Titulo:
Modelos predictivos de riesgo cardiovascular
.
Sumario:
Los modelos predictores estiman la probabilidad de desarrollar un evento cardiovascular (ECV) a futuro, son desarrollados con base en factores de poblaciones específi cas y han sido validados internamente midiendo su discriminación y calibración, con el objeto de apoyar una intervención primaria y secundaria, no obstante, si se desea aplicarlos en una población distinta, se debe realizar una validación externa.Diversas controversias rondan entorno a estos modelos, especialmente acerca de cuál aplicar en pacientes con diabetes y cuál usar en población colombiana. Se han planteado posturas sobre considerar la diabetes como equivalente de riesgo cardiovascular, evaluar estos pacientes igual que personas que carecen de ésta o brindar un enfoque... Ver más
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0121-2133
2500-7181
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2017-09-18
80
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Eduardo Antonio Burgos Martínez, Andrés Felipe Ramírez, Erika Sofía Villamil - 2017
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http://purl.org/coar/access_right/c_abf2
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Modelos predictivos de riesgo cardiovascular Predictive models of cardiovascular risk Los modelos predictores estiman la probabilidad de desarrollar un evento cardiovascular (ECV) a futuro, son desarrollados con base en factores de poblaciones específi cas y han sido validados internamente midiendo su discriminación y calibración, con el objeto de apoyar una intervención primaria y secundaria, no obstante, si se desea aplicarlos en una población distinta, se debe realizar una validación externa.Diversas controversias rondan entorno a estos modelos, especialmente acerca de cuál aplicar en pacientes con diabetes y cuál usar en población colombiana. Se han planteado posturas sobre considerar la diabetes como equivalente de riesgo cardiovascular, evaluar estos pacientes igual que personas que carecen de ésta o brindar un enfoque separado; y aunque hay estudios que defi enden diferentes posturas, es un tema que aún es controvertido y entre las guías internacionales aún no hay consenso sobre un modelo específi co.Por otra parte, se han llevado a cabo dos estudios en población colombiana usando la misma cohorte histórica, uno donde compara Framingham y PROCAM y otro SCORE, ACC/AHA y Framingham ajustado; el primer estudio determinó que PROCAM tenía mejor calibracióny discriminación mientras que Framingham sobreestima el riesgo, y el segundo concluyó que ACC/AHA es mejor que SCORE aunque subestima el riesgo y en realidad ninguno lo estima correctamente. Pese a ello, las guías recomiendan el uso del Framingham ajustado.Lo anterior refl eja la necesidad de ahondar en la investigación sobre la estimación del riesgo cardiovascular especialmente en nuestro medio para brindar un enfoque acertado a la población colombiana,disminuyendo los ECV, mejorando la calidad de vida del paciente y dando una reducción en los costos al sistema de salud. Predictive models estimate the likelihood of developing a cardiovascular event (CVD), are developed based on specific population factors and have been internally validated by measuring their discrimination and calibration, in order to support primary and secondary intervention, nonetheless, if it is necessary to make them in a different population, an external validation must be performed.Several controversies surround these models, on the use of diabetes and the use in the Colombian population. Postures have been considered about considering diabetes as a cardiovascular risk equivalent, assessing these patients as well as providing a separate approach; and there are even studies that defend different positions, is an issue that is still controversial and among the international guides.On the other hand, studies have been carried out in the Colombian population using the same historical cohort, one comparing Framingham and PROCAM and another SCORE, ACC / AHA and adjusted Framingham; The fi rst study determined that there was better calibration and discrimination while Framingham overestimated the risk, and the latter concluded that ACC / AHA is better than SCORE, but estimates the risk and in fact not one correctly estimates it. In spite of this, the guides favor the use of the adjusted Framingham.This refl ects the need to delve into research on cardiovascular risk, especially in our environment, to provide a successful approach to the Colombian