Effectiveness of endovascular treatment for in-hospital stroke vs. community-onset stroke: a propensity score-matched analysis

BACKGROUND

The effectiveness of endovascular treatment for in-hospital stroke remains debatable. We aimed to compare the outcomes between patients with in-hospital stroke and community-onset stroke who received endovascular treatment.

METHODS

This prospective registry-based cohort study included consecutive patients who underwent endovascular treatment from January 2013 to December 2022 and were registered in the Selection Criteria in Endovascular Thrombectomy and Thrombolytic Therapy study and Yonsei Stroke Cohort. Functional outcomes at day 90, radiological outcomes, and safety outcomes were compared between the in-hospital and community-onset groups using logistic regression and propensity score-matched analysis.

RESULTS

Of 1,219 patients who underwent endovascular treatment, 117 (9.6%) had in-hospital stroke. Patients with in-hospital onset were more likely to have a pre-stroke disability and active cancer than those with community-onset. The interval from the last known well to puncture was shorter in the in-hospital group than in the community-onset group (155 vs. 355 min, p<0.001). No significant differences in successful recanalization or safety outcomes were observed between the groups; however, the in-hospital group exhibited worse functional outcomes and higher mortality at day 90 than the community-onset group (all p<0.05). After propensity score matching including baseline characteristics, functional outcomes after endovascular treatment did not differ between the groups (OR: 1.19, 95% CI 0.78-1.83, p=0.4). Safety outcomes did not significantly differ between the groups.

CONCLUSION

Endovascular treatment is a safe and effective treatment for eligible patients with in-hospital stroke. Our results will help physicians in making decisions when planning treatment and counseling caregivers or patients.

Long-term outcomes of patients with embolic stroke of undetermined source according to subtype

The prognosis of patients with embolic stroke of undetermined source (ESUS) may vary according to the underlying cause. Therefore, we aimed to divide ESUS into subtypes and assess the long-term outcomes. Consecutive patients with acute ischemic stroke who underwent a comprehensive workup, including transesophageal echocardiography and prolonged electrocardiography monitoring, were enrolled. We classified ESUS into minor cardioembolic (CE) ESUS, arteriogenic ESUS, two or more causes ESUS, and no cause ESUS. Arteriogenic ESUS was sub-classified into complex aortic plaque (CAP) ESUS and non-stenotic (< 50%) relevant artery plaque (NAP) ESUS. A total of 775 patients were enrolled. During 1286 ± 748 days follow-up, 116 major adverse cardiovascular events (MACE) occurred (4.2 events/100 patient-years). Among the ESUS subtypes, CAP ESUS was associated with the highest MACE frequency (9.7/100 patient-years, p = 0.021). Cox regression analyses showed that CAP ESUS was associated with MACE (hazard ratio 2.466, 95% confidence interval 1.305-4.660) and any stroke recurrence (hazard ratio 2.470, 95% confidence interval, 1.108-5.508). The prognosis of ESUS varies according to the subtype, with CAP ESUS having the worst prognosis. Categorizing ESUS into subtypes could improve patient care and refine clinical trials.

Functional Outcomes Associated With Blood Pressure Decrease After Endovascular Thrombectomy

Importance

The associations between blood pressure (BP) decreases induced by medication and functional outcomes in patients with successful endovascular thrombectomy remain uncertain.

Objective

To evaluate whether BP reductions induced by intravenous BP medications are associated with poor functional outcomes at 3 months.

Design, Setting, and Participants

This cohort study was a post hoc analysis of the Outcome in Patients Treated With Intra-Arterial Thrombectomy-Optimal Blood Pressure Control trial, a comparison of intensive and conventional BP management during the 24 hours after successful recanalization from June 18, 2020, to November 28, 2022. This study included 302 patients who underwent endovascular thrombectomy, achieved successful recanalization, and exhibited elevated BP within 2 hours of successful recanalization at 19 stroke centers in South Korea.

Exposure

A BP decrease was defined as at least 1 event of systolic BP less than 100 mm Hg. Patients were divided into medication-induced BP decrease (MIBD), spontaneous BP decrease (SpBD), and no BP decrease (NoBD) groups.

Main Outcomes and Measures

The primary outcome was a modified Rankin scale score of 0 to 2 at 3 months, indicating functional independence. Primary safety outcomes were symptomatic intracerebral hemorrhage within 36 hours and mortality due to index stroke within 3 months.

Results

Of the 302 patients (median [IQR] age, 75 [66-82] years; 180 [59.6%] men), 47 (15.6%)were in the MIBD group, 39 (12.9%) were in the SpBD group, and 216 (71.5%) were in the NoBD group. After adjustment for confounders, the MIBD group exhibited a significantly smaller proportion of patients with functional independence at 3 months compared with the NoBD group (adjusted odds ratio [AOR], 0.45; 95% CI, 0.20-0.98). There was no significant difference in functional independence between the SpBD and NoBD groups (AOR, 1.41; 95% CI, 0.58-3.49). Compared with the NoBD group, the MIBD group demonstrated higher odds of mortality within 3 months (AOR, 5.15; 95% CI, 1.42-19.4). The incidence of symptomatic intracerebral hemorrhage was not significantly different among the groups (MIBD vs NoBD: AOR, 1.89; 95% CI, 0.54-5.88; SpBD vs NoBD: AOR, 2.75; 95% CI, 0.76-9.46).

Conclusions and Relevance

In this cohort study of patients with successful endovascular thrombectomy after stroke, MIBD within 24 hours after successful recanalization was associated with poor outcomes at 3 months. These findings suggested lowering systolic BP to below 100 mm Hg using BP medication might be harmful.

Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization

OBJECTIVES

This study explores whether textural features from initial non-contrast CT scans of infarcted brain tissue are linked to hemorrhagic transformation susceptibility.

