scholarly journals An update on statistical modeling for quality risk assessment of clinical trials

2021 ◽  
Author(s):  
Björn Koneswarakantha ◽  
Timothé Ménard
Keyword(s):  
Risk Factors ◽  
Operating Characteristic ◽  
Statistical Modelling ◽  
Adjusted Risk ◽  
Receiver Operating Characteristic Curves ◽  
Safety Reporting ◽  
Risk Estimates ◽  
Modelling Methodology ◽  
Model Finding ◽  
Reporting Data

Background - As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote Quality Assurance (QA) activities, we developed new quality indicators, building on a previously published statistical modelling methodology. Methods - We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 - 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints. Results - Resulting Area Under the Receiver Operating Characteristic Curves were between 0.57 - 0.66 with calibrated predictive ranges of 27 - 41%. The combined and adjusted risk factors could be used to easily interpret risk estimates. Conclusion - Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies and help to manage audit targets and audit focus also in post-pandemic times.

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.

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.

Violence in stalking situations

2009 ◽  
Vol 39 (9) ◽  
pp. 1469-1478 ◽  
Author(s):  
T. E. McEwan ◽  
P. E. Mullen ◽  
R. D. MacKenzie ◽  
J. R. P. Ogloff
Keyword(s):  
Risk Factors ◽  
Potential Risk ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Relationship Type ◽  
Logistic Regression Models ◽  
Receiver Operating Characteristic Curves ◽  
Increased Risk ◽  
Different Types ◽  
Potential Risk Factors

BackgroundStalking is often viewed as a precursor to violence, but determining which stalkers might attack is a difficult task. This study overcomes shortfalls in previous investigations by adopting a pseudo-prospective design and examining potential risk factors for different types of stalker.MethodDemographic, behavioural and diagnostic information was collected from stalkers referred to a community forensic mental health service (n=211). Potential risk factors for stalking violence were identified using odds ratios and χ2 tests, and entered into logistic regression models. Model utility was assessed using receiver operating characteristic curves.ResultsAmongst Rejected ex-intimate stalkers, violence was best predicted by previous violence, making threats and being employed (area under the curve=0.75), while for stalkers with other motives and relationships to the victim, being aged less than 30 years, substance use at the time of stalking and prior violence best predicted stalking violence (area under the curve=0.80).ConclusionsStalkers at increased risk of violence can be accurately identified by examining motivational and relationship type in conjunction with specific relevant risk factors. Previous violence is a particularly important risk factor, as are threats amongst ex-intimate stalkers. Approach behaviours and psychosis were shown to be less useful in predicting violence.

scholarly journals A risk score based on pediatric sequential organ failure assessment predicts 90-day mortality in children with Klebsiella pneumoniae bloodstream infection

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Shuang Li ◽  
Jingxian Liu ◽  
Feng Chen ◽  
Kang Cai ◽  
Jintong Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Organ Failure ◽  
Operating Characteristic ◽  
Pediatric Patients ◽  
Risk Groups ◽  
Receiver Operating Characteristic Curves ◽  
Source Of Infection ◽  
Failure Assessment ◽  
Medium Risk

Abstract Background Klebsiella pneumoniae bloodstream infection (Kp-BSI) is a serious threat to pediatric patients. The objective of this study was to explore the risk factors, validate the prediction efficiency of pediatric Sequential Organ Failure Assessment (SOFA) and establish better early predictors of mortality in pediatric patients with Kp-BSI. Methods All children diagnosed with Kp-BSI were included in this retrospective cohort study from January 2009 to June 2019. Basic characteristics, symptoms and physical examinations, treatments, laboratory statistics, and SOFA at the onset of Kp-BSI were recorded. The Cox proportional hazard model and receiver operating characteristic curves were used to assess the association between the variables and the 90-day mortality and their predictive value. DeLong’s test of receiver operating characteristic curves and integrated discrimination improvement index were used to determine the improvement in predictive capacity of the modified SOFA models. A predictive score was developed using multivariate logistic regression. Results Of the 146 children enrolled, 33 (22.6%) patients died within 90 days. Hospitalization in the last 6 months, intra-abdominal source of infection, presence of organ failure, and altered levels of blood biomarkers, including C-reactive protein, albumin, and lactate were significant risk factors for 90-day mortality. The area under the curve (AUC) of SOFA for predicting 90-day mortality was 0.80 (95% CI 0.71–0.89). Moreover, we found that a prediction model combining SOFA with two other parameters, namely hospitalization in the last 6 months and intra-abdominal source of infection, was better at predicting mortality (AUC = 0.89, 95% CI 0.82–0.96; sensitivity = 0.86; specificity = 0.84). According to this novel risk model, we defined three statistically different groups: low-risk, medium-risk and high-risk groups, with an observed 90-day mortality of 5.4, 35.7, and 72.0%, respectively. With reference to the low-risk patients, the medium-risk and high-risk groups had a higher mortality, with hazard ratios of 8.36 (95% CI 3.60–27.83) and 20.27 (95% CI 7.47–54.95), respectively. Conclusions The modified SOFA may be better than the original score to predict 90-day mortality in pediatric patients with Kp-BSI. Future prospective studies are required to validate this novel scoring system in external cohorts.

