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Published in: World Journal of Surgical Oncology 1/2020

Open Access 01-12-2020 | Colorectal Cancer | Research

A competing-risk nomogram to predict cause-specific death in elderly patients with colorectal cancer after surgery (especially for colon cancer)

Authors: Zhengbing Wang, Yawei Wang, Yan Yang, Yi Luo, Jiangtao Liu, Yingjie Xu, Xuan Liu

Published in: World Journal of Surgical Oncology | Issue 1/2020

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Abstract

Background

Clinically, when the diagnosis of colorectal cancer is clear, patients are more concerned about their own prognosis survival. Special population with high risk of accidental death, such as elderly patients, is more likely to die due to causes other than tumors. The main purpose of this study is to construct a prediction model of cause-specific death (CSD) in elderly patients using competing-risk approach, so as to help clinicians to predict the probability of CSD in elderly patients with colorectal cancer.

Methods

The data were extracted from Surveillance, Epidemiology, and End Results (SEER) database to include ≥ 65-year-old patients with colorectal cancer who had undergone surgical treatment from 2010 to 2016. Using competing-risk methodology, the cumulative incidence function (CIF) of CSD was calculated to select the predictors among 13 variables, and the selected variables were subsequently refined and used for the construction of the proportional subdistribution hazard model. The model was presented in the form of nomogram, and the performance of nomogram was bootstrap validated internally and externally using the concordance index (C-index).

Results

Dataset of 19,789 patients who met the inclusion criteria were eventually selected for analysis. The five-year cumulative incidence of CSD was 31.405% (95% confidence interval [CI] 31.402–31.408%). The identified clinically relevant variables in nomogram included marital status, pathological grade, AJCC TNM stage, CEA, perineural invasion, and chemotherapy. The nomogram was shown to have good discrimination after internal validation with a C-index of 0.801 (95% CI 0.795–0.807) as well as external validation with a C-index of 0.759 (95% CI 0.716–0.802). Both the internal and external validation calibration curve indicated good concordance between the predicted and actual outcomes.

