Learning competing risks across multiple hospitals: one-shot distributed algorithms

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.

A Bayesian adaptive biomarker stratified phase II randomized clinical trial design for radiotherapies with competing risk survival outcomes

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.

Long-term cardiovascular mortality risk in patients with bladder cancer: a real-world retrospective study of 129,765 cases based on the SEER database

Introduction

Among 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.

Methods

We 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).

Results

A 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.

Discussion

To 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.

Impact of Nosocomial Infection on in-Hospital Mortality Rate in Adult Patients Under Venoarterial Extracorporeal Membrane Oxygenation After Cardiac Surgery

Objective

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.

Prognostic value of prior malignancy history in stage I differentiated thyroid cancer: a SEER-based study

Background

Thyroid cancer is the most common endocrine cancer today. Differentiated thyroid cancer (DTC) comprises more than 95% of all thyroid cancers. With the increasing incidence of tumors and development of screening, more patients suffer from multiple cancers. The purpose of this study was to explore the prognostic value of a history of prior malignancy for stage I DTC.

Methods

Stage I DTC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method and Cox proportional hazards regression method were used to determine the risk factors for overall survival (OS) and disease-specific survival (DSS). A competing risk model was also used to determine the risk factors for DTC-related death after considering the competitive risks. In addition, conditional survival analysis in patients with stage I DTC was performed.

Results

A total of 49,723 patients with stage I DTC were enrolled in the study, and 4,982 (10.0%) had prior malignancy history. Prior malignancy history was a factor affecting OS (P<0.001) and DSS (P<0.001) in the Kaplan-Meier analysis and an independent risk factor for OS [hazard ratio (HR) =3.6, 95% confidence interval (CI): 3.17-4.088, P<0.001] and DSS (HR =4.521, 95% CI: 2.224-9.192, P<0.001) in the multivariate Cox proportional hazards regression analysis. In the competing risk model, in the multivariate analysis, prior malignancy history was a risk factor for the DTC-related deaths [subdistribution HR (SHR) =4.32, 95% CI: 2.233-8.3593, P<0.001] after considering the competitive risks. Conditional survival showed that the probability of achieving 5-year DSS was not changed in either the two groups with or without prior malignancy history. For the patients with prior malignancy history, the probability of achieving 5-year OS increased with each additional year survived, but for the patients without prior malignancy history, the improvement of conditional OS only appeared with 2 years already prior survived.

Conclusions

Prior malignancy history has an adverse impact on the survival of patients with stage I DTC. The probability of achieving 5-year OS for stage I DTC patients with prior malignancy history increases with each additional year survived. The inconsistent survival effects of prior malignancy history should be considered in clinical trial design and recruitment.

Risk factor for progression to kidney failure with replacement therapy in elderly patients with chronic kidney disease: A retrospective single-centre cohort study

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.

Competing risk model for prognostic comparison between clear cell type and common type hepatocellular carcinoma: A population-based propensity score matching study

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.

[Ultrasound combined with Ki67 detection for analyzing contributing factors of failure to cure and recurrence of hyperthyroidism in patients with Graves disease after (131)I treatment]

OBJECTIVE

To analyze factors associated with failure to cure or recurrence of hyperthyroidism in patients with Graves disease (GD) after ¹³¹I treatment using ultrasound combined with Ki67 detection.

METHODS

Eighty-nine patients with GD receiving ¹³¹I treatment in the Department of Nuclear Medicine at our hospital from January, 2020 to November, 2021 were enrolled. Before treatment, thyroid volume, shear wave elastic value and Ki67 expression in the follicular epithelial cells were measured using three-dimensional ultrasonic virtual organ computer-aided analysis, shear-wave elastic imaging and ultrasound-guided fine needle aspiration. The data including age, gender, antithyroid drug (ATD) history, dose of ¹³¹I, and TRAb were collected from all the cases. The patients were followed up for up to 1 year, starting at 1 month after ¹³¹I treatment, and the follow-up results of the patients were divided into failure to cure or recurrence of hyperthyroidism, premature hypothyroidism and euthyroidism or loss to follow-up. The proportional hazards model and fine-Gray test were used to estimate the adjusted hazard ratio (HR) and 95% confidence interval (95% CI) for patients with failure to cure or recurrence of hyperthyroidism.

RESULTS

Among the 89 patients, 27 patients were found to have failure to cure or recurrence of hyperthyroidism, 50 had premature hypothyroidism, 1 patient had euthyroidism, and 11 patients were lost to follow-up at the end of the 1-year follow-up. Analysis of the competitive risk model showed that status of Ki67 expression, ¹³¹I dose and thyroid volume were independently correlated with failure to cure or recurrence of hyperthyroidism after the treatment with HR (95% CI) of 0.36 (0.15, 0.86), 0.81 (0.68, 0.96) and 1.11 (1.07, 1.15), respectively.

CONCLUSION

In patients with GD, the expression of Ki67 in thyroid follicular epithelial cells, ¹³¹I dose and thyroid volume are independently correlated with failure to cure or recurrence of hyperthyroidism after ¹³¹I treatment. New ultrasound techniques can play an important role in evaluating the therapeutic outcome of ¹³¹I treatment in GD patients.

