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Patient Knowledge of Coronary Risk Profile
Improves the Effectiveness of Dyslipidemia Therapy

The CHECK-UP Study: A Randomized Controlled Trial
Steven A. Grover, MD, MPA, FRCPC; Ilka Lowensteyn, PhD; Lawrence Joseph, PhD;Mohammed Kaouache, MSc; Sylvie Marchand, RN; Louis Coupal, MSc; Ghislain Boudreau, PhD;for the Cardiovascular Health Evaluation to Improve Compliance and KnowledgeAmong Uninformed Patients (CHECK-UP) Study Group Background: Despite increasing evidence that treat-
total cholesterol to high-density lipoprotein cholesterol ra- ing dyslipidemia reduces cardiovascular events, many pa- tio were observed in patients receiving risk profiles (51.2 tients do not achieve recommended lipid targets.
mg/dL [to convert to millimoles per liter, multiply by 0.0259]and 1.5, respectively) vs usual care (48.0 mg/dL and 1.3, Methods: To determine whether showing physicians and
respectively), but the differences were small (−3.3 mg/dL; patients the patient’s calculated coronary risk can im- 95% confidence interval [CI], −5.4 to −1.1 mg/dL; and −0.1; prove the effectiveness of treating dyslipidemia in a pri- 95% CI, −0.2 to −0.1, respectively). Patients in the risk pro- mary care setting, patients were randomized to receive file group were also more likely to reach lipid targets (odds usual care or ongoing feedback regarding their calcu- ratio, 1.26; 95% CI, 1.07 to 1.48). A significant dose-response lated coronary risk and the change in this risk after life- effect was also noted when the impact of the risk profile was style changes, pharmacotherapy, or both to treat dyslip- stronger in those with worse profiles.
idemia. Outcomes, based on intention-to-treat analysis,included changes in blood lipid levels, coronary risk, and Conclusions: Discussing coronary risk with the pa-
the frequency of reaching lipid targets.
tient is associated with a small but measurable improve- Results: Two hundred thirty primary care physicians en-
ment in the efficacy of lipid therapy. The value of incor- rolled 3053 patients. After 12 months of follow-up, 2687 porating risk assessment in preventive care should be patients (88.0%) remained in the study. After adjustment for baseline lipid values, significantly greater mean reduc-tions in low-density lipoprotein cholesterol levels and the Arch Intern Med. 2007;167(21):2296-2303 Author Affiliations: McGill
Cardiovascular Health
Improvement Program and the
ALTHOUGHINCREASING inadequatetreatmentbyphysiciansand
therapy is targeted to those at high risk, a will be most effective and cost-effective if For editorial comment
therapy.7-9 Accordingly, as recently dis- see pages 2286 and 2288
cussed by Jackson et al,9 one of the chal- CME available online at
evitable in chronic illness. The active par- www.archinternmed.com
less essential. Simons et al18 showed that Group Information: The
approximately one-third of those who dis- CHECK-UP Study Groupmembers are listed on lipid targets.10-13 This is due, in part, to (REPRINTED) ARCH INTERN MED/ VOL 167 (NO. 21), NOV 26, 2007 2007 American Medical Association. All rights reserved.
et al15 suggested that better compliance might result fromimproving patients’ understanding of their coronary risk and the potential benefits of therapy.
Improving communication through shared decision making and clinical decision aids has been advocated by many as one approach to optimizing patient care in the presence of clinical uncertainty.22-24 Given that recent ex- pert guidelines7-9 recommend calculating the future risk of cardiovascular events to identify high-risk patients, we hypothesized that sharing this information with pa- tients might enhance the effectiveness of treating dys-lipidemia in a primary care setting. The Cardiovascular Health Evaluation to Improve Compliance and Knowl- edge Among Uninformed Patients (CHECK-UP) Study was a randomized clinical trial designed to test this hy- pothesis and demonstrate this proof of principle.
As a result of these changes, your cardiovascular age has dropped from 60.8 y to 53.8 y. Your 8-y cardiovascular risk has dropped from 24.5% to 7.5%.
