Nomogram for survival analysis in the presence of competing risks

Comparison of left- and right-sided colorectal cancer to explore prognostic signatures related to pyroptosis

Background

Colorectal cancer (CRC) is one of the most common malignancies, and pyroptosis exerts an immunoregulatory role in CRC. Although the location of the primary tumor is a prognostic factor for patients with CRC, the mechanisms of pyroptosis in left- and right-sided CRC remain unclear.

Methods

Expression and clinical data were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Differences in clinical characteristics, immune cell infiltration, and somatic mutations between left- and right-sided CRC were then compared. After screening for differentially expressed genes, Pearson correlation analysis was performed to select pyroptosis-related genes, followed by a gene set enrichment analysis. Univariate and multivariate Cox regression analyses were used to construct and validate the prognostic model and nomogram for predicting prognosis. Collected left- and right-sided CRC samples were subjected to reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key pyroptosis-related genes.

Results

Left- and right-sided CRC exhibited significant differences in clinical features and immune cell infiltration. Five prognostic signatures were identified from among 134 pyroptosis-related differentially expressed genes to construct a risk score-based prognostic model, and adverse outcomes for high-risk patients were further verified using an external cohort. A nomogram was also generated based on three independent prognostic factors to predict survival probabilities, while calibration curves confirmed the consistency between the predicted and actual survival. Experiment data confirmed the significant differential expression of five genes between left- and right-sided CRC.

Conclusion

The five identified pyroptosis-related gene signatures may be potential biomarkers for predicting prognosis in left- and right-sided CRC and may help improve the clinical outcomes of patients with CRC.

Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator

BACKGROUND

Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis.

PURPOSE

Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.

METHODS

Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors.

RESULTS

In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years).

LIMITATIONS

Lack of an external data set for model validation.

CONCLUSION/RELEVANCE

As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.

Causes of death among people living with metastatic cancer

Studying survivorship and causes of death in patients with advanced or metastatic cancer remains an important task. We characterize the causes of death among patients with metastatic cancer, across 13 cancer types and 25 non-cancer causes and predict the risk of death after diagnosis from the diagnosed cancer versus other causes (e.g., stroke, heart disease, etc.). Among 1,030,937 US (1992-2019) metastatic cancer survivors, 82.6% of patients (n = 688,529) died due to the diagnosed cancer, while 17.4% (n = 145,006) died of competing causes. Patients with lung, pancreas, esophagus, and stomach tumors are the most likely to die of their metastatic cancer, while those with prostate and breast cancer have the lowest likelihood. The median survival time among patients living with metastases is 10 months; our Fine and Gray competing risk model predicts 1 year survival with area under the receiver operating characteristic curve of 0.754 (95% CI [0.754, 0.754]). Leading non-cancer deaths are heart disease (32.4%), chronic obstructive and pulmonary disease (7.9%), cerebrovascular disease (6.1%), and infection (4.1%).

Development and validation of a nomogram for predicting cardiovascular mortality risk for diffuse large B-cell lymphoma in children, adolescents, and adults

Objective

This study aimed to construct and validate a nomogram for predicting cardiovascular mortality (CVM) for child, adolescent, and adult patients with diffuse large B-cell lymphoma (DLBCL).

Materials and methods

Patients with only one primary tumor of DLBCL first diagnosed between 2000 and 2019 in the SEER database were extracted. We used the cumulative incidence function (CIF) to evaluate the cumulative rate of CVM. The outcome of interest was CVM, which was analyzed using a competing risk model, accounting for death due to other causes. The total database was randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3. Adjustments were for demographics, tumor characteristics, and treatment modalities. Nomograms were constructed according to these risk factors to predict CVM risk at 5, 10, and 15 years. Validation included receiver operating characteristic (ROC) curves, time-dependent ROC, C-index, calibration curves, and decision curve analysis.

Results

One hundred four thousand six hundred six patients following initial diagnosis of DLBCL were included (58.3% male, median age 64 years, range 0-80, White 83.98%). Among them, 5.02% died of CVM, with a median follow-up time of 61 (31-98) months. Nomograms based on the seven risk factors (age at diagnosis, gender, race, tumor grade, Ann Arbor stage, radiation, chemotherapy) with hazard ratios ranging from 0.19-1.17 showed excellent discrimination, and calibration plots demonstrated satisfactory prediction. The 5-, 10-, and 15-year AUC and C-index of CVM in the training set were 0.716 (0.714-0.718), 0.713 (0.711-0.715), 0.706 (0.704-0.708), 0.731, 0.727, and 0.719; the corresponding figures for the validation set were 0.705 (0.688-0.722), 0.704 (0.689-0.718), 0.707 (0.693-0.722), 0.698, 0.698, and 0.699. Decision curve analysis revealed a clinically beneficial net benefit.

Conclusions

We first built the nomogram model for DLBCL patients with satisfactory prediction and excellent discrimination, which might play an essential role in helping physicians enact better treatment strategies at the time of initial diagnosis.

Development and validation of a nomogram to predict cervical lymph node metastasis in head and neck squamous cell carcinoma

Background

Head and neck cancers are a heterogeneous, aggressive, and genetically complex collection of malignancies of the oral cavity, nasopharynx, oropharynx, hypopharynx, larynx, paranasal sinuses and salivary glands, which are difficult to treat. Regional lymph nodes metastasis is a significant poor prognosis factor for head and neck squamous cell carcinoma. Metastasis to the regional lymph nodes reduces the 5-year survival rate by 50% compared with that of patients with early-stage disease. Accurate evaluation of cervical lymph node is a vital component in the overall treatment plan for patients with squamous cell carcinoma of the head and neck. However, current models are struggle to accurately to predict cervical lymph node metastasis. Here, we analyzed the clinical, imaging, and pathological data of 272 patients with HNSCC confirmed by postoperative pathology and sought to develop and validate a nomogram for prediction of lymph node metastasis in patients with head and neck squamous cell carcinoma.

Methods

We retrospectively analyzed the clinical, imaging, and pathological data of 272 patients with head and neck squamous cell carcinoma (HNSCC) confirmed by postoperative pathology at the Affiliated Hospital of Qingdao University from June 2017 to June 2021. Patients were randomly divided into the training and validation cohorts in a 3:1 ratio, and after screening risk factors by logistic regression, nomogram was developed for predicting lymph nodes metastasis, then the prediction model was verified by C-index, area under curve (AUC), and calibration curve.

Results

Of the 272 patients, seven variables were screened to establish the predictive model, including the differentiation degree of the tumor [95% confidence interval(CI):1.224~6.735, P=0.015], long-to-short axis ratio of the lymph nodes (95%CI: 0.019~0.217, P<0.001), uneven/circular enhancement (95%CI: 1.476~16.715, P=0.010), aggregation of lymph nodes (95%CI:1.373~10.849, P=0.010), inhomogeneous echo (95%CI: 1.337~23.389, P=0.018), unclear/absent medulla of lymph nodes (95%CI: 2.514~43.989, P=0.001), and rich blood flow (95%CI: 1.952~85.632, P=0.008). The C-index was 0.910, areas under the curve of training cohort and verification cohort were 0.953 and 0.938 respectively, indicating the discriminative ability of this nomogram. The calibration curve showed a favorable compliance between the prediction of the model and actual observations. The clinical decision curve showed this model is clinically useful and had better discriminative ability between 0.25 and 0.9 for the probability of cervical LNs metastasis.

Conclusions

We established a good prediction model for cervical lymph node metastasis in head and neck squamous cell carcinoma patients which can provide reference value and auxiliary diagnosis for clinicians in making neck management decisions of HNSCC patients.

Development and Validation of a New Score to Assess the Risk of Posttransplantation Diabetes Mellitus in Kidney Transplant Recipients

Background

Posttransplantation diabetes mellitus (PTDM) is a serious complication of solid organ transplantation. It is associated with major adverse cardiovascular events, which are a leading cause of morbidity and mortality in transplant patients. This study aimed to develop and validate a score to predict the risk of PTDM in kidney transplant recipients.

Methods

A single-center retrospective cohort study was conducted in a tertiary care hospital in Medellín, Colombia, between 2005 and 2019. Data from 727 kidney transplant recipients were used to develop a risk prediction model. Significant predictors with competing risks were identified using time-dependent Cox proportional hazard regression models. To build the prediction model, the score for each variable was weighted using calculated regression coefficients. External validation was performed using independent data, including 198 kidney transplant recipients from Tübingen, Germany.

Results

Among the 727 kidney transplant recipients, 122 developed PTDM. The predictive model was based on 5 predictors (age, gender, body mass index, tacrolimus therapy, and transient posttransplantation hyperglycemia) and exhibited good predictive performance (C-index: 0.7 [95% confidence interval, 0.65-0.76]). The risk score, which included 33 patients with PTDM, was used as a validation data set. The results showed good discrimination (C-index: 0.72 [95% confidence interval, 0.62-0.84]). The Brier score and calibration plot demonstrated an acceptable fit capability in external validation.

