AF is the most common cardiac arrhythmia, and the burden of AF has increased gradually, which now poses a major public healthcare burden.17)18) The temporal trends in the incidence and prevalence of AF in the entire adult population in Korea from 2008 to 2015 have been reported using the NHID.19) The incidence and prevalence of AF have increased gradually in this period. Moreover, the number of patients with a CHA₂DS₂-VASc score of more than 2 points increased dramatically due to an increase in the elderly population and the prevalence of comorbidities, such as heart failure and diabetes. Kim et al.20) reported that hospitalization for AF, especially due to major bleeding, has increased over the last ten years in Korea. Therefore, AF has resulted in an increment in hospitalization costs due to AF-related complications. AF increased the risk of stroke, hospitalization, and the risk of mortality after adjusting cardiovascular comorbidities.9)21) The risk of all-cause mortality in Korean patients with AF was 3.7-fold higher than that of the general population.22) The most common cause of death was cardiovascular disease (38.5%), and cerebral infarction was the leading specific cause, which also supports evidence suggesting the need to pay more attention to stroke prevention.

The prescription pattern of certain drugs has been analyzed using the NHID. Past findings include temporal trends of antithrombotic therapy for stroke prevention in Korean patients with non-valvular AF.23) Asians with AF are usually less likely to receive anticoagulation therapy compared to the Western population. The global registry on long-term oral antithrombotic treatment in patients with atrial fibrillation (GLORIA-AF) registry reports that only 55% of the Asian population with AF were prescribed oral anticoagulants (OAC), whereas 90% of the European population with AF received OAC, and the most common OAC was non-vitamin K antagonist oral anticoagulants (NOACs).24) In line with GLORIA-AF registry, a 50.6% OAC prescription rate was reported in Korean patients with non-valvular AF, and CHA₂DS₂-VASc score ≥2 in 2015.23) OAC underuse was predominant in female patients, and patients with presence of vascular disease and prior intracranial hemorrhage (ICH). The geographical location and income levels were also associated with AF prevalence and OAC prescription rates.25) Yu et al.26) reported the implications of NOAC introduction on the OAC prescription pattern in patients with AF after full reimbursement of NOAC in July 2015. The difference in penetration of NOAC was influenced by the different medical systems in tertiary referral hospitals, nursing, and public health centers, which resulted in social inequalities of stroke prevention despite full reimbursement. Lee et al.27) reported the changes in patients' characteristics treated with warfarin or NOAC, and whether OAC prescription patterns affected the clinical outcomes. Between 2015 and 2017, NOAC prescriptions increased significantly from 59% to 89%, whereas warfarin use decreased from 41% to 11%. Patients with warfarin changed to younger and with lower CHA₂DS₂-VASc score, which resulted in improved clinical outcomes. However, patients with NOAC showed consistent lower risks of the composite outcome compared with warfarin despite these changes. These primary results based on the NHID provide insights into improving outcomes by increasing the rate of OAC use for stroke prevention according to guideline recommendations and may also guide planning and decision making regarding health policies.

Optimal anticoagulation therapy in patients with AF who have undergone percutaneous coronary intervention (PCI) is gaining attention. Recent RCTs have highlighted the benefit of double antithrombotic therapy based on NOACs over triple antithrombotic therapy based on warfarin.28)29)30)31) In real-world clinical practice, OAC is under prescribed in patients with AF undergoing PCI due to the fear of bleeding. A recent report examining the prescription pattern of antithrombotic therapy in patients with AF undergoing PCI revealed that the number of patients with AF and PCI has increased constantly, but OAC prescription was only 40% in 2015.32) Furthermore, more than 70% of patients received antiplatelet agents only one-year after PCI.33) Considering that most of these patients have a high risk of stroke, optimal antithrombotic therapy may be necessary after PCI.

