Spontaneous Coronary Artery Dissection and Evidence-Based Medicine.

Spontaneous coronary artery dissection (SCAD) is a relatively newly diagnosed area, and evidence-based medicine (EBM) standards are emerging and currently include an aspirin, ß blocker, clopidogrel, angiotensin-converting enzyme inhibitor/angiotensin-receptor blocker for patients with hypertension, vascular abnormality imaging, and cardiac rehabilitation. Because SCAD is an uncommon condition, many providers are unfamiliar with EBM treatment standards which could affect the implementation of recommended treatment. This study documented the frequency of failure to meet EBM SCAD treatment standards and factors contributing to conformance failure. Patients who presented to a tertiary referral hospital from January 1, 2005, to July 6, 2020, were included. The electronic medical record was reviewed for EBM treatment. Patients who did not meet the criteria of EBM were contacted by phone for a phone interview. The study period included 118 patients with SCAD, 3 of whom (2.5%) died and were not eligible for this study. In the final cohort of 115 patients, the average age was 55 years, female gender (97%) and EBM standards were met in 30%. Of patients who participated in the phone interview, 38 (33%) reported frustration with SCAD misdiagnosis (39%), inadequate mental health resources (37%), and communication failure regarding the need for cardiologist follow-up (26%). Cardiac rehabilitation use was impacted by location, time of day, availability, and cost. The most common medication-limiting factor for ß-blocker usage was fatigue (15%). Most (59%) patients did not undergo fibromuscular dysplasia imaging. In conclusion, in this 15-year SCAD study from a single tertiary care hospital SCAD registry, only 30% met the current EBM for SCAD. Unique solutions that are both patient-informed and evidence-driven are needed to achieve the best clinical outcomes.

An algorithm that identifies coronary and heart failure events in the electronic health record.

INTRODUCTION: The advent of universal health care coverage in the United States and the use of electronic health records can make the medical record a disease surveillance tool. The objective of our study was to identify criteria that accurately categorize acute coronary and heart failure events by using electronic health record data exclusively so that the medical record can be used for surveillance without manual record review. METHODS: We serially compared 3 computer algorithms to manual record review. The first 2 algorithms relied on ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification) codes, troponin levels, electrocardiogram (ECG) data, and echocardiograph data. The third algorithm relied on a detailed coding system, Intelligent Medical Objects, Inc., (IMO) interface terminology, troponin levels, and echocardiograph data. RESULTS: Cohen's κ for the initial algorithm was 0.47 (95% confidence interval [CI], 0.41-0.54). Cohen's κ was 0.61 (95% CI, 0.55-0.68) for the second algorithm. Cohen's κ for the third algorithm was 0.99 (95% CI, 0.98-1.00). CONCLUSION: Electronic medical record data are sufficient to categorize coronary heart disease and heart failure events without manual record review. However, only moderate agreement with medical record review can be achieved when the classification is based on 4-digit ICD-9-CM codes because ICD-9-CM 410.9 includes myocardial infarction with elevation of the ST segment on ECG (STEMI) and myocardial infarction without elevation of the ST segment on ECG (nSTEMI). Nearly perfect agreement can be achieved using IMO interface terminology, a more detailed coding system that tracks to ICD9, ICD10 (International Classification of Diseases, Tenth Revision, Clinical Modification), and SnoMED-CT (Systematized Nomenclature of Medicine - Clinical Terms).

Accuracy of heart disease prevalence estimated from claims data compared with an electronic health record.

INTRODUCTION: We developed a decision support tool that can guide the development of heart disease prevention programs to focus on the interventions that have the most potential to benefit populations. To use it, however, users need to know the prevalence of heart disease in the population that they wish to help. We sought to determine the accuracy with which the prevalence of heart disease can be estimated from health care claims data. METHODS: We compared estimates of disease prevalence based on insurance claims to estimates derived from manual health records in a stratified random sample of 480 patients aged 30 years or older who were enrolled at any time from August 1, 2007, through July 31, 2008 (N = 474,089) in HealthPartners insurance and had a HealthPartners Medical Group electronic record. We compared randomly selected development and validation samples to a subsample that was also enrolled on August 1, 2005 (n = 272,348). We also compared the records of patients who had a gap in enrollment of more than 31 days with those who did not, and compared patients who had no visits, only 1 visit, or 2 or more visits more than 31 days apart for heart disease. RESULTS: Agreement between claims data and manual review was best in both the development and the validation samples (Cohen's κ, 0.92, 95% confidence interval [CI], 0.87-0.97; and Cohen's κ, 0.94, 95% CI, 0.89-0.98, respectively) when patients with only 1 visit were considered to have heart disease. CONCLUSION: In this population, prevalence of heart disease can be estimated from claims data with acceptable accuracy.

Identifying opportunities for a medical group to improve outcomes for patients with coronary artery disease and heart failure: an exploratory study.

CONTEXT: A decision-support tool was created to identify opportunities to improve outcomes for patients with coronary artery disease and heart failure by delivering all efficacious interventions; that is, "optimizing" care. When national data were applied, nearly 75% of the deaths that could be prevented or postponed by optimizing care for patients with heart disease would occur among ambulatory patients. OBJECTIVE: The purpose of this analysis is two-fold: 1) to determine whether medical group data are adequate to use in the decision-support tool, and 2) to determine whether the conclusions generated from the medical group data are similar to the conclusions generated from US data. DESIGN/MAIN OUTCOME MEASURE: The potential impact of optimizing care for patients age 40 to 75 years treated for coronary artery disease and heart failure by a multispecialty group between August 2007 and July 2008 was calculated using deaths that might be prevented or postponed if optimal care was achieved. RESULTS: The greatest opportunity to prevent or postpone deaths-70% of the total opportunity-lies with optimizing care for ambulatory patients. Optimizing care for patients hospitalized for acute myocardial infarction with or without ST-segment elevation on electrocardiography would prevent or postpone only 2% of deaths. CONCLUSIONS: This study demonstrates that 1) it is feasible to use the decision-support tool to analyze opportunities for improvement in a medical group, and 2) as concluded from national data analysis, optimizing ambulatory care presents the greatest opportunity to improve outcomes for patients with heart disease.