Evidence map›Paper›PMID 39753226›Full record

ArticleJournal of medical Internet research2024

Development and Validation of a Nomogram-Based Model to Predict Primary Hypertension Within the Next Year in Children and Adolescents: Retrospective Cohort Study.

Chenlong Qin, Li Peng, Yun Liu, Xiaoliang Zhang, Shumei Miao, Zhiyuan Wei, Wei Feng, Hongjian Zhang, Cheng Wan, Yun Yu and 3 more

Abstract readValidation Study
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Chenlong QinDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0009-0009-0912-5639
Li PengDepartment of Endocrinology and Metabolism, the Fourth Affiliated Hospital, Nanjing Medical University, Nanjing, China.ORCID 0009-0000-5017-1690
Yun LiuDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0000-0002-4311-3772
Xiaoliang ZhangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0009-0005-7598-1733
Shumei MiaoDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0000-0001-6101-8288
Zhiyuan WeiDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0009-0000-9341-4111
Wei FengDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0000-0002-6843-2067
Hongjian ZhangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0009-0001-2041-2702
Cheng WanDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0000-0002-2981-5970
Yun YuDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0000-0003-2042-8365
Shan LuWomen and Children Department of the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID 0009-0004-3088-5070
Ruochen HuangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0009-0006-5297-2970
Xin ZhangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.ORCID 0000-0003-0047-3134

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrimary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions.

objectiveThis study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents. It also aimed to establish and validate a nomogram-based model for predicting the next year's PH risk.

methodsA training cohort (n=3938, between January 1, 2008, and December 31, 2020) and a validation cohort (n=1269, between January 1, 2021, and July 1, 2023) were established for model training and validation. An independent cohort of 576 individuals was established for external validation of the model. The result of the least absolute shrinkage and selection operator regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomogram. The performance of the nomogram underwent assessment and validation through the area under the receiver operating characteristic curve, concordance index, calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis.

resultsThe PH risk factors that we have ultimately identified include gender (odds ratio [OR] 3.34, 95% CI 2.88 to 3.86; P<.001), age (OR 1.11, 95% CI 1.08 to 1.14; P<.001), family history of hypertension (OR 42.74, 95% CI 23.07 to 79.19; P<.001), fasting blood glucose (OR 6.07, 95% CI 4.74 to 7.78; P<.001), low-density lipoprotein cholesterol (OR 2.03, 95% CI 1.60 to 2.57; P<.001), and uric acid (OR 1.01, 95% CI 1.01 to 1.01; P<.001), while factor breastfeeding (OR 0.04, 95% CI 0.03 to 0.05; P<.001) has been identified as a protective factor. Subsequently, a nomogram has been constructed incorporating these factors. Areas under the receiver operating characteristic curves of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. Concordance indexes of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. The nomogram has been proven to have good clinical benefits and stability in calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. Finally, we observed noteworthy differences in uric acid levels and family history of hypertension among various subgroups, demonstrating a high correlation with PH. Moreover, the web-based calculator of the nomogram was built online.

conclusionsWe have developed and validated a stable and reliable nomogram that can accurately predict PH risk within the next year among children and adolescents in primary care and offer effective and cost-efficient support for clinical decisions for the risk prediction of PH.

Indexed as

NomogramsAdolescentChildChinaCohort StudiesEssential HypertensionFemaleHumansHypertensionMaleRetrospective StudiesRisk FactorsROC Curveclinical applicabilitydevelopmentelectronic health recordsindependent risk factorspediatricsprediction modelprimary hypertensionvalidation

Identifiers

PMID39753226
PMCPMC11730233

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.