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Background: Utilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk. Methods: Clinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer. Results: Involving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model's area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach. Conclusion: We created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first‐trimester screening for pre‐eclampsia in a large Asian population. Methods: This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11–13+6 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first‐trimester prediction of preterm pre‐eclampsia (<37 weeks), term pre‐eclampsia (≥37 weeks), and any pre‐eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model. Results: The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre‐eclampsia (0.82, 95% confidence interval [CI] 0.77–0.87 vs. 0.86, 95% CI 0.811–0.91, P = 0.019), term pre‐eclampsia (0.75, 95% CI 0.71–0.80 vs. 0.79, 95% CI 0.75–0.83, P = 0.006), and any pre‐eclampsia (0.78, 95% CI 0.74–0.81 vs. 0.82, 95% CI 0.79–0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre‐eclampsia, term pre‐eclampsia, and any pre‐eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80–0.89), 0.77 (95% CI 0.73–0.81), and 0.80 (95% CI 0.76–0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre‐eclampsia (P = 0.135) and term pre‐eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre‐eclampsia (P = 0.024). Conclusion: This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre‐eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population. Synopsis: Machine learning model achieved comparable predictive performance to that of the FMF competing risk model in predicting preterm and term pre‐eclampsia in an Asian population. [ABSTRACT FROM AUTHOR]

Abstract Background Utilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk. Methods Clinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer. Results Involving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model’s area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach. Conclusion We created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans.

Abstract 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.

Objective: Previous research has primarily focused on the incidence and mortality rates of Merkel cell carcinoma (MCC), neglecting the examination of cardiovascular mortality (CVM) risk among survivors, particularly older patients. This study aims to assess the risk of CVM in older individuals diagnosed with MCC. Methods: Data pertaining to older MCC patients were obtained from the Surveillance, Epidemiology, and End Results database (SEER). CVM risk was measured using standardized mortality ratio (SMR) and cumulative mortality. Multivariate Fine-Gray's competing risk model was utilized to evaluate the risk factors contributing to CVM. Results: Among the study population of 2,899 MCC patients, 465 (16.0%) experienced CVM during the follow-up period. With the prolongation of the follow-up duration, the cumulative mortality rate for CVM reached 27.36%, indicating that cardiovascular disease (CVD) became the second most common cause of death. MCC patients exhibited a higher CVM risk compared to the general population (SMR: 1.69; 95% CI: 1.54–1.86, p < 0.05). Notably, the SMR for other diseases of arteries, arterioles, and capillaries displayed the most significant elevation (SMR: 2.69; 95% CI: 1.16–5.29, p < 0.05). Furthermore, age at diagnosis and disease stage were identified as primary risk factors for CVM, whereas undergoing chemotherapy or radiation demonstrated a protective effect. Conclusion: This study emphasizes the significance of CVM as a competing cause of death in older individuals with MCC. MCC patients face a heightened risk of CVM compared to the general population. It is crucial to prioritize cardiovascular health starting from the time of diagnosis and implement personalized CVD monitoring and supportive interventions for MCC patients at high risk. These measures are essential for enhancing survival outcomes. [ABSTRACT FROM AUTHOR]

Astrocytoma is a type of adult-type diffuse gliomas that includes diffuse astrocytoma (DA) and anaplastic astrocytoma (AA). However, comprehensive investigations into the risk assessment and prognosis of DA and AA using population-based studies remain noticeably scarce. In this study, we developed 2 predictive nomograms to evaluate the susceptibility and prognosis associated with DA and AA. The study cohort comprised 3837 individuals diagnosed with DA or AA between 2010 and 2019 selected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent predictors were identified and used to construct the nomograms for overall death and cancer-specific death rates. The performance of the models was assessed using C-index, calibration curves, and receiver operating characteristic curve, and the clinical applicability was evaluated using decision curve analysis. The receiver operating characteristic curves in this study show excellent clinical applicability and predictive power. Notably, the area under the curves of the training and verification queues was higher than 0.80, thereby cementing the models' precision. Additionally, the calibration plots demonstrate that the anticipated mortality rates strikingly match the measured values. This alignment of figures is sustained in the validation cohort. Furthermore, the decision curve analysis corroborates the models' translational potential, reinforcing their relevance within real-world clinical settings. The presented nomograms have not only exhibited good predictive performance but also showcased pragmatic clinical utility in prognosticating patient outcomes. Significantly, this will undoubtedly serve as a valuable asset for oncologists, facilitating informed treatment decisions and meticulous follow-up planning. [ABSTRACT FROM AUTHOR]

Background: Young lung cancer is a rare subgroup accounting for 5% of lung cancer. The aim of this study was to compare the causes of death (COD) among lung cancer patients of different age groups and construct a nomogram to predict cancer‐specific survival (CSS) in young patients with advanced stage. Methods: Lung cancer patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and stratified into the young (18–45 years) and old (> 45 years) groups to compare their COD. Young patients diagnosed with advanced stage (IVa and IVb) from 2010 to 2015 were reselected and divided into training and validation cohorts (7:3). Independent prognostic factors were identified through the Fine‐Gray's test and further integrated to the competing risk model. The area under the receiver operating characteristic curve (AUC), consistency index (C‐index), and calibration curve were applied for validation. Results: The proportion of cancer‐specific death (CSD) in young patients was higher than that in old patients with early‐stage lung cancer (p < 0.001), while there was no difference in the advanced stage (p = 0.999). Through univariate and multivariate analysis, 10 variables were identified as independent prognostic factors for CSS. The AUC of the 1‐, 3‐, and 5‐year prediction of CSS was 0.688, 0.706, and 0.791 in the training cohort and 0.747, 0.752, and 0.719 in the validation cohort. The calibration curves demonstrated great accuracy. The C‐index of the competing risk model was 0.692 (95% CI: 0.636–0.747) in the young patient cohort. Conclusion: Young lung cancer is a distinct entity with a different spectrum of competing risk events. The construction of our nomogram can provide new insights into the management of young patients with lung cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to construct a competing risk prediction model for predicting specific mortality risks in endometrial cancer patients from the SEER database based on their demographic characteristics and tumor information. Methods: We collected relevant clinical data on patients with histologically confirmed endometrial cancer in the SEER database between 2010 and 2015. Univariate and multivariate competing risk models were used to analyze the risk factors for endometrial cancer‐specific death, and a predictive nomogram was constructed. C‐index and receiver operating characteristic curve (ROC) at different time points were used to verify the accuracy of the constructed nomogram. Results: There were 26 109 eligible endometrial cancer patients in the training cohort and 11 189 in the validation cohort. Univariate and multivariate analyses revealed that Age, Marriage, Grade, Behav, FIGO, Size, Surgery, SurgOth, Radiation, ParaAortic_Nodes, Peritonea, N positive, DX_liver, and DX_lung were independent prognostic factors for specific mortality in endometrial cancer patients. Based on these factors, a nomogram was constructed. Internal validation showed that the nomogram had a good discriminative ability (C‐index = 0.883 [95% confidence interval [CI]: 0.881–0.884]), and the 1‐, 3‐, and 5‐year AUC values were 0.901, 0.886 and 0.874, respectively. External validation indicated similar results (C‐index = 0.883 [95%CI: 0.882–0.883]), and the 1‐, 3‐, and 5‐ AUC values were 0.908, 0.885 and 0.870, respectively. Conclusion: We constructed a competing risk model to predict the specific mortality risk among endometrial cancer patients. This model has favorable accuracy and reliability and can provide a reference for the development and update of endometrial cancer prognostic risk assessment tools. [ABSTRACT FROM AUTHOR]

