Automated, High-Throughput Platform to Generate a High-Reliability, Comprehensive Rectal Cancer Database.

PURPOSE: Dynamic operations platforms allow for cross-platform data extraction, integration, and analysis, although application of these platforms to large-scale oncology enterprises has not been described. This study presents a pipeline for automated, high-fidelity extraction, integration, and validation of cross-platform oncology data in patients undergoing treatment for rectal cancer at a single, high-volume institution. METHODS: A dynamic operations platform was used to identify patients with rectal cancer treated at MD Anderson Cancer Center between 2016 and 2022 who had magnetic resonance imaging (MRI) imaging and preoperative treatment details available in the electronic health record (EHR). Demographic, clinicopathologic, tumor mutation, radiographic, and treatment data were extracted from the EHR using a methodology adaptable to any disease site. Data accuracy was assessed by manual review. Accuracy before and after implementation of synoptic reporting was determined for MRI data. RESULTS: A total of 516 patients with localized rectal cancer were included. In the era after institutional adoption of synoptic reports, the dynamic operations platform extracted T (tumor) category data from the EHR with 95% accuracy compared with 87% before the use of synoptic reports, and N (lymph node) category with 88% compared with 58%. Correct extraction of pelvic sidewall adenopathy was 94% compared with 78%, and extramural vascular invasion accuracy was 99% compared with 89%. Neoadjuvant chemotherapy and radiation data were 99% accurate for patients who had synoptic data sources. CONCLUSION: Using dynamic operations platforms enables automated cross-platform integration of multiparameter oncology data with high fidelity in patients undergoing multimodality treatment for rectal cancer. These pipelines can be adapted to other solid tumors and, together with standardized reporting, can increase efficiency in clinical research and the translation of actionable findings toward optimizing patient outcomes.

Pediatric Artificial Lung: A Low-Resistance Pumpless Artificial Lung Alleviates an Acute Lamb Model of Increased Right Ventricle Afterload.

Lung disease in children often results in pulmonary hypertension and right heart failure. The availability of a pediatric artificial lung (PAL) would open new approaches to the management of these conditions by bridging to recovery in acute disease or transplantation in chronic disease. This study investigates the efficacy of a novel PAL in alleviating an animal model of pulmonary hypertension and increased right ventricle afterload. Five juvenile lambs (20-30 kg) underwent PAL implantation in a pulmonary artery to left atrium configuration. Induction of disease involved temporary, reversible occlusion of the right main pulmonary artery. Hemodynamics, pulmonary vascular input impedance, and right ventricle efficiency were measured under 1) baseline, 2) disease, and 3) disease + PAL conditions. The disease model altered hemodynamics variables in a manner consistent with pulmonary hypertension. Subsequent PAL attachment improved pulmonary artery pressure (p = 0.018), cardiac output (p = 0.050), pulmonary vascular input impedance (Z.0 p = 0.028; Z.1 p = 0.058), and right ventricle efficiency (p = 0.001). The PAL averaged resistance of 2.3 ± 0.8 mm Hg/L/min and blood flow of 1.3 ± 0.6 L/min. This novel low-resistance PAL can alleviate pulmonary hypertension in an acute animal model and demonstrates potential for use as a bridge to lung recovery or transplantation in pediatric patients with significant pulmonary hypertension refractory to medical therapies.