American Heart JournalVolume 157, Number 3 Decker et al 557Methods Table I. Frequency of risk factors recalled from instructionsPatient sample received at or since hospital discharge Acute myocardial infarction patients (N = 2,498) were Recallconsecutively recruited between January 1, 2003, and June 28,2004, into the Prospective Registry Evaluating Myocardial Risk factor Yes NoInfarction: Events and Recovery (PREMIER) health statusstudy.10 Of 10,911 patients screened, 3,953 were eligible; and Medication 1663 (88.3%) 221 (11.7%)2,498 subsequently consented and were enrolled. This 19-center Diet 1067 (65.1%) 573 (34.9%)national registry included baseline data of chart abstractions Whom to call 1031 (71.5%) 410 (28.5%)(presentation, clinical comorbidities, in-hospital treatments, Cardiac rehabilitation 824 (78.5%) 226 (21.5%)discharge medications, discharge instructions, etc) and inter- Exercise 745 (70.9%) 306 (29.1%)views by trained data collectors within 24 to 72 hours of Smoking 457 (76.3%) 142 (23.7%) Cholesterol therapy 252 (40.5%) 371 (59.5%)admission. Each participating hospital obtained Institutional Diabetes management 182 (65.2%) 97 (34.8%)Research Board approval, and patients signed an informed Cholesterol check 126 (39.1%) 196 (60.9%)consent form for baseline and follow-up interviews. Warfarin 99 (75%) 33 (25%) Weight management 76 (49%) 79 (50.1%)Outcomes assessment Weight loss 29 (48.3%) 31 (51.7%) Patients general health status was measured by the Short Form The individual risk factor must have been documented as provided to the patient before(SF)–12 Physical Component Scale (PCS). A score of 50 reflects hospital discharge.the population average, and a 10-point deviation represents 1SD.11 Disease-specific health status was assessed with the Seattle and the 12-month follow-up interviews were used to assessAngina Questionnaire (SAQ), a 19-item disease-specific ques- patients health status outcomes using the SF-12 and SAQ.tionnaire. The SAQ Angina Frequency and Quality of Life (QoL)scales were used as outcomes in this study, with SAQ scores Additional variablesranging from 0 to 100, where higher scores represent fewer Patients were also asked, on the baseline interview, whethersymptoms and better quality of life.12-14 A mean difference of N5 they avoided obtaining medical care because of cost (yes/points is considered clinically significant.12 no),15 about the prevalence of depressive symptoms (using the Patients health status recovery was quantified through Patient Health Questionnaire [PHQ] score16,17), and about their1-month and 12-month telephone interviews conducted by an social support (using the Enhancing Recovery in Coronaryexperienced, central call center. The 30-minute phone inter- Heart Disease Social Support Instrument [ESSI]18). The avoid-views included questions about treatment after discharge ing care question was used as a proxy for reported income,(including hospitalizations, diagnostic tests, procedures, medi- which was missing on 39% of the baseline patient interviewscations, and outpatient visits) since their last study contact. because of sensitivity about answering the question, and hasMortality was determined through the Social Security Adminis- been reported in the past as a predictor of poor outcomes.19tration Death Master File. The PHQ assesses the presence of 9 depressive symptoms; and The baseline case report form abstracted from the medical the severity index ranges from 0 to 27, with a PHQ score ≥10record which of the 13 discharge instructions were docu- defined as moderate to severe depression, representing themented as being provided to the patient (exercise, medication minimum number of symptoms required for the diagnosis ofadherence, diet modification, smoking cessation, weight major depression.20 The ESSI is a 7-item questionnairemonitoring and loss, follow-up plans, to call a physician for assessing patients social network for support and assistance.21recurrent symptoms, cardiac rehabilitation, cholesterol mon-itoring, lipid therapy, diabetes management, and warfarin use). Statistical analysisAfterward, at 1 month, patients were asked if they had The frequency with which patients recalled RFM advicereceived instructions at, or since, discharge on any 1 of the was determined, along with the rate of very careful13 RFM items and how well they had followed these adherence to the individual items. Descriptive demographic,instructions. Responses included “very carefully,” “fairly well,” clinical, and treatment data for patients reporting adherence“somewhat,” “not at all,” or “not able to do for other reasons.” to all RFM instructions