MATERIALS AND METHODS

Stroke patients undergoing thrombolysis or thrombectomy from Jan 2012 to Jan 2022 were analyzed retrospectively. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging. A total of 94 radiomic features were extracted from the infarcted tissue on initial NCCT scans. Patients were divided into training and test sets (7:3 ratio). Two models were developed with fivefold cross-validation: one incorporating first-order and textural radiomic features, and another using only textural radiomic features. A clinical model was also constructed using logistic regression with clinical variables, and test set validation was performed.

RESULTS

Among 362 patients, 218 had hemorrhagic transformations. The LightGBM model with all radiomics features had the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.986 (95% confidence interval [CI], 0.971-1.000) on the test dataset. The ExtraTrees model performed best when textural features were employed, with an AUROC of 0.845 (95% CI, 0.774-0.916). Minimum, maximum, and ten percentile values were significant predictors of hemorrhagic transformation. The clinical model showed an AUROC of 0.544 (95% CI, 0.431-0.658). The performance of the radiomics models was significantly better than that of the clinical model on the test dataset (p < 0.001).

CONCLUSIONS

The radiomics model can predict hemorrhagic transformation using NCCT in stroke patients. Low Hounsfield unit was a strong predictor of hemorrhagic transformation, while textural features alone can predict hemorrhagic transformation.

CLINICAL RELEVANCE STATEMENT

Using radiomic features extracted from initial non-contrast computed tomography, early prediction of hemorrhagic transformation has the potential to improve patient care and outcomes by aiding in personalized treatment decision-making and early identification of at-risk patients.

KEY POINTS

• Predicting hemorrhagic transformation following thrombolysis in stroke is challenging since multiple factors are associated. • Radiomics features of infarcted tissue on initial non-contrast CT are associated with hemorrhagic transformation. • Textural features on non-contrast CT are associated with the frailty of the infarcted tissue.

Combined use of anticoagulant and antiplatelet on outcome after stroke in patients with nonvalvular atrial fibrillation and systemic atherosclerosis

This study aimed to investigate whether there was a difference in one-year outcome after stroke between patients treated with antiplatelet and anticoagulation (OAC + antiplatelet) and those with anticoagulation only (OAC), when comorbid atherosclerotic disease was present with non-valvular atrial fibrillation (NVAF). This was a retrospective study using a prospective cohort of consecutive patients with ischemic stroke. Patients with NVAF and comorbid atherosclerotic disease were assigned to the OAC + antiplatelet or OAC group based on discharge medication. All-cause mortality, recurrent ischemic stroke, hemorrhagic stroke, myocardial infarction, and bleeding events within 1 year after the index stroke were compared. Of the 445 patients included in this study, 149 (33.5%) were treated with OAC + antiplatelet. There were no significant differences in all outcomes between groups. After inverse probability of treatment weighting, OAC + antiplatelet was associated with a lower risk of all-cause mortality (hazard ratio 0.48; 95% confidence interval 0.23-0.98; P = 0.045) and myocardial infarction (0% vs. 3.0%, P < 0.001). The risk of hemorrhagic stroke was not significantly different (P = 0.123). OAC + antiplatelet was associated with a decreased risk of all-cause mortality and myocardial infarction but an increased risk of ischemic stroke among patients with NVAF and systemic atherosclerotic diseases.

Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT

OBJECTIVES

To develop and validate a deep learning model for predicting hemorrhagic transformation after endovascular thrombectomy using dual-energy computed tomography (CT).

MATERIALS AND METHODS

This was a retrospective study from a prospective registry of acute ischemic stroke. Patients admitted between May 2019 and February 2023 who underwent endovascular thrombectomy for acute anterior circulation occlusions were enrolled. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging or CT. The deep learning model was developed using post-thrombectomy dual-energy CT to predict hemorrhagic transformation within 72 h. Temporal validation was performed with patients who were admitted after July 2022. The deep learning model's performance was compared with a logistic regression model developed from clinical variables using the area under the receiver operating characteristic curve (AUC).

RESULTS

Total of 202 patients (mean age 71.4 years ± 14.5 [standard deviation], 92 men) were included, with 109 (54.0%) patients having hemorrhagic transformation. The deep learning model performed consistently well, showing an average AUC of 0.867 (95% confidence interval [CI], 0.815-0.902) upon five-fold cross validation and AUC of 0.911 (95% CI, 0.774-1.000) with the test dataset. The clinical variable model showed an AUC of 0.775 (95% CI, 0.709-0.842) on the training dataset (p < 0.01) and AUC of 0.634 (95% CI, 0.385-0.883) on the test dataset (p = 0.06).

CONCLUSION

A deep learning model was developed and validated for prediction of hemorrhagic transformation after endovascular thrombectomy in patients with acute stroke using dual-energy computed tomography.

CLINICAL RELEVANCE STATEMENT

This study demonstrates that a convolutional neural network (CNN) can be utilized on dual-energy computed tomography (DECT) for the accurate prediction of hemorrhagic transformation after thrombectomy. The CNN achieves high performance without the need for region of interest drawing.

KEY POINTS

• Iodine leakage on dual-energy CT after thrombectomy may be from blood-brain barrier disruption. • A convolutional neural network on post-thrombectomy dual-energy CT enables individualized prediction of hemorrhagic transformation. • Iodine leakage is an important predictor of hemorrhagic transformation following thrombectomy for ischemic stroke.

Intensive vs Conventional Blood Pressure Lowering After Endovascular Thrombectomy in Acute Ischemic Stroke: The OPTIMAL-BP Randomized Clinical Trial

Importance

Optimal blood pressure (BP) control after successful reperfusion with endovascular thrombectomy (EVT) for patients with acute ischemic stroke is unclear.

Objective

To determine whether intensive BP management during the first 24 hours after successful reperfusion leads to better clinical outcomes than conventional BP management in patients who underwent EVT.

Design, Setting, and Participants

Multicenter, randomized, open-label trial with a blinded end-point evaluation, conducted across 19 stroke centers in South Korea from June 2020 to November 2022 (final follow-up, March 8, 2023). It included 306 patients with large vessel occlusion acute ischemic stroke treated with EVT and with a modified Thrombolysis in Cerebral Infarction score of 2b or greater (partial or complete reperfusion).

