Competing Risk Nomogram Research Articles

Development and validation of the competing risk nomogram and risk classification system for predicting cancer-specific mortality in patients with cervical adenosquamous carcinoma treated via radical hysterectomy.

In this study, we established and validated a competing risk nomogram for predicting the cumulative incidence of cervical adenosquamous carcinoma (ASC)-specific death in patients undergoing radical hysterectomy. Patients diagnosed with ASC between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function (CIF) for various variables influencing ASC-specific mortality was computed. A Fine-Gray competing risk model was used to identify independent predictors, formulating a competing risk nomogram. A multivariate Cox proportional hazards model was also applied for comparative analysis. The performance of the nomogram was assessed using metrics such as the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A corresponding risk classification system was constructed based on nomogram-derived scores. Factors such as advanced age, racial background (Black race), higher tumor grade, increased tumor size, advanced TNM stage, and receipt of radiotherapy without chemotherapy were found to be positively associated with elevated ASC-specific mortality. Additionally, age, T stage, M stage, and chemotherapy were identified as independent predictors correlated with ASC-specific mortality. The established nomogram exhibited accurate discriminatory capabilities and superior net benefits compared to the traditional TNM staging system. Additionally, the high-risk group consistently demonstrated higher probabilities of ASC-specific death in both the training and validation sets. The developed nomogram proficiently quantified the incidence of ASC-specific death in patients subjected to radical hysterectomy for ASC. This tool could help clinicians in formulating personalized treatment strategies and devising follow-up protocols.

A competing-risk nomogram for predicting gastric cancer-specific survival in patients over 70 years: A SEER-based study

BackgroundCancer-specific survival in older patients with gastric cancer is competitively affected by death from other causes. This study aimed to investigate cancer-specific survival and associated risk factors by competing-risk analysis in older patients with gastric cancer. MethodsThe data of this study are from the SEER database, using univariable and multivariable analysis of competitive risk model to weaken the impact of competitive events, explore the risk factors of cancer-specific survival, and developed a nomogram model. Then, the performance of the model is verified by C-index, ROC curve, calibration curve and DCA, and the new model is compared with the traditional TNM stage by NRI and IDI. ResultsOur study encompassed a total of 8183 patients, with 5731 in the training cohort and 2452 in the validation cohort. Univariable and multivariable analysis showed that age, years of diagnosis, race, site, SEER stage, TNM stage, surgery, radiation or chemotherapy, LNE, tumor grade and size are independent risk factors for cancer-specific survival in older patients with gastric cancer. Based on the risk factors, we developed a diagram model to predict cancer-specific survival. C-index, ROC curve, calibration curve and DCA also show good results. We compared the new model with the traditional TNM stage model, and the NRI and IDI showed the new model has been significantly improved. ConclusionThis study developed a nomogram to predict the cancer-specific survival of older patients with gastric cancer, which can accurately predict the prognosis and contribute to clinical treatment decision-making.

A Predictive Model for Gastric Cancer-Specific Death after Gastrectomy: A Competing-Risk Nomogram.

We aimed to assess the likelihood of cause-specific death and other causes of death after gastrectomy for gastric cancer (GC). Additionally, a competing-risk nomogram was developed for patient counseling and decision-making. Eligible GC patients who had gastrectomy between 2007 and 2015 were included in the study from the Surveillance, Epidemiology, and End Results (SEER) database. Death from gastric cancer and death from other causes were considered as separate competing events. Cumulative incidence functions (CIF) were calculated for each event, and a competing-risk nomogram was developed. Overall, 8,808 patients who underwent gastrectomy were analyzed. Among them, 4,659 (52.90%) died from gastric cancer and 1,284 (14.58%) died from other causes. The five-year cumulative incidence of cause-specific death for gastric cancer was 50.4%, and 10.2% for deaths from other causes. Several independent factors, such as age at diagnosis, tumor site, grade, size, lymph node examination results, pathological T status, pathological N status, metastatic status, Lauren classification, radiation, and chemotherapy, were found to be associated with gastric cancer-specific death. The nomogram, based on results from the competing risk regression model, demonstrated good performance. We have developed a nomogram aimed at predicting gastric cancer-specific mortality in patients following gastrectomy. The model has undergone internal validation, demonstrating good accuracy and reliability. It serves as useful tool that can assist physicians and patients in making more informed clinical decisions.