Abstract For patients with upper limb melanoma, the significance of specific death is more important than that of all-cause death, and traditional survival analysis may overestimate the mortality rate of patients. Therefore, the nomogram model for predicting the specific mortality risk of melanoma in the upper limbs was developed. A population with melanoma in the upper limbs, diagnosed from 2010 to 2015, were selected from the National Cancer Institute database of Surveillance, Epidemiology, and End Results (SEER). The independent predictive factors of specific death were confirmed by the competing risk model of one-factor analysis and multi-factor analysis, and the nomogram was constructed according to the independent predictive factors. 17,200 patients with upper limb melanoma were enrolled in the study (training cohort: n = 12,040; validation cohort: n = 5160). Multi-factor analysis of the competing risk model showed that age, marital status, gender, tumor stage, T stage, M stage, regional lymph node surgery information, radiotherapy, chemotherapy, mitotic cell count, ulcer and whether there were multiple primary cancers, were independent factors affecting the specific death of upper limb melanoma patients (P

Abstract Background The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients. Methods We extracted DLBCL patients’ data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model’s consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model’s effectiveness. Results This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts. Conclusion The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.

Introduction: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 + 6 weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia. Methods: This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation. Results: We ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively). Conclusions: Our models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes. [ABSTRACT FROM AUTHOR]

Objective This study aims to investigate the mortality among HIV/AIDS patients receiving antiretroviral therapy (ART) in Fangchenggang City, Guangxi Province, and to identify associated factors with AIDS-related and non-AIDS-related deaths, providing scientific evidence for reducing AIDS mortality. Methods We collected data from the National Comprehensive AIDS Prevention and Control Information System. These data included socio-demographic and follow-up records of 2 728 HIV-1 infected individuals receiving Antiretroviral Therapy (ART) treatment in Fangchenggang City from January 1, 2005 to July 5, 2022. This dataset included socio-demographic and follow-up records during the specified period, providing a comprehensive insight into the therapeutic responses, survival rates, and potential associated complications among ART-treated HIV/AIDS patients within Fangchenggang City. Socio-demographic and follow-up data were analyzed using the cumulative incidence function (CIF) under a competing risk framework and the Fine-Gray subdistribution hazard regression model to assess the associated factors with both AIDS-related and non-AIDS-related deaths. Results With an average follow-up period of 6.7 person-years, 295 cases experienced AIDS-related death with a mortality rate of 1.06/100 person-years, while 227 cases died from non-AIDS-related causes, with a mortality rate of 1.2/100 person-years. Under consideration of competing risks, the cumulative incidence of AIDS-related death at 1 year, 5 years, and 13 years post-diagnosis was 2.5%, 8.5%, and 15.0%, respectively. The factors associated with a higher risk of AIDS-related death included: age 60 years or older (aHR = 1.5, 95% CI: 1.05~2.15), current spouse's infection status unknown/not investigated (aHR = 1.39, 95 % CI: 1.03~1.90), history of prophylactic treatment for opportunistic infections (aHR = 1.4, 95% CI: 1.06~1.84), and occurrence of opportunistic infections or tumors (aHR = 1.65, 95% CI: 1.12~2.45) . On the contrary, factors associated with a lower risk of AIDS-related death included: being female (aHR = 0.61, 95% CI: 0.49~0.90), initial treatment regimen containing EFV (aHR = 0.41, 95% CI: 0.25~0.68), having changed the treatment regimen (aHR = 0.19, 95% CI: 0.12~0.29), first CD4 cell count ≥ 200 cells/µL (aHR = 0.30, 95% CI: 0.20~0.45), initial viral load (VL) between 50 to 1000 copies/ml (aHR = 0.31, 95% CI: 0.18-0.54), and VL < 50 copies/ml (aHR = 0.61, 95% CI: 0.44~0.84). Regarding non-AIDS related deaths, passive detection (aHR = 1.41, 95% CI: 1.0~1.98), uninvestigated/unknown spouse's infection status (aHR = 1.40, 95% CI: 1.01~1.95), and first CD4 cell count ≥200 cells/ (JLL (aHR = 1.68, 95% CI: 1.27~2.22) were found to be associated with increased risk. In contrast, lower risk factors included being female (aHR = 0.55, 95 % CI: 0.37-0.81), having experienced a change in antiviral treatment regimen post-initiation (aHR = 0.33, 95%CI :0.21-0.50), initial treatment regimens containing EFV (aHR = 0.55, 95% CI: 0.34-0.88), and NVP (aHR = 0.50, 95% CI: 0.3-0.83). Conclusion The mortality rate among HIV/AIDS patients receiving ART in Fangchenggang City is relatively low. This study underscores the importance of preventing and treating opportunistic infections or tumors for improving the survival of HIV/AIDS patients. HIV/AIDS care clinics should particularly focus on monitoring and following up on female patients, older patients, and those detected passively, enhancing medication adherence, expanding HIV testing among key populations, and thereby striving to reduce the mortality rate among AIDS patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents. Materials and Methods Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data. Results The meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data. Discussion Our proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions. Conclusion Our study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework. [ABSTRACT FROM AUTHOR]

For patients with upper limb melanoma, the significance of specific death is more important than that of all-cause death, and traditional survival analysis may overestimate the mortality rate of patients. Therefore, the nomogram model for predicting the specific mortality risk of melanoma in the upper limbs was developed. A population with melanoma in the upper limbs, diagnosed from 2010 to 2015, were selected from the National Cancer Institute database of Surveillance, Epidemiology, and End Results (SEER). The independent predictive factors of specific death were confirmed by the competing risk model of one-factor analysis and multi-factor analysis, and the nomogram was constructed according to the independent predictive factors. 17,200 patients with upper limb melanoma were enrolled in the study (training cohort: n = 12,040; validation cohort: n = 5160). Multi-factor analysis of the competing risk model showed that age, marital status, gender, tumor stage, T stage, M stage, regional lymph node surgery information, radiotherapy, chemotherapy, mitotic cell count, ulcer and whether there were multiple primary cancers, were independent factors affecting the specific death of upper limb melanoma patients (P < 0.05). The nomogram has good predictive ability regarding the specific mortality risk of melanoma in the upper limbs, and could be of great help to formulate prognostic treatment strategies and follow-up strategies that are conducive to survival. [ABSTRACT FROM AUTHOR]

