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 Log Odds of Positive Lymph Node- (LODDS-) Based Competing-Risk Nomogram for Predicting Prognosis of Resected Rectal Cancer: A Development and Validation Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Rui-zhe Zheng ◽  
Jiang Xie ◽  
Shui-qiang Zhang ◽  
Wen Li ◽  
Bo Dong ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Death Rate ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
Tnm Stage ◽  
Competing Risk ◽  
Brier Score ◽  
Log Odds

Background and Aims. Cancer-specific survival (CSS) of rectal cancer (RC) is associated with several factors. We aimed to build an efficient competing-risk nomogram based on log odds of positive lymph nodes (LODDS) to predict RC survival. Methods. Medical records of 8754 patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database, of 4895 patients from SEER during 2011–2014 and of 478 patients from an Eastern center as a development cohort, validation cohort, and test cohort, respectively. Univariate and multivariate competing-risk analyses were performed to build competing-risk nomogram for predicting the CSS of RC patients. Prediction efficacy was evaluated and compared with reference to the 8th TNM classification using the factor areas under the receiver operating characteristic curve (AUC) and Brier score. Results. The competing-risk nomogram was based on 6 variables: size, M stage, LODDS, T stage, grade, and age. The competing-risk nomogram showed a higher AUC value in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.81 vs. 0.76), validation cohort (0.85 vs. 0.82), and test cohort (0.71 vs. 0.66). The competing-risk nomogram also showed a higher Brier score in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.120 vs. 0.127), validation cohort (0.123 vs. 0.128), and test cohort (0.202 vs. 0.226). Conclusion. We developed and validated a competing-risk nomogram for RC death, which could provide the probability of survival averting competing risk to facilitate clinical decision-making.

scholarly journals A Novel Autophagy-Related Prognostic Risk Model and a Nomogram for Survival Prediction of Oral Cancer Patients

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Hongjun Fei ◽  
Xiongming Chen
Keyword(s):  
Survival Data ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Interaction Network ◽  
Receiver Operator Characteristic Curve ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Sequencing Data

Background. This study is aimed at constructing a risk signature to predict survival outcomes of ORCA patients. Methods. We identified differentially expressed autophagy-related genes (DEARGs) based on the RNA sequencing data in the TCGA database; then, four independent survival-related ARGs were identified to construct an autophagy-associated signature for survival prediction of ORCA patients. The validity and robustness of the prognostic model were validated by clinicopathological data and survival data. Subsequently, four independent prognostic DEARGs that composed the model were evaluated individually. Results. The expressions of 232 autophagy-related genes (ARGs) in 127 ORCA and 13 control tissues were compared, and 36 DEARGs were filtered out. We performed functional enrichment analysis and constructed protein–protein interaction network for 36 DEARGs. Univariate and multivariate Cox regression analyses were adopted for searching prognostic ARGs, and an autophagy-associated signature for ORCA patients was constructed. Eventually, 4 desirable independent survival-related ARGs (WDR45, MAPK9, VEGFA, and ATIC) were confirmed and comprised the prognostic model. We made use of multiple ways to verify the accuracy of the novel autophagy-related signature for survival evaluation, such as receiver-operator characteristic curve, Kaplan–Meier plotter, and clinicopathological correlational analyses. Four independent prognostic DEARGs that formed the model were also associated with the prognosis of ORCA patients. Conclusions. The autophagy-related risk model can evaluate OS for ORCA patients independently since it is accurate and stable. Four prognostic ARGs that composed the model can be studied deeply for target treatment.

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 Hospital admission risk stratification of patients with gout presenting to the emergency department

2022 ◽  
Author(s):  
Wang Han ◽  
Nur Azizah Allameen ◽  
Irwani Ibrahim ◽  
Preeti Dhanasekaran ◽  
Feng Mengling ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prognostic Model ◽  
Hospital Admissions ◽  
Brier Score ◽  
Calibration Plot ◽  
Acute Gout ◽  
Web Based ◽  
Derivation Cohort ◽  
Risk Estimator ◽  
Gout Flare

Abstract To characterise gout patients at high risk of hospitalisation and to develop a web-based prognostic model to predict the likelihood of gout-related hospital admissions. This was a retrospective single-centre study of 1417 patients presenting to the emergency department (ED) with a gout flare between 2015 and 2017 with a 1-year look-back period. The dataset was randomly divided, with 80% forming the derivation and the remaining forming the validation cohort. A multivariable logistic regression model was used to determine the likelihood of hospitalisation from a gout flare in the derivation cohort. The coefficients for the variables with statistically significant adjusted odds ratios were used for the development of a web-based hospitalisation risk estimator. The performance of this risk estimator model was assessed via the area under the receiver operating characteristic curve (AUROC), calibration plot, and brier score. Patients who were hospitalised with gout tended to be older, less likely male, more likely to have had a previous hospital stay with an inpatient primary diagnosis of gout, or a previous ED visit for gout, less likely to have been prescribed standby acute gout therapy, and had a significant burden of comorbidities. In the multivariable-adjusted analyses, previous hospitalisation for gout was associated with the highest odds of gout-related admission. Early identification of patients with a high likelihood of gout-related hospitalisation using our web-based validated risk estimator model may assist to target resources to the highest risk individuals, reducing the frequency of gout-related admissions and improving the overall health-related quality of life in the long term. Key points • We reported the characteristics of gout patients visiting a tertiary hospital in Singapore. • We developed a web-based prognostic model with non-invasive variables to predict the likelihood of gout-relatedhospital admissions.

