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Microvascular dysfunction in septic and dengue shock: Pathophysiology and implications for clinical management

[No abstract, showing first paragraph of article]The microcirculation is the terminal vascular network of the systemic circulation, whose primary function is to distribute oxygen to, and remove metabolic by-products from living cells. In health, tissue perfusion is regulated by control of microvascular tone and endothelial permeability. In states of shock, however, there is a mismatch between demand for, and delivery or utilisation of oxygen in the tissues.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

BackgroundDengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam.MethodsWe performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation.FindingsWe included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients.InterpretationWe demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care.FundingEPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

An artificial intelligence-based approach to identify volume status in patients with severe dengue using wearable PPG data.

Dengue shock syndrome (DSS) is a serious complication of dengue infection which occurs when critical plasma leakage results in haemodynamic shock. Treatment is challenging as fluid therapy must balance the risk of hypoperfusion with volume overload. In this study, we investigate the potential utility of wearable photoplethysmography (PPG) to determine volume status in DSS. In this prospective observational study, we enrolled 250 adults and children with a clinical diagnosis of dengue admitted to the Hospital for Tropical Diseases, Ho Chi Minh City. PPG monitoring using a wearable device was applied for a 24-hour period. Clinical events were then matched to the PPG data by date and time. We predefined two clinical states for comparison: (1) the 2-hour period before a shock event was an "empty" volume state and (2) the 2-hour period between 1 and 3 hours after a fluid initiation event was a "full" volume state. PPG data were sampled from these states for analysis. Variability and waveform morphology features were extracted and analyzed using principal components analysis and random forest. Waveform images were used to develop a computer vision model. Of the 250 patients enrolled, 90 patients experienced the predefined outcomes, and had sufficient data for the analysis. Principal components analysis identified four principal components (PCs), from the 23 pulse wave features. Logistic regression using these PCs showed that the empty state is associated with PCs 1 (p = 0.016) and 4 (p = 0.036) with both PCs denoting increased sympathetic activity. Random forest showed that heart rate and the LF-HF ratio are the most important features. A computer vision model had a sensitivity of 0.81 and a specificity of 0.70 for the empty state. These results provide proof of concept that an artificial intelligence-based approach using continuous PPG monitoring can provide information on volume states in DSS.

Climate change and health in Southeast Asia – defining research priorities and the role of the Wellcome Trust Africa Asia Programmes

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.

Metformin as adjunctive therapy for dengue in overweight and obese patients: a protocol for an open-label clinical trial (MeDO)

Background: Dengue is a disease of major global importance. While most symptomatic infections are mild, a small proportion of patients progress to severe disease with risk of hypovolaemic shock, organ dysfunction and death. In the absence of effective antiviral or disease modifying drugs, clinical management is solely reliant on supportive measures. Obesity is a growing problem among young people in Vietnam and is increasingly recognised as an important risk factor for severe dengue, likely due to alterations in host immune and inflammatory pathways. Metformin, a widely used anti-hyperglycaemic agent with excellent safety profile, has demonstrated potential as a dengue therapeutic in vitro and in a retrospective observational study of adult dengue patients with type 2 diabetes. This study aims to assess the safety and tolerability of metformin treatment in overweight and obese dengue patients, and investigate its effects on several clinical, immunological and virological markers of disease severity. Methods: This open label trial of 120 obese/overweight dengue patients will be performed in two phases, with a metformin dose escalation if no safety concerns arise in phase one. The primary endpoint is identification of clinical and laboratory adverse events. Sixty overweight and obese dengue patients aged 10-30 years will be enrolled at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam. Participants will complete a 5-day course of metformin therapy and be compared to a non-treated group of 60 age-matched overweight and obese dengue patients. Discussion: Previously observed antiviral and immunomodulatory effects of metformin make it a promising dengue therapeutic candidate in appropriately selected patients. This study will assess the safety and tolerability of adjunctive metformin in the management of overweight and obese young dengue patients, as well as its effects on markers of viral replication, endothelial dysfunction and host immune responses. Trial registration: ClinicalTrials.gov: NCT04377451 (May 6 th 2020).