Conclusion

Using the large sample database and competing-risk analysis, a postoperative prediction model for elderly patients with colorectal cancer was established with satisfactory accuracy. The individualized estimates of CSD outcome for the elderly patients were realized.
Literature
1.
go back to reference Hyodo I, Suzuki H, Takahashi K, Saito Y, Tanaka S, Chiu HM, et al. Present status and perspectives of colorectal cancer in Asia: colorectal cancer working group report in 30th Asia-Pacific Cancer Conference. Jpn J Clin Oncol. 2010;40(Suppl 1):i38.PubMedCrossRef Hyodo I, Suzuki H, Takahashi K, Saito Y, Tanaka S, Chiu HM, et al. Present status and perspectives of colorectal cancer in Asia: colorectal cancer working group report in 30th Asia-Pacific Cancer Conference. Jpn J Clin Oncol. 2010;40(Suppl 1):i38.PubMedCrossRef
2.
go back to reference Ruscelli P, Renzi C, Polistena A, Sanguinetti A, Avenia N, Popivanov G, et al. Clinical signs of retroperitoneal abscess from colonic perforation: two case reports and literature review. Medicine (Baltimore). 2018;97:e13176.CrossRef Ruscelli P, Renzi C, Polistena A, Sanguinetti A, Avenia N, Popivanov G, et al. Clinical signs of retroperitoneal abscess from colonic perforation: two case reports and literature review. Medicine (Baltimore). 2018;97:e13176.CrossRef
4.
go back to reference Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. . Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. .
6.
go back to reference Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how. BMJ. 2009;338:b375.PubMedCrossRef Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how. BMJ. 2009;338:b375.PubMedCrossRef
7.
go back to reference Smith T, Muller DC, Moons KGM, Cross AJ, Johansson M, Ferrari P, et al. Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies. Gut. 2018; gutjnl-2017-315730. Smith T, Muller DC, Moons KGM, Cross AJ, Johansson M, Ferrari P, et al. Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies. Gut. 2018; gutjnl-2017-315730.
8.
go back to reference Kawai K, Sunami E, Yamaguchi H, Ishihara S, Kazama S, Nozawa H, et al. Nomograms for colorectal cancer: a systematic review. World J Gastroenterol. 2015;21:11877.PubMedPubMedCentralCrossRef Kawai K, Sunami E, Yamaguchi H, Ishihara S, Kazama S, Nozawa H, et al. Nomograms for colorectal cancer: a systematic review. World J Gastroenterol. 2015;21:11877.PubMedPubMedCentralCrossRef
9.
go back to reference Fiocco M, Putter H, Van Houwelingen JC. Reduced rank proportional hazards model for competing risks. Biostatistics. 2005;6:465.PubMedCrossRef Fiocco M, Putter H, Van Houwelingen JC. Reduced rank proportional hazards model for competing risks. Biostatistics. 2005;6:465.PubMedCrossRef
10.
go back to reference Cox DR. Regression models and life-tables. J Royal Stat Soc. 1972;34:187–220. Cox DR. Regression models and life-tables. J Royal Stat Soc. 1972;34:187–220.
11.
12.
go back to reference Varadhan R, Weiss CO, Segal JB, Wu AW, Scharfstein D, Boyd C. Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications. Med Care. 2010;48:S96–105.PubMedCrossRef Varadhan R, Weiss CO, Segal JB, Wu AW, Scharfstein D, Boyd C. Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications. Med Care. 2010;48:S96–105.PubMedCrossRef
13.
go back to reference Hinchliffe SR, Lambert PC. Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions. BMC Med Res Methodol. 2013;13:13.PubMedPubMedCentralCrossRef Hinchliffe SR, Lambert PC. Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions. BMC Med Res Methodol. 2013;13:13.PubMedPubMedCentralCrossRef
14.
go back to reference Gray RJ. A Class of $K$-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat. 1988;16:1141–54.CrossRef Gray RJ. A Class of $K$-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat. 1988;16:1141–54.CrossRef
15.
go back to reference Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Pub Am Stat Assoc. 1999;94:496–509.CrossRef Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Pub Am Stat Assoc. 1999;94:496–509.CrossRef
16.
go back to reference Wolbers M, Koller MT, Witteman JCM, Steyerberg EW. Prognostic models with competing risks. Epidemiology. 2009;20:555–61.PubMedCrossRef Wolbers M, Koller MT, Witteman JCM, Steyerberg EW. Prognostic models with competing risks. Epidemiology. 2009;20:555–61.PubMedCrossRef
17.
go back to reference Jr FEH. rms: regression modeling strategies; 2012. Jr FEH. rms: regression modeling strategies; 2012.
18.
19.
go back to reference Koller MT, Raatz H, Steyerberg EW, Wolbers M. Competing risks and the clinical community: irrelevance or ignorance. Stat Med. 2012;31:1089–97.PubMedCrossRef Koller MT, Raatz H, Steyerberg EW, Wolbers M. Competing risks and the clinical community: irrelevance or ignorance. Stat Med. 2012;31:1089–97.PubMedCrossRef
21.
go back to reference Klein JP, Andersen PK. Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics. 2015;61:223–9.CrossRef Klein JP, Andersen PK. Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics. 2015;61:223–9.CrossRef
