scholarly journals Development and validation of a preoperative nomogram for predicting patients with impacted ureteral stone: a retrospective analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chenglu Wang ◽  
Lu Jin ◽  
Xinyang Zhao ◽  
Boxin Xue ◽  
Min Zheng
Keyword(s):  
Calibration Curve ◽  
Ureteral Stone ◽  
Multivariate Logistic Regression Analysis ◽  
Curve Analysis ◽  
Ureteral Stones ◽  
Ureteral Calculus ◽  
Validation Group ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Development And Validation

Abstract Background To develop and validate a practical nomogram for predicting the probability of patients with impacted ureteral stone. Methods Between June 2020 to March 2021, 214 single ureteral stones received ureteroscopy lithotripsy (URSL) were selected in development group. While 82 single ureteral stones received URSL between April 2021 to May 2021 were included in validation group. Independent factors for predicting impacted ureteral stone were screened by univariate and multivariate logistic regression analysis. The relationship between preoperative factors and stone impaction was modeled according to the regression coefficients. Discrimination and calibration were estimated by area under the receiver operating characteristic (AUROC) curve and calibration curve respectively. Clinical usefulness of the nomogram was evaluated by decision curve analysis. Results Age, ipsilateral stone treatment history, hydronephrosis and maximum ureteral wall thickness (UWTmax) at the portion of stone were identified as independent predictors for impacted stone. The AUROC curve of development and validation group were 0.915 and 0.882 respectively. Calibration curve of two groups showed strong concordance between the predicted and actual probabilities. Decision curve analysis showed that the predictive nomogram had a superior net benefit than UWTmax for all examined probabilities. Conclusions We developed and validated an individualized model to predict impacted ureteral stone prior to surgery. Through this prediction model, urologists can select an optimal treatment method and decrease intraoperative and postoperative complications for patients with impacted ureteral calculus.

Development and Validation of Nomogram to Predict Very Early Recurrence of Combined Hepatocellular-Cholangiocarcinoma After Hepatic Resection: A Multi-Institutional Study

2021 ◽  
Author(s):  
Yijun Wu ◽  
Hongzhi Liu ◽  
Jianxing Zeng ◽  
Yifan Chen ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Risk Groups ◽  
Early Recurrence ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Good Discrimination ◽  
Decision Curve Analysis ◽  
High Incidence ◽  
Combined Hepatocellular Cholangiocarcinoma ◽  
Development And Validation

Abstract Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.

scholarly journals Prognostic nomogram for predicting 5-year overall survival in Chinese patients with high-grade osteosarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhihong Yao ◽  
Zunxian Tan ◽  
Jifei Yang ◽  
Yihao Yang ◽  
Cao Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Calibration Curve ◽  
Curve Analysis ◽  
Cox Proportional Hazards Model ◽  
Chinese Patients ◽  
Gamma Glutamyl Transferase ◽  
High Grade ◽  
Decision Curve Analysis ◽  
High Grade Osteosarcoma ◽  
Glutamyl Transferase

AbstractThis study aimed to construct a widely accepted prognostic nomogram in Chinese high-grade osteosarcoma (HOS) patients aged ≤ 30 years to provide insight into predicting 5-year overall survival (OS). Data from 503 consecutive HOS patients at our centre between 12/2012 and 05/2019 were retrospectively collected. Eighty-four clinical features and routine laboratory haematological and biochemical testing indicators of each patient at the time of diagnosis were collected. A prognostic nomogram model for predicting OS was constructed based on the Cox proportional hazards model. The performance was assessed by the concordance index (C-index), receiver operating characteristic curve and calibration curve. The utility was evaluated by decision curve analysis. The 5-year OS was 52.1% and 2.6% for the nonmetastatic and metastatic patients, respectively. The nomogram included nine important variables based on a multivariate analysis: tumour stage, surgical type, metastasis, preoperative neoadjuvant chemotherapy cycle, postoperative metastasis time, mean corpuscular volume, tumour-specific growth factor, gamma-glutamyl transferase and creatinine. The calibration curve showed that the nomogram was able to predict 5-year OS accurately. The C-index of the nomogram for OS prediction was 0.795 (range, 0.703–0.887). Moreover, the decision curve analysis curve also demonstrated the clinical benefit of this model. The nomogram provides an individualized risk estimate of the 5-year OS in patients with HOS aged ≤ 30 years in a Chinese population-based cohort.