Novel Clinical Monitoring Approaches for Reemergence of Diphtheria Myocarditis, Vietnam.

Diphtheria is a life-threatening, vaccine-preventable disease caused by toxigenic Corynebacterium bacterial species that continues to cause substantial disease and death worldwide, particularly in vulnerable populations. Further outbreaks of vaccine-preventable diseases are forecast because of health service disruptions caused by the coronavirus disease pandemic. Diphtheria causes a spectrum of clinical disease, ranging from cutaneous forms to severe respiratory infections with systemic complications, including cardiac and neurologic. In this synopsis, we describe a case of oropharyngeal diphtheria in a 7-year-old boy in Vietnam who experienced severe myocarditis complications. We also review the cardiac complications of diphtheria and discuss how noninvasive bedside imaging technologies to monitor myocardial function and hemodynamic parameters can help improve the management of this neglected infectious disease.

Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses.

Optimal management of infectious diseases is guided by up-to-date information at the individual and public health levels. For infections of global importance, including emerging pandemics such as COVID-19 or prevalent endemic diseases such as dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging, considering that the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare settings. Deployment of wearables in hospital settings for the management of infectious diseases, or in the community to support syndromic surveillance during outbreaks, could provide significant, cost-effective advantages and improve healthcare delivery. We highlight a range of promising technologies employed by wearable devices and discuss the technical and ethical issues relating to implementation in the clinic, focusing on low- and middle- income countries. Finally, we propose a set of essential criteria for the rollout of wearable technology for clinical use.

Predicting deterioration in dengue using a low cost wearable for continuous clinical monitoring.

Close vital signs monitoring is crucial for the clinical management of patients with dengue. We investigated performance of a non-invasive wearable utilising photoplethysmography (PPG), to provide real-time risk prediction in hospitalised individuals. We performed a prospective observational clinical study in Vietnam between January 2020 and October 2022: 153 patients were included in analyses, providing 1353 h of PPG data. Using a multi-modal transformer approach, 10-min PPG waveform segments and basic clinical data (age, sex, clinical features on admission) were used as features to continuously forecast clinical state 2 h ahead. Prediction of low-risk states (17,939/80,843; 22.1%), defined by NEWS2 and mSOFA 

The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality.

BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of <72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.ResultsWe included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84-0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV >90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account-this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.

The compensatory reserve index predicts recurrent shock in patients with severe dengue.

BackgroundDengue shock syndrome (DSS) is one of the major clinical phenotypes of severe dengue. It is defined by significant plasma leak, leading to intravascular volume depletion and eventually cardiovascular collapse. The compensatory reserve Index (CRI) is a new physiological parameter, derived from feature analysis of the pulse arterial waveform that tracks real-time changes in central volume. We investigated the utility of CRI to predict recurrent shock in severe dengue patients admitted to the ICU.MethodsWe performed a prospective observational study in the pediatric and adult intensive care units at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Patients were monitored with hourly clinical parameters and vital signs, in addition to continuous recording of the arterial waveform using pulse oximetry. The waveform data was wirelessly transmitted to a laptop where it was synchronized with the patient's clinical data.ResultsOne hundred three patients with suspected severe dengue were recruited to this study. Sixty-three patients had the minimum required dataset for analysis. Median age was 11 years (IQR 8-14 years). CRI had a negative correlation with heart rate and moderate negative association with blood pressure. CRI was found to predict recurrent shock within 12 h of being measured (OR 2.24, 95% CI 1.54-3.26), P ConclusionCRI is a useful non-invasive method for monitoring intravascular volume status in patients with severe dengue.

