scholarly journals Scoring system development for prediction of extravesical bladder cancer

2014 ◽  
Vol 71 (9) ◽  
pp. 851-857 ◽  
Author(s):  
Rade Prelevic ◽  
Miroslav Stojadinovic ◽  
Dejan Simic ◽  
Aleksandar Spasic ◽  
Nikola Petrovic
Keyword(s):  
Bladder Cancer ◽  
Scoring System ◽  
Clinical Decision Making ◽  
External Validation ◽  
Multivariable Analysis ◽  
Curve Analysis ◽  
Clinical Usefulness ◽  
Clinical Staging ◽  
Surgical Specimen ◽  
Decision Curve Analysis

Background/Aim. Staging of bladder cancer is crucial for optimal management of the disease. However, clinical staging is not perfectly accurate. The aim of this study was to derive a simple scoring system in prediction of pathological advanced muscle-invasive bladder cancer (MIBC). Methods. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk in prediction of pathological advanced MIBC using precystectomy clinicopathological data: demographic, initial transurethral resection (TUR) [grade, stage, multiplicity of tumors, lymphovascular invasion (LVI)], hydronephrosis, abdominal and pelvic CT radiography (size of the tumor, tumor base width), and pathological stage after radical cystectomy (RC). Advanced MIBC in surgical specimen was defined as pT3-4 tumor. Receiving operating characteristic (ROC) curve quantified the area under curve (AUC) as predictive accuracy. Clinical usefulness was assessed by using decision curve analysis. Results. This single-center retrospective study included 233 adult patients with BC undergoing RC at the Military Medical Academy, Belgrade. Organ confined disease was observed in 101 (43.3%) patients, and 132 (56.7%) had advanced MIBC. In multivariable analysis, 3 risk factors most strongly associated with advanced MIBC: grade of initial TUR [odds ratio (OR) = 4.7], LVI (OR = 2), and hydronephrosis (OR = 3.9). The resultant total possible score ranged from 0 to 15, with the cut-off value of > 8 points, the AUC was 0.795, showing good discriminatory ability. The model showed excellent calibration. Decision curve analysis showed a net benefit across all threshold probabilities and clinical usefulness of the model. Conclusion. We developed a unique scoring system which could assist in predicting advanced MIBC in patients before RC. The scoring system showed good performance characteristics and introducing of such a tool into daily clinical decision-making may lead to more appropriate integration of perioperative chemotherapy. Clinical value of this model needs to be further assessed in external validation cohorts.

scholarly journals External validation of nomogram for predicting malignant intraductal papillary mucinous neoplasm (IPMN): from the theory to the clinical practice using the decision curve analysis model

2021 ◽  
Author(s):  
Riccardo Casadei ◽  
Claudio Ricci ◽  
Carlo Ingaldi ◽  
Alessandro Cornacchia ◽  
Marina Migliori ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pancreatic Resection ◽  
External Validation ◽  
Curve Analysis ◽  
Clinical Usefulness ◽  
Analysis Model ◽  
Net Benefit ◽  
Threshold Probability ◽  
Branch Duct ◽  
Decision Curve Analysis

AbstractThe management of IPMNs is a challenging and controversial issue because the risk of malignancy is difficult to predict. The present study aimed to assess the clinical usefulness of two preoperative nomograms for predicting malignancy of IPMNs allowing their proper management. Retrospective study of patients affected by IPMNs. Two nomograms, regarding main (MD) and branch duct (BD) IPMN, respectively, were evaluated. Only patients who underwent pancreatic resection were collected to test the nomograms because a pathological diagnosis was available. The analysis included: 1-logistic regression analysis to calibrate the nomograms; 2-decision curve analysis (DCA) to test the nomograms concerning their clinical usefulness. 98 patients underwent pancreatic resection. The logistic regression showed that, increasing the score of both the MD-IPMN and BD-IPMN nomograms, significantly increases the probability of IPMN high grade or invasive carcinoma (P = 0.029 and P = 0.033, respectively). DCA of MD-IPMN nomogram showed that there were no net benefits with respect to surgical resection in all cases. DCA of BD-IPMN nomogram, showed a net benefit only for threshold probability between 40 and 60%. For these values, useless pancreatic resection should be avoided in 14.8%. The two nomograms allowed a reliable assessment of the malignancy rate. Their clinical usefulness is limited to BD-IPMN with threshold probability of malignancy of 40–60%, in which the patients can be selected better than the “treat all” strategy.

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 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.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

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.

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 Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia

2020 ◽  
Vol 9 (5) ◽  
pp. 1587
Author(s):  
Hamid Y. Hassen ◽  
Seifu H. Gebreyesus ◽  
Bilal S. Endris ◽  
Meselech A. Roro ◽  
Jean-Pierre Van Geertruyden
Keyword(s):  
Prediction Model ◽  
Pregnant Women ◽  
Risk Score ◽  
Low Income ◽  
Low Birthweight ◽  
Multivariable Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Maternal Characteristics ◽  
Trained Manpower

At least one ultrasound is recommended to predict fetal growth restriction and low birthweight earlier in pregnancy. However, in low-income countries, imaging equipment and trained manpower are scarce. Hence, we developed and validated a model and risk score to predict low birthweight using maternal characteristics during pregnancy, for use in resource limited settings. We developed the model using a prospective cohort of 379 pregnant women in South Ethiopia. A stepwise multivariable analysis was done to develop the prediction model. To improve the clinical utility, we developed a simplified risk score to classify pregnant women at high- or low-risk of low birthweight. The accuracy of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration plot. All accuracy measures were internally validated using the bootstrapping technique. We evaluated the clinical impact of the model using a decision curve analysis across various threshold probabilities. Age at pregnancy, underweight, anemia, height, gravidity, and presence of comorbidity remained in the final multivariable prediction model. The AUC of the model was 0.83 (95% confidence interval: 0.78 to 0.88). The decision curve analysis indicated the model provides a higher net benefit across ranges of threshold probabilities. In general, this study showed the possibility of predicting low birthweight using maternal characteristics during pregnancy. The model could help to identify pregnant women at higher risk of having a low birthweight baby. This feasible prediction model would offer an opportunity to reduce obstetric-related complications, thus improving the overall maternal and child healthcare in low- and middle-income countries.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document