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery.

Cancer-specific survival (CSS) in multiple primary colorectal cancer (MPCC) patients is competitively affected by death from other causes. This study aimed to investigate CSS and associated risk factors by competing risk analysis in MPCC patients. The data of this study is from the SEER database. Using univariable and multivariable analysis of the competitive risk model to weaken the impact of competitive events, it explores the risk factors of CSS and develops a nomogram model. Then, the performance of the model is verified by the ROC curve, calibration curve, and DCA. The study encompasses a total of 8931 patients, with 6255 in the training cohort and 2676 in the validation cohort. Univariable and multivariable analyses showed that sex, Tumor Node Metastasis (TNM) stage, and tumor grade are independent risk factors for cancer-specific survival in MPCC patients. Based on the risk factors, we developed a diagram model to predict CSS. ROC curve, calibration curve, and DCA also show good results. In conclusion, a nomogram is developed that serves as a valuable tool for predicting CSS in MPCC patients, providing clinicians with crucial insights for personalized treatment planning.

A nomogram to predict cancer-specific mortality in adult patients with malignant meningioma: a competing risk analysis

BackgroundComprehensive investigations of the prognosis factors and treatment strategies with adjustment of competing causes of death for patients with malignant meningioma (MM) is still lacking.Patient and methodThe surveillance, Epidemiology, and End Results (SEER) database were used to include adult patients with this rare disease between 2004 and 2018. The probability of MM-caused mortality (MMCM) and non-MM-caused mortality (non-MMCM) were presented by cumulative incidence function curves. Then, the association between variates with non-MMCM was evaluated by the cox proportional hazard model, and the prognostic factors of MMCM were identified by Fine-Gray competing risk regression model. Furthermore, a nomogram was developed to predict the 1-year, 2-year, and 5-year MMCM and the performance was tested by a time-dependent area under the receiver operating characteristic (ROC) curve and calibration.Result577 patients were included, with a median age of 62 (18–100) years old and a median overall survival time of 36 (0–176) months. The percentage of non-MMCM was 15.4% (n = 89) in the entire population and 21.7% (n = 54) in elderly patients. The multivariable Cox proportional hazard regression model revealed that older age and other tumor(s) before or after MM had an independently significant association with higher non-MMCM. After adjustment of competing causes of death, the multivariable Fine-gray regression model identified age group ≥ 65 year, tumor size > 5.3 cm, recurrent MM, and histologic type 9530/3 (Meningioma, malignant) had an independently significant association with higher MMCM. Compared with gross total (GTR) of tumor, subtotal resection of tumor (HR 1.66, 95%CI 1.08–2.56, P = 0.02), partial resection of lobe (HR 2.26, 95%CI 1.32–3.87, P = 0.003), and gross total resection of lobe (HR 1.69, 95%CI 1.12–2.51, P = 0.01) had an independently significant association with higher MMCM.ConclusionThe competing risk nomogram including age group, tumor size, initial status, histologic type, and extent of resection is discriminative and clinically useful. This study emphasized the importance of the GTR of tumor in the treatment of MM patients, which had a significantly lower incidence of MMCM compared with biopsy, STR of tumor, partial resection of lobe, and GTR of lobe.