Figure 1. A coronary risk profile of a hypothetical patient demonstrating the
reduction in risk after risk factor modification. The broken horizontal lines
represent risk tertiles for Canadians of the same age and sex based on data
STUDY DESIGN
from the Canadian Heart Health Surveys. The associated decrease incardiovascular age represents the forecasted increased life expectancy Physicians were identified from multiple sources, including pro- associated with lifelong risk reduction. For an explanation of cardiovascular fessional association databases. Interested investigators were age, see the “Coronary Risk Profile” subsection below.
invited to 1 of 4 regional investigator meetings, which con-sisted of a full-day educational session, including information visit, patients completed a fasting lipid profile, and risk pro- on the national lipid guidelines, the study protocol, and how files were completed at the central coordinating center. At each to interpret the risk profiles. Of 330 physicians who attended visit, study physicians discussed the risk profile with patients one of the investigator meetings, 230 participated in the study.
randomly assigned to receive it. Profiles were withheld from Using office medical record reviews or prebooked clinic ap- patients in the usual care group, who received routine care as pointments, patients were identified who were likely to have practiced by their physician. Routine care could include a cal- untreated hyperlipidemia, including those who had diabetes culation of coronary risk. However, physicians were unlikely mellitus, established CVD, or multiple risk factors for CVD. Pa- to systematically estimate risk in their practice because lipid tient inclusion criteria were based on the 2000 Canadian Work- guidelines first recommended using risk tables in the year pre- ing Group on Hypercholesterolemia and Other Dyslipidemias lipid guidelines and included men and women aged 30 to 70years with CVD or diabetes mellitus or men aged 45 to 70 yearsand women aged 55 to 70 years who had a calculated 10-year CORONARY RISK PROFILE
coronary risk of at least 10% based on Framingham equa-tions.25 At screening, patients provided written informed con- The coronary risk profile is a 1-page computer printout that sent and had a complete medical evaluation, including a full displays a patient’s probability of developing coronary disease lipid profile. The study protocol and informed consent were (Figure 1). For individuals with previously diagnosed CVD,
approved by local ethics review boards. Randomization was com- these estimates were calculated using the previously pub- pleted at a central coordinating center, where patients, not phy- lished and validated Cardiovascular Life Expectancy Model based sicians, were randomized to receive risk profiles or usual care.
on data from the Lipid Research Clinics Follow-up Cohort.26 Patients were eligible for the study if (1) they had CVD or For individuals without CVD, these risk estimates were based diabetes mellitus or a calculated 10-year coronary risk greater on Framingham equations,27 and life expectancy was calcu- than 30%, with a low-density lipoprotein cholesterol (LDL-C) lated using the Cardiovascular Life Expectancy Model. For these level of 97 mg/dL or greater (to convert to millimoles per liter, primary prevention patients, the profile also included their “car- multiply by 0.0259) or a total cholesterol to high-density lipo- diovascular age,” calculated as the patient’s age minus the dif- protein cholesterol (TC:HDL-C) ratio of 4 or greater; (2) the ference between his or her estimated remaining life expec- calculated 10-year risk was 20% to 30%, with an LDL-C level tancy (adjusted for coronary and stroke risk) and the average of 116 mg/dL or greater or a TC:HDL-C ratio of 5 or greater; remaining life expectancy of Canadians of the same age and or (3) the calculated 10-year risk was 10% to 20%, with an LDL-C sex.27,28 For example, a 50-year-old with a life expectancy of level of 155 mg/dL or greater or a TC:HDL-C ratio of 6 or greater.
25 more years (vs 30 more years for the average Canadian) would Exclusion criteria included hypersensitivity to statins, risk of be assigned a cardiovascular age of 55 years. Once the study pregnancy, breastfeeding, active liver disease or elevated as- was completed, the risk profile became freely available at the partate aminotransferase or alanine aminotransferase levels (Ն3 McGill Cardiovascular Health Improvement Program Web site times normal), elevated creatine kinase levels (Ն5 times nor- mal), elevated triglyceride levels (Ͼ939 mg/dL [to convert to At entry into the study, patients randomized to the risk pro- millimoles per liter, multiply by 0.0113]), a history of pancre- file group were shown their coronary risk profile. The relative atitis, and significant renal insufficiency.
risk was graphically summarized by comparing this risk with To replicate the usual barriers to adherence, all medica- a representative sample of Canadians of the same age and sex tions were purchased at a pharmacy chosen by the patient. Drug using data from the Canadian Heart Health Surveys.29 Popula- costs were borne by patients using private insurance, public tion risk tertiles were constructed for each profile based on these drug plans, or out-of-pocket payment. A few weeks before each data so that each patient could see his or her absolute risk com- (REPRINTED) ARCH INTERN MED/ VOL 167 (NO. 21), NOV 26, 2007 2007 American Medical Association. All rights reserved.
