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.

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.

Development And Validation Of Models To Predict Cesarean Delivery Among Low-Risk Nulliparous Women At Term: A Retrospective Study In China

2020 ◽  
Author(s):  
Fangcan Sun ◽  
Bing Han ◽  
Fangfang Wu ◽  
Qianqian Shen ◽  
Minhong Shen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Cesarean Delivery ◽  
Prediction Models ◽  
External Validation ◽  
Curve Analysis ◽  
Low Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Nulliparous Women

Abstract Background: Cesarean delivery after failure of trial of labor is associated with adverse maternal and perinatal outcomes. A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict cesarean delivery for low-risk nulliparous women in Chinese population.Methods: This retrospective study analyzed the low-risk nulliparous women with singleton cephalic full-term fetus delivered in two medical centers. After the clinical data of the women who delivered at the tertiary referral center (n=6 551) was collected and was used univariate and multivariable logistic regression analysis, the prediction model was fitted. We performed external validation using data from nulliparous who delivered from another hospital(secondary referral center, n=7 657). A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance. Results: The cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6 551) and 7.82% (599/7 657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. Because sex of the fetuses were unknown until they born(China's Fertility Policy), we established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The Hosmer-Lemeshow goodness-of-fit test showed that these two models fitted well. Decision curve analysis demonstrated that the models were clinically useful. And internal validation using Bootstrap method showed that these prediction models perform well. On the external validation set, the AUC were 0.775 and 0.775, respectively. The calibration plots for the probability of cesarean showed a good correlation. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions: Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.

scholarly journals Analysis of risk factors for determining the need for prostate biopsy in patients with negative MRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linghui Liang ◽  
Feng Qi ◽  
Yifei Cheng ◽  
Lei Zhang ◽  
Dongliang Cao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Detection Efficiency ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Multivariable Logistic Regression Analysis ◽  
Detection Rates ◽  
Prostate Biopsies ◽  
Clinically Significant ◽  
Definition Of ◽  
Medical University

AbstractTo analyze the clinical characteristics of patients with negative biparametric magnetic resonance imaging (bpMRI) who didn’t need prostate biopsies (PBs). A total of 1,012 male patients who underwent PBs in the First Affiliated Hospital of Nanjing Medical University from March 2018 to November 2019, of 225 had prebiopsy negative bpMRI (defined as Prostate Imaging Reporting and Data System (PI-RADS 2.1) score less than 3). The detection efficiency of clinically significant prostate cancer (CSPCa) was assessed according to age, digital rectal examination (DRE), prostate volume (PV) on bpMRI, prostate-specific antigen (PSA) and PSA density (PSAD). The definition of CSPCa for Gleason score > 6. Univariate and multivariable logistic regression analysis were used to identify predictive factors of absent CSPCa on PBs. Moreover, absent CSPCa contained clinically insignificant prostate cancer (CIPCa) and benign result. The detection rates of present prostate cancer (PCa) and CSPCa were 27.11% and 16.44%, respectively. Patients who were diagnosed as CSPCa had an older age (P < 0.001), suspicious DRE (P < 0.001), a smaller PV (P < 0.001), higher PSA value (P = 0.008) and higher PSAD (P < 0.001) compared to the CIPCa group and benign result group. PSAD < 0.15 ng/ml/cm3 (P = 0.004) and suspicious DRE (P < 0.001) were independent predictors of absent CSPCa on BPs. The negative forecast value of bpMRI for BP detection of CSPCa increased with decreasing PSAD, mainly in patients with naive PB (P < 0.001) but not in prior negative PB patients. 25.33% of the men had the combination of negative bpMRI, PSAD < 0.15 ng/ml/cm3 and PB naive, and none had CSPCa on repeat PBs. The incidence of PB was determined, CSPCa was 1.59%, 0% and 16.67% in patients with negative bpMRI and PSAD < 0.15 ng/ml/cm3, patients with negative bpMRI, PSAD < 0.15 ng/ml/cm3 and biopsy naive and patients with negative bpMRI, PSAD < 0.15 ng/ml/cm3 and prior negative PB, separately. We found that a part of patients with negative bpMRI, a younger age, no suspicious DRE and PSAD < 0.15 ng/ml/cm3 may securely avoid PBs. Conversely PB should be considered in patients regardless of negative bpMRI, especially who with a greater age, obviously suspicious DRE, significantly increased PSA value, a significantly small PV on MRI and PSAD > 0.15 ng/ml/cm3.

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.

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

scholarly journals MultiParametric Magnetic Resonance Imaging-Based Nomogram for Predicting Prostate Cancer and Clinically Significant Prostate Cancer in Men Undergoing Repeat Prostate Biopsy

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Cong Huang ◽  
Gang Song ◽  
He Wang ◽  
Guangjie Ji ◽  
Jie Li ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Prostate Biopsy ◽  
Curve Analysis ◽  
Resonance Imaging ◽  
Decision Curve Analysis ◽  
Multiparametric Magnetic Resonance Imaging ◽  
Clinically Significant ◽  
Systematic Prostate Biopsy

Objective. To develop and internally validate nomograms based on multiparametric magnetic resonance imaging (mpMRI) to predict prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with a previous negative prostate biopsy. Materials and Methods. The clinicopathological parameters of 231 patients who underwent a repeat systematic prostate biopsy and mpMRI were reviewed. Based on Prostate Imaging and Reporting Data System, the mpMRI results were assigned into three groups: Groups “negative,” “suspicious,” and “positive.” Two clinical nomograms for predicting the probabilities of PCa and csPCa were constructed. The performances of nomograms were assessed using area under the receiver operating characteristic curves (AUCs), calibrations, and decision curve analysis. Results. The median PSA was 15.03 ng/ml and abnormal DRE was presented in 14.3% of patients in the entire cohort. PCa was detected in 75 patients (32.5%), and 59 (25.5%) were diagnosed with csPCa. In multivariate analysis, age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE), and mpMRI finding were significantly independent predictors for PCa and csPCa (all p < 0.01). Of those patients diagnosed with PCa or csPCa, 20/75 (26.7%) and 18/59 (30.5%) had abnormal DRE finding, respectively. Two mpMRI-based nomograms with super predictive accuracy were constructed (AUCs = 0.878 and 0.927, p < 0.001), and both exhibited excellent calibration. Decision curve analysis also demonstrated a high net benefit across a wide range of probability thresholds. Conclusion. mpMRI combined with age, PSA, PV, and DRE can help predict the probability of PCa and csPCa in patients who underwent a repeat systematic prostate biopsy after a previous negative biopsy. The two nomograms may aid the decision-making process in men with prior benign histology before the performance of repeat prostate biopsy.

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&lt;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&gt;=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&lt;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.

Sign in / Sign up

Export Citation Format

Share Document