Variety of software solutions are being used for clinical use. This special contribution focuses on the personal experience of developing several software solutions concerning stroke. Stroke119 application was developed to inform the patient of the closest hospital available for thrombolytic therapy and provides a simple three-step self-test for detection of acute stroke. A multi-center web-based registry solution named SMART DB was developed to facilitate multi-center studies. Over 650,000 records were created by 25 centers in SMART DB. An artificial intelligence-based web solution for prediction of coronary artery disease in stroke patients was developed named S2CAD. A clinical decision support platform for thrombi acquired from endovascular thrombectomy named ARIA Cloud was developed. Software for stroke is actively being developed in Korea. Software solutions are expected to increase efficiency by providing clinical decision support in the near future.

PURPOSE: The benefits of thrombolytic treatment are time-dependent. We developed a smartphone application that aids stroke patient self-screening and hospital selection, and may also decrease hospital arrival time. MATERIALS AND METHODS: The application was developed for iPhone and Android smartphones. Map data for the application were adopted from the open map. For hospital registration, a web page (http://stroke119.org) was developed using PHP and MySQL. RESULTS: The Stroke 119 application includes a stroke screening tool and real-time information on nearby hospitals that provide thrombolytic treatment. It also provides information on stroke symptoms, thrombolytic treatment, and prescribed actions when stroke is suspected. The stroke screening tool was adopted from the Cincinnati Prehospital Stroke Scale and is displayed in a cartoon format. If the user taps a cartoon image that represents abnormal findings, a pop-up window shows that the user may be having a stroke, informs the user what to do, and directs the user to call emergency services. Information on nearby hospitals is provided in map and list views, incorporating proximity to the user's location using a Global Positioning System (a built-in function of smartphones). Users can search for a hospital according to specialty and treatment levels. We also developed a web page for hospitals to register in the system. Neurology training hospitals and hospitals that provide acute stroke care in Korea were invited to register. Seventy-seven hospitals had completed registration. CONCLUSION: This application may be useful for reducing hospital arrival times for thrombolytic candidates.
*Cellular Phone ; Geographic Information Systems ; Hospitals ; Humans ; Republic of Korea ; Stroke/*diagnosis

Purpose: We previously developed learning models for predicting the need for intensive care and oxygen among patients with coronavirus disease (COVID-19). Here, we aimed to prospectively validate the accuracy of these models. Materials and Methods: Probabilities of the need for intensive care [intensive care unit (ICU) score] and oxygen (oxygen score) were calculated from information provided by hospitalized COVID-19 patients (n=44) via a web-based application. The performance of baseline scores to predict 30-day outcomes was assessed. Results: Among 44 patients, 5 and 15 patients needed intensive care and oxygen, respectively. The area under the curve of ICU score and oxygen score to predict 30-day outcomes were 0.774 [95% confidence interval (CI): 0.614–0.934] and 0.728 (95% CI:0.559–0.898), respectively. The ICU scores of patients needing intensive care increased daily by 0.71 points (95% CI: 0.20–1.22) after hospitalization and by 0.85 points (95% CI: 0.36–1.35) after symptom onset, which were significantly different from those in individuals not needing intensive care (p=0.002 and <0.001, respectively). Trends in daily oxygen scores overall were not markedly different; however, when the scores were evaluated within <7 days after symptom onset, the patients needing oxygen showed a higher daily increase in oxygen scores [1.81 (95% CI: 0.48–3.14) vs. -0.28 (95% CI: 1.00–0.43), p=0.007]. Conclusion Our machine learning models showed good performance for predicting the outcomes of COVID-19 patients and could thus be useful for patient triage and monitoring.

Background: and Purpose Left atrial or left atrial appendage (LA/LAA) thrombi are frequently observed during cardioembolic evaluation in patients with ischemic stroke. This study aimed to investigate stroke outcomes in patients with LA/LAA thrombus. Methods: This retrospective study included patients admitted to a single tertiary center in Korea between January 2012 and December 2020. Patients with nonvalvular atrial fibrillation who underwent transesophageal echocardiography or multi-detector coronary computed tomography were included in the study. Poor outcome was defined as modified Rankin Scale score >3 at 90 days. The inverse probability of treatment weighting analysis was performed. Results: Of the 631 patients included in this study, 68 (10.7%) had LA/LAA thrombi. Patients were likely to have a poor outcome when an LA/LAA thrombus was detected (42.6% vs. 17.4%, P<0.001). Inverse probability of treatment weighting analysis yielded a higher probability of poor outcomes in patients with LA/LAA thrombus than in those without LA/LAA thrombus (P<0.001). Patients with LA/LAA thrombus were more likely to have relevant arterial occlusion on angiography (36.3% vs. 22.4%, P=0.047) and a longer hospital stay (8 vs. 7 days, P<0.001) than those without LA/LAA thrombus. However, there was no difference in early neurological deterioration during hospitalization or major adverse cardiovascular events within 3 months between the two groups. Conclusions Patients with ischemic stroke who had an LA/LAA thrombus were at risk of a worse functional outcome after 3 months, which was associated with relevant arterial occlusion and prolonged hospital stay.

