Risk factors of left ventricular hypertrophy for chronic kidney disease patients and construction of a predictive model

XIAO Zhimei; DING Chenhui; LUO Chen; QIAN Leilei

doi:10.16766/j.cnki.issn.1674-4152.003635

Volume 22 Issue 8

Aug. 2024

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Article Navigation > Chinese Journal of General Practice > 2024 > 22(8): 1354-1358

XIAO Zhimei, DING Chenhui, LUO Chen, QIAN Leilei. Risk factors of left ventricular hypertrophy for chronic kidney disease patients and construction of a predictive model[J]. Chinese Journal of General Practice, 2024, 22(8): 1354-1358. doi: 10.16766/j.cnki.issn.1674-4152.003635

Citation:

XIAO Zhimei, DING Chenhui, LUO Chen, QIAN Leilei. Risk factors of left ventricular hypertrophy for chronic kidney disease patients and construction of a predictive model[J]. Chinese Journal of General Practice, 2024, 22(8): 1354-1358. doi: 10.16766/j.cnki.issn.1674-4152.003635

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Risk factors of left ventricular hypertrophy for chronic kidney disease patients and construction of a predictive model

doi: 10.16766/j.cnki.issn.1674-4152.003635

Department of Nephrology, Rugao Hospital Affiliated to Nantong University (Rugao People ' s Hospital, Jiangsu Province), Rugao, Jiangsu 226500, China

Funds:

LGY201926

QA2020051

Received Date: 2023-09-25
Available Online: 2024-11-19

Abstract

Abstract

Objective To construct a predictive model for assessing the risk of left ventricular hypertrophy (LVH) in patients with chronic kidney disease (CKD) and evaluate its clinical application value. Methods The clinical data of 302 patients with stage Ⅲ-Ⅴ CKD, who were treated at Rugao Hospital Affiliated to Nantong University from January 2018 to August 2022 were collected. Patients were randomly divided into a modeling group (n=200) and a validation group (n=102) based on their ID number. Logistic regression was used to determine risk factors for LVH, and a nomogram model was constructed. The predictive ability of the model was evaluated by receiver operating characteristic curve and correction curve. Results The incidence of LVH in CKD patients was 37.4% (113/302). Older age (OR=1.054, 95% CI: 1.020-1.089, P=0.002), cardiovascular history (OR=5.826, 95% CI: 2.263-15.003, P < 0.001), CKD Ⅴ stage (OR=5.831, 95% CI: 2.142-15.873, P=0.009), elevated systolic blood pressure (OR=1.019, 95% CI: 1.002-1.036, P=0.025) and hyperphosphatemia (OR=1.109, 95% CI: 1.029-1.195, P=0.007) were independent risk factors for LVH. The prediction model of LVH was constructed based on the above factors, and had AUC of 0.850 (95% CI: 0.796-0.904) in the modeling group and 0.792 (95% CI: 0.707-0.877) in validation group. The calibration curve showed that the nomogram model exhibits good predictive ability for LVH. Conclusion The nomogram model, which is based on age, history of cardiovascular disease, CKD stage, systolic blood pressure, and blood phosphorus, provides clinicians a simple and effective tool to identify CKD patients with high risk of LVH.
- Chronic kidney disease,
- Left ventricular hypertrophy,
- Risk factors,
- Nomogram,
- Predictive model

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References(21)

References

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