population, reducing CVD, improving the quality of life of the patient and reducing the costs of the health system. Burgos Martínez, Eduardo Antonio Ramírez, Andrés Felipe Villamil, Erika Sofía cardiovascular model cardiovascular risk risk scoring prognostic models modelo cardiovascular riesgo cardiovascular puntaje de riesgo modelos pronósticos 22 2 Artículo de revista Journal article 2017-09-18T10:16:55Z 2017-09-18T10:16:55Z 2017-09-18 application/pdf Fundación Universitaria Juan N. Corpas Revista Cuarzo 0121-2133 2500-7181 https://revistas.juanncorpas.edu.co/index.php/cuarzo/article/view/165 10.26752/cuarzo.v22.n2.165 https://doi.org/10.26752/cuarzo.v22.n2.165 spa https://creativecommons.org/licenses/by-nc-sa/4.0/ Eduardo Antonio Burgos Martínez, Andrés Felipe Ramírez, Erika Sofía Villamil - 2017 80 91 Pan A, Wang Y, Talaei M, Hu FB. Relation of smoking with total mortality and cardiovascular events among patients with diabetes mellitus: a meta-analysis and systematic review. Circulation. 2015;132(19):1795–1804. González-Clemente J, Palma S, Arroyo J, Vilardell C, Caixàs A, Giménez-Palop O et al. ¿La diabetes mellitus es un equivalente de riesgo coronario? Resultados de un metaanálisis de estudios prospectivos. Revista Española de Cardiología. 2007;60(11):1167-1176. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. Evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee. JAMA. 2014;311(5): 507–520. Levit R, Wenger N. High Risk, High Stakes: Optimizing Cardiovascular Risk Assessment in Women. Current Cardiovascular Risk Reports. 2012;6(2):176-184. Modelos predictivos de riesgo cardiovascular Burgos EA. y cols. 91 Steiropoulos P. Is There Evidence of Early Vascular Disease in Patients with Obstructive Sleep Apnoea Without Known Comorbidities? Preliminary Findings. The Open Cardiovascular Medicine Journal. 2013;6(1):61-68. Haffner S, Lehto S, Ronnemaa T et al. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. NEJM. 1998;339 (1998) 229–234. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation. 2002;106(25):3143–3421. Rana et al. Diabetes and Prior Coronary Heart Disease are Not Necessarily Risk Equivalent for Future Coronary Heart Disease Events. JGIM. 2016;31(4):387-393. Daniels et al. Is Diabetes Mellitus a Heart Disease Equivalent in Women? Results From an International Study of Postmenopausal Women in the Raloxifene Use for the Heart (RUTH) Trial Circulation: Cardiovascular Quality and Outcomes. Circulation. 2013;6(2):164-170. Standards of Medical Care. Diabetes-2016: summary of revisions. Diabetes Care. 2016;39(1):1-2. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–2381. Stone N, Robinson J, Lichtenstein A, Bairey C et al. 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults. JACC. 2014;63(25):2889–2934. Kothari V, Stevens R, Adler A, et al. UKPDS 60 risk of stroke in type 2 diabetes estimated by the UK prospective diabetes study risk engine, Stroke. 2002;33(7):1776-1781. Van der Leeuw J, Van Dieren S, Beulens J, Boeing H, Spijkerman A, Van der Graaf Y et al. The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus. Heart.2014;101(3):222-229. Paynter N. Cardiovascular Risk Prediction in Diabetic Men and Women Using Hemoglobin A1c vs Diabetes as a High-Risk Equivalent.Archives of Internal Medicine. 2011;171(19):1712-18. Allan GM, Nouri F, Korownyk C, Kolber MR, Vandermeer B, McCormack J. Agreement among cardiovascular disease risk calculators. Circulation. 2013;127(19):1948–1956. Helfand M, Buckley DI, Freeman M, et al. Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Ann Intern Med. 2009;151(7):496–507. Allan G, Garrison S, McCormack J. Comparison of cardiovascular disease risk calculators. Current Opinion in Lipidology. 2014;25(4):254-265. Graham I, D’Agostino R. Therapeutic strategies in cardiovascular risk. Glob. Heart. 2013;8(1):11–23. Damen J, Hooft L, Schuit E, Debray T, Collins G, Tzoulaki I et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;16;353(2416):1-11. Khanji M, Bicalho V, van Waardhuizen C, Ferket B, Petersen S, Hunink M. Cardiovascular Risk Assessment. Annals of Internal Medicine. 2016;165(10):713. Schargrodsky et al. The CARMELA Study. The American Journal of Medicine. 2008;121:58-65. Cortes-Bergoderi M, Thomas R, Albuquerque F, Batsis J, Burdiat G, Perez-Terzic C et al. Validity of cardiovascular risk prediction models in Latin America and among Hispanics in the United States of America: a systematic review. Revista Panamericana de Salud Pública. 