Interventions

Participants were randomly assigned to receive intensive BP management (systolic BP target <140 mm Hg; n = 155) or conventional management (systolic BP target 140-180 mm Hg; n = 150) for 24 hours after enrollment.

Main Outcomes and Measures

The primary outcome was functional independence at 3 months (modified Rankin Scale score of 0-2). The primary safety outcomes were symptomatic intracerebral hemorrhage within 36 hours and death related to the index stroke within 3 months.

Results

The trial was terminated early based on the recommendation of the data and safety monitoring board, which noted safety concerns. Among 306 randomized patients, 305 were confirmed eligible and 302 (99.0%) completed the trial (mean age, 73.0 years; 122 women [40.4%]). The intensive management group had a lower proportion achieving functional independence (39.4%) than the conventional management group (54.4%), with a significant risk difference (-15.1% [95% CI, -26.2% to -3.9%]) and adjusted odds ratio (0.56 [95% CI, 0.33-0.96]; P = .03). Rates of symptomatic intracerebral hemorrhage were 9.0% in the intensive group and 8.1% in the conventional group (risk difference, 1.0% [95% CI, -5.3% to 7.3%]; adjusted odds ratio, 1.10 [95% CI, 0.48-2.53]; P = .82). Death related to the index stroke within 3 months occurred in 7.7% of the intensive group and 5.4% of the conventional group (risk difference, 2.3% [95% CI, -3.3% to 7.9%]; adjusted odds ratio, 1.73 [95% CI, 0.61-4.92]; P = .31).

Conclusions and Relevance

Among patients who achieved successful reperfusion with EVT for acute ischemic stroke with large vessel occlusion, intensive BP management for 24 hours led to a lower likelihood of functional independence at 3 months compared with conventional BP management. These results suggest that intensive BP management should be avoided after successful EVT in acute ischemic stroke.

Trial Registration

ClinicalTrials.gov Identifier: NCT04205305.

Cancer Prediction With Machine Learning of Thrombi From Thrombectomy in Stroke: Multicenter Development and Validation

BACKGROUND

We aimed to develop and validate machine learning models to diagnose patients with ischemic stroke with cancer through the analysis of histopathologic images of thrombi obtained during endovascular thrombectomy.

METHODS

This was a retrospective study using a prospective multicenter registry which enrolled consecutive patients with acute ischemic stroke from South Korea who underwent endovascular thrombectomy. This study included patients admitted between July 1, 2017 and December 31, 2021 from 6 academic university hospitals. Whole-slide scanning was performed for immunohistochemically stained thrombi. Machine learning models were developed using transfer learning with image slices as input to classify patients into 2 groups: cancer group or other determined cause group. The models were developed and internally validated using thrombi from patients of the primary center, and external validation was conducted in 5 centers. The model was also applied to patients with hidden cancer who were diagnosed with cancer within 1 month of their index stroke.

RESULTS

The study included 70 561 images from 182 patients in both internal and external datasets (119 patients in internal and 63 in external). Machine learning models were developed for each immunohistochemical staining using antibodies against platelets, fibrin, and erythrocytes. The platelet model demonstrated consistently high accuracy in classifying patients with cancer, with area under the receiver operating characteristic curve of 0.986 (95% CI, 0.983-0.989) during training, 0.954 (95% CI, 0.937-0.972) during internal validation, and 0.949 (95% CI, 0.891-1.000) during external validation. When applied to patients with occult cancer, the model accurately predicted the presence of cancer with high probabilities ranging from 88.5% to 99.2%.

CONCLUSIONS

Machine learning models may be used for prediction of cancer as the underlying cause or detection of occult cancer, using platelet-stained immunohistochemical slide images of thrombi obtained during endovascular thrombectomy.

Prognostic value of central blood pressure on the outcomes of embolic stroke of undetermined source

We investigated the prognostic impact of central blood pressure (BP) on outcomes in patients with embolic stroke of undetermined source (ESUS). The prognostic value of central BP according to ESUS subtype was also evaluated. We recruited patients with ESUS and data on their central BP parameters (central systolic BP [SBP], central diastolic BP [DBP], central pulse pressure [PP], augmentation pressure [AP], and augmentation index [AIx]) during admission. ESUS subtype classification was arteriogenic embolism, minor cardioembolism, two or more causes, and no cause. Major adverse cardiovascular event (MACE) was defined as recurrent stroke, acute coronary syndrome, hospitalization for heart failure, or death. Over a median of 45.8 months, 746 patients with ESUS were enrolled and followed up. Patients had a mean age of 62.8 years, and 62.2% were male. Multivariable Cox regression analysis showed that central SBP and PP were associated with MACE. All-cause mortality was independently associated with AIx. In patients with no cause ESUS, central SBP and PP, AP, and AIx were independently associated with MACE. AP and AIx were independently associated with all-cause mortality (all p < 0.05). We demonstrated that central BP can predict poor long-term prognosis in patients with ESUS, especially those with the no cause ESUS subtype.

Impact of Left Atrial or Left Atrial Appendage Thrombus on Stroke Outcome: A Matched Control Analysis

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.

Association between blood viscosity and early neurological deterioration in lacunar infarction

Background

Understanding the factors related to early neurologic deterioration (END) is crucial in the management of patients with lacunar infarction. Blood viscosity is a significant factor for microvascular perfusion. We investigated the association between blood viscosity and occurrence of END in lacunar infarction.

Methods

We included consecutive patients admitted for lacunar infarction within 72 h from symptoms onset. END was defined as an increase in the National Institute of Health Stroke Scale (NIHSS) score ≥2 within 24 h of admission. Viscosity was measured within 24 h of hospitalization with a scanning capillary tube viscometer. Viscosity measured at a shear rate of 300 s⁻¹ was defined as systolic blood viscosity (SBV), whereas that measured at a shear rate of 5 s⁻¹ as diastolic blood viscosity (DBV).