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

Development and validation of competing risk nomograms for predicting cancer‑specific mortality in non-metastatic patients with non‑muscle invasive urothelial bladder cancer

We aimed to assess the cumulative incidences of cancer-specific mortality (CSM) in non-metastatic patients with non‑muscle invasive urothelial bladder cancer (NMIUBC) and establish competing risk nomograms to predict CSM. Patient data was sourced from the Surveillance, Epidemiology, and End Results database, as well as the electronic medical record system in our institution to form the external validation cohort. Sub-distribution proportional hazards model was utilized to determine independent risk factors influencing CSM in non-metastatic NMIUBC patients. Competitive risk nomograms were constructed to predict 3-year, 5-year, and 8-year cancer-specific survival (CSS) in all patients group, TURBT group and cystectomy group, respectively. The discrimination and accuracy of the model were validated through the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. Decision curve analysis (DCA) and a risk stratification system was employed to evaluate the clinical utility of the model. Race, age, marital status, surgery in other sites, tumor size, histological type, histological grade, T stage and N stage were identified as independent risk factors to predict CSS in all patients group. The C-index for 3-year CSS was 0.771, 0.770 and 0.846 in the training, testing and external validation sets, respectively. The ROC curves showed well discrimination and the calibration plots were well fitted and consistent. Moreover, DCA demonstrated well clinical effectiveness. Altogether, the competing risk nomogram displayed excellent discrimination and accuracy for predicting CSS in non-metastatic NMIUBC patients, which can be applied in clinical practice to help tailor treatment plans and make clinical decisions.

The hepatocellular carcinoma risk in patients with HBV-related cirrhosis: a competing risk nomogram based on a 4-year retrospective cohort study.

The study aimed to build and validate a competitive risk nomogram to predict the cumulative incidence of hepatocellular carcinoma (HCC) for patients with hepatitis B virus (HBV)-related cirrhosis. A total of 1401 HBV-related cirrhosis patients were retrospectively enrolled from January 1, 2011 to December 31, 2014. Application of 20 times imputation dealt with missing data using multiple imputation by chained equations (MICE). The patients were randomly divided into a training set (n = 1017) and a validation set (n = 384) at a ratio of 3:1. A prediction study was carried out using a competing risk model, where the event of interest was HCC and the competing events were death and liver transplantation, and subdistribution hazard ratios (sHRs) with 95% CIs were reported. The multivariate competing risk model was constructed and validated. There was a negligible difference between the original database and the 20 imputed datasets. At the end of follow-up, the median follow-up time was 69.9 months (interquartile range: 43.8-86.6). There were 31.5% (442/1401) of the patients who developed HCC, with a 5-year cumulative incidence of 22.9 (95%CI, 20.8%-25.2%). The univariate and multivariate competing risk regression and construction of the nomogram were performed in 20 imputed training datasets. Age, sex, antiviral therapy history, hepatitis B e antigen, alcohol drinking history, and alpha-fetoprotein levels were included in the nomogram. The area under receiver operating characteristic curve values at 12, 24, 36, 60, and 96 months were 0.68, 0.69, 0.70, 0.68, and 0.80, and the Brier scores were 0.30, 0.25, 0.23, 0.21, and 0.20 in the validation set. According to the cumulative incidence function, the nomogram effectively screened out high-risk HCC patients from low-risk patients in the presence of competing events (Fine-Gray test p < 0.001). The competitive risk nomogram was allowed to be used for predicting HCC risk in individual patients with liver cirrhosis, taking into account both the association between risk factors and HCC and the modifying effect of competition events on this association.

Predicting cancer-specific mortality in T1/2 hepatocellular carcinoma after radiofrequency ablation by competing risk nomogram: A population-based analysis

