Abstract
Background
The aim of this study was to assess the associations of comorbidities with primary treatment of prostate cancer (PCa) patients and of comorbidities with PCa-specific mortality (PCSM) compared to other-cause mortality (OCM) in Switzerland.
Patients and methods
We included 1527 men diagnosed with PCa in 2000 and 2001 in the canton of Zurich. Multiple imputation methods were applied to missing data for stage, grade and comorbidities. Multinomial logistic regression analyses were used to explore the associations of comorbidities with treatment. Cox regression models were used to estimate all-cause mortality, and Fine and Gray competing risk regression models to estimate sub-distribution hazard ratios for the outcomes PCSM and OCM.
Results
Increasing age was associated with a decreasing probability of receiving curative treatment, whereas an increasing Charlson Comorbidity Index (CCI) did not influence the treatment decision as strongly as age. The probability of OCM was higher for patients with comorbidities compared to those without comorbidities [CCI 1: hazard ratio 2.07 (95% confidence interval 1.51–2.85), CCI 2+: 2.34 (1.59–3.44)]; this was not observed for PCSM [CCI 1: 0.79 (0.50–1.23), CCI 2+: 0.97 (0.59–1.59)]. In addition, comorbidities had a greater impact on the patients’ mortality than age.
Conclusions
The results of the current study suggest that chronological age is a stronger predictor of treatment choices than comorbidities, although comorbidities have a larger influence on patients’ mortality. Hence, inclusion of comorbidities in treatment choices may provide more appropriate treatment for PCa patients to counteract over- or undertreatment.
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References
Abdollah F et al (2011) Cancer-specific and other-cause mortality after radical prostatectomy versus observation in patients with prostate cancer: competing-risks analysis of a large North American population-based cohort. Eur Urol 60:920–930. https://doi.org/10.1016/j.eururo.2011.06.039
Adejoro O, Alishahi A, Konety B (2016) Association of comorbidity, age, and radical surgical therapy for prostate cancer, bladder cancer, and renal. Cell Carcinoma Urol 97:130–137.e131. https://doi.org/10.1016/j.urology.2016.06.015
Alibhai SM, Krahn MD, Cohen MM, Fleshner NE, Tomlinson GA, Naglie G (2004) Is there age bias in the treatment of localized prostate carcinoma? Cancer 100:72–81. https://doi.org/10.1002/cncr.11884
Aus G, Abbou CC, Pacik D, Schmid HP, van Poppel H, Wolff JM, Zattoni F (2001) EAU guidelines on prostate cancer. Eur Urol 40:97–101. https://doi.org/10.1159/000049758
Beyersmann J, Schumacher M (2007) Misspecified regression model for the subdistribution hazard of a competing risk. Stat Med 26:1649–1651. https://doi.org/10.1002/sim.2727
Beyersmann J, Dettenkofer M, Bertz H, Schumacher M (2007) A competing risks analysis of bloodstream infection after stem-cell transplantation using subdistribution hazards and cause-specific hazards. Stat Med 26:5360–5369. https://doi.org/10.1002/sim.3006
Beyersmann J, Latouche A, Buchholz A, Schumacher M (2009) Simulating competing risks data in survival analysis. Stat Med 28:956–971. https://doi.org/10.1002/sim.3516
Beyersmann J, Allignol A, Schumacher M (2012) Competing risks and multistate models with R. Springer, New York. https://doi.org/10.1007/978-1-4614-2035-4
Bill-Axelson A et al (2011) Radical prostatectomy versus watchful waiting in early prostate cancer N. Engl J Med 364:1708–1717. https://doi.org/10.1056/NEJMoa1011967
Bodner TE (2008) What improves with increased missing data imputations? Struct Equ Model Multidiscip J 15:651–675. https://doi.org/10.1080/10705510802339072
Boehm K et al (2017) Comorbidity and age cannot explain variation in life expectancy associated with treatment of non-metastatic prostate cancer. World J Urol 35:1031–1036. https://doi.org/10.1007/s00345-016-1963-7
Bratt O et al (2015) Undertreatment of men in their seventies with high-risk nonmetastatic. Prostate Cancer Eur Urol 68:53–58. https://doi.org/10.1016/j.eururo.2014.12.026
Briganti A et al (2013) Impact of age and comorbidities on long-term survival of patients with high-risk prostate cancer treated with radical prostatectomy: a multi-institutional competing-risks analysis. Eur Urol 63:693–701. https://doi.org/10.1016/j.eururo.2012.08.054
