Describing Anisocoria in Neurocritically Ill Patients

BACKGROUND

Anisocoria (unequal pupil size) has been defined using cut points ranging from greater than 0.3 mm to greater than 2.0 mm for absolute difference in pupil size. This study explored different pupil diameter cut points for assessing anisocoria as measured by quantitative pupillometry before and after light stimulus.

METHODS

An exploratory descriptive study of international registry data was performed. The first observations in patients with paired left and right quantitative pupillometry measurements were included. Measurements of pupil size before and after stimulus with a fixed light source were used to calculate anisocoria.

RESULTS

The sample included 5769 patients (mean [SD] age, 57.5 [17.6] years; female sex, 2558 patients [51.5%]; White race, 3669 patients [75.5%]). Anisocoria defined as pupil size difference of greater than 0.5 mm was present in 1624 patients (28.2%) before light stimulus; 645 of these patients (39.7%) also had anisocoria after light stimulus (P < .001). Anisocoria defined as pupil size difference of greater than 2.0 mm was present in 79 patients (1.4%) before light stimulus; 42 of these patients (53.2%) also had anisocoria after light stimulus (P < .001).

DISCUSSION

The finding of anisocoria significantly differed before and after light stimulus and according to the cut point used. At most cut points, fewer than half of the patients who had anisocoria before light stimulus also had anisocoria after light stimulus.

CONCLUSION

The profound difference in the number of patients adjudicated as having anisocoria using different cut points reinforces the need to develop a universal definition for anisocoria.

Wearing a face mask during controlled-intensity exercise is not a risk factor for exertional heatstroke: A pilot study

Aim

This study aimed to measure the influence of wearing face masks on individuals' physical status in a hot and humid environment.

Methods

Each participant experienced different physical situations: (i) not wearing a mask (control), (ii) wearing a surgical mask, (iii) wearing a sport mask. An ingestible capsule thermometer was used to measure internal core body temperature during different exercises (standing, walking, and running, each for 20 min) in an artificial weather room with the internal wet-bulb globe temperature set at 28°C. The change in the participants' physical status and urinary liver fatty acid-binding protein (L-FABP) were measured.

Results

Six healthy male volunteers were enrolled in the study. In each participant, significant changes were observed in the heart rate and internal core temperatures after increased exercise intensity; however, no significant differences were observed between these parameters and urinary L-FABP among the three intervention groups.

Conclusion

Mask wearing is not a risk factor for heatstroke during increased exercise intensity.

The impact of intraoperative pericardial three-dimensional echocardiography for the atrioventricular valve repair in the patient with congenital heart disease

Background

Atrioventricular valve (AVV) regurgitation enormously affected the survival outcome of the patients with congenital heart disease (CHD). However, the image quality by use of transthoracic echocardiography has not reached a level that is sufficient, and also, three-dimensional echocardiography, which is useful to clarify complex AVV anatomy, cannot be applied for the patients less than 15kg, to guide for the AVV repair in pediatric patients. We try to show surgeons more precise three-dimensional images about an AVV by using intraoperative pericardial three-dimensional echocardiography (IP3DE) and improve the surgical outcome.

Purpose

To determine the efficacy of IP3DE by assessing the surgical outcome of an AVV repair and re-intervention rate.

Method

Eighty-five patient with CHD who underwent atrioventricular repair with significant regurgitation (Grade 2–4+) before operation were divided into two groups imaged IP3DE or not, in our hospital from 1993 to 2020. We assessed the surgical outcome and re-intervention rate between two arms and re-evaluate AVV images before surgery compared to the IP3DE.

Result

IP3DE was performed in forty-six patients (IP3DE group) and thirty-nine patients were not (control group). Median age at AVV repair was 3.0/2.8 years, respectively. The AVV was tricuspid (n=25), mitral (n=41), or common (n=19). The IP3DE group had a significantly higher improvement in regurgitation of AVV (IP3DE: Grade 3.2±0.3 → 1.7±0.3 vs Control: Grade 2.8±0.3 → 1.8±0.3, p&lt;0.05). Fifty-nine percent of the IP3DE group was successful outcome (Grade&lt;1+ after repair). There was no significant difference in the rate of re-intervention after surgery between two groups. In multivariate analysis, using IP3DE contributed to successful outcome for AVV repair (OR: 4.66, 95% CI: 1.46–14.8, p&lt;0.01). The different and/or additional anatomical AVV findings were obtained in sixty-one percent of patients by the IP3DE.