BackgroundRadiofrequency ablation (RFA) is one of the primary treatment methods for T1/2 hepatocellular carcinoma (HCC), but the risk factors after RFA remain controversial. This study aims to identify the key factors associated with cancer-specific mortality (CSM) in patients with T1/2 HCC after RFA using competing risk analysis and to establish a prognostic nomogram for improved clinical management. MethodsA total of 2,135 T1/2 HCC patients treated with RFA were obtained from the Surveillance, Epidemiology, and End Results (SEER) database and randomly categorized into training and validation sets. Univariate and multivariable competing risk analyses were performed to identify risk factors associated with CSM and construct a competing risk nomogram. Receiver operating characteristic (ROC) curves, concordance indices (C-indexes), calibration plots, and decision curve analysis (DCA) were conducted to evaluate the predictive efficiency and clinical applicability of the nomogram in the training and validation sets. Patients were stratified according to their nomogram score, and the different risk groups were compared using cumulative incidence function (CIF) curves and Gray's validation . ResultsThe 5-year CSM rate for HCC patients treated with RFA was 30.1 %. Grade, tumor size, tumor number, cirrhosis, and AFP level were identified as independent risk factors for CSM. A prognostic nomogram was developed based on these risk factors. The time-dependent C-indexes (0.65) were greater than those of the AJCC stage model (0.55) during the 12 to 60 months of follow-up. The calibration plots of the competing risk nomograms demonstrated excellent consistency between actual survival and nomogram predictions. ROC analyses showed that the 1-, 3-, and 5-year AUC values in both the training and validation cohorts were all greater than 0.63 and exceeded those of the AJCC stage model. DCA demonstrated the clinical usefulness of the nomogram. Patients were classified into low-, moderate-, and high-risk groups based on the nomogram scores, with the high-risk group showing significantly higher CSM rates after RFA compared to the other two groups. ConclusionsWe identified Grade, AFP, cirrhosis, tumor size, and tumor number as independent risk factors associated with CSM. The competing risk nomogram exhibited high performance in predicting the probability of CSM for HCC patients undergoing RFA.

Competing risk nomogram and risk classification system for evaluating overall and cancer-specific survival in neuroendocrine carcinoma of the cervix: a population-based retrospective study

ObjectiveNeuroendocrine carcinoma of the cervix (NECC) is a rare malignancy with poor clinical prognosis due to limited therapeutic options. This study aimed to establish a risk-stratification score and nomogram models to predict prognosis in NECC patients.MethodsData on individuals diagnosed with NECC between 2000 and 2019 were retrieved from the Surveillance Epidemiology and End Results (SEER) database and then randomly classified into training and validation cohorts (7:3). Univariate and multivariate Cox regression analyses evaluated independent indicators of prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis further assisted in confirming candidate variables. Based on these factors, cancer-specific survival (CSS) and overall survival (OS) nomograms that predict survival over 1, 3, and 5 years were constructed. The receiver operating characteristic (ROC) curve, the concordance index (C-index), and the calibration curve estimated the precision and discriminability of the competing risk nomogram for both cohorts. Finally, we assessed the clinical value of the nomograms using decision curve analysis (DCA).ResultsData from 2348 patients were obtained from the SEER database. Age, tumor stage, T stage, N stage, chemotherapy, radiotherapy, and surgery predicted OS. Additionally, histological type was another standalone indicator of CSS prognosis. For predicting CSS, the C-index was 0.751 (95% CI 0.731 ~ 0.770) and 0.740 (95% CI 0.710 ~ 0.770) for the training and validation cohorts, respectively. Furthermore, the C-index in OS prediction was 0.757 (95% CI 0.738 ~ 0.776) and 0.747 (95% CI 0.718 ~ 0.776) for both cohorts. The proposed model had an excellent discriminative ability. Good accuracy and discriminability were also demonstrated using the AUC and calibration curves. Additionally, DCA demonstrated the high clinical potential of the nomograms for CSS and OS prediction. We constructed a corresponding risk classification system using nomogram scores. For the whole cohort, the median CSS times for the low-, moderate-, and high-risk groups were 59.3, 19.5, and 7.4 months, respectively.ConclusionNew competing risk nomograms and a risk classification system were successfully developed to predict the 1-, 3-, and 5-year CSS and OS of NECC patients. The models are internally accurate and reliable and may guide clinicians toward better clinical decisions and the development of personalized treatment plans.

Competing risk nomogram predicting cause-specific mortality in older patients with testicular germ cell tumors.

