ArticleESMO open2023
Development and validation of a risk score (Delay-7) to predict the occurrence of a treatment delay following cycle 1 chemotherapy.
Article in ESMO open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator.Frontiers in oncology · 2026Article
- A Review of the Utilization of Relative Dose Intensity and Dose Delay Factor in Health Technology Appraisals of Oncology Drugs in Solid Tumors.Advances in therapy · 2025Review
- Impact of Time-to-Treatment Initiation and First Inter-Cycle Delay in Patients with Hodgkin Lymphoma.Journal of clinical medicine · 2025Article
- From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.BMJ oncology · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundThe risk of toxicity-related dose delays, with cancer treatment, should be included as part of pretreatment education and be considered by clinicians upon prescribing chemotherapy. An objective measure of individual risk could influence clinical decisions, such as escalation of standard supportive care and stratification of some patients, to receive proactive toxicity monitoring. PATIENTS AND
methodsWe developed a logistic regression prediction model (Delay-7) to assess the overall risk of a chemotherapy dose delay of 7 days for patients receiving first-line treatments for breast, colorectal and diffuse large B-cell lymphoma. Delay-7 included hospital treated, age at the start of chemotherapy, gender, ethnicity, body mass index, cancer diagnosis, chemotherapy regimen, colony stimulating factor use, first cycle dose modifications and baseline blood values. Baseline blood values included neutrophils, platelets, haemoglobin, creatinine and bilirubin. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Net benefit was used to understand the risk thresholds where the model would perform better than the 'treat all' or 'treat none' strategies.
resultsA total of 4604 patients were included in our study of whom 628 (13.6%) incurred a 7-day delay to the second cycle of chemotherapy. Delay-7 showed good discrimination and calibration, with c-statistic of 0.68 (95% confidence interval 0.66-0.7), following internal validation and calibration-in-the-large of -0.006.
conclusionsDelay-7 predicts a patient's individualised risk of a treatment-related delay at cycle two of treatment. The score can be used to stratify interventions to reduce the occurrence of treatment-related toxicity.
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