Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.

Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: a comparative analysis across two New York hospitals

A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. In doing so, we quantified the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.4% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (79.2%) versus those who died (61.0%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

Constructing Prediction Models for Freezing of Gait by Nomogram and Machine Learning: A Longitudinal Study

Objectives: Although risk factors for freezing of gait (FOG) have been reported, there are still few prediction models based on cohorts that predict FOG. This 1-year longitudinal study was aimed to identify the clinical measurements closely linked with FOG in Chinese patients with Parkinson's disease (PD) and construct prediction models based on those clinical measurements using Cox regression and machine learning.Methods: The study enrolled 967 PD patients without FOG in the Hoehn and Yahr (H&Y) stage 1–3 at baseline. The development of FOG during follow-up was the end-point. Neurologists trained in movement disorders collected information from the patients on a PD medication regimen and their clinical characteristics. The cohort was assessed on the same clinical scales, and the baseline characteristics were recorded and compared. After the patients were divided into the training set and test set by the stratified random sampling method, prediction models were constructed using Cox regression and random forests (RF).Results: At the end of the study, 26.4% (255/967) of the patients suffered from FOG. Patients with FOG had significantly longer disease duration, greater age at baseline and H&Y stage, lower proportion in Tremor Dominant (TD) subtype, a higher proportion in wearing-off, levodopa equivalent daily dosage (LEDD), usage of L-Dopa and catechol-O-methyltransferase (COMT) inhibitors, a higher score in scales of Unified Parkinson's Disease Rate Scale (UPDRS), 39-item Parkinson's Disease Questionnaire (PDQ-39), Non-Motor Symptoms Scale (NMSS), Hamilton Depression Rating Scale (HDRS)-17, Parkinson's Fatigue Scale (PFS), rapid eye movement sleep behavior disorder questionnaire-Hong Kong (RBDQ-HK), Epworth Sleepiness Scale (ESS), and a lower score in scales of Parkinson's Disease Sleep Scale (PDSS) (P < 0.05). The risk factors associated with FOG included PD onset not being under the age of 50 years, a lower degree of tremor symptom, impaired activities of daily living (ADL), UPDRS item 30 posture instability, unexplained weight loss, and a higher degree of fatigue. The concordance index (C-index) was 0.68 for the training set (for internal validation) and 0.71 for the test set (for external validation) of the nomogram prediction model, which showed a good predictive ability for patients in different survival times. The RF model also performed well, the C-index was 0.74 for the test set, and the AUC was 0.74.Conclusions: The study found some new risk factors associated with the FOG including a lower degree of tremor symptom, unexplained weight loss, and a higher degree of fatigue through a longitudinal study, and constructed relatively acceptable prediction models.

Association Between Obesity and Post-stroke Anxiety in Patients With Acute Ischemic Stroke

Post-stroke anxiety (PSA) is serious psychosomatic comorbidity among patients with stroke, but whether obesity could be positively associated with PSA is currently unknown. The purpose of this study was to investigate the potential association between obesity and subsequent anxiety risk in patients with stroke. A total of 441 patients with acute ischemic stroke (AIS) onset were consecutively recruited within 7 days, and PSA and post-stroke depression (PSD) were evaluated by using a 14-item Hamilton anxiety scale (HAMA) and 17-item Hamilton depression scale (HAMD) at the end of 1-month follow-up. The odds ratio (OR) with 95% CI was estimated for the incidental PSA by using logistic regression analysis. The incidence of PSA was 25.85% at the end of 1-month follow-up, with a significant difference between patients with and without abdominal obesity. Relative fat mass (RFM) and abdominal obesity were significantly associated with an elevated risk of PSA, and the crude ORs were 1.04 (95% CI: 1.01–1.08) and 1.93 (95% CI: 1.11–3.34), respectively. Even after adjustment for obesity-related risk factors and PSA-related clinical measurements, the association remained to be pronounced with abdominal obesity. However, RFM (OR = 1.03, 95% CI: 0.99–1.06, P = 0.053) and abdominal obesity (OR = 1.31, 95% CI: 0.80–2.15, P = 0.280) were not significantly associated with an elevated risk of PSD. Abdominal obesity was independently associated with the PSA instead of PSD, which may help predict PSA risk in clinical practice. Further prospective clinical studies with a long follow-up duration are warranted to verify this finding.