Establishment and validation of a nomogram for predicting immune-related prognostic features in trunk melanoma-specific death

Background

Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application.

Methods

Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram.

Results

We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree.

Conclusions

This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events.

Adjuvant chemotherapy improves survival in high-risk stage II colon cancer: a retrospective cohort study

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.

Efficacy and safety of lenvatinib for preventing tumor recurrence after liver transplantation in hepatocellular carcinoma beyond the Milan criteria

Background

Lenvatinib is one of the first-line treatments for unresectable hepatocellular carcinoma (HCC). However, data are lacking on lenvatinib in the postoperative setting.

Methods

This retrospective analysis enrolled 242 patients with HCC who underwent liver transplantation (LTx). Eligible patients were divided into 2 groups according to their use of adjuvant lenvatinib following LTx (lenvatinib, n=42; control, n=200). The primary outcome measures were overall survival (OS), time to recurrence (TTR), and safety. Kaplan-Meier analysis was applied to calculate the OS, while a competing risk model was used to estimate the cumulative incidence of recurrence.

Results

The lenvatinib group showed more advanced tumors and a higher proportion of HCC beyond the Milan criteria (P<0.001) than the control group. There were no significant differences in both the OS and TTR between the 2 groups. After focusing on the patients with HCC beyond the Milan criteria, baseline characteristics were similar in the lenvatinib group (n=38) and the control group (n=102). Competing risk analysis showed lenvatinib significantly prolonged TTR after LTx versus the control group [sub-hazard ratio (sHR), 0.40; 95% confidence interval (CI): 0.17 to 0.93; P=0.031]. In the multivariate competing risk model, adjuvant lenvatinib was an independent protective factor for tumor recurrence after LTx in patients with HCC beyond the Milan criteria (sHR, 0.33; 95% CI: 0.13 to 0.83; P=0.018). The rate of early recurrence within t2 years after LTx was also significantly decreased in the lenvatinib group (15.8% vs. 33.3%, P=0.041). However, the lenvatinib group exhibited comparable OS with the control group in patients with HCC beyond the Milan criteria. Treatment-related adverse events (TRAEs) and Grade ≥3 TRAEs occurred in 40 (95.2%) and 13 (31%) patients who received adjuvant lenvatinib, respectively. No treatment-related death was reported.

Conclusions

Postoperative lenvatinib administration may provide clinical benefits and is well tolerated in patients with HCC beyond the Milan criteria who undergo LTx.

E-Bayesian and H-Bayesian Inferences for a Simple Step-Stress Model with Competing Failure Model under Progressively Type-II Censoring

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.

Serum PlGF compared with PAPP-A in first trimester screening for preterm pre-eclampsia: Adjusting for the effect of aspirin treatment

OBJECTIVE

To compare the predictive performance for preterm-pre-eclampsia (PE) in first-trimester screening by serum placental growth factor (PlGF) versus pregnancy associated plasma protein-A (PAPP-A), in combination with maternal risk factors, mean arterial pressure (MAP) and uterine artery pulsatility index (UtA-PI), after adjustment for the effect of aspirin in women receiving this treatment.

DESIGN

Non-intervention multicentre screening studies for PE in singleton pregnancies.

SETTING

Maternity hospitals.

POPULATION

Two independent prospective studies of 8775 and 16 451 women with singleton pregnancies attending for routine assessment at 11⁺⁰ -13⁺⁶ weeks' gestation.

METHODS

The competing risks model was used to estimate patient-specific risks of delivery with PE at <37 weeks' gestation based on maternal risk factors and combinations with MAP, UtA-PI and either PlGF or PAPP-A. McNemar's test was used to compare the detection rate (DR) of preterm-PE of screening utilising PlGF versus PAPP-A, after adjustments for the effects of aspirin.

MAIN OUTCOME MEASURE

Predictive performance for preterm-PE.

RESULTS

In the combined data of 25 226 women, including 678 (2.7%) who developed PE, there were 194(0.8%) with preterm-PE. Addition of PlGF improved the DR of preterm-PE, at 10% screen positive rate, by 18.4% (95% CI 12.2-24.6) in screening by maternal risk factors, by 19.9% (95% CI 13.6-26.2) in screening by maternal factors and MAP, and by 7.0% (95% CI 2.3-11.6) in screening by maternal factors, MAP and UtA-PI. PAPP-A did not significantly improve the DR provided by any combination of biomarkers.

CONCLUSION

The predictive performance of first trimester PlGF for preterm-PE is superior to that of PAPP-A.

The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

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.

Risk Prediction of Second Primary Malignancies in Primary Early-Stage Ovarian Cancer Survivors: A SEER-Based National Population-Based Cohort Study

Purpose

This study aimed to characterize the clinical features of early-stage ovarian cancer (OC) survivors with second primary malignancies (SPMs) and provided a prediction tool for individualized risk of developing SPMs.