2012;32(2):131-139. Muñoz O, Ruiz J, Mariño A. Bustos M. Concordancia entre los modelos de SCORE y Framingham y las ecuaciones AHA/ACC como evaluadores de riesgo cardiovascular. Revista Colombiana de Cardiología. 2016;24(2): 110-116. Assmann G. Simple Scoring Scheme for Calculating the Risk of Acute Coronary Events Based on the 10-Year Follow-Up of the Prospective Cardiovascular Munster (PROCAM) Study. Circulation. 2002;105(3):310-315. Rücker V, Keil U, Fitzgerald A, Malzahn U, Prugger C, Ertl G et al. Predicting 10-Year Risk of Fatal Cardiovascular Disease in Germany: An Update Based on the SCORE-Deutschland Risk Charts. PLoS One. 2016;11(19): e0162188. Merry, A. H. et al. Risk prediction of incident coronary heart disease in The Netherlands:re-estimation and improvement of the SCORE risk function. SAGE Journals.2012;19(4), 840–848. DeFilippis A, Young R, McEvoy J, Michos E, Sandfort V et al. Risk score overestimation: the impact of individual cardiovascular risk factors and preventive therapies on the performance of the American Heart Association-American College of Cardiology-Atherosclerotic Cardiovascular Disease risk score in a modern multi-ethnic cohort. European Heart Journal. 2017;38(8):598-608. Cook N, Ridker P. Calibration of the Pooled Cohort Equations for Atherosclerotic Cardiovascular Disease. Annals of Internal Medicine. 2016;165(11):786-794. Muntner P, Colantonio L, Cushman M et al. Validation of the atherosclerotic cardiovascular disease pooled cohort risk equations. JAMA. 2014;311(14):1406–1415. https://revistas.juanncorpas.edu.co/index.php/cuarzo/article/download/165/163 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_dcae04bc http://purl.org/redcol/resource_type/ARTREV info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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FUNDACION UNIVERSITARIA JUAN N. CORPAS |
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https://nuevo.metarevistas.org/FUNDACIONUNIVERSITARIAJUANN.CORPAS/logo.png |
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Colombia |
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Revista Cuarzo |
| title |
Modelos predictivos de riesgo cardiovascular |
| spellingShingle |
Modelos predictivos de riesgo cardiovascular Burgos Martínez, Eduardo Antonio Ramírez, Andrés Felipe Villamil, Erika Sofía cardiovascular model cardiovascular risk risk scoring prognostic models modelo cardiovascular riesgo cardiovascular puntaje de riesgo modelos pronósticos |
| title_short |
Modelos predictivos de riesgo cardiovascular |
| title_full |
Modelos predictivos de riesgo cardiovascular |
| title_fullStr |
Modelos predictivos de riesgo cardiovascular |
| title_full_unstemmed |
Modelos predictivos de riesgo cardiovascular |
| title_sort |
modelos predictivos de riesgo cardiovascular |
| title_eng |
Predictive models of cardiovascular risk |
| description |
Los modelos predictores estiman la probabilidad de desarrollar un evento cardiovascular (ECV) a futuro, son desarrollados con base en factores de poblaciones específi cas y han sido validados internamente midiendo su discriminación y calibración, con el objeto de apoyar una intervención primaria y secundaria, no obstante, si se desea aplicarlos en una población distinta, se debe realizar una validación externa.Diversas controversias rondan entorno a estos modelos, especialmente acerca de cuál aplicar en pacientes con diabetes y cuál usar en población colombiana. Se han planteado posturas sobre considerar la diabetes como equivalente de riesgo cardiovascular, evaluar estos pacientes igual que personas que carecen de ésta o brindar un enfoque separado; y aunque hay estudios que defi enden diferentes posturas, es un tema que aún es controvertido y entre las guías internacionales aún no hay consenso sobre un modelo específi co.Por otra parte, se han llevado a cabo dos estudios en población colombiana usando la misma cohorte histórica, uno donde compara Framingham y PROCAM y otro SCORE, ACC/AHA y Framingham ajustado; el primer estudio determinó que PROCAM tenía mejor calibracióny discriminación mientras que Framingham sobreestima el riesgo, y el segundo concluyó que ACC/AHA es mejor que SCORE aunque subestima el riesgo y en realidad ninguno lo estima correctamente. Pese a ello, las guías recomiendan el uso del Framingham ajustado.Lo anterior refl eja la necesidad de ahondar en la investigación sobre la estimación del riesgo cardiovascular especialmente en nuestro medio para brindar un enfoque acertado a la población colombiana,disminuyendo los ECV, mejorando la calidad de vida del paciente y dando una reducción en los costos al sistema de salud.
