Development of a clinical model to predict vagus nerve stimulation response in pediatric patients with drug-resistant epilepsy

Introduction Epilepsy impacts 470,000 children in the United States, and children with epilepsy are estimated to expend 6 times more on healthcare than those without epilepsy. For patients with antiseizure medication (ASM)-resistant epilepsy and unresectable seizure foci, vagus nerve stimulation (VNS) is a treatment option. Predicting response to VNS has been historically challenging. We aimed to create a clinical prediction score which could be utilized in a routine outpatient clinical setting. Methods We performed an 11-year, single-center retrospective analysis of patients <21 years old with ASM-resistant epilepsy who underwent VNS. The primary outcome was >50% seizure frequency reduction after one year. Univariate and multivariate logistic regressions were performed to assess clinical factors associated with VNS response; 70% and 30% of the sample were used to train and validate the multivariate model, respectively. A prediction score was developed based on the multivariate regression. Sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. Results This analysis included 365 patients. Multivariate logistic regression revealed that variables associated with VNS response were: <4 years of epilepsy duration before VNS (p=0.008) and focal motor seizures (p=0.037). The variables included in the clinical prediction score were: epilepsy duration before VNS, age at seizure onset, number of pre-VNS ASMs, if VNS was the patient's first therapeutic epilepsy surgery, and predominant seizure semiology. The final AUC was 0.7013 for the "fitted" sample and 0.6159 for the "validation" sample. Conclusions We developed a clinical model to predict VNS response in one of the largest samples of pediatric VNS patients to date. While the presented clinical prediction model demonstrated an acceptable AUC in the training cohort, clinical variables alone likely do not accurately predict VNS response. This score may be useful upon further validation, though its predictive ability underscores the need for more robust biomarkers of treatment response.

Sleep Stage Classification For Medical Purposes: Machine Learning Evaluation For Imbalanced Data

Background: Sleep is commonly associated with physical and mental health status. Sleep quality can be determined from the dynamic of sleep stages during the night. Data from the wearable device can potentially be used as predictors to classify the sleep stage. Robust Machine Learning (ML) model is needed to learn the pattern within wearable data to be associated with the sleep-wake classification, especially to handle the imbalanced proportion between wake and sleep stages. In this study, we incorporated a publicy available dataset consists of three features captured from a consumer wearable device and the labelled sleep stages from a polysomnogram. We implemented Random Forest, Support Vector Machine , Extreme Gradiet Boosting Tree, Densed Neural Network (DNN), and Long Short-Term Memory (LSTM), complemented by three strategies to handle the imbalanced data problem. Results: In total, we included more than 24,815 rows of preprocessed data from 31 samples. The proportion of minority-majority data is 1:10. In classifying this extreme imbalanced data, the DNN model was found to have the best performance compared to the previous best model, which is based on basic Multi-Layer Perceptron. Our best model successfully achieved a 12% higher specificity score (prediction score for minority class) and 1% improvement on the sensitivity score (prediction score for majority class) by including all features in the model. This achievement was affected by the implementation of custom class weight and oversampling strategy. In contrast, when we only used two features, XGB achieved a specificity improvement only by 1%, while keeping the sensitivity at the same level.Conclusions: The non-linear operation within the DNN model could successfully learn the hidden pattern from the combination of three features. Additionally, the class weight parameter avoided the model ignoring the minority class by giving more weight for this class in the loss function. The feature engineering process seemed to obscure the time-series characteristics within the data. This is why LSTM, as one of the best methods for time-series data, failed to perform well in this classification task.

Deep Venous Thrombosis (DVT) risk assessment on routine hemodialysis patient using Padua prediction score

Introduction: Hemodialysis requires invasive vascular access (VA) procedure which could emerge deep venous thrombosis (DVT) complication. Apart from VA, other risk factors, either modifiable or unmodifiable, could increase DVT risk. Those factors can be assessed by Padua Prediction Score (PPS). This study aims to assess which risk factors in PPS increase the risk of developing DVT in routine hemodialysis patients at BHCC main clinic. Methods: This research is a descriptive observational study with simple random sampling. The participants were 58 routine hemodialysis patients in BHCC. The inclusion criteria of this study were the ages above 17 years old, had history hemodialysis more than one, the patient willing to become of the sample subject. The patient that incompletely fulfills the questionnaire were already treated with anticoagulation were admitted for VTE, and had a history of discontinuing hemodialysis were excluded. The data were gathered using a questionnaire according to PPS. The data was analyzed by using SPP 25.0. The descriptive data was provided in a table and pie chart. Results: Based on the results of the PPS, 11 patients (18.96%) were among the high risk, and 47 patients (81.04%) were at low risk. The most potent risk factor in increasing the risk of DVT is reduced mobility with a risk priority number (RPN) of 30 (severity=3, occurrence=10). Recent (≤one month) trauma and surgery entail on second with an RPN of 24 (severity=2, occurrence=12). The third is occupied by heart and/or respiratory failure with a RPN of 14 (severity=1, occurrence=14). Previous VTE history with a RPN of 12 (severity=3, occurrence=4) placed fourth, followed by age≥ 70 (RPN=8, severity=1, occurrence=8) and obesity (BMI>= 30) with a RPN of 4 (severity=1, occurrence=4) at fifth and sixth respectively. Conclusion: "Reduced mobility" is the most prominent risk factor to increase DVT risk in routine hemodialysis patients, followed by other risk factors. Reduced mobility and obesity are modifiable risk factors that should be eliminated by educating routine hemodialysis patients.