Applying artificial intelligence and digital health technologies, Viet Nam

Core outcome measurement set for clinical trials in dengue: an international Delphi consensus study (DEN-CORE)

Early NK-cell and T-cell dysfunction marks progression to severe dengue in patients with obesity and healthy weight

Abstract Dengue is a mosquito-borne virus infection affecting half of the world’s population for which therapies are lacking. The role of T and NK-cells in protection/immunopathogenesis remains unclear for dengue. We performed a longitudinal phenotypic, functional and transcriptional analyses of T and NK-cells in 124 dengue patients using flow cytometry and single-cell RNA-sequencing. We show that T/NK-cell signatures early in infection discriminate patients who develop severe dengue (SD) from those who do not. These signatures are exacerbated in patients with overweight/obesity compared to healthy weight patients, supporting their increased susceptibility to SD. In SD, CD4 + /CD8 + T-cells and NK-cells display increased co-inhibitory receptor expression and decreased cytotoxic potential compared to non-SD. Using transcriptional and proteomics approaches we show decreased type-I Interferon responses in SD, suggesting defective innate immunity may underlie NK/T-cell dysfunction. We propose that dysfunctional T and NK-cell signatures underpin dengue pathogenesis and may represent novel targets for immunomodulatory therapy in dengue.

A modified Sequential Organ Failure Assessment score for dengue: development, evaluation and proposal for use in clinical trials

Abstract Background Dengue is a neglected tropical disease, for which no therapeutic agents have shown clinical efficacy to date. Clinical trials have used strikingly variable clinical endpoints, which hampers reproducibility and comparability of findings. We investigated a delta modified Sequential Organ Failure Assessment (delta mSOFA) score as a uniform composite clinical endpoint for use in clinical trials investigating therapeutics for moderate and severe dengue. Methods We developed a modified SOFA score for dengue, measured and evaluated its performance at baseline and 48 h after enrolment in a prospective observational cohort of 124 adults admitted to a tertiary referral hospital in Vietnam with dengue shock. The modified SOFA score included pulse pressure in the cardiovascular component. Binary logistic regression, cox proportional hazard and linear regression models were used to estimate association between mSOFA, delta mSOFA and clinical outcomes. Results The analysis included 124 adults with dengue shock. 29 (23.4%) patients required ICU admission for organ support or due to persistent haemodynamic instability: 9/124 (7.3%) required mechanical ventilation, 8/124 (6.5%) required vasopressors, 6/124 (4.8%) required haemofiltration and 5/124 (4.0%) patients died. In univariate analyses, higher baseline and delta (48 h) mSOFA score for dengue were associated with admission to ICU, requirement for organ support and mortality, duration of ICU and hospital admission and IV fluid use. Conclusions The baseline and delta mSOFA scores for dengue performed well to discriminate patients with dengue shock by clinical outcomes, including duration of ICU and hospital admission, requirement for organ support and death. We plan to use delta mSOFA as the primary endpoint in an upcoming host-directed therapeutic trial and investigate the performance of this score in other phenotypes of severe dengue in adults and children.

Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam.

BackgroundIdentifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.MethodsWe developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set.FindingsThe final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76-0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98.InterpretationThe study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Kinetics of cardiovascular and inflammatory biomarkers in paediatric dengue shock syndrome

