Development of stroke identification algorithm for claims data using the multicenter stroke registry database

Network analysis of stroke systems of care in Korea

Background

The landscape of stroke care has shifted from stand-alone hospitals to cooperative networks among hospitals. Despite the importance of these networks, limited information exists on their characteristics and functional attributes.

Methods

We extracted patient-level data on acute stroke care and hospital connectivity by integrating national stroke audit data with reimbursement claims data. We then used this information to transform interhospital transfers into a network framework, where hospitals were designated as nodes and transfers as edges. Using the Louvain algorithm, we grouped densely connected hospitals into distinct stroke care communities. The quality and characteristics in given stroke communities were analysed, and their distinct types were derived using network parameters. The clinical implications of this network model were also explored.

Results

Over 6 months, 19 113 patients with acute ischaemic stroke initially presented to 1009 hospitals, with 3114 (16.3%) transferred to 246 stroke care hospitals. These connected hospitals formed 93 communities, with a median of 9 hospitals treating a median of 201 patients. Derived communities demonstrated a modularity of 0.904 , indicating a strong community structure, highly centralised around one or two hubs. Three distinct types of structures were identified: single-hub (n=60), double-hub (n=22) and hubless systems (n=11). The endovascular treatment rate was highest in double-hub systems, followed by single-hub systems, and was almost zero in hubless systems. The hubless communities were characterised by lower patient volumes, fewer hospitals, no hub hospital and no stroke unit.

Conclusions

This network analysis could quantify the national stroke care system and point out areas where the organisation and functionality of acute stroke care could be improved.

Developing a national surveillance system for stroke and acute myocardial infarction using claims data in the Republic of Korea: a retrospective study

BACKGROUND

Limited information is available concerning the epidemiology of stroke and acute myocardial infarction (AMI) in the Republic of Korea. This study aimed to develop a national surveillance system to monitor the incidence of stroke and AMI using national claims data.

METHODS

We developed and validated identification algorithms for stroke and AMI using claims data. This validation involved a 2-stage stratified sampling method with a review of medical records for sampled cases. The weighted positive predictive value (PPV) and negative predictive value (NPV) were calculated based on the sampling structure and the corresponding sampling rates. Incident cases and the incidence rates of stroke and AMI in the Republic of Korea were estimated by applying the algorithms and weighted PPV and NPV to the 2018 National Health Insurance Service claims data.

RESULTS

In total, 2,200 cases (1,086 stroke cases and 1,114 AMI cases) were sampled from the 2018 claims database. The sensitivity and specificity of the algorithms were 94.3% and 88.6% for stroke and 97.9% and 90.1% for AMI, respectively. The estimated number of cases, including recurrent events, was 150,837 for stroke and 40,529 for AMI in 2018. The age- and sex-standardized incidence rate for stroke and AMI was 180.2 and 46.1 cases per 100,000 person-years, respectively, in 2018.

CONCLUSION

This study demonstrates the feasibility of developing a national surveillance system based on claims data and identification algorithms for stroke and AMI to monitor their incidence rates.

Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study

BACKGROUND

Many studies have evaluated stroke using claims data; most of these studies have defined ischemic stroke using an operational definition following the rule-based method. Rule-based methods tend to overestimate the number of patients with ischemic stroke.

OBJECTIVES

We aimed to identify an appropriate algorithm for identifying stroke by applying machine learning (ML) techniques to analyze the claims data.

METHODS

We obtained the data from the Korean National Health Insurance Service database, which is linked to the Ilsan Hospital database (n = 30,897). The performance of prediction models (extreme gradient boosting [XGBoost] or gated recurrent unit [GRU]) was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under precision-recall curve (AUPRC), and calibration curve.

RESULTS

In total, 30,897 patients were enrolled in this study, 3145 of whom (10.18%) had ischemic stroke. XGBoost, a tree-based ML technique, had the AUROC was 94.46% and AUPRC was 92.80%. GRU showed the highest accuracy (99.81%), precision (99.92%) and recall (99.69%).

CONCLUSIONS

We proposed recurrent neural network-based deep learning techniques to improve stroke phenotyping. This can be expected to produce rapid and more accurate results than the rule-based methods.

Incidence and case fatality of stroke in Korea, 2011-2020

OBJECTIVES

Stroke remains the second leading cause of death in Korea. This study was designed to estimate the crude, age-adjusted and age-specific incidence rates, as well as the case fatality rate of stroke, in Korea from 2011 to 2020.

