Risk factors associated with skeletal-related events following discontinuation of denosumab treatment among patients with bone metastases from solid tumors: A real-world machine learning approach

Dionna Jacobson; Benoit Cadieux; Celestia S Higano; David H Henry; Basia A Bachmann; Marko Rehn; Alison T Stopeck; Hossam Saad

doi:10.1016/j.jbo.2022.100423

Risk factors associated with skeletal-related events following discontinuation of denosumab treatment among patients with bone metastases from solid tumors: A real-world machine learning approach

J Bone Oncol. 2022 Mar 17:34:100423. doi: 10.1016/j.jbo.2022.100423. eCollection 2022 Jun.

Authors

Dionna Jacobson¹, Benoit Cadieux¹, Celestia S Higano², David H Henry³, Basia A Bachmann¹, Marko Rehn¹, Alison T Stopeck⁴, Hossam Saad¹

Affiliations

¹ Amgen Inc., Thousand Oaks, CA, USA.
² University of British Columbia, Vancouver, Canada.
³ University of Pennsylvania, Pennsylvania Hospital, Philadelphia, PA, USA.
⁴ Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA.

Abstract

Background: Clinical practice guidelines recommend the use of bone-targeting agents for preventing skeletal-related events (SREs) among patients with bone metastases from solid tumors. The anti-RANKL monoclonal antibody denosumab is approved for the prevention of SREs in patients with bone metastases from solid tumors. However, real-world data are lacking on the impact of individual risk factors for SREs, specifically in the context of denosumab discontinuation.

Purpose: We aim to identify risk factors associated with SRE incidence following denosumab discontinuation using a machine learning approach to help profile patients at a higher risk of developing SREs following discontinuation of denosumab treatment.

Methods: Using the Optum PanTher Electronic Health Record repository, patients diagnosed with incident bone metastases from primary solid tumors between January 1, 2007, and September 1, 2019, were evaluated for inclusion in the study. Eligible patients received ≥ 2 consecutive 120 mg denosumab doses on a 4-week (± 14 days) schedule with a minimum follow-up of ≥ 1 year after the last denosumab dose, or an SRE occurring between days 84 and 365 after denosumab discontinuation. Extreme gradient boosting was used to develop an SRE risk prediction model evaluated on a test dataset. Multiple variables associated with patient demographics, comorbidities, laboratory values, treatments, and denosumab exposures were examined as potential factors for SRE risk using Shapley Additive Explanations (SHAP). Univariate analyses on risk factors with the highest importance from pooled and tumor-specific models were also conducted.

Results: A total of 1,414 adult cancer patients (breast: 40%, prostate: 30%, lung: 13%, other: 17%) were eligible, of whom 1,133 (80%) were assigned to model training and 281 (20%) to model evaluation. The median age at inclusion was 67 (range, 19-89) years with a median duration of denosumab treatment of 253 (range, 88-2,726) days; 490 (35%) patients experienced ≥ 1 SRE 83 days after denosumab discontinuation. Meaningful model performance was evaluated by an area under the receiver operating curve score of 77% and an F1 score of 62%; model precision was 60%, with 63% sensitivity and 78% specificity. SHAP identified several significant factors for the tumor-agnostic and tumor-specific models that predicted an increased SRE risk following denosumab discontinuation, including prior SREs, shorter denosumab treatment duration, ≥ 4 clinic visits per month with at least one hospitalization (all-cause) event from the baseline period up to discontinuation of denosumab, younger age at bone metastasis, shorter time to denosumab initiation from bone metastasis, and prostate cancer.

Conclusion: This analysis showed a higher cumulative number of SREs, prior SREs relative to denosumab initiation, a higher number of hospital visits, and a shorter denosumab treatment duration as significant factors that are associated with an increased SRE risk after discontinuation of denosumab, in both the tumor-agnostic and tumor-specific models. Our machine learning approach to SRE risk factor identification reinforces treatment guidance on the persistent use of denosumab and has the potential to help clinicians better assess a patient's need to continue denosumab treatment and improve patient outcomes.

Keywords: AUC, area under the curve; AUROC, area under the receiver operating curve; BTA, bone-targeting agent; Bone-targeting agents; CPT, Current Procedural Terminology; Classification model; Denosumab; ECOG, Eastern Cooperative Oncology Group; EHR, electronic health record; ESMO, European Society for Medical Oncology; HCPCS, Healthcare Common Procedure Coding System; ICD, International Classification of Diseases; ONJ, osteonecrosis of the jaw; PHI, protected health information; Q4W, every four weeks; RANKL, receptor activator of nuclear factor κβ ligand; ROC, receiver operating characteristic; Retrospective observational study; SHAP, Shapley Additive Explanations; SRE, skeletal-related event; Skeletal-related events; XGB, Extreme Gradient Boost.