Risk Assessment Score and Chi-Square Automatic Interaction Detection Algorithm for Hypertension Among Africans: Models From the SIREN Study.
blood pressure
body mass index
hypertension
machine learning
risk assessment
Journal
Hypertension (Dallas, Tex. : 1979)
ISSN: 1524-4563
Titre abrégé: Hypertension
Pays: United States
ID NLM: 7906255
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
pmc-release:
01
12
2024
medline:
17
11
2023
pubmed:
13
10
2023
entrez:
13
10
2023
Statut:
ppublish
Résumé
This study aimed to develop a risk-scoring model for hypertension among Africans. In this study, 4413 stroke-free controls were used to develop the risk-scoring model for hypertension. Logistic regression models were applied to 13 risk factors. We randomly split the dataset into training and testing data at a ratio of 80:20. Constant and standardized weights were assigned to factors significantly associated with hypertension in the regression model to develop a probability risk score on a scale of 0 to 1 using a logistic regression model. The model accuracy was assessed to estimate the cutoff score for discriminating hypertensives. Mean age was 59.9±13.3 years, 56.0% were hypertensives, and 8 factors, including diabetes, age ≥65 years, higher waist circumference, (BMI) ≥30 kg/m The novel risk-scoring model discriminated hypertensives with good accuracy and will be helpful in the early identification of community-based Africans vulnerable to hypertension for its primary prevention.
Sections du résumé
BACKGROUND
UNASSIGNED
This study aimed to develop a risk-scoring model for hypertension among Africans.
METHODS
UNASSIGNED
In this study, 4413 stroke-free controls were used to develop the risk-scoring model for hypertension. Logistic regression models were applied to 13 risk factors. We randomly split the dataset into training and testing data at a ratio of 80:20. Constant and standardized weights were assigned to factors significantly associated with hypertension in the regression model to develop a probability risk score on a scale of 0 to 1 using a logistic regression model. The model accuracy was assessed to estimate the cutoff score for discriminating hypertensives.
RESULTS
UNASSIGNED
Mean age was 59.9±13.3 years, 56.0% were hypertensives, and 8 factors, including diabetes, age ≥65 years, higher waist circumference, (BMI) ≥30 kg/m
CONCLUSIONS
UNASSIGNED
The novel risk-scoring model discriminated hypertensives with good accuracy and will be helpful in the early identification of community-based Africans vulnerable to hypertension for its primary prevention.
Identifiants
pubmed: 37830199
doi: 10.1161/HYPERTENSIONAHA.122.20572
pmc: PMC10715722
mid: NIHMS1934924
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2581-2590Subventions
Organisme : NHGRI NIH HHS
ID : U24 HG009780
Pays : United States
Organisme : FIC NIH HHS
ID : D43 TW012030
Pays : United States
Organisme : NHLBI NIH HHS
ID : UE5 HL172183
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS115944
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS107900
Pays : United States
Organisme : NINDS NIH HHS
ID : R13 NS115395
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS114045
Pays : United States
Organisme : NHGRI NIH HHS
ID : U54 HG007479
Pays : United States
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