scholarly journals Determining the Target Population That Would Most Benefit from Screening for Hepatic Fibrosis in a Primary Care Setting

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1605
Author(s):  
Su Hyun Park ◽  
Jong Hyun Lee ◽  
Dae Won Jun ◽  
Kyung A Kang ◽  
Ji Na Kim ◽  
...  
Keyword(s):  
Primary Care ◽  
Fatty Liver ◽  
Hepatic Fibrosis ◽  
Liver Enzyme ◽  
Elevated Liver Enzyme ◽  
Curve Analysis ◽  
Low Risk ◽  
Net Benefit ◽  
Diabetes Group ◽  
Decision Curve Analysis

Due to its high prevalence, screening for hepatic fibrosis in the low-risk population is called for action in the primary care clinic. However, current guidelines provide conflicting recommendations on populations to be screened. We aimed to identify the target populations that would most benefit from screening for hepatic fibrosis in clinical practice. This study examined 1288 subjects who underwent magnetic resonance elastography. The diagnostic performance of the Fibrosis-4 (FIB-4) index and NAFLD fibrosis score was compared in the following groups: (1) ultrasonography (USG)-diagnosed NAFLD, (2) elevated liver enzyme, (3) metabolic syndrome, (4) impaired fasting glucose, and (5) type 2 diabetes regardless of fatty liver. Decision curve analysis was performed to express the net benefit of groups over a range of probability thresholds (Pts). The diabetes group showed a better area under the receiver operating characteristic curve (AUROC: 0.69) compared with subjects in the USG-diagnosed NAFLD (AUROC: 0.57) and elevated liver enzyme (AUROC: 0.55) groups based on the FIB-4 index. In decision curve analysis, the diabetes group showed the highest net benefit for the detection of significant fibrosis across a wide range of Pts. Patients with diabetes, even in the absence of fatty liver, would be preferable for hepatic fibrosis screening in low-risk populations.

scholarly journals Equivocal PI-RADS Three Lesions on Prostate Magnetic Resonance Imaging: Risk Stratification Strategies to Avoid MRI-Targeted Biopsies

2020 ◽  
Vol 10 (4) ◽  
pp. 270
Author(s):  
Daniël F. Osses ◽  
Christian Arsov ◽  
Lars Schimmöller ◽  
Ivo G. Schoots ◽  
Geert J.L.H. van Leenders ◽  
...  
Keyword(s):  
Specific Antigen ◽  
Curve Analysis ◽  
Low Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Lesion Diameter ◽  
Prostate Specific Antigen Density ◽  
Clinically Significant ◽  
First Time

We aimed to investigate the relation between largest lesion diameter, prostate-specific antigen density (PSA-D), age, and the detection of clinically significant prostate cancer (csPCa) using first-time targeted biopsy (TBx) in men with Prostate Imaging—Reporting and Data System (PI-RADS) 3 index lesions. A total of 292 men (2013–2019) from two referral centers were included. A multivariable logistic regression analysis was performed. The discrimination and clinical utility of the built model was assessed by the area under the receiver operation curve (AUC) and decision curve analysis, respectively. A higher PSA-D and higher age were significantly related to a higher risk of detecting csPCa, while the largest index lesion diameter was not. The discrimination of the model was 0.80 (95% CI 0.73–0.87). When compared to a biopsy-all strategy, decision curve analysis showed a higher net benefit at threshold probabilities of ≥2%. Accepting a missing ≤5% of csPCa diagnoses, a risk-based approach would result in 34% of TBx sessions and 23% of low-risk PCa diagnoses being avoided. In men with PI-RADS 3 index lesions scheduled for first-time TBx, the balance between the number of TBx sessions, the detection of low-risk PCa, and the detection of csPCa does not warrant a biopsy-all strategy. To minimize the risk of missing the diagnosis of csPCa but acknowledging the need of avoiding unnecessary TBx sessions and overdiagnosis, a risk-based approach is advisable.

P9 Novel use of CHA2DS2VASC score to select patients to undergo repeat atrial fibrillation screening

2020 ◽  
Vol 41 (Supplement_1) ◽  
Author(s):  
W Sun ◽  
B P Y Yan
Keyword(s):  
Atrial Fibrillation ◽  
High Risk ◽  
Clinical Outcomes ◽  
Cross Validation ◽  
Risk Group ◽  
Curve Analysis ◽  
Low Risk ◽  
Decision Curve Analysis ◽  
Risk Patients

Abstract Background We have previously demonstrated unselected screening for atrial fibrillation (AF) in patients ≥65 years old in an out-patient setting yielded 1-2% new AF each time screen-negative patients underwent repeated screening at 12 to 18 month interval. Selection criteria to identify high-risk patients for repeated AF screening may be more efficient than repeat screening on all patients. Aims This study aimed to validate CHA2DS2VASC score as a predictive model to select target population for repeat AF screening. Methods 17,745 consecutive patients underwent 24,363 index AF screening (26.9% patients underwent repeated screening) using a handheld single-lead ECG (AliveCor) from Dec 2014 to Dec 2017 (NCT02409654). Adverse clinical outcomes to be predicted included (i) new AF detection by repeated screening; (ii) new AF clinically diagnosed during follow-up and (ii) ischemic stroke/transient ischemic attack (TIA) during follow-up. Performance evaluation and validation of CHA2DS2VASC score as a prediction model was based on 15,732 subjects, 35,643 person-years of follow-up and 765 outcomes. Internal validation was conducted by method of k-fold cross-validation (k = n = 15,732, i.e., Leave-One-Out cross-validation). Performance measures included c-index for discriminatory ability and decision curve analysis for clinical utility. Risk groups were defined as ≤1, 2-3, or ≥4 for CHA2DS2VASC scores. Calibration was assessed by comparing proportions of actual observed events. Results CHA2DS2VASC scores achieved acceptable discrimination with c-index of 0.762 (95%CI: 0.746-0.777) for derivation and 0.703 for cross-validation. Decision curve analysis showed the use of CHA2DS2VASC to select patients for rescreening was superior to rescreening all or no patients in terms of net benefit across all reasonable threshold probability (Figure 1, left). Predicted and observed probabilities of adverse clinical outcomes progressively increased with increasing CHA2DS2VASC score (Figure 1, right): 0.7% outcome events in low-risk group (CHA2DS2VASC ≤1, predicted prob. ≤0.86%), 3.5% intermediate-risk group (CHA2DS2VASC 2-3, predicted prob. 2.62%-4.43%) and 11.3% in high-risk group (CHA2DS2VASC ≥4, predicted prob. ≥8.50%). The odds ratio for outcome events were 4.88 (95%CI: 3.43-6.96) for intermediate-versus-low risk group, and 17.37 (95%CI: 12.36-24.42) for high-versus-low risk group.  Conclusion Repeat AF screening on high-risk population may be more efficient than rescreening all screen-negative individuals. CHA2DS2VASC scores may be used as a selection tool to identify high-risk patients to undergo repeat AF screening. Abstract P9 Figure 1

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.

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.

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.

scholarly journals Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

2020 ◽  
Author(s):  
Ruyi Zhang ◽  
Mei Xu ◽  
Xiangxiang Liu ◽  
Miao Wang ◽  
Qiang Jia ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
External Validation ◽  
Cancer Staging ◽  
Curve Analysis ◽  
Calibration Plot ◽  
Net Benefit ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set ◽  
Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

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