METHODS

We utilized data from the National Health Insurance Services from January 1, 2002 to December 31, 2020, to calculate incidence rates and 30-day and 1-year case fatality rates of stroke. Additionally, we determined sex and age-specific incidence rates and computed age-standardized incidence rates by direct standardization to the 2005 population.

RESULTS

The crude incidence rate of stroke hovered around 200 (per 100,000 person-years) from 2011 to 2015, then surged to 218.4 in 2019, before marginally declining to 208.0 in 2020. Conversely, the age-standardized incidence rate consistently decreased by 25% between 2011 and 2020. When stratified by sex, the crude incidence rate increased between 2011 and 2019 for both sexes, followed by a decrease in 2020. Age-standardized incidence rates displayed a downward trend throughout the study period for both sexes. Across all age groups, the 30-day and 1-year case fatality rates of stroke consistently decreased from 2011 to 2019, only to increase in 2020.

CONCLUSIONS

Despite a decrease in the age-standardized incidence rate, the total number of stroke events in Korea continues to rise due to the rapidly aging population. Moreover, 2020 witnessed a decrease in incidence but an increase in case fatality rates.

Weekend effect on 30-day mortality for ischemic and hemorrhagic stroke analyzed using severity index and staffing level

BACKGROUND AND PURPOSE

Previous studies on the weekend effect-a phenomenon where stroke outcomes differ depending on whether the stroke occurred on a weekend-mostly targeted ischemic stroke and showed inconsistent results. Thus, we investigated the weekend effect on 30-day mortality in patients with ischemic or hemorrhagic stroke considering the confounding effect of stroke severity and staffing level.

METHODS

We retrospectively analyzed data of patients hospitalized for ischemic or hemorrhagic stroke between January 1, 2015, and December 31, 2018, which were extracted from the claims database of the National Health Insurance System and the Medical Resource Report by the Health Insurance Review & Assessment Service. The primary outcome measure was 30-day all-cause mortality.

RESULTS

In total, 278,632 patients were included, among whom 84,240 and 194,392 had a hemorrhagic and ischemic stroke, respectively, with 25.8% and 25.1% of patients, respectively, being hospitalized during the weekend. Patients admitted on weekends had significantly higher 30-day mortality rates (hemorrhagic stroke 16.84%>15.55%, p<0.0001; ischemic stroke 5.06%>4.92%, p<0.0001). However, in the multi-level logistic regression analysis adjusted for case-mix, pre-hospital, and hospital level factors, the weekend effect remained consistent in patients with hemorrhagic stroke (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.00-1.10), while the association was no longer evident in patients with ischemic stroke (OR 1.01, 95% CI 0.96-1.06).

CONCLUSIONS

Weekend admission for hemorrhagic stroke was significantly associated with a higher mortality rate after adjusting for confounding factors. Further studies are required to understand factors contributing to mortality during weekend admission.

Analysis of medical service utilization for post-stroke sequelae in Korea between 2016 and 2018: a cross-sectional study

In this retrospective cross-sectional observational study, the medical service utilization of post-stroke sequelae patients was examined using a national patient sample. The Korean Health Insurance Review and Assessment Service-National Patients Sample database was used to investigate the medical service utilization of 19,562 patients, diagnosed with post-stroke sequelae of cerebrovascular disease (I69) in Korea between January 2016 and December 2018. We compared the demographic characteristics, diagnosis code subtypes, frequency of healthcare utilization, medical costs, and comorbidities of standard care (SC) and Korean medicine (KM) users. Overall, patients aged ≥ 65 years accounted for the highest percentage, and utilization of medical services increased among patients aged ≥ 45 years. Outpatient care was higher among SC (79.23%) and KM (99.38%) users. Sequelae of cerebral infarction accounted for the highest percentage of diagnosis subtypes. Physical therapy and rehabilitation therapy were most frequent in SC, whereas injection/procedure and acupuncture were most frequent in KM. Cerebrovascular circulation/dementia drugs were prescribed most frequently in SC. Circulatory, digestive, endocrine, and metabolic disorders were the most common comorbidities in SC, whereas musculoskeletal and connective tissue disorders were most common in KM. Overall, SC and KM users showed differences in the number of medical service claims, cost of care, and comorbidities. Our findings provide basic research data for clinicians, researchers, and policy makers.

Five-Year Risk of Acute Myocardial Infarction After Acute Ischemic Stroke in Korea

Background

The long‐term incidence of acute myocardial infarction (AMI) in patients with acute ischemic stroke (AIS) has not been well defined in large cohort studies of various race‐ethnic groups.