Results

Of the 178 patients included (median age, 65.5; interquartile range [IQR], 56.0, 76.0], END occurred in 33 (18.5%). DBV was significantly higher in patients with END than those without END (13.3 mPa·s [IQR 11.8, 16.0] vs. 12.3 mPa·s [IQR11.0, 13.5]; P = 0.023). In the multivariate analysis, DBV was independently associated with the occurrence of END (odds ratio 1.17; 95% confidence interval 1.01–1.36; P = 0.043). Subgroup analysis showed no heterogeneity in the effect of viscosity on the occurrence of END.

Conclusions

Blood viscosity at a low shear rate (DBV) was associated with the occurrence of END in patients with lacunar infarction. Blood rheology may be important in pathophysiology of END in patients with lacunar infarction.

Early depression screening and short-term functional outcome in hospitalized patients for acute ischemic stroke

Background

Patients with ischemic stroke are at high risk for post-stroke depression (PSD). There are limited data regarding the clinical impact of early PSD, assessed in hospitalized patients with acute ischemic stroke.

Methods

This hospital-based observational cohort study included consecutive patients with acute ischemic stroke or transient ischemic attack between July 2019 and June 2021. In the study hospital, all admitted patients were systematically screened for depression. The depression was screened using the Patient Health Questionnaire-9 (PHQ-9), and PHQ-9 positivity indicated early PSD, which was defined as a score of &gt;4. Logistic regression analyses were used to compare the rates of poor functional outcomes at 3 months in patients with and without PHQ-9 positivity.

Results

Among 1339 patients admitted during the study period, 775 were included, with a median age of 68.0 years, and 316 (40.8%) were women. A total of 111 (14.3%) patients were PHQ-9 positive. History of cancer and early neurological deterioration were independently associated with PHQ-9 positivity. Poor functional outcomes at 3 months were observed in 147 patients (18.8%). PHQ-9 positivity independently showed a 2.2-fold increased risk of poor functional outcome at 3 months (Odds ratio 2.23; 95% confidence interval 1.05–4.73, P = 0.037).

Conclusions

Patients with history of cancer and early neurological deterioration were at risk for early PSD. Early PSD was independently associated with poor functional outcomes at 3 months. The identification of early depression could offer opportunities for further questioning and exploration of symptoms, as well as interventions.

Validation of Machine Learning Models to Predict Adverse Outcomes in Patients with COVID-19: A Prospective Pilot Study

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.

Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

A machine learning technique for identifying hidden coronary artery disease (CAD) might be useful. We developed and validated machine learning models to predict patients with hidden CAD and assess long-term outcomes in patients with acute ischemic stroke.

METHODS

Multidetector coronary computed tomography was performed for patients without known history of CAD. Primary outcomes were defined as having any degree of CAD and having obstructive CAD (≥50% stenosis). Demographic variables, risk factors, laboratory results, Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, NIH Stroke Scale score, blood pressure, and carotid artery stenosis were used to develop and validate machine learning models to predict CAD. Area under the receiver operating characteristic curves (AUC) was calculated for performance analysis, and Kaplan-Meier and Cox survival analyses of long-term outcomes were performed. Major adverse cardiovascular events (MACE) were defined as ischemic stroke, myocardial infarction, unstable angina, urgent coronary revascularization, and cardiovascular mortality.

RESULTS

Overall, 1,710 patients were included for the training dataset and 348 patients for the validation dataset. An Extreme Gradient Boosting model was developed to predict any degree of CAD, which showed an AUC of 0.763 (95% CI 0.711-0.814) on validation. A logistic regression model was used to predict obstructive CAD and had an AUC of 0.714 (95% CI 0.692-0.799). During the first 5 years of follow-up, MACE occurred more frequently when predicted of any CAD (P = 0.022) or obstructive CAD (P < 0.001). Cox proportional analysis showed that the hazard ratio of MACE was 1.5 (95% CI 1.1-2.2; P = 0.016) when predicted of any CAD, whereas it was 1.9 (95% CI 1.3-2.6; P < 0.001) for obstructive CAD.

DISCUSSION

We demonstrated that machine learning may help identify hidden CAD in patients with acute ischemic stroke. Long-term outcomes were also associated with prediction results.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with acute ischemic stroke with CAD risk factors but no known history of CAD, a machine learning model predicts CAD on multidetector coronary computed tomography with an AUC of 0.763 (95% CI 0.711-0.814).

Impact of Sarcopenia on Functional Outcomes Among Patients With Mild Acute Ischemic Stroke and Transient Ischemic Attack: A Retrospective Study

Introduction

Sarcopenia, a age-related disease characterized by loss of muscle mass accompanied by loss of function, is associated with nutrition imbalance, physical inactivity, insulin resistance, inflammation, metabolic syndrome, and atherosclerosis which are risk factors for cardiovascular disease. However, its association with outcomes after ischemic stroke has not been well-established. This study investigated whether functional outcomes of patients with acute ischemic stroke is associated with sarcopenia.

Methods

Data were collected from 568 consecutive patients with acute ischemic stroke with National Institute of Health Stroke Scale 0–5 or transient ischemic attack who underwent bioelectrical impedance analysis between March 2018 and March 2021. Sarcopenia was defined, as low muscle mass, as measured by bioelectrical impedance analysis, and low muscle strength, as indicated by the Medical Research Council score. Unfavorable functional outcome was defined as mRS score of 2–6 at 90 days after discharge. The relationship between functional outcomes and the presence of sarcopenia or its components was determined.

Results

Of the 568 patients included (mean age 65.5 ± 12.6 years, 64.6% male), sarcopenia was detected in 48 (8.5%). After adjusting for potential confounders, sarcopenia was independently and significantly associated with unfavorable functional outcome (odds ratio 2.37, 95% confidence interval 1.15–4.73 for unfavorable functional outcome, odds ratio 2.10, 95% confidence interval 1.18–3.71 for an increase in the mRS score). Each component of sarcopenia was also independently associated with unfavorable functional outcome (odds ratio 1.76, 95% confidence interval 1.05–2.95 with low muscle mass, odds ratio 2.64, 95% confidence interval 1.64–4.23 with low muscle strength). The impact of low muscle mass was larger in men than in women, and in patients with lower muscle mass of the lower extremities than in those with lower muscle mass of the upper extremities.

