Sarrio-Sanz Pau, Martinez-Cayuelas Laura, Beltran-Perez Abraham, Muñoz-Montoya Milagros, Segura-Heras Jose-Vicente, Gil-Guillen Vicente F, Gomez-Perez Luis
Urology Services, University Hospital of San Juan de Alicante, 03550 San Juan de Alicante, Alicante, Spain.
Public Health, Science History and Gynaecology Department, Miguel Hernández University, 03550 San Juan de Alicante, Alicante, Spain.
J Clin Med. 2024 Apr 10;13(8):2177. doi: 10.3390/jcm13082177.
: The aim was to develop a decision tree and a new prognostic tool to predict cancer-specific survival in patients with urothelial bladder cancer treated with radical cystectomy. : A total of 11,834 patients with bladder cancer treated with radical cystectomy between 2004 and 2019 from the SEER database were randomly split into the derivation ( = 7889) and validation cohorts ( = 3945). Survival curves were estimated using conditional decision tree analysis. We used Multiple Imputation by Chained Equations for the treatment of missing values and the pec package to compare the predictive performance. We extracted data from our model following CHARMS and assessed the risk of bias and applicability with PROBAST. : A total of 4824 (41%) patients died during the follow-up period due to bladder cancer. A decision tree was made and 12 groups were obtained. Patients with a higher AJCC stage and older age have a worse prognosis. The risk groups were summarized into high, intermediate and low risk. The integrated Brier scores between 0 and 191 months for the bootstrap estimates of the prediction error are the lowest for our conditional survival tree (0.189). The model showed a low risk of bias and low concern about applicability. The results must be externally validated. : Decision tree analysis is a useful tool with significant discrimination. With this tool, we were able to stratify patients into 12 subgroups and 3 risk groups with a low risk of bias and low concern about applicability.
目的是开发一种决策树和一种新的预后工具，以预测接受根治性膀胱切除术的尿路上皮膀胱癌患者的癌症特异性生存率。：从监测、流行病学和最终结果（SEER）数据库中选取了2004年至2019年间接受根治性膀胱切除术的11834例膀胱癌患者，随机分为推导队列（n = 7889）和验证队列（n = 3945）。使用条件决策树分析估计生存曲线。我们使用链式方程多重填补法处理缺失值，并使用pec软件包比较预测性能。我们按照CHARMS从模型中提取数据，并使用PROBAST评估偏倚风险和适用性。：共有4824例（41%）患者在随访期间死于膀胱癌。构建了一个决策树，得到了12个组。美国癌症联合委员会（AJCC）分期较高和年龄较大的患者预后较差。风险组被总结为高、中、低风险。对于预测误差的自举估计，我们的条件生存树在0至191个月之间的综合Brier评分最低（0.189）。该模型显示偏倚风险较低，对适用性的担忧也较低。结果必须进行外部验证。：决策树分析是一种具有显著区分能力的有用工具。使用该工具，我们能够将患者分为12个亚组和3个风险组，偏倚风险低，对适用性的担忧也低。
