republic of China.

出版信息

Sci Rep. 2017 Oct 24;7(1):13889. doi: 10.1038/s41598-017-14489-4.

DOI:10.1038/s41598-017-14489-4
PMID:29066841
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5654762/
Abstract

Cardiovascular mortality risk is high for peritoneal dialysis (PD) patients but it varies considerably among individuals. There is no clinical tool to predict cardiovascular mortality for PD patients yet. Therefore, we developed a cardiovascular mortality risk nomogram in a PD patient cohort. We derived and internally validated the nomogram in incident adult PD patients randomly assigned to a training (N = 918) or a validation (N = 460) dataset. The nomogram was built using the LASSO Cox regression model. Increasing age, history of cardiovascular disease or diabetes were consistent predictors of cardiovascular mortality. Low hemoglobin and serum albumin, high hypersensitive C-reactive protein and decreasing 24 hours urine output were identified as non-traditional cardiovascular risk predictors. In the validation dataset, the above nomogram performed good discrimination (1 year c-statistic = 0.83; 3 year c-statistic = 0.78) and calibration. This tool can classify patients between those at high risk of cardiovascular mortality (high-risk group) and those of low risk (low-risk group). Cardiovascular mortality was significantly different in the internal validation set of patients for the high-risk group compared to the low-risk group (HR 3.77, 2.14-6.64; p < 0.001). This novel nomogram can accurately predict cardiovascular mortality risk in incident PD patients.

摘要

心血管疾病死亡风险对腹膜透析(PD)患者较高,但个体间差异较大。目前尚无临床工具可预测 PD 患者的心血管疾病死亡风险。因此,我们在 PD 患者队列中开发了一种心血管疾病死亡风险列线图。我们在随机分配到训练(N=918)或验证(N=460)数据集的新发病例成年 PD 患者中推导并内部验证了该列线图。该列线图使用 LASSO Cox 回归模型构建。年龄增加、心血管疾病或糖尿病史是心血管疾病死亡的一致预测因素。低血红蛋白和血清白蛋白、高超敏 C 反应蛋白和 24 小时尿量减少被确定为非传统心血管风险预测因素。在验证数据集中,上述列线图具有良好的区分度(1 年 c 统计量=0.83;3 年 c 统计量=0.78)和校准度。该工具可将患者分为心血管疾病死亡风险高(高危组)和低(低危组)的两组。高危组患者的心血管疾病死亡率在内部验证集中与低危组患者相比存在显著差异(HR 3.77,2.14-6.64;p<0.001)。这种新的列线图可以准确预测新发病例 PD 患者的心血管疾病死亡风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/21ffbac0d3bc/41598_2017_14489_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/8f60cd539900/41598_2017_14489_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/ab00990e5369/41598_2017_14489_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/1a2f2a685161/41598_2017_14489_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/b765dc43d27d/41598_2017_14489_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/2903e6ee4fab/41598_2017_14489_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/21ffbac0d3bc/41598_2017_14489_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/8f60cd539900/41598_2017_14489_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/ab00990e5369/41598_2017_14489_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/1a2f2a685161/41598_2017_14489_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/b765dc43d27d/41598_2017_14489_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/2903e6ee4fab/41598_2017_14489_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b1d/5654762/21ffbac0d3bc/41598_2017_14489_Fig6_HTML.jpg

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