In this analysis, we asked two questions: 1) are medical group data adequate to identify opportunities to prevent or postpone death among individuals with heart disease?, and 2) if the data are adequate, are the conclusions generated from medical group data similar to those we previously generated from US statistics? We found that, with the exception of physical activity data, the medical group data were adequate to identify opportunities to prevent or postpone deaths and that the conclusions for a single medical group were consistent with previous conclusions based on national data.1
We found that nearly 70% of the total opportunity to increase the DPP would accrue from optimizing care of ambulatory patients. Among hospitalized patients, the greatest DPP would accrue from optimizing care for patients with HF with an LVEF ≤35% and patients with UA. Optimizing care for hospitalized patients with either STEMI or nSTEMI would prevent or postpone only about 2% of deaths. This is in part because of the fact that presentation with STEMI or nSTEMI is infrequent relative to other presentations and to the nearly optimal care that patients with STEMI or nSTEMI already receive.
We acknowledge that limitations in the data weaken the conclusions that can be drawn. For example, fatality rates and sheer numbers of patients suggest that many of the patients we classified as having heart disease newly diagnosed in the ambulatory setting actually had chronic prevalent disease. However, even if this were true, the conclusion would not change: The most significant opportunities to improve outcomes for patients with heart disease lie in the ambulatory setting. Another significant limitation is that we needed to use somewhat different data sets to estimate the number of cases in the population and the opportunities to improve care because we did not have easy access to medical records from other care-delivery systems. This would not be a problem if every medical group analyzed their opportunities to improve outcomes for patients with heart disease as we did. In addition, LVEF was quantified both in the acute setting and outpatient setting, and it is possible that a patient may have had an improvement in their ejection fraction after appropriate medical therapy was given. However, we always selected the highest LVEF and this does not negate the fact that the highest prevalence of patients had higher LVEF's and still accounted for the highest attributable risk for mortality.
Another limitation is the assumption that the effects of multiple interventions are cumulative. We used the method of Mant and Hicks to prevent intervention effects from potentially summing to greater than 100%, but this calculation may not have taken full account for multiple interventions.4
Although it is possible to collect nearly all of the data used in this analysis with currently available commercial software, we did need to manually review medical records of patients with acute MI to distinguish between STEMI and nSTEMI; the ICD-9-CM diagnostic codes did not reliably distinguish between STEMI and nSTEMI, a computer-based search of text in the medical record for words indicating STEMI or nSTEMI was unreliable, and the ECG analysis software used by the Medical Group did not permit searching for patients' ECGs for patterns of interest (eg, the words “acute myocardial infarction with ST elevation”). If newer ECG reporting software with the capability to search ECGs for patterns of interest had been available, there would have been no need to manually review any medical record.
A conundrum for quality-improvement efforts that use mortality as an outcome is that death certificate data always lag behind clinical data. We feel that, for the purposes of care-improvement initiatives, it is most important to analyze current clinical practice; because mortality rates are relatively stable for ambulatory cohorts and populations with acute events, using a relatively recent historical cohort to estimate mortality rates should not introduce significant error into the calculations.
Although it would be attractive to have a model that includes all heart disease, which is possible, we chose to limit our codes to CAD and HF for this pilot study. Arrhythmias and valvular heart disease could be included in the analysis as specific conditions, but doing so would add a level of complexity that we wished to avoid. It would also be possible to use the same model to analyze the opportunities to prevent and treat several chronic diseases simultaneously. For example, cerebrovascular disease, peripheral arterial disease, and chronic obstructive pulmonary disorder could be added to the analysis of heart disease opportunities.
… there is relatively little opportunity to improve outcomes by improving care during acute events other than heart failure with an left ventricular ejection fraction <35%.
This study raises important questions about the current focus of efforts to improve heart disease outcomes in the US. To the extent that the nearly optimal care given to hospitalized patients in this study is representative of the care received by all Americans hospitalized with heart disease, there is relatively little opportunity to improve outcomes by improving care during acute events other than HF with an LVEF ≤35%. The large size of the ambulatory population with CAD and/or HF magnifies the care deficiencies they experience. Although we acknowledge that it is highly unlikely that all patients with heart disease will become physically active, eat a healthy diet, and abstain from tobacco, this analysis shows that even modest improvements in the rates of these behaviors will have the largest impact on outcomes for these patients.