Table 1. Baseline Characteristics of the 3053 Study Patients
Risk Profile
Usual Care
Characteristic
(n = 1510)
(n = 1543)
Figure 2. Flow of patients through the trial.
pared with that of peers. Finally, a copy of the profile was given The second profile at the 3-month follow-up visit com- pared baseline risk with the risk after statin therapy or life- style modification. Each subsequent profile compared the pa- tient’s current global risk status with all profiles obtained at previous visits so that patients could follow their response to STUDY VISITS
The baseline visit occurred 2 to 4 weeks after screening. Fol- Abbreviations: ACE, angiotensin-converting enzyme; BMI, body mass index low-up visits occurred at 3, 6, 9, and 12 months, with a re- (calculated as weight in kilograms divided by height in meters squared); evaluation of lipids and safety variables 2 to 4 weeks before each CVD, cardiovascular disease; HDL, high-density lipoprotein; LDL, low-density visit. Patients receiving lifestyle modification who did not reach lipid targets at the 3-month follow-up visit were asked to start SI conversion factors: To convert total, HDL, and LDL cholesterol to statin therapy as per the national guidelines. Those who reached millimoles per liter, multiply by 0.0259; triglycerides to millimoles per liter,multiply by 0.0113.
lipid targets could continue lifestyle modification. When phar- a Differences between baseline and target lipid levels before treatment.
macotherapy was initiated, the choice of statin and the start- b Cardiovascular age is calculated as the patient’s age minus the difference ing dose were chosen by the physician, and at each visit, statin between his or her estimated remaining life expectancy (adjusted for coronary therapy could be modified based on physician and patient pref- risk) and the average remaining life expectancy of Canadians of the same age erences. Although the primary objective was to treat patients to achieve recommended lipid levels, the study protocol didnot force physicians to switch or titrate medication to achievethese targets.
interval [CI], 1.9%-15.9%). Given the lower bound of 1.9%,2282 patients would be required. Further adjusting for an an- SAMPLE SIZE CONSIDERATIONS
ticipated 30% dropout rate required 3260 patients to be ran-domized.
Sample size calculations were performed using classic and Bayesian sample size calculations were based on ensuring Bayesian approaches. It was assumed that each enrolled pa- sufficiently accurate interval estimation of the between-group tient would provide a baseline and at least 1 follow-up assess- difference in LDL-C reduction. A mixed Bayesian/likelihood ment, with a 1:1 randomization between study arms.
average coverage criterion was used.31 This criterion uses pre- The classic sample size calculation was performed to pro- vious information to predict which data are likely to arise in vide adequate power (90%) to detect anticipated changes in the trial, but then it ensures accurate estimation assuming LDL-C levels when tested using a 2-sided t test (␣=.05). In a that standard CIs will be used for final inferences. A gamma previous study,30 risk assessment feedback resulted in an in- density with settings of 23.08 and 2652.93 was used for the cremental reduction in LDL-C levels of 8.9% (95% confidence variance in each treatment group, which was derived assum- (REPRINTED) ARCH INTERN MED/ VOL 167 (NO. 21), NOV 26, 2007 2007 American Medical Association. All rights reserved.
Table 2. Outcomes After 12 Monthsa
Risk Profile Group
Usual Care Group
Difference
P Valueb
Baseline
Absolute Change
Baseline
Absolute Change
10-y risk of CVD (for patients without CVD) Abbreviations: CVD, cardiovascular disease; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol.
SI conversion factors: To convert total, HDL, and LDL cholesterol to millimoles per liter, multiply by 0.0259.
a Data are given as mean (SD) unless otherwise indicated.
b The P values are for the difference between the 2 groups adjusting for baseline values using analysis of covariance.