|
| description_eng |
Predictive models estimate the likelihood of developing a cardiovascular event (CVD), are developed based on specific population factors and have been internally validated by measuring their discrimination and calibration, in order to support primary and secondary intervention, nonetheless, if it is necessary to make them in a different population, an external validation must be performed.Several controversies surround these models, on the use of diabetes and the use in the Colombian population. Postures have been considered about considering diabetes as a cardiovascular risk equivalent, assessing these patients as well as providing a separate approach; and there are even studies that defend different positions, is an issue that is still controversial and among the international guides.On the other hand, studies have been carried out in the Colombian population using the same historical cohort, one comparing Framingham and PROCAM and another SCORE, ACC / AHA and adjusted Framingham; The fi rst study determined that there was better calibration and discrimination while Framingham overestimated the risk, and the latter concluded that ACC / AHA is better than SCORE, but estimates the risk and in fact not one correctly estimates it. In spite of this, the guides favor the use of the adjusted Framingham.This refl ects the need to delve into research on cardiovascular risk, especially in our environment, to provide a successful approach to the Colombian population, reducing CVD, improving the quality of life of the patient and reducing the costs of the health system.
|
| author |
Burgos Martínez, Eduardo Antonio Ramírez, Andrés Felipe Villamil, Erika Sofía |
| author_facet |
Burgos Martínez, Eduardo Antonio Ramírez, Andrés Felipe Villamil, Erika Sofía |
| topic |
cardiovascular model cardiovascular risk risk scoring prognostic models modelo cardiovascular riesgo cardiovascular puntaje de riesgo modelos pronósticos |
| topic_facet |
cardiovascular model cardiovascular risk risk scoring prognostic models modelo cardiovascular riesgo cardiovascular puntaje de riesgo modelos pronósticos |
| topicspa_str_mv |
modelo cardiovascular riesgo cardiovascular puntaje de riesgo modelos pronósticos |
| citationvolume |
22 |
| citationissue |
2 |
| publisher |
Fundación Universitaria Juan N. Corpas |
| ispartofjournal |
Revista Cuarzo |
| source |
https://revistas.juanncorpas.edu.co/index.php/cuarzo/article/view/165 |
| language |
spa |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-sa/4.0/ Eduardo Antonio Burgos Martínez, Andrés Felipe Ramírez, Erika Sofía Villamil - 2017 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references |
Pan A, Wang Y, Talaei M, Hu FB. Relation of smoking with total mortality and cardiovascular events among patients with diabetes mellitus: a meta-analysis and systematic review. Circulation. 2015;132(19):1795–1804. González-Clemente J, Palma S, Arroyo J, Vilardell C, Caixàs A, Giménez-Palop O et al. ¿La diabetes mellitus es un equivalente de riesgo coronario? Resultados de un metaanálisis de estudios prospectivos. Revista Española de Cardiología. 2007;60(11):1167-1176. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. Evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee. JAMA. 2014;311(5): 507–520. Levit R, Wenger N. High Risk, High Stakes: Optimizing Cardiovascular Risk Assessment in Women. Current Cardiovascular Risk Reports. 2012;6(2):176-184. Modelos predictivos de riesgo cardiovascular Burgos EA. y cols. 91 Steiropoulos P. Is There Evidence of Early Vascular Disease in Patients with Obstructive Sleep Apnoea Without Known Comorbidities? Preliminary Findings. The Open Cardiovascular Medicine Journal. 2013;6(1):61-68. Haffner S, Lehto S, Ronnemaa T et al. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. NEJM. 1998;339 (1998) 229–234. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation. 