scholarly journals Novel Gene Signatures Predictive of Patient Recurrence-Free Survival and Castration Resistance in Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 917
Author(s):  
Jun A ◽  
Baotong Zhang ◽  
Zhiqian Zhang ◽  
Hailiang Hu ◽  
Jin-Tang Dong
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Treatment Decision ◽  
Rank Aggregation ◽  
Lymph Node Status ◽  
Calibration Plot ◽  
Recurrence Free Survival ◽  
Castration Resistance ◽  
Free Survival

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa. This signature included NPEPL1, VWF, LMO7, ALDH2, NUAK1, and TPT1, and was named CRPC-derived prognosis signature (CRPCPS). Interestingly, three of these 6 genes constituted another signature capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and remained valid in patients stratified by tumor stage, Gleason score, and lymph node status. The signature also predicted overall survival and metastasis-free survival. The signature’s robustness was demonstrated by the C-index (0.55–0.74) and the calibration plot in all nine cohorts and the 3-, 5-, and 8-year area under the receiver operating characteristic curve (0.67–0.77) in three cohorts. The nomogram analyses demonstrated CRPCPS’ clinical applicability. The CRPCPS thus appears useful for RFS prediction in PCa.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

scholarly journals Prognostic nomogram for the severity of acute organophosphate insecticide self-poisoning: a retrospective observational cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e042765
Author(s):  
Ning Dong ◽  
Shaokun Wang ◽  
Xingliang Li ◽  
Wei Li ◽  
Nan Gao ◽  
...  
Keyword(s):  
New York ◽  
Characteristic Curve ◽  
Oral Intake ◽  
Heart Association ◽  
Northern China ◽  
Primary Outcome Measure ◽  
Plasma Cholinesterase ◽  
Hospital Death ◽  
Calibration Plot ◽  
Organophosphate Insecticide

ObjectiveTo develop a convenient nomogram for the bedside evaluation of patients with acute organophosphorus poisoning (AOPP).DesignThis was a retrospective study.SettingTwo independent hospitals in northern China, the First Hospital of Jilin University and the Lequn Hospital of the First Hospital of Jilin University.ParticipantsA total of 1657 consecutive patients admitted for the deliberate oral intake of AOPP within 24 hours from exposure and aged >18 years were enrolled between 1 January 2013 and 31 December 2018. The exclusion criteria were: normal range of plasma cholinesterase, exposure to any other type of poisonous drug(s), severe chronic comorbidities including symptomatic heart failure (New York Heart Association III or IV) or any other kidney, liver and pulmonary diseases. Eight hundred and thirty-four patients were included.Primary outcome measureThe existence of severely poisoned cases, defined as patients with any of the following complications: cardiac arrest, respiratory failure requiring ventilator support, hypotension or in-hospital death.Results440 patients from one hospital were included in the study to develop a nomogram of severe AOPP, whereas 394 patients from the other hospital were used for the validation. Associated risk factors were identified by multivariate logistic regression. The nomogram was validated by the area under the receiver operating characteristic curve (AUC). A nomogram was developed with age, white cells, albumin, cholinesterase, blood pH and lactic acid levels. The AUC was 0.875 (95% CI 0.837 to 0.913) and 0.855 (95% CI 0.81 to 0.9) in the derivation and validation cohorts, respectively. The calibration plot for the probability of severe AOPP showed an optimal agreement between the prediction by nomogram and actual observation in both derivation and validation cohorts.ConclusionA convenient severity evaluation nomogram for patients with AOPP was developed, which could be used by physicians in making clinical decisions and predicting patients’ prognosis.

Development and Validation of a Nomogram for Predicting Bladder Calculi Risk in Patients With Benign Prostatic Hyperplasia

2021 ◽  
Author(s):  
Euxu Xie ◽  
Xuelian Gu ◽  
Chen Ma ◽  
Li Guo ◽  
Man Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Characteristic Curve ◽  
Prostatic Hyperplasia ◽  
Calibration Plot ◽  
Lasso Regression ◽  
Net Benefit ◽  
Bladder Calculi ◽  
Validation Set ◽  
Selection Operator

Abstract Objective To develop and validate a nomogram for predicting bladder calculi risk in patients with benign prostatic hyperplasia (BPH).Methods A total of 368 patients who underwent transurethral resection of the prostate (TURP) and had histologically proven BPH from January 2018 to January 2021 were retrospectively collected. Eligible patients were randomly assigned to the training and validation datasets. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal risk factors. A prediction model was established based on the selected characteristics. The performance of the nomogram was assessed by calibration plots and the area under the receiver operating characteristic curve (AUROC). Furthermore, decision curve analysis (DCA) was used to determine the net benefit rate of of the nomogram. Results Among 368 patients who met the inclusion criteria, older age, a history of diabetes and hyperuricemia, longer intravesical prostatic protrusion (IPP)and larger prostatic urethral angulation (PUA) were independent risk factors for bladder calculi in patients with BPH. These factors were used to develop a nomogram, which had a good identification ability in predicting the risk of bladder calculi in patients, with AUROCs of 0.911 (95% CI: 0.876–0.945) in the training set and 0.884 (95% CI: 0.820–0.948) in the validation set. The calibration plot showed that the model had good calibration. Moreover, DCA indicated that the model had a goodclinical benefit. Conclusion We developed and internally validated the first nomogram to date to help physicians assess the risk of bladder calculi in patients with BPH, which may help physicians improve individual interventions and make better clinical decisions.

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