Buuren Sv (2012) Flexible imputation of missing data. Chapman and Hall/CRC. https://doi.org/10.1201/b11826
Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383
Choudhury JB (2002) Non-parametric confidence interval estimation for competing risks analysis: application to contraceptive data. Stat Med 21:1129–1144
Daskivich TJ, Chamie K, Kwan L, Labo J, Dash A, Greenfield S, Litwin MS (2011a) Comorbidity and competing risks for mortality in men with prostate cancer. Cancer 117:4642–4650. https://doi.org/10.1002/cncr.26104
Daskivich TJ et al (2011b) Overtreatment of men with low-risk prostate cancer and significant comorbidity. Cancer 117:2058–2066. https://doi.org/10.1002/cncr.25751
Daskivich TJ et al (2013) Effect of age, tumor risk, and comorbidity on competing risks for survival in a US population-based cohort of men with prostate cancer. Ann Intern Med 158:709–717. https://doi.org/10.7326/0003-4819-158-10-201305210-00005
Daskivich TJ et al (2015) Prediction of long-term other-cause mortality in men with early-stage prostate cancer: results from the Prostate. Cancer Outcomes Study Urol 85:92–100. https://doi.org/10.1016/j.urology.2014.07.003
de Camargo Cancela M, Comber H, Sharp L (2013) Age remains the major predictor of curative treatment non-receipt for localised prostate cancer: a population-based study. Br J Cancer 109:272–279. https://doi.org/10.1038/bjc.2013.268
Droz JP et al. (2017) Management of prostate cancer in elderly patients: recommendations of a task force of the international society of geriatric oncology. Eur Urol. https://doi.org/10.1016/j.eururo.2016.12.025
Eisemann N, Waldmann A, Katalinic A (2011) Imputation of missing values of tumour stage in population-based cancer registration. BMC Med Res Methodol 11:129 https://doi.org/10.1186/1471-2288-11-129
Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509. https://doi.org/10.1080/01621459.1999.10474144
Grambauer N, Schumacher M, Beyersmann J (2010) Proportional subdistribution hazards modeling offers a summary analysis even if misspecified. Stat Med 29:875–884. https://doi.org/10.1002/sim.3786
Grambsch PM, Therneau TM (1994) Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81:515–526. https://doi.org/10.1093/biomet/81.3.515
Gray B (2014) cmprsk: subdistribution analysis of competing risks
Harlan LC et al (2001) Factors associated with initial therapy for clinically localized prostate cancer: prostate cancer outcomes study. J Natl Cancer Inst 93:1864–1871
Heidenreich A et al (2014) EAU guidelines on prostate cancer. part 1: screening, diagnosis, and local treatment with curative intent-update 2013. Eur Urol 65:124–137. https://doi.org/10.1016/j.eururo.2013.09.046
Jacobs BL, Lopa SH, Yabes JG, Nelson JB, Barnato AE, Degenholtz HB (2016) Association of functional status and treatment choice among older men with prostate cancer in the Medicare. Adv Popul Cancer 122:3199–3206. https://doi.org/10.1002/cncr.30184
Kataja VV (2003) ESMO minimum clinical recommendations for diagnosis, treatment and follow-up of prostate cancer. Ann Oncol 14:1010–1011. https://doi.org/10.1093/annonc/mdg293
Krebsregister der Kantone Zuerich und Zug (2017) Jahresbericht 2016. http://www.krebsregister.usz.ch/fachwissen/Documents/Jahresbericht_Krebsregister%202016.pdf. Accessed 05 May 2017
Latouche A, Boisson V, Chevret S, Porcher R (2007) Misspecified regression model for the subdistribution hazard of a competing risk. Stat Med 26:965–974. https://doi.org/10.1002/sim.2600
Lee JY et al (2014) Charlson comorbidity index is an important prognostic factor for long-term survival outcomes in Korean men with prostate cancer after radical prostatectomy. Yonsei Med J 55:316–323. https://doi.org/10.3349/ymj.2014.55.2.316
Lunardi P, Ploussard G, Grosclaude P, Roumiguie M, Soulie M, Beauval JB, Malavaud B (2017) Current impact of age and comorbidity assessment on prostate cancer treatment choice and over/undertreatment risk. World J Urol 35:587–593. https://doi.org/10.1007/s00345-016-1900-9
Mariotto AB, Wang Z, Klabunde CN, Cho H, Das B, Feuer EJ (2013) Life tables adjusted for comorbidity more accurately estimate noncancer survival for recently diagnosed cancer patients. J Clin Epidemiol 66:1376–1385. https://doi.org/10.1016/j.jclinepi.2013.07.002