Testicular germ cell tumor (TGCT) is the most common type of malignancy in young men, but rarely in older adults. We aimed to construct a competing risk model to predict the prognosis for older patients with TGCT. We collected TGCT patients aged 50 years or older diagnosed between 2004 and 2015 from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. We estimated the cumulative incidences of cause-specific death (CSD) and other causes of death and established a nomogram predicting cause-specific mortality in older patients with TGCT by Fine-Gray competing risk regression. The concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC), and decision analysis curves (DCA) were used to evaluate the differentiation, accuracy, and clinical significance of the nomogram. A total of 2,751 older TGCT patients were included in the study. The 3-, 5-, and 10-year cumulative incidences were 4.4, 5.0 and 6.1%, respectively, for cause-specific death, and 3.8, 6.2, 13.1%, respectively, for other causes of death. Predictors of cause-specific mortality in older TGCT included age, marital status, annual household income, histology, tumor size, stage and surgery. In the training and validation sets, the C-indexes were greater than 0.8, indicating that the nomogram had good discrimination. The AUC revealed the same result. The calibration curves showed good agreement between the predicted and observed results of the nomogram. DCA curves indicated that the nomogram had more clinical significance than the conventional American Joint Committee on Cancer (AJCC) staging. Based on the total nomogram score of each case, all patients were categorized into low-risk and high-risk groups, and risk categorization allowed the identification of cases with a high risk of death. We established a competing risk nomogram with good performance that may help clinicians accurately predict the prognosis of older TGCT patients.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma.

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Mortality patterns of patients with tonsillar squamous cell carcinoma: a population-based study.

Tonsillar squamous cell carcinoma (TSCC) and second primary malignancies (SPMs) are the most common causes of mortality in patients with primary TSCC. However, the competing data on TSCC-specific death (TSD) or SPM-related death in patients with TSCC have not been evaluated. This study aimed to analyze the mortality patterns and formulate prediction models of mortality risk caused by TSCC and SPMs. Data on patients with a first diagnosis of TSCC were extracted as the training cohort from the 18 registries comprising the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk approach of cumulation incidence function was used to estimate cumulative incidence curves. Fine and gray proportional sub-distributed hazard model analyses were performed to investigate the risk factors of TSD and SPMs. A nomogram was developed to predict the 5- and 10-year risk probabilities of death caused by TSCC and SPMs. Moreover, data from the 22 registries of the SEER database were also extracted to validate the nomograms. In the training cohort, we identified 14,530 patients with primary TSCC, with TSCC (46.84%) as the leading cause of death, followed by SPMs (26.86%) among all causes of death. In the proportion of SPMs, the lungs and bronchus (22.64%) were the most common sites for SPM-related deaths, followed by the larynx (9.99%), esophagus (8.46%), and Non-Melanoma skin (6.82%). Multivariate competing risk model showed that age, ethnicity, marital status, primary site, summary stage, radiotherapy, and surgery were independently associated with mortality caused by TSCC and SPMs. Such risk factors were selected to formulate prognostic nomograms. The nomograms showed preferable discrimination and calibration in both the training and validation cohorts. Patients with primary TSCC have a high mortality risk of SPMs, and the competing risk nomogram has an ideal performance for predicting TSD and SPMs-related mortality. Routine follow-up care for TSCC survivors should be expanded to monitor SPMs.

A nomogram for predicting breast cancer specific survival in elderly patients with breast cancer: a SEER population-based analysis

