HIV-infected patients are at increased risk for cardiovascular disease (CVD). However, general population CVD risk prediction equations that identify HIV-infected patients at elevated risk have not been widely assessed in sub-Saharan African (SSA).
HIV-infected adults from 30–50 years of age with documented viral suppression were enrolled into a cross-sectional study in Gaborone, Botswana. Participants were screened for CVD risk factors. Bilateral carotid intima-media thickness (cIMT) was measured and 10-year predicted risk of cardiovascular disease was calculated using the Pooled Cohorts Equation for atherosclerotic CVD (ASCVD) and the 2008 Framingham Risk Score (FRS) (National Cholesterol Education Program III–NCEP III). ASCVD ≥7.5%, FRS ≥10%, and cIMT≥75th percentile were considered elevated risk for CVD. Agreement in classification of participants as high-risk for CVD by cIMT and FRS or ASCVD risk score was assessed using McNemar`s Test. The optimal cIMT cut off-point that matched ASCVD predicted risk of ≥7.5% was assessed using Youden’s J index.
Among 208 HIV-infected patients (female: 55%, mean age 38 years), 78 (38%) met criteria for ASCVD calculation versus 130 (62%) who did not meet the criteria. ASCVD classified more participants as having elevated CVD risk than FRS (14.1% versus 2.6%, McNemar’s exact test p = 0.01), while also classifying similar proportion of participants as having elevated CVD like cIMT (14.1% versus 19.2%, McNemar’s exact test p = 0.34). Youden’s J calculated the optimal cut point at the 81st percentile for cIMT to correspond to an ASCVD score ≥7.5% (sensitivity = 72.7% and specificity = 88.1% with area under the curve for the receiver operating characteristic [AUC] of 0.82, 95% Mann-Whitney CI: 0.66–0.99).
While the ASCVD risk score classified more patients at elevated CVD risk than FRS, ASCVD score classified similar proportion of patients as high risk when compared with established subclinical atherosclerosis. However, potential CVD risk category misclassification by established equations such as ASCVD may still exist among HIV-infected patients; hence there is still a need for development of a CVD risk prediction equation tailored to HIV-infected patients in SSA.
Citation: Mosepele M, Hemphill LC, Palai T, Nkele I, Bennett K, Lockman S, et al. (2017) Cardiovascular disease risk prediction by the American College of Cardiology (ACC)/American Heart Association (AHA) Atherosclerotic Cardiovascular Disease (ASCVD) risk score among HIV-infected patients in sub-Saharan Africa. PLoS ONE 12(2): e0172897. https://doi.org/10.1371/journal.pone.0172897
Editor: Giuseppe Vittorio De Socio, Azienda Ospedaliera Universitaria di Perugia, ITALY
Received: October 30, 2016; Accepted: February 10, 2017; Published: February 24, 2017
Copyright: © 2017 Mosepele et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data are available from the Botswana-Harvard AIDS Partnership Institute (once approved by Botswana Ministry of Health Ethics Committee) for researchers who meet the criteria for access to confidential data. Study data is available through an application to the Botswana Ministry of Health Human Research Development Committee (HRDC). Contacts for the Committee: Mr Lemphi Moremi, Ministry of Health & Wellness, Government Enclave, Gaborone, BOTSWANA. Email: firstname.lastname@example.org Telephone: 00267 391 4467.
Funding: This work was funded by The National Institute of Allergy and Infectious Diseases, 5P30AI060354-0; Harvard Global Health Institute, BWH#2013D002079, Dr. Mosepele Mosepele; Fogarty International Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Author KB is a self-employed biostatistician working under the name Bennett Statistical Consulting Inc. who performed biostatistical analysis as recommended by the study team. This work was paid for using approved NIH funds. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.
