Taiwan’s National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan’s Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.

Atrial fibrillation (AF) can be newly detected in approximately one-fourth of patients with ischemic stroke and transient ischemic attack without previously recognized AF. We present updated evidence supporting that AF detected after stroke or transient ischemic attack (AFDAS) may be a distinct clinical entity from AF known before stroke occurrence (known atrial fibrillation). Data suggest that AFDAS can arise from the interplay of cardiogenic and neurogenic forces. The embolic risk of AFDAS can be understood as a gradient defined by the prevalence of vascular comorbidities, the burden of AF, neurogenic autonomic changes, and the severity of atrial cardiopathy. The balance of existing data indicates that AFDAS has a lower prevalence of cardiovascular comorbidities, a lower degree of cardiac abnormalities than known atrial fibrillation, a high proportion (52%) of very brief (<30 seconds) AF paroxysms, and is more frequently associated with insular brain infarction. These distinctive features of AFDAS may explain its recently observed lower associated risk of stroke than known atrial fibrillation. We present an updated ad-hoc meta-analysis of randomized clinical trials in which the association between prolonged cardiac monitoring and reduced risk of ischemic stroke was nonsignificant (incidence rate ratio, 0.90 [95% CI, 0.71–1.15]). These findings highlight that larger and sufficiently powered randomized controlled trials of prolonged cardiac monitoring assessing the risk of stroke recurrence are needed. Meanwhile, we call for further research on AFDAS and stroke recurrence, and a tailored approach when using prolonged cardiac monitoring after ischemic stroke or transient ischemic attack, focusing on patients at higher risk of AFDAS and, more importantly, at higher risk of cardiac embolism.

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.

Ascertaining stroke severity in claims data-based studies is difficult because clinical information is unavailable. We assessed the predictive validity of a claims-based stroke severity index (SSI) and determined whether it improves case-mix adjustment. We analyzed patients with acute ischemic stroke (AIS) from hospital-based stroke registries linked with a nationwide claims database. We estimated the SSI according to patient claims data. Actual stroke severity measured with the National Institutes of Health Stroke Scale (NIHSS) and functional outcomes measured with the modified Rankin Scale (mRS) were retrieved from stroke registries. Predictive validity was tested by correlating SSI with mRS. Logistic regression models were used to predict mortality. The SSI correlated with mRS at 3 months (Spearman rho = 0.578; 95 % confidence interval [CI], 0.556–0.600), 6 months (rho = 0.551; 95 % CI, 0.528–0.574), and 1 year (rho = 0.532; 95 % CI 0.504–0.560). Mortality models with the SSI demonstrated superior discrimination to those without. The AUCs of models including the SSI and models with the NIHSS did not differ significantly. The SSI correlated with functional outcomes after AIS and improved the case-mix adjustment of mortality models. It can act as a valid proxy for stroke severity in claims data-based studies.

Purpose: The performance of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for identifying acute hemorrhagic stroke in Taiwan’s National Health Insurance claims database has not been assessed. This study aimed to construct and validate the case definitions for acute hemorrhagic stroke based on ICD-10-CM diagnostic codes. Patients and Methods: From January 2018 to December 2019, all inpatient records with ICD-10-CM code of I60 or I61 in any field of the discharge diagnoses were retrieved from the hospitalization claims data and all hospitalizations with a final diagnosis of subarachnoid hemorrhage (SAH) or intracerebral hemorrhage (ICH) were identified from the stroke registry databases. The clinical diagnosis in the stroke registry was treated as the reference standard. For hospitalizations not recorded in the stroke registry, manual review of the medical records and images was done to ascertain the diagnosis. The positive predictive value (PPV) and sensitivity of various case definitions for acute hemorrhagic stroke were estimated. Results: Among the 983 hospitalizations, 860, 111, and 12 were determined to be true-positive, false-positive, and false-negative episodes of acute hemorrhagic stroke, respectively. The PPV and sensitivity of the ICD-10-CM codes of I60 or I61 for identifying acute hemorrhagic stroke were 88.6% and 98.6%, respectively. The PPV increased to 98.2%, whereas the sensitivity decreased to 93.1% when acute hemorrhagic stroke was defined as hospitalizations in which the primary diagnosis field contained I60 or I61. Hemorrhagic transformation of ischemic stroke and concomitant cerebrovascular diseases other than SAH or ICH were the main reasons for a false-positive and false-negative diagnosis of acute hemorrhagic stroke, respectively. Conclusion: This study demonstrated the performance of ICD-10-CM codes for identifying acute hemorrhagic stroke and may offer a reference for future claims-based stroke studies.

Stroke severity is an important outcome predictor for intracerebral hemorrhage (ICH) but is typically unavailable in administrative claims data. We validated a claims-based stroke severity index (SSI) in patients with ICH in Taiwan. Consecutive ICH patients from hospital-based stroke registries were linked with a nationwide claims database. Stroke severity, assessed using the National Institutes of Health Stroke Scale (NIHSS), and functional outcomes, assessed using the modified Rankin Scale (mRS), were obtained from the registries. The SSI was calculated based on billing codes in each patient's claims. We assessed two types of criterion-related validity (concurrent validity and predictive validity) by correlating the SSI with the NIHSS and the mRS. Logistic regression models with or without stroke severity as a continuous covariate were fitted to predict mortality at 3, 6, and 12 months. The concurrent validity of the SSI was established by its significant correlation with the admission NIHSS (r = 0.731; 95% confidence interval [CI], 0.705-0.755), and the predictive validity was verified by its significant correlations with the 3-month (r = 0.696; 95% CI, 0.665-0.724), 6-month (r = 0.685; 95% CI, 0.653-0.715) and 1-year (r = 0.664; 95% CI, 0.622-0.702) mRS. Mortality models with NIHSS had the highest area under the receiver operating characteristic curve, followed by models with SSI and models without any marker of stroke severity. The SSI appears to be a valid proxy for the NIHSS and an effective adjustment for stroke severity in studies of ICH outcome with administrative claims data.