Methods and Results

A prospective cohort of patients with AIS who were registered in a multicenter nationwide stroke registry (CRCS‐K [Clinical Research Collaboration for Stroke in Korea] registry) was followed up for the occurrence of AMI through a linkage with the National Health Insurance Service claims database. The 5‐year cumulative incidence and annual risk were estimated according to predefined demographic subgroups, stroke subtypes, a history of coronary heart disease (CHD), and known risk factors of CHD. A total of 11 720 patients with AIS were studied. The 5‐year cumulative incidence of AMI was 2.0%. The annual risk was highest in the first year after the index event (1.1%), followed by a much lower annual risk in the second to fifth years (between 0.16% and 0.27%). Among subgroups, annual risk in the first year was highest in those with a history of CHD (4.1%) compared with those without a history of CHD (0.8%). The small‐vessel occlusion subtype had a much lower incidence (0.8%) compared with large‐vessel occlusion (2.2%) or cardioembolism (2.4%) subtypes. In the multivariable analysis, history of CHD (hazard ratio, 2.84; 95% CI, 2.01–3.93) was the strongest independent predictor of AMI after AIS.

Conclusions

The incidence of AMI after AIS in South Korea was relatively low and unexpectedly highest during the first year after stroke. CHD was the most substantial risk factor for AMI after stroke and conferred an approximate 5‐fold greater risk.

Performance of ICD-10-CM Diagnosis Codes for Identifying Acute Ischemic Stroke in a National Health Insurance Claims Database

Purpose

The validity of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding for the identification of acute ischemic stroke (AIS) in Taiwan’s National Health Insurance claims database has not been investigated. This study aimed to construct and validate the case definition algorithms for AIS based on ICD-10-CM diagnostic codes.

Patients and Methods

This study identified all hospitalizations with ICD-10-CM code of I63* in any position of the discharge diagnoses from the inpatient claims database and all patients with a final diagnosis of AIS from the stroke registry between Jan 2018 and Dec 2019. Hospitalizations in the claims data that could be successfully linked to those in the registry data were regarded as true episodes of AIS. Otherwise, their electronic medical records and images were manually reviewed to ascertain whether they were true episodes of AIS. Using the true episodes of AIS as the reference standard, the positive predictive value (PPV) and sensitivity of various case definition algorithms for AIS were calculated.

Results

A total of 1227 hospitalizations were successfully linked. Among the 155 hospitalizations that could not be linked, 54 were determined to be true episodes of AIS. Using ICD-10-CM code of I63* in any position of the discharge diagnoses to identify AIS yielded a PPV and sensitivity of 92.7% and 99.4%, respectively. The PPV increased to 99.8% with >12% decrease in the sensitivity when AIS was restricted to those with I63* as the primary diagnosis. When AIS was defined to be I63* as the primary, first secondary, or second secondary diagnosis, both PPV and sensitivity were greater than 97%.

Conclusion

This study demonstrated the validity of various case definition algorithms for AIS based on ICD-10-CM coding and can provide a reference for future claims-based stroke research.

Incidence, Risk Factors, and Prediction of Myocardial Infarction and Stroke in Farmers: A Korean Nationwide Population-based Study

Objectives

This study was conducted to determine the incidence and risk factors of myocardial infarction (MI) and stroke in farmers compared to the general population and to establish 5-year prediction models.

Methods

The farmer cohort and the control cohort were generated using the customized database of the National Health Insurance Service of Korea database and the National Sample Cohort, respectively. The participants were followed from the day of the index general health examination until the events of MI, stroke, or death (up to 5 years).

Results

In total, 734 744 participants from the farmer cohort and 238 311 from the control cohort aged between 40 and 70 were included. The age-adjusted incidence of MI was 0.766 and 0.585 per 1000 person-years in the farmer and control cohorts, respectively. That of stroke was 0.559 and 0.321 per 1000 person-years in both cohorts, respectively. In farmers, the risk factors for MI included male sex, age, personal history of hypertension, diabetes, current smoking, creatinine, metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). Those for stroke included male sex, age, personal history of hypertension, diabetes, current smoking, high γ-glutamyl transferase, and metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). The prediction model showed an area under the receiver operating characteristic curve of 0.735 and 0.760 for MI and stroke, respectively, in the farmer cohort.

Conclusions

Farmers had a higher age-adjusted incidence of MI and stroke. They also showed distinct patterns in cardiovascular risk factors compared to the general population.