BackgroundThe number of elderly patients diagnosed with breast cancer is increasing worldwide. However, treatment decisions for these patients are highly variable. Although researchers have identified the effects of surgery, radiotherapy, endocrine therapy, and chemotherapy in elderly patients with breast cancer, clinicians still struggle to make appropriate decisions for these patients.MethodsWe identified 75,525 female breast cancer patients aged ≥ 70 years in the Surveillance, Epidemiology, and End Results (SEER) database treated between January 1, 2010, and December 31, 2016. The patients were further divided into training and testing cohorts. The cumulative occurrence of breast cancer-specific deaths (BCSDs) and other cause-specific deaths (OCSD) was calculated using the cumulative incidence function. In the univariate analysis, risk factors were screened using the Fine-Gray model. In the multivariate analysis for competing risks, the sub-distribution hazard ratio with a 95% confidence interval for each independent predictor associated with BCSD was calculated for the construction of nomograms. Based on the above analyses, a competing risk nomogram was constructed to predict the probability of BCSD in the 1st, 3rd, and 5th years after treatment. During validation, the concordance index (C-index) was selected to quantify the predictive ability of the competing risk model.ResultsA total of 33,118 patients were included in this study, with 24,838 in the training group and 8,280 in the testing group. Age, race, marital status, cancer grade, tumor stage, node stage, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor--2 status, and treatment including surgery, radiation, and chemotherapy were used to establish a nomogram. The C-index of 0.852 (0.842-0.862) in the training cohort and 0.876 (0.868-0.892) in the testing cohort indicated satisfactory discriminative ability of the nomogram. Calibration plots showed favorable consistency between the nomogram predictions and actual observations in both the training and validation cohorts.ConclusionsOur study identified independent predictors of BCSD in elderly patients with breast cancer. A prognostic nomogram was developed and validated to aid clinical decision-making.

Prognostic factors for competing risk in patients with AIDS-related Kaposi's sarcoma: A SEER population-based study.

Despite the improved survival of patients with AIDS and Kaposi's sarcoma (KS), competing events are a non-negligible issue affecting the survival of such patients. In this study, we explored the prognostic factors of KS-specific and non-KS-specific mortality in patients with AIDS-related KS (AIDS-KS), accounting for competing risk. We identified 17 103 patients with AIDS-KS aged 18-65 years between 1980 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) 18 registry database. Prognostic factors for KS-specific and non-KS-specific mortality were determined by the Fine and Grey proportional subdistribution hazard model. We built competing risk nomograms and assessed their predictive performance based on the identified prognostic factors. In total, 12 943 (75.68%) patients died, 1965 (15.50%) of whom died from competing events. The KS-specific mortality rate was 14 835 per 100 000 person-years, and the non-KS specific mortality rate was 2719 per 100 000 person-years. Specifically, age >44 years was associated with an 11% decrease in the subdistribution hazard of KS-specific mortality compared with age <43 years but a 50% increase in the subdistribution hazard of non-KS-specific mortality. Being male was associated with a 26% increase in the subdistribution hazard of KS-specific mortality compared with being female but a 32% decrease in the subdistribution hazard of non-KS-specific mortality. Notably, being in the antiretroviral therapy (ART) era consistently showed a decrease in the subdistribution hazard of both KS-specific and non-KS-specific mortality than being in the pre-ART era. Competing events commonly occurred among patients with AIDS-KS, which deserves further attention to improve the prognosis of these patients.

Nomogram for predicting cardiac death and heart transplantation in arrhythmogenic right ventricular cardiomyopathy.

In this study, we aimed to develop and validate a competing risk nomogram for predicting cardiac death and heart transplantation (HT) in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). We retrospectively enrolled 149 consecutive patients with ARVC diagnosed at our institution between 2008 and 2022. Cox proportional hazards model was primarily used to identify variables associated with cardiac death and HT. On the basis of these indicators, a competing risk nomogram was developed to predict the 1, 3, and 5 year probabilities of cardiac death and HT. The area under the receiver operating characteristic curve (AUC), Harrell's C-index, and calibration curves were used to evaluate and internally validate the performance of the model. Decision curve analysis was performed to assess the clinical utility of the nomogram. Of the 149 patients with ARVC, the mean age was 38.77 ± 15.94 years, and most of the patients were men (67.11%, 100/149). Fourteen patients experienced cardiac death and nine underwent HT, during a median follow-up period of 5.8 years (interquartile range, 0.62-5.56 years). Multivariable COX analysis revealed that extent of TWI in the anterior and inferior leads (P = 0.0057), right atrial diameter on transthoracic echocardiography (P = 0.0498), RVEF (P = 0.1036), and LVEF (P < 0.001) all showed statistical significance. The 1-, 3-, and 5-year cumulative incidence of cardiac death and HT were 3.35%, 8.05%, and 11.4%, respectively. The area under the receiver operating characteristic curve of the nomogram for predicting cardiac death and HT at 1, 3, and 5years after diagnosis of ARVC were 0.860, 0.935, and 0.956. The value of Harrell's C-index is 0.9273 (95% confidence interval 0.8954-0.9590; P<0.001), indicating that the model had good discriminative ability in internal validation. Decision curve analysis revealed that our model was clinically useful within the entire range of potential treatment thresholds in most cases. The cumulative incidence of the primary outcomes was significantly different between the three risk groups according to nomogram-derived scores (P<0.001). On the basis of a retrospective review of patients with ARVC at a single centre, we developed a novel nomogram for predicting the risk of cardiac death and HT after ARVC diagnosis. This competing risk nomogram based on four readily available clinical parameters (right atrial diameter, right and left ventricular ejection fraction, and T-wave inversion) is a potentially useful tool for individualized prognostic assessment in patients with ARVC.