ing that standard deviations will be within 9% to 14%.31 A (23%). These community physicians screened 4310 pa- sample size of 2200 patients provides an accuracy of ±1% in tients, 3053 of whom were eligible for the study, were estimating LDL-C reduction differences, which is sufficiently randomized, and completed the baseline visit accurate for clinical decision making. Further adjusting for (Figure 2). The study started May 10, 2001, and ended
the dropout rate of approximately 30% again means that ap- August 25, 2003, when all the patients had completed proximately 3000 patients need to be randomized to retain 12 months of follow-up or were withdrawn. Of the 366 patients who did not complete the study (166 in the DATA ANALYSIS
risk profile group and 200 in the usual care group), 82(22.4%) withdrew consent, 89 (24.3%) were lost to fol- All the end points were prespecified, and the data from all en- low-up, 48 (13.1%) experienced an adverse event, and rolled patients were analyzed on an intention-to-treat basis. The prespecified primary end points were the change in LDL-C lev- Baseline characteristics of all eligible patients are sum- els, the TC:HDL-C ratio, and the percentage of patients who marized in Table 1. Patients not completing the 12-
reached national lipid targets. Secondary end points included month follow-up (n=366) were compared with those who the change in nonlipid risk factors and global 10-year risk. We did (n = 2687) and were, on average, slightly older used the summary measures approach for continuous vari- (mean ± SD, 56.6 ± 8.0 vs 54.6 ± 8.8 years) and less likely ables. Changes in continuous variables were analyzed using the to be smokers (27.9% vs 35.8%). There were no other difference between baseline and the mean of all follow-up mea- important differences among early termination patients surements. Analysis of covariance was used to adjust for anydifferences at baseline. Differences in binary characteristics at the 12-month follow-up visit between the study arms were evalu-ated using the ␹2 test, and multiple logistic regression was used BLOOD LIPID CHANGES
to adjust for any important differences in baseline values. Whenpatients withdrew prematurely, the results from the last visit Despite similar statin dosages, the mean change in LDL-C level from baseline for the risk profile group was −51.2 In this study, the unit of analysis was the patient. However, mg/dL (95% CI, −52.8 to −49.7 mg/dL) and for the usual the possibility remained that between-physician differences could care group was −48.0 mg/dL (95% CI, −49.5 to −46.4 mg/ have an effect on estimated treatment efficacy.32 Accordingly,a mixed-effects model was also fitted to estimate the effect of dL), with a significant mean difference of −3.3 mg/dL (95% the intervention compared with the control group after adjust- CI, −5.4 to −1.1 mg/dL; P = .02) (Table 2). Between-
ment for between-physician variability.33 To adjust for the ef- group differences were also observed for TC level (−3.9 fectiveness of different statins at various doses, we defined a mg/dL; 95% CI, −6.4 to −1.4 mg/dL) and the TC:HDL-C standardized statin dose as atorvastatin calcium, 10 mg, equal ratio (−0.1; 95% CI, −0.2 to −0.1). Using a random- to any of the following: simvastatin, 30 mg; pravastatin so- effects model to account for between-physician differ- dium, 60 mg; lovastatin, 60 mg; fluvastatin sodium, 90 mg; and ences, these results between study arms remained essen- REACHING LIPID TARGETS
PARTICIPANT ENROLLMENT
Overall, patients in the risk profile group were no more likely AND FOLLOW-UP
to reach lipid targets than those receiving usual care (55.2%vs 52.2%) (odds ratio [OR], 1.13; 95% CI, 0.98-1.30). How- Physician participation (n = 230) in each of the 10 prov- ever, at baseline, patients in the risk profile group had higher inces approximated the population distribution across levels of TC and LDL-C and a higher TC:HDL-C ratio Canada, including the Maritime provinces (8%), Que- (Table 1). Accordingly, after adjusting for baseline differ- bec (26%), Ontario (43%), and the western provinces ences in LDL-C levels and the TC:HDL-C ratio risk pro- (REPRINTED) ARCH INTERN MED/ VOL 167 (NO. 21), NOV 26, 2007 2007 American Medical Association. All rights reserved.
Table 3. Probability of Reaching Recommended Lipid Targets Stratified by Clinical Status
Age, Mean, y
Patients Identified
Patients Reaching
Study Participants
as High Risk, %a
Lipid Targets, %
OR (95% CI)d
Cardiovascularb
Abbreviations: CI, confidence interval; CVD, cardiovascular disease, NA, not applicable; OR, odds ratio.
a High risk is defined as the upper tertile for Canadians of the same age and sex.
b Cardiovascular age is calculated as the patient’s age minus the difference between his or her estimated remaining life expectancy (adjusted for coronary risk) and the average remaining life expectancy of Canadians of the same age and sex.
c The age gap is defined as an individual’s cardiovascular age minus his or her actual age.
d The ORs are adjusted for the difference between the baseline total cholesterol to high-density lipoprotein cholesterol ratio and target and the difference between baseline low-density lipoprotein cholesterol level and the target.
als with preexisting CVD (OR, 1.25; 95% CI, 0.89-1.75)
(Table 3). Cardiovascular age could not be calculated
in this subgroup, but 95% to 96% of individuals withCVD were in the highest risk tertile for their age and sex. In the presence of symptomatic disease, it seemsthat a risk profile did not substantially improve theeffectiveness of treatment. On the other hand, it was in individuals without CVD that a risk profile increasedthe likelihood of reaching targets (OR, 1.26; 95% CI, 1.04-1.53). This was primarily due to the impact onindividuals with diabetes mellitus (OR, 1.42; 95% CI, The risk profile assigned each patient to a risk tertile compared with Canadians of the same age and sex.