Methods

Data were obtained from the Surveillance, Epidemiology and End Results (SEER) database during 1998-2013. Considering non-SPM death as a competing event, the Fine and Gray model and the corresponding nomogram were used to identify the risk factors for SPMs and predict the SPM probabilities after the initial OC diagnosis. The decision curve analysis (DCA) was performed to evaluate the clinical utility of our proposed model.

Results

A total of 14,314 qualified patients were enrolled. The diagnosis rate and the cumulative incidence of SPMs were 7.9% and 13.6% [95% confidence interval (CI) = 13.5% to 13.6%], respectively, during the median follow-up of 8.6 years. The multivariable competing risk analysis suggested that older age at initial cancer diagnosis, white race, epithelial histologic subtypes of OC (serous, endometrioid, mucinous, and Brenner tumor), number of lymph nodes examined (<12), and radiotherapy were significantly associated with an elevated SPM risk. The DCA revealed that the net benefit obtained by our proposed model was higher than the all-screening or no-screening scenarios within a wide range of risk thresholds (1% to 23%).

Conclusion

The competing risk nomogram can be potentially helpful for assisting physicians in identifying patients with different risks of SPMs and scheduling risk-adapted clinical management. More comprehensive data on treatment regimens and patient characteristics may help improve the predictability of the risk model for SPMs.

Nomogram Predicts the Role of Primary Tumor Surgery on De Novo Stage-IV Breast Cancer Patients: A SEER-Based Competing Risk Analysis Model

Objective

The efficacy of primary tumor surgery on survival in female patients with de novo stage IV breast cancer (BC) remains unclear. Our study endeavored to develop comprehensive competing risk nomograms to predict clinical outcomes and guide precision treatment in these patients.

Participants and Methods

A total of 12281 patients who had distant metastasis at initial BC diagnosis between 2010 and 2017 in the Surveillance Epidemiology and End Results (SEER) database, were enrolled in this study. First, we assessed the impacts of primary tumor surgery on overall survival (OS) and breast cancer-specific survival (BCSS) using the Kaplan-Meier curves. Then subgroup analyses stratified by different metastatic patterns were performed using Cox and competing risk models (CRM). Based on the filtered independent prognostic parameters by CRM, we established two nomograms to predict the probability of breast cancer-specific death (BCSD) at 1-,2- and 3-year intervals. Furthermore, calibration curves and area under the curves (AUC) were conducted for validation.

Results

Kaplan-Meier analysis revealed that surgery was associated with better OS and BCSS (P<0.001). Subgroup analyses demonstrated that in bone-only metastases pattern, relative to breast-conserving surgery (BCS), patients receiving mastectomy had worse prognosis and the poorest survival belonged to non-surgery individuals (BCSS: mastectomy: HR=1.35; 95%CI=1.15-1.60; non-surgery: 2.42; 2.08-2.82; OS: mastectomy: 1.44; 1.23-1.68; non-surgery: 2.40; 2.08-2.78). Additionally, no survival difference was observed between BCS and reconstruction recipients (BCSS: HR=1.10; 95%CI=0.85-1.43; OS: 1.11; 0.86-1.44). Furthermore, patients undergoing BCS possessed similar BCSS with mastectomy recipients as well as reconstruction recipients in viscera metastases pattern, whereas non-surgery individuals had a worse survival (mastectomy: HR=1.04; 95%CI=0.92-1.18; reconstruction: 0.86; 0.69-1.06; non-surgery: 1.83; 1.63-2.05). Two competing risk nomograms of distinct metastatic patterns were established to comprehensively predict the survival of patients. Calibration curves indicated the terrific consistency of the models. Moreover, the AUC values in the training and validation sets were in the range of 0.70–0.80, exhibiting good specificity and sensitivity.

Conclusion

The surgery implementation was associated with a lower probability of BCSD in de novo stage-IV BC patients. Our nomograms could offer a relatively accurate and individualized prediction of the cumulative incidence rate of BCSD after primary tumor resection.

Establishment of a Competing Risk Nomogram in Patients with Pulmonary Sarcomatoid Carcinoma

Background and aim: Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of nonsmall cell lung cancer with a poor prognosis. This study aimed to analyze the clinicopathological characteristics and survival outcomes among patients with PSC, lung squamous cell cancer (SCC), and lung adenocarcinoma (LAC), and to construct a competing risk nomogram for patients with PSC. Method: Data of 3 groups of patients diagnosed with PSC, SCC, or LAC from the surveillance, epidemiology, and end results (SEER) database between 1988 and 2015 were retrospectively reviewed. A 1:1 propensity score matching (PSM) analysis was used to balance the baseline data of patients. Independent risk factors associated with survival outcomes were screened by the least absolute shrinkage and selection operator and further determined by univariate and multivariate Cox proportional risk regression analyses. The overall survival (OS) of patients was evaluated by Kaplan–Meier analysis and compared with a log-rank test. The cumulative incidence function was used to estimate the 5-year probabilities of the cancer-specific mortality of PSC. A nomogram was constructed to illustrate the competing risk model to predict the 3- and 5-year OS, and corresponding concordance indexes (C-indexes) and calibration curves were used to assess and validate the competing risk nomogram. Results: A total of 2285 patients with PSC were included in this study. Compared with SCC and LAC patients, the Kaplan–Meier analysis showed that patients with PSC had a worse prognosis, with a median survival of 5 months (95% confidence interval [CI]: 5-6 months) and a 5-year OS rate of 15.3% (95% CI: 13.9%-16.9%). Similar outcomes were demonstrated after 1:1 PSM. Moreover, the competing risk model showed that age, T stage, M stage, tumor size, lymph node ratio (LNR), surgery, and chemotherapy were associated with PSC-specific mortality. The 5-year C-index of the nomogram was 0.718. Calibration curves illustrated that the nomogram was well-validated and had great accuracy. Conclusions: Patients with PSC had a worse survival outcome compared with SCC or LAC patients. Age, T stage, M stage, tumor size, LNR, surgery, and chemotherapy were associated with PSC-specific mortality. The competing risk nomogram displayed excellent discrimination in predicting PSC-specific mortality.