P0560POST-DISCHARGE FOLLOW UP AFTER AKI. AN EXTERNAL VALIDATION AND DECISION CURVE ANALYSIS OF TWO RISK MODELS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Simon Sawhney ◽  
Zhi Tan ◽  
Corri Black ◽  
Brenda Hemmelgarn ◽  
Angharad Marks ◽  
...  
Keyword(s):  
Risk Model ◽  
Discharge Planning ◽  
External Validation ◽  
Curve Analysis ◽  
Net Benefit ◽  
Post Discharge ◽  
Decision Curve Analysis ◽  
One Year ◽  
Unplanned Admissions

Abstract Background and Aims There is limited evidence to inform which people should receive follow up after AKI and for what reasons. Here we report the external validation (geographical and temporal) and potential clinical utility of two complementary models for predicting different post-discharge outcomes after AKI. We used decision curve analysis, a technique that enables visualisation of the trade-off (net benefit) between identifying true positives and avoiding false positives across a range of potential risk thresholds for a risk model. Based on decision curve analysis we compared model guided approaches to follow up after AKI with alternative strategies of standardised follow up – e.g. follow up of all people with AKI, severe AKI, or a discharge eGFR<30. Method The Alberta AKI risk model predicts the risk of stage G4 CKD at one year after AKI among those with a baseline GFR>=45 and at least 90 days survival (2004-2014, n=9973). A trial is now underway using this tool at a 10% threshold to identify high risk people who may benefit from specialist nephrology follow up. The Aberdeen AKI risk model provides complementary predictions of early mortality or unplanned readmissions within 90 days of discharge (2003, n=16453), aimed at supporting non-specialists in discharge planning, with a threshold of 20-40% considered clinically appropriate in the study. For the Alberta model we externally validated using Grampian residents with hospital AKI in 2011-2013 (n=9382). For the Aberdeen model we externally validated using all people admitted to hospital in Grampian in 2012 (n=26575). Analysis code was shared between the sites to maximise reproducibility. Results Both models discriminated well in the external validation cohorts (AUC 0.855 for CKD G4, and AUC 0.774 for death and readmissions model), but as both models overpredicted risks, recalibration was performed. For both models, decision curve analysis showed that prioritisation of patients based on the presence or severity of AKI would be inferior to a model guided approach. For predicting CKD G4 progression at one year, a strategy guided by discharge eGFR<30 was similar to a model guided approach at the prespecified 10% threshold (figure 1). In contrast for early unplanned admissions and mortality, model guided approaches were superior at the prespecified 20-40% threshold (figure 2). Conclusion In conclusion, prioritising AKI follow up is complex and standardised recommendations for all people may be an inefficient and inadequate way of guiding clinical follow-up. Guidelines for AKI follow up should consider suggesting an individualised approach both with respect to purpose and prioritisation.

Prognosis viewing for nasopharyngeal carcinoma treated with intensity-modulated radiation therapy: application of nomogram and decision curve analysis

2019 ◽  
Vol 50 (2) ◽  
pp. 159-168
Author(s):  
Zhaodong Fei ◽  
Xiufang Qiu ◽  
Mengying Li ◽  
Chuanben Chen ◽  
Yi Li ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Intensity Modulated Radiation Therapy ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Intensity Modulated ◽  
T Stage ◽  
N Stage

Abstract Objective To view and evaluate the prognosis factors in patients with nasopharyngeal carcinoma (NPC) treated with intensity modulated radiation therapy using nomogram and decision curve analysis (DCA). Methods Based on a primary cohort comprising consecutive patients with newly confirmed NPC (n = 1140) treated between January 2014 and December 2015, we identified independent prognostic factors of overall survival (OS) to establish a nomogram. The model was assessed by bootstrap internal validation and external validation in an independent validation cohort of 460 patients treated between January 2013 and December 2013. The predictive accuracy and discriminative ability were measured by calibration curve, concordance index (C-index) and risk-group stratification. The clinical usefulness was assessed by DCA. Results The nomogram incorporated T-stage, N-stage, age, concurrent chemotherapy and primary tumour volume (PTV). The calibration curve presented good agreement for between the nomogram-predicted OS and the actual measured survival probability in both the primary and validation cohorts. The model showed good discrimination with a C-index of 0.741 in the primary cohort and 0.762 in the validation cohort. The survival curves of different risk-groups were separated clearly. Decision curve analysis demonstrated that the nomogram provided a higher net benefit (NB) across a wider reasonable range of threshold probabilities for predicting OS. Conclusion This study presents a predictive nomogram model with accurate prediction and independent discrimination ability compared with combination of T-stage and N-stage. The results of DCA supported the point that PTV can help improve the prognostic ability of T-stage and should be added to the TNM staging system.