A competing risk nomogram to predict cancer-specific mortality of patients with late-onset colorectal cancer.

This study aimed to compare the clinical characteristics and survival differences between early-onset colorectal cancer (EOCRC) patients and late-onset colorectal cancer (LOCRC) patients, identify the risk factors for cancer-specific mortality (CSM) in LOCRC patients and construct a mortality risk assessment nomogram. CRC patients diagnosed pathologically between 2010 and 2019 in the SEER database were included and divided into the early-onset group and the late-onset group, and the late-onset group was divided into the training and validation sets. The Fine-Gray competing risk model was applied to analyze the prognostic factors of LOCRC patients and establish a competing risk nomogram for CSM. There are differences in the distribution of multiple clinical features between the early-onset group and the late-onset group. Age, tumor size, histological type, pathological grading, T stage, N stage, M stage, SEER stage, primary tumor surgery, metastatic lesion surgery, radiotherapy, chemotherapy, neural invasion, and carcinoembryonic antigen (CEA) were independent influencing factors of the CSM rate in LOCRC patients. The C-index of the prognosis model outweighed 0.8, and the calibration curves fitted the reference line well. The CSM competing risk nomogram for LOCRC patients in this study had acceptable predictive performance that could be applied to the clinic.

Prognostic value and mode selection of locoregional treatment in Stage-IV breast cancer patients.

This study aimed to assess the actual prognostic significance of different locoregional treatment (LRT) (surgery and radiotherapy) modalities for stage-IV breast cancer (BC) patients and construct a competing risk nomogram to make precise predictions of the breast cancer-specific death (BCSD) risk among LRT recipients. A total of 9279 eligible stage-IV BC patients from the Surveillance Epidemiology and End Results (SEER) database were included in this study. Initially, we evaluated the impact of LRT on survival both before and after the propensity score matching (PSM). Then, we used the Cox hazard proportional model and competing risk model to identify the independent prognostic factors for LRT recipients. Based on the screened variables, a comprehensive nomogram was established. Kaplan-Meier curves demonstrated that LRT significantly prolonged overall survival (OS) and breast cancer-specific survival (BCSS) (P < 0.001). In addition, patients treated with surgery combined with postoperative radiotherapy (PORT) possessed the optimal survival (P < 0.001). Regardless of the surgical modalities, primary tumor resection combined with radiotherapy could ameliorate the prognosis (P < 0.05). Subgroup analysis showed that in patients with T2-T4 stage, PORT had a survival benefit compared with those undergoing surgery combined with preoperative radiotherapy (PRRT) and surgery only. Based on the screened independent prognostic factors, we established a comprehensive nomogram to forecast BCSD in 1year, 2years and 3years, which showed robust predictive ability. PORT was associated with a lower BCSD in stage-IV BC patients. The practical nomogram could provide a precise prediction of BCSD for LRT recipients, which was meaningful for patients' individualized management.

