Dataset Information

Development of a competing risk nomogram for the prediction of cause-specific mortality in patients with thymoma: a population-based analysis.

ABSTRACT:

Background

This study was developed to assess the odds of cause-specific mortality and other types of mortality in thymoma patients. In addition, these analyses were leveraged to develop a comprehensive competing risk model-based nomogram capable of predicting cause-specific mortality as a result of thymoma.

Methods

Thymoma patients included within the Surveillance, Epidemiology, and End Results (SEER) database from 2004-2016 were identified, and the odds of cause-specific mortality due to thymoma and other forms of mortality for these patients were estimated. In addition, Fine and Gray's proportional subdistribution hazard model was constructed, and a competing risk nomogram was developed using this model that was capable of predicting the odds of 3-, 5-, and 10-year cause-specific mortality in thymoma patients.

Results

In total, 1,591 relevant cases in the SEER database were selected for analysis. In this patient cohort, the respective 5-year cumulative incidence rates for cause-specific mortality and mortality attributable to other causes were 12.4% and 8.2%. Variables significantly associated with cause-specific mortality included age, chemotherapy, surgery, and Masaoka stage. Additionally, the odds of other-cause-specific mortality rose with increasing patient age, and chemotherapy was correlated with other-cause-specific mortality. The competing risk nomogram that was developed exhibited good discriminative ability as a means of predicting cause-specific mortality, as evidenced by a concordance index (C-index) value of 0.84. Calibration curves further revealed excellent consistency between predicted and actual mortality when using this nomogram.

Conclusions

In summary, we herein assessed the odds of cause-specific and other-cause-specific mortality among thymoma patients, and we designed a novel nomogram capable of predicting cause-specific mortality for thymoma, providing a promising tool that may be of value in the context of individualized patient prognostic evaluation.

SUBMITTER: Zhang T

PROVIDER: S-EPMC8743403 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Development of a competing risk nomogram for the prediction of cause-specific mortality in patients with thymoma: a population-based analysis.

Zhang Tao T Liu Lipin L Qiu Bin B

Journal of thoracic disease 20211201 12

<h4>Background</h4>This study was developed to assess the odds of cause-specific mortality and other types of mortality in thymoma patients. In addition, these analyses were leveraged to develop a comprehensive competing risk model-based nomogram capable of predicting cause-specific mortality as a result of thymoma.<h4>Methods</h4>Thymoma patients included within the Surveillance, Epidemiology, and End Results (SEER) database from 2004-2016 were identified, and the odds of cause-specific mortali ...[more]

PMID: 35070368

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Project description:IntroductionParotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.MethodsSpecific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.ResultsWe finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.ConclusionWe have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Project description:BackgroundFor clinical workers, disease-specific death is a better indicator of tumor severity. Breast cancer is the most prevalent malignancy in women. Luminol type B breast cancer is one of the biggest threats to women's health, and few studies have paid attention to its specific death. Early recognition of luminol type B breast cancer allows clinicians to assess the prognosis and develop more optimal treatment plans.MethodsIn this study, the basic information of luminal B population, clinical and pathological characteristics, treatment regimen and survival data were collected from the SEER database. The patients were randomly divided into a training group and a validation group. The single-factor and multi-factor competitive risk models were used to analyze the independent influencing factors of tumor-specific death, and the predictive nomogram based on the competitive risk model was constructed. The consistency index (C-index) and calibration curves over time were used to evaluate the accuracy of the predicted nomograms.ResultsThis study included a total of 30,419 luminal B patient. The median follow-up period was 60 (IQR: 44-81) months. Among the 4,705 deaths during the follow-up period, 2,863 patients died specifically, accounting for 60.85% of the deaths. The independent predictive factors of cancer-specific mortality were: married, primary site, grade, stage, the primary site of operation, radiotherapy, chemotherapy, metastasis (lymph node, bone, brain, liver, lung), and Estrogen Receptor and Progesterone Receptor status. In the training cohort, the C-index of the predictive nomogram was 0.858, and the area under the receiver operating characteristic curve (AUC) for the first, third, and fifth years was 0.891, 0.864, and 0.845. The C-index of the validation cohort was 0.862, and the AUC for the first, third, and fifth years was 0.888, 0.872, and 0.849. The calibration curves of the training and validation cohorts showed that the predicted probability of the model was very consistent with the actual probability. And the 5-year survival rate according to the traditional survival analysis was 9.49%, while the 5-year specific mortality rate was only 8.88%.ConclusionsThe luminal B competing risk model we established has ideal accuracy and calibration.

Project description:ObjectiveThis research aims to construct and authenticate a comprehensive predictive model for all-cause mortality, based on a multifaceted array of risk factors.MethodsThe derivation cohort for this study was the Chinese Longitudinal Healthy Longevity Survey (CLHLS), while the Healthy Ageing and Biomarkers Cohort Study (HABCS) and the China Health and Retirement Longitudinal Study (CHARLS) were used as validation cohorts. Risk factors were filtered using lasso regression, and predictive factors were determined using net reclassification improvement. Cox proportional hazards models were employed to establish the mortality risk prediction equations, and the model's fit was evaluated using a discrimination concordance index (C-index). To evaluate the internal consistency of discrimination and calibration, a 10x10 cross-validation technique was employed. Calibration plots were generated to compare predicted probabilities with observed probabilities. The prediction ability of the equations was demonstrated using nomogram.ResultsThe CLHLS (mean age 88.08, n = 37074) recorded 28158 deaths (179683 person-years) throughout the course of an 8-20 year follow-up period. Additionally, there were 1384 deaths in the HABCS (mean age 86.74, n = 2552), and 1221 deaths in the CHARLS (mean age 72.48, n = 4794). The final all-cause mortality model incorporated demographic characteristics like age, sex, and current marital status, as well as functional status indicators including cognitive function and activities of daily living. Additionally, lifestyle factors like past smoking condition and leisure activities including housework, television viewing or radio listening, and gardening work were included. The C-index for the derivation cohort was 0.728 (95% CI: 0.724-0.732), while the external validation results for the CHARS and HABCS cohorts were 0.761 (95% CI: 0.749-0.773) and 0.713 (95% CI: 0.697-0.729), respectively.ConclusionThis study introduces a reliable, validated, and acceptable mortality risk predictor for older adults in China. These predictive factors have potential applications in public health policy and clinical practice.

Dataset Information

Development of a competing risk nomogram for the prediction of cause-specific mortality in patients with thymoma: a population-based analysis.

Background

Methods

Results

Conclusions

Publications

Development of a competing risk nomogram for the prediction of cause-specific mortality in patients with thymoma: a population-based analysis.

Similar Datasets

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