However, the baseline risk tertile did not seem to affectpatient responses. Individuals without CVD were also Figure 3. Mean adjusted odds ratios (ORs) for reaching lipid targets in
given their cardiovascular age, their actual age, and the individuals receiving a risk profile (vs usual care) are stratified by age gap resulting “age gap” (cardiovascular age − actual age).
quintiles (Qs). Using multiple logistic regression analysis, the impact of therisk profile is adjusted for the difference between baseline low-density This variable seemed to modify the degree to which pa- lipoprotein cholesterol level and target, baseline total cholesterol to tients responded to the risk profile. For example, high-density lipoprotein cholesterol ratio and target, and statin dosage. Also among patients with diabetes mellitus who received a included is the significant interaction (P = .04) between the risk profile andthe age gap, indicating that the positive impact of the risk profile increases risk profile, the increased probability of reaching lipid with an increasing age gap. For example, in individuals at relatively low risk targets may have been associated with the large age gap for cardiovascular disease, the age gap is small or even negative (quintile 1: (cardiovascular age − actual age) of approximately 6 −6.10 to 0.43 years), indicating that the estimated life expectancy is greater years. This hypothesis was further evaluated in all pri- than or equal to the average life expectancy for Canadians of the same ageand sex. The corresponding adjusted OR for reaching lipid targets in mary prevention patients. Patients in the risk profile individuals who are reassured that they are at low risk is 0.92. With an group with a cardiovascular age greater than their increasing age gap, the positive impact of the risk profile also increases so chronologic age (age gap Ͼ0) demonstrated larger that the OR of reaching lipid targets is highest (OR, 1.69) for those in thehighest age gap quintile. Horizontal bars represent 95% confidence intervals.
LDL-C reductions compared with patients receivingusual care. On the other hand, LDL-C reductions weresmaller in patients in the risk profile group who were file, patients demonstrated a greater likelihood of reach- reassured that their risk was low because their age gap ing lipid targets (OR, 1.26; 95% CI, 1.07-1.48).
was less than 0 (Figure 3).
When the probability of reaching lipid targets was The interaction between the risk profile and an in- examined according to the patient’s clinical status, the creased age gap in individuals without CVD was further risk profile did not have a significant effect in individu- examined by plotting the adjusted OR for reaching lipid (REPRINTED) ARCH INTERN MED/ VOL 167 (NO. 21), NOV 26, 2007 2007 American Medical Association. All rights reserved.
Lorne W. Adams, MD; Mario Coˆte´, MD; John P. Hopkins, MD; Ronald Akhras, MD; Donald E. Craig, MD; Anita S. Hunt, MD;Mohamed M. Ali, MD; Thomas R. Crawford, MD; Antony M. Irving, MD; Cathy V. Andrew, MD; Wesley S. Cutbush, MD; WesleyD. Jackson, MD; Donald M. Andrew, MD; Oliver A. David, MD; David H. C. James, MD; Haig Ashikian, MD; Guy Deslauriers,MD; Peter Jechel, MD; David Attwell, MD; Charles Dewar, MD; Anthony F. Jeraj, MD; Abdoulaye Bah, MD; Marcel De´ziel, MD;Thomas E. Johnson, MD; Denis A. Beaulieu, MD; Ripple Dhillon, MD; Jean-Franc¸ois Julien, MD; Margaret H. Bennett, MD; JeanD. Dion, MD; Pierre Julien, MD; Samuel Bergman, MD; Leonard Direnfeld, MD; Bharat B. Kalra, MD; Ranbir S. Bhatia, MD; WayneB. Domanko, MD; Nicholas Karellis, MD; Gunvant S. Bhatt, MD; Bernard Dufour, MD; Martin L. Kates, MD; Krzysztof W. Bienkowski,MD; Thomas H. Echlin, MD; Ian K. Kendal, MD; Clair Biglow, MD; Mark Essak, MD; Bertram W. King, MD; David B. I. Birbrager,MD; Ronald G. Esterbauer, MD; Judy Komosky, MD; Gregory L. Black, MD; Brian Fagan, MD; Arthur M. Kushner, MD; CiffordP. Blais, MD; Roland Faucher, MD; Donald Lafortune, MD; Richard Blanchet, MD; Alcantro B. Fernandez, MD; Roch Lambert,MD; Denys Blouin, MD; George F. Fitzpatrick, MD; Yves P. Langlois, MD; Raynald C. Boily, MD; David L. Fleck, MD; BrodieLantz, MD; Jacques Boisselle, MD; Curtis S. Folkerson, MD; He´lène Laporte, MD; Serge Boucher, MD; Anne H. Fong, MD; Franc¸oisLaurendeau, MD; Jean Bouffard, MD; Dennis H. G. Forrester, MD; Brian W. Laursen, MD; Jean Bouthillier, MD; Carl Fournier,MD; John Law, MD; Christiane Bovo, MD; Norman L. Fox, MD; Michael Leckie, MD; Boris W. Boyko, MD; E´velyne Fraser, MD;Frank R. Lee, MD; Richard Brassard, MD; Robert C. Frechette, MD; Marc-Fre´de´rick Lee, MD; Jeannot Breton, MD; Maude Gagnon,MD; Cheuk-Hon Li, MD; Gilles D. Brousseau, MD; Eamon N. Gamble, MD; Shao-Jin Li, MD; Gerald Brown, MD; Edward Gee,MD; Pierre Liboiron, MD; Jerzy T. Brzeski, MD; Roland J. Genge, MD; Hanson K. Lo, MD; Brian J. Buckley, MD; Michael A.