Cardiovascular disease (CVD) risk is elevated in patients with HIV [1–3], and emerging data suggest that HIV-infected patients in sub-Saharan Africa (SSA) confront a similarly increased burden of CVD [4–6]. However, identifying individual HIV-infected patients who are at increased risk and candidates for primary CVD risk prevention remains a challenge [7–9]. In most studies, available general population CVD prediction equations are applied among HIV-infected patients to predict risk of hard end-points such as myocardial infarction or stroke. In some instances, the equations are compared to each other in the same clinical cohort to assess agreement in classifying patients as either low or high CVD risk. For instance, studies that applied the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) risk prediction tool for atherosclerotic CVD (ASCVD) among HIV-infected patients have reported mixed results. In some HIV-cohorts, ASCVD underestimated CVD risk as compared to actual occurrence of CVD end-points [10, 11] or presence of high risk morphology plaque . Other studies have demonstrated good  versus poor  agreement with the well-established Framingham Risk Score (FRS). The FRS has been reported to accurately predict CVD risk [10, 15], underestimate CVD risk [12, 14] or overestimate risk . When applied to HIV cohorts, the HIV-specific CVD risk prediction equation from the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) cohort has also been noted to both underestimate predicted CVD risk [10, 11] and overestimate it  based on expected CVD endpoints.
Given these mixed data on performance of CVD risk prediction rules across different HIV-infected populations, surrogate CVD end points such as carotid atherosclerosis as assessed by carotid intima-media thickness (cIMT) have been used as an alternate approach to estimate risk of CVD [16, 17] with a cIMT cut-off of ≥75th percentile indicating high CVD risk . Importantly, cIMT has been used as a surrogate CVD endpoint to assess the new ASCVD risk score performance in new populations without their own validated CVD risk prediction scores [19, 20]. The use of cIMT as a surrogate CVD end-point is of particular interest in an African HIV-infected patient population because cIMT is a stronger predictor of stroke than myocardial infarction  and HIV-infected patients in SSA experience strokes more frequently than myocardial infarctions [5, 22–24]. We therefore sought to assess correlation between estimated 10 year risk of CVD using the ASCVD risk score or Framingham Risk Score (FRS) with cIMT as a surrogate CVD end-point among virally suppressed HIV-infected patients in Botswana.
HIV-infected adults between 30–50 years of age with documented viral suppression were enrolled into a cross-sectional study in Gaborone, Botswana. All participants were screened for the following CVD risk factors (CVDRF): bilateral carotid intima media thickness (cIMT), elevated blood pressure or use of anti-hypertensive treatment, weight, height, waist circumference, cigarette smoking, non-fasting lipid profile, and glycosylated haemoglobin (HBA1C). Bilateral distal 1-cm of the common carotid artery was assessed using ultrasound as per the American Society of Echocardiography Carotid Intima Media Thickness Task Force . All participants had FRS and ASCVD risk scores calculated [25–27]. The proportions of participants categorized as having elevated versus not elevated CVD risk were calculated, with ASCVD predicted risk ≥7.5% and FRS predicted risk ≥10% indicating elevated risk. Agreement in classifying participants as elevated risk for CVD by both cIMT (≥75th percentile and ≥90th percentile, separately, using percentiles based on HIV-negative controls) and the ASCVD score was assessed using McNemar`s Test. Since we hypothesized that ASCVD would classify significantly more participants as elevated CVD risk than FRS, agreement between FRS and cIMT was not assessed. In an exploratory analysis, the optimal cIMT cut off-point that matched ASCVD risk of ≥7.5% was assessed using Youden’s J index; calculated as J = sensitivity + specificity– 1. The maximum value of J may be used to determine an optimal cut-off in scenarios where the costs associated with false positives and false negatives are equal. This index was used along with the receiver operating characteristic (ROC) and corresponding area under this curve (AUC). A historical cohort of 224 HIV-negative controls, mean age 37(±5) years, from the same population with cIMT data was available to define the population’s cIMT 75th percentile cut-off point.
All participants provided written informed consent. The Botswana Ministry of Health Research & Development Committee, Princess Marina Hospital Ethics Committee and Brigham & Women`s Hospital / Massachusetts General Hospital Institutional Review Board all approved the study.
