Effect of Transport Time on the Use of Reperfusion Therapy for Patients with Acute Ischemic Stroke in Korea

Comparison of Factors Associated With Direct Versus Transferred-in Admission to Government-Designated Regional Centers Between Acute Ischemic Stroke and Myocardial Infarction in Korea

BACKGROUND

There has been no comparison of the determinants of admission route between acute ischemic stroke (AIS) and acute myocardial infarction (AMI). We examined whether factors associated with direct versus transferred-in admission to regional cardiocerebrovascular centers (RCVCs) differed between AIS and AMI.

METHODS

Using a nationwide RCVC registry, we identified consecutive patients presenting with AMI and AIS between July 2016 and December 2018. We explored factors associated with direct admission to RCVCs in patients with AIS and AMI and examined whether those associations differed between AIS and AMI, including interaction terms between each factor and disease type in multivariable models. To explore the influence of emergency medical service (EMS) paramedics on hospital selection, stratified analyses according to use of EMS were also performed.

RESULTS

Among the 17,897 and 8,927 AIS and AMI patients, 66.6% and 48.2% were directly admitted to RCVCs, respectively. Multivariable analysis showed that previous coronary heart disease, prehospital awareness, higher education level, and EMS use increased the odds of direct admission to RCVCs, but the odds ratio (OR) was different between AIS and AMI (for the first 3 factors, AMI > AIS; for EMS use, AMI < AIS). EMS use was the single most important factor for both AIS and AMI (OR, 4.72 vs. 3.90). Hypertension and hyperlipidemia increased, while living alone decreased the odds of direct admission only in AMI; additionally, age (65-74 years), previous stroke, and presentation during non-working hours increased the odds only in AIS. EMS use weakened the associations between direct admission and most factors in both AIS and AMI.

CONCLUSIONS

Various patient factors were differentially associated with direct admission to RCVCs between AIS and AMI. Public education for symptom awareness and use of EMS is essential in optimizing the transportation and hospitalization of patients with AMI and AIS.

Annual Case Volume and One-Year Mortality for Endovascular Treatment in Acute Ischemic Stroke

BACKGROUND

The association between endovascular treatment (EVT) case volume per hospital and clinical outcomes has been reported, but the exact volume threshold has not been determined. This study aimed to examine the case volume threshold in this context.

METHODS

National audit data on the quality of acute stroke care in patients admitted via emergency department, within 7 days of onset, in hospitals that treated ≥ 10 stroke cases during the audit period were analyzed. Ischemic stroke cases treated with EVT during the last three audits (2013, 2014, and 2016) were selected for the analysis. Annual EVT case volume per hospital was estimated and analyzed as a continuous and a categorical variable (in quartiles). The primary outcome measure was 1-year mortality as a surrogate of 3-month functional outcome. As post-hoc sensitivity analysis, replication of the study results was examined using the 2018 audit data.

RESULTS

We analyzed 1,746 ischemic stroke cases treated with EVT in 120 acute care hospitals. The median annual EVT case volume was 12.0 cases per hospital, and mortality rates at 1 month, 3 months, and 1 year were 12.7%, 16.6%, and 23.3%, respectively. Q3 and Q4 had 33% lower odds of 1-year mortality than Q1. Adjustments were made for predetermined confounders. Annual EVT case volume cut-off value for 1-year mortality was 15 cases per year (P < 0.02). The same cut-off value was replicated in the sensitivity analysis.

CONCLUSION

Annual EVT case volume was associated with 1-year mortality. The volume threshold per hospital was 15 cases per year.

Combination of Radiological and Clinical Baseline Data for Outcome Prediction of Patients With an Acute Ischemic Stroke

Background

Accurate prediction of clinical outcome is of utmost importance for choices regarding the endovascular treatment (EVT) of acute stroke. Recent studies on the prediction modeling for stroke focused mostly on clinical characteristics and radiological scores available at baseline. Radiological images are composed of millions of voxels, and a lot of information can be lost when representing this information by a single value. Therefore, in this study we aimed at developing prediction models that take into account the whole imaging data combined with clinical data available at baseline.