Characteristics, Prognosis, and Competing Risk Nomograms of Cutaneous Malignant Melanoma: Evidence for Pigmentary Disorders

PURPOSE

Cutaneous malignant melanoma (CMM) always presents as a complex disease process with poor prognosis. The objective of the present study was to explore the influence of solitary or multiple cancers on the prognosis of patients with CMM to better understand the landscape of CMM.

METHODS

We reviewed the records of CMM patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results Program. The cumulative incidence function was used to represent the probabilities of death. A novel causal inference method was leveraged to explore the risk difference to death between different types of CMM, and nomograms were built based on competing risk models.

RESULTS

The analysis cohort contained 165,043 patients with CMM as the first primary malignancy. Patients with recurrent CMM and multiple primary tumors had similar overall survival status (p = 0.064), while their demographics and cause-specific death demonstrated different characteristics than those of patients with solitary CMM (p < 0.001), whose mean survival times are 75.4 and 77.3 months and 66.2 months, respectively. Causal inference was further applied to unveil the risk difference of solitary and multiple tumors in subgroups, which was significantly different from the total population (p < 0.05), and vulnerable groups with high risk of death were identified. The established competing risk nomograms had a concordance index >0.6 on predicting the probabilities of death of CMM or other cancers individually across types of CMM.

CONCLUSION

Patients with different types of CMM had different prognostic characteristics and different risk of cause-specific death. The results of this study are of great significance in identifying the high risk of cause-specific death, enabling targeted intervention in the early period at both the population and individual levels.

Recurrent patterns after postoperative radiotherapy for early stage endometrial cancer: A competing risk analysis model

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 < 0.001). The LR and IR groups showed late metastasis behaviors for distant metastasis. The HR group presented a large magnitude of distant metastasis with an early peak that increased beyond 3 years. Subgroup analysis revealed that EBRT±VBT tended to reduce the locoregional relapse rate compared with VBT in the HIR-HR group (2.36% vs. 7.73%, Gray's test, p = 0.08).

CONCLUSION

The established competing risk modeling demonstrated different recurrence patterns across the risk groups and radiotherapy modes. A better understanding of the change in site-specific recurrence behavior allows more targeted adjuvant treatment and surveillance regimens.

Predicting cancer-specific mortality in patients with parotid gland carcinoma by competing risk nomogram

BACKGROUND

The aim of the present study was to establish a competing risk nomogram to predict parotid gland cancer-specific mortality (PGC-SM).

METHODS

Seven thousand nine hundred and sixty-two patients extracted from SEER database were randomly categorized into training and validation sets. The competing risk model was used to identify factors associated with PGC-SM. The nomogram was evaluated via concordance indexes (C-indexes), calibration plots, and decision curve analysis (DCA).

RESULTS

Male, elderly, white, widowed, larger tumor, no surgery, advanced tumor grade, lymph node (LN) metastasis, adenocarcinoma (ADC), and higher TNM stage were associated with higher incidence of PGC-SM. Calibration plots showed that the nomogram was well calibrated. C-indexes for nomogram were 0.84 (95% CI: 0.81-0.86) and 0.84 (95% CI: 0.82-0.86) in training and validation sets, respectively. DCA demonstrated the clinical usefulness of nomogram.

CONCLUSIONS

The competing risk nomogram shows high performance in predicting PGC-SM, which might enable clinicians formulate suitable treatment protocols for patients with parotid gland carcinoma (PGC).

Developing and validating a novel nomogram used a competing-risks model for predicting the prognosis of primary fallopian tube carcinoma: a retrospective study based on the SEER database

Background

The current prognostic methods for primary fallopian tube carcinoma (PFTC) are inadequate. This study is the first to use a competing-risks model to perform an accurate analysis of the prognostic factors for PFTC cause-specific death (CSD). We used the model to established a nomogram for the 3-, 5-, and 8-year CSD rates based on the identified prognostic factors.

Methods

This study selected 1,924 patients from the SEER (Surveillance, Epidemiology, and End Results) database. The cumulative incidence function (CIF) was used in univariate analyses, and Gray’s test was used to determine the intergroup difference in the CIF. We then used the subdistribution proportional hazards model in a multivariate analysis. We finally used the prognostic factors identified in the analysis of the competing-risks model to construct a 3-, 5-, and 8-year CSD nomogram for PFTC patients. The concordance index (C-index) and calibration plots were used to evaluate the discrimination ability and consistency of the model.