Cohort baseline characteristics
Among the 78 participants eligible for ASCVD calculation, 34 (44%) were females with a mean age of 45 years. All participants in this cohort were virally suppressed with mean HIV disease and ART exposure duration of 10.9 and 9.3 years respectively. Baseline demographic, CVD risk factors, and HIV-associated factors are summarized in Table 1 for these 78 eligible participants as well as the 130 participants who were ineligible to have ASCVD calculated.
Predicted 10 year CVD risk by ASCVD and FRS
Among the 208 HIV-infected participants with cIMT results, 130 participants did not have an ASCVD risk score calculated for primary prevention of CVD: 122 were less than 40 years old (ASCVD risk score not applicable); of those greater than 40 years old, 6 were already on statin therapy, one had prior diagnosis of diabetes mellitus and one had total cholesterol < 100. The mean predicted 10-year CVD risk for the cohort eligible for risk score calculation (n = 78) was 4% by ASCVD versus 1.97% by FRS. ASCVD classified more participants at elevated CVD risk as compared to FRS (14.1% versus 2.6% respectively, McNemar`s p = 0.01).
Correlation between ASCVD risk and cIMT
Of the 78 HIV-infected participants with ASCVD scores, cIMT led to categorization of 63 (80.8%) as low CVD risk and 15 (19.2%) as elevated CVD risk (using the cIMT cut-off point determined from a cohort of 65 similar HIV-negative participants between 40 and 50 years old). ASCVD categorized similar proportion of participants as elevated CVD risk compared with cIMT using the ≥75th percentile as a cutoff: 14.1% versus 19.2%, respectively (McNemar’s p = 0.34, Table 2). When the criterion for elevated CVD risk by cIMT was increased to 90th percentile, 76 (97.4%) participants were categorized as low CVD risk versus 2 (2.6%) as high CVD risk by cIMT. Applying the higher cIMT criteria for elevated CVD risk, significantly fewer participants were categorized as elevated risk by ASCVD versus cIMT: 14.1% versus 2.6%, respectively (McNemar`s p = 0.02, Table 3).
We used Youden’s J to calculate the optimal cut point to be the 81st percentile for cIMT, based on ASCVD score ≥7.5% (sensitivity = 72.7% and specificity = 88.1% with AUC 0.82, 95% Mann-Whitney CI: 0.66–0.99). This 81st percentile categorized participants identically to our original 75th percentile cut-off based on HIV-uninfected controls within the 40–50 year age band (cIMT = 0.698mm and 0.707mm for 81st percentile among HIV-infected ASCVD eligible participants versus and 75th percentile among HIV-uninfected controls between 40 and 50 years old, respectively).
In this pilot study of CVD risk prediction in sub-Saharan Africa, using the ACC/AHA CVD risk equation, we demonstrated that among HIV-infected patients age 40–50 years with viral suppression, there was relatively good agreement in risk classification between the ASCVD risk score and cIMT, a validated marker of subclinical atherosclerosis, in classifying participants at high risk for CVD. Agreement in risk classification by the ASCVD risk score and cIMT lost significance with a higher and less clinically relevant cut-point of cIMT of 90th percentile was applied, suggesting that ASCVD algorithm may appropriately to classify patients in the high risk category at least based on observed sub-clinical atherosclerosis.
The relevance of CVD risk prediction tools developed for the general population is unclear in both HIV-infected populations and in sub-Saharan African settings. When the ASCVD risk score has been evaluated in special populations for whom a tailored CVD risk score has not been developed, it has been found to have moderate agreement with surrogate markers of CVD such as cIMT . The Mediators of Atherosclerosis study of South Asians living in America (MASALA) clinical cohort of 849 South Asian adults between 40–75 years old living in San Francisco Bay and greater Chicago showed good agreement between the ASCVD risk score and cIMT. Similarly, in a Korean cohort of 201 adults, high risk classification by ASCVD correlated with high CVD risk classification by cIMT . However, this agreement has not been consistent across studies. In a US population of patients with head and neck cancer, ASCVD under-estimated the proportion of patients classified high risk by the ASCVD when compared with cIMT . While radiation may have induced excess atherosclerosis that could not be predicted by ASCVD, but is clearly detectable on cIMT measurement, this study highlights the utility of cIMT in identifying atherosclerosis induced by novel mechanisms other than traditional CVD risk factors, as is likely the case in our study population.
