Evidence map›Paper›PMID 38514675›Full record

ArticleScientific reports2024

Development and validation of a model for predicting the risk of cardiovascular events in maintenance hemodialysis patients.

Meijie Qin, Yuqi Yang, Lu Dai, Jie Ding, Yan Zha, Jing Yuan

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

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6citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Meijie QinZunyi Medical University, Guizhou, 563003, China.
Yuqi YangDepartment of Nephrology, Guizhou Provincial People's Hospital, Guizhou, 550002, China.
Lu DaiDepartment of Nephrology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guizhou, 550002, China.
Jie DingThe Second Clinical Medical College of Guizhou University of Traditional Chinese Medicine, Guizhou, 550002, China.
Yan ZhaDepartment of Nephrology, Guizhou Provincial People's Hospital, Guizhou, 550002, China. zhayan72@126.com.
Jing YuanDepartment of Nephrology, Guizhou Provincial People's Hospital, Guizhou, 550002, China. yuanjinger@126.com.

Funding

the Basic Research Plan of Guizhou Province in 2023 (Natural Science Project) QKH-ZK [2023] General 218the Guizhou Administration of Traditional Chinese Medicine QZYY-2023-135the National Natural Science Foundation of China 823601480the Science and Technology Department of Guizhou Province QKHCG2023-ZD010the Science and Technology Department of Guizhou Province QKHPTPC 2018-5636-2the Talent Fund of Guizhou provincial People's Hospital [2022]-1
6 · The paper itself

Abstract

The mortality rates for patients undergoing hemodialysis (HD) remain unacceptably high compared to the general population, and more specific information about the causes of death is not known. The study aimed to develop and validate a risk prediction model that uses common clinical factors to predict the probability of cardiovascular events in maintenance hemodialysis (MHD) patients. The study involved 3488 adult patients who received regular scheduled hemodialysis treatment at 20 hemodialysis centers in southwest China between June 2015 and August 2020, with follow-up until August 2021. The optimal parameter set was identified by multivariable Cox regression analyses and Cross-LASSO regression analyses and was used to establish a nomogram for predicting the risk of cardiovascular events in maintenance hemodialysis patients at 3 and 5 years. The performance of the model was evaluated using the consistency index (Harrell's C-index), the area under the receiver operating characteristic (ROC) curve, and calibration plots. The model was validated by tenfold cross-validation and bootstrapping with 1000 resamples. In the derivation cohort, the model yields an AUC of 0.764 [95% confidence interval (CI), 0.737-0.790] and 0.793 [CI, 0.757-0.829] for predicting the risk of cardiovascular events of MHD patients at 3 and 5 years. In the internal validation cohort AUC of 0.803 [95% CI, 0.756-0.849], AUC of 0.766 [95% CI, 0.686-0.846], and the external validation cohort AUC of 0.826 [95% CI, 0.765-0.888], AUC of 0.817 [95% CI, 0.745-0.889] at 3 and 5 years. The model's calibration curve is close to the ideal diagonal. By tenfold cross-validation analyses, the 3- and 5-year risk of cardiovascular events (AUC 0.732 and 0.771, respectively). By the bootstrap resampling method, the derivation cohort and validation cohort (Harrell's C-index 0.695 and 0.667, respectively) showed good uniformity with the model. The constructed model accurately predicted cardiovascular events of MHD patients in the 3rd and 5th years after dialysis. And the further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.

Indexed as

Cardiovascular DiseasesRenal DialysisAdultCalibrationChinaHumansNomogramsRisk Factors

Identifiers

PMID38514675
PMCPMC10958022

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.