Once-weekly versus twice-weekly bortezomib in newly diagnosed multiple myeloma: a real-world analysis.

Induction regimens for multiple myeloma (MM) commonly include bortezomib, which has typically been administered twice weekly despite studies demonstrating comparable efficacy and less peripheral neuropathy (PN) with once-weekly bortezomib. We aimed to analyze the real-world prevalence and efficacy of once-weekly versus twice-weekly bortezomib regimens in newly diagnosed MM. We analyzed 2497 US patients aged 18-70 years treated with commercial first-line bortezomib using nationwide Flatiron Health electronic health record-derived data, including 910 (36.4%) patients who received twice-weekly and 1522 (63.2%) who received once-weekly bortezomib. Once-weekly bortezomib use increased over time, from 57.7% in 2017 to 73.1% in 2022. Multivariate analysis identified worsened performance status and more recent year of diagnosis with higher odds of receiving once-weekly bortezomib. Real-world progression-free survival (median 37.2 months with once-weekly versus 39.6 months with twice-weekly, p = 0.906) and overall survival (medians not reached in either cohort, p = 0.800) were comparable. PN rates were higher in patients receiving twice-weekly bortezomib (34.7% versus 18.5%, p < 0.001). In conclusion, once-weekly bortezomib is clearly associated with similar efficacy and fewer toxicities compared to twice-weekly bortezomib. Our findings support once-weekly bortezomib as a standard-of-care regimen for newly diagnosed patients with MM.

RWD-derived response in multiple myeloma.

Real-world data (RWD) are important for understanding the treatment course and response patterns of patients with multiple myeloma. This exploratory pilot study establishes a way to reliably assess response from incomplete laboratory measurements captured in RWD. A rule-based algorithm, adapted from International Myeloma Working Group response criteria, was used to derive response using RWD. This derived response (dR) algorithm was assessed using data from the phase III BELLINI trial, comparing the number of responders and non-responders assigned by independent review committee (IRC) versus the dR algorithm. To simulate a real-world scenario with missing data, a sensitivity analysis was conducted whereby available laboratory measurements in the dataset were artificially reduced. Associations between dR and overall survival were evaluated at 1) individual level and 2) treatment level in a real-world patient cohort obtained from a nationwide electronic health record-derived de-identified database. The algorithm's assignment of responders was highly concordant with that of the IRC (Cohen's Kappa 0.83) using the BELLINI data. The dR replicated the differences in overall response rate between the intervention and placebo arms reported in the trial (odds ratio 2.1 vs. 2.3 for IRC vs. dR assessment, respectively). Simulation of missing data in the sensitivity analysis (-50% of available laboratory measurements and -75% of urine monoclonal protein measurements) resulted in a minor reduction in the algorithm's accuracy (Cohen's Kappa 0.75). In the RWD cohort, dR was significantly associated with overall survival at all landmark times (hazard ratios 0.80-0.81, p<0.001) at the individual level, while the overall association was R2 = 0.67 (p<0.001) at the treatment level. This exploratory pilot study demonstrates the feasibility of deriving accurate response from RWD. With further confirmation in independent cohorts, the dR has the potential to be used as an endpoint in real-world studies and as a comparator in single-arm clinical trials.

Exploring racial disparities in treatment patterns and outcomes for patients with multiple myeloma using real world data.

This retrospective observational study evaluated racial disparities among Black and White patients with multiple myeloma (MM). We included patients from a longitudinal de-identified EHR-derived database who had ≥2 visits recorded on or after 1/1/2011, documented treatment, and race listed as White or Black. Black patients (n = 1172) were more likely female (54.8%/42.9%) and younger (<65 years, 40.8%/30.8%) than White patients (n = 4637). Unadjusted median real-world overall survival (rwOS) indexed to first-line of therapy (LOT) was 64.6 months (95% CI: 57.8-74.0) for Blacks and 54.5 months (95% CI: 50.9-56.2) for Whites. Adjusted rwOS estimates (for sex, age at index date, and practice type) to either first- (aHR = 0.94; 95% CI: 0.84-1.06) or second-LOT (aHR = 0.90; 95% CI: 0.77-1.05) were similar. Unadjusted derived response rate (dRR) during first-LOT was 84.8% (95% CI: 80.7-88.1) for Blacks and 86.9% (95% CI: 85.0-88.5) for Whites (odds ratio [OR] = 0.78 [95% CI: 0.57-1.10]); in second-LOT, 67.2% (95% CI: 58.4-75.0) for Blacks and 72.4% (95% CI: 68.1-76.3) for Whites (OR = 0.72 [95% CI: 0.46-1.13]). High representation of Black patients enabled this robust analysis, albeit with limitations inherent to the observational data source, the retrospective design, and the analytic use of newly derived endpoints requiring further validation.

Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.

BACKGROUND: Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction. METHODS: We applied the Super Learner (SL) algorithm to the 2016-2018 thyroidectomy-specific NSQIP database to predict complications following thyroidectomy. Cross-validation was used to assess model discrimination and precision. RESULTS: For the 17,987 patients undergoing thyroidectomy, rates of recurrent laryngeal nerve injury, post-operative hypocalcemia prior to discharge or within 30 days, and neck hematoma were 6.1%, 6.4%, 9.0%, and 1.8%, respectively. SL improved prediction of thyroidectomy-specific outcomes when compared with benchmark logistic regression approaches. For postoperative hypocalcemia prior to discharge, SL improved the cross-validated AUROC to 0.72 (95%CI 0.70-0.74) compared to 0.70 (95%CI 0.68-0.72; p < 0.001) when using a manually curated logistic regression algorithm. CONCLUSION: Ensemble machine learning modestly improves prediction for thyroidectomy-specific outcomes. SL holds promise to provide more accurate patient-level risk prediction to inform treatment decisions.