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李思儒, 李静, 杨栖, 等. 转移性黑色素瘤患者早期死亡风险的预测模型构建及验证[J]. koko体育app 学报(医学版), 2024, 55(2): 367-374. DOI:
引用本文: 李思儒, 李静, 杨栖, 等. 转移性黑色素瘤患者早期死亡风险的预测模型构建及验证[J]. koko体育app 学报(医学版), 2024, 55(2): 367-374. DOI:
LI Siru, LI Jing, YANG Qi, et al. Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(2): 367-374. DOI:
Citation: 𒀰 LI Siru, LI Jing, YANG Qi, et al. Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(2): 367-374. DOI:

转移性黑色素瘤患者早期死亡风险的预测模型构建及验证

Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma

  • 摘要:
    目的 构建预测转移性黑色素瘤(metastatic melanoma, MM)患者早期死亡风险的列线图模型。
    方法 本研究纳入监测、流行病学和最终结果(Surveillance, Epidemiology, and End Results Program, SEER)数据库中(2010–2015年)2138例被诊断为MM的患者。使用logistic回归分析确定影响MM患者早期死亡的独立风险因素。并利用这些因素构建全因早死和癌症特异性早期死亡的列线图。通过受试者操作特征(receiver operating characteristic, ROC)曲线、校准曲线和决策曲线分析(decision curve analysis, DCA)评估该模型的效能,并使用四川省肿瘤医院2015年1月–2020年1月被诊断为MM的105例患者的临床病理资料进行外部验证。
    结果 logistic回归分析显示,婚姻状况、原发部位、N分期、手术、化疗、骨转移、肝转移、肺转移和脑转移可以被确定为早期死亡的独立预测因素。基于这些因素开发了两个列线图分别预测全因早死和癌症特异性早死风险。在全因和癌症特异性早期死亡模型中,训练组的曲线下面积(the area under the curve, AUC)分别为0.751〔95%置信区间(confidence interval, CI):0.726~0.776〕和0.740(95%CI:0.714~0.765))。内部验证组的AUC分别为0.759(95%CI:0.722~0.797)和0.757(95%CI:0.718~0.780),外部验证组的AUC分别为0.750(95%CI:0.649~0.850)和0.741(95%CI :0.644~0.838)。校准曲线表明预测概率与观察概率的一致性高,DCA分析表明该模型的临床应用价值较高。
    结论 列线图模型预测MM患者的早期死亡表现出良好的预测能力,能够帮助临床医师制定更个性化的治疗策略。
     
    Abstract:
    Objective To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM).
    Methods The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020.
    Results According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval CI: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models.
    Conclusion The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.
     
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