Development of Electronic Health Record-Based Machine Learning Models to Predict Barrett's Esophagus and Esophageal Adenocarcinoma Risk.

INTRODUCTION: Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. METHODS: The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models. RESULTS: We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes. DISCUSSION: ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

Biochemical changes in heat exposed buffalo heifers supplemented with yeast.

Serum electrolytes, minerals, blood biochemical and plasma enzymes were studied in heat exposed buffalo heifers supplemented with yeast powder in feed to alleviate the adverse effects of heat stress. Eighteen healthy Murrah buffalo heifers (270-280) kg were divided into three groups. Animals of group I were kept in shed and served as control, while group II and group III were exposed in a psychrometric chamber at 40°C for 4 hrs daily for 16 days continuously. The animals in group III were also supplemented with yeast powder at 10 g per animal per day. The serum sodium, potassium and chloride levels decreased significantly (P < 0.05) in group II and their levels tended to be maintained in group III. Serum calcium, magnesium, phosphorus, glucose and total cholesterol concentration decreased (P < 0.05) significantly whereas total protein increased in group II due to heat stress. The levels of these minerals tended to be normal in group III. The plasma concentration of SGOT and SGPT increased significantly (P < 0.05), whereas the concentration of plasma alkaline phosphatase and acid phosphatase decreased due to thermal exposure. The levels of these enzymes tended to be normal in group III. The results indicated significant deviations in blood biochemical due to thermal stress and their levels tended to be normal in yeast supplemented group of buffalo heifers.