Conclusions

In this study, the prevalence of sarcopenia in patients with stroke was lower than most of previous studies and patients with sarcopenia showed higher likelihood for unfavorable functional outcomes at 90 days after acute ischemic stroke or TIA. Further investigation of the interventions for treating sarcopenia and its impact on the outcome of ischemic stroke patients is needed.

Risks and Benefits of Early Rhythm Control in Patients With Acute Strokes and Atrial Fibrillation: A Multicenter, Prospective, Randomized Study (the RAFAS Trial)

Background The purpose of the RAFAS (Risk and Benefits of Urgent Rhythm Control of Atrial Fibrillation in Patients With Acute Stroke) trial was to explore the risks and benefits of early rhythm control in patients with newly documented atrial fibrillation (AF) during an acute ischemic stroke (IS). Method and Results An open-label, randomized, multicenter trial design was used. If AF was diagnosed, the patients in the early rhythm control group started rhythm control within 2 months after the occurrence of an IS, unlikely the usual care. The primary end points were recurrent IS within 3 and 12 months. The secondary end points were a composite of all deaths, unplanned hospitalizations from any cause, and adverse arrhythmia events. Patients (n=300) with AF and an acute IS (63.0% men, aged 69.6±8.5 years; 51.2% with paroxysmal AF) were randomized 2:1 to early rhythm control (n=194) or usual care (n=106). A total of 273 patients excluding those lost to follow-up (n=27) were analyzed. The IS recurrences did not differ between the groups within 3 months of the index stroke (2 [1.1%] versus 4 [4.2%]; hazard ratio [HR], 0.257 [log-rank P=0.091]) but were significantly lower in the early rhythm control group at 12 months (3 [1.7%] versus 6 [6.3%]; HR, 0.251 [log-rank P=0.034]). Although the rates of overall mortality, any cause of hospitalizations (25 [14.0%] versus 16 [16.8%]; HR, 0.808 [log-rank P=0.504]), and arrhythmia-related adverse events (5 [2.8%] versus 1 [1.1%]; HR, 2.565 [log-rank P=0.372]) did not differ, the proportion of sustained AF was lower in the early rhythm control group than the usual care group (60 [34.1%] versus 59 [62.8%], P<0.001) in 12 months. Conclusions The early rhythm control strategy of an acute IS decreased the sustained AF and recurrent IS within 12 months without an increase in the composite adverse outcomes. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02285387.

Effect of total cholesterol level variabilities on cerebrovascular disease

OBJECTIVE

Hyperlipidemia is a risk factor of cerebrovascular disease (CVD). However, the relationship between CVD and cholesterol variability is less clear. This study assesses the relationship between cholesterol change and CVD risk.

PATIENTS AND METHODS

We reviewed 480,830 people from 20 to 99 years with 2 health check-ups from 2002 to 2015 from the Korean National Health Insurance (KNHI) database. People's baseline and follow-up cholesterol levels were classified into low (<180 mg/dL), moderate (≥180 mg/dL and <240 mg/dL), and high (≥240 mg/dL). Participants were divided into 9 groups (low-to-low, low-to-moderate, low-to-high, moderate-to-low, moderate-to-moderate, moderate-to-high, high-to-low, high-to-moderate, high-to-high).

RESULTS

Low to high cholesterol level is associated with hemorrhagic stroke (aHR1 = 1.59; 95% CI 1.12-2.28 and aHR2 = 1.56; 95% CI 1.07-2.25). Low to moderate/high cholesterol level is associated with ischemic stroke and occlusion/stenosis (for low to moderate, aHR1 = 1.11; 95% CI 1.04-1.17 and aHR2 = 1.14; 95% CI 1.07-1.21 for ischemic stroke and aHR1 = 1.18; 95% CI 1.07-1.29 and aHR2 = 1.20; 95% CI 1.08-1.32 for occlusion/stenosis, for low to high, aHR1 = 1.42; 95% CI 1.20-1.67 and aHR2 = 1.28; 95% CI 1.08-1.52 for ischemic stroke and aHR1 = 1.86; 95% CI 1.46-2.36 and aHR2= 1.74; 95% CI 1.36-2.23 for occlusion/stenosis). Moderate to high cholesterol level is associated with ischemic stroke and occlusion/stenosis (for ischemic stroke, aHR1 = 1.12; 95% CI 1.05-1.20 and aHR2 = 1.10; 95% CI 1.03-1.17, for occlusion/stenosis, aHR1 = 1.21; 95% CI 1.10-1.33 and aHR2 = 1.19; 95% CI 1.08-1.32). Moderate to low cholesterol level is associated with ischemic and hemorrhagic stroke and occlusion/stenosis (for ischemic, aHR1 = 1.15; 95% CI 1.09-1.21, for hemorrhagic, aHR1 = 1.14; 95% CI 1.01-1.28, for occlusion/stenosis, aHR1 = 1.14; 95% CI 1.05-1.23). High to low cholesterol level is associated with ischemic stroke and occlusion/stenosis (for ischemic stroke, aHR1 = 1.51; 95% CI 1.33-1.71 and aHR2 = 1.20; 95% CI 1.05-1.36, for occlusion/stenosis, aHR1 = 1.50; 95% CI 1.24-1.81).

CONCLUSIONS

Our study shows that cholesterol changes, especially larger changes, lead to an increase in CVD, which demonstrates that cholesterol variability may increase CVD.

Outcome in Patients Treated with Intra-arterial thrombectomy: The optiMAL Blood Pressure control (OPTIMAL-BP) Trial

RATIONALE

Very early stage blood pressure (BP) levels may affect outcome in stroke patients who have successfully undergone recanalization following intra-arterial treatment, but the optimal target of BP management remains uncertain.

