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976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients

BACKGROUND: HIV-infected individuals are at higher risk for developing cardiovascular disease (CVD). We aimed to develop a model to predict 10-year cardiovascular (CV) risk given that commonly used CVD risk assessment tools might not be accurate for HIV-infected patients. METHODS: We conducted a ret...

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Autores principales: Karanika, Styliani, Karantanos, Theodoros, Carneiro, Herman, Assoumou, Sabrina A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809309/
http://dx.doi.org/10.1093/ofid/ofz359.078
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author Karanika, Styliani
Karantanos, Theodoros
Carneiro, Herman
Assoumou, Sabrina A
author_facet Karanika, Styliani
Karantanos, Theodoros
Carneiro, Herman
Assoumou, Sabrina A
author_sort Karanika, Styliani
collection PubMed
description BACKGROUND: HIV-infected individuals are at higher risk for developing cardiovascular disease (CVD). We aimed to develop a model to predict 10-year cardiovascular (CV) risk given that commonly used CVD risk assessment tools might not be accurate for HIV-infected patients. METHODS: We conducted a retrospective cohort study of HIV-infected patients seen at Boston Medical Center between March 2012 and January 2017. Exclusion criteria are shown in Figure 1. Patients were divided into model development and validation cohorts. Logistic regression was used to create a risk model for CV events using data from the development cohort. The relationship between risk factors and CVD risk was summarized using a point-based risk-scoring system. Areas under the receiver-operating-characteristics curve (AUC) were used to evaluate model discrimination. The model was subsequently tested using the validation cohort. RESULTS: Of 3,867 eligible HIV-infected patients, 1,914 individuals met inclusion criteria (Figure 1). There were 256 CV events in the development cohort. Ten independent prognostic factors were incorporated into the prediction function (P(model) < 0.001). The model had excellent discrimination for CVD risk [AUC 0.94; (95% CI:0.93–0.96)] (Figure 2) and included the following variables: male sex (P < 0.001), African-American ethnicity (P = 0.023), current age (P = 0.020), age at HIV diagnosis (P = 0.006), peak HIV viral load (P = 0.012), nadir CD4 lymphocyte count (P < 0.001), hypertension (P < 0.001), hyperlipidemia (P = 0.001), diabetes (P < 0.001), and chronic kidney disease (P < 0.001). Scoring system and score sheets of risk estimates were developed to predict CV events in a 10-year follow-up period (Figures 3 and 4). The 10-parameter multiple logistic regression model also had excellent discrimination [AUC 0.96; (95% CI: 0.89–0.99)] when applied to the validation cohort. CONCLUSION: We developed and validated a risk-scoring system based on 10 clinical factors that accurately predict the 10-year risk for CV events in an HIV-infected population. This assessment tool may provide clinicians with a rapid assessment of cardiovascular disease risk among HIV-infected patients and inform prevention measures during the era of effective antiretroviral therapy. [Image: see text] [Image: see text] [Image: see text] [Image: see text] DISCLOSURES: All Authors: No reported Disclosures.
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spelling pubmed-68093092019-10-28 976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients Karanika, Styliani Karantanos, Theodoros Carneiro, Herman Assoumou, Sabrina A Open Forum Infect Dis Abstracts BACKGROUND: HIV-infected individuals are at higher risk for developing cardiovascular disease (CVD). We aimed to develop a model to predict 10-year cardiovascular (CV) risk given that commonly used CVD risk assessment tools might not be accurate for HIV-infected patients. METHODS: We conducted a retrospective cohort study of HIV-infected patients seen at Boston Medical Center between March 2012 and January 2017. Exclusion criteria are shown in Figure 1. Patients were divided into model development and validation cohorts. Logistic regression was used to create a risk model for CV events using data from the development cohort. The relationship between risk factors and CVD risk was summarized using a point-based risk-scoring system. Areas under the receiver-operating-characteristics curve (AUC) were used to evaluate model discrimination. The model was subsequently tested using the validation cohort. RESULTS: Of 3,867 eligible HIV-infected patients, 1,914 individuals met inclusion criteria (Figure 1). There were 256 CV events in the development cohort. Ten independent prognostic factors were incorporated into the prediction function (P(model) < 0.001). The model had excellent discrimination for CVD risk [AUC 0.94; (95% CI:0.93–0.96)] (Figure 2) and included the following variables: male sex (P < 0.001), African-American ethnicity (P = 0.023), current age (P = 0.020), age at HIV diagnosis (P = 0.006), peak HIV viral load (P = 0.012), nadir CD4 lymphocyte count (P < 0.001), hypertension (P < 0.001), hyperlipidemia (P = 0.001), diabetes (P < 0.001), and chronic kidney disease (P < 0.001). Scoring system and score sheets of risk estimates were developed to predict CV events in a 10-year follow-up period (Figures 3 and 4). The 10-parameter multiple logistic regression model also had excellent discrimination [AUC 0.96; (95% CI: 0.89–0.99)] when applied to the validation cohort. CONCLUSION: We developed and validated a risk-scoring system based on 10 clinical factors that accurately predict the 10-year risk for CV events in an HIV-infected population. This assessment tool may provide clinicians with a rapid assessment of cardiovascular disease risk among HIV-infected patients and inform prevention measures during the era of effective antiretroviral therapy. [Image: see text] [Image: see text] [Image: see text] [Image: see text] DISCLOSURES: All Authors: No reported Disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6809309/ http://dx.doi.org/10.1093/ofid/ofz359.078 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Karanika, Styliani
Karantanos, Theodoros
Carneiro, Herman
Assoumou, Sabrina A
976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients
title 976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients
title_full 976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients
title_fullStr 976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients
title_full_unstemmed 976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients
title_short 976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients
title_sort 976. development and validation of a risk score for predicting cardiovascular events in hiv-infected patients
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809309/
http://dx.doi.org/10.1093/ofid/ofz359.078
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