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

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This study investigated SRE risk factors after densomuab treatment discontinuation.

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An unbiased machine learning approach was developed to evaluate >60 variables.

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Prior SREs and short denosumab treatment duration were primary risk factors.

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The results can guide denosumab persistence decisions and improve patient outcomes.

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.

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

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Background: The anti-RANKL monoclonal antibody denosumab has been shown to be superior to the bisphosphonate zoledronate for the prevention of skeletal-related events (SREs) in patients with incident bone metastases (BM) from solid tumors (ST). Clinical guidelines recommend the use of a bone-targeting agent for SRE prevention for ≥ 2 years. However, real-world treatment patterns in the U.S. suggest that the denosumab treatment duration is often < 1 year. Applying a machine learning approach, we sought to identify risk factors associated with SRE incidence following cessation of denosumab to help inform optimal clinical SRE prevention strategies. Methods: Using the Optum PanTher Electronic Health Record repository, patients diagnosed with incident BM from a primary ST between 1 Jan 2007 and 1 Sep 2019 were evaluated for inclusion in the study. Eligible patients had to receive ≥ 2 consecutive 120 mg denosumab doses on an every 4-week (± 14 days) schedule and have a minimum follow-up ≥ 1 year after the last denosumab dose or an SRE occurring between days 84 and 365 after denosumab cessation. Extreme gradient boosting was used to develop an SRE risk prediction model evaluated on a test dataset. Impact and relative importance of available medical, clinical, and treatment factors on SRE risk following denosumab cessation were extracted from the model 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 patients (breast, n = 563 [40%]; prostate, 421 [30%]; lung, 180 [13%]; other cancers, 250 [17%]) met inclusion criteria, with a median of 253 (min, 88; max, 2726) days of denosumab treatment; 490 (35%) experienced ≥ 1 SRE following denosumab cessation. With a meaningful model performance based on an area under the receiver operating characteristic (AUROC) score of 77%, SHAP identified several significant factors that predicted an increased SRE risk following denosumab cessation, including prior SREs, shorter denosumab treatment duration, and a higher number of clinic visits as the top-ranked factors (Table). Conclusions: A machine learning approach to SRE risk factor identification may help clinicians better assess the individualized patient’s need for denosumab treatment persistence and improve patient outcomes. Results from tumor-specific groups will be presented at the meeting.[Table: see text]