Conectar
MRI is increasingly recognised as a valuable tool for assessing prognosis and predicting outcomes following traumatic spinal cord injury (SCI). Several potential MRI biomarkers have been identified, but efforts are still needed to improve the accuracy and feasibility of these biomarkers in clinical practice. This study aims to build a national Canadian SCI imaging repository for storing and analysing imaging data for SCI, with the goal of improving SCI MRI biomarkers to predict outcomes and inform clinical management.
As a substudy of the Rick Hansen SCI Registry (RHSCIR), this retrospective multisite study includes individuals who sustained a traumatic cervical SCI between 2015 and 2021, were previously enrolled in RHSCIR, and had MRI scans acquired within 72 hours of injury and before any surgical intervention. Individuals with a penetrating trauma and/or with any prior spine surgery are excluded. The study principal investigator and research associates, experienced with data curation and with the standardised format and specifications of the Brain Imaging Data Structure standard, guide the site’s curator on the steps to perform image deidentification and curation to create standardised datasets across all sites. These datasets are transferred to a Digital Research Alliance of Canada (‘the Alliance’) server designated for this project and concatenated to form the national Canadian SCI imaging repository (Neurogitea). We are using a semiautomated processing pipeline to quantify lesion morphology, together with additional imaging measures that are manually extracted from the images (for instance, the relative maximal spinal cord compression and the maximum canal compromise). Through linkage to RHSCIR clinical and epidemiological data already available on eligible participants, regression analysis is planned to predict neurological outcomes at discharge, including the American Spinal Injury Association Impairment Scale grade, upper and lower extremity motor and sensory scores.
This protocol has been submitted by the participating sites to obtain ethics and institutional approvals prior to the study initiation at each site. All 12 sites across Canada have now obtained ethics and institutional approvals. Study results will be disseminated at local, national and international conferences and by journal publications.
by Ikgyu Shin, Nilay Bhatt, Alaa Alashi, Keervani Kandala, Karthik Murugiah
ObjectivesTo develop and compare prediction models for 30-day and 1-year mortality in Heart failure with preserved ejection fraction (HFpEF) using EHR data, utilizing both traditional and machine learning (ML) techniques.
BackgroundHFpEF represents 1 in 2 heart failure patients. Predictive models in HFpEF, specifically those derived from electronic health record (EHR) data, are less established.
MethodsUsing MIMIC-IV EHR data from 2008−2019, patients aged ≥ 18 years admitted with a primary diagnosis of HFpEF were identified using ICD-9 and 10 codes. Demographics, vital signs, prior diagnoses, and lab data were extracted. Data was partitioned into 80% training, 20% test sets. Prediction models from seven model classes (Support Vector Classifier (SVC), Logistic Regression, Lasso Regression, Elastic Net, Random Forest, Histogram-based Gradient Boosting Classifier (HGBC), and eXtreme Gradient Boosting (XGBoost)) were developed using various imputation and oversampling techniques with 5-fold cross-validation. Model performance was compared using several metrics, and individual feature importance assessed using SHapley Additive exPlanations (SHAP) analysis.
ResultsAmong 3,235 hospitalizations for HFpEF, 30-day mortality was 6.3%, and 1- year mortality was 29.2%. Logistic regression performed well for 30-day mortality (Area Under the Receiver operating characteristic curve (AUC) 0.83), whereas Random Forest (AUC 0.79) and HGBC (AUC 0.78) for 1-year mortality. Age and NT-proBNP were the strongest predictors in SHAP analyses for both outcomes.
ConclusionModels derived from EHR data can predict mortality after HFpEF hospitalization with comparable performance to models derived from registry or trial data, highlighting the potential for clinical implementation.
FreshRSS 