Risk prediction of second primary malignancies after gynecological malignant neoplasms resection with and without radiation therapy: a population-based surveillance, epidemiology, and end results (SEER) analysis

PurposeThe association between post-resection radiotherapy for primary gynecological malignant neoplasms (GMNs) and the development of secondary primary malignancies (SPMs) remains a subject of debate. This study represents the first population-based analysis employing a multivariate competitive risk model to assess risk factors for this relationship and to develop a comprehensive competing-risk nomogram for quantitatively predicting SPM probabilities.Materials and methodsIn our study, data on patients with primary GMNs were retrospectively collected from the Epidemiology, Surveillance and End Results (SEER) database from 1973 to 2015. The incidence of secondary malignant tumors diagnosed at least six months after GMN diagnosis was compared to determine potential risk factors for SPMs in GMN patients using the Fine and Gray proportional sub-distribution hazard model. A competing-risk nomogram was constructed to quantify SPM probabilities.ResultsA total of 109,537 patients with GMNs were included in the study, with 76,675 and 32,862 GMN patients in the training and verification sets, respectively. The competing-risk model analysis identified age, primary tumor location, tumor grade, disease stage, chemotherapy, and radiation as risk factors for SPMs in GMN patients. Calibration curves and ROC curves in both training and verification cohorts demonstrated the predictive accuracy of the established nomogram, which exhibited a good ability to predict SPM occurrence.ConclusionsThis study presents the nomogram developed for quantitatively predicting SPM probabilities in GMN patients for the first time. The constructed nomogram can assist clinicians in designing personalized treatment strategies and facilitate clinical decision-making processes.

Prediction of death probability in adenocarcinoma of the transverse colon: competing-risk nomograms based on 21,469 patients.

Tumors in parts of the colon other than the transverse colon have been well studied, but little is known about adenocarcinoma of the transverse colon (ATC).The aim of this study was to construct nomograms using competing-risk model for accurately predicting the probability of cancer-specific and non-cancer-specific death in patients with ATC. Data on eligible patients recorded during 2000-2019 in the Surveillance, Epidemiology, and End Results database were extracted and screened. Factors that influencing prognosis were screened for death from ATC (DATC) and death from other causes (DOC) using competing-risk analysis, including univariate and multivariate analyses based on Gray's test and the Fine-Gray model, respectively. Independent prognostic factors were identified and nomograms were constructed. For comparison, we also constructed a Cox model and an AJCC stage-only competing-risk model (AJCC model) for patients with DATC. Performance evaluations of the nomograms and comparison between the models were performed using calibration plots, Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, and the areas under the ROC curve (AUCs). The nomograms and models were validated using a validation cohort. The net reclassification index, integrated discrimination improvement, decision curves, and risk stratification were not assessed because no accepted methods were suited for competing-risk model. This study included 21,469 patients with ATC, and 17 and 9 independent influencing factors were identified for the construction of DATC nomograms (DATCN) and DOC nomograms (DOCN), respectively. In both the training and validation cohorts, the calibration curves indicated good agreement between the nomogram-based predictions and the actual observations in the two nomograms, respectively. The C-index of the DATCN was higher than 80% (80.3-83.3%) at 1, 3 and 5 years in both the training and validation cohorts, significantly outperforming the AJCC (76.7-78%) and Cox (75.4-79.5%) model. The C-index of the DOCN was also higher than 69% (69.0-73.6%). In terms of ROC curves at each time point, those of the DATCN were very close to the upper-left corner of the coordinate axis in both the training and validation cohorts, and their AUCs were larger than 84% (84.2-85.4%).The AUCs of the AJCC (78.4-81.1%) and Cox (79.4-81.5%) models were significantly lower (p < 0.05), and the curves were closer to the diagonal. The ROC curves of the DOCN was similar to those of the DATCN, and the AUCs were 68.5-74%. The DATCN and DOCN therefore had good consistency, accuracy, and stability, respectively. This study was the first to construct competing-risk nomograms for ATC. These nomograms have proved useful for accurately assessing patient prognoses and allowing more-individualized follow-up strategies, thereby reducing the mortality.