Conclusions

We proposed and validated a prognostic model to predict the risk of PTDM, which performed well in discrimination and calibration, and is a simple score for use and implementation by means of a nomogram for routine clinical application.

De-escalating chemotherapy for stage I-II gastric neuroendocrine carcinoma? A real-world competing risk analysis

BACKGROUND

The role of adjuvant chemotherapy in gastric neuroendocrine neoplasms (GNEC) has not been well clarified yet. The study was designed to investigate the potential effect of adjuvant chemotherapy in stage I-II GNEC patients and construct a predictive nomogram.

METHOD

Stage I-II GNEC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database and divided into chemotherapy and no-chemotherapy groups. We used Kaplan-Meier survival analyses, propensity score matching (PSM), and competing risk analyses. The predictive nomogram was then built and validated.

RESULTS

Four hundred four patients with stage I-II GNEC were enrolled from the SEER database while 28 patients from Hangzhou TCM Hospital were identified as the external validation cohort. After PSM, similar 5-year cancer-specific survival was observed in two groups. The outcomes of competing risk analysis indicated a similar 5-year cumulative incidence of cancer-specific death (CSD) between the two cohorts (35.4% vs. 31.4%, p = 0.731). And there was no significant relation between chemotherapy and CSD in the multivariate competing risks regression analysis (HR, 0.79; 95% CI, 0.48-1.31; p = 0.36). Furthermore, based on the variables from the multivariate analysis, a competing event nomogram was created to assess the 1-, 3-, and 5-year risks of CSD. The 1-, 3-, and 5-year area under the receiver operating characteristic curve (AUC) values were 0.770, 0.759, and 0.671 in the training cohort, 0.809, 0.782, and 0.735 in the internal validation cohort, 0.786, 0.856, and 0.770 in the external validation cohort. Furthermore, calibration curves revealed that the expected and actual probabilities of CSD were relatively consistent.

CONCLUSION

Stage I-II GNEC patients could not benefit from adjuvant chemotherapy after surgery. De-escalation of chemotherapy should be considered for stage I-II GNEC patients. The proposed nomogram exhibited excellent prediction ability.

New score for predicting major bleeding in patients with atrial fibrillation using direct oral anticoagulants

PURPOSE

Our aim was to identify factors associated with major bleeding in patients with atrial fibrillation (AF) on direct oral anticoagulants (DOACs) and to construct and externally validate a predictive model that would provide a validated tool for clinical assessment of major bleeding.

METHODS

In the development cohort, prediction model was built by logistic regression, the area under the curve (AUC), and Nomogram. External validation, analytical identification and calibration of the model using AUC, calibration curves and Hosmer-Lemeshow test.

RESULTS

The development cohort consisted of 4209 patients from 7 centers and the external validation cohort consisted of 1800 patients from 12 centers. Multifactorial analysis showed that age > 65 years, history of bleeding, anemia, vascular disease, antiplatelet therapy/non-steroidal anti-inflammatory drugs and rivaroxaban were independent risk factors for major bleeding, and gastrointestinal protective agents was a protective factor. The Alfalfa-MB model was constructed using these seven factors (AUC = 0.807), and in the external validation cohort, the model showed good discriminatory power (AUC = 0.743) and good calibration (Hosmer-Lemeshow test P value of 0.205). The predictive power of the six bleeding scores was ORBIT (AUC = 0.706), HAS-BLED (AUC = 0.648), ATRIA (AUC = 0.645), HEMORR2 HAGES (AUC = 0.632), ABC (AUC = 0.619) and Shireman (AUC = 0.599) in descending order.

CONCLUSION

Based on 7 factors, we derived and externally validated a predictive model for major bleeding with DOACs in patients with AF (Alfalfa-MB). The model has good predictive value and may be an effective tool to help reduce the occurrence of major bleeding in patients with DOACs.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model

Objectives

To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium.

Materials and methods

Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay.

Results

Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities.

Conclusion

As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.

Risk Factors of Relapse After Smoking Cessation: Results in China Family Panel Studies From 2010 to 2018

Background

Tobacco use is still highly prevalent globally in spite of the tobacco control efforts made by the governments. In view of the harm of smoking and relapse after smoking cessation, the purpose of this study is to establish a competitive risk model to determine potential risk factors for smoking relapse.

Methods

The population-based cohort of ex-smokers over the age of 18 years was obtained from the China Family Panel Studies (CFPS) database from 2010 to 2018. Competing risk models were conducted to identify the risk factors for relapse.

Results

A total of 1,019 subjects were included in this study, of which 311 (30.52%) subjects relapsed during the follow-up period. A multivariate analysis indicated that age < 40 years [hazard ratio (HR) 19.142; 95% CI: 10.641–34.434, p < 0.01], cohabitation (HR: 1.422; 95% CI: 1.081–1.87, p = 0.01), and often depression [HR 1.422; 95% CI, (1.081–1.87), p = 0.01] were associated with a great risk of relapse while the age of quitting smoking < 60 years (HR: 0. 436; 95% CI: 0.229–0.831, p < 0.01) and joining the Chinese Communist Party (CCP) (HR 0.611; 95% CI: 0.397–0.939, p = 0.03) were reduced risk factors for relapse.

Conclusions

Approximately 3 in 10 ex-smokers were observed to relapse. There are various risk factors for relapse as well. In the face of such a serious situation, it is urgent to take action to control smoking.

Fragility Fractures in Postmenopausal Women: Development of 5-Year Prediction Models Using the FRISBEE Study

CONTEXT

Individualized fracture risk may help to select patients requiring a pharmacological treatment for osteoporosis. FRAX and the Garvan fracture risk calculators are the most used tools, although their external validation has shown significant differences in their risk prediction ability.

OBJECTIVE AND METHODS

Using data from the Fracture Risk Brussels Epidemiological Enquiry study, a cohort of 3560 postmenopausal women aged 60 to 85 years, we aimed to construct original 5-year fracture risk prediction models using validated clinical risk factors (CRFs). Three models of competing risk analysis were developed to predict major osteoporotic fractures (MOFs), all fractures, and central fractures (femoral neck, shoulder, clinical spine, pelvis, ribs, scapula, clavicle, sternum).

RESULTS

Age, a history of fracture, and hip or spine BMD were predictors common to the 3 models. Excessive alcohol intake and the presence of comorbidities were specific additional CRFs for MOFs, a history of fall for all fractures, and rheumatoid arthritis for central fractures. Our models predicted the fracture probability at 5 years with an acceptable accuracy (Brier scores ≤ 0.1) and had a good discrimination power (area under the receiver operating curve of 0.73 for MOFs and 0.72 for central fractures) when internally validated by bootstrap. Three simple nomograms, integrating significant CRFs and the mortality risk, were constructed for different fracture sites. In conclusion, we derived 3 models predicting fractures with an acceptable accuracy, particularly for MOFs and central fractures. The models are based on a limited number of CRFs, and we constructed nomograms for use in clinical practice.

Idiopathic Pulmonary Fibrosis Mortality Risk Prediction Based on Artificial Intelligence: The CTPF Model

Background: Idiopathic pulmonary fibrosis (IPF) needs a precise prediction method for its prognosis. This study took advantage of artificial intelligence (AI) deep learning to develop a new mortality risk prediction model for IPF patients.

Methods: We established an artificial intelligence honeycomb segmentation system that segmented the honeycomb tissue area automatically from 102 manually labeled (by radiologists) cases of IPF patients’ CT images. The percentage of honeycomb in the lung was calculated as the CT fibrosis score (CTS). The severity of the patients was evaluated by pulmonary function and physiological feature (PF) parameters (including FVC%pred, DLco%pred, SpO2%, age, and gender). Another 206 IPF cases were randomly divided into a training set (n = 165) and a verification set (n = 41) to calculate the fibrosis percentage in each case by the AI system mentioned previously. Then, using a competing risk (Fine–Gray) proportional hazards model, a risk score model was created according to the training set’s patient data and used the validation data set to validate this model.

Result: The final risk prediction model (CTPF) was established, and it included the CT stages and the PF (pulmonary function and physiological features) grades. The CT stages were defined into three stages: stage I (CTS≤5), stage II (5 < CTS<25), and stage III (≥25). The PF grades were classified into mild (a, 0–3 points), moderate (b, 4–6 points), and severe (c, 7–10 points). The AUC index and Briers scores at 1, 2, and 3 years in the training set were as follows: 74.3 [63.2,85.4], 8.6 [2.4,14.8]; 78 [70.2,85.9], 16.0 [10.1,22.0]; and 72.8 [58.3,87.3], 18.2 [11.9,24.6]. The results of the validation sets were similar and suggested that high-risk patients had significantly higher mortality rates.

Conclusion: This CTPF model with AI technology can predict mortality risk in IPF precisely.

Is lobectomy superior to sub-lobectomy in non-small cell lung cancer with pleural invasion? A population-based competing risk analysis

Background

Pleural invasion (PL) has been regarded as an unfavorable prognostic factor for non-small cell lung cancer (NSCLC). But there was no agreement on the optimal surgical extent in NSCLC patients with PL. We aimed to compare the survival outcomes of lobectomy and sub-lobectomy in these patients.