provided, across the 4 adherenceTo assess the degree to which an individual patient adhered to groups, were compared with Cochran-Armitage trend test forRFM, we a priori defined adherence as the percentage of categorical data and analysis of variance trend tests forrelevant activities for which the patient reported “very continuous data.carefully.” Only those RFMs that were relevant and documen- To identify the independent association of adherence atted at baseline for that patient were included in the 1 month on 12-month outcomes, multivariable analyses weredenominator (ie, only diabetic patients were included in the performed. All multivariable models included age, sex, whiteassessment of diabetes management, only smokers were race, marital status, education Nhigh school, body mass index,considered for the smoking cessation advice, etc). We then currently smoking, medical insurance, avoid care because ofsummarized patients reports of adherence into the following cost, ESSI social support score, depression (PHQ score ≥10),4 classifications: poor (meaning that the patient adhered very history of diabetes, lung disease, hypercholesterolemia, con-carefully to b49% of their RFMs; 0%-49%), partial (50%-74%), gestive heart failure, hypertension, prior MI, prior percuta-careful (75%-99%), and very careful (100%). The 1-month neous coronary intervention, prior coronary artery bypassresponses were used to classify patients reported adherence, graft, ST elevation MI, revascularization during hospitalization,
American Heart Journal558 Decker et al March 2009 Table II. Baseline characteristics by category of reported adherence Very careful (100%) Careful (75%-99%) Partial (50%-74%) Poor (0%-49%) n = 393 n = 612 n = 677 n = 364 P valueSociodemographic Age (mean ± SD), y 64.6 ± 14 y 59.8 ± 11.6 y 60.1 ± 12.8 y 58.6y ± 12.4 y b.001 Gender (male) 257 (65.4%) 419 (68.5%) 464 (68.5%) 247 (67.9)% .477 Race (white) 305 (78%) 485 (79.4) 523 (77.6%) 266 (73.5%) .107 Married 233 (60.8%) 408 (67.4%) 429 (64.5%) 211 (58.8%) .384 Education (Nhigh school) 307 (80.8%) 496 (82.3%) 527 (78.7%) 289 (80.7%) .500 Low social support (BL)⁎ 41 (11%) 91 (15.4%) 95 (14.6%) 75 (21.4%) b.001Self-reported economic burden Avoid care because of cost 44 (11.6%) 109 (18.1%) 123 (18.5%) 75 (21%) .001 Payor:none/self-pay 29 (7.7%) 62 (10.6%) 85 (13.4%) 62 (18%) b.001Clinical comorbidities Final diagnosis: STEMI 169 (43%) 310 (50.7%) 315 (46.5%) 153 (42%) .465 NSTEMI 224 (57%) 302 (49.3%) 362 (53.5%) 211 (58%) .465 Prior MI 74 (18.8%) 109 (17.8%) 145 (21.4%) 83 (22.8%) .062 Prior PCI 73 (18.6%) 95 (15.5%) 143 (21.1%) 69 (19%) .252 Prior CABG 54 (13.7%) 73 (11.9%) 91 (13.4%) 42 (11.5%) .612 Congestive heart failure 42 (10.7%) 55 (9%) 60 (8.9%) 35 (9.6%) .596 Depression (BL)† 70 (18.9%) 108 (18.6%) 141 (22.2%) 82 (23.6%) .043 Diabetes 97 (24.7%) 158 (25.8%) 177 (26.1%) 114 (31.3%) .057 Hypertension 242 (61.6%) 366 (59.8%) 433 (64%) 222 (61%) .638 Hypercholesterolemia 179 (45.5%) 335 (54.7%) 333 (49.2%) 173 (47.5%) .841 COPD 44 (11.2%) 51 (8.3%) 100 (14.8%) 49 (13.5%) .022 Current smoker 74 (19.2%) 193 (31.7%) 244 (36.2%) 161 (44.5%) b.001 Body mass index (kg/m2) 28.5 ± 6.0 29.3 ± 6.0 29.5 ± 6.5 30 ± 6.6 .001Revascularization .856 PCI 119 (30.3%) 195 (31.9%) 235 (34.7%) 126 (34.6%) CABG 43 (10.9%) 84 (13.7%) 68 (10%) 38 (10.4%) Medical management 231 (58.8%) 333 (54.4%) 374 (55.2%) 200 (54.9%)Nitrate medication (1 m) 89 (68.5%) 115 (63.5%) 151 (67.1%) 58 (48.3%) .008β-Blocker medication (1 m) 264 (78.1%) 437 (80.3%) 482 (80.5%) 254 (77.9%) .997SAQ Angina Y/N (BL) 192 (49.5%) 310 (50.8%) 366 (54.1%) 221 (60.9%) b.001SAQ QoL (BL) 64 ± 22.7 64.5 ± 23.2 61.9 ± 23.4 61.5 ± 23.6 .052SAQ QoL (1 y) 88.1 ± 16.5 85.7 ± 16.4 84 ± 18.4 82.1 ± 19.6 b.001SF-12v2 PCS (BL) 43.9 ± 12.2 44.4 ± 12.2 42.8 ± 12.3 43.1 ± 12.8 .172SF-12v2 PCS (1 y) 46 ± 11.5 46.5 ± 11.2 43.4 ± 12 43.8 ± 11.3 .001SF-12v2 Mental Component Score (BL) 51.5 ± 11.4 50.2 ± 11.1 49.8 ± 11.4 47.6 ± 12 b.001SF-12v2 Mental Component Score (1 y) 54.6 ± 8.4 53.7 ± 9.1 53.6 ± 9.4 52.1 ± 9.6 .001BL, Baseline; STEMI, ST-segment elevation MI; NSTEMI, non–ST-segment elevation MI; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; COPD,chronic obstructive pulmonary disease.⁎ Based on the ESSI.† Based on the PHQ.