Geoghegan, MD; Lydia C. L. Lo, MD; Al-Beruni S. Buckridan, MD; Kamil Ghali, MD; Graham J. Loeb, MD; William A. Buckton,MD; Stuart R. Glaser, MD; Benoit Loranger, MD; Brent E. Bukovy, MD; Bronte L. Golda, MD; Terrence Magennis, MD; GeorgeBurwell, MD; Serge Goulet, MD; Thomas Maguire, MD; Ashok K. Chadha, MD; Moonsamy Govender, MD; Lorne A. Marsh,MD; Richard J. Champoux, MD; William C. Gracey, MD; Azaria Marthyman, MD; Hari S. Chana, MD; Robert D. Graham, MD;Peter D. McPhedran, MD; Pierre Charbonneau, MD; Russell Grimwood, MD; Upender K. Mehan, MD; Tak-Kee Cheung, MD;Andre´e A. Guay, MD; Pravinsagar Mehta, MD; John F. Chiu, MD; Magdi Y. Habra, MD; Shamsh Y. Merali, MD; Pan C. Chow,MD; Brian P. Hadley, MD; Denis Me´tivier, MD; Walter Chow, MD; Darlene Hammell, MD; Michel Meunier, MD; Margaret A.
Churcher, MD; Velizar A. Harizanov, MD; Maurice Milner, MD; John M. Collingwood, MD; Bruce A. Herman, MD; Donald Collins-Williams, MD; David W. Hillier, MD; Angelos Costaris, MD; Michael S. C. Ho, MD; Kenneth A. Mitton, MD; Brian S. Swarbreck,MD; Martin Model, MD; John P. Taliano, MD; Gloria M. Mok, MD; Jonny Tam, MD; Alan Munroe, MD; Margaret K. H. Tao, MD;Salma B. Murji, MD; Alain Tardif, MD; Kapila Narang, MD; Ivor Teitelbaum, MD; Derek S. Nesdoly, MD; Allison M. Theman,MD; Dung P. Nghiem, MD; Lyne The´riault, MD; Claire M. Nunes-Vas, MD; Nicole Thibault, MD; Richard Nuttall, MD; MorrisE. Trager, MD; Paul F. O’Brien, MD; Holtby M. Turner, MD; Helen Olijnik, MD; Steven L. Turner, MD; Chelvi A. M. Pandian,MD; Douglas Tweel, MD; Marilyn F. Paterson, MD; Richard H. Tytus, MD; Claude Patry, MD; Kandiah Vaithianathan, MD; PeterPetrosoniak, MD; Alain Valiquette, MD; Andre´ Poisson, MD; Phillip W. Van Der Merwe, MD; Charles Potter, MD; Trevor T. C.
Vu, MD; David G. Pow, MD; Robert J. Wahby, MD; Gerard Quinn, MD; Lyle H. Waldman, MD; Salim M. Quraishi, MD; Paul E.