Method

2717 patients were included in the Surveillance, Epidemiology, and End Results (SEER) database and divided into the lobectomy and sub-lobectomy groups. The propensity score matching (PSM) and competing risk analysis were implemented. Then the predictive nomogram was constructed and validated.

Results

2230 Patients received lobectomy while the other 487 patients underwent sub-lobectomy. After 1:1 PSM, the cumulative incidence of cancer-specific death (CSD) was lower in the lobectomy group compared with the sub-lobectomy group (1-year: 12% vs. 15%; 3-year: 30% vs. 37%, 5-year: 34% vs. 45%, P = 0.04). According to the subgroup analysis, the patients who underwent lobectomy suffered lower CSD in the N0–1 stage, adenocarcinoma, and PL-2 cohort (p < 0.05). And there was a significant relationship between the sub-lobectomy group and CSD in the multivariate competing risks regression analysis (HR, 1.26; 95%CI, 1.02–1.56; P = 0.034). Furthermore, a competing event nomogram was constructed to assess the 1-, 3-, and 5-year chances of CSD based on the variables from the multivariate analysis. The 1-, 3-, 5-year area under the receiver operating characteristic curve (AUC) values were 0.720, 0.706, and 0.708 in the training cohort, and 0.738, 0.696, 0.680 in the validation cohorts, respectively. And calibration curves demonstrated ideal consistency between the predicted and observed probabilities of CSD.

Conclusion

Lobectomy should be considered the preferred surgery compared to sub-lobectomy for NSCLC patients with PL. The proposed nomograms presented great prediction ability for these patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09634-w.

A Novel Nomogram Based on a Competing Risk Model Predicting Cardiovascular Death Risk in Patients With Chronic Kidney Disease

Objective

Chronic kidney disease (CKD) patients are more likely to die from cardiovascular disease (CVD) than develop renal failure. This study aimed to develop a new nomogram for predicting the risk of cardiovascular death in CKD patients.

Methods

This study enrolled 1656 CKD patients from NHANES 2003 to 2006 survey. Data sets from 2005 to 2006 survey population were used to build a nomogram for predicting the risk of cardiovascular death, and the nomogram was validated using data from 2003 to 2004 survey population. To identify the main determinants of cardiovascular death, we performed univariate analysis and backward-stepwise regression to select the key factors. The probability of cardiovascular death for each patient in 5, 7, and 9 years was calculated using a nomogram based on the predictors. To assess the nomogram’s performance, the area under receiver operating characteristic curve (AUC) and the calibration curve with 1,000 bootstraps resamples were utilized. The prediction model’s discrimination was examined using cumulative incidence function (CIF).

Results

Age, homocysteine, potassium levels, CKD stage, and anemia were included in the nomogram after screening risk factors using univariate analysis and backward-stepwise regression. Internal validation revealed that this nomogram possesses high discrimination and calibration (AUC values of 5–, 7–, and 9-years were 0.79, 0.81, and 0.81, respectively). External validation confirmed the same findings (AUC values of 5–, 7– and 9-years were 0.76, 0.73, and 0.73, respectively). According to CIF, the established nomogram effectively differentiates patients at a high risk of cardiovascular death from those at low risk.

Conclusion

This work develops a novel nomogram that integrates age, homocysteine, potassium levels, CKD stage, and anemia and can be used to more easily predict cardiovascular death in CKD patients, highlighting its potential value in clinical application.

Clinical Significance and Potential Mechanisms of ATP Binding Cassette Subfamily C Genes in Hepatocellular Carcinoma

The purpose of this investigation was to assess the diagnostic and prognostic significance of ATP binding cassette subfamily C (ABCC) genes in hepatocellular carcinoma (HCC). The Student t-test was used to compare the expression level of ABCCs between HCC and paraneoplastic tissues. Receiver operating characteristic curve (ROC) analysis was applied for diagnostic efficiency assessment. The Kaplan-Meier method and Cox proportional hazards model were respectively applied for survival analysis. Genes with prognostic significance were subsequently used to construct prognostic models. From the perspective of genome-wide enrichment analysis, the mechanisms of prognosis-related ABCC genes were attempted to be elaborated by gene set enrichment analysis (GSEA). It was observed in the TCGA database that ABCC1, ABCC4, ABCC5, and ABCC10 were significantly upregulated in tumor tissues, while ABCC6 and ABCC7 were downregulated in HCC tissues. Receiver operating characteristic analysis revealed that ABCC7 might be a potential diagnostic biomarker in HCC. ABCC1, ABCC4, ABCC5, and ABCC6 were significantly related to the prognosis of HCC in the TCGA database. The prognostic significance of ABCC1, ABCC4, ABCC5, and ABCC6 was also observed in the Guangxi cohort. In the Guangxi cohort, both polymerase chain reaction and IHC (immunohistochemical) assays demonstrated higher expression of ABCC1, ABCC4, and ABCC5 in HCC compared to liver tissues, while the opposite was true for ABCC6. GSEA analysis indicated that ABCC1 was associated with tumor differentiation, nod-like receptor signal pathway, and so forth. It also revealed that ABCC4 might play a role in HCC by regulating epithelial-mesenchymal transition, cytidine analog pathway, met pathway, and so forth. ABCC5 might be associated with the fatty acid metabolism and KRT19 in HCC. ABCC6 might impact the cell cycle in HCC by regulating E2F1 and myc. The relationship between ABCC genes and immune infiltration was explored, and ABCC1,4,5 were found to be positively associated with infiltration of multiple immune cells, while ABCC6 was found to be the opposite. In conclusion, ABCC1, ABCC4, ABCC5, and ABCC6 might be prognostic biomarkers in HCC. The prognostic models constructed with ABCC1, ABCC4, ABCC5, and ABCC6 had satisfactory efficacy.

Nomogram to predict cause-specific mortality of patients with rectal adenocarcinoma undergoing surgery: a competing risk analysis

BACKGROUND

Rectal adenocarcinoma is one of major public health problems, severely threatening people's health and life. Cox proportional hazard models have been applied in previous studies widely to analyze survival data. However, such models ignore competing risks and treat them as censored, resulting in excessive statistical errors. Therefore, a competing-risk model was applied with the aim of decreasing risk of bias and thereby obtaining more-accurate results and establishing a competing-risk nomogram for better guiding clinical practice.

METHODS

A total of 22,879 rectal adenocarcinoma cases who underwent primary-site surgical resection were collected from the SEER (Surveillance, Epidemiology, and End Results) database. Death due to rectal adenocarcinoma (DRA) and death due to other causes (DOC) were two competing endpoint events in the competing-risk regression analysis. The cumulative incidence function for DRA and DOC at each time point was calculated. Gray's test was applied in the univariate analysis and Gray's proportional subdistribution hazard model was adopted in the multivariable analysis to recognize significant differences among groups and obtain significant factors that could affect patients' prognosis. Next, A competing-risk nomogram was established predicting the cause-specific outcome of rectal adenocarcinoma cases. Finally, we plotted calibration curve and calculated concordance indexes (c-index) to evaluate the model performance.

RESULTS

22,879 patients were included finally. The results showed that age, race, marital status, chemotherapy, AJCC stage, tumor size, and number of metastasis lymph nodes were significant prognostic factors for postoperative rectal adenocarcinoma patients. We further successfully constructed a competing-risk nomogram to predict the 1-year, 3-year, and 5-year cause-specific mortality of rectal adenocarcinoma patients. The calibration curve and C-index indicated that the competing-risk nomogram model had satisfactory prognostic ability.

CONCLUSION

Competing-risk analysis could help us obtain more-accurate results for rectal adenocarcinoma patients who had undergone surgery, which could definitely help clinicians obtain accurate prediction of the prognosis of patients and make better clinical decisions.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Background

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

Methods

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

Results

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

Conclusions

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

Prediction of an Imminent Fracture After an Index Fracture - Models Derived From the Frisbee Cohort

Patients who sustain a fracture are at greatest risk of recurrent fracture during the next 2 years. We propose three models to identify subjects most at risk of an imminent fracture, according to fracture site (any fracture, major osteoporotic fracture [MOF] or central). They were constructed using data of the prospective Frisbee cohort, which includes 3560 postmenopausal women aged 60 to 85 years who were followed for at least 5 years. A total of 881 subjects had a first incident validated fragility fracture before December 2018. Among these, we validated 130 imminent fractures occurring within the next 2 years; 79 were MOFs, and 88 were central fractures. Clinical risk factors were re-evaluated at the time of the index fracture. Fine and Gray proportional hazard models were derived separately for each group of fractures. The following risk factors were significantly associated with the risk of any imminent fracture: total hip bone mineral density (BMD) (p < 0.001), a fall history (p < 0.001), and comorbidities (p = 0.03). Age (p = 0.05 and p = 0.03, respectively) and a central fracture as the index fracture (p = 0.04 and p = 0.005, respectively) were additional predictors of MOFs and central fractures. The three prediction models are presented as nomograms. The calibration curves and the Brier scores based on bootstrap resampling showed calibration scores of 0.089 for MOF, 0.094 for central fractures, and 0.132 for any fractures. The predictive accuracy of the models expressed as area under the receiver operating characteristic (AUROC) curve (AUC) were 0.74 for central fractures, 0.72 for MOFs, and 0.66 for all fractures, respectively. These AUCs compare well with those of FRAX and Garvan to predict the 5- or 10-year fracture probability. In summary, five predictors (BMD, age, comorbidities, falls, and central fracture as the incident fracture) allow the calculation with a reasonable accuracy of the imminent risk of fracture at different sites (MOF, central fracture, and any fracture) after a recent sentinel fracture. © 2021 American Society for Bone and Mineral Research (ASBMR).

