Statins after acute coronary syndrome: a missed opportunity
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* Gregory A Roth

During and immediately following a myocardial infarction, care providers routinely explain the role of statins as a way to reduce the chance of further coronary events and even death. Hospitalised and recalling their recent chest pain, patients are usually highly receptive to this idea of taking a statin medication every day. Some may be reassured that clinical guidelines, supported by large clinical trials, give statins their strongest level of evidence.1 Physicians who follow these patients in clinic might conclude that statin use is widely adopted and well maintained, but these physicians could not possibly know for sure. Statins are among the most effective of medical therapies, but our ability to know if they are actually being used remains severely limited.

Into this information gap steps researchers who are able to find and follow the ‘entire denominator’—every patient in a country that has received care for an acute coronary syndrome. The paper by Grey *et al*2 is an excellent example, and tells the story of New Zealand's experience delivering statins to acute coronary syndrome (ACS) survivors in fine detail. Using a national data system that links public hospital records, outpatient pharmaceutical claims and death certificates, they were able to identify almost every ACS patient in their country and follow their pharmacy purchases for 3 years. This retrospective analysis included a cohort of over 11 000 individuals, most of whom experienced a non-ST elevation myocardial infarction (MI) or unstable angina. As would be expected for those suffering from coronary artery disease, this was an older population (mean age of 67 years) and predominantly male (63%). As would be expected from this kind of real-world study, the cohort included a distribution of ethnicity and socioeconomic background that looks much like the country as a whole. They found that full adherence to statin therapy was maintained by only 69% of the cohort during the first year following the index event, falling to 66% over the next 3 years. Independent predictors of lower adherence included age 35–44 years, female sex, and Maori and Pacific ethnicity, but not a neighbourhood-level measure of socioeconomic deprivation.

While data collection is far easier and less costly in this kind of retrospective analysis than in prospective studies, significant challenges remain nonetheless. Grey *et al* have expertly navigated these methodological challenges and, in doing so, provide an example of careful pharmacoepidemiology. By investigating ACS, they are using administrative ICD10 codes that are well validated against clinical records. This data is recent, covering the years 2007–2010 and, therefore, reflects current clinical practice and public perception. They appropriately excluded those patients who died soon after leaving the hospital, or spent most of their time in a chronic care facility. They also excluded patients at the extremes of the age spectrum where diagnoses, clinical care and patient decision making may differ from common practice. They adopted a simple and widely used metric, the medication possession ratio (MPR), as an outcome measure. MPR divides the number of pills dispensed at pharmacy by the number of days during which they are needed. Attaining an MPR of 80% is a common and accepted outcome that represents a lower bar for adherence in most patients.

Perhaps the most important conclusion to draw from this work, considering the current fascination with Big Data and social networks, is the ease with which some countries can link large datasets in order to yield important insights into health and healthcare. Data linkage is an old tool. Beginning in the 1970s, early MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) studies manually linked hospital care to death certificates.3 In the USA in the 1990s, the Cooperative Cardiovascular Project laboriously abstracted over 16 000 patient charts from billing records that reported ACS.4 In the present study, these intensive efforts have been replaced by an encrypted and unique national identifier that was used to connect data managed by different government departments. This kind of record linkage is becoming a new standard for population studies of pharmacotherapy. For example, with the advent of a government-sponsored prescription drug benefit in the USA, the pharmacy data of 60 million patients can now be studies.5

While Grey *et al* demonstrate the power of linking national health data systems, they mostly substantiate the poor adherence to statins found in prior studies. Phase Z of the A to Z trial randomised patients with ACS to placebo followed by 20 mg versus 40 mg, followed by 80 mg doses of simvastatin for 2 years.6 This study went so far as to assume a 15% discontinuation rate in their sample size calculations. In fact, treatment was discontinued by 32% and 34%, respectively. PROVE-IT 22 randomised patients following ACS to pravastatin 40 mg versus atorvastatin 80 mg, and found discontinuation rates of 21–23%.7 More directly relevant to the question of adherence in a real-world setting was the recent MI-FREEE (Mi-freee Post-Myocardial Infarction Free Rx Event and Economic Evaluation) trial, which randomised patients with myocardial infarction to standard or waived copayment for their cardiovascular prescriptions. Over the following 3 years, patients only achieved an 80% MPR for statins of 31.6% and 38.6%, respectively (p<0.001), rising to around 45% if those who never filled any statin prescription were excluded.8 New Zealand appears to achieve considerably better results as a nation than the participants in the MI-FREEE trial, approaching adherence patterns seen in the rarified cohorts of randomised clinical trials. Regardless, it is clear that statin therapy is a missed opportunity for many patients following ACS.

But what are we to do with these disappointing results? If it is true, as concluded by the Cholesterol Treatment Trialists’ Collaboration, that statins after ACS lower recurrent vascular events by approximately 20% for every 20 mg/dL reduction in low density lipoprotein (LDL) cholesterol, than failure to deliver statins to 30–50% of ACS survivors results in a great deal of preventable death and disability.9 In this context, linked datasets can help to identify a problem in healthcare delivery and also serve as the basis for the solution. Large linked datasets are already being used to predict risk of hospitalisation among participants across the US Veterans healthcare system.10 Following the observation of Grey *et al* that non-adherence was significantly elevated among younger, female and ethnic-minority patients, similar approaches could be used to target interventions that improve adherence. This same data could be used for benchmarking and tracking performance over time at the regional or clinic level.

Several unanswered questions remain as important areas of future research. There is need to examine predictors of medication non-adherence beyond easily captured demographic characteristics. For example, Grey *et al* used a census-based, neighborhood-level measure of socioeconomic deprivation that may not have adequately captured deprivation in actual patients. And while prescription data is available, less is known about the full chain of events required for successful delivery of statin, beginning with appropriate prescribing by a physician and ending with ingestion of the pill by the patient. In our search for low-cost, high-value health interventions, improving adherence to statins is likely to pay off.

## Footnotes

*   Competing interests None.

*   Provenance and peer review Commissioned; internally peer reviewed.

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