Our direct comparison of ASCVD to FRS in categorizing patient as elevated CVD risk was important to perform as neither risk prediction rule was developed for HIV-infected patients in SSA. In our cohort, ASCVD classified more patients as elevated CVD risk than FRS as has been observed when these two prediction equations are compared among geographically diverse general populations beyond the US general population [29–32] and some HIV-specific patient population studies  but not all [11, 13]. It is possible that the difference in categorizing patients at elevated risk by ASCVD versus FRS in out cohort may reflect the findings that the ASCVD may “over-estimate” risk, even in the general population. We would expect this effect to be more significant among a younger HIV-infected patient population in whom traditional CVD risk factors do not seem to be the main drivers of observed CVD risk such as occurrence of stroke among HIV-infected patients in the SSA setting [22, 23]. Ultimately, either ASCVD or FRS will need to be assessed for the ability to categorize HIV-infected patients who experience CVD end-points such as stroke in SSA as predicted high CVD risk.
Like other imaging modalities, cIMT provides a direct measure of observed subclinical atherosclerosis that result from a combination of risk factors present in the population. In contrast, ASCVD provides an estimate of predicted CVD risk based on traditional CVD risk factors only. Applying the ASCVD risk equation to our cohort of HIV-infected participants, similar proportion of patients were classified as having elevated predicted CVD risk using the risk score versus using an estimate of subclinical atherosclerosis measured by cIMT. Despite this concordance, the finding that nearly ten percent of patients at elevated risk by cIMT measurement were classified as low risk by risk score suggests that a component of CVD risk observed on carotid imaging in our study may be due to increasingly recognized HIV-specific novel risk factors for CVD such as inflammation [33–36] and immune dysregulation [37, 38], among other mechanistic factors, especially in relation to relatively younger HIV-infected patients as was the case in our study . Our data suggests that the ASCVD score may fail to identify HIV-infected patients at higher CVD risk (based on our surrogate CVD endpoint of cIMT). Applying an even higher threshold for classification of high risk by ASCVD of ≥10%, as was recently recommended by the US Preventative Services Task Force , is likely to accurately classify even fewer HIV-infected patients at elevated CVD risk.
The main limitations of our study are the small sample size and cross-sectional design. Larger heterogeneous HIV cohorts in SSA followed longitudinally for clinical CVD endpoints such as strokes and myocardial infarctions would provide a better assessment of utility of ASCVD in identifying patients at high CVD risk, and potentially assess the contribution of HIV-specific factors to the total predicted CVD. Using hard CVD clinical end-points such as stroke or myocardial infarction would be more robust measures for assessing CVD risk prediction model calibration and discrimination than subclinical carotid-intima media thickness, as the equations were not developed to evaluate cIMT as an outcome. Thus, we do not provide formal measures of discrimination or calibration. Further, given the strong effect of age on risk of CVD, future work should study HIV-infected patients over 50 years old However, our results provide a rigorous evaluation of CVD risk prediction equations in the sub-Saharan African setting, where hard CVD endpoints are not routinely available, by using carotid atherosclerosis as a surrogate CVD end point and comparing CVD risk classification.
Our results suggest that the ASCVD risk score is reasonably accurate in classifying patients in terms of CVD risk in HIV-infected adults age 40–50 in SSA when using subclinical atherosclerosis as the CVD endpoint. In light of these findings, the ASCVD risk score may be cautiously used among HIV-infected patients in SSA pending further validation of traditional CVD risk prediction tools in SSA using hard clinical endpoints. Developing accurate strategies for CVD risk assessment and primary CVD risk reduction will be an important priority as HIV-infected individuals in sub-Saharan Africa age. Accurate identification of HIV-infected patients who will benefit the most from statin therapy will efficiently guide national programs in SSA in considering resource allocation to reduce the burden of CVD-associated mortality and morbidity versus other urgent needs for HIV-infected populations.