Walsh, MD; Robert Ramsey, MD; Marvin Waxman, MD; John C. Rea, MD; Ronald S. Weiss, MD; Patrick A. Renchko, MD; RhondaWilansky, MD; Marcel Reny, MD; John S. Wilczynski, MD; Cyril Riche, MD; Bryan E. Williams, MD; Brian D. Ritchie, MD; SingMan Wu, MD; Julie Ross, MD; Stephen T. W. Wu, MD; Robert J. Roy, MD; Jack K. P. Yeung, MD; Herbert W. Sacks, MD; DavidK. W. Yip, MD; Paul Salciccioli, MD; Paul C. K. Yong, MD; David Saul, MD; Michael Zigman, MD; Georgia Savvidou, MD; MartinShack, MD; Ronald J. Smith, MD; Salim Somani, MD; Peter A. Souchen, MD; R. George H. Southey, MD; Nigel Spencer, MD;John S. Spiers, MD; Joseph M. Stander, MD; Michel St-Onge, MD; and Jack Sussman, MD. Scientific Advisory Board Members:Brian Gore, MD; Steven Grover, MD, MPA; Lawrence Joseph, PhD; Lyne Lalonde, PhD; Ruth McPherson, MD, PhD; and JohnStewart, MD.
targets against age gap quintiles (Figure 3). Multiple lo- ferences in blood lipid levels, the risk profile group was gistic regression analysis was used to adjust for the dif- also more likely to reach the recommended lipid tar- ference between baseline lipid levels and lipid targets gets. Finally, the significant interaction effect between (LDL-C and the TC:HDL-C ratio) and the statin dose.
the risk profile and the age gap (cardiovascular age−actual In individuals in the lowest age gap quintile (−6.10 to age) demonstrated that the higher a patient’s risk, as evi- 0.43 years), the OR for reaching lipid targets using a risk denced by increased cardiovascular age, the greater the profile (vs usual care) was 0.92. A dose-response effect impact associated with the risk profile.
was noted, with a significant interaction observed where The strengths and weaknesses of this study must be the risk profile was more effective in individuals with recognized. Despite positive results for the prespecified larger age gaps (highest quintile: OR, 1.69; 95% CI, 1.21- outcomes, the clinical impact of risk profile feedback was small. This was due, in part, to a study protocol that mayhave minimized the impact of the intervention by en- couraging physicians to treat patients in both treatmentarms to achieve nationally recommended targets and mini- These results demonstrate the proof of principle that statin mized loss to follow-up even among those receiving usual therapy can be enhanced by informing patients of their care. The lipid changes required to reach treatment tar- calculated coronary risk. The reductions in the LDL-C gets (see “Treatment gap” in Table 1) were also modest level and the TC:HDL-C ratio were greater for patients for LDL-C level and the TC:HDL-C ratio (−41 mg/dL and receiving risk profiles. After adjustment for baseline dif- −1.03, respectively, for the control group); hence, there (REPRINTED) ARCH INTERN MED/ VOL 167 (NO. 21), NOV 26, 2007 2007 American Medical Association. All rights reserved.
was a high probability of success in both treatment arms.
Kaouache, and Coupal. Drafting of the manuscript: Grover, Also, the inclusion in the study of some relatively low- Lowensteyn, and Coupal. Critical revision of the manu- risk patients, whose small or negative age gap may have script for important intellectual content: Grover, reassured them that their risk was low, may have re- Lowensteyn, Joseph, Kaouache, Marchand, Coupal, and duced the overall perceived need for treatment in the risk Boudreau. Statistical analysis: Grover, Joseph, Kaouache, profile group. Finally, this trial was not a cluster design and Coupal. Administrative, technical, and material sup- based on physician randomization because an earlier port: Grover, Lowensteyn, and Boudreau. Study supervi- study30 demonstrated low retention rates for physicians sion: Grover, Lowensteyn, and Marchand.
in the control arm. Randomizing patients may have re- Financial Disclosure: Drs Grover and Lowensteyn and Mr
duced the observed effectiveness of the intervention be- Coupal have received research grants from Pfizer, Sanofi cause physicians may have incorporated the knowledge Aventis, and AstraZeneca. Dr Grover has received speaker gained from treating patients in the risk profile group honoraria from Pfizer, Sanofi Aventis, and Orynx and has when treating individuals in the control group. Given this either been a consultant to or participated on an advisory possible contamination of the control group, these study board for AstraZeneca, Sanofi Aventis, and Pfizer.
results may underestimate the potential impact of the risk Funding/Support: This study was funded by Pfizer
profile. It is possible that the impact of a profile would be even greater in patients still contemplating the pros Role of the Sponsor: The study design was conceived by
and cons of therapy. Ideally, low-risk patients would be Dr Grover, and the protocol was reviewed and modified reassured, whereas higher-risk patients would be moti- by the Scientific Advisory Board members listed on the vated to start and adhere to treatment.
