Development and validation of a risk-prediction model for adverse drug reactions in real-world cancer patients treated with anlotinib
- Jiajia Qian 1, Cong Ruan 1, Yunyun Cai 1, Weiyi Yi 1, Jiyong Liu 2,3,4, Rui Xu 2
- Jiajia Qian 1, Cong Ruan 1, Yunyun Cai 1
- 1Minhang Branch, Fudan University Shanghai Cancer Center, Shanghai, China.
- 2Minhang Branch, Fudan University Shanghai Cancer Center, No. 106, Ruili Road, Shanghai 200240, China.
- 3Fudan University Shanghai Cancer Center, Shanghai, China.
- 4Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- 0Minhang Branch, Fudan University Shanghai Cancer Center, Shanghai, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study identified key risk factors for adverse drug reactions (ADRs) in anlotinib cancer patients. A validated prediction model helps manage ADRs and improve patient outcomes.
Area Of Science
- Oncology
- Pharmacovigilance
- Clinical Pharmacy
Background
- Anlotinib is a targeted therapy used in cancer treatment.
- Adverse drug reactions (ADRs) associated with anlotinib are a significant clinical concern.
- Risk factors and predictive models for anlotinib-induced ADRs remain underexplored, especially in China.
Purpose Of The Study
- To investigate the risk factors associated with anlotinib-related ADRs.
- To develop and validate a predictive model for anlotinib-induced ADRs in cancer patients.
- To enhance the management of ADRs and improve patient prognosis.
Main Methods
- Retrospective analysis of 300 cancer patients treated with anlotinib.
- Univariate and multivariate logistic regression to identify risk factors.
- Development and validation of a nomogram prediction model, assessed by AUC and concordance index.
Main Results
- A high incidence of ADRs (79.33%) was observed in patients receiving anlotinib.
- Independent risk factors identified include diagnosis, combination treatment, distant metastasis, treatment lines, and cumulative dose.
- The developed prediction model demonstrated good calibration (AUC: 0.790) and reliability through external validation.
Conclusions
- A validated, simple risk prediction model for anlotinib-induced ADRs has been developed.
- The model is well-calibrated and discriminative, aiding clinical decision-making.
- This tool supports ADR prevention, prognosis improvement, and rational drug use in anlotinib therapy.
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