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.
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
Journal
Journal of bone oncology
ISSN: 2212-1366
Titre abrégé: J Bone Oncol
Pays: Netherlands
ID NLM: 101610292
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
received:
16
11
2021
revised:
08
03
2022
accepted:
14
03
2022
entrez:
5
4
2022
pubmed:
6
4
2022
medline:
6
4
2022
Statut:
epublish
Résumé
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. 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. 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. 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. 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.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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.
Identifiants
pubmed: 35378840
doi: 10.1016/j.jbo.2022.100423
pii: S2212-1374(22)00013-6
pmc: PMC8976128
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100423Informations de copyright
© 2022 The Authors.
Déclaration de conflit d'intérêts
Dionna Jacobson, Benoit Cadieux, and Basia A. Bachmann, were employees of Amgen. Marko Rehn and Hossam Saad are employees and stockholders of Amgen. Celestia S. Higano – consulting fees: Bayer, Ferring, Clovis Oncology, Blue Earth Diagnostics, Janssen, Hinova, Pfizer, AstraZeneca, Carrick Therapeutics, Novartis, Merck Sharp & Dohme, Astellas Pharma, Myovant Sciences, Genentech, Menarini; contracted research support: Aragon Pharmaceuticals, AstraZeneca, Medivation, Emergent BioSolutions, Bayer, Roche, Astellas Pharma, Clovis Oncology, Ferring Pharmaceuticals, eFFECTOR Therapeutics; ownership interest: CTI BioPharma CORP; honoraria: Astellas Pharma. David H. Henry has nothing to disclose. Alison T. Stopeck – consulting fees: Amgen, AstraZeneca, Athenex, BeiGene, Exact Sciences; contracted research support: Amgen, Genomic Health, AstraZeneca, Seattle Genetics; speaker’s bureau: Genomic Health; honoraria: Amgen.
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