- Conceptualization: MM LCH SL VT KB.
- Data curation: MM LCH SL TP IN VT.
- Formal analysis: MM LCH SL VT KB.
- Funding acquisition: MM SL LCH VT.
- Investigation: MM LCH TP IN SL VT.
- Methodology: MM LCH SL VT.
- Project administration: MM IN LCH TP SL VT.
- Resources: MM SL VT.
- Supervision: MM LCH SL VT.
- Validation: MM LCH SL VT.
- Visualization: MM LCH SL VT.
- Writing – original draft: MM IN LCH TP SL VT.
- Writing – review & editing: MM IN LCH TP SL VT.
- 1. Triant VA, Lee H, Hadigan C, Grinspoon SK. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. The Journal of clinical endocrinology and metabolism. 2007;92(7):2506–12. Epub 2007/04/26. pmid:17456578
- 2. Chow FC, Regan S, Feske S, Meigs JB, Grinspoon SK, Triant VA. Comparison of ischemic stroke incidence in HIV-infected and non-HIV-infected patients in a US health care system. Journal of acquired immune deficiency syndromes (1999). 2012;60(4):351–8. Epub 2012/05/15.
- 3. Freiberg MS, Chang CC, Kuller LH, Skanderson M, Lowy E, Kraemer KL, et al. HIV infection and the risk of acute myocardial infarction. JAMA internal medicine. 2013;173(8):614–22. Epub 2013/03/06. pmid:23459863
- 4. Benjamin LA, Corbett EL, Connor MD, Mzinganjira H, Kampondeni S, Choko A, et al. HIV, antiretroviral treatment, hypertension, and stroke in Malawian adults: A case-control study. Neurology. 2016;86(4):324–33. Epub 2015/12/20. pmid:26683649
- 5. Bloomfield GS, Khazanie P, Morris A, Rabadan-Diehl C, Benjamin LA, Murdoch D, et al. HIV and noncommunicable cardiovascular and pulmonary diseases in low- and middle-income countries in the ART era: what we know and best directions for future research. Journal of acquired immune deficiency syndromes (1999). 2014;67 Suppl 1:S40–53. Epub 2014/08/15.
- 6. Kingery JR, Alfred Y, Smart LR, Nash E, Todd J, Naguib MR, et al. Short-term and long-term cardiovascular risk, metabolic syndrome and HIV in Tanzania. Heart (British Cardiac Society). 2016;102(15):1200–5. Epub 2016/04/24.
- 7. D'Agostino RB Sr. Cardiovascular risk estimation in 2012: lessons learned and applicability to the HIV population. The Journal of infectious diseases. 2012;205 Suppl 3:S362–7. Epub 2012/05/18.
- 8. Begovac J, Dragovic G, Viskovic K, Kusic J, Perovic Mihanovic M, Lukas D, et al. Comparison of four international cardiovascular disease prediction models and the prevalence of eligibility for lipid lowering therapy in HIV infected patients on antiretroviral therapy. Croatian medical journal. 2015;56(1):14–23. Epub 2015/03/03. pmid:25727038
- 9. Nery MW, Martelli CM, Silveira EA, de Sousa CA, Falco Mde O, de Castro Ade C, et al. Cardiovascular risk assessment: a comparison of the Framingham, PROCAM, and DAD equations in HIV-infected persons. TheScientificWorldJournal. 2013;2013:969281. Epub 2013/11/15. pmid:24228022
- 10. Thompson-Paul AM, Lichtenstein KA, Armon C, Palella FJ Jr., Skarbinski J, Chmiel JS, et al. Cardiovascular disease risk prediction in the HIV Outpatient Study. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2016. Epub 2016/09/11.
