Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation

Maternal ABO Blood Phenotype and Factors Associated with Preeclampsia Subtype

Preeclampsia affects 3-8% of all pregnancies and is a global issue that significantly effects the short and long-term health of women and neonates. The pathophysiology of preeclampsia remains unclear and there seems to be two distinct subtypes, early and late onset. Each subtype may have a unique pathophysiology and set of risk factors. Preeclampsia is linked to long-term risk of cardiovascular disease in previously affected women. Subsequently, risk factors shared between preeclampsia and cardiovascular disease should be explored. The main aim of this study was to determine the strength of association between maternal ABO blood type and preeclampsia subtype. This hospital-based case control study was completed at one community hospital in the Mid Atlantic, United States. The study included 126 female subjects with early onset preeclampsia (≤ 33 6/7 weeks gestation), 126 female subjects with late onset preeclampsia (≥ 34 weeks gestation) and 259 control subjects with no history of preeclampsia. Strict diagnostic criteria were used and preeclamptic subjects were classified by subtype based on gestational age at diagnosis. Data on ABO blood type, as well other physical and socio-demographic variables were extracted from the electronic health record. No significant association was noted between preeclampsia subtype and non-O blood type (p=.456) and ABO blood phenotype trended towards significance (p=.062). After exclusion of subjects with comorbidities (CHTN, GDM and DM) from the sample (n=403), there was a significant association noted between ABO blood type and preeclampsia subtype (p=.001). A significant association was also noted between preeclamptic subjects with growth restriction and ABO blood type (p= \u3c.001). Preeclamptic subjects with the B blood type had OR=3.44, 95% CI 1.58, 7.50 of having a growth-restricted fetus than did those with other blood types. Finally, when adjusting for race only, subjects with AB blood type had the following odds (OR=3.03, 95% CI 1.04, 8.80; OR=3.35, 95% CI 1.02, 11.03.) of developing pre-eclampsia and late onset preeclampsia respectively. When other clinical risk factors of preeclampsia are included in the model, AB blood type significantly predicts membership in the early onset preeclampsia subtype (OR=5.41, 95% CI, 1.19, 24.55) and was trend-level in the late onset group (p=.053). Preeclamptic women with B blood type had three times the odds of having a growth-restricted fetus, subsequently; they may require close ultrasound surveillance. AB blood type was significantly associated with three times increased odds of late onset preeclampsia. When included in a model with other common risk factors of preeclampsia, ABO blood type only accounted for a small amount of variability in the model. ABO blood type may not be a valuable addition to a preeclampsia-screening algorithm that already includes common clinical risk factors of preeclampsia. However, when controlling for other common clinical risk factors of preeclampsia, women with AB blood type had over 5 times the odds of developing early onset preeclampsia. Further research is necessary to examine if blood type regulates biomarkers that mediate the development of each preeclampsia subtype or in some way is associated with severe features of the disease

Secular Trends in Ischemic Stroke Subtypes

Background: With an aging population and an increasing prevalence of therapy for atherosclerosis, it might be expected that stroke subtypes would be changing over time. Limited information exists on the ischemic stroke subtypes in adults in Canada. Methods: Patients referred to the Urgent TIA Clinic, in London, Ontario, between 2002-2012 were included. Secular trends were analyzed using Poisson regression with spline trend function. Ischemic stroke subtype classification was validated. Results: 3,445 consecutive patients (mean age + SD 64.8 + 14.9) were included. Cardioembolic strokes/TIAs increased from 21% in 2002 to 56% in 2012, whereas all other ischemic stroke subtypes decreased (p\u3c0.05). Separate analysis in men and women showed similar results. Conclusions: The decrease in atherosclerotic risk factors resulted in fewer strokes/TIAs caused by large artery atherosclerosis. On the contrary, cardioembolic strokes/TIAs have increased. This has important implications for more intensive investigation and treatment to reduce the risk of recurrent embolic stroke/TIA

2023 ESC Guidelines for the management of acute coronary syndromes.

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Added value of acute multimodal CT-based imaging (MCTI) : a comprehensive analysis

Introduction: MCTI is used to assess acute ischemic stroke (AIS) patients.We postulated that use of MCTI improves patient outcome regardingindependence and mortality.Methods: From the ASTRAL registry, all patients with an AIS and a non-contrast-CT (NCCT), angio-CT (CTA) or perfusion-CT (CTP) within24 h from onset were included. Demographic, clinical, biological, radio-logical, and follow-up caracteristics were collected. Significant predictorsof MCTI use were fitted in a multivariate analysis. Patients undergoingCTA or CTA&CTP were compared with NCCT patients with regards tofavourable outcome (mRS ≤ 2) at 3 months, 12 months mortality, strokemechanism, short-term renal function, use of ancillary diagnostic tests,duration of hospitalization and 12 months stroke recurrence