Abstract Glycocalyx disruption and hyperinflammatory responses are implicated in the pathogenesis of dengue-associated vascular leak, however little is known about their association with clinical outcomes of patients with dengue shock syndrome (DSS). We investigated the association of vascular and inflammatory biomarkers with clinical outcomes and their correlations with clinical markers of vascular leakage. We performed a prospective cohort study in Viet Nam. Children ≥5 years of age with a clinical diagnosis of DSS were enrolled into this study. Blood samples were taken daily during ICU stay and 7–10 days after hospital discharge for measurements of plasma levels of Syndecan-1, Hyaluronan, Suppression of tumourigenicity 2 (ST-2), Ferritin, N-terminal pro Brain Natriuretic Peptide (NT-proBNP), and Atrial Natriuretic Peptide (ANP). The primary outcome was recurrent shock. Ninety DSS patients were enrolled. Recurrent shock occurred in 16 patients. All biomarkers, except NT-proBNP, were elevated at presentation with shock. There were no differences between compensated and decompensated DSS patients. Glycocalyx markers were positively correlated with inflammatory biomarkers, haematocrit, percentage haemoconcentration, and negatively correlated with stroke volume index. While Syndecan-1, Hyaluronan, Ferritin, and ST-2 improved with time, ANP continued to be raised at follow-up. Enrolment Syndecan-1 levels were observed to be associated with developing recurrent shock although the association did not reach the statistical significance at the P &lt; 0.01 (OR = 1.82, 95% CI 1.07–3.35, P = 0.038). Cardiovascular and inflammatory biomarkers are elevated in DSS, correlate with clinical vascular leakage parameters and follow different kinetics over time. Syndecan-1 may have potential utility in risk stratifying DSS patients in ICU.

Noninvasive Measurement of Lung Function Using the Inspired Sinewave Technique in Mechanically Ventilated Patients With Acute Brain Injury: A Feasibility Study.

Early neurological deterioration in minor stroke caused by small artery occlusion: Incidence, risk factors and treatment impact.

IntroductionEarly neurological deterioration (END) is a forecast factor in poor outcomes in minor strokes. END's prevalence and forecast factors in minor strokes caused by small artery occlusion (SAO) are still unclear.Patients and methodWe retrospectively analyzed 451 patients with minor stroke (NIHSS ≤ 5) caused by SAO hospitalized within an initial 24 h at BachMai Hospital's stroke center. END was defined as conditions with an elevated two or more NIHSS points within an initial 72 h. The primary outcome included the determination of the END incidence. The secondary outcome identified forecast factors for END through multivariate logistic regression analyses, and therapeutic impacts of antiplatelet and thrombolytic treatments.ResultsEND occurred in 9.5 % (43/451) of patients (62.7 % male, mean age 63.8 ± 11.8 years). Independent forecast included admission SBP ≥ 150 mmHg (OR = 1.99; 95 % CI: 1.01 - 3.94; p = 0.048), diabetes history (OR = 0.58; 95 % CI: 1.05 - 4.33; p = 0.036), admission blood glucose ≥ 14mmol/L (OR = 2.99; 95 % CI: 1.05 - 8.54; p = 0.04), and internal capsule infarction (OR = 2.23; 95 % CI: 1.01 - 4.92; p = 0.048). The patients group admitted within 4.5 h, DAPT has significantly lower END risk compared to SAPT (OR = 0.079; 95 % CI: 0.007 - 0.939; p = 0.04) and altepase (OR = 0.013; 95 % CI: 0.01 - 0.12; p < 0.01). END risk was similar between SAPT and altepase (p = 0.074).Discussion and conclusionEND is a 9.5 % incidence in minor acute ischemic stroke due to SAO. Independent forecasts are admission SBP and blood glucose, diabetes history, and internal capsule infarction. The DAPT group has significantly lower END risk than the SAPT and alteplase groups.

Prevalence of common autosomal recessive and X-linked conditions in pregnant women in Vietnam: a cross-sectional study.