β-blocker use at 1 month, nitrate use at 1 month, number of model.22 The SAQ QoL and SF-12 PCS were modeled usingrisk factors applicable to each patient, and baseline health multivariable hierarchical linear regression.status corresponding to each particular outcome. Lower One-year mortality and rehospitalization were modeled usingadherence categories were compared with the reference level multivariable proportional hazards regression stratified by site.of 100% adherence for effect size reporting, and linear trend Restricted cubic spline terms were added to models for alltests were used to calculate the P value for trend across all continuous variables to account for possible nonlinearity, and the4 adherence groups for each outcome. hierarchical model structures used also accounted for correlation Because of its skewed distribution (70% of patients of patients within site.23 Power was calculated for each outcomereported no angina), SAQ Frequency scores were dichot- using PASS software (PASS 2008 version 08.0.05, www.ncss.omized into any angina (scores b100) or no angina (scores = com). There was N80% power to detect a 5% difference for100) and were modeled using multivariable hierarchical mortality and presence of angina between the lowest- and themodified Poisson regression. Although typical analyses often highest-adherence group. There also was N80% power to detect ause logistic regression to estimate adjusted odds ratios, these 5-point mean difference in the SAQ QoL and SF-12 PCS as well asmay not provide accurate representations of relative risks 80% power to detect a 10% difference in all-cause rehospitaliza-when the outcomes are common. In this study, those events tion. All tests for statistical significance were 2-tailed with an αthat occurred in N25% of patients had their adjusted relative level of .05. Analyses were conducted using SAS software 9.1risks estimated directly using a modified Poisson regression (SAS Institute, Cary, NC) and R version 126.96.36.199
American Heart JournalVolume 157, Number 3 Decker et al 559Missing data Figure 1 The primary analyses included patients who participated in1-month follow-up interviews. Of the 2,498 patients enrolledin PREMIER, 47 died before the 1-month assessment, 122were contacted but refused an interview, 233 were lost tofollow-up, and 50 had incomplete RFM data. Thus, 2,046patients had a 1-month follow-up interview that wasanalyzed. Of these eligible patients, 84% provided 1-yearhealth status follow-up. Missing information on one or more covariates was presentfor 144 (7%) patients; 99 (4.8%) were missing N1 value. Missingcovariate data were assumed to be missing at random and wereimputed using multiple imputation methods to allow incor-poration of all patients and to correctly account for uncertaintydue to missingness.23 The imputation model included the fullarray of demographic, socioeconomic, patient history, treat-ment, and all quality of life subscales. To assess potential bias due to unavailable follow-up, wecreated a nonparsimonious model of the propensity to bemissing a 1-year interview.25 For those patients who refused1-year interviews or could not be contacted, propensity scoreswere computed using logistic regression analyses to predicttheir likelihood of unsuccessful follow-up. Predictor variablesincluded demographics, socioeconomic and lifestyle factors, “Very careful” reported adherence to individual RFM items.clinical characteristics, vital signs and laboratory studies,disease severity, baseline health status, medications, and acuteand nonacute treatments received during patients initial AMI cholesterol management or weight loss recalled receivinghospitalization. From these models, a probability of failure to these instructions.complete an interview was calculated. The reciprocal of thisprobability to complete an interview was then used as a Patient characteristics associated with RFM adherenceweight in the multivariable regression analyses to weight Baseline characteristics of patients who recalled RFMsthose patients with available data with similar patient and the percentage of reported adherence are reported incharacteristics as the patients who were lost to follow-upmore heavily. This method assesses potential observable bias Table II. Responses indicated that patients who veryfrom those lost to follow-up by overrepresenting the patient carefully adhered to their RFM were less likely to be currenttype that is more likely to be lost to follow-up.25 The smokers (19% vs 45%), were older (64.6 vs 58.6 years), andpropensity weighting did not change the clinical interpreta- reported higher levels of social support (89% vs 79%) astion or significance of the results and were comparable with compared with those whose adherence scores were b50%the primary data, suggesting little observable bias associated (P b .01 for all). Patients with greater adherence reportedwith loss to follow-up. Accordingly, only the unweighted continuing their nitrate medication more often then