Results

The subdistribution proportional hazards model showed that age, histological type, FIGO stage, and the log of the ratio between the numbers of positive and negative lymph nodes (LODDS) were independent prognostic factors for CSD. The 3-, 5-, and 8-year C-indexes were 0.744, 0.744, and 0.733 in the training cohort, and 0.737, 0.748, and 0.721 in the validation cohort. In the calibration plots, the forecast lines were very close to the reference lines.

Conclusions

This study is the first to analyze the prognostic factors for PFTC based on a competing-risks model. This model indicates that age, histological type, FIGO stage, and LODDS are significant prognostic factors affecting CSD in PFTC patients. We have also constructed the first 3-, 5-, and 8-year CSD nomogram for PFTC patients. This nomogram exhibits good discrimination ability and accuracy and can help clinicians to provide individualized prognostic analysis for PFTC patients.

Survival analysis of second primary malignancies after cervical cancer using a competing risk model: implications for prevention and surveillance

Background

Previous studies have reported an increased risk for second primary malignancies (SPMs) after cervical cancer (CC). This study aims to quantify and assess the risk of developing SPMs in long-term survivors of CC.

Methods

A population-based cohort of CC patients aged 20–79 years was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk model and corresponding nomogram were constructed to predict the 3-, 5-, and 10-year cumulative risks of SPMs. A Fine-Gray plot was created to validate the model. Finally, we performed decision curve analysis (DCA) to evaluate the clinical usefulness of the model by calculating the net benefit.

Results

A total of 34,295 patients were identified, and approximately 6.3% of the study participants developed SPMs. According to the multivariable competing-risk model, older black CC survivors with localized disease who were treated with radiation therapy were more susceptible to SPMs. The 3-, 5-, and 10-year cumulative incidences of SPMs were 2.5%, 3.6%, and 6.2%, respectively. Calibration curves showed good agreement between the predicted and observed models. The DCA yielded a wide range of risk thresholds at which the net benefits could be obtained from our proposed model.

Conclusions

This study provides physicians with a practical, individualized prognostic estimate to assess the risk of SPMs among CC survivors. CC survivors remain at a high risk of developing SPMs, and further surveillance should focus especially on the patients with black race, older age, localized disease, or those having received radiation therapy.

Research on the Role of Combined Chemotherapy and Radiotherapy in Patients With N+ Non-Metastatic Metaplastic Breast Carcinoma: A Competing Risk Analysis Model Based on the SEER database, 2000 to 2015

Purpose

Due to the rarity of metaplastic breast carcinoma (MpBC), no randomized trials have investigated the role of combined chemotherapy and radiotherapy (CCRP) in this condition. We aimed to explore and identify the effectiveness of CCRP in patients with regional lymph node metastasis (N+) non-metastatic MpBC.

Materials and Methods

Data were obtained from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program database. We assessed the effects of CCRP on overall survival (OS), breast cancer-specific survival (BCSS), and breast cancer-specific death (BCSD) using Kaplan-Meier analysis, competing risk model analysis, and competing risk regression mode analysis.

Results

A total of 707 women and 361 death cases were included in the unmatched cohort, of which 76.45% (276/361) were BCSD, and 23.55% (85/361) were non-breast cancer-specific deaths (non-BCSD). Both the ChemT and CCRP groups had better OS (ChemT group: HR: 0.59, 95% CI: 0.45–0.78, P<0.001; CCRP group: HR: 0.31, 95% CI: 0.23–0.41, P<0.001) and BCSS (ChemT group: HR: 0.63, 95% CI: 0.45–0.87, P<0.001; CCRP group: HR: 0.32, 95%CI: 0.22–0.46, P<0.001) than the non-therapy group. Subjects in the CCRP group tended to have significantly lower cumulative BCSD (Gray’s test, P=0.001) and non-BCSD (Gray’s test, P<0.001) than the non-therapy group or ChemT group. In competing risk regression model analysis, subjects in the CCRP group had a better prognosis in BCSD (HR: 0.710, 95% CI: 0.508–0.993, P=0.045) rather than the ChemT group (HR: 1.081, 95% CI: 0.761–1.535, P=0.660) than the non-therapy group.

Conclusion

Our study demonstrated that CCRP could significantly decrease the risk of death for both BCSD and non-BCSD and provided a valid therapeutic strategy for patients with N+ non-metastatic MpBC.

Prognostic Significance of Preoperative Lymphocyte-to-C-Reactive Protein Ratio in Patients with Non-Metastatic Colorectal Cancer

Background

The inflammatory indexes are attracting increasing attention as a prognostic predictor for colorectal cancer (CRC). However, the prognostic value of the preoperative lymphocyte-to-C-reactive protein ratio (LCR) in patients with non-metastatic CRC remains to be established.

