Using big data from health records from four countries to evaluate chronic disease outcomes: a study in 114 364 survivors of myocardial infarction

Abstract

AimsTo assess the international validity of using hospital record data to compare long-term outcomes in heart attack survivors.Methods and resultsWe used samples of national, ongoing, unselected record sources to assess three outcomes: cause death; a composite of myocardial infarction (MI), stroke, and all-cause death; and hospitalized bleeding. Patients aged 65 years and older entered the study 1 year following the most recent discharge for acute MI in 2002–11 [n = 54 841 (Sweden), 53 909 (USA), 4653 (England), and 961 (France)]. Across each of the four countries, we found consistent associations with 12 baseline prognostic factors and each of the three outcomes. In each country, we observed high 3-year crude cumulative risks of all-cause death (from 19.6% [England] to 30.2% [USA]); the composite of MI, stroke, or death [from 26.0% (France) to 36.2% (USA)]; and hospitalized bleeding [from 3.1% (France) to 5.3% (USA)]. After adjustments for baseline risk factors, risks were similar across all countries [relative risks (RRs) compared with Sweden not statistically significant], but higher in the USA for all-cause death [RR USA vs. Sweden, 1.14 (95% confidence interval 1.04–1.26)] and hospitalized bleeding [RR USA vs. Sweden, 1.54 (1.21–1.96)].ConclusionThe validity of using hospital record data is supported by the consistency of estimates across four countries of a high adjusted risk of death, further MI, and stroke in the chronic phase after MI. The possibility that adjusted risks of mortality and bleeding are higher in the USA warrants further study.

Cardiovascular events and death after myocardial infarction or ischemic stroke in an older Medicare population

“…We observed a high rate of MI and IS recurrence in the first year of survival after an MI or IS (7.7% and 6.7%, respectively), and the risk of recurrence persisted and continued to increase for up to 6 years of follow‐up (14.3% and 13.4%, respectively). These findings confirm results from other retrospective observational studies, in which the risk of recurrent events was highest in the first year but remained elevated in subsequent years . Moreover, many patients in this Medicare sample experienced multiple recurrent events (11.6 and 10.2 events per 100 patient‐years for MI and IS, respectively, in the 2012 cohort).…”

“…The prevalence of comorbidities such as hypertension and diabetes, was high in the study population, and as shown in other studies, diabetes is a significant risk factor for recurrent events . In the Hoorn cohort study, the incidence of a recurrent cardiovascular event during approximately 4 years of follow‐up was 60% higher for individuals with diabetes compared with those without, and higher glycated hemoglobin was one of the predictive risk factors for a recurrent event .…”

Clinical and economic outcomes among elderly myocardial infarction survivors in the United States

“…However, the large proportionate difference in outcome rates between STEMI and NSTEMI in this study would not likely be accounted for by the different treatment rates . The relative contribution of the cardiovascular risk factors and comorbidities to outcome rates and costs are consistent with previous shorter term studies that show that prior MIs are strongly associated with recurrent MI and cardiovascular death than with all‐cause mortality, while other comorbidities, including heart failure, diabetes, and renal disease, are strongly associated with all‐cause mortality . This continuity of risk patterns may indicate that the same mechanisms that drive short‐term post‐MI risk also drive risk in this more robust, survivor population.…”

Big data and precision medicine: challenges and strategies with healthcare data

“…In this respect, biomaterials stored in centralized biobanks need to be linked to rich clinical information of the patients' course of disease. Future biomedical insights can only be achieved if those biobanks are connected as large networks which are able to provide sufficient data even for very small disease subgroups [9,64]. Predictive models are typically trained in data-driven procedures.…”