The risk and number of comorbidities may be calculated using formulae, such as the Charlson Comorbidity Index and CHA₂DS₂-VASc score.34)35) The CHA₂DS₂-VASc score has been effective in predicting the risk of ischemic stroke in Korean patients with non-valvular AF not receiving OAC.36)37) Therefore, these results highlight the need to introduce OAC in patients with a CHA₂DS₂-VASc score of more than 2 points, which was adopted in the Korean Guideline of Atrial Fibrillation Management.38)

The prescription of NOACs has increased dramatically in Korea. However, only a small number of Asians with AF have been included in pivotal RCTs, thus, evidence based on Asian patients is not sufficient. Asians with AF have a higher risk of stroke and bleeding, especially ICH, compared to non-Asians.39)40) Therefore, evidence regarding the use of NOACs in Asians with AF is needed. Based on the recent increase in the use of NOAC in real-world clinical practice in the Korean population, comparative effectiveness and safety analyses among different OACs using observational cohorts from the administrative claims data may be useful in obtaining a comprehensive dataset studying the use of NOACs in a large-scale Asian population. For treatment comparison using observational cohorts, a number of statistical methods, including multiple regression analysis, propensity score matching, and inverse probability of treatment weighting, were used to minimize the difference among treatment groups. Researchers from Taiwan have published several papers comparing the effectiveness and safety of NOACs and warfarin.10)41) Researchers in Korea have also published several studies comparing the effectiveness and safety of NOACs with warfarin.42)43)44)45)46) Korean researchers have also analyzed special populations, such as patients with low body-weight and liver disease, which were not included in the pivotal RCTs of NOACs.47)48) Since the number of Asian patients with AF was smaller than non-Asian patients, only a small number of underweight patients were included in RCTs.47) Most patients with active liver disease were also excluded from the pivotal NOAC trials.49)50)51)52) However, patients with low-body weight or liver disease may be selected from the health screening database of the NHID. Future research using these variables may produce more novel findings, thus, highlighting one of the strengths of Korean NHID. Another important laboratory variable is serum creatinine. We calculated the creatinine clearance (Cockcroft-Gault Equation) using serum creatinine, body weight, age, and gender. Renal dysfunction is a criterion for dose reduction of NOACs, which aids in the classification of prescription into on-label or off-label dosing. A comparison between on-label rivaroxaban (20 mg daily, R20) and off-label rivaroxaban (15 mg daily, R15) in patients with AF and normal or mildly impaired renal function (CrCl ≥50 mL/min), revealed better outcomes for both R20 and R15 compared to warfarin.44) However, R20 was associated with a slightly lower risk of composite clinical outcome than off-label R15. Another study used creatinine clearance to compare the outcomes between edoxaban and warfarin,53) which revealed that a low dose edoxaban (30 mg daily) showed a higher risk of stroke and systemic embolism compared to warfarin at higher levels of CrCl (>95 mL/min). The FDA label also restricts the use of edoxaban in patients with a CrCl >95 mL/min due to concerns regarding an increased risk of stroke compared to warfarin.54) A recent study, which evaluated the net clinical benefit in the high-risk population, reported that AF patients with a previous history of ICH were excluded from pivotal NOAC trials, thus, there is limited data on optimal anticoagulation therapy among high-risk patients. Among a total of 5,712 patients with AF and prior ICH, NOAC was associated with a significantly lower risk of ICH and stroke compared to warfarin.55) Additionally, NOAC showed consistent benefits compared to warfarin in a population constituting OAC-naïve patients with non-valvular AF and a history of stroke.56)

The national health screening database constitutes lifestyle questionnaires (drinking, smoking, and exercise), laboratory results, and anthropometric measurements data. Heavy drinking of alcohol is a well-known risk factor for AF, but the impact of a high frequency or binge drinking on AF remains unclear. Kim et al.57) reported that frequent drinking and amount of alcohol consumption were significant risk factors for new-onset of AF, whereas heavy consumption per drink was not. The risk of AF could be evaluated using the laboratory parameters. Proteinuria and gamma-glutamyl transferase were found to have predictive values on the risk of AF.58)59)

Variability in the individual laboratory results recorded in the NHID over time may be an interesting topic for research. The health screening database contains serial data of the same patients who undergo biannual health screening examinations. Several variability indices, such as coefficient of variation, standard deviation, variability independent of the mean, and average real variability, could be calculated using serial variables, including body weight, total cholesterol, fasting blood glucose, and serum creatinine.60) High variability of metabolic parameters, such as fasting blood glucose, total cholesterol levels, systolic blood pressure, and body mass index, were independent predictors of mortality and cardiovascular events.60)61) High variabilities of lipid, renal function, blood pressure, and metabolic parameters were associated with the risk of AF, heart failure, myocardial infarction, stroke, and mortality.62)63)64)65)66)67) Mildly abnormal lipid levels were also associated with a higher risk of atherosclerotic cardiovascular disease, whereas lipid variability was not.68)

