BMJ Open (Jul 2021)

Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis

  • Xiang Li,
  • Qiao Huang,
  • Chengzhuo Li,
  • Jun Lyu,
  • Didi Han,
  • Fengshuo Xu,
  • Shuai Zheng,
  • Fanfan Zhao,
  • Xiaojie Feng

DOI
https://doi.org/10.1136/bmjopen-2020-048243
Journal volume & issue
Vol. 11, no. 7

Abstract

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Objective This study aimed to use a competing-risks model to establish a nomogram to accurately analyse the prognostic factors for upper tract urothelial carcinoma (UTUC) cancer-specific death (CSD).Design Retrospective observational cohort study.Setting The programme has yielded a database of all patients with cancer in 18 defined geographical regions of the USA.Participants We selected patients with UTUC from the latest edition of the Surveillance, Epidemiology, and End Results database from 1975 to 2016. After excluding patients with unknown histological grade, tumour size and lymph node status, 2576 patients were finally selected.Primary and secondary outcome measures We used the Fine-Gray proportional subdistribution hazards model for multivariate analysis and compared the results with cause-specific hazards model. We finally constructed a nomogram for 3-year, 5-year and 8-year CSD rates and tested these rates in a validation cohort.Results The proportional subdistribution hazards model showed that sex, tumour size, distant metastasis, surgery status, number of lymph nodes positive (LNP) and lymph nodes ratio (LNR) were independent prognostic factors for CSD. All significant factors associated with CSD were included in the nomogram. The 3-year, 5-year and 8-year concordance indexes were 0.719, 0.702 and 0.692 in the training cohort and 0.701, 0.675 and 0.668 in the validation cohort, respectively.Conclusions The competing-risks model showed that sex, tumour size, distant metastasis, surgery status, LNP and LNR were associated with CSD. The nomogram predicts the probability of CSD in patients with UTUC at 3, 5 and 8 years, which may help clinicians in predicting survival probabilities in individual patients.