- 11. Krikke M, Hoogeveen RC, Hoepelman AI, Visseren FL, Arends JE. Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models. HIV medicine. 2016;17(4):289–97. Epub 2015/08/14. pmid:26268806
- 12. Zanni MV, Fitch KV, Feldpausch M, Han A, Lee H, Lu MT, et al. 2013 American College of Cardiology/American Heart Association and 2004 Adult Treatment Panel III cholesterol guidelines applied to HIV-infected patients with/without subclinical high-risk coronary plaque. AIDS (London, England). 2014;28(14):2061–70. Epub 2014/09/30.
- 13. Elsamadisi P, Cha A, Kim E, Latif S. Statin Use With the ATP III Guidelines Compared to the 2013 ACC/AHA Guidelines in HIV Primary Care Patients. Journal of pharmacy practice. 2015. Epub 2015/11/21.
- 14. Clement ME, Park LP, Navar AM, Okeke NL, Pencina MJ, Douglas PS, et al. Statin Utilization and Recommendations Among HIV- and HCV-infected Veterans: A Cohort Study. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2016;63(3):407–13. Epub 2016/05/05.
- 15. Raggi P, De Francesco D, Manicardi M, Zona S, Bellasi A, Stentarelli C, et al. Prediction of hard cardiovascular events in HIV patients. The Journal of antimicrobial chemotherapy. 2016. Epub 2016/09/04.
- 16. Hodis HN, Mack WJ, LaBree L, Selzer RH, Liu CR, Liu CH, et al. The role of carotid arterial intima-media thickness in predicting clinical coronary events. Annals of internal medicine. 1998;128(4):262–9. Epub 1998/02/21. pmid:9471928
- 17. Ludwig M, von Petzinger-Kruthoff A, von Buquoy M, Stumpe KO. [Intima media thickness of the carotid arteries: early pointer to arteriosclerosis and therapeutic endpoint]. Ultraschall in der Medizin (Stuttgart, Germany: 1980). 2003;24(3):162–74. Epub 2003/06/21.
- 18. Stein JH, Korcarz CE, Hurst RT, Lonn E, Kendall CB, Mohler ER, et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography. 2008;21(2):93–111; quiz 89–90. Epub 2008/02/12.
- 19. Kandula NR, Kanaya AM, Liu K, Lee JY, Herrington D, Hulley SB, et al. Association of 10-year and lifetime predicted cardiovascular disease risk with subclinical atherosclerosis in South Asians: findings from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study. Journal of the American Heart Association. 2014;3(5):e001117. Epub 2014/10/04. pmid:25277669
- 20. Lee DG, Han JH, Kwon KY, Kim JH, Han KH, Lee EJ. Association of 10-Year Atherosclerotic Cardiovascular Disease Risk Score with Carotid Intima-Media Thickness and Plaque. Korean journal of family medicine. 2015;36(6):310–5. Epub 2015/12/04. pmid:26634098
- 21. Ebrahim S, Papacosta O, Whincup P, Wannamethee G, Walker M, Nicolaides AN, et al. Carotid plaque, intima media thickness, cardiovascular risk factors, and prevalent cardiovascular disease in men and women: the British Regional Heart Study. Stroke; a journal of cerebral circulation. 1999;30(4):841–50. Epub 1999/04/03.
- 22. Hoffmann M, Berger JR, Nath A, Rayens M. Cerebrovascular disease in young, HIV-infected, black Africans in the KwaZulu Natal province of South Africa. Journal of neurovirology. 2000;6(3):229–36. Epub 2000/07/06. pmid:10878712
- 23. Stroke risk factors in an incident population in urban and rural Tanzania: a prospective, community-based, case-control study. The Lancet Global health. 2013;1(5):e282–8. Epub 2014/04/22. pmid:24748275
- 24. Syed FF, Sani MU. Recent advances in HIV-associated cardiovascular diseases in Africa. Heart (British Cardiac Society). 2013;99(16):1146–53. Epub 2013/05/18.
