ObjectivesWe present an illustrative application of methods that account for covariates in receiver operating characteristic (ROC) curve analysis, using individual patient data on D-dimer testing... Show moreObjectivesWe present an illustrative application of methods that account for covariates in receiver operating characteristic (ROC) curve analysis, using individual patient data on D-dimer testing for excluding pulmonary embolism.Study Design and SettingBayesian nonparametric covariate-specific ROC curves were constructed to examine the performance/positivity thresholds in covariate subgroups. Standard ROC curves were constructed. Three scenarios were outlined based on comparison between subgroups and standard ROC curve conclusion: (1) identical distribution/identical performance, (2) different distribution/identical performance, and (3) different distribution/different performance. Scenarios were illustrated using clinical covariates. Covariate-adjusted ROC curves were also constructed.ResultsAge groups had prominent differences in D-dimer concentration, paired with differences in performance (Scenario 3). Different positivity thresholds were required to achieve the same level of sensitivity. D-dimer had identical performance, but different distributions for YEARS algorithm items (Scenario 2), and similar distributions for sex (Scenario 1). For the later covariates, comparable positivity thresholds achieved the same sensitivity. All covariate-adjusted models had AUCs comparable to the standard approach.ConclusionSubgroup differences in performance and distribution of results can indicate that the conventional ROC curve is not a fair representation of test performance. Estimating conditional ROC curves can improve the ability to select thresholds with greater applicability. Show less
Hany, M.; Demerdash, H.M.; Agayby, A.S.S.; Ibrahim, M.; Torensma, B. 2022
Introduction: Obesity is associated with metabolic syndrome (MBS), a cluster of components including central obesity, insulin resistance (IR), dyslipidemia, and hypertension. IR is the major risk... Show moreIntroduction: Obesity is associated with metabolic syndrome (MBS), a cluster of components including central obesity, insulin resistance (IR), dyslipidemia, and hypertension. IR is the major risk factor in the development and progression of type 2 diabetes mellitus in obesity and MBS. Predicting preoperatively whether a patient with obesity would have improved or non-improved IR after bariatric surgery would improve treatment decisions. Methods: A prospective cohort study was conducted between August 2019 and September 2021. We identified pre- and postoperative metabolic biomarkers in patients who underwent laparoscopic sleeve gastrectomy. Patients were divided into two groups: group A (IR < 2.5), with improved IR, and group B (IR >= 2.5), with non-improved IR. A prediction model and receiver operating characteristics (ROC) were used to determine the effect of metabolic biomarkers on IR. Results: Seventy patients with obesity and MBS were enrolled. At 12-month postoperative a significant improvement in lipid profile, fasting blood glucose, and hormonal biomarkers and a significant reduction in the BMI in all patients (p = 0.008) were visible. HOMA-IR significantly decreased in 57.14% of the patients postoperatively. Significant effects on the change in HOMA-IR >= 2.5 were the variables; preoperative BMI, leptin, ghrelin, leptin/ghrelin ratio (LGr), insulin, and triglyceride with an OR of 1.6,1.82, 1.33, 1.69, 1.77, and 1.82, respectively (p = 0.009 towards p = 0.041). Leptin had the best predictive cutoff value on ROC (86% sensitivity and 92% specificity), whereas ghrelin had the lowest (70% sensitivity and 73% specificity). Conclusion: Preoperative BMI, leptin, ghrelin, LGr, and increased triglycerides have a predictive value on higher postoperative, non-improved patients with HOMA-IR (>= 2.5). Therefore, assessing metabolic biomarkers can help decide on treatment/extra therapy and outcome before surgery. Show less
Berger, F.A.; Sijs, H. van der; Becker, M.L.; Gelder, T. van; Bemt, P.M.L.A. van den 2020
Background The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs... Show moreBackground The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. Methods A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated. Results The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. Conclusions A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. Show less