Studying a rare disease would be another interesting topic for research using the NHID. The number of patients and the accuracy of diagnosis would be important issues in rare disease research. As prescribed in the previous section, a combination of ICD-10 codes and RID code would increase the accuracy of diagnosis. We have validated the ICD-10 codes with RID code (V127) to define hypertrophic cardiomyopathy (HCM).69) The accuracy, sensitivity, and specificity of the RID code for HCM diagnosis were 92.6%, 91.5%, and 100%, respectively. The prevalence and incidence of HCM in Korea have increased substantially with increasing coexisting modifiable risk factors.70) The risk of AF using the RIF code with special populations, including those with HCM, inflammatory bowel disease, ankylosing spondylitis, and Behcet's disease, has also been reported.69)71)72)73) Considering the population size of the NIHD, research on rare diseases would be feasible, while the NHIS-NSC would not be an appropriate database to study rare diseases.

Hypertension is most commonly defined using a combination of three major criteria, including ICD-10 codes I10–I13 or I15 for hypertensive disease usually recorded twice in the outpatient clinic, or once in hospitalization, and a history of prescription of antihypertensive drugs classified as No. 214 in Drug Payment Classification List.74) The detailed information on antihypertensive drugs is summarized in Supplementary Table 1. Research based on the NHIS-National health examinee database reported that systolic blood pressure >140 or diastolic blood pressure >90 mmHg were used as an additional criteria for hypertension.60)75)76) These studies, however, did not include those with hypertensive end-organ damage, such as hypertensive renal (I12) or heart disease (I13), and secondary hypertension (I15). Definitions of the common cardiovascular diseases are summarized in Table 2.

The Taskforce Team of Diabetes Fact Sheet of the Korean Diabetes Association has summarized the operational definition of diabetes based on the Korean NHID.77) They have also evaluated the proportion of patients with diabetes according to different combinations of diagnosis and prescription data. The Taskforce team concluded the following criteria: 1) ICD-10 codes E11–E14 for diabetes as either principal diagnosis or 1st to 4th additional diagnosis at least once a year; and 2) prescription of antidiabetic agents (Table 2). Moreover, fasting glucose levels ≥126 mg/dL was considered as additional diagnostic criteria for diabetes. The prevalence of diabetes based on this definition was 11.4%. Many studies that did not include data from the Health check-up database used a combination of ICD-10 codes and antidiabetic agents only.78) The detailed information on antidiabetic agents is summarized in Supplementary Table 2.

Dyslipidemia was defined using the ICD-10 code E78 combined with prescribing lipid-lowering agents at least once a year.79) In addition to this operational definition, total cholesterol ≥240 mg/dL was another criteria for dyslipidemia.60)65)76)

Myocardial infarction was diagnosed using ICD-10 codes I21, I22, with more than one diagnosis during admission or at outpatient clinics. Hospitalization with primary diagnostic ICD-10-codes I21, I22 showed a positive predictive value of 92%.80) Heart failure was defined using ICD-10-code I50 with more than one diagnosis during admission or at outpatient clinics. The definitions of peripheral arterial disease, transient ischemic attack, and end-stage renal disease were summarized in Table 3.