No abstract available.
Cerebellum* ; Magnetic Resonance Imaging ; Wernicke Encephalopathy*

Spontaneous dissection of the middle cerebral artery could result in thromboembolic stroke caused by the intramural hematoma. Dissection should be considered as a possible etiology in a young stroke patient, but it is not straightforward in an emergency situation. Moreover, the efficacy and safety of thrombolytic treatment in the acute stage are unknown. We applied intravenous and intra-arterial stent thrombectomy with the Solitaire device successfully in a patient with acute left middle cerebral artery occlusion due to spontaneous dissection.
Emergencies ; Hematoma ; Humans ; Infarction* ; Infarction, Middle Cerebral Artery ; Middle Cerebral Artery* ; Stents* ; Stroke ; Thrombectomy*

Background: and Purpose We aimed to determine whether the care process and outcomes in patients with acute stroke who received recanalization therapy changed during the outbreak of coronavirus disease 2019 (COVID-19) in South Korea. Methods: We used data from a prospective multicenter reperfusion therapy registry to compare the care process including the time from symptom onset to treatment, number of treated patients, and discharge disposition and treatment outcomes between before and during the COVID-19 outbreak in South Korea. Results: Upon the COVID-19 outbreak in South Korea, the number of patients receiving endovascular treatment to decrease temporarily but considerably. The use of emergency medical services by stroke patients increased from 91.5% before to 100.0% during the COVID-19 outbreak (p=0.025), as did the median time from symptom onset to hospital visit [median (interquartile range), 91.0 minutes (39.8–277.0) vs. 176.0 minutes (56.0–391.5), p=0.029]. Furthermore, more functionally dependent patients with disabilities were discharged home (59.5% vs. 26.1%, p=0.020) rather than staying in a regional or rehabilitation hospital. In contrast, there were no COVID-19-related changes in the times from the hospital visit to brain imaging and treatment or in the functional outcome, successful recanalization rate, or rate of symptomatic intracerebral hemorrhage. Conclusions These findings suggest that a prehospital delay occurred during the COVID-19 outbreak, and that patients with acute stroke might have been reluctant to visit and stay in hospitals. Our findings indicate that attention should be paid to prehospital care and the behavior of patients with acute stroke during the COVID-19 outbreak.

Background: and Purpose We aimed to develop a model predicting early recanalization after intravenous tissue plasminogen activator (t-PA) treatment in large-vessel occlusion. Methods: Using data from two different multicenter prospective cohorts, we determined the factors associated with early recanalization immediately after t-PA in stroke patients with large-vessel occlusion, and developed and validated a prediction model for early recanalization. Clot volume was semiautomatically measured on thin-section computed tomography using software, and the degree of collaterals was determined using the Tan score. Follow-up angiographic studies were performed immediately after t-PA treatment to assess early recanalization. Results: Early recanalization, assessed 61.0±44.7 minutes after t-PA bolus, was achieved in 15.5% (15/97) in the derivation cohort and in 10.5% (8/76) in the validation cohort. Clot volume (odds ratio [OR], 0.979; 95% confidence interval [CI], 0.961 to 0.997; P=0.020) and good collaterals (OR, 6.129; 95% CI, 1.592 to 23.594; P=0.008) were significant factors associated with early recanalization. The area under the curve (AUC) of the model including clot volume was 0.819 (95% CI, 0.720 to 0.917) and 0.842 (95% CI, 0.746 to 0.938) in the derivation and validation cohorts, respectively. The AUC improved when good collaterals were added (derivation cohort: AUC, 0.876; 95% CI, 0.802 to 0.950; P=0.164; validation cohort: AUC, 0.949; 95% CI, 0.886 to 1.000; P=0.036). The integrated discrimination improvement also showed significantly improved prediction (0.097; 95% CI, 0.009 to 0.185; P=0.032). Conclusions The model using clot volume and collaterals predicted early recanalization after intravenous t-PA and had a high performance. This model may aid in determining the recanalization treatment strategy in stroke patients with large-vessel occlusion.

Background: and Purpose We aimed to develop a model predicting early recanalization after intravenous tissue plasminogen activator (t-PA) treatment in large-vessel occlusion. Methods: Using data from two different multicenter prospective cohorts, we determined the factors associated with early recanalization immediately after t-PA in stroke patients with large-vessel occlusion, and developed and validated a prediction model for early recanalization. Clot volume was semiautomatically measured on thin-section computed tomography using software, and the degree of collaterals was determined using the Tan score. Follow-up angiographic studies were performed immediately after t-PA treatment to assess early recanalization. Results: Early recanalization, assessed 61.0±44.7 minutes after t-PA bolus, was achieved in 15.5% (15/97) in the derivation cohort and in 10.5% (8/76) in the validation cohort. Clot volume (odds ratio [OR], 0.979; 95% confidence interval [CI], 0.961 to 0.997; P=0.020) and good collaterals (OR, 6.129; 95% CI, 1.592 to 23.594; P=0.008) were significant factors associated with early recanalization. The area under the curve (AUC) of the model including clot volume was 0.819 (95% CI, 0.720 to 0.917) and 0.842 (95% CI, 0.746 to 0.938) in the derivation and validation cohorts, respectively. The AUC improved when good collaterals were added (derivation cohort: AUC, 0.876; 95% CI, 0.802 to 0.950; P=0.164; validation cohort: AUC, 0.949; 95% CI, 0.886 to 1.000; P=0.036). The integrated discrimination improvement also showed significantly improved prediction (0.097; 95% CI, 0.009 to 0.185; P=0.032). Conclusions The model using clot volume and collaterals predicted early recanalization after intravenous t-PA and had a high performance. This model may aid in determining the recanalization treatment strategy in stroke patients with large-vessel occlusion.