scholarly journals A risk score based on pediatric Sequential Organ Failure Assessment predicts 90-day mortality in children with Klebsiella pneumoniae bloodstream infection

2020 ◽  
Author(s):  
Shuang Li ◽  
Jingxian Liu ◽  
Feng Chen ◽  
Kang Cai ◽  
Jintong Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Organ Failure ◽  
Operating Characteristic ◽  
Pediatric Patients ◽  
Risk Groups ◽  
Receiver Operating Characteristic Curves ◽  
Source Of Infection ◽  
Failure Assessment ◽  
Medium Risk

Abstract Background: Klebsiella pneumoniae bloodstream infection (Kp-BSI) is a serious threat to pediatric patients. The objective of this study was to explore the risk factors, validate the prediction efficiency of pediatric Sequential Organ Failure Assessment (SOFA) and establish better early predictors of mortality in pediatric patients with Kp-BSI.Methods: All children diagnosed with Kp-BSI were included in this retrospective cohort study from January 2009 to June 2019. Basic characteristics, symptoms and physical examinations, treatments, laboratory statistics, and SOFA at the onset of Kp-BSI were recorded. The Cox proportional hazard model and receiver operating characteristic curves were used to assess the association between the variables and the 90-day mortality and their predictive value. DeLong’s test of receiver operating characteristic curves and integrated discrimination improvement index were used to determine the improvement in predictive capacity of the modified SOFA models. A predictive score was developed using multivariate logistic regression.Results: Of the 146 children enrolled, 33 (22.6%) patients died within 90 days. Hospitalization in the last six months, intra-abdominal source of infection, presence of organ failure, and altered levels of blood biomarkers, including C-reactive protein, albumin, and lactate were significant risk factors for 90-day mortality. The area under the curve (AUC) of SOFA for predicting 90-day mortality was 0.80 (95% CI 0.71–0.89). Moreover, we found that a prediction model combining SOFA with two other parameters, namely hospitalization in the last six months and intra-abdominal source of infection, was better at predicting mortality (AUC = 0.89, 95% CI 0.82–0.96; sensitivity = 0.86; specificity = 0.84). According to this novel risk model, we defined three statistically different groups: low-risk, medium-risk and high-risk groups, with an observed 90-day mortality of 5.4%, 35.7%, and 72.0%, respectively. With reference to the low-risk patients, the medium-risk and high-risk groups had a higher mortality, with hazard ratios of 8.36 (95% CI 3.60–27.83) and 20.27 (95% CI 7.47–54.95), respectively.Conclusions: The modified SOFA may be better than the original score to predict 90-day mortality in pediatric patients with Kp-BSI. Future prospective studies are required to validate this novel scoring system in external cohorts.

scholarly journals A risk score based on pediatric sequential organ failure assessment predicts 90-day mortality in children with Klebsiella pneumoniae bloodstream infection

2020 ◽  
Author(s):  
Shuang Li ◽  
Jingxian Liu ◽  
Feng Chen ◽  
Kang Cai ◽  
Jintong Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Klebsiella Pneumoniae ◽  
Sequential Organ Failure Assessment ◽  
Bloodstream Infection ◽  
Operating Characteristic ◽  
Risk Groups ◽  
Receiver Operating Characteristic Curves ◽  
Failure Assessment ◽  
Medium Risk