Background: The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients. Methods: We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness. Results: This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts. Conclusion: The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Young lung cancer is a rare subgroup accounting for 5% of lung cancer. The aim of this study was to compare the causes of death (COD) among lung cancer patients of different age groups and construct a nomogram to predict cancer‐specific survival (CSS) in young patients with advanced stage. Methods Lung cancer patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and stratified into the young (18–45 years) and old (> 45 years) groups to compare their COD. Young patients diagnosed with advanced stage (IVa and IVb) from 2010 to 2015 were reselected and divided into training and validation cohorts (7:3). Independent prognostic factors were identified through the Fine‐Gray's test and further integrated to the competing risk model. The area under the receiver operating characteristic curve (AUC), consistency index (C‐index), and calibration curve were applied for validation. Results The proportion of cancer‐specific death (CSD) in young patients was higher than that in old patients with early‐stage lung cancer (p

Abstract Background Cervical cancer is the fourth most common malignant tumor troubling women worldwide. Whether marital status affects the prognosis of cervical cancer is still unclear. Here, we investigate the prognostic value of marital status in patients with cervical cancer based on the seer database. Material/Methods The demographic and clinical data of patients with cervical cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2017. Patients were divided into two groups (married and unmarried) according to marital status, and then the clinical characteristics of each group were compared using the chi-square test. Propensity score matching (PSM) was used to reduce differences in baseline characteristics. The overall survival (OS) and cervical cancer-specific survival (CCSS) were assessed by the Kaplan-Meier method, univariate and multivariate Cox regression models, and stratified analysis. Moreover, univariate and multivariate competing risk regression models were performed to calculate hazard ratios (HR) of death risk. Results A total of 21,148 patients were included in this study, including 10,603 married patients and 10,545 unmarried patients. Married patients had better OS(P

Gliosarcoma (GSM) is a highly aggressive variant of brain cancer with an extremely unfavorable prognosis. Prognosis is not feasible by traditional methods because of a lack of staging criteria, and the present study aims to screen more detailed demographic factors to predict the prognostic factors of the tumors. For this study, we extracted data of patients diagnosed with GSM from the SEER (Surveillance Epidemiology and End Results) database between 2000 and 2019. To account for the influence of competing risks, we used a Cumulative Incidence Function. Subsequently, univariate analysis was conducted to evaluate the individual variables under investigation. Specifically for patients with GSM, we generated cumulative risk curves for specific mortality outcomes and events related to competing risks. In addition, we used both univariate and multivariate Cox analysis to account for non–GSM-related deaths that may confound our research. The competing risk model showed that age, marital status, tumor size, and adjuvant therapy were prognostic factors in GSM-related death. The analysis results showed that older age (60–70 years, ≥71 years) and larger tumor size (≥5.3 cm) significantly increased the risk of GSM-related death. Conversely, surgical intervention, chemotherapy, and being single were identified as protective factors against GSM-related death. Our study using a competing risk model provided valuable insights into the prognostic factors associated with GSM-related death. Further research and clinical interventions targeted at minimizing these risk factors and promoting the use of protective measures may contribute to improved outcomes and reduced mortality for patients with GSM. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the inference for the competing risks model when the failure times follow Chen distribution. With assumption of two causes of failures, which are partially observed, are considered as independent. The existence and uniqueness of maximum likelihood estimates for model parameters are obtained under generalized progressive hybrid censoring. Also, we discussed the classical and Bayesian inferences of the model parameters under the assumption of restricted and nonrestricted parameters. Performance of classical point and interval estimators are compared with Bayesian point and interval estimators by conducting extensive simulation study. In addition to that, for illustration purpose, a real life example is discussed. Finally, some concluding remarks, regarding the presented model, are made. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is the fourth most common malignant tumor troubling women worldwide. Whether marital status affects the prognosis of cervical cancer is still unclear. Here, we investigate the prognostic value of marital status in patients with cervical cancer based on the seer database. Material/Methods: The demographic and clinical data of patients with cervical cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2017. Patients were divided into two groups (married and unmarried) according to marital status, and then the clinical characteristics of each group were compared using the chi-square test. Propensity score matching (PSM) was used to reduce differences in baseline characteristics. The overall survival (OS) and cervical cancer-specific survival (CCSS) were assessed by the Kaplan-Meier method, univariate and multivariate Cox regression models, and stratified analysis. Moreover, univariate and multivariate competing risk regression models were performed to calculate hazard ratios (HR) of death risk. Results: A total of 21,148 patients were included in this study, including 10,603 married patients and 10,545 unmarried patients. Married patients had better OS(P < 0.05) and CCSS (P < 0.05) compared to unmarried patients, and marital status was an independent prognostic factor for both OS (HR: 0.830, 95% CI: 0.798–0.862) and CCSS (HR: 0.892, 95% CI: 0.850–0.937). Moreover, after eliminating the competing risk, married patients (CCSD: HR:0.723, 95% CI: 0.683–0.765, P < 0.001) had a significantly decreased risk of death compared to unmarried patients. In stratified analysis, the married patients showed better OS and CCSS than the unmarried patients diagnosed in 1975–2000 and 2001–2017. Conclusions: Being married was associated with a favorable prognosis of cervical cancer, and marital status was an independent prognostic factor for cervical cancer. [ABSTRACT FROM AUTHOR]

In recent decades, many phase II clinical trials have used survival outcomes as the primary endpoints. If radiotherapy is involved, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, many existing research has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-size-fits-all" strategy often fails, and it is more relevant to evaluate the subgroup-specific treatment effect with the subgroup defined by the radiation susceptibility status. In this paper, we propose a Bayesian adaptive biomarker stratified phase II trial design evaluating the subgroup-specific treatment effects of radiotherapy. We use the cause-specific hazard approach to model the competing risk survival outcomes. We propose restricting the candidate radiation doses based on each patient's radiation susceptibility status. Only the clinically feasible personalized dose will be considered, which enhances the benefit for the patients in the trial. In addition, we propose a stratified Bayesian adaptive randomization scheme such that more patients will be randomized to the dose reporting more favorable survival outcomes. Numerical studies and an illustrative trial example have shown that the proposed design performed well and outperformed the conventional design ignoring the competing risk issue. [ABSTRACT FROM AUTHOR]

In this research, the statistical inference of unknown lifetime parameters is proposed in the presence of independent competing risks using a progressive Type-II censored dataset. The lifetime distribution associated with a failure mode is assumed to follow the new Pareto distribution, with consideration given to two distinct competing failure reasons. Maximum likelihood estimators (MLEs) for the unknown model parameters, as well as reliability and hazard functions, are derived, noting that they are not expressible in closed form. The Newton–Raphson, expectation maximization (EM), and stochastic expectation maximization (SEM) methods are employed to generate maximum likelihood (ML) estimations. Approximate confidence intervals for the unknown parameters, reliability, and hazard rate functions are constructed using the normal approximation of the MLEs and the normal approximation of the log-transformed MLEs. Additionally, the missing information principle is utilized to derive the closed form of the Fisher information matrix, which, in turn, is used with the delta approach to calculate confidence intervals for reliability and hazards. Bayes estimators are derived under both symmetric and asymmetric loss functions, with informative and non-informative priors considered, including independent gamma distributions for informative priors. The Monte Carlo Markov Chain sampling approach is employed to obtain the highest posterior density credible intervals and Bayesian point estimates for unknown parameters and reliability characteristics. A Monte Carlo simulation is conducted to assess the effectiveness of the proposed techniques, with the performances of the Bayes and maximum likelihood estimations examined using average values and mean squared errors as benchmarks. Interval estimations are compared in terms of average lengths and coverage probabilities. Real datasets are considered and examined for each topic to provide illustrative examples.