Methods

A total of 955 patients from 2010 to 2014 at a single center were included. Receiver operating characteristic curves (ROC) were generated to define the optimal cutoff value of the inflammatory indexes, and the areas under the curve (AUC) were calculated to compare the predictive value among the inflammatory indexes. The Fine and Gray competing risk regression model and Cox proportional hazard model were used to determine the prognostic factors for cancer-specific survival (CSS) and overall survival (OS) by using sub-distribution hazard ratio (SHR) and hazard ratio (HR) as size effects, respectively.

Results

A ratio of 6500 was defined as the optimal cutoff value for LCR for dividing CRC patients into the high (> 6500, n = 528) and low (≤ 6500, n = 427) LCR groups. The LCR had the highest value of prognostic prediction among all inflammation-based scores. Low LCR was significant correlated with several clinicopathological features of tumor invasion and development. The patients with low LCR had poorer CSS and OS as compared to those with high LCR. Multivariate analyses showed that low LCR was independently associated with worse OS (HR = 0.61, 95% CI: 0.53-0.70) and CSS (SHR = 0.55, 95% CI: 0.43-0.71).

Conclusion

Preoperative LCR can be a useful biomarker for prognostic prediction in non-metastatic CRC patients with a better predictive value than other inflammatory indexes.

Effect of antiretroviral therapy initiation time and baseline CD4+ cell counts on AIDS-related mortality among former plasma donors in China: a 21-year retrospective cohort study

Background

The conventional survival analysis model on HIV/AIDS prognosis is the Cox proportional hazard model, which deals with only one event type, death, regardless of the cause. Few studies have used a competing risk model to evaluate the predictors of AIDS-related mortality.

Objective

To estimate the influence of antiretroviral therapy (ART) initiation time and baseline CD4⁺ cell counts on acquired immunodeficiency syndrome (AIDS)-related death among former plasma donors.

Methods

A retrospective cohort study was conducted involving 11,905 human immunodeficiency virus (HIV) or AIDS patients in a high-risk area of Henan province in China between 1995 and 2016. Demographic and clinical data were collected. Sub-distribution hazard ratios (sHRs) for AIDS-related mortality with baseline CD4⁺ cell counts and ART initiation time were determined using a competing risk model.

Results

Patients who initiated ART within 90 days of HIV/AIDS diagnosis (sHR: 0.24, 95% CI: 0.22–0.27) or had baseline CD4⁺ counts of >500 cells/μL (sHR: 0.23, 95% CI: 0.19–0.28) were associated with lower AIDS-related mortality risk. Patients with ART initiation time >1 year but CD4⁺ counts >350 cells/μL (sHR: 4.42, 95% CI: 3.30–5.91) had a higher AIDS-related mortality risk than those with ART initiation time >90 days but CD4⁺ counts ≤350 cells/μL (sHR: 4.33, 95% CI: 3.58–5.23).

Conclusions

Our results demonstrate that patients with high CD4⁺ cell counts and late ART had a 9% higher risk of AIDS-related death than those with low CD4⁺ cell counts and early ART. This study confirms the great significance of immediate ART initiation among former plasma donor HIV patients in China.

To what extent is severe osteoarthritis preventable? Occupational and non-occupational risk factors for knee and hip osteoarthritis

OBJECTIVES

To explore the relative contribution of cumulative physical workload, sociodemographic and lifestyle factors, as well as prior injury to hospitalization due to knee and hip OA.

METHODS

We examined a nationally representative sample of persons aged 30-59 years, who participated in a comprehensive health examination (the Health 2000 Study). A total of 4642 participants were followed from mid-2000 to end-2015 for the first hospitalization due to knee or hip OA using the National Hospital Discharge Register. We examined the association of possible risk factors with the outcome using a competing risk regression model (death was treated as competing risk) and calculated population attributable fractions for statistically significant risk factors.

RESULTS

Baseline age and BMI as well as injury were associated with the risk of first hospitalization due to knee and hip OA. Composite cumulative workload was associated with a dose-response pattern with hospitalizations due to knee OA and with hospitalizations due to hip OA at a younger age only. Altogether, prior injury, high BMI and intermediate to high composite cumulative workload accounted for 70% of hospitalizations due to knee OA. High BMI alone accounted for 61% and prior injury only for 6% of hospitalizations due to hip OA.

CONCLUSION

Our results suggest that overweight/obesity, prior injury and cumulative physical workload are the most important modifiable risk factors that need to be targeted in the prevention of knee OA leading to hospitalization. A substantial proportion of hospitalizations due to hip OA can be reduced by controlling excess body weight.

A Competing Risk Analysis Model to Determine the Prognostic Value of Isolated Tumor Cells in Axillary Lymph Nodes for T1N0M0 Breast Cancer Patients Based on the Surveillance, Epidemiology, and End Results Database

Introduction

Knowledge of the association between isolated tumor cells (ITCs) in breast cancer patients and the outcome is very limited. We aimed to determine the prognostic value of axillary lymph node ITCs for T1N0M0 female breast cancer (FBC) patients.

Methods

Data for T1N0M0 FBC patients staged ITCs negative [pN0(i−)] and positive [pN0(i+)] were extracted from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. Prognostic predictors were identified by Kaplan–Meier analysis, competing risk model, and Fine–Gray multivariable regression model.