- 25. 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):3143–421. Epub 2002/12/18. pmid:12485966
- 26. Goff DC Jr., Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49–73. Epub 2013/11/14. pmid:24222018
- 27. Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Journal of the American College of Cardiology. 2014;63(25 Pt B):2889–934. Epub 2013/11/19. pmid:24239923
- 28. Jacoby D, Hajj J, Javaheri A, deGoma E, Lin A, Ahn P, et al. Carotid intima-media thickness measurement promises to improve cardiovascular risk evaluation in head and neck cancer patients. Clinical cardiology. 2015;38(5):280–4. Epub 2015/05/13. pmid:25962530
- 29. Johnson KM, Dowe DA. Accuracy of statin assignment using the 2013 AHA/ACC Cholesterol Guideline versus the 2001 NCEP ATP III guideline: correlation with atherosclerotic plaque imaging. Journal of the American College of Cardiology. 2014;64(9):910–9. Epub 2014/08/30. pmid:25169177
- 30. Cho YK, Jung CH, Kang YM, Hwang JY, Kim EH, Yang DH, et al. 2013 ACC/AHA Cholesterol Guideline Versus 2004 NCEP ATP III Guideline in the Prediction of Coronary Artery Calcification Progression in a Korean Population. Journal of the American Heart Association. 2016;5(8). Epub 2016/08/21.
- 31. Jung CH, Lee MJ, Kang YM, Yang DH, Kang JW, Kim EH, et al. 2013 ACC/AHA versus 2004 NECP ATP III Guidelines in the Assignment of Statin Treatment in a Korean Population with Subclinical Coronary Atherosclerosis. PloS one. 2015;10(9):e0137478. Epub 2015/09/16. pmid:26372638
- 32. Pursnani A, Massaro JM, D'Agostino RB Sr., O'Donnell CJ, Hoffmann U. Guideline-Based Statin Eligibility, Coronary Artery Calcification, and Cardiovascular Events. Jama. 2015;314(2):134–41. Epub 2015/07/15. pmid:26172893
- 33. Vos AG, Idris NS, Barth RE, Klipstein-Grobusch K, Grobbee DE. Pro-Inflammatory Markers in Relation to Cardiovascular Disease in HIV Infection. A Systematic Review. PloS one. 2016;11(1):e0147484. Epub 2016/01/26. pmid:26808540
- 34. Nou E, Lo J, Grinspoon SK. Inflammation, immune activation, and cardiovascular disease in HIV. AIDS (London, England). 2016;30(10):1495–509. Epub 2016/04/09.
- 35. Nou E, Lo J, Hadigan C, Grinspoon SK. Pathophysiology and management of cardiovascular disease in patients with HIV. The lancet Diabetes & endocrinology. 2016;4(7):598–610. Epub 2016/02/14.
- 36. Longenecker CT, Sullivan C, Baker JV. Immune activation and cardiovascular disease in chronic HIV infection. Current opinion in HIV and AIDS. 2016;11(2):216–25. Epub 2015/11/26. pmid:26599166
- 37. Lichtenstein KA, Armon C, Buchacz K, Chmiel JS, Buckner K, Tedaldi EM, et al. Low CD4+ T cell count is a risk factor for cardiovascular disease events in the HIV outpatient study. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2010;51(4):435–47. Epub 2010/07/06.
- 38. Kaplan RC, Kingsley LA, Gange SJ, Benning L, Jacobson LP, Lazar J, et al. Low CD4+ T-cell count as a major atherosclerosis risk factor in HIV-infected women and men. AIDS (London, England). 2008;22(13):1615–24. Epub 2008/08/02.
- 39. Hanna DB, Guo M, Buzkova P, Miller TL, Post WS, Stein JH, et al. HIV Infection and Carotid Artery Intima-media Thickness: Pooled Analyses Across 5 Cohorts of the NHLBI HIV-CVD Collaborative. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2016;63(2):249–56. Epub 2016/04/28.
- 40. Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW Jr., Garcia FA, et al. Statin Use for the Primary Prevention of Cardiovascular Disease in Adults: US Preventive Services Task Force Recommendation Statement. Jama. 2016;316(19):1997–2007. Epub 2016/11/14. pmid:27838723