Conclusion

IP3DE contributes to successful outcome for AVV repair by obtaining further information on complicated AVV anatomy in congenital heart disease. IP3DE also enables both cardiovascular surgeons and cardiologists to share the accurate and detail “surgeon's view” in the operating room for planning of AVV repair.

Funding Acknowledgement

Type of funding sources: None.

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Efficacy of Chest Radiography as a Primary Care Triage Tool in Severe Coronavirus Disease

Objective Severe acute respiratory syndrome coronavirus 2 has spread globally, and it is important to utilize medical resources properly, especially in critically ill patients. We investigated the validity of chest radiography as a tool for predicting aggravation in coronavirus disease (COVID-19) cases. Methods A total of 104 laboratory-confirmed COVID-19 cases were referred from the cruise ship "Diamond Princess" to the Self-Defense Forces Central Hospital in Japan from February 11 to 25, 2020. Fifty-nine symptomatic patients were selected. Chest radiography was performed upon hospitalization; subsequently, patients were categorized into the positive radiograph (Group A) and negative radiograph (Group B) groups. Radiographic findings were analyzed with a six-point semiquantitative score. Group A was further classified into two additional subgroups: patients who required oxygen therapy during their clinical courses (Group C) and patients who did not (Group D). Clinical records, laboratory data, and radiological findings were collected for an analysis. Results Among 59 patients, 34 were men with a median age of 60 years old. Groups A, B, C, and D consisted of 33, 26, 12, and 21 patients, respectively. The number of patients requiring oxygen administration was significantly larger in Group A than in Group B. The consolidation score on chest radiographs was significantly higher in Group C than in Group D. When chest radiographs showed consolidation in more than two lung fields, the positive likelihood ratio of deterioration was 10.6. Conclusions Chest radiography is a simple and easy-to-use clinic-level triage tool for predicting the severity of COVID-19 and may contribute to the allocation of medical resources.

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19

BACKGROUND

The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19.

METHODS

This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm.

RESULTS

In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for predictive capability was SpO₂, followed by age, respiratory rate, and systolic blood pressure.

CONCLUSION

In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier. Prospective validation studies are needed to verify the application of the tool in clinical practice.

Alcohol does not increase in-hospital mortality due to severe blunt trauma: an analysis of propensity score matching using the Japan Trauma Data Bank

Aim

Alcohol‐related problems, including trauma, are a great burden on global health. Alcohol metabolism in the Japanese population is genetically inferior to other races. This study aimed to evaluate the effects of alcohol use among a Japanese severe blunt trauma cohort.

Methods

This retrospective observational study analyzed the data of trauma patients registered in the Japan Trauma Data Bank between 2004 and 2019. The primary outcome of this study was in‐hospital mortality. The lengths of hospital and intensive care unit stay were the secondary outcomes. Propensity score matching was used to adjust the anatomical severity and patient background to reduce the potential alcohol use bias.

Results

We analyzed 46,361 patients categorized into nondrinking (n = 37,818) and drinking (n = 8,543) groups. After a 1:1 propensity score matching (n = 8,428, respectively), despite the Glasgow Coma Scale and Revised Trauma Score scores being significantly lower in the drinking group (14 vs. 13 and 7.84 vs. 7.55, P < 0.001, respectively) and intensive care unit length of stay being significantly longer in the drinking group (6 vs. 7 days, P = 0.002), in‐hospital mortality was significantly lower in the alcohol group (11.8% vs. 9.0%, P < 0.001) and there were no differences in the duration of hospital stay (19 vs. 19 days, P = 0.848).

Conclusion

Despite increasing physiological severity on admission, after adjusting for anatomical severity, alcohol consumption could be beneficial in severe blunt trauma patients as regards in‐hospital mortality.

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing.

Federated Learning used for predicting outcomes in SARS-COV-2 patients

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

Prediction of an increase in oxygen requirement of SARS-CoV-2 pneumonia using three different scoring systems

Introduction

In patients with severe coronavirus disease 2019 (COVID-19), respiratory failure is a major complication and its symptoms occur around one week after onset. The CURB-65, A-DROP and expanded CURB-65 tools are known to predict the risk of mortality in patients with community-acquired pneumonia. In this retrospective single-center retrospective study, we aimed to assess the correlations of the A-DROP, CURB-65, and expanded CURB-65 scores on admission with an increase in oxygen requirement in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia.

