scholarly journals Development and validation of a novel competing risk model for predicting survival of esophagogastric junction adenocarcinoma: a SEER population-based study and external validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tongbo Wang ◽  
Yan Wu ◽  
Hong Zhou ◽  
Chaorui Wu ◽  
Xiaojie Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Esophagogastric Junction ◽  
Risk Model ◽  
External Validation ◽  
Tnm Staging ◽  
Competing Risk ◽  
Brier Score ◽  
Cancer Center ◽  
Clinicopathological Feature ◽  
Prediction Ability

Abstract Background Adenocarcinoma in Esophagogastric Junction (AEG) is a severe gastrointestinal malignancy with a unique clinicopathological feature. Hence, we aimed to develop a competing risk nomogram for predicting survival for AEG patients and compared it with new 8th traditional tumor-node-metastasis (TNM) staging system. Methods Based on data from the Surveillance, Epidemiology, and End Results (SEER) database of AEG patients between 2004 and 2010, we used univariate and multivariate analysis to filter clinical factors and then built a competing risk nomogram to predict AEG cause-specific survival. We then measured the clinical accuracy by comparing them to the 8th TNM stage with a Receiver Operating Characteristic (ROC) curve, Brier score, and Decision Curve Analysis (DCA). External validation was performed in 273 patients from China National Cancer Center. Results A total of 1755 patients were included in this study. The nomogram was based on five variables: Number of examined lymph nodes, grade, invasion, metastatic LNs, and age. The results of the nomogram was greater than traditional TNM staging with ROC curve (1-year AUC: 0.747 vs. 0.641, 3-year AUC: 0.761 vs. 0.679, 5-year AUC: 0.759 vs. 0.682, 7-year AUC: 0.749 vs. 0.673, P < 0.001), Brier score (3-year: 0.198 vs. 0.217, P = 0.012; 5-year: 0.198 vs. 0.216, P = 0.008; 7-year: 0.199 vs. 0.215, P = 0.014) and DCA. In external validation, the nomogram also showed better diagnostic value than traditional TNM staging and great prediction accuracy. Conclusion We developed and validated a novel nomogram and risk stratification system integrating clinicopathological characteristics for AEG patients. The model showed superior prediction ability for AEG patients than traditional TNM classification.

scholarly journals Development of A Novel Competing Risk Survival Predicting Model for Esophagogastric Junction Adenocarcinoma: a SEER population-based study

2020 ◽  
Author(s):  
Tongbo Wang ◽  
Yan Wu ◽  
Hong Zhou ◽  
Chaorui Wu ◽  
Xiaojie Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Esophagogastric Junction ◽  
Tnm Classification ◽  
Population Based ◽  
Tnm Staging ◽  
Competing Risk ◽  
Brier Score ◽  
Clinicopathological Feature ◽  
Seer Database ◽  
Esophagogastric Junction Adenocarcinoma

Abstract Background: Adenocarcinoma in Esophagogastric Junction (AEG) is a severe gastrointestinal malignancy with a unique clinicopathological feature. To develop a competing risk nomogram for AEG patients and compared it with new 8th traditional tumor-node-metastasis (TNM) staging system.Methods: Based on AEG patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2010, we used the univariate and multivariate analysis to filter clinical factors and then built the competing risk nomogram to predicting the AEG cause-specific survival. We measured the clinical accuracy by comparing to the 8th TNM stage with receiver operating characteristic (ROC) curve, Brier score, and decision curve analysis (DCA).Results: Total of 1755 patients were included into this study. This nomogram was based on five variables: Number of examined lymph nodes, grade, invasion, metastatic LNs and age. The nomogram model was greater than traditional TNM staging with ROC curve (1-year AUC:0.747 vs. 0.641, 3-year AUC: 0.761 vs. 0.679, 5-year AUC: 0.759 vs. 0.682, 7-year AUC: 0.749 vs. 0.673, P<0.001), Brier score (3-year: 0.198 vs. 0.217, P=0.012; 5-year: 0.198 vs. 0.216, P=0.008; 7-year: 0.199 vs. 0.215, P=0.014) and DCA.Conclusions: Based on the SEER database with AEG patients, the competing risk nomogram showed the greater accurate individualized prediction of the survival compared with traditional TNM classification.

