Phenotyping Hepatic Immune-Related Adverse Events in the Setting of Immune Checkpoint Inhibitor Therapy.

PURPOSE: We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets. METHODS: We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI. RESULTS: A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74. CONCLUSION: The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.

Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C.

INTRODUCTION: Hepatitis C, which affects 71 million persons worldwide, is the most common blood-borne pathogen in the United States. Chronic infections can be treated effectively thanks to the availability of modern direct-acting antiviral (DAA) therapies. Real-world data on the duration of DAA therapy, which can be used to optimize and guide the course of therapy, may also be useful in determining quality of life enhancements based upon total required supply of medication and long-term improvements to quality of life. We developed a machine learning model to identify patient characteristics associated with prolonged DAA treatment duration. METHODS: A nationwide U.S. commercial managed care plan with claims data that covers about 60 million beneficiaries from 2009 to 2019 were used in the retrospective study. We examined differences in age, gender, and multiple comorbidities among patients treated with different durations of DAA treatment. We also examined the performance of machine learning models for predicting a prolonged course of DAA based on the area under the receiver operating characteristic curve (AUC). RESULTS: We identified 3943 cases with hepatitis C who received sofosbuvir/ledipasvir as the first course of DAA and were eligible for the study. Patients receiving prolonged treatment (n = 240, 6.1%) were more likely to have compensated cirrhosis, decompensated cirrhosis, and other comorbidities (P < 0.001). For distinguishing patients who received prolonged DAA treatment for hepatitis C from patients received standard treatment, the optimal predictive model, constructed with XGBoost, had an AUC of 0.745 ± 0.031 (P < 0.001). CONCLUSIONS: The risk of antiviral resistance and the cost of DAA are strong motivators to ensure that first-round DAA therapy is effective. For the dominant DAA treatment during the course of this analysis, we present a model that identifies factors already captured in established guidelines and adds to those age, comorbidity burden, and type 2 diabetes status; patient characteristics that are predictive of extended treatment.

An application to support COVID-19 occupational health and patient tracking at a Veterans Affairs medical center.

OBJECTIVE: Reducing risk of coronavirus disease 2019 (COVID-19) infection among healthcare personnel requires a robust occupational health response involving multiple disciplines. We describe a flexible informatics solution to enable such coordination, and we make it available as open-source software. MATERIALS AND METHODS: We developed a stand-alone application that integrates data from several sources, including electronic health record data and data captured outside the electronic health record. RESULTS: The application facilitates workflows from different hospital departments, including Occupational Health and Infection Control, and has been used extensively. As of June 2020, 4629 employees and 7768 patients and have been added for tracking by the application, and the application has been accessed over 46 000 times. DISCUSSION: Data captured by the application provides both a historical and real-time view into the operational impact of COVID-19 within the hospital, enabling aggregate and patient-level reporting to support identification of new cases, contact tracing, outbreak investigations, and employee workforce management. CONCLUSIONS: We have developed an open-source application that facilitates communication and workflow across multiple disciplines to manage hospital employees impacted by the COVID-19 pandemic.