Abstract Background: Klebsiella pneumoniae bloodstream infection (Kp-BSI) is a serious threat to pediatric patients. The objective of this study was to explore the risk factors for mortality, validate the prediction efficiency of pediatric sequential organ failure assessment (SOFA) and establish better specific early predictors. Methods: All children diagnosed with Kp-BSI were included in this retrospective cohort study from January 2009 to June 2019. Basic characteristics, symptoms and physical examinations, laboratory statistics, and SOFA at the onset of Kp-BSI were recorded. The Cox proportional hazard model and the receiver operating characteristic curves were used to assess the association of the variables with the 90-day mortality and their predictive values. The DeLong's test of receiver operating characteristic curves and integrated discrimination improvement index were calculated to investigate predictive improvement of the modified SOFA models. A predictive score was developed using multivariate logistic regression. Results: Of the 146 children enrolled, 33 (22.6%) died within 90 days. Hospitalization within the previous six months, intra-abdominal source of bloodstream infection, presence of organ failures, and blood biomarkers including the C-reactive protein, albumin, and lactate were identified as significant risk factors for the 90-day mortality. The area under the curve (AUC) of SOFA for predicting 90-day mortality was 0.80 (95% CI 0.71–0.89). We further found a better combined model when adding hospitalization within the previous six months and intra-abdominal source of bloodstream infection into the SOFA score (AUC = 0.89, 95% CI 0.82–0.96, sensitivity = 0.86, specificity = 0.84). According to this novel risk model, we defined three statistically different groups: low-risk, medium-risk and high-risk groups, with an observed 90-day mortality of 5.4%, 35.7%, and 72.0%, respectively. With the reference of the low-risk group, the medium-risk and high-risk groups had a higher mortality, with hazard ratios of 8.36 (95% CI 3.60–27.83) and 20.27 (95% CI 7.47–54.95), respectively. Conclusions: The modified SOFA model may better predict the 90-day mortality of Kp-BSI. Future perspective studies will be required to validate this novel scoring system in external cohorts.

scholarly journals A risk score based on pediatric Sequential Organ Failure Assessment predicts 90-day mortality in children with Klebsiella pneumoniae bloodstream infection

2020 ◽  
Author(s):  
Shuang Li ◽  
Jingxian Liu ◽  
Feng Chen ◽  
Kang Cai ◽  
Jintong Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Organ Failure ◽  
Operating Characteristic ◽  
Pediatric Patients ◽  
Risk Groups ◽  
Receiver Operating Characteristic Curves ◽  
Source Of Infection ◽  
Failure Assessment ◽  
Medium Risk

Abstract Background: Klebsiella pneumoniae bloodstream infection (Kp-BSI) is a serious threat to pediatric patients. The objective of this study was to explore the risk factors, validate the prediction efficiency of pediatric Sequential Organ Failure Assessment (SOFA) and establish better early predictors of mortality in pediatric patients with Kp-BSI.Methods: All children diagnosed with Kp-BSI were included in this retrospective cohort study from January 2009 to June 2019. Basic characteristics, symptoms and physical examinations, treatments, laboratory statistics, and SOFA at the onset of Kp-BSI were recorded. The Cox proportional hazard model and receiver operating characteristic curves were used to assess the association between the variables and the 90-day mortality and their predictive value. DeLong’s test of receiver operating characteristic curves and integrated discrimination improvement index were used to determine the improvement in predictive capacity of the modified SOFA models. A predictive score was developed using multivariate logistic regression.Results: Of the 146 children enrolled, 33 (22.6%) patients died within 90 days. Hospitalization in the last six months, intra-abdominal source of infection, presence of organ failure, and altered levels of blood biomarkers, including C-reactive protein, albumin, and lactate were significant risk factors for 90-day mortality. The area under the curve (AUC) of SOFA for predicting 90-day mortality was 0.80 (95% CI 0.71–0.89). Moreover, we found that a prediction model combining SOFA with two other parameters, namely hospitalization in the last six months and intra-abdominal source of infection, was better at predicting mortality (AUC = 0.89, 95% CI 0.82–0.96; sensitivity = 0.86; specificity = 0.84). According to this novel risk model, we defined three statistically different groups: low-risk, medium-risk and high-risk groups, with an observed 90-day mortality of 5.4%, 35.7%, and 72.0%, respectively. With reference to the low-risk patients, the medium-risk and high-risk groups had a higher mortality, with hazard ratios of 8.36 (95% CI 3.60–27.83) and 20.27 (95% CI 7.47–54.95), respectively.Conclusions: The modified SOFA may be better than the original score to predict 90-day mortality in pediatric patients with Kp-BSI. Future prospective studies are required to validate this novel scoring system in external cohorts.