Results

A total of 94,599 subjects were included, 88,632 of whom were staged at pN0(i−) and 5,967 were pN0(i+). Patients staged pN0(i+) had worse breast cancer-specific survival (BCSS) [hazard ratio (HR): 1.298, 95% CI = 1.069–1.576, P = 0.003] and higher breast cancer-specific death (BCSD) rate (Gray’s test, P = 0.002) than pN0(i−) group. In the Fine–Gray multivariable regression analysis, the pN0(i+) group had higher BCSD rate (HR: 1.321, 95% CI = 1.109–1.575, P = 0.002) than pN0(i−) group. In subgroup analyses, no significant difference in BCSD was shown between the chemotherapy and non-chemotherapy subgroup (Gray’s test, P = 0.069) or radiotherapy and non-radiotherapy subgroup (Gray’s test, P = 0.096).

Conclusion

ITC was independently related to the increase of the BCSD rate and could be identified as a reliable survival predictor for T1N0M0 FBC patients.

Risk of Second Primary Malignancies in Colon Cancer Patients Treated With Colectomy

Background: Second primary malignancy (SPM) attracts a growing attention. However, the clinical features of colon cancer (CC) survivors with SPMs are not clear and could help guide clinicians to develop a better surveillance strategy.

Methods: We reviewed 56,930 CC survivors treated with colectomy from the Surveillance, Epidemiology, and End Results (SEER) database during 1998–2011. Competing risk models and nomograms were conducted for predicting the risk of occurring SPMs. The clinical utility of the models was measured by decision curve analysis (DCA) using net benefit approaches.

Results: Five thousand thirteen (17.1%) of male patients developed SPMs and sites of SPMs included prostate (32.2%), lung and bronchus (11.6%), urinary bladder and kidney (10.8%), colon (10.0%), and melanoma of the skin (3.9%), while 3,592 (13.0%) of female patients occurred SPMs and sites of SPMs involved breast (25.8%), lung and bronchus (13.6%), colon (11.6%), uterus (8.2%), urinary bladder, and kidney (5.6%). Survivors with a second carcinoma of lung and bronchus showed the worst prognosis. Older age increased the risk of SPMs in both male (Subdistribution hazard ratio =2.85 [95% confidence interval = 2.53–3.21]) and female (1.80 [1.59–2.04]) survivors, especially for the risk of a second prostate carcinoma in male (5.33 [4.03–7.03]). Compared with white race, black male survivors remained at higher risk to develop the second prostate carcinoma (1.98 [1.74–2.26]). Competing-risk nomograms for CC survivors were established to help clinicians predict the probabilities of overall SPMs and prostate carcinoma. Validation of nomograms showed good discrimination and accuracy, and DCAs revealed the clinical effectiveness.

Conclusions: We profiled the clinical characteristics of a large population-based cohort of CC survivors with SPMs. These features may improve future follow-up management, especially for the surveillance of second prostate cancer in men and second breast cancer in women.

A novel model to predict cancer-specific survival in patients with early-stage uterine papillary serous carcinoma (UPSC)

OBJECTIVE

Stage I-II uterine papillary serous carcinoma (UPSC) has aggressive biological behavior and leads to poor prognosis. However, clinicopathologic risk factors to predict cancer-specific survival of patients with stage I-II UPSC were still unclear. This study was undertaken to develop a prediction model of survival in patients with early-stage UPSC.

METHODS

Using Surveillance, Epidemiology, and End Results (SEER) database, 964 patients were identified with International Federation of Gynecology and Obstetrics (FIGO) stage I-II UPSC who underwent at least hysterectomy between 2004 and 2015. By considering competing risk events for survival outcomes, we used proportional subdistribution hazards regression to compare cancer-specific death (CSD) for all patients. Based on the results of univariate and multivariate analysis, the variables were selected to construct a predictive model; and the prediction results of the model were visualized using a nomogram to predict the cancer-specific survival and the response to adjuvant chemotherapy and radiotherapy of stage I-II UPSC patients.

RESULTS

The median age of the cohort was 67 years. One hundred and sixty five patients (17.1%) died of UPSC (CSD), while 8.6% of the patients died from other causes (non-CSD). On multivariate analysis, age ≥ 67 (HR = 1.45, P = .021), tumor size ≥ 2 cm (HR = 1.81, P = .014) and >10 regional nodes removed (HR = 0.52, P = .002) were significantly associated with cumulative incidence of CSD. In the age ≥67 cohort, FIGO stage IB-II was a risk factor for CSD (HR = 1.83, P = .036), and >10 lymph nodes removed was a protective factor (HR = 0.50, P = .01). Both adjuvant chemotherapy combined with radiotherapy and adjuvant chemotherapy alone decreased CSD of patients with stage I-II UPSC older than 67 years (HR = 0.47, P = .022; HR = 0.52, P = .024, respectively). The prediction model had great risk stratification ability as the high-risk group had higher cumulative incidence of CSD than the low-risk group (P < .001). In the high-risk group, patients with post-operative adjuvant chemoradiotherapy had improved CSD compared with patients who did not receive radiotherapy nor chemotherapy (P = .037). However, there was no such benefit in the low-risk group.