Non-cancer deaths are now becoming a great threat to the health of cancer survivors. There are no comprehensive and systematic reports on chronic liver disease and cirrhosis mortality (CLDCM) among patients with digestive system cancers (DSCs). This research aimed to quantitatively assess the risks and patterns of CLDCM among patients with DSCs. From the surveillance, epidemiology and end results (SEER) program, we extracted the data of patients diagnosed with DSCs between 2000 and 2017. Trends in incidence-based mortality rate (IBMR) were calculated using Joinpoint software. The standardized mortality ratio (SMR) was obtained based on the reference of the general United States population. The cumulative incidence function curves were constructed by all causes of death. Independent indicators were identified using the multivariate Fine and Gray competing risk model. We included 906,292 eligible patients from the SEER program, of which 3068 (0.34%) died from chronic liver disease and cirrhosis (CLDC). The IBMR of CLDC continued to increase during the study period [average annual percent change (APC): 6.7%; 95% confidence interval (CI) 5.1–8.2] and the SMR was significantly increased (SMR: 3.19; 95% CI 3.08–3.30). The cumulative mortality of CLDC was the lowest in all causes of death. Furthermore, the age at diagnosis, race, gender, marital status, year of diagnosis, SEER stage, surgery, chemotherapy and radiotherapy were identified as independent indicators. Better screening, diagnostic and management approaches need to be implemented as a preferred method to protect the liver among patients with DSCs. [ABSTRACT FROM AUTHOR]

Objective: 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. Methods: 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. Results: 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. Conclusion: The CSM competing risk nomogram for LOCRC patients in this study had acceptable predictive performance that could be applied to the clinic. [ABSTRACT FROM AUTHOR]

Purpose: It was of great significance to identify someone with a high risk of hepatocellular carcinoma (HCC) occurrence and make a diagnosis as early as possible. Therefore, we aimed to develop and validate a new, objective, and accurate prediction model, and convert it into a nomogram to make a personalized prediction of cancer occurrence in cirrhotic patients with different etiologies. Methods: The present study included 938 patients with cirrhosis from January 1, 2011, to December 31, 2012. Patients were prospectively followed-up until January 1, 2018. We used a competing risk model and the Fine–Gray test to develop and validate the prediction model and to plot a nomogram based on the model established. Results: At the end of follow-up, 202 (21.5%) patients developed HCC, with a 5-year incidence of 19.0% (corrected for competing risk model). Based on the competing risk regression method, we built a prediction model including age, gender, etiology, lymphocyte, and A/G ratio. Three groups with different risks were generated on account of tertiles of the 5-year risk predicted by the model. The cumulative 1-, 3-, and 5-year incidences of HCC were 2.0%, 20.8%, and 42.3% in high-risk group, 0.9%, 10.1%, and 17.7% in medium-risk group, and 0%, 2.0%, 8.5% in low-risk group (P < 0.001). The model showed excellent discrimination and calibration in predicting the risk of HCC occurrence in patients with all-cause cirrhosis. Conclusion: The model could make an individual prediction of cancer occurrence and stratify patients based on predicted risk, regardless of the causes of cirrhosis. [ABSTRACT FROM AUTHOR]

Xiyuan Li,1,2 Xiaomeng Wang,1 Liangshan Wang,1 Chenglong Li,1 Xing Hao,1 Zhongtao Du,1 Haixiu Xie,1 Feng Yang,1 Hong Wang,1 Xiaotong Hou1 1Center for Cardiac Intensive Care, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People’s Republic of China; 2Department of Intensive Care Unit, Aviation General Hospital of China Medical University, Beijing, 100012, People’s Republic of ChinaCorrespondence: Xiaotong Hou; Hong Wang, Center for Cardiac Intensive Care, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, People’s Republic of China, Tel +86 10 64456631 ; +86 15010516438, Email xt.hou@ccmu.edu.cn; 914286855@qq.comObjective: There was no consensus on the impact of nosocomial infection on In-hospital mortality rate in patients receiving ECMO. This study aimed to investigate the impact of nosocomial infection (NI) on In-hospital mortality rate in adult patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO) after cardiac surgery.Materials and Methods: This retrospective study included 503 adult patients who underwent VA-ECMO after cardiac surgery. The impact of time-dependent NIs on In-hospital mortality rate within 28 days of ECMO initiation was investigated using a Cox regression model. The cumulative incidence function for death was compared between patients with NIs and those without NIs using a competing risk model.Results: Within 28 days after ECMO initiation, 206 (41.0%) patients developed NIs, and 220 (43.7%) patients died. The prevalence rates of NIs were 27.8% and 20.3% during and after ECMO therapy, respectively. The incidence rates of NIs during and after ECMO therapy were 49‰ and 25‰, respectively. Time-dependent NI was an independent risk factor for predicting death (hazard ratio = 1.05, 95% confidence interval = 1.00– 1.11). The cumulative incidence of death in patients with NI was significantly higher than that in patients without NI at each time point within 28 days of ECMO initiation. (Z = 5.816, P = 0.0159).Conclusion: NI was a common complication in adult patients who received VA-ECMO after cardiac surgery, and time-dependent NI was an independent risk factor for predicting mortality in these patients. Using a competing risk model, we confirmed that NIs increased the risk of In-hospital mortality rate in these patients.Keywords: venoarterial extracorporeal membrane oxygenation, nosocomial infection, in-hospital mortality rate, Cox regression, competing risk model

