Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning

Clinical audit of invasive mold disease (IMD) in hematology patients is inefficient due to the difficulties of case finding. This results in antifungal stewardship (AFS) programs preferentially reporting drug cost and consumption rather than measures that actually reflect quality of care. We used machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions and benchmarked against key AFS measures. NLP screened 3014 reports from 1 September 2008 to 31 December 2017, generating 784 positives that after review, identified 205 IMD episodes (44% probable-proven) in 185 patients from 50,303 admissions. Breakthrough-probable/proven-IMD on antifungal prophylaxis accounted for 60% of episodes with serum monitoring of voriconazole or posaconazole in the 2 weeks prior performed in only 53% and 69% of episodes, respectively. Fiberoptic bronchoscopy within 2 days of CT scan occurred in only 54% of episodes. The average turnaround of send-away bronchoalveolar galactomannan of 12 days (range 7-22) was associated with high empiric liposomal amphotericin consumption. A random audit of 10% negative reports revealed two clinically significant misses (0.9%, 2/223). This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. Compared to current methods of clinical audit, semi-automated surveillance using NLP is more efficient and inclusive by avoiding restrictions based on any underlying hematologic condition, and has the added advantage of being potentially scalable

Characteristics of post hoc subgroup analyses of oncology clinical trials: A systematic review

BACKGROUND: Subgroup analyses in clinical trials assess intervention effects on specific patient subgroups, ensuring generalizability. However, they are usually only able to generate hypotheses rather than definitive conclusions. This study examined the prevalence and characteristics of post hoc subgroup analysis in oncology. METHODS: We systematically reviewed published subgroup analyses from 2000 to 2022. We included articles presenting secondary, post hoc, or subgroup analyses of interventional clinical trials in oncology, cancer survivorship, or cancer screening, published separately from the original clinical trial publication. We collected cancer type, year of publication, where and how subgroup analyses were reported, and funding. RESULTS: Out of 16 487 screened publications, 1612 studies were included, primarily subgroup analyses of treatment trials for solid tumors (82%). Medical writers contributed to 31% of articles, and 58% of articles reported conflicts of interest. Subgroup analyses increased significantly over time, with 695 published between 2019 and 2022, compared to 384 from 2000 to 2014. Gastrointestinal tumors (25%) and lymphoid lineage tumors (39%) were the most frequently studied solid and hematological malignancies, respectively. Industry funding and reporting of conflicts of interest increased over time. Subgroup analyses often neglected to indicate their secondary nature in the title. Most authors were from high-income countries, most commonly North America (45%). CONCLUSIONS: This study demonstrates the rapidly growing use of post hoc subgroup analysis of oncology clinical trials, revealing that the majority are supported by pharmaceutical companies, and they frequently fail to indicate their secondary nature in the title. Given the known methodological limitations of subgroup analyses, caution is recommended among authors, readers, and reviewers when conducting and interpreting these studies