Risk of Conversion to Arthroplasty After Hip Arthroscopy: Validation of a Published Risk Score Using an Independent, Prospectively Collected Database

2021 ◽  
pp. 036354652199382
Author(s):  
Mario Hevesi ◽  
Devin P. Leland ◽  
Philip J. Rosinsky ◽  
Ajay C. Lall ◽  
Benjamin G. Domb ◽  
...  
Keyword(s):  
Risk Score ◽  
Roc Curve ◽  
Hip Arthroscopy ◽  
External Validation ◽  
Area Under The Curve ◽  
Harris Hip Score ◽  
Brier Score ◽  
Level Of Evidence ◽  
Center Edge ◽  
Modified Harris Hip Score

Background: Hip arthroscopy is rapidly advancing and increasingly commonly performed. The most common surgery after arthroscopy is total hip arthroplasty (THA), which unfortunately occurs within 2 years of arthroscopy in up to 10% of patients. Predictive models for conversion to THA, such as that proposed by Redmond et al, have potentially substantial value in perioperative counseling and decreasing early arthroscopy failures; however, these models need to be externally validated to demonstrate broad applicability. Purpose: To utilize an independent, prospectively collected database to externally validate a previously published risk calculator by determining its accuracy in predicting conversion of hip arthroscopy to THA at a minimum 2-year follow-up. Study Design: Cohort study (diagnosis); Level of evidence, 1. Methods: Hip arthroscopies performed at a single center between November 2015 and March 2017 were reviewed. Patients were assessed pre- and intraoperatively for components of the THA risk score studied—namely, age, modified Harris Hip Score, lateral center-edge angle, revision procedure, femoral version, and femoral and acetabular Outerbridge scores—and followed for a minimum of 2 years. Conversion to THA was determined along with the risk score’s receiver operating characteristic (ROC) curve and Brier score calibration characteristics. Results: A total of 187 patients (43 men, 144 women, mean age, 36.0 ± 12.4 years) underwent hip arthroscopy and were followed for a mean of 2.9 ± 0.85 years (range, 2.0-5.5 years), with 13 patients (7%) converting to THA at a mean of 1.6 ± 0.9 years. Patients who converted to THA had a mean predicted arthroplasty risk of 22.6% ± 12.0%, compared with patients who remained arthroplasty-free with a predicted risk of 4.6% ± 5.3% ( P < .01). The Brier score for the calculator was 0.04 ( P = .53), which was not statistically different from ideal calibration, and the calculator demonstrated a satisfactory area under the curve of 0.894 ( P < .001). Conclusion: This external validation study supported our hypothesis in that the THA risk score described by Redmond et al was found to accurately predict which patients undergoing hip arthroscopy were at risk for converting to subsequent arthroplasty, with satisfactory discriminatory, ROC curve, and Brier score calibration characteristics. These findings are important in that they provide surgeons with validated tools to identify the patients at greatest risk for failure after hip arthroscopy and assist in perioperative counseling and decision making.

scholarly journals Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruohui Mo ◽  
Rong Shi ◽  
Yuhong Hu ◽  
Fan Hu
Keyword(s):  
Diabetic Retinopathy ◽  
Regression Analysis ◽  
Roc Curve ◽  
External Validation ◽  
Curve Analysis ◽  
Postprandial Blood Glucose ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Decision Curve Analysis ◽  
Course Of Disease

Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

External validation of a Dutch predictive nomogram for complete response to T-VEC in an independent American patient cohort.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 9563-9563
Author(s):  
Emma H.A. Stahlie ◽  
Michael Carr ◽  
Jonathan S. Zager ◽  
Alexander Christopher Jonathan Van Akkooi
Keyword(s):  
External Validation ◽  
Complete Response ◽  
Stage Iiib ◽  
Validation Dataset ◽  
Brier Score ◽  
Cancer Center ◽  
Discriminative Ability ◽  
Patient Cohort ◽  
American Patient ◽  
Overall Performance