previous page. The sponsor participated in discussions Given that masking is impossible in a decision aid regarding study design and protocol development and study, the Hawthorne effect (a change in behavior re- provided logistical support during the trial. The inves- sulting from the knowledge that one is being studied) is tigators were responsible for data collection, data analy- always a concern. However, patients in both interven- sis, and preparation of the manuscript independent of the tions signed informed consent documents and under- funding source. The sponsor was permitted to review the stood that they were participating in a study. Moreover, manuscript, but all final decisions regarding content re- the profile was most helpful in those with the largest age mained the responsibility of the principal investigator.
gap, demonstrating a dose-response effect consistent withthe underlying study hypothesis. Patient behavior seemsto have been modified as the odds of reaching lipid tar- gets increased approximately 25% after adjustment forstatin dose and baseline lipid levels. This suggests greater 1. Scandinavian Simvastatin Survival Study Group. Randomised trial of choles- adherence with statins or other lifestyle changes.
terol lowering in 4444 patients with coronary heart disease: the ScandinavianSimvastatin Survival Study (4S). Lancet. 1994;344(8934):1383-1389.
On the other hand, the strengths of the study include 2. Sacks FM, Pfeffer MA, Moye LA, et al. The effect of pravastatin on coronary events a cross-country randomized trial in a primary care set- after myocardial infarction in patients with average cholesterol levels. N Engl J ting. The choice of medication and the decision to switch or titrate this medication was left to the individual phy- 3. Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group.
Prevention of cardiovascular events and death with pravastatin in patients with sician. The cost of medication and the effort to obtain it coronary heart disease and a broad range of initial cholesterol levels. N Engl J also reflected patient care as currently practiced under a 4. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study Given the enormous clinical and economic burden of of cholesterol lowering with simvastatin in 20 536 high-risk individuals: a ran- CVD in our communities, primary prevention cannot be domised placebo-controlled trial. Lancet. 2002;360(9326):7-22.
5. Colhoun HM, Betteridge DJ, Durrington PN, et al. Primary prevention of cardio- avoided. Communicating risk is consistent with many of vascular disease with atorvastatin in type 2 diabetes in the Collaborative Ator- the recommendations to improve adherence, including vastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled enhancing self-monitoring and using the support of fam- trial. Lancet. 2004;364(9435):685-696.
ily and friends.7,8 Informing patients of their coronary risk 6. Sever PS, Dahlof B, Poulter NR, et al. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than- may also increase the effectiveness of primary preven- average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Out- tion by identifying individuals most likely to benefit from comes Trial–Lipid Lowering Arm (ASCOT-LLA): a multicenter randomised con- treatment while reassuring those at low risk. This infor- trolled trial. Lancet. 2003;361(9364):1149-1158.
mation may also assist physicians in treatment selection 7. De Backer G, Ambrosioni E, Borch-Johnsen K, et al. European guidelines on car- diovascular disease prevention in clinical practice: Third Joint Task Force of Eu-ropean and other Societies on Cardiovascular Disease Prevention in ClinicalPractice. Eur Heart J. 2003;24(17):1601-1610.
Accepted for Publication: July 18, 2007.
8. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol Correspondence: Steven A. Grover, MD, MPA, FRCPC,
in Adults. Executive Summary of the Third Report of the National Cholesterol Research Institute of the McGill University Health Cen- Education Program (NCEP): Expert Panel on Detection, Evaluation, and Treat-ment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001; tre, Royal Victoria Hospital, 687 Pine Ave W, V- Building, Montreal, QC H3A 1A1, Canada (steven.grover 9. Jackson R, Lawes C, Bennet D, Milne R, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual’s absolute Author Contributions: Study concept and design: Grover,
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Joseph, Kaouache, Coupal, and Boudreau. Acquisition of 10. Pearson TA, Laurora I, Chu H, Kafonek S. The Lipid Treatment Assessment Project (L-TAP): a multicenter survey to evaluate the percentages of dyslipidemic pa- data: Grover, Lowensteyn, Marchand, and Coupal. Analy- tients receiving lipid-lowering therapy and achieving low-density lipoprotein cho- sis and interpretation of data: Grover, Lowensteyn, Joseph, lesterol goals. Arch Intern Med. 2000;160(4):459-467.
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27. Grover SA, Abrahamowicz M, Joseph L, Brewer C, Coupal L, Suissa S. The ben- 13. McBride P, Schrott HG, Plane MB, Underbakke G, Brown RL. Primary care prac- efits of treating hyperlipidemia to prevent coronary heart disease: estimating tice adherence to national cholesterol education program guidelines for patients changes in life expectancy and morbidity. JAMA. 1992;267(6):816-822.
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