Comparison Between Non–High-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol to Estimate Cardiovascular Risk Using a Multivariate Model

BACKGROUND:Although studies exist comparing low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (HDL-C) in the development of cardiovascular disease (CVD), most have limitations in the mathematical models used to evaluate their prognostic power adjusted for the o...

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Veröffentlicht in:The Journal of cardiovascular nursing 2018-11, Vol.33 (6), p.E17-E23
Hauptverfasser: Palazón-Bru, Antonio, Carbayo-Herencia, Julio Antonio, Simarro-Rueda, Marta, Artigao-Ródenas, Luis Miguel, Divisón-Garrote, Juan Antonio, Molina-Escribano, Francisca, Ponce-García, Isabel, Gil-Guillén, Vicente Francisco
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container_end_page E23
container_issue 6
container_start_page E17
container_title The Journal of cardiovascular nursing
container_volume 33
creator Palazón-Bru, Antonio
Carbayo-Herencia, Julio Antonio
Simarro-Rueda, Marta
Artigao-Ródenas, Luis Miguel
Divisón-Garrote, Juan Antonio
Molina-Escribano, Francisca
Ponce-García, Isabel
Gil-Guillén, Vicente Francisco
description BACKGROUND:Although studies exist comparing low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (HDL-C) in the development of cardiovascular disease (CVD), most have limitations in the mathematical models used to evaluate their prognostic power adjusted for the other risk factors (cardiovascular risk). OBJECTIVE:The aim of this study was to compare LDL-C and non-HDL-C in patients with CVD to determine whether both parameters predict CVD similarly. METHODS:A cohort of 1322 subjects drawn from the general population of a Spanish region was followed between 1992 and 2006. The outcome was time to CVD. Secondary variables were gender, age, hypertension, diabetes, personal history of CVD, current smoker, body mass index, LDL-C, and non-HDL-C. Two CVD prediction models were constructed with the secondary variables, with only the lipid parameter varying (non-HDL-C or LDL-C). In the construction of the models, the following were consideredmultiple imputation, events per variable of 10 or more, and continuous predictors as powers. The validation was conducted by bootstrapping obtaining the distribution of the C statistic (discrimination) and the probabilities observed by smooth curves. These results were compared in both models using graphical and analytical testing. RESULTS:There were a total of 137 CVD events. The models showed no differences in the distributions of the C statistic (discrimination, P = .536) or in the calibration plot. CONCLUSIONS:In our population, LDL-C and non-HDL-C were equivalent at predicting CVD. More studies using this methodology are needed to confirm these results.
doi_str_mv 10.1097/JCN.0000000000000534
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OBJECTIVE:The aim of this study was to compare LDL-C and non-HDL-C in patients with CVD to determine whether both parameters predict CVD similarly. METHODS:A cohort of 1322 subjects drawn from the general population of a Spanish region was followed between 1992 and 2006. The outcome was time to CVD. Secondary variables were gender, age, hypertension, diabetes, personal history of CVD, current smoker, body mass index, LDL-C, and non-HDL-C. Two CVD prediction models were constructed with the secondary variables, with only the lipid parameter varying (non-HDL-C or LDL-C). In the construction of the models, the following were consideredmultiple imputation, events per variable of 10 or more, and continuous predictors as powers. The validation was conducted by bootstrapping obtaining the distribution of the C statistic (discrimination) and the probabilities observed by smooth curves. These results were compared in both models using graphical and analytical testing. RESULTS:There were a total of 137 CVD events. The models showed no differences in the distributions of the C statistic (discrimination, P = .536) or in the calibration plot. CONCLUSIONS:In our population, LDL-C and non-HDL-C were equivalent at predicting CVD. More studies using this methodology are needed to confirm these results.</description><identifier>ISSN: 0889-4655</identifier><identifier>EISSN: 1550-5049</identifier><identifier>DOI: 10.1097/JCN.0000000000000534</identifier><identifier>PMID: 30273261</identifier><language>eng</language><publisher>United States: Copyright Wolters Kluwer Health, Inc. All rights reserved</publisher><subject>Cardiovascular disease ; Cholesterol ; Health risk assessment ; Lipoproteins ; Multivariate analysis ; Nursing ; Risk factors</subject><ispartof>The Journal of cardiovascular nursing, 2018-11, Vol.33 (6), p.E17-E23</ispartof><rights>Copyright © 2018 Wolters Kluwer Health, Inc. 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OBJECTIVE:The aim of this study was to compare LDL-C and non-HDL-C in patients with CVD to determine whether both parameters predict CVD similarly. METHODS:A cohort of 1322 subjects drawn from the general population of a Spanish region was followed between 1992 and 2006. The outcome was time to CVD. Secondary variables were gender, age, hypertension, diabetes, personal history of CVD, current smoker, body mass index, LDL-C, and non-HDL-C. Two CVD prediction models were constructed with the secondary variables, with only the lipid parameter varying (non-HDL-C or LDL-C). In the construction of the models, the following were consideredmultiple imputation, events per variable of 10 or more, and continuous predictors as powers. The validation was conducted by bootstrapping obtaining the distribution of the C statistic (discrimination) and the probabilities observed by smooth curves. These results were compared in both models using graphical and analytical testing. RESULTS:There were a total of 137 CVD events. The models showed no differences in the distributions of the C statistic (discrimination, P = .536) or in the calibration plot. CONCLUSIONS:In our population, LDL-C and non-HDL-C were equivalent at predicting CVD. 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subjects Cardiovascular disease
Cholesterol
Health risk assessment
Lipoproteins
Multivariate analysis
Nursing
Risk factors
title Comparison Between Non–High-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol to Estimate Cardiovascular Risk Using a Multivariate Model
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