Improved method to stratify lymphoma patients with risk of secondary central nervous system involvement: A multicenter retrospective analysis

Secondary central nervous system (SCNS) involvement is an infrequent but universally fatal event in diffused large B-cell lymphoma. The occurrence rate of SCNS involvement is approximately 5% but comes with a poor prognosis ever after. However, existing risk models to predict the incidence and prognosis of these patients with SCNS involvement lack both efficiency and accuracy. Controversy has also been reported regarding which risk factor may best identify the population with a high CNS relapse rate. In this study, we retrospectively analyzed 831 patients with diffused large B-cell lymphoma, diagnosed between March 2008 and June 2018 in Tianjin Medical University Cancer Institute and Hospital, Beijing Cancer Hospital, and Cancer Hospital of The University of Chinese Academy of Science. Risk factors and nomogram were identified and established based on Fine and Gray's competing risk analysis. Among these patients, 55 (6.6%) of them eventually developed SCNS involvement. The 1- and 2-year incidence for SCNS involvement were 3.9% and 4.7%, respectively. The median time from de novo diagnosis to CNS relapse was 8 months, and the median overall survival of these patients was 28 months. Considering the competing mortality before SCNS involvement, Fine and Gray's competing risk model was performed to analyze the characteristics related to SCNS involvement, and identified risk factors as the multiple extranodal involvements, elevated LDH and AMC level, and the involvement of breast, adrenal gland/kidney, pulmonary and bone. Corresponding factors were integrated into the competing nomogram for SCNS involvement (c-index = 0.778). In conclusion, we present the first predictive nomogram to evaluate the risk to develop SCNS involvement in de novo DLBCL patients, which may help in both prognostic evaluation and clinical decision for this subgroup.

Post-discharge arrhythmic risk stratification of patients with acute myocarditis and life-threatening ventricular tachyarrhythmias

AIMS

The outcomes of patients presenting with acute myocarditis and life-threatening ventricular arrhythmias (LT-VA) are unclear. The aim of this study was to assess the incidence and predictors of recurrent major arrhythmic events (MAEs) after hospital discharge in this patient population.

METHODS AND RESULTS

We retrospectively analysed 156 patients (median age 44 years; 77% male) discharged with a diagnosis of acute myocarditis and LT-VA from 16 hospitals worldwide. Diagnosis of myocarditis was based on histology or the combination of increased markers of cardiac injury and cardiac magnetic resonance (CMR) Lake Louise criteria. MAEs were defined as the relapse, after discharge, of sudden cardiac death or successfully defibrillated ventricular fibrillation, or sustained ventricular tachycardia (sVT) requiring implantable cardioverter-defibrillator therapy or synchronized external cardioversion. Median follow-up was 23 months [first to third quartile (Q1-Q3) 7-60]. Fifty-eight (37.2%) patients experienced MAEs after discharge, at a median of 8 months (Q1-Q3 2.5-24.0 months; 60.3% of MAEs within the first year). At multivariable Cox analysis, variables independently associated with MAEs were presentation with sVT [hazard ratio (HR) 2.90, 95% confidence interval (CI) 1.38-6.11]; late gadolinium enhancement involving ≥2 myocardial segments (HR 4.51, 95% CI 2.39-8.53), and absence of positive short-tau inversion recovery (STIR) (HR 2.59, 95% CI 1.40-4.79) at first CMR.

CONCLUSIONS

Among patients discharged with a diagnosis of myocarditis and LT-VA, 37.2% had recurrences of MAEs during follow-up. Initial CMR pattern and sVT at presentation stratify the risk of arrhythmia recurrence.

Model to predict cause-specific mortality in patients with olfactory neuroblastoma: a competing risk analysis

Purpose

The main objective of this study was to evaluate the cumulative incidence of cause-specific mortality and other causes of mortality for patients with olfactory neuroblastoma (ONB). The secondary aim was to model the probability of cause-specific death and build a competing risk nomogram to predict cause-specific mortality for this disease.

Methods

Patients with ONB from 1975 to 2016 were identified from the Surveillance, Epidemiology, and End Results database. We estimated the cumulative incidence function (CIF) for cause-specific mortality and other causes of mortality, and constructed the Fine and Gray’s proportional subdistribution hazard model, as well as a competing-risk nomogram based on Fine and Gray’s model, to predict the probability of cause-specific mortality for patients with ONB.

Results

After data selection, 826 cases were included for analysis. Five-year cumulative incidence of cause-specific mortality was 19.5% and cumulative incidence of other causes of mortality was 11.3%. Predictors of cause-specific mortality for ONB included tumor stage, surgery and chemotherapy. Age was most strongly predictive of other causes of mortality: patients aged > 60 years exhibited subdistribution hazard ratios of 1.063 (95 % confidence interval [CI] 1.05–1.08; p = 0.001). The competing risk nomogram for cause-specific mortality was well-calibrated, and had good discriminative ability (concordance index = 0.79).

Conclusions

We calculated the CIF of cause-specific mortality and other causes of mortality in patients with the rare malignancy ONB. We also built the first competing risk nomogram to provide useful individualized predictive information for patients with ONB.

The first competing risk survival nomogram in patients with papillary renal cell carcinoma

There is still a lack of competing risk analysis of patients with papillary renal cell carcinoma (pRCC) following surgery. We performed the cumulative incidence function (CIF) to estimate the absolute risks of cancer-specific mortality (CSM) and other-cause mortality (OCM) of pRCC over time, and constructed a nomogram predicting the probability of 2-, 3- and 5-year CSM based on competing risk regression. A total of 5993 pRCC patients who underwent nephrectomy between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The 2-, 3-, 5-year CSM rates were 3.2%, 4.4% and 6.5%, respectively, and that of OCM were 3.2%, 5.0% and 9.3%, respectively. The estimates of 5-year cumulative mortality were most pronounced among patients aged > 75 years in OCM (17.0%). On multivariable analyses, age, tumor grade, T stage, N stage, and with or without bone, liver and lung metastases were identified as independent predictors of CSM following surgery and were integrated to generate the nomogram. The nomogram achieved a satisfactory discrimination with the AUC_t of 0.730 at 5-year, and the calibration curves presented impressive agreements. Taken together, age-related OCM is a significant portion of all-cause mortality in elderly patients and our nomogram can be used for decision-making and patient counselling.

Application of competing risk model in the prognostic prediction study of patients with follicular thyroid carcinoma

BACKGROUND

Follicular thyroid carcinoma (FTC) is an indolent carcinoma. The cumulative incidence of death from patients with FTC and the nomogram built based on the competing risks model have not been described.

METHODS

The data from patients diagnosed with primary FTC were identified and extracted from the surveillance, epidemiology, and end results (SEER) program (1988-2015). The cumulative incidence function was utilized to calculate the likelihood of death resulting from thyroid cancer and other causes, respectively. Gray's test was used to examine the difference in the cumulative incidence of death between the groups. A tenfold cross-validation was applied to assess the discrimination and calibration of the model.

RESULTS

A total of 9210 patients diagnosed with primary FTC were included. The median follow-up time was 92 months (1-347 months). The 5-year, 10-year, and 20-year probabilities of death from FTC were 2.84%, 5.23%, and 8.61%, respectively. The age at diagnosis, sex, tumor size, pathological subtypes, tumor extension, lymph node involvement, as well as surgical and radiotherapy methods used, were related to the cumulative incidence of death. Multivariate analysis identified several risk factors for patient survival. The model behaved well in terms of performance. A nomogram based on the model allowed the prediction of the probability of death among patients with FTC.

CONCLUSIONS

The prognosis of FTC is excellent. The likelihood of death caused by thyroid cancer increases with age. Male sex, tumors larger than 4 cm, invasion, extrathyroidal extension, lymph node involvement, and distant metastases increase the risk of dying of thyroid carcinoma. The nomogram constructed on the basis of the model is potentially useful for both clinicians and patients.

Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

Background

Lymph node metastasis (LNM) is a well-established prognostic factor for colon cancer. Preoperative LNM evaluation is relevant for planning colon cancer treatment. The aim of this study was to construct and evaluate a nomogram for predicting LNM in primary colon cancer according to pathological features.