AIM

We hypothesized that the clinical outcome after intensive BP-lowering is superior to conventional BP control after successful recanalization by intra-arterial treatment.

SAMPLE-SIZE ESTIMATES

We aim to randomize 668 patients (334 per arm), 1:1.

METHODS AND DESIGN

We initiated a multicenter, prospective, randomized, open-label trial with a blinded end-point assessment (PROBE) design. After successful recanalization (thrombolysis in cerebral infarction score ≥ 2 b), patients with elevated systolic BP level, defined as the mean of two readings ≥ 140 mmHg, will be randomly assigned to the intensive BP-lowering (systolic BP < 140 mm Hg) group or the conventional BP-lowering (systolic BP, 140-180 mm Hg) group.

STUDY OUTCOMES

The primary efficacy outcomes are from dichotomized analysis of modified Rankin Scale (mRS) scores at three months (mRS scores: 0-2 vs. 3-6). The primary safety outcomes are symptomatic intracerebral hemorrhage and death within three months.

DISCUSSION

The OPTIMAL-BP trial will provide evidence for the effectiveness of active BP control to achieve systolic BP < 140 mmHg during 24 h in patients with successful recanalization after intra-arterial treatment.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04205305.

Prediction of Early Recanalization after Intravenous Thrombolysis in 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.

Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea

BACKGROUND

Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage.

METHODS

This is a retrospective study using cohort data from the Korean Centers for Disease Control and Prevention that included all admitted patients with COVID-19 between January 19 and June 3, 2020, in South Korea. The primary outcome was patients requiring intensive care defined as actual admission to the intensive care unit; at any time use of an extracorporeal life support device, mechanical ventilation, or vasopressors; and death. Patients admitted until March 20 were included for the training dataset to develop the prediction models and externally validated for the patients admitted afterward. Two logistic regression models were developed with different predictors and the predictive performance was compared: one with patient-provided variables and the other with added radiologic and laboratory variables. An integer-based scoring system was developed based on the developed logistic regression model.

RESULTS

A total of 5193 patients were considered, with 4663 patients included after excluding patients with age under 18 or insufficient data. For the training dataset, 3238 patients were included. Of the included patients, 444 (9.5%) patients required intensive care. The model developed with only the clinical variables showed an area under the curve of 0.884 for the validation set. The performance did not differ when radiologic and laboratory variables were added. Seven variables were selected for developing an integer-based scoring system: age, sex, initial body temperature, dyspnea, hemoptysis, history of chronic kidney disease, and activities of daily living. The area under the curve of the scoring system was 0.880.

CONCLUSIONS

An integer-based scoring system was developed for predicting patients with COVID-19 requiring intensive care, with high performance. This system may aid decision support for prioritizing the patient for hospitalization and intensive care, particularly in a situation with limited medical resources.

Care Process of Recanalization Therapy for Acute Stroke during the COVID-19 Outbreak in South Korea

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.

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

Background

Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available.

Objective

The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics—baseline demographics, comorbidities, and symptoms.

Methods

A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC).

Results

A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively.

Conclusions

We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis

BACKGROUND

Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened.

OBJECTIVE

This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak.

METHODS

The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group.

RESULTS

The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application.

CONCLUSIONS

We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.

COVID-19 Outcome Prediction and Monitoring Solution for Military Hospitals in South Korea: Development and Evaluation of an Application

BACKGROUND

COVID-19 has officially been declared as a pandemic, and the spread of the virus is placing sustained demands on public health systems. There are speculations that the COVID-19 mortality differences between regions are due to the disparities in the availability of medical resources. Therefore, the selection of patients for diagnosis and treatment is essential in this situation. Military personnel are especially at risk for infectious diseases; thus, patient selection with an evidence-based prognostic model is critical for them.

OBJECTIVE

This study aims to assess the usability of a novel platform used in the military hospitals in Korea to gather data and deploy patient selection solutions for COVID-19.

METHODS

The platform's structure was developed to provide users with prediction results and to use the data to enhance the prediction models. Two applications were developed: a patient's application and a physician's application. The primary outcome was requiring an oxygen supplement. The outcome prediction model was developed with patients from four centers. A Cox proportional hazards model was developed. The outcome of the model for the patient's application was the length of time from the date of hospitalization to the date of the first oxygen supplement use. The demographic characteristics, past history, patient symptoms, social history, and body temperature were considered as risk factors. A usability study with the Post-Study System Usability Questionnaire (PSSUQ) was conducted on the physician's application on 50 physicians.

RESULTS

The patient's application and physician's application were deployed on the web for wider availability. A total of 246 patients from four centers were used to develop the outcome prediction model. A small percentage (n=18, 7.32%) of the patients needed professional care. The variables included in the developed prediction model were age; body temperature; predisease physical status; history of cardiovascular disease; hypertension; visit to a region with an outbreak; and symptoms of chills, feverishness, dyspnea, and lethargy. The overall C statistic was 0.963 (95% CI 0.936-0.99), and the time-dependent area under the receiver operating characteristic curve ranged from 0.976 at day 3 to 0.979 at day 9. The usability of the physician's application was good, with an overall average of the responses to the PSSUQ being 2.2 (SD 1.1).

CONCLUSIONS

The platform introduced in this study enables evidence-based patient selection in an effortless and timely manner, which is critical in the military. With a well-designed user experience and an accurate prediction model, this platform may help save lives and contain the spread of the novel virus, COVID-19.