scholarly journals An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jie Cui ◽  
Qingquan Wen ◽  
Xiaojun Tan ◽  
Jinsong Piao ◽  
Qiong Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Calibration Curve ◽  
Cox Regression ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Decision Curve Analysis ◽  
Clinicopathologic Factors

AbstractLong non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

Utility of PCA3 and TMPRSS2:ERG urinary biomarkers in African American men undergoing prostate biopsy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 126-126
Author(s):  
Allison H. Feibus ◽  
A. Oliver Sartor ◽  
Krishnarao Moparty ◽  
Michael W. Kattan ◽  
Kevin M. Chagin ◽  
...  
Keyword(s):  
Prostate Biopsy ◽  
Prediction Models ◽  
African American Men ◽  
Standard Of Care ◽  
Curve Analysis ◽  
High Grade ◽  
Net Benefit ◽  
Racially Diverse ◽  
Decision Curve Analysis ◽  
History Of

126 Background: To determine the performance characteristics of urinary PCA3 andTMPRSS2:ERG (T2:ERG) in a racially diverse group of men. Methods: Following IRB approval, from 2013-2015, post digital rectal exam (DRE) urine was prospectively collected in patients without known prostate cancer (PCa), prior to biopsy. PCA3 and T2:ERG RNA copies were quantified and normalized to PSA mRNA copies using Progensa assay (Hologic, San Diego, CA). Prediction models for PCa and high-grade PCa were created using standard of care (SOC) variables (age, race, family history of PCa, prior prostate biopsy and abnormal DRE) plus PSA. Decision Curve Analysis was performed to compare the net benefit of using SOC, plus PSA, with the addition of PCA3 and T2:ERG. Results: Of 304 patients, 182 (60%) were AA; 139(46%) were diagnosed with PCa (69% AA). PCA3 and T2:ERG scores were greater in men with PCa, ≥ 3 cores, ≥ 33.3% cores, > 50% involvement of greatest biopsy core and Epstein significant PCa (p-values < 0.04). PCA3 added to the SOC plus PSA model for the detection of any PCa in the overall cohort (0.747 vs 0.677; p < 0.0001), in AA only (0.711 vs 0.638; p = 0.0002) and non-AA (0.781 vs 0.732; p = 0.0016). PCA3 added to the model for the prediction of high-grade PCa for the overall cohort (0.804 vs 0.78; p = 0.0002) and AA only (0.759 vs 0.717; p = 0.0003) but not non-AA. Decision curve analysis demonstrated significant net benefit with the addition of PCA3 compared with SOC plus PSA. For AA, T2:ERG did not improve concordance statistics for the detection any or high-grade PCa. Conclusions: For AA, urinary PCA3 improves the ability to predict the presence of any and high-grade PCa. However for this population, T2:ERG urinary assay does not add significantly to standard detection and risk stratification tools.

Screening for aggressive prostate cancer: A single-center experience using the 4Kscore and multiparametric MRI for the detection of Gleason 7 or higher prostate cancer.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 84-84
Author(s):  
Vivek Venkatramani ◽  
Bruno Nahar ◽  
Tulay Koru-Sengul ◽  
Nachiketh Soodana-Prakash ◽  
Mark L. Gonzalgo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Significant Proportion ◽  
Curve Analysis ◽  
Risk Scores ◽  
Consecutive Series ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Aggressive Prostate Cancer ◽  
Prostate Cancer Detection