Abstract Objective Classical Cox proportional hazard models tend to overestimate the event probability in a competing risk setup. Due to the lack of quantitative evaluation of competitive risk data for colon cancer (CC), the present study aims to evaluate the probability of CC-specific death and construct a nomogram to quantify survival differences among CC patients. Methods Data on patients diagnosed with CC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Patients were divided into a training dataset for the establishment of the model and a validation dataset to evaluate the performance the model at a ratio of 7:3. To evaluate the ability of multiple variables to predict cause-specific death in CC patients, univariate and multivariate analyses with Fine-Gray models were performed to screen the predictors of cause-specific death, and a nomogram for predicting cause-specific mortality was constructed. Then, the receiver operating characteristic (ROC) curve and the calibration curve were plotted to evaluate the prognostic performance of the nomogram. Results The dataset was randomly divided into a training (n = 16,655) dataset and a validation (n = 7,139) dataset at a ratio of 7:3. In the training dataset, variables including pathological subtypes of tumors, pathological grading (degree of differentiation), AJCC staging, T-staging, surgical type, lymph node surgery, chemotherapy, tumor deposits, lymph node metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for cause-specific death of CC patients. Among these factors, the AJCC stage had the strongest predictive ability, and these features were used to construct the final model. In the training dataset, the consistency index (C-index) of the model was 0.848, and the areas under the receiver operating characteristic curve (AUC) at 1, 3, and 5 years was 0.852, 0.861, and 0.856, respectively. In the validation dataset, the C-index of the model was 0.847, and the AUC at 1 year, 3 years, and 5 years was 0.841, 0.862, and 0.852, respectively, indicating that this nomogram had an excellent and robust predictive performance. Conclusion This study can help clinical doctors make better clinical decisions and provide better support for patients with CC.

Abstract Background Clear cell type hepatocellular carcinoma (HCC) is an uncommon neoplasm with an ambivalent prognosis compared to common type HCC. Methods First, patients with clear cell or common type HCC were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database, and their demographic and clinical characteristics were identified. Next, overall survival (OS), disease‐specific survival (DSS), and subgroup analysis of the two types of HCC were performed. Next, we utilized a competing risk model to focus on cancer‐caused death. Finally, propensity score matching (PSM) was employed to reduce the confounding factors based on the histopathological type, and sensitivity analysis was conducted. Results A total of 205 cases of clear cell type HCC and 29,954 cases of common type HCC were enrolled in our study. Patients with clear cell type HCC were older and predominantly female than those with common type HCC. OS and DSS were not significantly different between the two groups, and histopathological type was not a prognostic factor of HCC, as verified by the competing risk model. Patient characteristics adjusted by PSM and sensitivity analysis confirmed this conclusion. In subgroup analysis, patients with clear cell type HCC at grade III ~ IV and with lymph nodes metastasis had a better prognosis compared to common type HCC. Conclusions This study revealed that the prognosis of clear cell type HCC is similar to common type HCC. Tumor differentiation grade and status of lymph node metastasis affect the prognosis of HCC.

IntroductionAmong 28 cancer types, bladder cancer (BC) patients have the highest risk of dying from cardiovascular disease (CVD). We aimed to identify the independent risk factors and develop a novel nomogram for predicting long-term cardiovascular mortality in patients with BC.MethodsWe extracted data from the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with bladder cancer (BC) between 2000 and 2017. The cumulative incidence function (CIF) was computed for both CVD-related death and other causes of death. Then we performed univariate and multivariate analyses to explore the independent risk factors and further develop a novel nomogram to predict cardiovascular mortality at 5- and 10-year for patients with BC by using the Fine-Gray competing risk model. The efficacy of the developed nomogram was assessed by the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 12,9765 patients were randomly divided into training (n = 90,835, 70%), and validation (n = 38,930, 30%) cohorts. During the follow-up period, 31,862 (46.4%) patients died from BC, and 36793 (53.6%) patients died from non-BC, of which CVD-related death accounted for 17,165 (46.7%), being the major cause of non-cancer deaths. The multivariate analysis showed that age, sex, race, marital status, histologic type, tumor grade, summary stage, and chemotherapy were independent risk factors of CVD-related death in BC patients. The nomogram based on the above eight factors showed good discrimination power, excellent consistency, and clinical practicability: (1) the areas under the curve of the ROC for 5- and 10-year CVD-related death of 0.725 and 0.732 in the training cohort and 0.726 and 0.734 in the validation cohort; (2) the calibration curves showed that the prediction probabilities were basically consistent with the observed probabilities; (3) the DCA curves revealed that the nomogram had high positive net benefits.DiscussionTo our knowledge, this was the first study to identify the independent risk factors and develop a novel nomogram for predicting long-term cardiovascular mortality in patients with BC based on the competing risk model. Our results could help clinicians comprehensively and effectively manage the co-patient of BC and CVD, thereby reducing the risk of cardiovascular mortality in BC survivors.

Purpose: To explore the prognostic factors and the optimal treatment modalities for patients with stage IVA laryngeal squamous cell carcinoma (LSCC), so as to improve the survival rate of patients. Methods: Patients with stage IVA LSCC between 2004 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. We used competing risk models to build nomograms for predicting cancer-specific survival (CSS). The effectiveness of the model was assessed using the calibration curves and the concordance index (C-index). The above results were compared with the nomogram established by Cox regression analysis. The patients were grouped into low-risk and high-risk groups by competing risk nomogram formula. And the Kaplan–Meier (K–M) method and log-rank test were used to make sure that these groups had a survival difference. Results: Overall, 3612 patients were included. Older age, black race, a higher N stage, a higher pathological grade, and a larger tumor size were independent risk factors for CSS; married marital status, total/radical laryngectomy, and radiotherapy were protective factors. The C-index was 0.663, 0.633, and 0.628 in the train set and 0.674, 0.639, and 0.629 in the test set of the competing risk model, and 0.672, 0.640, and 0.634 in the traditional Cox nomogram for 1, 3, and 5 years. In overall survival and CSS, the prognosis of the high-risk group was poorer than that of the low-risk group. Conclusion: For patients with stage IVA LSCC, a competing risk nomogram was created to help screen risk population and guide clinical decision-making. [ABSTRACT FROM AUTHOR]

Abstract Objective Radiotherapy is one of the effective ways to treat glioblastoma multiforme (GBM). We aimed to explore the prognostic difference between external beam radiotherapy (EBRT) and EBRT combined with brachytherapy (EBRT + BT). Methods The GBM patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into two cohorts: the EBRT cohort and the EBRT + BT cohort. Kaplan–Meier (KM) analysis and Cox proportional hazards regression were used to determine the underlying risk factors for overall survival (OS) and disease-specific survival (DSS). And the competing risk model and propensity score matching (PSM) was adopted to eliminate potential biases. We also conducted subgroup analyses and interaction tests as well. Results There was a total of 41,010 eligible GBM patients. The median OS (15 months) and DSS (17 months) of the EBRT + BT cohort were significantly longer than that of the EBRT cohort (OS = 11 months, DSS = 12 months). After using the competing risk model and PSM, we found that only advanced age was the independent risk factor, while only EBRT + BT was the independent protective factor (HR = 0.84, 95%CI [0.74,0.96], p = 0.01). EBRT had universal effects in the treatment of GBM, and EBRT + BT had a more pronounced protective effect in the subgroups of males (HR = 0.81, 95%CI [0.68,0.97], p = 0.02) and local excision (HR = 0.82, 95%CI [0.34,0.95], p = 0.01). Conclusions The therapeutical effect of EBRT + BT treatment is better than that of EBRT alone, especially in male patients or patients who have undergone local resection. Our findings may provide novel evidence to develop a better radiotherapy strategy for GBM patients.

