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.

Development and validation of a new clinical prediction model of catheter-related thrombosis in cancer patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14031-e14031
Author(s):  
Binliang Liu ◽  
Junying Xie ◽  
Xiaoying Sun ◽  
Yanfeng Wang ◽  
Zhong Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Cancer Patients ◽  
Retrospective Cohort ◽  
Operating Characteristic ◽  
Risk Group ◽  
Cancer Center ◽  
Catheter Related Thrombosis

e14031 Background: The central venous catheter brings convenience for drug delivery and improves comfort for cancer patients, it also causes serious complications. The most common one is catheter-related thrombosis (CRT). This study aimed to evaluate the incidence and risk factors of CRT in cancer patients, and to develop an effective prediction model for CRT in cancer patients. Methods: The development of our prediction model was based on the data of a retrospective cohort (n = 3131) from National Cancer Center. The validation of our prediction model was done in a prospective cohort from National Cancer Center (n = 685) and a retrospective cohort from Hunan Cancer Hospital (n = 61). The predictive accuracy and the discriminative ability were determined by the receiver operating characteristic curves and calibration plots. Results: Multivariate analysis demonstrated that sex, cancer type, catheter type, position of the catheter tip, chemotherapy status, and antiplatelet/anticoagulation status at baseline were independent risk factors for CRT. The area under receiver operating characteristic (ROC) curve of our prediction model was 0.741 (CI: 0.715-0.766) in the primary cohort; 0.754 (CI: 0.704-0.803) and 0.658 (CI: 0.470-0.845) in validation cohorts respectively. Good calibration and clinical impact were also shown in primary and validation cohorts. The high-risk group had a higher incidence of CRTs than the low-risk group in the primary cohort and two validation cohort (p < 0.001). Conclusions: Our model is a novel prediction tool for CRT risk which helps to assigning cancer patients into high-risk or low-risk group accurately. Our model will be valuable for clinicians in decision making of thromboprophylaxis.

scholarly journals Metabolic Syndrome and Its Components Reduce Coronary Collateralization in Chronic Total Occlusion: an Observational Study

2021 ◽  
Author(s):  
Tong Liu ◽  
Zheng Wu ◽  
Jinghua Liu ◽  
Yun Lv ◽  
Wenzheng Li
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Receiver Operating Characteristic ◽  
Chronic Total Occlusion ◽  
Operating Characteristic ◽  
Total Occlusion ◽  
Score System ◽  
Receiver Operating Characteristic Curves ◽  
Score Systems ◽  
Receiver Operating

Abstract Background: Metabolic syndrome (METs) is an independent risks for the incidence of cardiovascular diseases. We investigated whether or to what extent the METs and its components was associated with coronary collateralization (CC) in chronic total occlusion (CTO).Methods: This study involved 1709 inpatients with CTO. Data on demographic and clinical characteristics were collected by cardiovascular doctors. The CC condition was defined by Rentrop score system. Subgroup analysis, mixed models regression analysis, score systems and receiver-operating characteristic curves (ROC) analysis were done. Results: Overall, 1709 inpatients were assigned to the Poor CC group (n = 370), good CC group (n = 1339) with or without METs. Compared to good CC, the incidence of METs was higher in poor CC for overall patients. Poor collateralization was present in 9.1%, 14.4%, 19.9%, 18.1%, 35.1% and 54.2% of the six groups, who met the diagnostic criteria of MetS 0, 1, 2, 3, 4 and 5 times. For multivariable logistic regression, quartiles of BMI remained the risk factors of CC growth in all subgroups (adjusted OR = 1.728, 95% CI 1.518-1.967, P < 0.001 all patient group , adjusted OR = 1.827, 95% CI 1.484-2.249, P < 0.001 No-METs group and adjusted OR = 1.771, 95% CI 1.484-2.115,P < 0.001 METs group). After adjustment for potential confounding factors, METs was an independent risk factors of CC growth in several models. Assigning a score of one for each components, this score system had significant predictive value for the CC conditions by Receiver-operating characteristic(AUC: 0.622, 95%CI: 0.588-0.655) .Conclusions: METs, especially for body mass index, confers greater risk for CC formation in CTO. Score systems may help to predict CC condition.

scholarly journals Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuyuan Chen ◽  
Changxing Chi ◽  
Dedian Chen ◽  
Sanjun Chen ◽  
Binbin Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Training Set ◽  
Characteristic Curves ◽  
Decision Curve Analysis ◽  
Receiver Operating Characteristic Curves ◽  
Independent Risk Factors

Background. The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients. Methods. Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis. Results. All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates. Conclusion. We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

scholarly journals Impact of the preoperative prognostic nutritional index as a predictor for postoperative complications after resection of locally recurrent rectal cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masakatsu Paku ◽  
Mamoru Uemura ◽  
Masatoshi Kitakaze ◽  
Shiki Fujino ◽  
Takayuki Ogino ◽  
...  
Keyword(s):  
Risk Factors ◽  
Rectal Cancer ◽  
Postoperative Complications ◽  
Blood Loss ◽  
Receiver Operating Characteristic ◽  
Odds Ratio ◽  
Inflammatory Markers ◽  
Operating Characteristic ◽  
Recurrent Rectal Cancer ◽  
Prognostic Nutrition Index

Abstract Background Local recurrence is common after curative resections for rectal cancer. Surgical intervention is among the best treatment choices. However, achieving a negative resection margin often requires extensive pelvic organ resections; thus, the postoperative complication rate is quite high. Recent studies have reported that the inflammatory index could predict postoperative complications. This study aimed to validate the correlation between clinical factors, including inflammatory markers, and severe complications after surgery for local recurrent rectal cancer. Methods This retrospective study included 99 patients that underwent radical resections for local recurrences of rectal cancer. Postoperative complications were graded according to the Clavien-Dindo classification. Grades ≥3 were defined as severe complications. Risk factors for severe complications were identified with univariate and multivariate logistic regression models and assessed with receiver-operating characteristic curves. Results Severe postoperative complications occurred in 38 patients (38.4%). Analyses of correlations between inflammatory markers and severe postoperative complications revealed that the strongest correlation was found between the prognostic nutrition index and severe postoperative complications. The receiver-operating characteristic analysis showed that the optimal prognostic nutrition index cut-off value was 42.2 (sensitivity: 0.790, specificity: 0.508). In univariate and multivariate analyses, a prognostic nutrition index ≤44.2 (Odds ratio: 3.007, 95%CI:1.171–8.255, p = 0.02) and a blood loss ≥2850 mL (Odds ratio: 2.545, 95%CI: 1.044–6.367, p = 0.04) were associated with a significantly higher incidence of severe postoperative complications. Conclusions We found that a low preoperative prognostic nutrition index and excessive intraoperative blood loss were risk factors for severe complications after surgery for local recurrent rectal cancer.

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.

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