84 Background: While non-invasive biomarkers and multi-parametric MRI (mpMRI) are routinely used for prostate cancer detection, few studies have assessed their performance together. We evaluated the performance of mpMRI and the 4Kscore for the detection of significant prostate cancer. Methods: We identified a consecutive series of men who underwent an mpMRI and 4Kscore for evaluation of prostate cancer at the University of Miami. We selected those who underwent a biopsy of the prostate. The primary outcome was the presence of Gleason 7 or higher cancer on biopsy. The 4Kscore was modeled as a continuous variable, but also categorized into low ( < 7.5%), intermediate (7.5-20%), and high ( > 20) risk scores. The mpMRI was categorized as being either negative or positive for a suspicion of cancer. We used logistic regression and Decision Curve Analysis to report the discrimination and clinical utility of using mpMRI and the 4Kscore for prostate cancer detection. Finally, we modeled the probability of harboring a Gleason 7 or higher prostate cancer based on various categories of the 4Kscore and mpMRI. Results: Among 235 men who underwent a 4Kscore and mpMRI, only 112 (52%) were referred for a biopsy, allowing a significant proportion of men to avoid a biopsy. Among those who had a biopsy, the AUC for using the 4Kscore and mpMRI together [0.81(0.72-0.90)] was superior to using the 4Kscore [0.71(0.61-0.81);p = 0.004] and mpMRI [0.74(0.65-0.83);p = 0.02] alone. Similarly, decision curve analysis revealed the highest net benefit for using both tests together, compared to either test alone. Finally, we found that having a positive mpMRI was a predictor of aggressive cancer in the higher two 4Kscores, but not in the lowest category, suggesting that men with a low 4Kscore may not benefit from getting an mpMRI, most likely due to the low likelihood of cancer and having a positive mpMRI. Conclusions: The 4Kscore and mpMRI provides independent, but complementary, information to enhance the prediction of aggressive prostate cancer. Prospective trials are required to confirm these findings.

Development of a composite biomarker-based calculator to predict the probability of pathologic complete response (pT0) after neoadjuvant pembrolizumab (pembro) in muscle invasive bladder cancer (MIBC).

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 539-539
Author(s):  
Andrea Necchi ◽  
Joshua J. Meeks ◽  
Marco Bandini ◽  
Leigh Ann Fall ◽  
Daniele Raggi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clinical Decision Making ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Clinical Decision ◽  
Curve Analysis ◽  
Risk Calculator ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Post Therapy

539 Background: The PURE01 study (NCT02736266) evaluates the use of pembro before radical cystectomy (RC) in MIBC. We assessed selected individual and combined biomarkers for predicting pT0 response after pembro, and developed a tool that may be used as an aid for clinical decision-making. Methods: Patients (pts) enrolled in the PURE01 were clinical (c) stage T≤4aN0M0 MIBC. Analysis to date included a comprehensive genomic profiling (FoundationONE assay), programmed cell-death-ligand-1 (PD-L1) combined positive score assessment (CPS, Dako 22C3 antibody) and whole transcriptome (Decipher assay) and RNA-seq profiling of pre/post therapy samples. Multivariable logistic regression analyses (MVA) evaluated baseline cT-stage and biomarkers in association with pT0 response. Corresponding coefficients were used to develop a risk calculator based on the tumor mutational burden (TMB), CPS, Immune190 signature score, and cT-stage. Decision-curve analysis was performed. Results: Complete biomarker data was available for 84 pts. Increasing TMB, CPS, and Immune190 scores showed a linear positive correlation with the pT0 probability in logistic regression (p=0.02, p=0.004, p=0.02). The c-index of the risk calculator was 0.79. Decision-curve analysis found the net-benefit of the model was higher than the “treat-all” option within the clinically-meaningful threshold probabilities of achieving a pT0 of 40-60%. Within this range, adding the Immune190 score improved the model over TMB and CPS. A significant decrease in median TMB values was observed (p=0.005) in 24 matched RC, versus a non-significant change in median CPS in 38 matched RC. Molecular subtyping switching was observed in 20/31 matched cases (64.5%), most frequently to the luminal-infiltrated subtype (80%). Conclusions: The study presents the first composite biomarker-based pT0 probability calculator for optimal pt selection. Pending validation, the model may be used to recommend neoadjuvant pembro to very selected MIBC pts. The observed changes in biomarker features in post-therapy samples may have an impact on future adjuvant strategies. Clinical trial information: NCT02736266.

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