Background: Although the number of elderly patients with chronic kidney disease (CKD) has increased, few studies have examined their prognosis. Methods: The study design was a retrospective cohort study at a single centre. We evaluated 301 patients aged ≥75 years old with CKD stage G3a to G5. The primary endpoint was kidney failure with replacement therapy (KFRT) and secondary endpoints were all-cause mortality and annual decline rates of estimated glomerular filtration rate (eGFR). The incidence of KFRT was estimated using the cumulative incidence method considering the competing risk of death. To identify the independent risk factors related to KFRT, multivariate Fine-Gray regression model analysis were performed. Results: The median age of the patients was 79 years and the median eGFR was 24.0 mL/min/1.73 m² at baseline. Urinary protein was positive in 70% of patients. With a median follow-up of 24.5 months, 35% of the patients developed KFRT and 9% died. Kidney survival significantly decreased according to the CKD stage at baseline. In patients without proteinuria, the cumulative incidence of KFRT increased in CKD stage G5 patients, while in patients with proteinuria, the incidence of KFRT increased from patients with CKD stage G3b. Multivariate Fine-Gray regression model revealed that less aged, CKD stage G5, baseline data such as proteinuria, hypoalbuminemia, hyperphosphatemia, and hyperuricemia were independent risk factors for KFRT. Conclusion: Elderly CKD patients with proteinuria need to be carefully monitored even at an early CKD stage because of the risk of developing KFRT. [ABSTRACT FROM AUTHOR]

Objective: Classical Cox proportional hazard models tend to overestimate the event probability in a competing risk setup. Due to the lack of quantitative evaluation of competitive risk data for colon cancer (CC), the present study aims to evaluate the probability of CC-specific death and construct a nomogram to quantify survival differences among CC patients. Methods: Data on patients diagnosed with CC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Patients were divided into a training dataset for the establishment of the model and a validation dataset to evaluate the performance the model at a ratio of 7:3. To evaluate the ability of multiple variables to predict cause-specific death in CC patients, univariate and multivariate analyses with Fine-Gray models were performed to screen the predictors of cause-specific death, and a nomogram for predicting cause-specific mortality was constructed. Then, the receiver operating characteristic (ROC) curve and the calibration curve were plotted to evaluate the prognostic performance of the nomogram. Results: The dataset was randomly divided into a training (n = 16,655) dataset and a validation (n = 7,139) dataset at a ratio of 7:3. In the training dataset, variables including pathological subtypes of tumors, pathological grading (degree of differentiation), AJCC staging, T-staging, surgical type, lymph node surgery, chemotherapy, tumor deposits, lymph node metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for cause-specific death of CC patients. Among these factors, the AJCC stage had the strongest predictive ability, and these features were used to construct the final model. In the training dataset, the consistency index (C-index) of the model was 0.848, and the areas under the receiver operating characteristic curve (AUC) at 1, 3, and 5 years was 0.852, 0.861, and 0.856, respectively. In the validation dataset, the C-index of the model was 0.847, and the AUC at 1 year, 3 years, and 5 years was 0.841, 0.862, and 0.852, respectively, indicating that this nomogram had an excellent and robust predictive performance. Conclusion: This study can help clinical doctors make better clinical decisions and provide better support for patients with CC. [ABSTRACT FROM AUTHOR]

This article explores how organizational mobility and foreign nationality affect a researcher's chances of an internal career promotion in university systems that do not have rules preventing inbreeding and where teaching occurs mostly not in English but a local language. As a case study, we have examined the Flemish university system, the Dutch speaking part of Belgium, and developed expectations on the chances of promotion for mobile and foreign researchers compared to non-mobile and nationals. We use data for all postdoctoral and professorial staff between 1991 and 2017, for a total of 14,135 scientists. We calculated the chances of promotion with a competing risk model to take time into account and to disentangle the probability of two mutually exclusive risk events: promotion and leaving the university. The results show that international mobility and foreign nationality reduced the chances of promotion in the same university, and that mobile and foreign scientists were also more likely to leave any given university. These effects were particularly strong at an early stage: in the study period, 21.9% of non-mobile national postdocs became professor compared to just 1.2% of internationally mobile foreigners. These results would suggest that internationally mobile and foreign scientists struggle to advance in universities that lack rules preventing inbreeding and with little opportunity to teach in English. [ABSTRACT FROM AUTHOR]

Background: Adult brainstem high-grade glioma (HGG) is a refractory disease, and the treatment strategy of resection is still controversial. Objective: To investigate the prognostic value of brainstem HGG resection in adults. Methods: We collected 126,386 samples from the Surveillance, Epidemiology, and End Results (SEER) database between 1998 and 2016, and screened 973 patients diagnosed with adult brainstem HGG, who were in turn, grouped into 899 cases of non-resections and 74 cases of resections. Competing risk models were used to screen independent prognostic factors. Propensity score matching (PSM) was performed to reduce the influence of confounding factors. Conditional survival (CS) rate was considered to evaluate the changes in overall survival (OS) and disease-specific survival (DSS) of patients with HGG over time. Results: Based on the competing risk model and PSM, univariate analysis showed that age ≥ 45 years and male gender were poor prognostic factors for adult brainstem HGG. No previous history of glioma was a beneficial factor. Multivariate analysis revealed only the absence of a history of glioma to be a favorable prognostic factor. Considering the CS rate of the resection group, after the patient had survived for 3 years, the OS and DSS remained unchanged at 100% during the fourth and fifth years, whereas in the non-resection group, the OS and DSS of the patients were 82% and 74%, respectively. Conclusion: Adult brainstem HGG resection has a poor prognosis in the early stage; however, patients have a potentially significant survival benefit after 3 years of survival. [ABSTRACT FROM AUTHOR]

Background: Clear cell type hepatocellular carcinoma (HCC) is an uncommon neoplasm with an ambivalent prognosis compared to common type HCC. Methods: First, patients with clear cell or common type HCC were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database, and their demographic and clinical characteristics were identified. Next, overall survival (OS), disease‐specific survival (DSS), and subgroup analysis of the two types of HCC were performed. Next, we utilized a competing risk model to focus on cancer‐caused death. Finally, propensity score matching (PSM) was employed to reduce the confounding factors based on the histopathological type, and sensitivity analysis was conducted. Results: A total of 205 cases of clear cell type HCC and 29,954 cases of common type HCC were enrolled in our study. Patients with clear cell type HCC were older and predominantly female than those with common type HCC. OS and DSS were not significantly different between the two groups, and histopathological type was not a prognostic factor of HCC, as verified by the competing risk model. Patient characteristics adjusted by PSM and sensitivity analysis confirmed this conclusion. In subgroup analysis, patients with clear cell type HCC at grade III ~ IV and with lymph nodes metastasis had a better prognosis compared to common type HCC. Conclusions: This study revealed that the prognosis of clear cell type HCC is similar to common type HCC. Tumor differentiation grade and status of lymph node metastasis affect the prognosis of HCC. [ABSTRACT FROM AUTHOR]

