A Risk Profile Using Simple Hematologic Parameters to Assess Benefits From Baricitinib in Patients Hospitalized With COVID-19: A Post Hoc Analysis of the Adaptive COVID-19 Treatment Trial-2.

BACKGROUND: The ACTT risk profile, which was developed from ACTT-1 (Adaptive COVID-19 Treatment Trial-1), demonstrated that hospitalized patients with COVID-19 in the high-risk quartile (characterized by low absolute lymphocyte count [ALC], high absolute neutrophil count [ANC], and low platelet count at baseline) benefited most from treatment with the antiviral remdesivir. It is unknown which patient characteristics are associated with benefit from treatment with the immunomodulator baricitinib. OBJECTIVE: To apply the ACTT risk profile to the ACTT-2 cohort to investigate potential baricitinib-related treatment effects by risk quartile. DESIGN: Post hoc analysis of ACTT-2, a randomized, double-blind, placebo-controlled trial. (ClinicalTrials.gov: NCT04401579). SETTING: Sixty-seven trial sites in 8 countries. PARTICIPANTS: Adults hospitalized with COVID-19 (n = 999; 85% U.S. participants). INTERVENTION: Baricitinib+remdesivir versus placebo+remdesivir. MEASUREMENTS: Mortality, progression to invasive mechanical ventilation (IMV) or death, and recovery, all within 28 days; ALC, ANC, and platelet count trajectories. RESULTS: In the high-risk quartile, baricitinib+remdesivir was associated with reduced risk for death (hazard ratio [HR], 0.38 [95% CI, 0.16 to 0.86]; P = 0.020), decreased progression to IMV or death (HR, 0.57 [CI, 0.35 to 0.93]; P = 0.024), and improved recovery rate (HR, 1.53 [CI, 1.16 to 2.02]; P = 0.002) compared with placebo+remdesivir. After 5 days, participants receiving baricitinib+remdesivir had significantly larger increases in ALC and significantly larger decreases in ANC compared with control participants, with the largest effects observed in the high-risk quartile. LIMITATION: Secondary analysis of data collected before circulation of current SARS-CoV-2 variants. CONCLUSION: The ACTT risk profile identifies a subgroup of hospitalized patients who benefit most from baricitinib treatment and captures a patient phenotype of treatment response to an immunomodulator and an antiviral. Changes in ALC and ANC trajectory suggest a mechanism whereby an immunomodulator limits severe COVID-19. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.

Exploration of a Potential DOOR Endpoint for Hospital-acquired Bacterial Pneumonia and Ventilator-associated Bacterial Pneumonia Using Six Registrational Trials for Antibacterial Drugs.

BACKGROUND: Desirability of outcome ranking (DOOR) is an innovative approach to clinical trial design and analysis that uses an ordinal ranking system to incorporate the overall risks and benefits of a therapeutic intervention into a single measurement. Here we derived and evaluated a disease-specific DOOR endpoint for registrational trials for hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia (HABP/VABP). METHODS: Through comprehensive examination of data from nearly 4000 participants enrolled in six registrational trials for HABP/VABP submitted to the Food and Drug Administration (FDA) between 2005 and 2022, we derived and applied a HABP/VABP specific endpoint. We estimated the probability that a participant assigned to the study treatment arm would have a more favorable overall DOOR or component outcome than a participant assigned to comparator. RESULTS: DOOR distributions between treatment arms were similar in all trials. DOOR probability estimates ranged from 48.3% to 52.9% and were not statistically different. There were no significant differences between treatment arms in the component analyses. Although infectious complications and serious adverse events occurred more frequently in ventilated participants compared to non-ventilated participants, the types of events were similar. CONCLUSIONS: Through a data-driven approach, we constructed and applied a potential DOOR endpoint for HABP/VABP trials. The inclusion of syndrome-specific events may help to better delineate and evaluate participant experiences and outcomes in future HABP/VABP trials and could help inform data collection and trial design.

Predictive Modeling of Drug-Related Adverse Events with Real-World Data: A Case Study of Linezolid Hematologic Outcomes.

Electronic health records (EHRs) provide meaningful knowledge of drug-related adverse events (AEs) that are not captured in standard drug development and postmarketing surveillance. Using variables obtained from EHR data in the University of California San Francisco de-identified Clinical Data Warehouse, we aimed to evaluate the potential of machine learning to predict two hematological AEs, thrombocytopenia and anemia, in a cohort of patients treated with linezolid for 3 or more days. Features for model input were extracted at linezolid initiation (index), and outcomes were characterized from index to 14 days post-treatment. Random forest classification (RFC) was used for AE prediction, and reduced feature models were evaluated using cumulative importance (cImp) for feature selection. Grade 3+ thrombocytopenia and anemia occurred in 31% of 2,171 and 56% of 2,170 evaluable patients, respectively. Of the total 53 features, as few as 7 contributed at least 50% cImp, resulting in prediction accuracies of 70% or higher and area under the receiver operating characteristic curves of 0.886 for grade 3+ thrombocytopenia and 0.759 for grade 3+ anemia. Sensitivity analyses in strictly defined patient subgroups revealed similarly high predictive performance in full and reduced feature models. A logistic regression model with the same 50% cImp features showed similar predictive performance as RFC and good concordance with RFC probability predictions after isotonic calibration, adding interpretability. Collectively, this work demonstrates potential for machine learning prediction of AE risk in real-world patients using few variables regularly available in EHRs, which may aid in clinical decision making and/or monitoring.