Development and Validation of a Nomogram to Better Predict Hypertension Based on a 10-year Retrospective Cohort Study in China

Overview

Journal Elife

Specialty Biology

Date 2021 May 28

PMID 34047697

Citations 16

Authors

Xinna Deng

Huiqing Hou

Xiaoxi Wang

Qingxia Li

Xiuyuan Li

Zhaohua Yang

Haijiang Wu

Affiliations

Soon will be listed here.

Abstract

Background: Hypertension is a highly prevalent disorder. A nomogram to estimate the risk of hypertension in Chinese individuals is not available.

Methods: 6201 subjects were enrolled in the study and randomly divided into training set and validation set at a ratio of 2:1. The LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomograms. The performance of the nomograms was assessed and validated by AUC, C-index, calibration curves, DCA, clinical impact curves, NRI, and IDI.

Results: The nomogram was developed with the parameters of family history of hypertension, age, SBP, DBP, BMI, MCHC, MPV, TBIL, and TG. AUCs of nomogram were 0.750 in the training set and 0.772 in the validation set. C-index of nomogram were 0.750 in the training set and 0.772 in the validation set. The nomogram was developed with the parameters of family history of hypertension, age, SBP, DBP, RDWSD, and TBIL. AUCs of nomogram were 0.705 in the training set and 0.697 in the validation set. C-index of nomogram were 0.705 in the training set and 0.697 in the validation set. Both nomograms demonstrated favorable clinical consistency. NRI and IDI showed that the nomogram exhibited superior performance than the nomogram. Therefore, the web-based calculator of nomogram was built online.

Conclusions: We have constructed a nomogram that can be effectively used in the preliminary and in-depth risk prediction of hypertension in a Chinese population based on a 10-year retrospective cohort study.

Funding: This study was supported by the Hebei Science and Technology Department Program (no. H2018206110).

Citing Articles

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PMID: 39753226 PMC: 11730233. DOI: 10.2196/58686.

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Islam M, Alam J, Kumar S, Islam A, Khan M, Rabby S Heliyon. 2024; 10(22):e40246.

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Tang J, Yang R, Li H, Wei X, Yang Z, Cai W Elife. 2024; 13.

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AlKetbi L, Al Hashaikeh B, Fahmawee T, Sahalu Y, Alkuwaiti M, Nagelkerke N J Hypertens. 2024; 43(2):308-317.

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Dai X, Li Y, Wang H, Dai Z, Chen Y, Liu Y Front Oncol. 2024; 14:1402847.

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