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Comparing Cardiovascular Disease Risk Scores for Use in HIV-Infected
Individuals.......Optimal cardiovascular disease risk score for HIV
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"Accurate CVD risk assessment is essential to effectively balance the risks and benefits of therapy in primary prevention.....3 of the 4 CVD risk scores were not developed for use among HIV-infected individuals...... CVD risk scores were developed using different endpoints as outcomes (e.g. CVD vs. MI)...... Current standard of care for primary CVD prevention such as prescribing a statin is to use the ASCVD score in the general population, no one knows how this works in HIV but these findings suggest that it works fairly well..... ASCVD performed better than the other risk scores, doing better than both older more outdated scores and an HIV score with antiretroviral medications"
[Background: Crane said why is this so important? cardiovascular risk scores/assessment are an important, even key driver of cardiovascular disease prevention in the general population driving a number of actions including most importantly likely initiation of statin use, And how this applies or should apply in HIV where we have an increasingly aging cohort is just not clear. We compared the expected and observed event rates for 4 risk scores in 10, 832 HIV-infected individuals who had 229 incident (new) MI events. Regarding calibration for all MIs, all 4 tests failed the goodness of fit suggesting even the best of these have room for improvement. Small changes in predicted vs observed risk could have a significant impact on the likelihood of a patient receiving a treatment such as a statin. Framingham overall fairly well calibrated but in a clinically significant range, overestimated risk (8% vs 5%), ATP underestimated risk for people at low & intermediate risk, DAD predicted risk grossly underestimates risk, ASCVD is a much better calibrated region compared to DAD although slightly underestimating risk in looking at clinically most relevant it is a fairly well calibrated measure, Regarding type ! MIs, artherolsclerotic MIs, perhaps the most important risk to predict, which may be the most benefitted for in deciding to initiate statin ASCVD, Framingham & ATP3 overestimate risk & DAD underestimates risk, but when considering calibration overall ASCVD is substantially better. . "ASCVD had better discrimination than the other 3 risk scores for Type 1 MIs, Type 2 MIs, and all MIs."]
WEBCAST:http://www.croiwebcasts.org/console/player/29443?mediaType=audio&
Heidi M. Crane1; Robin Nance1; Joseph A. Delaney2; Daniel Drozd1; Susan Heckbert1; Rebekah Young1; Matthew J. Feinstein3; Richard Moore4; Michael Saag5; Mari M. Kitahata1
1Univ of Washington, Seattle, WA, USA;2Univ of Washington Sch of PH and Community Med, Seattle, WA, USA;3Northwestern Univ, Feinberg Sch of Med, Chicago, IL, USA;4Johns Hopkins Univ, Baltimore, MD, USA;5Univ of Alabama at Birmingham, Birmingham, AL, USA
Reported by Jules Levin
CROI 2016 Feb 22-24 Boston
Program Abstract
While cardiovascular disease (CVD) risk stratification tools exist for use in the general population, they may not accurately estimate risk in persons living with HIV (PLWH). Examining the performance of CVD risk scores in PLWH requires large studies with comprehensive clinical data and well-validated outcomes.
We developed a state-of-the-art screening algorithm and central adjudication protocol for the validation of incident myocardial infarction (MI) in the CFAR Network of Integrated Clinical Systems (CNICS), which harmonizes comprehensive clinical data on PLWH in routine care at multiple US sites. Among PLWH enrolled between 1996-2014, we compared the performance of 3 CVD risk scores developed in the general population (Framingham, ATP-3, and 2013 ACC/AHA ASCVD) and one developed for use in PLWH (D:A:D) using area under the curve (AUC). The Universal Definition of MI classifies MI by type. Type 1 MI (T1MI) result spontaneously from atherosclerotic plaque instability, whereas type 2 MI (T2MI) occur secondary to oxygen demand/supply mismatch of any cause such as sepsis. We compared the AUC for risk scores for T1MI, T2MI, and all MIs combined. Beginning in 2007, CNICS patients completed clinical assessments every 4-6 months that included tobacco use. We repeated analyses among this subset to ensure smoking status was updated for those who quit or started smoking.
There were 243 incident MIs among 11,338 PLWH during a mean follow-up of 4.3 years. ASCVD had a significantly better AUC than other scores for all MI and for T2MI (Table) including the DAD AUC (p<0.001), and was not inferior to the other AUCs for T1MI. Our results were similar in the subset of PLWH with time-updated smoking status.
The large size, comprehensive clinical data and central adjudication of MI by type in CNICS allows for direct comparison of clinical risk scores in PLWH. Some variations across risk scores are to be expected given differences in the outcome (i.e. predicting CVD vs. MI). The addition of HIV-specific variables as in the DAD score did not improve discrimination compared with ASCVD, however inclusion of different HIV-specific measures may lead to improved discrimination and is planned in future analyses. ASCVD performed as well or better than other risk scores across all MI events and the superior performance in detecting T2MI is worthy of additional investigation.
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