Affiliation:
1. Leon H. Charney Division of Cardiology Department of Medicine New York University School of Medicine New York NY
2. Department of Population Health New York University School of Medicine New York NY
3. Department of Population Health Sciences University of Utah Salt Lake City UT
4. Department of Pharmacy Kaiser Permanente Colorado Aurora CO
5. George E. Wahlen Department of Veterans Affairs Medical Center Salt Lake City UT
6. Department of Clinical Pharmacy University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences Aurora CO
7. Cardiology Section Veterans Affairs Medical Center Manhattan NY
8. Department of Internal Medicine University of Utah Salt Lake City UT
9. Department of Pharmacotherapy University of Utah Salt Lake City UT
Abstract
Background
Canadian Cardiovascular Society (
CCS
) angina severity classification is associated with mortality, myocardial infarction, and coronary revascularization in clinical trial and registry data. The objective of this study was to determine associations between
CCS
class and all‐cause mortality and healthcare utilization, using natural language processing to extract
CCS
classifications from clinical notes.
Methods and Results
In this retrospective cohort study of veterans in the United States with stable angina from January 1, 2006, to December 31, 2013, natural language processing extracted
CCS
classifications. Veterans with a prior diagnosis of coronary artery disease were excluded. Outcomes included all‐cause mortality (primary), all‐cause and cardiovascular‐specific hospitalizations, coronary revascularization, and 1‐year healthcare costs. Of 299 577 veterans identified, 14 216 (4.7%) had ≥1
CCS
classification extracted by natural language processing. The mean age was 66.6±9.8 years, 99% of participants were male, and 81% were white. During a median follow‐up of 3.4 years, all‐cause mortality rates were 4.58, 4.60, 6.22, and 6.83 per 100 person‐years for
CCS
classes I,
II
,
III
, and
IV
, respectively. Multivariable adjusted hazard ratios for all‐cause mortality comparing
CCS II
,
III
, and
IV
with those in class I were 1.05 (95% CI, 0.95–1.15), 1.33 (95% CI, 1.20–1.47), and 1.48 (95% CI, 1.25–1.76), respectively. The multivariable hazard ratio comparing
CCS IV
with
CCS
I was 1.20 (95% CI, 1.09–1.33) for all‐cause hospitalization, 1.25 (95% CI, 0.96–1.64) for acute coronary syndrome hospitalizations, 1.00 (95% CI, 0.80–1.26) for heart failure hospitalizations, 1.05 (95% CI, 0.88–1.25) for atrial fibrillation hospitalizations, 1.92 (95% CI, 1.40–2.64) for percutaneous coronary intervention, and 2.51 (95% CI, 1.99–3.16) for coronary artery bypass grafting surgery.
Conclusions
Natural language processing–extracted
CCS
classification was positively associated with all‐cause mortality and healthcare utilization, demonstrating the prognostic importance of anginal symptom assessment and documentation.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Cardiology and Cardiovascular Medicine