22.
go back to reference Takakura Y, Okajima M, Kanemitsu Y, Kuroda S, Egi H, Hinoi T, et al. External validation of two nomograms for predicting patient survival after hepatic resection for metastatic colorectal cancer. World J Surg. 2011;35:2275–82.PubMedCrossRef Takakura Y, Okajima M, Kanemitsu Y, Kuroda S, Egi H, Hinoi T, et al. External validation of two nomograms for predicting patient survival after hepatic resection for metastatic colorectal cancer. World J Surg. 2011;35:2275–82.PubMedCrossRef
23.
go back to reference Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2015;26:1364–70.CrossRef Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2015;26:1364–70.CrossRef
24.
go back to reference Shi RL, Chen Q, Yang Z, Pan G, Zhang Z, Wang WH, et al. Marital status independently predicts gastric cancer survival after surgical resection--an analysis of the SEER database. Oncotarget. 2016;7:13228–35.PubMedPubMedCentral Shi RL, Chen Q, Yang Z, Pan G, Zhang Z, Wang WH, et al. Marital status independently predicts gastric cancer survival after surgical resection--an analysis of the SEER database. Oncotarget. 2016;7:13228–35.PubMedPubMedCentral
25.
go back to reference Saha S, Shaik M, Johnston G, Saha SK, Berbiglia L, Hicks M, et al. Tumor size predicts long-term survival in colon cancer: an analysis of the National Cancer Data Base. Am J Surg. 2015;209:570–4.PubMedCrossRef Saha S, Shaik M, Johnston G, Saha SK, Berbiglia L, Hicks M, et al. Tumor size predicts long-term survival in colon cancer: an analysis of the National Cancer Data Base. Am J Surg. 2015;209:570–4.PubMedCrossRef
26.
go back to reference Dai W, Li Y, Meng X, Cai S, Li Q, Cai G. Does tumor size have its prognostic role in colorectal cancer? Re-evaluating its value in colorectal adenocarcinoma with different macroscopic growth pattern. Int J Surg. 2017;45:105–12.PubMedCrossRef Dai W, Li Y, Meng X, Cai S, Li Q, Cai G. Does tumor size have its prognostic role in colorectal cancer? Re-evaluating its value in colorectal adenocarcinoma with different macroscopic growth pattern. Int J Surg. 2017;45:105–12.PubMedCrossRef
27.
go back to reference Storli KE, Søndenaa K, Bukholm IRK, Nesvik I, Bru T, Furnes B, et al. Overall survival after resection for colon cancer in a national cohort study was adversely affected by TNM stage, lymph node ratio, gender, and old age. Int J Colorectal Dis. 2011;26:1299–307.PubMedPubMedCentralCrossRef Storli KE, Søndenaa K, Bukholm IRK, Nesvik I, Bru T, Furnes B, et al. Overall survival after resection for colon cancer in a national cohort study was adversely affected by TNM stage, lymph node ratio, gender, and old age. Int J Colorectal Dis. 2011;26:1299–307.PubMedPubMedCentralCrossRef
28.
go back to reference Wang J, Hassett JM, Dayton MT, Kulaylat MN. Lymph node ratio: role in the staging of node-positive colon cancer. Ann Surg Oncol. 2008;15:1600–8.PubMedCrossRef Wang J, Hassett JM, Dayton MT, Kulaylat MN. Lymph node ratio: role in the staging of node-positive colon cancer. Ann Surg Oncol. 2008;15:1600–8.PubMedCrossRef
29.
go back to reference Chen SL, Steele SR, Eberhardt J, Zhu K, Bilchik A, Stojadinovic A. Lymph node ratio as a quality and prognostic indicator in stage III colon cancer. Ann Surg. 2011;253:82–7.PubMedCrossRef Chen SL, Steele SR, Eberhardt J, Zhu K, Bilchik A, Stojadinovic A. Lymph node ratio as a quality and prognostic indicator in stage III colon cancer. Ann Surg. 2011;253:82–7.PubMedCrossRef
30.
go back to reference Gleisner AL, Mogal H, Dodson R, Efron J, Gearhart S, Wick E, et al. Nodal status, number of lymph nodes examined, and lymph node ratio: what defines prognosis after resection of colon adenocarcinoma. J Am Coll Surg. 2013;217:1090–100.PubMedCrossRef Gleisner AL, Mogal H, Dodson R, Efron J, Gearhart S, Wick E, et al. Nodal status, number of lymph nodes examined, and lymph node ratio: what defines prognosis after resection of colon adenocarcinoma. J Am Coll Surg. 2013;217:1090–100.PubMedCrossRef
31.
go back to reference Yang J, Xing S, Li J, Yang S, Hu J, Liu H, et al. Novel lymph node ratio predicts prognosis of colorectal cancer patients after radical surgery when tumor deposits are counted as positive lymph nodes: a retrospective multicenter study. Oncotarget. 2016;7:73865–75.PubMedPubMedCentral Yang J, Xing S, Li J, Yang S, Hu J, Liu H, et al. Novel lymph node ratio predicts prognosis of colorectal cancer patients after radical surgery when tumor deposits are counted as positive lymph nodes: a retrospective multicenter study. Oncotarget. 2016;7:73865–75.PubMedPubMedCentral
32.
go back to reference Tarantino I, Warschkow R, Worni M, Meratikashani K, Köberle D, Schmied BM, et al. Elevated preoperative CEA is associated with worse survival in stage I|[ndash]|III rectal cancer patients. Br J Cancer. 2012;107:266.PubMedPubMedCentralCrossRef Tarantino I, Warschkow R, Worni M, Meratikashani K, Köberle D, Schmied BM, et al. Elevated preoperative CEA is associated with worse survival in stage I|[ndash]|III rectal cancer patients. Br J Cancer. 2012;107:266.PubMedPubMedCentralCrossRef
