Latent heterogeneity of muscle‐invasive bladder cancer in patient characteristics and survival: A population‐based nation‐wide study in the Bladder Cancer Data Base Sweden (BladderBaSe)

Autor: Christel Häggström, Mark Rowley, Fredrik Liedberg, Anthony C. C. Coolen, Lars Holmberg
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Cancer Medicine, Vol 12, Iss 12, Pp 13856-13864 (2023)
Druh dokumentu: article
ISSN: 2045-7634
DOI: 10.1002/cam4.5981
Popis: Abstract Background Patients with muscle‐invasive bladder cancer (MIBC) constitute a heterogenous group in terms of patient and tumour characteristics (‘case‐mix’) and prognosis. The aim of the current study was to investigate whether differences in survival could be used to separate MIBC patients into separate classes using a recently developed latent class regression method for survival analysis with competing risks. Methods We selected all participants diagnosed with MIBC in the Bladder Cancer Data Base Sweden (BladderBase) and analysed inter‐patient heterogeneity in risk of death from bladder cancer and other causes. Results Using data from 9653 MIBC patients, we detected heterogeneity with six distinct latent classes in the studied population. The largest, and most frail class included 50% of the study population and was characterised by a somewhat larger proportion of women, higher age at diagnosis, more advanced disease and lower probability of curative treatment. Despite this, patients in this class treated with curative intent by radical cystectomy or radiotherapy had a lower association to risk of death. The second largest class included 23% and was substantially less frail as compared to the largest class. The third and fourth class included each around 9%–10%, whereas the fifth and sixth class included each 3%–4% of the population. Conclusions Results from the current study are compatible with previous research and the method can be used to adjust comparisons in prognosis between MIBC populations for influential differences in the distribution of sub‐classes.
Databáze: Directory of Open Access Journals
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