Methods

We retrospectively analyzed 207 patients who were hospitalized with SARS-CoV-2 pneumonia at the Self-Defense Forces Central Hospital in Tokyo, Japan. Performance of A-DROP, CURB-65, and the expanded CURB-65 scores were validated. In addition, we assessed whether there were any associations between an increase in oxygen requirement and known risk factors for critical illness in COVID-19, including elevation of liver enzymes and C-reactive protein (CRP), lymphocytopenia, high D-dimer levels and the chest computed tomography (CT) score.

Results

The areas under the curve for the ability of CURB-65, A-DROP, and the expanded CURB-65 scores to predict an increase in oxygen requirement were 0.6961, 0.6980 and 0.8327, respectively, and the differences between the three groups were statistically significant (p < 0.001). Comorbid cardiovascular disease, lymphocytopenia, elevated CRP, liver enzyme and D-dimer levels, and higher chest CT score were significantly associated with an increase in oxygen requirement

Conclusions

The expanded CURB-65 score can be a better predictor of an increase in oxygen requirement in patients with SARS-CoV-2 pneumonia.

CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings

Objectives

The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2.

Methods

Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression.

Results

Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms.

Conclusions

COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure.

Key Points

• Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings.

• CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5).

• KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Recent Advances in Cell-Based Therapies for Ischemic Stroke

Stroke is the most prevalent cardiovascular disease worldwide, and is still one of the leading causes of death and disability. Stem cell-based therapy is actively being investigated as a new potential treatment for certain neurological disorders, including stroke. Various types of cells, including bone marrow mononuclear cells, bone marrow mesenchymal stem cells, dental pulp stem cells, neural stem cells, inducible pluripotent stem cells, and genetically modified stem cells have been found to improve neurological outcomes in animal models of stroke, and there are some ongoing clinical trials assessing their efficacy in humans. In this review, we aim to summarize the recent advances in cell-based therapies to treat stroke.

Comparison of COVID-19 disease between young and elderly patients: Hidden viral shedding of COVID-19

Objectives

The symptoms of Coronavirus disease 2019 (COVID-19) vary among patients. The aim of this study was to investigate the clinical manifestation and disease duration in young versus elderly patients.

Methods

We retrospectively analyzed 187 patients (87 elderly and 100 young patients) with confirmed COVID-19. The clinical characteristics and chest computed tomography (CT) extent as defined by a score were compared between the two groups.

Results

The numbers of asymptomatic cases and severe cases were significantly higher in the elderly group (elderly group vs. young group; asymptomatic cases, 31 [35.6%] vs. 10 [10%], p < 0.0001; severe cases, 25 [28.7%] vs. 8 [8.0%], p = 0.0002). The proportion of asymptomatic patients and severe patients increased across the 10-year age groups. There was no significant difference in the total CT score and number of abnormal cases. A significant positive correlation between the disease duration and patient age was observed in asymptomatic patients (ρ = 0.4570, 95% CI 0.1198–0.6491, p = 0.0034).

Conclusions

Although the extent of lung involvement did not have a significant difference between the young and elderly patients, elderly patients were more likely to have severe clinical manifestations. Elderly patients were also more likely to be asymptomatic and a source of COVID-19 viral shedding.

Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: a retrospective analysis

BACKGROUND

The ongoing COVID-19 pandemic is a global threat. Identification of markers for symptom onset and disease progression is a pressing issue. We described the clinical features of people infected on board the Diamond Princess cruise ship who were diagnosed with asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or mild or severe COVID-19, on admission to the Self-Defense Forces Central Hospital (Tokyo, Japan) and at the end of observation.

METHODS

This retrospective, single-centre study included participants with laboratory-detected SARS-CoV-2 infection who were admitted to the Self-Defense Forces Central Hospital from Feb 11 to Feb 25, 2020. Clinical records, laboratory data, and radiological findings were analysed. Clinical outcomes were followed up until discharge or Feb 26, 2020, whichever came first. We defined asymptomatic infection as SARS-CoV-2 infection with no history of clinical signs and symptoms, severe COVID-19 as clinical symptoms of pneumonia (dyspnoea, tachypnoea, peripheral capillary oxygen saturation <93%, and need for oxygen therapy), and mild COVID-19 as all other symptoms. Clinical features on admission were compared among patients with different disease severity, including asymptomatic infection, at the end of observation. We used univariable analysis to identify factors associated with symptomatic illness among asymptomatic people infected with SARS-CoV-2 and disease progression in patients with COVID-19.