Patients and Methods

Six-hundred patients with clinicopathologically confirmed colon cancer (481 cases in the training set and 119 cases in the validation set) were enrolled in the Affiliated Cancer Hospital of Guangxi Medical University from January 2010 to December 2019. The expression of molecular markers (p53 and β-catenin) was determined by immunohistochemistry. Multivariate logistic regression was used to screen out independent risk factors, and a nomogram was established. The accuracy and discriminability of the nomogram were evaluated by consistency index and calibration curve.

Results

Univariate logistic analysis revealed that LNM in colon cancer is significantly correlated (P <0.05) with tumor size, grading, stage, preoperative carcinoembryonic antigen (CEA) level, and peripheral nerve infiltration (PNI). Multivariate logistic regression analysis confirmed that CEA, grading, and PNI were independent prognostic factors of LNM (P <0.05). The nomogram for predicting LNM risk showed acceptable consistency and calibration capability in the training and validation sets.

Conclusions

Preoperative CEA level, grading, and PNI were independent risk factor for LNM. Based on the present parameters, the constructed prediction model of LNM has potential application value.

Nomograms for Estimating Cause-Specific Death Rates of Patients With Inflammatory Breast Cancer: A Competing-Risks Analysis

Purpose:

Inflammatory breast cancer (IBC) is a rare, aggressive and special subtype of primary breast cancer. We aimed to establish competing-risks nomograms to predict the IBC-specific death (BCSD) and other-cause-specific death (OCSD) of IBC patients.

Methods:

We extracted data on primary IBC patients from the SEER (Surveillance, Epidemiology, and End Results) database by applying specific inclusion and exclusion criteria. Cumulative incidence function (CIF) was used to calculate the cumulative incidence rates and Gray’s test was used to evaluate the difference between groups. Fine-Gray proportional subdistribution hazard method was applied to identify the independent predictors. We then established nomograms to predict the 1-, 3-, and 5-year cumulative incidence rates of BCSD and OCSD based on the results. The calibration curves and concordance index (C-index) were adopted to validate the nomograms.

Results:

We enrolled 1699 eligible IBC patients eventually. In general, the 1-, 3-, and 5-years cumulative incidence rates of BCSD were 15.3%, 41.0%, and 50.7%, respectively, while those of OCSD were 3.0%, 5.1%, and 7.4%. The following 9 variables were independent predictive factors for BCSD: race, lymph node ratio (LNR), AJCC M stage, histological grade, ER (estrogen receptor) status, PR (progesterone receptor) status, HER-2 (human epidermal growth factor-like receptor 2) status, surgery status, and radiotherapy status. Meanwhile, age, ER, PR and chemotherapy status could predict OCSD independently. These factors were integrated for the construction of the competing-risks nomograms. The results of calibration curves and C-indexes indicated the nomograms had good performance.

Conclusions:

Based on the SEER database, we established the first competing-risks nomograms to predict BCSD and OCSD of IBC patients. The good performance indicated that they could be incorporated in clinical practice to provide references for clinicians to make individualized treatment strategies.

Integrating Prostate-specific Antigen Kinetics into Contemporary Predictive Nomograms of Salvage Radiotherapy After Radical Prostatectomy

BACKGROUND

Salvage radiotherapy (SRT) is an established treatment for men with biochemical recurrence following radical prostatectomy (RP). There are several risk factors associated with adverse outcomes; however, the value of postoperative prostate-specific antigen (PSA) kinetics is less clear in the ultrasensitive PSA era.

OBJECTIVE

To characterize the impact of PSA kinetics on outcomes following SRT and generate nomograms to aid in identifying patients with an increased risk of adverse clinical outcomes.

DESIGN, SETTING, AND PARTICIPANTS

A multi-institutional analysis was conducted of 1005 patients with prostate cancer treated with SRT after RP, with a median follow-up of 5 years.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Variables examined include immediate postoperative PSA, postoperative PSA doubling time (DT), and pre-SRT PSA, in addition to previously identified predictive factors. Multivariable survival analyses were completed using Fine-Gray competing risk regression. Rates of biochemical failure (BF), distant metastasis (DM), and prostate cancer-specific mortality (PCSM) were estimated by the cumulative incidence method. Nomograms were generated from multivariable competing risk regression with bootstrap cross-validation.

RESULTS AND LIMITATIONS

Factors associated with BF after SRT include PSA DT <6 mo, initial postoperative PSA ≥0.2 ng/ml, higher pre-SRT PSA, lack of androgen deprivation therapy, a higher Gleason score (GS), negative margins, seminal vesicle invasion, lack of pelvic nodal radiation, radiation total dose <66 Gy, a longer RP to SRT interval, and older age (p < 0.05 for each). Factors associated with DM include PSA DT <6 mo, pre-SRT PSA, a higher GS, and negative margins. Factors associated with PCSM include PSA DT not calculable or <6 mo and a higher GS. Nomograms were generated to estimate the risks of BF (concordance index [CI] 0.74), DM (CI 0.77), and PCSM (CI 0.77). Limitations include retrospective nature, broad treatment eras, institutional variations, and multiple methods available for the estimation of PSA DT.

CONCLUSIONS

Postoperative PSA kinetics, particularly pre-SRT PSA and PSA DT, are strongly associated with adverse oncologic outcomes following SRT and should be considered in management decisions.

PATIENT SUMMARY

In this report of men with prostate cancer who developed a prostate-specific antigen (PSA) recurrence after prostatectomy, we found that PSA levels after surgery and how quickly a PSA level doubles significantly impact the chance of prostate cancer recurrence after salvage radiation therapy. Based on this information, we created a tool to calculate a man's chance of cancer recurrence after salvage radiation therapy, and these estimations can be used to discuss whether additional treatment with radiation should be considered.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis

INTRODUCTION

Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.

METHODS

Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.

RESULTS

We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Competing risk model to determine the prognostic factors and treatment strategies for elderly patients with glioblastoma

The prognostic factors and optimal treatment for the elderly patient with glioblastoma (GBM) were poorly understood. This study extracted 4975 elderly patients (≥ 65 years old) with histologically confirmed GBM from Surveillance, Epidemiology and End Results (SEER) database. Firstly, Cumulative incidence function and cox proportional model were utilized to illustrate the interference of non-GBM related mortality in our cohort. Then, the Fine-Gray competing risk model was applied to determine the prognostic factors for GBM related mortality. Age ≥ 75 years old, white race, size > 5.4 cm, frontal lobe tumor, and overlapping lesion were independently associated with more GBM related death, while Gross total resection (GTR) (HR 0.87, 95%CI 0.80-0.94, P = 0.010), radiotherapy (HR 0.64, 95%CI 0.55-0.74, P < 0.001), chemotherapy (HR 0.72, 95%CI 0.59-0.90, P = 0.003), and chemoRT (HR 0.43, 95%CI 0.38-0.48, P < 0.001) were identified as independently protective factors of GBM related death. Based on this, a corresponding nomogram was conducted to predict 3-, 6- and 12-month GBM related mortality, the C-index of which were 0.763, 0.718, and 0.694 respectively. The calibration curve showed that there was a good consistency between the predicted and the actual mortality probability. Concerning treatment options, GTR followed by chemoRT is suggested as optimal treatment. Radiotherapy and chemotherapy alone also provide moderate clinical benefits.

Rate of Progression of Aortic Stenosis in Patients With Cancer

Patients with cancer and aortic stenosis (AS) are exposed to several factors that could accelerate the progression of AS. This study aimed to determine the cumulative incidence of AS progression and associated factors in these patients. This retrospective cohort study included patients with cancer, mild or moderate AS and at least two echocardiograms 6 months apart between 1996 and 2016 at MD Anderson Cancer Center. AS progression was defined by an increase in mean gradient of 20 mmHg or peak velocity of 2 m/s by spectral Doppler echocardiography or as requiring aortic valve replacement. Univariate and multivariable Fine-Gray models to account for the competing risk of death were used. One hundred and two patients were included and median follow-up was 7.3 years. Overall, 30 patients (29%) developed AS progression, while 48 (47%) died without it. Yearly rate of mean gradient change was 4.9 ± 3.9 mmHg and yearly rate of peak velocity change was 0.23 ± 0.29 m/s for patients who developed AS progression. In the univariate analysis, coronary artery disease (CAD), dyspnea, prevalent cyclophosphamide and beta-blocker use were associated with AS progression. In multivariable analysis, CAD and prevalent cyclophosphamide use for the time interval of more than 3 years of follow-up remained significantly associated with increased cumulative incidence of AS progression. In conclusion, patients with mild or moderate AS and cancer are more likely to die before having AS progression. AS progression is associated with CAD and prevalent cyclophosphamide use.

Competing-Risk Nomograms for Predicting the Prognosis of Patients With Infiltrating Lobular Carcinoma of the Breast

BACKGROUND

Infiltrating lobular carcinoma (ILC) is the second most common histologic subtype of breast cancer. We assessed the rates of cause-specific death in ILC patients with the aim of establishing competing-risk nomograms for predicting their prognosis.