theprimary data are reported. poorly adherent patients (68.5% vs 48.3%, trend P = .008). A significant finding was that patients who reported Funding for the PREMIER Registry was through CVTherapeu- avoiding care because of cost were less likely to reporttics, Palo Alto, CA. adhering very carefully to RFM instructions. Data collected on the frequency patients reportedResults adhering very carefully to RFMs demonstrated that mostPrevalence of RFM recall patients (82%) very carefully adhered to N50% of discharge Overall, PREMIER patients were, on average, 61 years instructions given to them. Nineteen percent (393/2,046)old, male (67%), and white (74%). The frequency of recall very carefully adhered to all instructions given.of individual instructions they received at discharge orsince is reported in Table I. Eligible patients were those Prevalence of RFM adherencewith documentation at baseline as having received the Strong reported adherence at 1 month occurred mostindividual RFM instruction. The most frequently docu- frequently with “taking medications as prescribed” andmented discharge instruction was medication directions, “warfarin use” (94% and 86%, respectively) (Figure 1).although only 88% (1,663/1,884) recalled receiving the Diet instructions was the second most commonlyinstruction. The second most common RFM was diet documented instruction on the medical record, as(n = 1,640), although only 65% recalled receiving this described above, with a 65.1% recall rate, but wasinstruction during the 1-month interview. Less than half reported as being adhered to very carefully by only 51% ofof the patients who had received instructions about the patients. The least frequent RFM adhered to very
American Heart Journal560 Decker et al March 2009 Table III. Summary of adjusted effect estimates of 12-month health status outcomes Incidence of angina SAQ QOL SF-12 PCS1-m reported verycareful adherence Trend Mean Trend Mean Trendto RFMs RR P value difference P value difference P value100% – .015 – .173 – .04975%-99% 1.39 (0.85, 2.25) 0.10 (−2.40, 2.59) 0.43 (−1.11, 1.88)50%-74% 1.53 (1.00, 2.33) −1.01 (−3.46, 1.43) −1.51 (−2.97, −0.05)b50% 1.58 (1.05, 2.37) −1.62 (−4.40, 1.16) −1.08 (−2.76, 0.59)All models included age, sex, white race, marital status, education Nhigh school, body mass index, currently smoking, medical insurance, avoid care because of cost, ESSI socialsupport score, depression (PHQ score ≥10), history of diabetes, lung disease, hypercholesterolemia, congestive heart failure, hypertension, prior MI, prior percutaneous coronaryintervention, prior coronary artery bypass graft, ST elevation MI, revascularization during hospitalization, β-blocker use at 1 month, nitrate use at 1 month, number of risk factorsapplicable to each patient, and baseline health status corresponding to each particular outcome.carefully was “losing weight” (43%) and cardiac rehabi- recommendations. Potential explanations might includelitation (33%). Incidentally, though, individuals who that the patients were preoccupied during discharge, thereported they were “very carefully” adherent to cardiac patients were given written material that they could notrehabilitation participation were also “very carefully” read or understand, the provision of instructions went toadherent to the other RFMs that they were eligible for. family members, or no instructions were actually presented despite documentation to the contrary.Association of RFM adherence with health We found that patients who reported stronger adher-status outcomes ence to RFM were more likely to not have angina 1 year In multivariable models adjusting for sociodemo- after their AMI, although other health status outcomesgraphic characteristics, β-blocker and nitrate use at 1 were not found to be associated with RFM adherence.month, clinical differences, and angina symptoms at 1 Although patients who continued their nitrate medicationmonth, patients who reported being b50% adherent were more often were also those who more closely adhered to68% more likely to report angina at 1 year versus those RFMs, this minimally changed the multivariable modelwith scores of 100% (relative risk [RR] 1.68, 95% CI 1.08- estimates, thus not explaining all the difference in2.64, trend P = .01). The addition of depression severity reported angina. To date, we did not identify any studiesand social support to the multivariable model did not correlating patient adherence to RFM instructions andattenuate our estimates; and thus, only the fully adjusted their 1-year angina incidence. For example, the Lifestylemodels are displayed in Table III (RR 1.58, 95% CI 1.05- Heart