Cost savings across bone marrow testing strategies in persons with suspected monoclonal gammopathy of undetermined significance

Abstract Introduction: Monoclonal gammopathy of undetermined significance (MGUS) affects over 2 million individuals aged ≥70 in the US. Earlier detection of smoldering multiple myeloma (SMM) and multiple myeloma (MM) in individuals with suspected MGUS via bone marrow biopsy allows for earlier active monitoring and potential intervention, including with daratumumab for high-risk SMM. Risk prediction for the presence of SMM or MM with the iStopMM and Mayo Clinic models leads to decreased individual-patient and population-level resource utilization burden of bone marrow testing with the tradeoff of some missed non-MGUS diagnoses. We sought to quantify the value of earlier diagnosis, while additionally accounting for the recent results of the AQUILA trial, by assessing the lifetime cost-effectiveness of bone marrow testing strategies for patients with suspected MGUS. Methods: In this independent analysis free of industry influence, we used a decision tree with nested Markov cohort models to evaluate diagnosis and disease progression from MGUS and SMM to MM across 3 diagnostic strategies employing: (1) iStopMM risk prediction model versus (2) Mayo Clinic risk stratification and prediction model versus (3) a test-all strategy. Both risk prediction model performance characteristics were derived from the largest population-level prospective cohort study. Individuals in the MGUS state were monitored annually for disease progression with blood tests. The SMM state included two groups: those correctly diagnosed through biopsy and those misclassified as MGUS due to not undergoing biopsy under the prediction model triage rules (missed SMM diagnosis). As recommended by guidelines, diagnosed patients with non–low-risk SMM were monitored with blood tests every three months; those with low-risk SMM were tested every six months. All diagnosed SMM patients underwent imaging for bone lesions at diagnosis and annually for up to five years. Missed SMM patients were monitored at the same frequency as those with MGUS until disease progression. Early versus delayed myeloma diagnosis complications were informed by real-world multi-institutional quasi-experimental data of consecutively diagnosed patients with MM. MM treatment and outcomes across up to four lines of therapy were modeled exactly as previously published in a validated model. In additional scenario analyses, we assessed the impact of daratumumab monotherapy in the care of people with high-risk SMM across all 3 testing strategies by incorporating the progression free survival and overall survival benefit as reported in the phase 3 AQUILA study. The primary outcome was the incremental net monetary benefit (iNMB), reported across all accepted US willingness-to-pay (WTP) thresholds from a US health system perspective. We concluded with extensive deterministic sensitivity analyses and additionally quantified uncertainty across all model parameters simultaneously with 10,000 Monte Carlo simulations. Results: The iStopMM model was the cost-effective strategy yielding the highest NMB of all strategies at every WTP threshold, with per-patient cost and effectiveness of $188,300 and 10.21 QALYs, respectively. This strategy was dominant—costing less and producing more QALYs—than both the Mayo Clinic and test-all strategies. At the population level, use of the iStopMM model over the Mayo Clinic model and the test-all strategy yields iNMBs of $0.67 [95% CI: 0.2 – 1.14] billion and $3.13 [95%CI: 2.80-3.47] billion, respectively, in favor of the iStopMM strategy. The iStopMM strategy was the cost-effective strategy in 100% of 10,000 second order Monte Carlo iterations. In deterministic sensitivity analyses, the top three parameters the iNMB was most sensitive to were prediction model test characteristics (i.e., sensitivity and specificity) as well as the baseline prevalence of SMM. However, no parameter variation could change the conclusion that iStopMM is the cost-effective strategy. In scenario analyses incorporating benefit of daratumumab monotherapy in the care of individuals with high-risk SMM, the iStopMM model remained the cost-effective strategy. Conclusion: Using the iStopMM risk prediction model for marrow sampling in patients with suspected MGUS leads to cost savings per patient evaluated, both with and without incorporating daratumumab benefit in the care of individuals with high-risk SMM, as compared to both the Mayo Clinic model and testing all with bone marrow biopsy.