CONCLUSION

Our prediction model of CSD based on proportional subdistribution hazards regression showed a good performance in predicting the cancer-specific survival of early-stage UPSC patients and contributed to guide clinical treatment decision, helping oncologists and patients with early-stage UPSC to decide whether to choose adjuvant therapy or not.

Pretreatment Aspartate Aminotransferase-to-Alanine Aminotransferase (De Ritis) Ratio Predicts the Prognosis of Nonmetastatic Nasopharyngeal Carcinoma

Background

The pretreatment aspartate aminotransferase-to-alanine aminotransferase (De Ritis) ratio is reportedly valuable in prognosis prediction of various malignancies. However, its value in the prognosis of nasopharyngeal carcinoma (NPC) has not yet been reported. This study aimed to evaluate the effect of the De Ritis ratio on the survival outcomes of patients with nonmetastatic NPC.

Methods

We retrospectively reviewed the medical data of 1023 patients with nonmetastatic NPC admitted between 2009 and 2013 at a single center. The Fine and Gray competing risk regression model was used to analyze the associations between the De Ritis ratio and the survival outcomes of cancer-specific survival (CSS) and progression-free survival (PFS) by using the subdistribution hazard ratio (SHR) and 95% confidence interval (CI) as size effects. The Cox proportional hazard model was used to evaluate the correlation between the De Ritis ratio and overall survival (OS) by using hazard ratio (HR) and 95% CI as size effects.

Results

Patients were divided into two groups in accordance with the pretreatment De Ritis ratio by using an optimal cutoff value of 1.65. Compared with the patients with low De Ritis ratio (< 1.65), those with elevated De Ritis ratio (≥ 1.65) had poorer prognosis with regard to CSS, PFS, and OS. Notably, multivariate analyses showed that high De Ritis ratio was independently associated with poor CSS (SHR = 1.64, 95% CI: 1.25–2.16), PFS (SHR = 1.69, 95% CI: 1.30–2.19), and OS (HR = 1.81, 95% CI: 1.39–2.40).

Conclusion

Pretreatment De Ritis ratio can be an independent prognostic predictor for patients with nonmetastatic NPC.

Risk factors of CVD mortality among the elderly in Beijing, 1992 - 2009: an 18-year cohort study

Few researchers have examined the effects of multiple risk factors of cardiovascular disease (CVD) mortality simultaneously. This study was to determine the associations of combined lifestyle and other factors with CVD mortality among the elderly (n = 3,257), in Beijing, China, through data mining of the Beijing Longitudinal Study of Aging (BLSA). BLSA is a representative cohort study from 1992 to 2009, hosted by Xuan Wu Hospital. Competing risk survival analysis was conducted to explore the association between risk factors and CVD mortality. The factors focused mainly on lifestyle, physical condition, and the model was adjusted for age and gender. There were 273 of the 1,068 recorded deaths caused by CVD among the 2010 participants. Living in a suburban area (HR = 0.614, 95% CI: 0.410-0.921) was associated with lower CVD mortality. Increasing age (66-75: HR = 1.511, 95% CI: 1.111-2.055; ≥ 76: HR = 1.847, 95% CI: 1.256-2.717), high blood pressure (HR = 1.407, 95% CI: 1.031-1.920), frequent consumption of meat (HR = 1.559, 95% CI: 1.079-2.254) and physical inactivity (p = 0.046) were associated with higher CVD mortality. The study provides an instructional foundation for the control and prevention of CVD in Beijing, China.

A competing risk approach for the European Heart SCORE model based on cause-specific and all-cause mortality

BACKGROUND

The European Heart SCORE model constitutes the basis for national guidelines for primary prevention and treatment of cardiovascular disease (CVD) in several European countries. The model estimates individuals' 10-year CVD mortality risks from age, sex, smoking status, systolic blood pressure, and total cholesterol level. The SCORE model, however, is not mathematically consistent and does not estimate all-cause mortality. Our aim is to modify the SCORE model to allow consistent estimation of both CVD-specific and all-cause mortality.

METHODS

Using a competing risk approach, we first re-estimated the cause-specific risk of dying from cardiovascular disease, and secondly we incorporated non-CVD mortality. Finally, non-CVD mortality was allowed to also depend on smoking status, and not only age and sex. From the models, we estimated CVD-specific and all-cause 10-year mortality risk, and the expected residual lifetime together with corresponding expected effects of statin treatment.

RESULTS

The modified model provided CVD-specific 10-year mortality risks similar to those of the European Heart SCORE model. Incorporation of non-CVD mortality increased 10-year mortality risks, in particular for older individuals. When non-CVD mortality was assumed unaffected by smoking status, the absolute risk reduction due to statin treatment ranged from 0.0% to 3.5%, whereas the gain in expected residual lifetime ranged from 3 to 11 months. Statin effectiveness increased for non-smokers and declined for smokers, when smoking was allowed to influence non-CVD mortality.

CONCLUSION

The modified model provides mathematically consistent estimates of mortality risk and expected residual lifetime together with expected benefits from statin treatment.