Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

Detecting Cerebral Ischemia From Electroencephalography During Carotid Endarterectomy Using Machine Learning.

Monitoring cerebral neuronal activity via electroencephalography (EEG) during surgery can detect ischemia, a precursor to stroke. However, current neurophysiologist-based monitoring is prone to error. In this study, we evaluated machine learning (ML) for efficient and accurate ischemia detection. We trained supervised ML models on a dataset of 802 patients with intraoperative ischemia labels and evaluated them on an independent validation dataset of 30 patients with refined labels from five neurophysiologists. Our results show moderate-to-substantial agreement between neurophysiologists, with Cohen's kappa values between 0.59 and 0.74. Neurophysiologist performance ranged from 58-93% for sensitivity and 83-96% for specificity, while ML models demonstrated comparable ranges of 63-89% and 85-96%. Random Forest (RF), LightGBM (LGBM), and XGBoost RF achieved area under the receiver operating characteristic curve (AUROC) values of 0.92-0.93 and area under the precision-recall curve (AUPRC) values of 0.79-0.83. ML has the potential to improve intraoperative monitoring, enhancing patient safety and reducing costs.