2002;106(25):3143–3421. Rana et al. Diabetes and Prior Coronary Heart Disease are Not Necessarily Risk Equivalent for Future Coronary Heart Disease Events. JGIM. 2016;31(4):387-393. Daniels et al. Is Diabetes Mellitus a Heart Disease Equivalent in Women? Results From an International Study of Postmenopausal Women in the Raloxifene Use for the Heart (RUTH) Trial Circulation: Cardiovascular Quality and Outcomes. Circulation. 2013;6(2):164-170. Standards of Medical Care. Diabetes-2016: summary of revisions. Diabetes Care. 2016;39(1):1-2. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–2381. Stone N, Robinson J, Lichtenstein A, Bairey C et al. 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults. JACC. 2014;63(25):2889–2934. Kothari V, Stevens R, Adler A, et al. UKPDS 60 risk of stroke in type 2 diabetes estimated by the UK prospective diabetes study risk engine, Stroke. 2002;33(7):1776-1781. Van der Leeuw J, Van Dieren S, Beulens J, Boeing H, Spijkerman A, Van der Graaf Y et al. The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus. Heart.2014;101(3):222-229. Paynter N. Cardiovascular Risk Prediction in Diabetic Men and Women Using Hemoglobin A1c vs Diabetes as a High-Risk Equivalent.Archives of Internal Medicine. 2011;171(19):1712-18. Allan GM, Nouri F, Korownyk C, Kolber MR, Vandermeer B, McCormack J. Agreement among cardiovascular disease risk calculators. Circulation. 2013;127(19):1948–1956. Helfand M, Buckley DI, Freeman M, et al. Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Ann Intern Med. 2009;151(7):496–507. Allan G, Garrison S, McCormack J. Comparison of cardiovascular disease risk calculators. Current Opinion in Lipidology. 2014;25(4):254-265. Graham I, D’Agostino R. Therapeutic strategies in cardiovascular risk. Glob. Heart. 2013;8(1):11–23. Damen J, Hooft L, Schuit E, Debray T, Collins G, Tzoulaki I et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;16;353(2416):1-11. Khanji M, Bicalho V, van Waardhuizen C, Ferket B, Petersen S, Hunink M. Cardiovascular Risk Assessment. Annals of Internal Medicine. 2016;165(10):713. Schargrodsky et al. The CARMELA Study. The American Journal of Medicine. 2008;121:58-65. Cortes-Bergoderi M, Thomas R, Albuquerque F, Batsis J, Burdiat G, Perez-Terzic C et al. Validity of cardiovascular risk prediction models in Latin America and among Hispanics in the United States of America: a systematic review. Revista Panamericana de Salud Pública. 2012;32(2):131-139. Muñoz O, Ruiz J, Mariño A. Bustos M. Concordancia entre los modelos de SCORE y Framingham y las ecuaciones AHA/ACC como evaluadores de riesgo cardiovascular. Revista Colombiana de Cardiología. 2016;24(2): 110-116. Assmann G. Simple Scoring Scheme for Calculating the Risk of Acute Coronary Events Based on the 10-Year Follow-Up of the Prospective Cardiovascular Munster (PROCAM) Study. Circulation. 2002;105(3):310-315. Rücker V, Keil U, Fitzgerald A, Malzahn U, Prugger C, Ertl G et al. Predicting 10-Year Risk of Fatal Cardiovascular Disease in Germany: An Update Based on the SCORE-Deutschland Risk Charts. PLoS One. 2016;11(19): e0162188. Merry, A. H. et al. Risk prediction of incident coronary heart disease in The Netherlands:re-estimation and improvement of the SCORE risk function. SAGE Journals.2012;19(4), 840–848. DeFilippis A, Young R, McEvoy J, Michos E, Sandfort V et al. Risk score overestimation: the impact of individual cardiovascular risk factors and preventive therapies on the performance of the American Heart Association-American College of Cardiology-Atherosclerotic Cardiovascular Disease risk score in a modern multi-ethnic cohort. European Heart Journal. 2017;38(8):598-608. Cook N, Ridker P. Calibration of the Pooled Cohort Equations for Atherosclerotic Cardiovascular Disease. Annals of Internal Medicine. 2016;165(11):786-794. Muntner P, Colantonio L, Cushman M et al. Validation of the atherosclerotic cardiovascular disease pooled cohort risk equations. JAMA. 2014;311(14):1406–1415. |
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