Bifidobacterium longum DS0956 and Lactobacillus rhamnosus DS0508 culture-supernatant ameliorate obesity by inducing thermogenesis in obese-mice

Excessive body fat and the related dysmetabolic diseases affect both developed and developing countries. The aim of this study was to investigate the beneficial role of a bacterial culture supernatant (hereafter: BS) of Lactobacillus and Bifidobacterium and their potential mechanisms of action on white-fat browning and lipolysis. For selection of four candidates among 55 Lactic acid producing bacteria (LAB) from human infant faeces, we evaluated by Oil Red O staining and Ucp1 mRNA quantitation in 3T3-L1 preadipocytes. The expression of browning and lipolysis markers was examined along with in vitro assays. The possible mechanism was revealed by molecular and biological experiments including inhibitor and small interfering RNA (siRNA) assays. In a mouse model, physiological, histological, and biochemical parameters and expression of some thermogenesis-related genes were compared among six experimental groups fed a high-fat diet and one normal-diet control group. The results allow us to speculate that BS treatment promotes browning and lipolysis both in vitro and in vivo. Moreover, the BS may activate thermogenic programs via a mechanism involving PKA-CREB signaling in 3T3-L1 cells. According to our data, we can propose that two LAB strains, Bifidobacterium longum DS0956 and Lactobacillus rhamnosus DS0508, may be good candidates for a dietary supplement against obesity and metabolic diseases; however, further research is required for the development as dietary supplements or drugs.

How Do Caregivers of Children with Congenital Heart Disease Navigate the Health Care System in Ethiopia?

Research Objective

Global surgery is becoming an increasingly important global health agenda. Cardiovascular disease is the major cause of mortality around the world, and congenital heart disease is the leading cause of morbidity in children. This study aimed to investigate and illustrate the caregivers’ experiences of accessing the health care system and undergoing pediatric cardiac surgery for children with congenital heart disease (CHD).

Study Design

A qualitative study was conducted. Interviews were conducted in December 2019 in Amharic, then translated into English using trained local interpreters. Data were transcribed verbatim and analyzed according to the principles of interpretive thematic analysis, informed by the candidacy framework, using NVivo. The candidacy framework explores the access to health care utilization by seven elements: candidacy, navigation, the permeability of services, appearances at health services, adjudications, offers and resistance, and operating conditions and the local production of candidacy.

Population Studied

Interviews were conducted with 13 caregivers of 10 patients with congenital heart disease that received cardiac surgery during the week of the interview.

Principal Findings

The following three themes emerged from the interviews: (a) Recognition of CHD mostly took place at birth, but for those born at home, they found out much later (max 14 years); (b) CHD was misdiagnosed multiple times prior to seeking care at a large hospital; and (c) patients were waiting for the surgery for more than a year, (d) being scheduled for surgery induced both anxiety and hopefulness. In the discussion, caregivers had financial difficulties and struggled in a fragmented delivery system and experienced poor service quality such as the inaccuracy of diagnosis while navigating the Ethiopian health care system.

Conclusions

Major care‐seeking delays were related to the inefficient and complex health care system, largely due to lack of early CHD recognition and financial hardships. Financial protection is low despite the availability of fee waivers for medications. Low education attainment and distance to hospitals are contributing to this challenge.

Implications for Policy or Practice

Overall, Ethiopia needs to prioritize policies that protect the financial status of low‐income households that need health care services. Along with increasing health care workforce capacity for pediatric cardiac surgeries in Ethiopia, there is a need to strengthen the district‐level screening capacity to facilitate earlier diagnosis at easily accessible health care settings.

Primary Funding Source

Search Results Web results Korea International Cooperation Agency.

Improving the Clinical Outcome in Stroke Patients Receiving Thrombolytic or Endovascular Treatment in Korea: from the SECRET Study

We investigated whether there was an annual change in outcomes in patients who received the thrombolytic therapy or endovascular treatment (EVT) in Korea. This analysis was performed using data from a nationwide multicenter registry for exploring the selection criteria of patients who would benefit from reperfusion therapies in Korea. We compared the annual changes in the modified Rankin scale (mRS) at discharge and after 90 days and the achievement of successful recanalization from 2012 to 2017. We also investigated the determinants of favorable functional outcomes. Among 1230 included patients, the improvement of functional outcome at discharge after reperfusion therapy was noted as the calendar year increased (p < 0.001). The proportion of patients who were discharged to home significantly increased (from 45.6% in 2012 to 58.5% in 2017) (p < 0.001). The successful recanalization rate increased over time from 78.6% in 2012 to 85.1% in 2017 (p = 0.006). Time from door to initiation of reperfusion therapy decreased over the years (p < 0.05). These secular trends of improvements were also observed in 1203 patients with available mRS data at 90 days (p < 0.05). Functional outcome was associated with the calendar year, age, initial stroke severity, diabetes, preadmission disability, intervals from door to reperfusion therapy, and achievement of successful recanalization. This study demonstrated the secular trends of improvement in functional outcome and successful recanalization rate in patients who received reperfusion therapy in Korea.

Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke

Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Methods- This was a retrospective study using a prospective cohort that enrolled patients with acute ischemic stroke. Favorable outcome was defined as modified Rankin Scale score 0, 1, or 2 at 3 months. We developed 3 machine learning models (deep neural network, random forest, and logistic regression) and compared their predictability. To evaluate the accuracy of the machine learning models, we also compared them to the Acute Stroke Registry and Analysis of Lausanne (ASTRAL) score. Results- A total of 2604 patients were included in this study, and 2043 (78%) of them had favorable outcomes. The area under the curve for the deep neural network model was significantly higher than that of the ASTRAL score (0.888 versus 0.839; P<0.001), while the areas under the curves of the random forest (0.857; P=0.136) and logistic regression (0.849; P=0.413) models were not significantly higher than that of the ASTRAL score. Using only the 6 variables that are used for the ASTRAL score, the performance of the machine learning models did not significantly differ from that of the ASTRAL score. Conclusions- Machine learning algorithms, particularly the deep neural network, can improve the prediction of long-term outcomes in ischemic stroke patients.