33.
go back to reference Nikberg M, Kindler C, Chabok A, Letocha H, Shetye J, Smedh K. Circumferential resection margin as a prognostic marker in the modern multidisciplinary management of rectal cancer. Dis Colon Rectum. 2015;58:275.PubMedCrossRef Nikberg M, Kindler C, Chabok A, Letocha H, Shetye J, Smedh K. Circumferential resection margin as a prognostic marker in the modern multidisciplinary management of rectal cancer. Dis Colon Rectum. 2015;58:275.PubMedCrossRef
34.
go back to reference Mayo E, Llanos AAM, Yi X, Duan S, Zhang L. Prognostic value of tumour deposit and perineural invasion status in colorectal cancer patients: a SEER-based population study. Histopathology. 2016;69:230–8.PubMedCrossRef Mayo E, Llanos AAM, Yi X, Duan S, Zhang L. Prognostic value of tumour deposit and perineural invasion status in colorectal cancer patients: a SEER-based population study. Histopathology. 2016;69:230–8.PubMedCrossRef
35.
go back to reference Peng J, Ding Y, Tu S, Shi D, Sun L, Li X, et al. Prognostic nomograms for predicting survival and distant metastases in locally advanced rectal cancers. PLoS One. 2014;9:e106344.PubMedPubMedCentralCrossRef Peng J, Ding Y, Tu S, Shi D, Sun L, Li X, et al. Prognostic nomograms for predicting survival and distant metastases in locally advanced rectal cancers. PLoS One. 2014;9:e106344.PubMedPubMedCentralCrossRef
36.
go back to reference Klement RJ, Abbasi-Senger N, Adebahr S, Alheid H, Allgaeuer M, Becker G, et al. The impact of local control on overall survival after stereotactic body radiotherapy for liver and lung metastases from colorectal cancer: a combined analysis of 388 patients with 500 metastases. BMC Cancer. 2019;19:173.PubMedPubMedCentralCrossRef Klement RJ, Abbasi-Senger N, Adebahr S, Alheid H, Allgaeuer M, Becker G, et al. The impact of local control on overall survival after stereotactic body radiotherapy for liver and lung metastases from colorectal cancer: a combined analysis of 388 patients with 500 metastases. BMC Cancer. 2019;19:173.PubMedPubMedCentralCrossRef
37.
go back to reference Petrelli F, Comito T, Barni S, Pancera G, Scorsetti M, Ghidini A, et al. Stereotactic body radiotherapy for colorectal cancer liver metastases: a systematic review. Radiother Oncol. 2018;129:427–34.PubMedCrossRef Petrelli F, Comito T, Barni S, Pancera G, Scorsetti M, Ghidini A, et al. Stereotactic body radiotherapy for colorectal cancer liver metastases: a systematic review. Radiother Oncol. 2018;129:427–34.PubMedCrossRef
38.
go back to reference Takada T, Tsutsumi S, Takahashi R, Ohsone K, Tatsuki H, Suto T, et al. Control of primary lesions using resection or radiotherapy can improve the prognosis of metastatic colorectal cancer patients. J Surg Oncol. 114:75–9.PubMedCrossRef Takada T, Tsutsumi S, Takahashi R, Ohsone K, Tatsuki H, Suto T, et al. Control of primary lesions using resection or radiotherapy can improve the prognosis of metastatic colorectal cancer patients. J Surg Oncol. 114:75–9.PubMedCrossRef
39.
go back to reference Kattan MW, Heller G, Brennan MF. A competing-risks nomogram for sarcoma-specific death following local recurrence. Stat Med. 2003;22:3515–25.PubMedCrossRef Kattan MW, Heller G, Brennan MF. A competing-risks nomogram for sarcoma-specific death following local recurrence. Stat Med. 2003;22:3515–25.PubMedCrossRef
40.
go back to reference Ross PL, Gerigk C, Gonen M, et al. Comparisons of nomograms and urologists' predictions in prostate cancer. Semin Urol Oncol. 2002;20(2):82–8.PubMedCrossRef Ross PL, Gerigk C, Gonen M, et al. Comparisons of nomograms and urologists' predictions in prostate cancer. Semin Urol Oncol. 2002;20(2):82–8.PubMedCrossRef
41.
go back to reference Nam RK, Kattan MW, Chin JL, et al. Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators. J Clin Oncol. 2011;29(22):2959–64.PubMedCrossRef Nam RK, Kattan MW, Chin JL, et al. Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators. J Clin Oncol. 2011;29(22):2959–64.PubMedCrossRef
43.
go back to reference Shen W, Sakamoto N, Yang L. Cancer-specific mortality and competing mortality in patients with head and neck squamous cell carcinoma: a competing risk analysis. Ann Surg Oncol. 2015;22:264–71.PubMedCrossRef Shen W, Sakamoto N, Yang L. Cancer-specific mortality and competing mortality in patients with head and neck squamous cell carcinoma: a competing risk analysis. Ann Surg Oncol. 2015;22:264–71.PubMedCrossRef
Metadata
Title
A competing-risk nomogram to predict cause-specific death in elderly patients with colorectal cancer after surgery (especially for colon cancer)
Authors
Zhengbing Wang
Yawei Wang
Yan Yang
Yi Luo
Jiangtao Liu
Yingjie Xu
Xuan Liu
Publication date
01-12-2020
Publisher
BioMed Central
Published in
World Journal of Surgical Oncology / Issue 1/2020
Electronic ISSN: 1477-7819
DOI
https://doi.org/10.1186/s12957-020-1805-3

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