Background: Understanding the effects of a delayed time-to-treatment initiation(TTI) for non-small cell lung cancer (NSCLC) is vital. Methods: We analyzed NSCLC data from the Surveillance, Epidemiology, and End Results database, focusing on lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC). TTI was studied as both continuous and dichotomous variables. Restricted cubic splines were employed to identify potential nonlinear dependency between the hazard ratio (HR) and TTI. Propensity score matching was used to ensure a balanced patient allocation, and then survival differences between groups were assessed using Kaplan-Meier analysis and competing risk models. We used overall survival (OS) as the primary outcome and cancer-specific cumulative mortality (CSCM) as a complementary indicator. Finally, sensitivity analyses were performed on censored data. Results: A total of 80,020 with NSCLC were analyzed. TTI was assessed as a continuous variable, showing a noticeable increase in the HR for stage I to II NSCLC with TTI >1 month. Conversely, the trend for stage III to IV NSCLC was the opposite. In stage I LUAD, the 'early' group demonstrated a higher OS compared to the 'delayed' group (Log-rank P = 0.002), while there was no significant difference in CSCM (Fine-gray P = 0.321). In stage I LUSC, there was no significant difference in OS(Log-rank P = 0.260), but the 'early' group had a lower CSCM (Fine-gray P = 0.018). For stage II-IV NSCLC, the 'delayed' group did not exhibit a negative impact on OS or CSCM. The sensitivity analysis further supported the results of the main analysis. Conclusion: Prolongation of TTI ≥31 days has a negative impact on OS or CSCM in stage I NSCLC only. Further exploration and validation are needed to determine whether these results can be used as evidence for a 'safe' TTI threshold setting for future NSCLC.

In a reliability theory, the statistical inference under accelerated competing risks model have a great significance. This model proposes the assumption of independence of variables for convenience. But, in general this assumption doesn't agree with the nature of the problem at hand. In this paper, we proposed a step stress accelerated life testing (ALT) model on the dependent competing risk modes of life Lomax distribution (LD). The structural problem of the dependence between variables discussed copula approach. Also, we are considering the units of the product fails under two dependent fatal risks and the failure times accelerated under step stress ALTs and censored with type-I generalized hybrid censoring scheme (GHCS). The parameters of this model are estimated by maximum likelihood method. Also, the confidence intervals are discussed with asymptotic and two bootstrap confidence intervals. The Monte Carlo simulation study conducted to measure the quality of the proposed methods. Finally, we report some points to describe the numerical results.

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Grating rulers are high-precision linear displacement sensors used in the servo control system of CNC machine tools. Due to the long lifetime of these rulers, evaluating their reliability level by using traditional methods takes huge amounts of time and resources. Meanwhile, some incomplete data are observed in the failure data, which will cause inaccurate evaluation results. In this study, an accelerated life reliability evaluation method of a grating ruler based on a competing risk model and incomplete data is proposed. Firstly, according to the characteristics of the grating ruler, the life distribution model based on competitive risk is established. The representation and conversion methods of failure data, considering incomplete data, are also proposed. Secondly, the accelerated life test (ALT) system is set up, and the 4500 h step stress ALT is conducted. Results indicate that the reliability level of the grating ruler is MTBFt = 8864.12 h, and that of each subsystem is MTBFt(MS) = 13,351.98 h, MTBFt(OS) = 14,283.48 h and MTBFt(ES) = 17,038.65 h. The advantages of the proposed method are that it is helpful in determining the subsystem with the highest failure rate in each stage and puts forward targeted suggestions for subsequent improvement designs. [ABSTRACT FROM AUTHOR]

Abstract Background Antiretroviral therapy (ART) is known to reduce tuberculosis (TB) incidence among people living with HIV (PLWH). However, studies describing the impact of long-term ART and CD4 count recovery on TB incidence remain scarce due to limited follow up in previous studies. We evaluated TB incidence in a long-term cohort of PLWH on ART in Thailand. Methods We conducted an analysis of PLWH aged ≥ 18 years who started ART between 1996 and December 2020. Participants were followed up every 6 months for routine HIV care. TB risk factors, body mass index (BMI), physical examination and full differential blood counts were evaluated at each clinic visit, and CD4 cell counts and HIV RNA every 12 months. Participants diagnosed with TB > 3 months after starting ART were classified as incident cases. Time to event models with death as a competing risk, were used to derive the TB cumulative incidence function (CIF) after ART initiation, and assess time-updated factors associated with incident TB using a six month lag. Results A total of 2,636 PLWH contributing 24,229 person years (PY) of follow-up on ART were analysed. Median age was 32.0 (IQR 27.4–37.6) years; 67.5% were male. Median CD4 cell count at ART initiation was 264 (IQR 167–379) cells/mm3 and median follow-up duration was 7.6 (IQR 1.9–15.7) years. During follow-up, 113 PLWH developed TB. The probability of incident TB was 0.7%, 1.7%, 3.3% and 4.3%, at 1, 2, 5 and 7 years after ART initiation, respectively. TB CIF was highest among participants with CD4

Abstract Objective The study aimed to evaluate site‐specific recurrent patterns via competing risks analysis and hazard function to provide evidence for adjuvant treatment and follow‐up for early staged endometrial cancer (EC). Methods A total of 858 patients with International Federation of Gynecology and Obstetrics stage I–II EC who received adjuvant radiotherapy at our institution (2000–2017) were included. The radiotherapy modality comprised external beam radiotherapy (EBRT) with or without vaginal brachytherapy (VBT) or VBT alone. Competing risks analysis and hazard rate function were employed to evaluate the recurrence rate according to the ESMO–ESGO–ESTRO risk classification. Results The 5‐year overall survival rates of the low‐risk (LR), intermediate‐risk (IR), high–intermediate risk (HIR), and high‐risk (HR) groups were 96.1%, 95%, 93%, and 89.7%, respectively (p = 0.018). Sixty‐eight patients developed recurrence. The 5‐year incidence of distant recurrence was the highest in the HR group (14.87%), followed by the HIR (7.71%), IR (5.27%), and LR (1.26%) groups (Gray's test, p