Premature death in dogs with nontraumatic hemoabdomen and splenectomy with benign histopathologic findings

OBJECTIVE To determine whether premature death occurred among dogs with nonmalignant splenic histopathologic findings after splenectomy for nontraumatic hemoabdomen. ANIMALS 197 dogs with nontraumatic hemoabdomen that underwent splenectomy and histopathologic evaluation between 2005 and 2018. PROCEDURES Information was obtained from electronic medical records, dog owners, and referring veterinarians to determine patient characteristics, histopathologic findings, survival information, and cause of death. Dogs were grouped based on histopathological diagnosis and outcome, and median survival times (MSTs) and risk factors for death were determined. RESULTS Histopathologic findings indicated malignancy in 144 of the 197 (73.1%) dogs with nontraumatic hemoabdomen. Hemangiosarcoma was diagnosed in 126 dogs (87.5% of those with malignancies and 64.0% of all dogs). Nine of 53 (17%) dogs with nonmalignant histopathologic findings had an adverse outcome and premature death, with an MST of 49 days. Risk factors for this outcome included low plasma total solids concentration, an elevated hemangiosarcoma likelihood prediction score, and a medium or high hemangiosarcoma likelihood prediction score category. CONCLUSIONS AND CLINICAL RELEVANCE This study showed that there is a group of dogs with nontraumatic hemoabdomen due to splenic disease that have nonmalignant histopathologic findings after splenectomy, but nonetheless suffer an adverse outcome and die prematurely of a suspected malignancy. Further evaluation of potential at-risk populations may yield detection of otherwise overlooked malignancies.

OP129 The Use Of A Text-Mining Screening Tool For Systematic Review Of Treatments For Relapsed/Refractory Diffuse Large B-Cell Lymphoma

IntroductionHuman screening of title and abstracts in a systematic literature review (SLR) is labor intensive and time-consuming. In many instances, thousands of citations may be retrieved; the vast majority excluded upon screening. Text-mining semi-automates and accelerates screening by identifying patterns in relevant and irrelevant citations, as labelled by the screener. One such text-mining tool, Abstrackr, uses an algorithm within an active-learning framework to predict the likelihood of citations being relevant. The objective of this study was to assesses the performance of Abstrackr for title and abstract screening in an SLR of treatments for relapsed/refractory diffuse large B-cell lymphoma.MethodsCitations identified from searches of electronic databases were imported to Abstrackr. An investigator-selected database of terms indicating relevance of title and abstract to the research question were uploaded. These terms were partly informed by the SLR inclusion/exclusion criteria. Citations deemed most relevant by Abstrackr were screened first (screening prioritization). Screening was carried out until a maximum prediction score of 0.4 or less, based on previous experience in the literature, was reached. Remaining citations were deemed unlikely to be relevant and did not undergo screening (screening truncation). Separately, a single-human screener screened all citations using Covidence.ResultsA total of 7,723 citations and 154 initial terms were uploaded to Abstrackr. Of these citations, 2,572 (33 percent) were screened before a prediction score of 0.39 was reached. Compared to single-human screening (conducted on all citations), the workload saving associated with Abstrackr was 5 days. A total of 451 (6 percent) citations proceeded to full-text screening; ten (0.1 percent) were included in the final evidence base. No citations predicted to be irrelevant by Abstrackr were included in the final evidence base.ConclusionsText-mining tools such as Abstrackr have the potential to reduce workload associated with title and abstract screening, without missing relevant citations.

Difficult Airway Prediction Score for Intubation in Emergency Departments: A Retrospective Cohort Study

Objective: Difficult intubation is associated with an increasing number of endotracheal intubation attempts. Repeated endotracheal intubation attempts are in turn associated with an increased risk of adverse events. Clinical prediction tools to predict difficult airway have limited application in emergency airway situations. This study was performed to develop a new model for predicting difficult intubation in the emergency department.Methods: This retrospective study was conducted using an exploratory model at the Emergency Medicine of Ramathibodi Hospital, a university-affiliated super-tertiary care hospital in Bangkok, Thailand. The study was conducted from June 2018 to July 2020. The inclusion criteria were an age of ≥15 years and treatment by emergency intubation in the emergency department. Difficult intubation was defined as a Cormack–Lehane grade III or IV laryngoscopic view. The predictive model and prediction score for detecting difficult intubation were developed by multivariable regression analysis.Results: During the study period, 617 patients met the inclusion criteria; of these, 83 (13.45%) had difficult intubation. Five independent factors were predictive of difficult intubation. The difficult airway assessment score that we developed to predict difficult airway intubation had an accuracy of 89%. A score of >4 increased the likelihood ratio of difficult intubation by 7.62 times.Conclusion: A difficult airway assessment score of >4 was associated with difficult intubation.