The ICD-10 codes for AF were expressed as ‘I48,’ ‘I48.0–I48.4, I48.9’ or ‘I48, I48.0, I48.1’ in different studies, but these expressions included the same AF patients over different study periods (Table 4).19)20)81)82)83) The KCD was updated in 2011 and 2016, according to the revision of ICD-10-codes. For the accuracy, either one diagnosis during hospitalization or more than twice at outpatient clinics was requited for the diagnosis.81) This criterion was consistent with a Taiwan nationwide population study.84)85) A study validating the accuracy, which included reviewing 628 randomly chosen patients with ICD-10 codes I48 and matching ECG records, revealed a positive predictive value of 94.1% for the definition of AF.83) To confine the analysis on non-valvular AF, patients with a diagnosis of mitral stenosis (I05.0, I05.2, I05.9) or presence of mechanical heart valves (Z95.2–Z95.4) were regarded as valvular AF and were excluded from the final anlysis.19) A washout period of more than one year was set to ensure accuracy of AF incidence.81)82) The prevalence of AF was influenced by the inclusion of a previous history of AF. Yang et al.82) compared different approaches for assessing the prevalence and incidence of AF using the NHID. The formal approach involved examining patients with individual AF diagnosis history and mortality, while the medical use approach involved examining patients who claimed medical expenses annually. The overall prevalence of AF using a formal approach was approximately double that of a medical approach. The formal approach would be more suitable to compare the AF epidemiology among different countries. However, overestimation of patients with AF would pose a limitation for the formal approach. The formal approach may include patients with transient AF, who had not been formally diagnosed and did not visit the hospital for AF for several years. Therefore, the medical use approach would be more suitable to estimate usage of medical services. The prescription pattern of OACs by the medical use approach was 30–50% in Korea, which is consistent with that of Asian AF patients in the GLORIA-AF registry.23)24) Considering the wide variability in the prevalence and incidence of AF in different analysis approaches, careful interpretation, and attention would be needed for comparison with other datasets.

Ischemic stroke was defined by the diagnosis of ICD-10 codes I63 and I64 with hospitalization and concomitant brain imaging studies, involving computed tomography or magnetic resonance image,36)42)43)44)45)46)47)48)86) or related death (Table 4).37)53) This definition has been widely adopted in previous studies using the claims database. The combination of primary diagnostic code of the ICD-10 codes I65 and I64, and brain imaging showed positive predictive value and sensitivity of 92.2% and 91.2%, respectively.80) A study examining patients with a prior history of ischemic stroke revealed that a primary diagnosis at index hospitalization is a prerequisite of the operational definition of ischemic stroke.87)

ICH was defined by the ICD-10 codes I60, I61, and I62 with hospitalization or red blood cell (RBC) transfusion.36)42)43)44)45)46)47)48)86) However, other studies also included hospitalization and concomitant brain imaging studies or related death in the definition of ICH.53)87) Similar to ischemic stroke, when the study population included patients with prior history of ICH, a primary diagnosis at index hospitalization was a prerequisite.87)

The operational definition of major gastrointestinal (GI) bleeding varied across research groups (Table 4). Major GI bleeding using the diagnostic codes with hospitalization and RBC transfusion.43)44)45)46)47)48)86) The ICD-10-CM codes for the definition of major GI bleeding varied across working groups. Yu et al.53) defined GI bleeding in accordance with diagnostic codes, including K25-28 (subcodes 0–2 and 4–6 only), K92.0, K92.1, K92.2, K62.5, I85.0, and I98.3, from admission diagnosis or related death. Cho et al.87) reported GI bleeding as per the following codes: I85.0, K22.1, K22.8, K25.0, K25.2, K25.4, K25.6, K26.0, K26.2, K26.4, K26.6, K27.0, K27.2, K27.4, K27.6, K28.0, K28.2, K28.4, K28.6, K29.0, K31.8, K55.2, K57.0, 57.1, K57.2, K57.3, K57.4, K57.5, K57.8, K57.9, K62.5, K66.1, K92.0, K92.1, and K92.2.

Major bleeding was defined as ICH or major GI bleeding.43)44)45)46)47)48)86) Essentially, the definition of major bleeding includes ICH and major GI bleeding; however, other relevant diagnoses include hospitalization for extracranial or unclassified sites. Cho et al.87) defined major bleeding as fatal bleeding, bleeding necessitating hospitalization, or bleeding that occurred in critical sites, such as intracranial, intraspinal, intraocular, retroperitoneal, or intramuscular with compartment syndrome. Yu et al.53) defined major bleeding as ICH, gastrointestinal bleeding, or anemia caused by bleeding. The diagnostic codes of extracranial or unclassified sites include D62 (acute posthemorrhagic anemia), H05.2 (hemorrhage of orbit), H35.6 (retinal hemorrhage), H43.1 (vitreous hemorrhage), J94.2 (hemothorax), M25.0 (hemarthrosis), and R04 (hemorrhage from respiratory passages including epistaxis and hemoptysis) (Table 4).53)87)