Factors Related to Locomotive Syndrome in School-Aged Children in Okazaki: A Cross-Sectional Study

The relationship of locomotive syndrome with other physical characteristics and lifestyle habits in children has not been fully elucidated. The aim of this study was to assess the prevalence of children’s locomotive syndrome, and to determine its relationship with the above-mentioned factors. This was a cross-sectional study of 285 elementary school children who volunteered to participate in a medical checkup for physical function. Data was collected via medical examination, clinical measurements, and questionnaires. A multivariable logistic regression model was used to determine the relationship (odds ratios; ORs) of participants’ characteristics, physical functions, and other outcomes determined by questionnaire on locomotive syndrome. The following factors were related to locomotive syndrome: older age (OR = 1.421, 95% confidence interval [CI] [1.039, 1.945]), male sex (OR = 4.011, 95% CI [2.189, 7.347]), and more time spent watching television per day (OR = 1.281, 95% CI [1.001, 1.640]). These results may assist in the encouragement of children to perform appropriate physical activities and avoid unhealthy lifestyle habits, reducing the occurrence of locomotive syndrome.

Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease

Here we present a modular agent-based mathematical model of the human cardiovascular and renal systems. It integrates the previous models primarily developed by A. C. Guyton, F. Karaaslan, K. M. Hallow, and Y. V. Solodyannikov. We performed the model calibration to find an equilibrium state within the normal vital sign ranges for a healthy adult. We verified the model’s abilities to reproduce equilibrium states with abnormal physiological values related to different combinations of cardiovascular diseases (such as systemic hypertension, chronic heart failure, pulmonary hypertension, etc.). For the model creation and validation, we involved over 200 scientific studies covering known models of the human cardiovascular and renal functions, biosimulation platforms, and clinical measurements of physiological quantities in normal and pathological conditions. We compiled detailed documentation describing all equations, parameters and variables of the model with justification of all formulas and values. The model is implemented in BioUML and available in the web-version of the software.

Structural variants in the Chinese population and their impact on phenotypes, diseases and population adaptation

A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing has improved the sensitivity of structural variant discovery. Here, we conduct the long-read sequencing-based structural variant analysis for 405 unrelated Chinese individuals, with 68 phenotypic and clinical measurements. We discover a landscape of 132,312 nonredundant structural variants, of which 45.2% are novel. The identified structural variants are of high-quality, with an estimated false discovery rate of 3.2%. The concatenated length of all the structural variants is approximately 13.2% of the human reference genome. We annotate 1,929 loss-of-function structural variants affecting the coding sequence of 1,681 genes. We discover rare deletions in HBA1/HBA2/HBB associated with anemia. Furthermore, we identify structural variants related to immunity which differentiate the northern and southern Chinese populations. Our study describes the landscape of structural variants in the Chinese population and their contribution to phenotypes and disease.

Locked-in Guillain-Barré syndrome: ‘my living nightmare’

A 48-year-old man with severe Guillain-Barré syndrome suffered complete paralysis, and for 31 days could not communicate with the outside world, while remaining fully conscious. After recovery, he provided feedback on aspects of his care, such as mechanical ventilation, physical therapy, and communication. Conventional low tidal volume normocapnic ventilation induced ongoing and profound dyspnoea, occasionally relieved by modest increases in minute ventilation. Routine and apparently benign physical therapy was extremely painful, which was not reflected in heart rate or blood pressure changes. When he eventually re-established communication after many weeks, via slight eye movements, his first message was to express a particular distressing symptom. His case is a valuable reminder of the sometimes large gap between clinical measurements and assumptions and the subjective patient experience. We propose several approaches to address such issues in other paralysed but conscious patients.

Adults with Loeys-Dietz syndrome and vascular Ehlers-Danlos syndrome: A cross-sectional study of life satisfaction

Objective: To explore life satisfaction among adults with Loeys-Dietz and those with vascular Ehlers-Danlos syndrome. Design: Postal survey in 2018. Participants and methods: Persons with molecularly verified Loeys-Dietz syndrome or vascular Ehlers-Danlos syndrome were recruited through the TRS [AQ2] National Resource Centre for Rare Disorders in Norway. The study used the Life Satisfaction Questionnaire 11, Hospital Anxiety and Depression Scale, Fatigue Severity Scale, and questions about physical activity and disease burden. Descriptive statistics were conducted with Bonferroni corrections. Results: The response rate was 74%, 52 participants, age range 18–68 years, and 58% were women. Only half of the participants were satisfied with their lives as a whole. Participants reported dissatisfaction with vocation, somatic health, and sexual life in particular. Participants with vascular Ehlers-Danlos syndrome (n = 18) were satisfied with more areas of life than those with Loeys-Dietz syndrome (n = 34). Low overall satisfaction was significantly associated with severe fatigue (p = 0.002) and symptoms of anxiety (p = 0.001). Conclusion: This study provides important information about living with Loeys-Dietz syndrome and vascular Ehlers-Danlos syndrome. Future studies should identify factors that reduce/increase life satisfaction. Professionals in welfare systems need more information about the unique challenges of living with these diagnoses. Guidelines for research and clinical measurements of life satisfaction should be updated.