PATIENTS AND METHODS

Data on ILC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function was used to calculate the cumulative incidence rates of cause-specific death, and Gray's test was applied to test the differences in cumulative incidence rates among groups. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard analysis method and established nomograms based on the results. Calibration curves and the concordance index were employed to validate the nomograms.

RESULTS

The study enrolled 11,361 patients. The 3-, 5-, and 10-year overall cumulative incidence rates for those who died of ILC were 3.1%, 6.2%, and 12.2%, respectively, whereas the rates for those who died from other causes were 3.2%, 5.8%, and 14.1%. Age, marriage, grade, size, regional node positivity, American Joint Committee on Cancer M stage, progesterone receptor, and surgery were independent prognostic factors for dying of ILC, whereas the independent prognostic factors for dying of other causes were age, race, marriage, size, radiation, and chemotherapy. The nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We applied competing-risk analysis to ILC patients based on the SEER database and established nomograms that perform well in predicting the cause-specific death rates at 3, 5, and 10 years after the diagnosis.

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named "stacked ensemble of machine learning models for methylation-correlated blocks" (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

Nomogram predicting survival to assist decision-making of radical prostatectomy in patients with metastatic prostate cancer

Background

Radical prostatectomy (RP) has heterogeneous effects on survival of patients with metastatic prostate cancer (mPCa). A reliable model to predict risk of cancer-specific mortality (CSM) and the potential benefit derived from RP is needed.

Methods

Patients diagnosed with mPCa were identified using the Surveillance, Epidemiology, and End Results database (2004–2015) and categorized in RP versus nonlocal treatment (NLT). Based on the Fine and Gray competing risks model in 8,463 NLT patients, a nomogram was created to predict CSM in mPCa patients. Decision tree analysis was then utilized for patient stratification. The effect of RP was evaluated among 3 different subgroups.

Results

A total of 8,863 patients were identified for analysis. Four hundred (4.5%) patients received RP. The 5-year cumulative incidence of CSM was 52.4% for the entire patients. Based on nomogram scores, patients were sorted into three risk groups using decision tree analysis. In the low- and intermediate-risk group, RP was found to be significantly correlated with a 21.7% risk reduction of 5-year CSM, and 25.0% risk reduction of 5-year CSM, respectively, whereas RP was not associated with CSM in high-risk group (hazard ratio =0.748, 95% confidence interval 0.485–1.150; P=0.190).

Conclusions

We developed a novel nomogram and corresponding patient stratification predicting CSM in mPCa patients. A newly identified patient subgroup with low-, and intermediate-risk of CSM might benefit more from RP. These results should be further validated and improved by ongoing prospective trials.

Competing Risks of Cancer and Non-Cancer Mortality When Accompanied by Lifestyle-Related Factors-A Prospective Cohort Study in Middle-Aged and Older Adults

Background

The study aimed to identify the association between the lifestyle-related factors and the cancer-specific, or non-cancer-specific mortality, when accompanied by a competing risk. Two statistical methods were applied, i.e., cause-specific hazard (CSH), and sub-distribution hazard ratio (SHR). Their respective key advantages, relative to the actual study design, were addressed, as was overall application potential.

Methods

Source data from 4,584 residents (34.2% men), aged 45–64 years, were processed using two different families of regression models, i.e., CSH and SHR; principal focus upon the impact of lifestyle-related factors on the competing risk of cancer and non-cancer mortality. The results were presented as hazard ratios (HR) with 95% confidence intervals (95% CI).

Results

Age, smoking status, and family history of cancer were found the leading risk factors for cancer death; the risk of non-cancer death higher in the elderly, and smoking individuals. Non-cancer mortality was strongly associated with obesity and hypertension. Moderate to vigorous physical activity decreased the risk of death caused by cancer and non-cancer causes.

Conclusions

Specific, lifestyle-related factors, instrumental in increasing overall, and cancer-specific mortality, are modifiable through health-promoting, individually pursued physical activities. Regular monitoring of such health-awareness boosting pursuits seems viable in terms of public health policy making.

Development and external validation of nomograms to predict sarcoma-specific death and disease progression after surgical resection of localized high-grade conventional primary central chondrosarcoma and dedifferentiated chondrosarcoma

AIMS

Our aim was to develop and validate nomograms that would predict the cumulative incidence of sarcoma-specific death (CISSD) and disease progression (CIDP) in patients with localized high-grade primary central and dedifferentiated chondrosarcoma.

METHODS

The study population consisted of 391 patients from two international sarcoma centres (development cohort) who had undergone definitive surgery for a localized high-grade (histological grade II or III) conventional primary central chondrosarcoma or dedifferentiated chondrosarcoma. Disease progression captured the first event of either metastasis or local recurrence. An independent cohort of 221 patients from three additional hospitals was used for external validation. Two nomograms were internally and externally validated for discrimination (c-index) and calibration plot.

RESULTS

In the development cohort, the CISSD at ten years was 32.9% (95% confidence interval (CI) 19.8% to 38.4%). Age at diagnosis, grade, and surgical margin were found to have significant effects on CISSD and CIDP in multivariate analyses. Maximum tumour diameter was also significantly associated with CISSD. In the development cohort, the c-indices for CISSD and CIDP at five years were 0.743 (95% CI 0.700 to 0.819) and 0.761 (95% CI 0.713 to 0.800), respectively. When applied to the validation cohort, the c-indices for CISSD and CIDP at five years were 0.839 (95% CI 0.763 to 0.916) and 0.749 (95% CI 0.672 to 0.825), respectively. The calibration plots for these two nomograms demonstrated good fit.

CONCLUSION

Our nomograms performed well on internal and external validation and can be used to predict CISSD and CIDP after resection of localized high-grade conventional primary central and dedifferentiated chondrosarcomas. They provide a new tool with which clinicians can assess and advise individual patients about their prognosis. Cite this article: Bone Joint J 2020;102-B(12):1752-1759.

DNA methylation-based biomarkers of age acceleration and all-cause death, myocardial infarction, stroke, and cancer in two cohorts: The NAS, and KORA F4

Background

DNA methylation (DNAm) may play a role in age-related outcomes. It is not yet known which DNAm-based biomarkers of age acceleration (BoAA) has the strongest association with age-related endpoints.

Methods

We collected the blood samples from two independent cohorts: the Normative Ageing Study, and the Cooperative Health Research in the Region of Augsburg cohort. We measured epigenome-wide DNAm level, and generated five DNAm BoAA at baseline. We used Cox proportional hazards model to analyze the relationships between BoAA and all-cause death. We applied the Fine and Gray competing risk model to estimate the risk of BoAA on myocardial infarction (MI), stroke, and cancer, accounting for death of other reasons as the competing risks. We used random-effects meta-analyses to pool the individual results, with adjustment for multiple testing.

Findings

The mean chronological ages in the two cohorts were 74, and 61, respectively. Baseline GrimAgeAccel, and DNAm-related mortality risk score (DNAmRS) both had strong associations with all-cause death, MI, and stroke, independent from chronological age. For example, a one standard deviation (SD) increment in GrimAgeAccel was significantly associated with increased risk of all-cause death [hazard ratio (HR): 2.01; 95% confidence interval (CI), 1.15, 3.50], higher risk of MI (HR: 1.44; 95% CI, 1.16, 1.79), and elevated risk of stroke (HR: 1.42; 95% CI, 1.06, 1.91). There were no associations between any BoAA and cancer.

Interpretation

From the public health perspective, GrimAgeAccel is the most useful tool for identifying at-risk elderly, and evaluating the efficacy of anti-aging interventions.

Funding

National Institute of Environmental Health Sciences of U.S., Harvard Chan-NIEHS Center for Environmental Health, German Federal Ministry of Education and Research, and the State of Bavaria in Germany.

A risk stratification model for predicting brain metastasis and brain screening benefit in patients with metastatic triple-negative breast cancer

BACKGROUND

Patients with metastatic triple-negative breast cancer (mTNBC) frequently experience brain metastasis. This study aimed to identify prognostic factors and construct a nomogram for predicting brain metastasis possibility and brain screening benefit in mTNBC patients.

METHODS

Patients with mTNBC treated at our institution between January 2011 and December 2018 were retrospectively analyzed. Fine and Gray's competing risks model was used to identify independent prognostic factors. By integrating these prognostic factors, a competing risk nomogram and risk stratification model were developed and evaluated with concordance index (C-index) and calibration curves.

RESULTS

A total of 472 patients were retrospectively analyzed, including 305 patients in the training set, 78 patients in the validation set I and 89 patients in the validation set II. Four clinicopathological factors were identified as independent prognostic factors in the nomogram: lung metastasis, number of metastatic organ sites, hilar/mediastinal lymph node metastasis and KI-67 index. The C-indexes and calibration plots showed that the nomogram exhibited a sufficient level of discrimination. A risk stratification was further generated to divide all the patients into three prognostic groups. The cumulative incidence of brain metastasis at 18 months was 5.3% (95% confidence interval [CI], 2.5%-9.7%) for patients in the low-risk group, while 14.3% (95% CI, 9.3%-20.4%) for patients with intermediate risk and 34.3% (95% CI, 26.8%-41.9%) for patients with high risk. Routine brain MRI screening improved overall survival in high-risk group (HR 0.67, 95% CI 0.46-0.98, P = .039), but not in low-risk group (HR 0.93, 95% CI 0.57-1.49, P = .751) and intermediate-risk group (HR 0.83, 95% CI 0.55-1.27, P = .386).

