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Angina Severity, Mortality, and Healthcare Utilization Among Veterans With Stable Angina
Mina Owlia1, John A Dodson1,2, Jordan B King3,4,5
1Leon H. Charney Division of Cardiology Department of Medicine New York University School of Medicine New York NY.
Insights
The Canadian Cardiovascular Society (CCS) angina severity classification, extracted using natural language processing, is linked to increased mortality and healthcare use in veterans. This highlights the prognostic value of documenting angina symptoms.
Area of Science:
- Cardiology
- Health Informatics
- Data Science
Background:
- The Canadian Cardiovascular Society (CCS) angina severity classification is a known predictor of adverse cardiovascular outcomes.
- Previous studies primarily used clinical trial and registry data to establish these associations.
- The utility of CCS classification extracted from clinical notes for predicting outcomes in real-world settings remains less explored.
Purpose of the Study:
- To determine the association between CCS angina severity classes and all-cause mortality.
- To investigate the relationship between CCS classes and healthcare utilization, including hospitalizations and revascularization procedures.
- To evaluate the effectiveness of natural language processing (NLP) in extracting CCS classifications from electronic health records.
Main Methods:
- A retrospective cohort study was conducted on US veterans with stable angina between 2006 and 2013.
- Natural language processing (NLP) was employed to extract CCS angina severity classifications from clinical notes.
- Outcomes included all-cause mortality, hospitalizations (all-cause and cardiovascular-specific), coronary revascularization, and healthcare costs.
Main Results:
- Of 299,577 veterans, 14,216 had CCS classifications extracted by NLP.
- Higher CCS classes (III and IV) were significantly associated with increased all-cause mortality compared to CCS class I.
- Increased CCS class also correlated with higher rates of all-cause hospitalization and coronary revascularization (PCI and CABG).
Conclusions:
- NLP-extracted CCS classification is a valuable tool for assessing prognosis in patients with stable angina.
- Angina symptom severity, as documented in clinical notes, is a significant independent predictor of mortality and healthcare utilization.
- Emphasizes the importance of accurate and consistent documentation of angina severity in clinical practice.
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.
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Angina I: Introduction
Angina II: Classification
Angina IV: Management
Angina V: Nursing Management
Angina III: Clinical Manifestations and Assessment
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