9563 Background: Talimogene Laherparepvec (T-VEC) is a genetically modified herpes simplex type 1 virus and known as an effective oncolytic immunotherapy for injectable cutaneous, subcutaneous, and nodal melanoma lesions in stage IIIB-IVM1a patients. Recently, Stahlie et al. published (Cancer Immunol Immunother '21) a model for predicting a complete response (CR) to T-VEC based on 3 easily accessible tumor characteristics identified using univariable and multivariable logistic regression analysis. The aim of this study was to externally validate this model in an independent, American patient cohort. Methods: A total of 76 patients with stage IIIB-IVM1a melanoma treated with T-VEC at Moffitt Cancer Center were included. A second nomogram was built incorporating the same predictive factors: tumor size (diameter of largest metastasis in mm), type of metastases (cutaneous, subcutaneous and nodal) and number of metastases (cut-off: <20 and >20). Predictive accuracy was assessed through calculation of overall performance, discriminative ability, and calibration. Outcomes and previously published outcomes were compared. Statistical analyses were done using R software. Results: Overall performance of the validation dataset nomogram was calculated with the Brier score and found to be 0.195, demonstrating good overall performance and similar to the original model Brier score of 0.182. Discriminative power, assessed by calculating the area under the receiver operating characteristic (ROC) curve was similar for both models, 0.767 and 0.755 for the NKI and Moffitt, respectively, resulting in a fair discriminative ability. The calibration curve showed mostly slight underestimation for predicated probabilities >0.37 and slight overestimation <0.37. Conclusions: An independent dataset externally validated a recently published predictive nomogram for CR to T-VEC in stage IIIB-IVM1a melanoma, with both models resulting in overall performances that were comparable and good. The second model reinforces the conclusion that for the best response to T-VEC, it should be used early on in the course of the disease, when the patient’s tumor burden is cutaneous with smaller diameter and fewer of metastases.[Table: see text]

scholarly journals How to Predict Cancer-Specific Death of Patients with Chondrosarcoma: A Prognostic Model Based on Competing Risk

2021 ◽  
Author(s):  
Binxiang Zhu ◽  
Yinmin Dong ◽  
Hongyu Zhu ◽  
Zijian Dong ◽  
Feng Li
Keyword(s):  
Prognostic Model ◽  
Risk Model ◽  
Current Model ◽  
Characteristic Curve ◽  
Radical Resection ◽  
Competing Risk ◽  
Brier Score ◽  
Calibration Plot ◽  
Plot Area ◽  
Probability Of Cancer

Abstract Background. As chondrosarcoma is the second highest primary malignant tumor of bone, it is necessary to find a way to predict the prognosis of chondrosarcoma. But the current model rarely involves the study of competing risk. This is a retrospective study with the aim of establishing a prognostic model and a nomogram based on competing risk to predict the probability of cancer-specific death (CSD) at 3 and 5 years. The Fine and Gray regression is a targeted statistical method, which makes the results more authentic and reliable.Methods. A total of 1674 chondrosarcoma patients were identified from the SEER database, and they were divided into training cohort and validation cohort by year of diagnosis. These two cohorts were used to develop and validate the prognostic model to predict the 3-year and 5-year probabilities of CSD, with non-CSD as the competing risk. Model accuracy made use of some verification functions, such as C-index, receiver operating characteristic curve (ROC), calibration plot, area under curve (AUC) and Brier score.Results. According to the outcomes of the model: older age (subdistribution hazards ratio(95%CI): 1.02 (1.01-1.03); P<0.001), dedifferentiated CHS (SHR(95%CI): 2.16 (1.30-3.59); P=0.003), high grade (SHR(95%CI): 2.60 (1.83-3.68); P<0.001), Regional involvement (SHR(95%CI): 3.15 (2.01-4.93); P<0.001), Distant metastasis (SHR(95%CI): 11.56 (6.82-19.59); P<0.001), tumor excision (SHR(95%CI): 0.47 (0.25-0.87); P=0.02) and Radical resection (SHR(95%CI): 0.54 (0.32-0.90); P=0.02) were significantly. They obviously promoted the increase of CSD.Conclusion. This prognostic model considered the competing risks of chondrosarcoma, and the nomogram can effectively predict the probability of CSD in patients with chondrosarcoma, which is suitable for clinical application.

scholarly journals Validation of the BATT score for prehospital risk stratification of traumatic haemorrhagic death: usefulness for tranexamic acid treatment criteria