CONCLUSIONS

We have developed a robust tool that is able to predict subsequent brain metastasis in mTNBC patients. Our model will allow selection of patients at high risk for brain metastasis who might benefit from routine bran MRI screening.

Comparative effectiveness study of chemotherapy in follicular lymphoma patients in the rituximab era: a Japanese claims database study

Aim: To evaluate comparative effectiveness of rituximub (R)-based versus non-R-based therapies for follicular lymphoma patients in Japan, where limited studies have been reported. Materials & methods: Patients who received R-based index regimens were propensity score matched to those who did not receive R, based on patient baseline attributes and clinical characteristics using Japanese retrospective claims database to assess clinical and economic outcomes. Results & conclusion: A total of 1947 patients remained in the overall follicular lymphoma cohorts: 1294 receiving an R-based and 653 a non-R-based regimen. Patients on R-based therapy underwent fewer hospitalizations and had a shorter length of stay, but had higher costs during the first year of intensive R-based therapy. Improved clinical outcomes were associated with patients who were younger, female and chose R-based regimens in first index line.

Evaluating overall survival and competing risks of survival in patients with early-stage breast cancer using a comprehensive nomogram

BACKGROUND

Patients with early-stage breast cancer (BC) live long but have competing comorbidities. This study aimed to estimate the effect of cancer and other causes of death in patients with early-stage BC and further quantify the survival differences.

MATERIALS AND METHODS

Data of patients diagnosed with BC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results database. The cumulative incidence function for breast cancer-specific mortality (BCSM) and other cause-specific mortality (OCSM) was estimated, and the differences were tested using the Gray test. The nomogram for estimating 3-, 4-, and 5-year overall survival (OS), breast cancer-specific survival, and other cause-specific survival was established based on Cox regression analysis and Fine and Gray competing risk analysis. The discriminative ability, calibration, and precision of the nomogram were evaluated and compared using C statistics, calibration plots, and area under the receiver operating characteristic curve.

RESULTS

A total of 196 304 eligible patients with early-stage BC were identified in this study. Of these, 12 417 (6.3%) patients died: 5628 (45.3%) due to BC and 6789 (54.7%) due to other causes. Five validated variables were incorporated to develop the prognostic nomogram: age, grade, tumor size, subtype, and surgery of primary site (Figure 3). Age was a strong predictive factor, which was more obvious in OCSM. The effect of surgery was more prominent in BCSM. Increased tumor size was correlated with OS and BCSM and slightly correlated with OCSM. Grade and subtype differences were more predominant in BCSM than in OCSM. The established nomogram was well calibrated and displayed good discrimination.

CONCLUSIONS

We evaluate OS and competing risks of death in patients with early-stage BC, establishing the first comprehensive prognostic nomogram.

Competing risk nomogram to predict cancer-specific survival in esophageal cancer during the intensity-modulated radiation therapy era: A single institute analysis

The present study aimed to investigate the probability of cancer-associated mortality of patients with esophageal cancer undergoing intensity-modulated radiation therapy (IMRT), and to establish a competing risk nomogram to predict the esophageal cancer-specific survival (EC-SS) of these patients. A total of 213 patients with EC who underwent IMRT between January 2014 and May 2017 were selected to establish nomograms according to Fine and Gray's competing risk analysis. Predictive accuracy and discriminative ability of the model were determined using the concordance index (C-index), calibration curves and the area under receiver operating characteristic curves. Decision tree analysis was also constructed for patient grouping. With a median follow-up of 19 months (range, 3–50), the 2-year EC-specific mortality (EC-SM) and the non-esophageal cancer specific mortality (NEC-SM) of the cohort were 35.4 and 3.51%, respectively. Furthermore, an elevated 2-year EC-SM was observed in patients with tumor length ≥4.5 cm compared with patients with tumor length <4.5 cm (45.8% vs. 21.4%; P<0.001), patients with non-squamous cell carcinoma compared with patients with squamous cell carcinoma (49.9 vs. 33.7%; P=0.025) and patients with N3 stage (43.2%; P=0.005). The 2-year NEC-SM of patients with tumor length ≥4.5 cm was 6% vs. 0% in patients with tumor length <4.5 cm (P=0.016). Three independent risk factors for survival, including tumor length, histological type and N stage, were integrated to build competing nomograms for the EC-SS model (C-index=0.72; 95% confidence interval, 0.66–0.77). In addition, the nomograms displayed better discrimination power than the 7th edition of the Tumor-Node-Metastasis staging system for predicting EC-SS (area under the curve=0.707 vs. 0.634). Furthermore, the results from the classification tree analysis demonstrated that N stage was the initial node and that primary tumor length was a determinant for EC-SM in these patients. In conclusion, NEC-SM represented a competing event for patients with EC with a tumor length ≥4.5 cm. The competing risk nomograms may therefore be considered as convenient individualized predictive tools for cancer-specific survival in patients with EC undergoing IMRT treatment.

A competing-risk nomogram to predict cause-specific death in elderly patients with colorectal cancer after surgery (especially for colon cancer)

Background

Clinically, when the diagnosis of colorectal cancer is clear, patients are more concerned about their own prognosis survival. Special population with high risk of accidental death, such as elderly patients, is more likely to die due to causes other than tumors. The main purpose of this study is to construct a prediction model of cause-specific death (CSD) in elderly patients using competing-risk approach, so as to help clinicians to predict the probability of CSD in elderly patients with colorectal cancer.

Methods

The data were extracted from Surveillance, Epidemiology, and End Results (SEER) database to include ≥ 65-year-old patients with colorectal cancer who had undergone surgical treatment from 2010 to 2016. Using competing-risk methodology, the cumulative incidence function (CIF) of CSD was calculated to select the predictors among 13 variables, and the selected variables were subsequently refined and used for the construction of the proportional subdistribution hazard model. The model was presented in the form of nomogram, and the performance of nomogram was bootstrap validated internally and externally using the concordance index (C-index).

Results

Dataset of 19,789 patients who met the inclusion criteria were eventually selected for analysis. The five-year cumulative incidence of CSD was 31.405% (95% confidence interval [CI] 31.402–31.408%). The identified clinically relevant variables in nomogram included marital status, pathological grade, AJCC TNM stage, CEA, perineural invasion, and chemotherapy. The nomogram was shown to have good discrimination after internal validation with a C-index of 0.801 (95% CI 0.795–0.807) as well as external validation with a C-index of 0.759 (95% CI 0.716–0.802). Both the internal and external validation calibration curve indicated good concordance between the predicted and actual outcomes.

Conclusion

Using the large sample database and competing-risk analysis, a postoperative prediction model for elderly patients with colorectal cancer was established with satisfactory accuracy. The individualized estimates of CSD outcome for the elderly patients were realized.

Prediction and clinical utility of a contralateral breast cancer risk model

BACKGROUND

Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.

METHODS

We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.

RESULTS

In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

CONCLUSIONS

We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Survival of patients with resected primary colorectal mucinous adenocarcinoma: A competing risk nomogram analysis

The aim of the present study was to use a competing risk model to analyze the prognostic value of mucinous adenocarcinoma (MAC) in patients with colorectal cancer (CRC). An additional aim was to construct nomograms for estimating the 3- and 5-year overall survival (OS) and cancer specific survival (CSS) rates of patients with primary CRC with MAC. The data were extracted from the Surveillance, Epidemiology, and End Results database, and a Multivariate Cox model and competing risk model were applied to assess the OS and CSS. Cox-based and competing risk-based nomograms were constructed and internally validated by discrimination and calibration, using the bootstrapping method with 1,000 times replicates. A total of 13,035 MAC and 61,958 non-mucinous adenocarcinoma (NMAC) CRC patients were enrolled in the present study. Compared with NMAC, MAC patients had a poorer OS and CSS time in the overall population, and in subgroups that comprised metastatic, non-metastatic, male, site of sigmoid colon, rectosigmoid junction and rectal CRC cases (HR>1; P<0.05). The Cox and competing risk-based nomograms showed effective discrimination and calibration. In conclusion, MAC was associated with poor OS and CSS in patients with CRC of the distal colon and rectum. The nomograms of primary CRC patients with MAC may aid the identification of individual patients with a high risk of overall mortality and cancer-associated mortality within 3 or 5 years.

Nomograms for predicting overall survival and cancer-specific survival in patients with surgically resected intrahepatic cholangiocarcinoma

Purpose

To develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with surgically resected intrahepatic cholangiocarcinoma (ICC).