Synthesis and characterization of iodovanadinite using PdI2, an iodine source for the immobilisation of radioiodine

The synthesis of a palladium-containing iodovanadinite derivative, hypothetically “PdPb₉(VO₄)₆I₂”, was attempted using PdI₂ as a source of iodine in searching for a novel waste form for radioiodine. Stoichiometric amounts of Pb₃(VO₄)₂ and PdI₂ were batched and reacted at elevated temperatures in sealed vessels. Batched material was also subjected to high-energy ball-milling (HEBM) in order to reduce reaction time and the potential for iodine volatilization during subsequent reaction at 200–500 °C. The resulting products were characterized using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray analysis, IR spectroscopy, thermal analysis and Pd K XANES. Results showed that PdI₂ can function as a sacrificial iodine source for the formation of iodovanadinite, prototypically Pb₁₀(VO₄)₆I₂, however, the incorporation of Pd into this phase was not definitively observed. The sacrificial reaction mechanism involved the decomposition of PdI₂ to Pd metal and nascent I₂, with the latter incorporated into the iodovanadinite Pb₁₀(VO₄)₆I₂ phase. In comparison to processing using standard solid state reaction techniques, the use of HEBM prior to high temperature reaction generates a more homogeneous end-product with better iodine retention for this system. Overall, the key novelty and importance of this work is in demonstrating a method for direct immobilisation of undissolved PdI₂ from nuclear fuel reprocessing, in a composite wasteform in which I-129 is immobilised within a durable iodovandinite ceramic, encapsulating Pd metal.

The synthesis and characterisation of a composite wasteform, comprising iodovanadinite Pb₁₀(VO₄)₆I₂ and Pd metal, is reported, for immobilisation of radioiodine PdI₂; the formation of Pd incorporated iodovanadinite “PdPb₉(VO₄)₆I₂” was not observed.

Vaccinia-based oncolytic immunotherapy Pexastimogene Devacirepvec in patients with advanced hepatocellular carcinoma after sorafenib failure: a randomized multicenter Phase IIb trial (TRAVERSE)

Pexastimogene devacirepvec (Pexa-Vec) is a vaccinia virus-based oncolytic immunotherapy designed to preferentially replicate in and destroy tumor cells while stimulating anti-tumor immunity by expressing GM-CSF. An earlier randomized Phase IIa trial in predominantly sorafenib-naïve hepatocellular carcinoma (HCC) demonstrated an overall survival (OS) benefit. This randomized, open-label Phase IIb trial investigated whether Pexa-Vec plus Best Supportive Care (BSC) improved OS over BSC alone in HCC patients who failed sorafenib therapy (TRAVERSE).

129 patients were randomly assigned 2:1 to Pexa-Vec plus BSC vs. BSC alone. Pexa-Vec was given as a single intravenous (IV) infusion followed by up to 5 IT injections. The primary endpoint was OS. Secondary endpoints included overall response rate (RR), time to progression (TTP) and safety.

A high drop-out rate in the control arm (63%) confounded assessment of response-based endpoints. Median OS (ITT) for Pexa-Vec plus BSC vs. BSC alone was 4.2 and 4.4 months, respectively (HR, 1.19, 95% CI: 0.78–1.80; p = .428). There was no difference between the two treatment arms in RR or TTP. Pexa-Vec was generally well-tolerated. The most frequent Grade 3 included pyrexia (8%) and hypotension (8%). Induction of immune responses to vaccinia antigens and HCC associated antigens were observed.

Despite a tolerable safety profile and induction of T cell responses, Pexa-Vec did not improve OS as second-line therapy after sorafenib failure. The true potential of oncolytic viruses may lie in the treatment of patients with earlier disease stages which should be addressed in future studies.

ClinicalTrials.gov: NCT01387555

Concurrent hyperthermia and re-irradiation for recurrent high-grade gliomas

Salvage therapy for recurrent high grade gliomas (HGG) includes surgery, radiotherapy and chemotherapy, however, standard treatment does not exist. We evaluated the tolerability and efficacy of re-irradiation (re-RT) with hyperthermia (HT) for patients with recurrent HGG. From September 2010 to July 2015, 20 patients with recurrent HGG were treated with re-RT and HT. The radiotherapy dose of 30 Gray (Gy) was delivered with 2 Gy per fraction daily, and HT was performed twice weekly. Primary endpoints were treatment compliance and toxicity. Second endpoints were overall survival (OS) and progression free survival (PFS). The median interval between initial RT and re-RT was 11 months. During re-RT with HT, there were no significant acute morbidities over grade 3. Median overall survival (OS) from re-irradiation was 8.4 months and the 6 and 12 months survival rate were 67% and 30%, respectively. The median progression free survival (PFS) from re-irradiation was 4.1 month. Our findings suggested that concurrent re-RT with HT was a safe and well-tolerated. In addition, the combination re-RT and HT could be a valuable salvage treatment option for selected recurrent HGG patients with poor performance status.

Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems

The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing composite scoring systems. DIC scores are simple and rapidly applicable. However, optimal fibrin-related markers and their cut-off values remain to be defined, requiring optimization for use. The aim of this study is to optimize the use of DIC-related parameters through machine learning (ML)-approach. Further, we evaluated whether this approach could provide a diagnostic value in DIC diagnosis. For this, 46 DIC-related parameters were investigated for both clinical findings and laboratory results. We retrospectively reviewed 656 DIC-suspected cases at an initial order for full DIC profile and labeled their evaluation results (Set 1; DIC, n = 228; non-DIC, n = 428). Several ML algorithms were tested, and an artificial neural network (ANN) model was established via independent training and testing using 32 selected parameters. This model was externally validated from a different hospital with 217 DIC-suspected cases (Set 2; DIC, n = 80; non-DIC, n = 137). The ANN model represented higher AUC values than the three scoring systems in both set 1 (ANN 0.981; ISTH 0.945; JMHW 0.943; and JAAM 0.928) and set 2 (AUC ANN 0.968; ISTH 0.946). Additionally, the relative importance of the 32 parameters was evaluated. Most parameters had contextual importance, however, their importance in ML-approach was different from the traditional scoring system. Our study demonstrates that ML could optimize the use of clinical parameters with robustness for DIC diagnosis. We believe that this approach could play a supportive role in physicians' medical decision by integrated into electrical health record system. Further prospective validation is required to assess the clinical consequence of ML-approach and their clinical benefit.