Matthes KL, Limam M, Dehler S, Korol D, Rohrmann S (2017) Primary treatment choice over time and relative survival of prostate cancer patients: influence of age, grade, and stage oncology research and treatment. https://doi.org/10.1159/000477096
Moons KG, Donders RA, Stijnen T, Harrell FE Jr (2006) Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 59:1092–1101. https://doi.org/10.1016/j.jclinepi.2006.01.009
Ng SP, Duchesne G, Tai KH, Foroudi F, Kothari G, Williams S (2017) Support for the use of objective comorbidity indices in the assessment of noncancer death risk in prostate cancer patients. Prostate Int 5:8–12. https://doi.org/10.1016/j.prnil.2016.12.001
Nur U, Shack LG, Rachet B, Carpenter JR, Coleman MP (2010) Modelling relative survival in the presence of incomplete data: a tutorial. Int J Epidemiol 39:118–128. https://doi.org/10.1093/ije/dyp309
Parker C, Gillessen S, Heidenreich A, Horwich A (2015) Cancer of the prostate: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 26 (Suppl 5):v69–v77. https://doi.org/10.1093/annonc/mdv222
Rajan P et al (2017) Effect of comorbidity on prostate cancer-specific mortality: a prospective observational study. J Clin Oncol 35:3566–3574. https://doi.org/10.1200/jco.2016.70.7794
Roberts CB, Albertsen PC, Shao YH, Moore DF, Mehta AR, Stein MN, Lu-Yao GL (2011) Patterns and correlates of prostate cancer treatment in older men. Am J Med 124:235–243. https://doi.org/10.1016/j.amjmed.2010.10.016
Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York
Sammon JD et al (2015) Predicting life expectancy in men diagnosed with prostate. Cancer Eur Urol 68:756–765. https://doi.org/10.1016/j.eururo.2015.03.020
Scrucca L, Santucci A, Aversa F (2007) Competing risk analysis using R: an easy guide for clinicians. Bone Marrow Transpl 40:381–387. https://doi.org/10.1038/sj.bmt.1705727
Showalter TN, Mishra MV, Bridges JF (2015) Factors that influence patient preferences for prostate cancer management options: a systematic review. Patient Prefer Adher 9:899–911. https://doi.org/10.2147/ppa.s83333
Struthers CA, Kalbfleisch JD (1986) Misspecified proportional hazard models. Biometrika 73:363–369. https://doi.org/10.1093/biomet/73.2.363
Therneau TM (2015) A package for survival analysis in S
van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate Imputation by chained equations in R. J Stat Softw 45:1–67. https://doi.org/10.18637/jss.v045.i03
Van Hemelrijck M, Folkvaljon Y, Adolfsson J, Akre O, Holmberg L, Garmo H, Stattin P (2016) Causes of death in men with localized prostate cancer: a nationwide population-based study. BJU Int 117:507–514. https://doi.org/10.1111/bju.13059
Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York
Vink G, Frank LE, Pannekoek J, van Buuren S (2014) Predictive mean matching imputation of semicontinuous variables. Stat Neerl 68:61–90. https://doi.org/10.1111/stan.12023
Walz J et al (2008) Accuracy of life tables in predicting overall survival in patients after radical prostatectomy. BJU Int 102:33–38. https://doi.org/10.1111/j.1464-410X.2008.07614.x
White IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: issues and guidance for practice. Stat Med 30:377–399. https://doi.org/10.1002/sim.4067
Funding
This work was supported by Kurt und Senta Hermann-Stiftung. Manuela Limam was supported by Cancer League Zurich.
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Research involving human patients and/or animals and informed consent
Cancer cases in the Canton of Zurich are registered with presumed consent and registration based on a decision by the Zurich Government Council from 1980 and the general registry approval by the Federal Commission of Experts for professional secrecy in medical research from 1995. All data were used anonymously in this analysis, and no approval from the Ethical Committee of the Canton of Zurich was necessary.
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Matthes, K.L., Limam, M., Pestoni, G. et al. Impact of comorbidities at diagnosis on prostate cancer treatment and survival. J Cancer Res Clin Oncol 144, 707–715 (2018). https://doi.org/10.1007/s00432-018-2596-6
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DOI: https://doi.org/10.1007/s00432-018-2596-6