The prevalence of recessive disorder carriers among Vietnamese women is still indistinct. This study aims to assess the prevalence of carriers for common autosomal recessive and X-linked conditions among Vietnamese pregnant women and to identify common mutations within these genes. A cross-sectional study was conducted with 8,464 Vietnamese pregnant women with indications for carrier screening tests for recessive disorders from November 2022 to August 2023 at the Institute of DNA Technology and Genetic Analysis. The survey includes demographic information, and the genetic screening was conducted using next-generation sequencing (NGS) techniques, focusing on 13 specific recessive conditions. 8,464 Vietnamese pregnant women's records were involved in this study. 1,928 of them carried at least one genetic recessive condition, representing the frequency of a recessive disorder was 22.8%. The highest recessive disorders rate among pregnant women was found for the G6PD gene mutation (G6PD deficiency) at a rate of about 1 in 20 individuals, followed by the HBA1 and HBA2 gene mutations (Alpha Thalassemia) at a rate of about 1 in 25. Other common recessive carrier genes included SRD5A2 (5-alpha reductase deficiency) at a rate of about 1 in 27, HBB (Beta Thalassemia) at a rate of about 1 in 28, ATP7B (Wilson's disease) at a rate of about 1 in 40, PAH (Phenylketonuria) at a rate of about 1 in 40, and SLC25A13 (Citrin deficiency) at a rate of about 1 in 45. The prevalence of recessive carriers among Vietnamese pregnant women is high, and at least 1 in 5 pregnant women carries one recessive gene. It is essential to encourage Vietnamese pregnant women to conduct recessive carrier screening tests to reduce mortality rates among children and to implement effective pregnancy planning and childbirth.

Early neurological deterioration in patients with minor stroke: A single-center study conducted in Vietnam

A minor ischemic stroke is associated with a higher likelihood of poor clinical outcomes at 90 days when there is early neurological deterioration (END). The objective of this case-control study conducted in a comprehensive stroke facility in Vietnam is to examine the frequency, forecast, and outcomes of patients with END in minor strokes. The study employs a descriptive observational design, longitudinally tracking patients with minor strokes admitted to Bach Mai Hospital’s Stroke Center between December 1, 2023, and August 31, 2024. Hospitalized within 24 hours of symptom onset, minor stroke patients with National Institutes of Health Stroke Scale (NIHSS) scores ≤ 5 and items 1a, 1b, and 1c on the NIHSS scale, each equal to 0, were included in the study. The primary measure of interest is the END rate, defined as a rise of 2 or more points in the NIHSS score during the first 72 hours after admission. We conduct a logistic regression analysis to identify forecasting factors for END. Out of 839 patients, 88 (10.5%) had END. In the END group, we found that most patients had complications within the first 24 hours of stroke, accounting for 43.2%; the 24 – 48-hour window accounted for 35.2%, and the 48 – 72-hour window accounted for 21.6%. END was associated with a higher likelihood of poor outcomes (mRS 2 – 6) at discharge (OR = 22.76; 95% CI 11.22 – 46.20; p < 0.01), 30 days post-stroke(OR = 24.38; 95% CI 14.40 – 41.29; p < 0.01), and 90 days post-stroke (OR = 21.74; 95% CI 12.63 – 37.43; p < 0.01). Some of the prognostic factors for END were admission NIHSS score (OR = 1.24; 95% CI 1.03 – 1.49; p = 0.02), admission systolic blood pressure greater than 150mmHg (OR = 1.70; 95% CI 1.03 – 2.81; p = 0.04), admission blood glucose (OR = 1.07; 95% CI 1.01 – 1.14; p = 0.02), reperfusion therapy (OR = 3.35; 95% CI 1.50 – 7.49; p < 0.01), use of antiplatelet monotherapy (OR = 3.69; 95% CI 2.24 – 6.08; p < 0.01), internal capsule infarction (OR = 2.54; 95% CI 1.37 – 4.71; p < 0.01), hemorrhagic transformation (OR = 5.72; 95% CI 1.07 – 30.45; p = 0.04), corresponding extracranial carotid artery occlusion (OR = 4.84; 95% CI 1.26 – 18.65; p = 0.02), and middle cerebral artery occlusion OR = 3.06; 95% CI 1.29 – 7.30; p = 0.01). END in minor stroke patients accounts for 10.5% and is a risk factor for poor neurological outcomes. Admission NIHSS score, higher systolic blood pressure, admission blood glucose, reperfusion therapy, use of antiplatelet monotherapy, internal capsule infarction, hemorrhagic transformation, corresponding extracranial carotid artery occlusion, and middle cerebral artery occlusion were some of the prognostic factors for END in our observational study.