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      Competing-risk nomogram for predicting survival in patients with advanced (stage III/IV) gallbladder cancer: A SEER population-based study

      1 , 1 , 2 , 3 , 1 , 1 , 1
      Japanese Journal of Clinical Oncology
      Oxford University Press (OUP)

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          Abstract

          Objective

          The primary aim of this study was to assess the cumulative incidence of cause-specific mortality (CSM) and other cause-specific mortality (OCSM) for patients with advanced gallbladder cancer (GBC), and then to develop a nomogram based on competing-risk analysis to forecast CSM.

          Methods

          We identified the patients with GBC with specific screening criteria and from the Surveillance Epidemiology and End Results (SEER) database. We calculated the cumulative incidence function for CSM and OCSM, and constructed a competing-risk nomogram based on the Fine and Gray’s proportional subdistribution hazard regression model to forecast the probability of CSM of these patients. In addition, the concordance index and calibration plot were performed to validate the novel established model.

          Results

          A total of 1411 patients were included in this study. The 1-, 2-, and 3-year overall cumulative mortalities were 46.2, 62.2, and 69.6% for CSM, respectively, while they were 6.2, 8.7, and 10.4% for OCSM. Additionally, the 1-, 2-, and 3-year estimates of overall survival were 47.6, 29.1, and 19.9% for above these patients, respectively. We also developed a competing-risk nomogram to estimate the CSM. The concordance index was 0.775 (95% confidence interval (CI): 0.750–0.800) in the training set and that was 0.765 (95% CI: 0.730–0.800) in the internal validation set, which suggests the robustness of the novel established model. Furthermore, the calibration curves and concordance index demonstrated that the nomogram was well-calibrated and demonstrated good discriminative ability.

          Conclusions

          The ample sample allowed us to develop a reliable model which demonstrated better calibration and discrimination for predicting the probability of CSM of patients with advanced GBC.

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          Most cited references25

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          Prognostic models with competing risks: methods and application to coronary risk prediction.

          Clinical decision-making often relies on a subject's absolute risk of a disease event of interest. However, in a frail population, competing risk events may preclude the occurrence of the event of interest. We review competing-risk regression models with a view toward predictive modeling. We show how measures of prognostic performance (such as calibration and discrimination) can be adapted to the competing-risks setting. An example of coronary heart disease (CHD) prediction in women aged 55-90 years in the Rotterdam study is used to illustrate the proposed methods, and to compare the Fine and Gray regression model to 2 alternative approaches: (1) a standard Cox survival model, which ignores the competing risk of non-CHD death, and (2) a cause-specific hazards model, which combines proportional hazards models for the event of interest and the competing event. The Fine and Gray model and the cause-specific hazards model perform similarly. However, the standard Cox model substantially overestimates 10-year risk of CHD; it classifies 18% of the individuals as high risk (>20%), compared with only 8% according to the Fine and Gray model. We conclude that competing risks have to be considered explicitly in frail populations such as the elderly.
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            When do we need competing risks methods for survival analysis in nephrology?

            Survival analyses are commonly applied to study death or other events of interest. In such analyses, so-called competing risks may form an important problem. A competing risk is an event that either hinders the observation of the event of interest or modifies the chance that this event occurs. For example, when studying death on dialysis, receiving a kidney transplant is an event that competes with the event of interest. Conventional methods for survival analysis ignoring the competing event(s), such as the Kaplan-Meier method and standard Cox proportional hazards regression, may be inappropriate in the presence of competing risks, and alternative methods specifically designed for analysing competing risks data should then be applied. This problem deserves more attention in nephrology research and in the current article, we therefore explain the problem of competing risks in survival analysis and how using different techniques may affect study results.
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              Population-based study evaluating and predicting the probability of death resulting from thyroid cancer and other causes among patients with thyroid cancer.

              The purpose of this study was to evaluate the probability of death for patients with thyroid cancer and construct a comprehensive nomogram based on a competing risks model to predict cumulative incidence of death resulting from thyroid cancer, other cancers, and non-cancer-related causes. Patients diagnosed with thyroid cancer between 1988 and 2003 were selected for the study from the Surveillance, Epidemiology, and End Results program. We estimated probabilities of death resulting from thyroid cancer, other cancers, and noncancer causes and analyzed associations of patient and tumor characteristics with probability of death. A nomogram for predicting probability of death was built using a proportional subdistribution hazard competing risks model. The entire cohort comprised 29,225 patients with malignant thyroid cancer. Median duration of follow-up until censoring or death was 85 months (range, 0 to 239 months). Five-year probabilities of death resulting from thyroid cancer, other cancer, and noncancer causes were 1.9%, 0.8%, and 1.7%, respectively. Increasing age and tumor size, male sex, poorly differentiated carcinoma, lymph node involvement, and regional and metastatic disease were associated with increased cumulative incidence of death resulting from thyroid cancer. A nomogram based on a competing risks model was developed for predicting the probability of death for patients with thyroid cancer. Performance of the model was excellent. This nomogram may be useful for patients and clinicians when predictions are needed.
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                Author and article information

                Contributors
                Journal
                Japanese Journal of Clinical Oncology
                Oxford University Press (OUP)
                1465-3621
                April 2022
                April 06 2022
                February 08 2022
                April 2022
                April 06 2022
                February 08 2022
                : 52
                : 4
                : 353-361
                Affiliations
                [1 ]Department of Liver Surgery & Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
                [2 ]State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing 100094, China
                [3 ]Department of Cardiology, Medical College of Soochow University, Suzhou 215006, China
                Article
                10.1093/jjco/hyab212
                35137118
                cfdb0802-8c7b-4ca5-bdfa-6bb2a22eca49
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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