Development and Validation of a Nomogram for Predicting Bladder Calculi Risk in Patients With Benign Prostatic Hyperplasia

2021 ◽  
Author(s):  
Euxu Xie ◽  
Xuelian Gu ◽  
Chen Ma ◽  
Li Guo ◽  
Man Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Characteristic Curve ◽  
Prostatic Hyperplasia ◽  
Calibration Plot ◽  
Lasso Regression ◽  
Net Benefit ◽  
Bladder Calculi ◽  
Validation Set ◽  
Selection Operator

Abstract Objective To develop and validate a nomogram for predicting bladder calculi risk in patients with benign prostatic hyperplasia (BPH).Methods A total of 368 patients who underwent transurethral resection of the prostate (TURP) and had histologically proven BPH from January 2018 to January 2021 were retrospectively collected. Eligible patients were randomly assigned to the training and validation datasets. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal risk factors. A prediction model was established based on the selected characteristics. The performance of the nomogram was assessed by calibration plots and the area under the receiver operating characteristic curve (AUROC). Furthermore, decision curve analysis (DCA) was used to determine the net benefit rate of of the nomogram. Results Among 368 patients who met the inclusion criteria, older age, a history of diabetes and hyperuricemia, longer intravesical prostatic protrusion (IPP)and larger prostatic urethral angulation (PUA) were independent risk factors for bladder calculi in patients with BPH. These factors were used to develop a nomogram, which had a good identification ability in predicting the risk of bladder calculi in patients, with AUROCs of 0.911 (95% CI: 0.876–0.945) in the training set and 0.884 (95% CI: 0.820–0.948) in the validation set. The calibration plot showed that the model had good calibration. Moreover, DCA indicated that the model had a goodclinical benefit. Conclusion We developed and internally validated the first nomogram to date to help physicians assess the risk of bladder calculi in patients with BPH, which may help physicians improve individual interventions and make better clinical decisions.

scholarly journals Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head

2020 ◽  
Author(s):  
Qiang Xu ◽  
Hangjun Chen ◽  
Sihai Chen ◽  
Jing Shan ◽  
Guoming Xia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Femoral Head ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Model Development ◽  
Lasso Regression ◽  
Early Screening ◽  
Good Discrimination ◽  
Internal Validation ◽  
Selection Operator

Abstract Background Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to estimate the probability of NONFH using clinical risk factors other than corticosteroids and alcohol consumption. Methods A training cohort of 790 patients (n=434, NONFH; n=356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. Results The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination (area under the receiver operating characteristic curve [AUC]=0.80) and calibration. Good discrimination (AUC=0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. Conclusions The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis.

scholarly journals Risk factors for bladder calculi in patients with benign prostatic hyperplasia

Medicine ◽  
2017 ◽  
Vol 96 (32) ◽  
pp. e7728 ◽  
Author(s):  
Wei Huang ◽  
Jun-Jie Cao ◽  
Min Cao ◽  
Hong-Shen Wu ◽  
Yong-Yi Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Prostatic Hyperplasia ◽  
Bladder Calculi

Development and Validation of a Nomogram for Renal Involvement in Primary Sjögren Syndrome Patients: A Retrospective Analysis

2021 ◽  
Author(s):  
Xinshi Huang ◽  
Xiaobing Wang ◽  
Dinglai Yu
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Renal Involvement ◽  
Calibration Plot ◽  
Receiver Operating Characteristic Curves ◽  
Logistic Analysis ◽  
Validation Set ◽  
Multivariate Logistic Analysis

Abstract Objective To establish and validate a nomogram for individualized prediction of renal involvement in pSS patients. Methods A total of 1293 patients with pSS from the First Affiliated Hospital of Wenzhou Medical University between January 2008 to January 2020 were recruited and further analyzed retrospectively. The patients were randomly divided into a development set (70%, n = 910) and a validation set (30%, n = 383). The univariable and multivariate logistic regression were performed to analyze the risk factors of renal involvement in pSS. Based on the regression β coefficients derived from multivariate logistic analysis, an individualized nomogram prediction model was developed. The prediction model of discrimination and calibration was evaluated with the area under the receiver operating characteristic curves and Calibration plot. Results Multivariate logistic analysis showed that hypertension, anemia, albumin, uric acid, anti-Ro52, hematuria and Chisholm-Mason grade were independent risk factors of renal involvement in pSS. The area under the receiver operating characteristic curves were 0.797 and 0.750 respectively in development set and validation set, indicating the nomogram had a good discrimination capacity. The Calibration plot showed nomogram had a strong concordance performance between the prediction probability and the actual probability. Conclusion The individualized nomogram for pSS patients those who had renal involvement could be used by clinicians to predict the risk of pSS patients developing into renal involvement and improve early screening and intervention.

The risk factors for acute urinary retention in patients with benign prostatic hyperplasia

2012 ◽  
Vol 38 (2) ◽  
pp. 65-69
Author(s):  
Huseyin Cihan Demirel ◽  
Cevdet Serkan Gokkaya ◽  
Cuneyt Ozden ◽  
Binhan Kagan Aktas ◽  
Suleyman Bulut ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Urinary Retention ◽  
Acute Urinary Retention ◽  
Prostatic Hyperplasia

scholarly journals Insulin and free oestradiol are independent risk factors for benign prostatic hyperplasia

2008 ◽  
Vol 12 (2) ◽  
pp. 160-165 ◽  
Author(s):  
J Hammarsten ◽  
J-E Damber ◽  
M Karlsson ◽  
T Knutson ◽  
Ö Ljunggren ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Prostatic Hyperplasia ◽  
Independent Risk Factors

scholarly journals Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Jenna Wiens ◽  
Wayne N. Campbell ◽  
Ella S. Franklin ◽  
John V. Guttag ◽  
Eric Horvitz
Keyword(s):  
Risk Factors ◽  
Clostridium Difficile ◽  
Risk Stratification ◽  
Characteristic Curve ◽  
Data Driven ◽  
First Year ◽  
High Risk Population ◽  
Urban Hospital ◽  
Small Set ◽  
Validation Set

Abstract Background.  Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile. Methods.  We consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model. Results.  Applied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79–.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69–.75). Conclusions.  Automated risk stratification of patients based on the contents of their EMRs can be used to accurately ide.jpegy a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI.

RISK FACTORS FOR DEVELOPING CLINICAL BENIGN PROSTATIC HYPERPLASIA: THE KRIMPEN STUDY

2008 ◽  
Vol 179 (4S) ◽  
pp. 526-526 ◽  
Author(s):  
Esther T Kok ◽  
Boris W Schouten ◽  
Arthur M Bohnen ◽  
Frans PMW Groeneveld ◽  
Siep Thomas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Prostatic Hyperplasia
Sign in / Sign up

Export Citation Format

Share Document