Background: The survival advantage of postoperative chemotherapy for high-risk stage II colon cancer (CC) patients remains unclear. Objectives: The purpose was to evaluate the survival of high-risk stage II CC cases treated with chemotherapy and to construct survival prediction models to predict the survival benefit from chemotherapy. Design: The study is a retrospective observational cohort study. Methods: Data on patients with stage II CC diagnosed from 2005 to 2019 who underwent radical surgery were obtained from the Surveillance, Epidemiology and End Results (SEER) database. A 1:1 propensity score matching (PSM) was applied to obtain two cohorts, chemotherapy versus no chemotherapy. A chi-square analysis was used to assess the differences before and after PSM in the above two groups. Kaplan–Meier survival analysis and Cox proportional hazards regression were applied to investigate the 5- and 10-year overall survival (OS) and cancer cause-specific survival (CSS). The predictive power of the constructed models was assessed by the concordance index (C-index) and calibration curves. Results: Of the 37,050 cases, 14,744 (39.8%) stage II CC were at high-risk and 29.2% of them received chemotherapy. Age, T stage, marital status, histologic grade, gender, and site independently influenced the reception of chemotherapy. The survival advantage of chemotherapy in the high-risk patients remained positive before and after PSM. The estimated 3, 5, and 10 years OS rates of chemotherapy group were 9.3, 10.7, and 15.6% higher than the nonchemotherapy group, respectively. Four nomograms predicting OS and CSS were established, with great discrimination (C-index between 0.627 and 0.691) and excellent calibration. Conclusion: Postoperative chemotherapy is beneficial for high-risk stage II CC patients, including the elderly patients (over 65 years of age). Our study developed nomograms to quantify the survival benefit of chemotherapy among high-risk stage II CC patients to develop personalized treatment recommendations and guide management decisions. [ABSTRACT FROM AUTHOR]

Purpose: Lymph node (LN) involvement is a key factor in ovarian clear cell carcinoma (OCCC) although, there several indicators can be used to define prognosis. This study examines the prognostic performances of each indicator for OCCC patients by comparing the number of lymph nodes examined (TNLE), the number of positive lymph nodes (PLN), lymph node ratio (LNR), and log odds of metastatic lymph nodes (LODDS). Methods: 1,300 OCCC patients who underwent lymphadenectomy between 2004 and 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Primary outcomes were Overall Survival (OS) and the cumulative incidence of Cancer-Specific Survival (CSS). Kaplan–Meier’s and Fine-Gray’s tests were implemented to assess OS and CSS rates. After conducting multivariate analysis, nomograms using OS and CSS were constructed based upon an improved LN system. Each nomograms’ performance was assessed using Receiver Operating Characteristics (ROC) curves, calibration curves, and the C-index which were compared to traditional cancer staging systems. Results: Multivariate Cox’s regression analysis was used to assess prognostic factors for OS, including age, T stage, M stage, SEER stage, and LODDS. To account for the CSS endpoint, a proportional subdistribution hazard model was implemented which suggested that the T stage, M stage, SEER stage, and LNR are all significant. This enabled us to develop a LODDS-based nomogram for OS and a LNR-based nomogram for CSS. C-indexes for both the OS and CSS nomograms were higher than the traditional American Joint Committee on Cancer (AJCC), 8th edition, staging system. Area Under the Curve (AUC) values for predicting 3- and 5-year OS and CSS between nomograms also highlighted an improvement upon the AJCC staging system. Calibration curves also performed with consistency, which was verified using a validation cohort. Conclusions: LODDS and LNR may be better predictors than N stage, TNLE, and PLNs. For OCCC patients, both the LODDS-based and LNR-based nomograms performed better than the AJCC staging system at predicting OS and CSS. However, further large sample, real-world studies are necessary to validate the assertion. [ABSTRACT FROM AUTHOR]

Competing risk ( C o R ) models are frequently disregarded in failure rate analysis, and traditional statistical approaches are used to study the event of interest. In this paper, we proposed a new lifetime distribution by generalizing the length biased exponential (LBE) distribution using the transmuted Topp-Leone-G ( T T L -G) family of distributions. The new three parameter model is called the transmuted Topp-Leone length biased exponential ( T T L L B E ) distribution. A comprehensive account of various mathematical features of the T T L L B E model are derived. The unknown parameters of the proposed distribution are estimated by six classical approaches: the maximum likelihood (ML) approach, maximum product spacing (MPS) approach, least square (LS) approach, Weighted LS (WLS) approach, Cramér-Von Mises (CVN) approach, Anderson–Darling (AD) approach, and Bayesian approach. The stability of the model parameters is examined through the simulation study. The applications of our proposed distribution are explained through real data and its performance is illustrated through its comparison with the competent existing distributions. The T T L L B E model depend on the C o R model has been obtained and estimated parameter of this model by ML and Bayesian estimation approaches. In electrical appliances, we found two main causes of failure, and the data of electrical appliances are fitted to our model. Therefore, we analyzed the T T L L B E model depend on the C o R model to obtain the strong cause of failure. [ABSTRACT FROM AUTHOR]

Objective: Radiotherapy is one of the effective ways to treat glioblastoma multiforme (GBM). We aimed to explore the prognostic difference between external beam radiotherapy (EBRT) and EBRT combined with brachytherapy (EBRT + BT).Methods: The GBM patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into two cohorts: the EBRT cohort and the EBRT + BT cohort. Kaplan-Meier (KM) analysis and Cox proportional hazards regression were used to determine the underlying risk factors for overall survival (OS) and disease-specific survival (DSS). And the competing risk model and propensity score matching (PSM) was adopted to eliminate potential biases. We also conducted subgroup analyses and interaction tests as well.Results: There was a total of 41,010 eligible GBM patients. The median OS (15 months) and DSS (17 months) of the EBRT + BT cohort were significantly longer than that of the EBRT cohort (OS = 11 months, DSS = 12 months). After using the competing risk model and PSM, we found that only advanced age was the independent risk factor, while only EBRT + BT was the independent protective factor (HR = 0.84, 95%CI [0.74,0.96], p = 0.01). EBRT had universal effects in the treatment of GBM, and EBRT + BT had a more pronounced protective effect in the subgroups of males (HR = 0.81, 95%CI [0.68,0.97], p = 0.02) and local excision (HR = 0.82, 95%CI [0.34,0.95], p = 0.01).Conclusions: The therapeutical effect of EBRT + BT treatment is better than that of EBRT alone, especially in male patients or patients who have undergone local resection. Our findings may provide novel evidence to develop a better radiotherapy strategy for GBM patients. [ABSTRACT FROM AUTHOR]

Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed "cure" of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the statistical analysis of a simple step-stress accelerated competing failure model under progressively Type-II censoring. It is assumed that there is more than one cause of failure, and the lifetime of the experimental units at each stress level follows exponential distribution. The distribution functions under different stress levels are connected through the cumulative exposure model. The maximum likelihood, Bayesian, Expected Bayesian, and Hierarchical Bayesian estimations of the model parameters are derived based on the different loss function. Based on Monte Carlo Simulations. We also get the average length and the coverage probability of the 95% confidence intervals and highest posterior density credible intervals of the parameters. From the numerical studies, it can be seen that the proposed Expected Bayesian estimations and Hierarchical Bayesian estimations have better performance in terms of the average estimates and mean squared errors, respectively. Finally, the methods of statistical inference discussed here are illustrated with a numerical example. [ABSTRACT FROM AUTHOR]