Patients and methods

The nomograms were developed using a development cohort of 947 ICC patients after surgery selected from Surveillance, Epidemiology, and End Results database, and externally validated using a training cohort of 159 patients admitted at our institution. Nomograms for OS and CSS were established based on the independent prognostic factors identified by COX regression models and Fine and Grey’s models, respectively. The performance of the nomograms was validated internally and externally by using the concordance index (c-index), and calibration plot, and compared with that of AJCC 8th edition TNM staging system by using c-index and decision curve analysis.

Results

Age, T stage, M stage, lymph node ratio (LNR) level and tumor grade were independent prognostic predictors for OS in ICC patients, while T stage, M stage, LNR level and tumor grade were independent prognostic predictors for CSS. Nomogram predicting OS was with a c-index of 0.751 on internal validation and 0.725 up to external validation, while nomogram for CSS was with a c-index of 0.736 on internal validation and 0.718 up to external validation. Calibration plots exhibited that the nomograms-predicted and actual OS/CSS probabilities were fitted well on both internal and external validation. Additionally, the nomograms exhibited superiority over AJCC 8th edition TNM staging system with higher c-indices and net benefit gains, in predicting OS and CSS in ICC patients after surgery.

Conclusion

The constructed nomograms could predict OS and CSS with good performance, which could be served as an effective tool for prognostic evaluation and individual treatment strategies optimization in ICC patients after surgery in clinical practice.

Competing risk nomogram predicting initial loco-regional recurrence in gastric cancer patients after D2 gastrectomy

Background

Lacking quantitative evaluations of clinicopathological features and the risk factors for loco-regional recurrence (LRR) in gastric cancer after D2 gastrectomy, we aimed to develop a competing risk nomogram to identify the risk predictors for initial LRR.

Methods

We retrospectively analysed 1105 patients who underwent radical gastrectomy with D2 resection for stage I-III gastric cancer. A nomogram predicting initial LRR of gastric cancer was conducted based on Fine and Grey’s competing risk analysis. The predictive accuracy and discriminative ability of the model were determined using the concordance index (C-index) and calibration curve. Decision tree analysis was performed for patient grouping.

Results

At a median follow-up of 28.4 months, 274 patients developed 373 first recurrence events (local, regional, and distant disease). The median recurrence-free survival (RFS) was 16.7 months. Multivariate competing risk analysis showed that age (SHR, 1.72; 95% CI, 1.10–2.83, p = 0.031), CEA (SHR, 1.94; 95% CI, 1.09–3.46, p = 0.024), pT4 (SHR, 2.77; 95% CI, 1.01–7.57, p = 0.047), lymph node metastasis (SHR 1.92, 95% CI: 1.09–3.38, p = 0.024) and LVI (SHR, 1.84; 95% CI, 1.06–3.20, p = 0.028) were independent risk factors for LRR (all p < 0.05). The nomogram incorporating these factors achieved good agreement between prediction and actual observation with a concordance index of 0.738 (95% CI, 0.767 to 0.709). In a subgroup analysis of node-positive patients, pN3b was associated with increased peritoneal and distant metastasis (p = 0.048). The para-aortic lymph nodes were the most frequent sites (n = 71) of LRR, and among them, the 16a2 and 16b1 nodes exhibited even more prevalence (90.1 and 81.7%).

Conclusions

Adjuvant radiotherapy might be recommended in gastric cancer patients ≥65 years old or those with pN+, pT4, LVI, or increased CEA levels, particularly in high-risk or pN1-3a patients. The competing risk nomograms may be considered as convenient and individualized predictive tools for LRR in gastric cancer after D2 gastrectomy. It is also recommended that the clinical target volume (CTV) include 16a2 and 16b1 regions of para-aortic lymph nodes.

Transfers to Hemodialysis Among US Patients Initiating Renal Replacement Therapy With Peritoneal Dialysis

RATIONALE & OBJECTIVE

Identifying patients who are likely to transfer from peritoneal dialysis (PD) to hemodialysis (HD) before transition could improve their subsequent care. This study developed a prediction tool for transition from PD to HD.

STUDY DESIGN

Retrospective cohort study.

SETTING & PARTICIPANTS

Adults initiating PD between January 2008 and December 2011, followed up through June 2015, for whom data were available in the US Renal Data System (USRDS).

PREDICTORS

Clinical characteristics at PD initiation and peritonitis claims.

OUTCOMES

Transfer to HD, with the competing outcomes of death and kidney transplantation.

ANALYTICAL APPROACH

Outcomes were ascertained from USRDS treatment history files. Subdistribution hazards (competing-risk) models were fit using clinical characteristics at PD initiation. A nomogram was developed to classify patient risk at 1, 2, 3, and 4 years. These data were used to generate quartiles of HD transfer risk; this quartile score was incorporated into a cause-specific hazards model that additionally included a time-dependent variable for peritonitis.

RESULTS

29,573 incident PD patients were followed up for a median of 21.6 (interquartile range, 9.0-42.3) months, during which 41.2% transferred to HD, 25.9% died, 17.1% underwent kidney transplantation, and the rest were followed up to the study end in June 2015. Claims for peritonitis were present in 11,733 (40.2%) patients. The proportion of patients still receiving PD decreased to <50% at 22.6 months and 14.2% at 5 years. Peritonitis was associated with a higher rate of HD transfer (HR, 1.82; 95% CI, 1.76-1.89; P < 0.001), as were higher quartile scores of HD transfer risk (HRs of 1.31 [95% CI, 1.25-1.37), 1.51 [95% CI, 1.45-1.58], and 1.78 [95% CI, 1.71-1.86] for quartiles 2, 3, and 4 compared to quartile 1 [P < 0.001 for all]).

LIMITATIONS

Observational data, reliant on the Medical Evidence Report and Medicare claims.

CONCLUSIONS

A large majority of the patients who initiated renal replacement therapy with PD discontinued this modality within 5 years. Transfer to HD was the most common outcome. Patient characteristics and comorbid diseases influenced the probability of HD transfer, death, and transplantation, as did episodes of peritonitis.

A Competing-Risks Nomogram in Patients with Metastatic Pancreatic Duct Adenocarcinoma

BACKGROUND The primary objective of this study was to assess the cumulative incidence of cause-specific mortality (CSM) and other causes of mortality (OCM) for patients with metastatic pancreatic duct adenocarcinoma (mPDAC). The secondary objective was to calculate the probability of CSM and build a competing risk nomogram to predict CSM for mPDAC. MATERIAL AND METHODS We identified patients with mPDAC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. We assessed the cumulative incidence function (CIF) for cause-specific mortality and other causes of mortality. We used Gray's test to investigate the differences. The Fine and Gray proportional subdistribution hazard model was applied to model CIF. And a competing risk nomogram was built to predict the probability of CSM for mPDAC. RESULTS There were 10 527 eligible patients diagnosed with mPDAC from 2010 to 2015 who were included in our formal analysis. The 6-month cumulative incidence of CSM was 60.3% and 5.9% for other causes. Predictors of SCM for mPDAC included surgery, age, tumor size, chemotherapy, radiation therapy, bone metastasis, and liver metastasis. The nomogram was proven to be well calibrated, and had good model discriminative ability. CONCLUSIONS We assessed the CIF of CSM and competing risk mortality in patients with mPDAC using the SEER database. The Fine and Gray proportional subdistribution hazard model performance was good, with a concordance index of 0.74, and the competing-risks nomogram was built, which can be a helpful predictive tool for cases with mPDAC. However, a validation sample data set and further verification are still needed to assess a profile for prognostic use in a prospective study.

Nomogram Predicting Cause-Specific Mortality in Nonmetastatic Male Breast Cancer: A Competing Risk Analysis

Introduction: Male breast cancer (MBC) is a rare tumor with few cases for research. Using the Surveillance, Epidemiology, and End Results program database, we carried out a competing risk analysis in patients with primary nonmetastatic MBC and built a predictive nomogram. Materials and Methods: We extracted primary nonmetastatic MBC patients according to the inclusion and exclusion criteria. Cumulative incidence function (CIF) and proportional subdistribution hazard model were adopted to explore risk factors for breast cancer-specific death (BCSD) and other cause-specific death (OCSD). Then we built a nomogram to predict the 3-year, 5-year and 8-year probabilities of BCSD and OCSD. C-indexes, Brier scores and calibration curves were chosen for validation. Results: We identified 1,978 nonmetastatic MBC patients finally. CIF analysis showed that the 3-year, 5-year and 8-year mortalities were 5.2%, 10.6% and 16.5% for BCSD, and 6.1%, 9.6% and 14.4% for OCSD. After adjustment of Fine and Gray models, black race, PR (-), advanced T/N/grade and no surgery were independently associated with BCSD. Meanwhile, elderly, unmarried status, advanced AJCC stage and no chemotherapy resulted in OCSD more possibly. A graphic nomogram was developed according to the coefficients from the Fine and Gray models. The calibration curves displayed exceptionally, with C-indexes nearly larger than 0.700 and Brier scores nearly smaller than 0.100. Conclusion: The competing risk nomogram showed good accuracy for predictive prognosis in nonmetastatic MBC patients. It was a useful implement to evaluate crude mortalities of BCSD and OCSD, and help clinicians to choose appropriate therapeutic plans.