Trial demonstrated a correlation between intensive2.37, trend P = .015). lifestyle change and the regression of coronary athero- There was no independent effect of RFM reported sclerosis without assessing patient health status.2 Theadherence on quality of life, physical functioning, current study extends such work by examining adherencerehospitalization, or mortality after adjusting for all in routine clinical care. Follow-up of patient health statuscovariates. Although a small mean difference of 1.5 points for a greater length of time than 1 year would be important(trend P = .049) for the SF-12 PCS was found for the to study, as this may not be sufficient time for potentialpartially adherent group versus those who adhered very beneficial effect of RFM.carefully to their RFMs, this would not be considered Several patient characteristics were observed to beclinically meaningful. associated with reported lower adherence to RFM instructions, including younger age and lower socialDiscussion support. This latter finding is congruent with a study by In light of the importance of risk factor modification on Conn et al26 that found that the presence of social supportsecondary prevention after AMI, this study examined creates a significant difference in patients behaviorspatients recall of RFM instructions and their reported related to cardiovascular health. They demonstrated acompliance with these recommendations. Our findings direct effect between social support and MI, a findingdo not necessarily support the impact RFM has on supported by our more contemporary investigation.previously reported patient outcomes. This is the first Our study also confirms previous observations ofstudy, of which we are aware, to document the marked important patient characteristics and RFM adherence.variation in the types of RFM recalled and adhered to by Previous cardiovascular studies have shown that the costAMI patients. We found that there were many RFMs that of medications and related health care is one of thepatients did not recall receiving the instruction regarding, potential reasons for poor medication-taking behavior.27such as cholesterol monitoring and management. This Our study found that patients who avoid care because ofimplies that, despite documentation, there is a deficiency cost also reported lower adherence to dischargein recall that patients may not be “receiving” these instructions. We also validated previous observations
American Heart JournalVolume 157, Number 3 Decker et al 561that current smokers and those with significant depres- Referencessive symptoms have poor adherence to RFM instruc- 1. Aldana SG, Whitmer WR, Greenlaw R, et al. Cardiovas-tions, although they did not strongly influence the cular risk reductions associated with aggressive lifestylemultivariable model.26,28 modification and cardiac rehabilitation. Heart Lung 2003;32: All RFMs are not of equal importance. Perhaps, the RFM 374-82. 2. Ornish D, Scherwitz LW, Billings JH, et al. Intensive lifestylewith the strongest association with improved outcomes is changes for reversal of coronary heart disease. JAMA 1998;280:cardiac rehabilitation, a method of simultaneous sup- 2001-7.porting many RFMs. Cardiac rehabilitation serves as a 3. American Heart Association. Available at: http://americanheart.source of education and reenforces crucial life-changing org/downloadable/heart/1140534985281Statsupdate06book.habits for improved survival after AMI.29-31 In fact, the pdf. Last accessed September 18, 2006.evidence is so strong in support of cardiac rehabilitation 4. Makaryus AN, Friedman EA. Patients understanding of theirthat it has recently been endorsed as a performance treatment plans and diagnosis at discharge. Mayo Clin Proc 2005;measure of quality among patients recovering from 80:991-4.AMI.31 Prior research has shown a survival benefit from 5. Hayes KS. Literacy for health information of adult patients and caregivers in a rural emergency department. Clin Excell Nurse Practcardiac rehabilitation and improvement in quality of life 2000;4:35-40.with decreased frequency of angina.32,33 6. Sdringola S, Nakagawa K, Nakagawa Y, et al. Combined intense Our findings should be considered in light of several lifestyle and pharmacologic lipid treatment further reduce coronarypotential limitations. First, only 84% of patients partici- events and myocardial perfusion abnormalities compared with usual-pated in the 1-month follow-up. Although extensive care cholesterol-lowering drugs in coronary artery disease. J Am Collpropensity-based models for incomplete follow-up sug- Cardiol 2003;41:263-72.gested no observable bias secondary to patients being 7. Smith Jr SC, Allen J, Blair SN, et al. AHA/ACC guidelines forlost to follow-up, residual confounding cannot be secondary prevention for patients with coronary and other athero-definitively excluded. A second potential concern is that sclerotic vascular disease: 2006 update: endorsed by the National Heart, Lung, and Blood Institute. Circulation 2006;113:2363-72.this is an observational registry that is exploratory in 8. Bradley EH, Herrin J, Elbel B, et al. 