Methods

We included 3,279 patients from the MR CLEAN Registry; a prospective, observational, multicenter registry of patients with ischemic stroke treated with EVT. We developed two approaches to combine the imaging data with the clinical data. The first approach was based on radiomics features, extracted from 70 atlas regions combined with the clinical data to train machine learning models. For the second approach, we trained 3D deep learning models using the whole images and the clinical data. Models trained with the clinical data only were compared with models trained with the combination of clinical and image data. Finally, we explored feature importance plots for the best models and identified many known variables and image features/brain regions that were relevant in the model decision process.

Results

From 3,279 patients included, 1,241 (37%) patients had a good functional outcome [modified Rankin Scale (mRS) ≤ 2] and 1,954 (60%) patients had good reperfusion [modified Thrombolysis in Cerebral Infarction (eTICI) ≥ 2b]. There was no significant improvement by combining the image data to the clinical data for mRS prediction [mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.81 vs. 0.80] above using the clinical data only, regardless of the approach used. Regarding predicting reperfusion, there was a significant improvement when image and clinical features were combined (mean AUC of 0.54 vs. 0.61), with the highest AUC obtained by the deep learning approach.

Conclusions

The combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values.

The effects of socioeconomic and geographic factors on chronic phase long-term survival after stroke in South Korea

The stroke incidence has increased rapidly in South Korea, calling for a national-wide system for long-term stroke management. We investigated the effects of socioeconomic status (SES) and geographic factors on chronic phase survival after stroke. We retrospectively enrolled 6994 patients who experienced a stroke event in 2009 from the Korean National Health Insurance database. We followed them up from 24 to 120 months after stroke onset. The endpoint was all-cause mortality. We defined SES using a medical-aid group and four groups divided by health insurance premium quartiles. Geographic factors were defined using Model 1 (capital, metropolitan, city, and county) and Model 2 (with or without university hospitals). The higher the insurance premium, the higher the survival rate tended to be (P < 0.001). The patient survival rate was highest in the capital city and lowest at the county level (P < 0.001). Regions with a university hospital(s) showed a higher survival rate (P = 0.006). Cox regression revealed that the medical-aid group was identified as an independent risk factor for chronic phase mortality. Further, NHIP level had a more significant effect than geographic factors on chronic stroke mortality. From these results, long-term nationwide efforts to reduce inter-regional as well as SES discrepancies affecting stroke management are needed.

Sex and Economic Disparity Related to Reperfusion Therapies for Patients with Acute Ischemic Stroke in South Korea across a 10-Year Period: A Nationwide Population-Based Study Using the National Health Insurance Database

A complete enumeration study was conducted to evaluate trends related to reperfusion therapies (intravenous thrombolysis (IVT) and endovascular treatment (EVT)) in acute ischemic stroke (AIS) in South Korea, according to sex, economic status, and age, over a 10-year period retrospectively, using the National Health Information Database (NHIS-2020-1-481). This study included AIS patients aged ≥20 years who were hospitalized in a general hospital or tertiary hospital for ≥4 days and underwent brain imaging during the same period. Study participants were classified by sex, economic status (Medical Aid beneficiaries and National Health Insurance beneficiaries) and age (20-44, 45-64, 65-79, and ≥80 years). Women showed a significantly lower OR (Odds ratio) than men in IVT (OR: 0.75; 95% CI: 0.73-0.77), EVT (OR: 0.96; 95% CI: 0.93-0.99), and any therapy (OR: 0.82; 95% CI: 0.80-0.84). The Medical Aid beneficiaries showed significantly lower OR in IVT (OR 0.91, 95% CI 0.88-0.95), EVT (OR 0.93, 95% CI 0.89-0.98), and either therapy (OR 0.92, 95% CI 0.90-0.95) than the National Health Insurance beneficiaries. This study showed sex and economic disparity related to reperfusion therapies in patients with AIS in Korea.