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Francois-Xavier Ageron ◽  
Timothy J. Coats ◽  
Vincent Darioli ◽  
Ian Roberts
Keyword(s):  
Tranexamic Acid ◽  
Acid Treatment ◽  
Injury Severity ◽  
External Validation ◽  
Research Network ◽  
Brier Score ◽  
Trauma Patients ◽  
Using Data ◽  
The Uk ◽  
The Impact

Abstract Background Tranexamic acid reduces surgical blood loss and reduces deaths from bleeding in trauma patients. Tranexamic acid must be given urgently, preferably by paramedics at the scene of the injury or in the ambulance. We developed a simple score (Bleeding Audit Triage Trauma score) to predict death from bleeding. Methods We conducted an external validation of the BATT score using data from the UK Trauma Audit Research Network (TARN) from 1st January 2017 to 31st December 2018. We evaluated the impact of tranexamic acid treatment thresholds in trauma patients. Results We included 104,862 trauma patients with an injury severity score of 9 or above. Tranexamic acid was administered to 9915 (9%) patients. Of these 5185 (52%) received prehospital tranexamic acid. The BATT score had good accuracy (Brier score = 6%) and good discrimination (C-statistic 0.90; 95% CI 0.89–0.91). Calibration in the large showed no substantial difference between predicted and observed death due to bleeding (1.15% versus 1.16%, P = 0.81). Pre-hospital tranexamic acid treatment of trauma patients with a BATT score of 2 or more would avoid 210 bleeding deaths by treating 61,598 patients instead of avoiding 55 deaths by treating 9915 as currently. Conclusion The BATT score identifies trauma patient at risk of significant haemorrhage. A score of 2 or more would be an appropriate threshold for pre-hospital tranexamic acid treatment.

scholarly journals QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index

2014 ◽  
Vol 79 (8) ◽  
pp. 965-975 ◽  
Author(s):  
Long Jiao ◽  
Xiaofei Wang ◽  
LI. Hua ◽  
Yunxia Wang
Keyword(s):  
Partition Coefficient ◽  
Cross Validation ◽  
External Validation ◽  
Quantitative Relationship ◽  
Structure Property ◽  
Prediction Ability ◽  
Vector Index ◽  
Artificial Neural Network Ann ◽  
Leave One Out ◽  
Edge Vector

The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

scholarly journals Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
David Gikungu ◽  
Jacob Wakhungu ◽  
Donald Siamba ◽  
Edward Neyole ◽  
Richard Muita ◽  
...  
Keyword(s):  
Rift Valley ◽  
Rift Valley Fever ◽  
Risk Model ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Weather Data ◽  
Brier Score ◽  
Valley Fever ◽  
Dynamic Risk ◽  
Normalised Difference Vegetation Index

Rift Valley fever (RVF) is a mosquito-borne viral zoonotic disease that occurs throughout sub-Saharan Africa, Egypt and the Arabian Peninsula, with heavy impact in affected countries. Outbreaks are episodic and related to climate variability, especially rainfall and flooding. Despite great strides towards better prediction of RVF epidemics, there is still no observed climate data-based warning system with sufficient lead time for appropriate response and mitigation. We present a dynamic risk model based on historical RVF outbreaks and observed meteorological data. The model uses 30-year data on rainfall, temperature, relative humidity, normalised difference vegetation index and sea surface temperature data as predictors. Our research on RVF focused on Garissa, Murang’a and Kwale counties in Kenya using a research design based on a correlational, experimental, and evaluational approach. The weather data were obtained from the Kenya Meteorological Department while the RVF data were acquired from International Livestock Research Institute, and the Department of Veterinary Services. Performance of the model was evaluated by using the first 70% of the data for calibration and the remaining 30% for validation. The assessed components of the model accurately predicted already observed RVF events. The Brier score for each of the models (ranging from 0.007 to 0.022) indicated high skill. The coefficient of determination (R2) was higher in Garissa (0.66) than in Murang’a (0.21) and Kwale (0.16). The discrepancy was attributed to data distribution differences and varying ecosystems. The model outputs should complement existing early warning systems to detect risk factors that predispose for RVF outbreaks.

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