scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

15-lncRNAs-based classifier-clinicopathologic nomogram improves the prediction of recurrence in patients with hepatocellular carcinoma

2019 ◽  
Author(s):  
Qiong Zhang ◽  
Gang Ning ◽  
Hongye Jiang ◽  
Yanlin Huang ◽  
Jinsong Piao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Clinicopathologic Factors ◽  
Validation Set ◽  
Better Than

Abstract Background: Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma(HCC) patients.Methods: The lncRNAs profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from the Cancer Genome Atlas (TCGA). Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier and compared our classifier to existing six-lncRNAs signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with TNM staging system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram.Results: Fifteen relapse-free survival (RFS) -related lncRNAs were identified and the classifer, consisting of the identified15 lncRNAs, could effectively classify patients into high-risk and low-risk subgroup. The prediction accuracy of the 15-lncRNAs-based classifier for predicting 2- year and 5-year RFS were 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, which was better than the existing six-lncRNAs signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better 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. Conclusion: Compared to TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

External validation of a sunitinib prognostic nomogram in patients (pts) with metastatic renal cell carcinoma (mRCC).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15070-e15070
Author(s):  
Changhong Yu ◽  
Michael W. Kattan ◽  
Thomas E. Hutson ◽  
Gary R. Hudes ◽  
Jinyu Yuan ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
External Validation ◽  
Curve Analysis ◽  
Medical Decision ◽  
Phase 2 ◽  
Concordance Index ◽  
Decision Curve Analysis ◽  
Significant Difference

e15070 Background: A nomogram was previously developed from pretreatment clinical features to predict the probability of achieving 12-month progression-free survival (PFS) with sunitinib in treatment (Tx)-naïve mRCC pts from a randomized, phase 3 trial (Cancer 2008;113:1552). Here, validation and update of this nomogram using pts from a phase 2 sunitinib mRCC study (Renal EFFECT Trial) is reported, as is evaluation of its usefulness for clinical decision making. Methods: The Tx-naïve mRCC pts included in the current analysis were randomized 1:1 to sunitinib 50 mg/d on a 4-weeks-on-2-weeks-off schedule (Schedule 4/2; n=146) or 37.5 mg/d on a continuous daily dosing (CDD) schedule (n=146). The variables included in the prior nomogram and used here for validation purposes were corrected serum calcium, number of metastatic sites, hemoglobin, prior nephrectomy, presence of lung and liver metastases, ECOG performance status, thrombocytosis, time from diagnosis to treatment, alkaline phosphatase, and lactate dehydrogenase. The nomogram was updated by removing prior nephrectomy as a variable, including baseline neutrophils and presence of bone metastases, and replacing thrombocytosis with baseline platelets. Validation of the existing and updated nomograms consisted of quantification of the discrimination with the concordance index. A decision curve analysis was used to examine whether this prediction model is useful for medical decision making. Results: With comparable pt characteristics and no significant difference in PFS (8.5 vs. 7.0 months; P=0.070) between the Schedule 4/2 and CDD arms of the phase 2 trial, the combined pt population (N=292) was used to validate the existing nomogram. The overall concordance index was 0.615. Based on the decision curve analysis, the existing nomogram has clinical utility when the probability of 12-month PFS exceeds 60%. Using Schedule 4/2 pts only, the concordance index was 0.594 for the updated nomogram; however, its utility showed more variability. Conclusions: The sunitinib nomogram has been validated in a similar pt cohort; however, its clinical utility may be limited and more research is needed to refine the tool further.

scholarly journals 15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiong Zhang ◽  
Gang Ning ◽  
Hongye Jiang ◽  
Yanlin Huang ◽  
Jinsong Piao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Clinicopathologic Factors ◽  
Validation Set ◽  
Better Than

Background. Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients. Methods. The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram. Results. Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally. Conclusion. Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

scholarly journals Established the first clinical prediction model regarding the risk of hyperuricemia in IgA nephropathy

2021 ◽  
Author(s):  
Yin-Hong Geng ◽  
Zhe Zhang ◽  
Jun-Jun Zhang ◽  
Bo Huang ◽  
Zui-Shuang Guo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Iga Nephropathy ◽  
Interstitial Fibrosis ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
University Hospital ◽  
Prediction Ability ◽  
Decision Curve Analysis ◽  
Incidence Risk

Abstract Objective. To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN) . Methods. Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Results. Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24-hour urinaryprotein (24h TP), Gross and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 ((95% CI 0.804–0.864)). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable.Conclusion. Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.

scholarly journals Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Bin Zhang ◽  
Qin Liu ◽  
Xiao Zhang ◽  
Shuyi Liu ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Clinical Utility ◽  
Predictive Accuracy ◽  
Fasting Blood Glucose ◽  
External Validation ◽  
Predictive Performance ◽  
Curve Analysis ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

scholarly journals Prediction and clinical utility of a contralateral breast cancer risk model

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Giardiello ◽  
Ewout W. Steyerberg ◽  
Michael Hauptmann ◽  
Muriel A. Adank ◽  
Delal Akdeniz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Germline Mutation ◽  
Clinical Utility ◽  
Contralateral Breast Cancer ◽  
Contralateral Breast ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Mutation Status

Abstract 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.

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.

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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