FINDINGS

Among the 104 participants included in the final analysis, the median age was 68 years (IQR 47-75) and 54 (52%) were male. On admission, 43 (41%) participants were classified as asymptomatic, 41 (39%) as having mild COVID-10, and 20 (19%) as having severe COVID-19. At the end of observation, 33 (32%) participants were confirmed as being asymptomatic, 43 (41%) as having mild COVID-19, and 28 (27%) as having severe COVID-19. Serum lactate hydrogenase concentrations were significantly higher in the ten participants who were asymptomatic on admission but developed symptomatic COVID-19 compared with the 33 participants who remained asymptomatic throughout the observation period (five [50%] vs four [12%] participants; odds ratio 7·25, 95% CI 1·43-36·70; p=0·020). Compared with patients with mild disease at the end of observation, patients with severe COVID-19 were older (median age 73 years [IQR 55-77] vs 60 years [40-71]; p=0·028) and had more frequent consolidation on chest CT (13 [46%] of 28 vs nine [21%] of 43; p=0·035) and lymphopenia (16 [57%] vs ten [23%]; p=0·0055) on admission.

INTERPRETATION

Older age, consolidation on chest CT images, and lymphopenia might be risk factors for disease progression of COVID-19 and contribute to improved clinical management.

FUNDING

None.

Indicators of Acute Kidney Injury as Biomarkers to Differentiate Heatstroke from Coronavirus Disease 2019: A Retrospective Multicenter Analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) and heat-related illness are systemic febrile diseases. These illnesses must be differentiated during a COVID-19 pandemic in summer. However, no studies have compared and distinguished heat-related illness and COVID-19. We compared data from patients with early heat-related illness and those with COVID-19.

METHODS

This retrospective observational study included 90 patients with early heat-related illness selected from the Heatstroke STUDY 2017-2019 (nationwide registries of heat-related illness in Japan) and 86 patients with laboratory-confirmed COVID-19 who had fever or fatigue and were admitted to one of two hospitals in Tokyo, Japan.

RESULTS

Among vital signs, systolic blood pressure (119 vs. 125 mm Hg, p = 0.02), oxygen saturation (98% vs. 97%, p < 0.001), and body temperature (36.6°C vs. 37.6°C, p<0.001) showed significant between-group differences in the heatstroke and COVID-19 groups, respectively. The numerous intergroup differences in laboratory findings included disparities in white blood cell count (10.8 × 10³/μL vs. 5.2 × 10³/μL, p<0.001), creatinine (2.2 vs. 0.85 mg/dL, p<0.001), and C-reactive protein (0.2 vs. 2.8 mg/dL, p<0.001), although a logistic regression model achieved an area under the curve (AUC) of 0.966 using these three factors. A Random Forest machine learning model achieved an accuracy, precision, recall, and AUC of 0.908, 0.976, 0.842, and 0.978, respectively. Creatinine was the most important feature of this model.

CONCLUSIONS

Acute kidney injury was associated with heat-related illness, which could be essential in distinguishing or evaluating patients with fever in the summer during a COVID-19 pandemic.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Serial serum SARS-CoV-2 RNA results in two COVID-19 cases with severe respiratory failure

Coronavirus disease 2019 (COVID-19) is spreading worldwide and poses an imminent threat to public health. We encountered 2 cases of COVID-19 with progression resulting in severe respiratory failure and improvement without any specific treatment.

To examine the course of infection, we performed reverse-transcription (RT) polymerase chain reaction assay with serum specimens, and serum SARS-CoV-2 RNA was detected in both cases when body temperature increased and respiratory status deteriorated. We, then examined, retrospectively and prospectively, the clinical course during hospitalization by performing serial examinations of serum SARS-CoV-2 RNA status. The findings from our cases suggest that not only is detection of viremia useful as a predictive marker of severity, but also serial serum SARS-CoV-2 RNA results can be helpful for predicting the clinical course.