scholarly journals A risk score based on pediatric Sequential Organ Failure Assessment predicts 90-day mortality in children with Klebsiella pneumoniae bloodstream infection

2020 ◽  
Author(s):  
Shuang Li ◽  
Jingxian Liu ◽  
Feng Chen ◽  
Kang Cai ◽  
Jintong Tan ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Organ Failure ◽  
Operating Characteristic ◽  
Pediatric Patients ◽  
Risk Groups ◽  
Receiver Operating Characteristic Curves ◽  
Source Of Infection ◽  
Failure Assessment ◽  
Medium Risk

Abstract Background: Klebsiella pneumoniae bloodstream infection (Kp-BSI) is a serious threat to pediatric patients. The objective of this study was to explore the risk factors, validate the prediction efficiency of pediatric Sequential Organ Failure Assessment (SOFA) and establish better early predictors of mortality in pediatric patients with Kp-BSI. Methods: All children diagnosed with Kp-BSI were included in this retrospective cohort study from January 2009 to June 2019. Basic characteristics, symptoms and physical examinations, treatments, laboratory statistics, and SOFA at the onset of Kp-BSI were recorded. The Cox proportional hazard model and receiver operating characteristic curves were used to assess the association between the variables and the 90-day mortality and their predictive value. DeLong's test of receiver operating characteristic curves and integrated discrimination improvement index were used to determine the improvement in predictive capacity of the modified SOFA models. A predictive score was developed using multivariate logistic regression. Results: Of the 146 children enrolled, 33 (22.6%) patients died within 90 days. Hospitalization in the last six months, intra-abdominal source of infection, presence of organ failure, and altered levels of blood biomarkers, including C-reactive protein, albumin, and lactate were significant risk factors for 90-day mortality. The area under the curve (AUC) of SOFA for predicting 90-day mortality was 0.80 (95% CI 0.71–0.89). Moreover, we found that a prediction model combining SOFA with two other parameters, namely hospitalization in the last six months and intra-abdominal source of infection, was better at predicting mortality (AUC = 0.89, 95% CI 0.82–0.96; sensitivity = 0.86; specificity = 0.84). According to this novel risk model, we defined three statistically different groups: low-risk, medium-risk and high-risk groups, with an observed 90-day mortality of 5.4%, 35.7%, and 72.0%, respectively. With reference to the low-risk patients, the medium-risk and high-risk groups had a higher mortality, with hazard ratios of 8.36 (95% CI 3.60–27.83) and 20.27 (95% CI 7.47–54.95), respectively. Conclusions: The modified SOFA may be better than the original score to predict 90-day mortality in pediatric patients with Kp-BSI. Future prospective studies are required to validate this novel scoring system in external cohorts.

scholarly journals 888Discriminating between risk discriminators: OPERA, AUC, and polygenic variance

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
John Hopper ◽  
James Dowty
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Standard Deviation ◽  
Risk Model ◽  
Disease Risk ◽  
Cumulative Distribution ◽  
Receiver Operating Curve ◽  
Pedigree Data ◽  
Adjusted Risk ◽  
Risk Estimates

Abstract Focus of Presentation Epidemiological risk estimates are often adjusted for age (and, if necessary, sex) by design or analysis, and for other factors. Consequently, risk estimates do not pertain to the crude risk factor, but to its population residual after adjusting for age, sex and other covariates. For disease risk, the change in Odds PER Adjusted standard deviation (OPERA) estimates the risk gradient on an appropriate scale; log(OPERA) is the natural measure to compare estimates. Findings Under a multiplicative risk model for a normally distributed (adjusted) risk factor, log(OPERA) = the difference in mean between cases and controls. The area under the receiver operating curve (AUC) = □(log(OPERA)/√2), where □ is the standard normal cumulative distribution function. The risk discrimination from combining risk factors can be predicted from their OPERAs. The polygenic standard deviation estimated from pedigree data = log(OPERA). The OPERA for knowing all familial risk factors can be calculated. Conclusions/Implications We present examples from the breast cancer literature where the wrong conclusions can be made by not using the OPERA concept. We give examples of the value of OPERA estimates in predicting the risk discrimination of their combination and demonstrate why the better one predicts the disease the harder it is to predict it better. Key messages OPERA overcomes problems about interpreting risk factors, and combinations of risk factors, in a way not apparent using changes in AUC. OPERA also puts an upper bound on the role of genetic factors in explaining differences in risk across the population.

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