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A 12-item short-form health survey:measure for many outcomes.34 construction of scales and preliminary tests of reliability and validity. Med Care 1996;34:220-33. 12. Spertus JA, Winder JA, Dewhurst TA, et al. Development andClinical implications evaluation of the Seattle Angina Questionnaire: a new functional We found that patient recall of RFMs that were status measure for coronary artery disease. J Am Coll Cardiol 1995;documented as having been instructed is less than ideal, 25:333-41.ranging from 39% to 88%. Patients reported adherence to 13. Spertus JA, Winder JA, Dewhurst TA, et al. Monitoring the quality of lifethe recalled risk factor was even more distressing. in patients with coronary artery disease. Am J Cardiol 1994;74:1240-4.Variations in reported adherence to different RFM items 14. Spertus JA, Jones P, McDonell M, et al. Health status predicts long- term outcome in outpatients with coronary disease. Circulation 2002;were found, yet moderate or poor adherence to RFMs 106:43-9.was associated with greater reported angina at 1 year. 15. Spertus J, Decker C, Woodman C, et al. Effect of difficulty affordingChanging lifelong habits and overcoming nonmodifiable health care on health status after coronary revascularization.barriers such as age or gender requires motivation and Circulation 2005;111:2572-8.encouragement along with individualized patient educa- 16. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-tion. More research is needed to explore if these findings report version of PRIME-MD: the PHQ primary care study. Primaryare seen in larger cardiac populations, as well as studying Care Evaluation of Mental Disorders. Patient Health Questionnaire.methods to improve RFM processes for AMI patients, all JAMA 1999;282:1737-44.targeted to increase adherence and optimize their 17. Kroenke K, Spitzer RL, Williams JB. 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Full text of this article is available at no charge at our website: www.ahjonline.comCoronary Artery DiseaseAssessment of P2Y12 inhibition with the point-of-care deviceVerifyNow P2Y12 in patients treated with prasugrel or clopidogrelcoadministered with aspirinChristoph Varenhorst, MD, a Stefan James, MD, PhD, a David Erlinge, MD, PhD, b Oscar O. Braun, MD, PhD, b ¨John T. Brandt, MD, c Kenneth J. Winters, MD, c Joseph A. Jakubowski, PhD, c Sylvia Olofsson, MSci, aLars Wallentin, MD, PhD, a Agneta Siegbahn, MD, PhD, d Uppsala and Lund, Sweden; and Indianapolis, INBackground Variability in response to thienopyridines has led to Results Dose- and time-dependent inhibition of P2Y12 was evidentthe development of point-of-care devices to assess adenosine diphosphate with VN-P2Y12. There was strong correlation with VN-P2Y12 and VASP or(ADP)-induced platelet aggregation. These tests need to be evaluated in LTA for all treatments through a wide range of P2Y12 function. At high levelscomparison to reference measurements of P2Y12 function during different of P2Y12 inhibition, platelet function measured by VN-P2Y12 was maximallythienopyridine treatments. inhibited and could not reflect further changes seen with VASP or LTA methods. Correlation was also observed between exposure to clopidogrelsMethods After a run-in on 75 mg aspirin, 110 subjects were active metabolite and VN-P2Y12 during MD and LD, whereas it wasrandomized to double-blind treatment with clopidogrel 600 mg loading observed only with prasugrel MD.dose (LD)/75 mg maintenance dose (MD) or prasugrel 60 mg LD/10 mgMD. Antiplatelet effects were evaluated by VerifyNow P2Y12 (VN-P2Y12) Conclusion The VN-P2Y12 correlated strongly with inhibition ofdevice (Accumetrics, San Diego, CA), vasodilator-stimulated phosphopro- P2Y12 function, as measured with either VASP or LTA. VN-P2Y12 alsotein (VASP) phosphorylation assay, and light transmission aggregometry correlated to exposure to the active metabolite of prasugrel and clopidogrel(LTA). Prasugrels and clopidogrels active metabolite concentration were up to levels associated with assumed saturation of the P2Y12 receptor.also determined. (Am Heart J 2009;157:562.e1-562.e9.)