![]() ![]() Next, we used a combination of pharmacy fill data, Current Procedural Terminology, and ICD‐9‐CM codes to identify veterans with a diagnosis of stable angina using the protocol adapted from Phelps et al (Table S1). First, to develop the NLP tool, we identified veterans with at least 1 inpatient or outpatient encounter with an International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD‐9‐CM) code of 413.x (angina pectoris) between January 1, 1999, and December 31, 2006. We conducted a retrospective cohort study using the Veterans Health Administration (VHA) clinical and administrative databases in 2 phases. We hypothesized that an NLP algorithm could accurately identify CCS classification within a large electronic health record system and that higher CCS classification would be associated with progressively higher all‐cause mortality and healthcare utilization, independent of other baseline characteristics. We undertook this study with 2 objectives: (1) identify CCS documentation in clinical notes within a large, integrated health system using natural language processing (NLP) techniques and (2) determine the association between initial CCS classification and all‐cause mortality and healthcare utilization. As such, data demonstrating the importance of CCS classification and outcomes in large cohorts of community‐treated angina patients is limited, particularly in patients with newly diagnosed stable angina. This is predominantly because a patient's CCS class is documented as unstructured free text within clinic notes and thus is not easily extractable for research purposes. 5, 6, 7 However, these associations have not been replicated in large, population‐based cohorts using electronic health record data sets. ![]() 2, 4 CCS angina classification has been associated with coronary revascularization, myocardial infarction, cognitive impairment, and mortality in clinical trials and prospective registries. The CCS angina classification is a physician‐reported symptom severity scale used to assess and grade physical‐activity symptoms on 4 levels: class I indicates angina with strenuous exertion class II indicates angina with walking >200 yards on flat surfaces, climbing stairs rapidly, or in cold or emotional situations class III indicates angina with walking 100–200 yards on flat surfaces and class IV indicates angina at rest or with any physical activity. Stable angina severity can be assessed in clinical practice and research settings by the physician using grading measures such as the Canadian Cardiovascular Society (CCS) angina classification system. 2 An accurate, comprehensive assessment of symptom severity at the point of care is difficult to estimate but holds important implications for shared medical decision‐making between patient and clinician, who work together to determine the specific types and intensity of interventions for angina treatment based on symptomatology. 1, 2, 3 Contemporary evidence‐based interventions for stable angina include aggressive lifestyle modifications, pharmacotherapy, and coronary revascularization to improve anginal symptoms and overall health status. Stable angina affects >10 million Americans and is the presenting symptom in approximately half of patients with coronary artery disease. 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. 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. 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. The mean age was 66.6☙.8 years, 99% of participants were male, and 81% were white. Of 299 577 veterans identified, 14 216 (4.7%) had ≥1 CCS classification extracted by natural language processing. Outcomes included all‐cause mortality (primary), all‐cause and cardiovascular‐specific hospitalizations, coronary revascularization, and 1‐year healthcare costs. Veterans with a prior diagnosis of coronary artery disease were excluded. 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |