Positive Net Reclassification Improvement Research Articles

Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank.

Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). We analyzed data from 126,490 post-menopausal women of "White British" ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected. The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053-0.104) and 0.051 (95% CI, 0.030-0.073), respectively], with negligible impact on controls. The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification. These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.

Discrimination and net-reclassification of cardiovascular disease risk with Lipoprotein(a) levels: The ATTICA study (2002–2022)

Lipoprotein(a) [Lp(a)] is a recognized as risk factor for atherosclerotic cardiovascular disease (ASCVD). However, its influence on clinical risk evaluations remains unclear. This study aimed to determine whether Lp(a) improves CVD risk prediction among apparently healthy adults from the general population. In 2002, n = 3,042 adults free of CVD, residing in Athens metropolitan area, in Greece, were recruited. A 20-year follow-up was conducted in 2022, comprising n = 2,169 participants, of which n = 1,988 had complete data for CVD incidence. Lp(a) levels were significantly associated with 20-year ASCVD incidence in the crude model (Hazard Ratio per 1 mg/dL: 1.004, p = 0.048), but not in multi-adjusted models considering demographic, lifestyle, and clinical factors. Adding Lp(a) to the Reynolds Risk Score (RRS) and Framingham Risk Score (FRS) variables resulted in positive Net Reclassification Improvement (NRI) values (0.159 and 0.160 respectively), indicating improved risk classification. Mediation analysis suggested that C-reactive protein, Interleukin-6, and Fibrinogen mediate the relationship between Lp(a) and ASCVD. No significant interaction was observed between Lp(a) and potential moderators. Lp(a) levels can predict 20-year CVD outcomes and improve CVD risk prediction within the general population, possibly via the intricate relationship between Lp(a), systemic inflammation, atherothrombosis.

Development and validation of a simple model to predict functionally significant coronary artery disease in Chinese populations: A two-center retrospective study

ObjectivesThis study sought to derive and validate a simple model combining traditional clinical risk factors with biomarkers and imaging indicators easily obtained from routine preoperative examinations to predict functionally significant coronary artery disease (CAD) in Chinese populations. MethodsWe developed five models from a derivation cohort of 320 patients retrospective collected. In the derivation cohort, we assessed each model discrimination using the area under the receiver operating characteristic curve (AUC), reclassification using the integrated discrimination improvement (IDI) and net reclassification improvement (NRI), calibration using the Hosmer-Lemeshow test, and clinical benefit using decision curve analysis (DCA) to derive the optimal model. The optimal model was internally validated by bootstrapping, and external validation was performed in another cohort including 96 patients. ResultsThe optimal model including 5 predictors (age, sex, hyperlipidemia, hs-cTnI and LVEF) achieved an AUC of 0.807 with positive NRI and IDI in the derivation cohort. Moreover, the Hosmer-Lemeshow test showed a good fit, and the DCA demonstrated good clinical net benefit. The C-statistic calculated by bootstrapping internal validation was 0.798, and the calibration curve showed adequate calibration (Brier score = 0.179). In the external validation cohort, the optimal model performance was acceptable (AUC = 0.704; Brier score = 0.20). Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. ConclusionsA simple model combined clinical risk factors with hs-cTnI and LVEF improving the prediction of functionally significant CAD in Chinese populations. This attractive model may be a choice for clinicians to risk stratification for CAD.

Sex Differences in Two International Guidelines for Assessing Obstructive Coronary Artery Disease in Symptomatic Outpatients by Coronary Computed Tomographic Angiography.

Low-risk individuals are unlikely to benefit from noninvasive testing, and women tend to have a lower prevalence of coronary artery disease (CAD). This study compared the performance of two current guidelines that differ by sex to assess s a'q's't chest pain outpatients, including symptom-based (2016 National Institute for Health and Care Excellence, NICE) and risk-based strategies (2019 European Society of Cardiology, ESC). A total of 542 outpatients referred for coronary computed tomography angiography (CCTA) at a single-centre were retrospectively included in this study. A risk assessment was calculated for each outpatient according to the two guidelines. Patients were classified into low and high-risk groups according to each strategy. The presence of coronary artery disease was the endpoint. Net reclassification improvement (NRI) was used to assess the performance of the two strategies. The prevalence of CAD was 27%. The sensitivity, specificity, positive predictive value and negative predictive value for ESC and NICE were 90.4%, 54.3%, 42.2%, 93.9% and 78.8%, 35.6%, 31.1% and 82.0% respectively. Compare to NICE, the NRI for ESC were 30.32%. The ESC guidelines classified 55.56% of women and 28.14% of men into the low-risk group. The ESC guidelines had a higher predictive value for coronary artery disease compared to the NICE guidelines, with a positive NRI in men (15.55%) and women (34.46%) respectively. The ESC guidelines offered a more accurate calculation of risk assessment than the NICE guidelines. Patient sex influenced applying the recent ESC guidelines, which would result in a significant decrease in inappropriate testing of women but an increase in appropriate noninvasive testing of men.

Comparison of risk assessment strategies incorporating coronary artery calcium score with estimation of pretest probability to defer cardiovascular testing in patients with stable chest pain

BackgroundThe risk assessment of patients with stable chest pain (SCP) to defer further cardiovascular testing is crucial, but the most appropriate risk assessment strategy remains unknown. We aimed to compare current strategies to identify low risk SCP patients.Methods5289 symptomatic patients who had undergone coronary artery calcium score (CACS) and coronary computed tomographic angiography scan were identified and followed. Pretest probability (PTP) of obstructive coronary artery disease (CAD) for every patient was estimated according to European Society of Cardiology (ESC)-PTP model and CACS-weighted clinical likelihood (CACS-CL) model, respectively. Based on the 2019 ESC guideline-determined risk assessment strategy (ESC strategy) and CACS-CL model-based risk assessment strategy (CACS-CL strategy), all patients were divided into low and high risk group, respectively. Area under receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) was used.ResultsCACS-CL model provided more robust estimation of PTP than ESC-PTP model did, with a larger AUC (0.838 versus 0.735, p < 0.0001), positive IDI (9%, p < 0.0001) and less discrepancy between observed and predicted probabilities. As a result, compared to ESC strategy which only applied CACS-CL model to patients with borderline ESC-PTP, CACS-CL strategy incorporating CACS with estimation of PTP to entire SCP patients indicated a positive NRI (19%, p < 0.0001) and a stronger association to major adverse cardiovascular events, with hazard ratios: 3.97 (95% confidence intervals: 2.75–5.72) versus 5.11 (95% confidence intervals: 3.40–7.69).ConclusionThe additional use of CACS for all SCP patients in CACS-CL strategy improved the risk assessment of SCP patients to identify individuals at low risk.

Simple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project

To assess whether anthropometric measures (body mass index [BMI], waist-hip ratio [WHR], and estimated fat mass [EFM]) are independently associated with major adverse cardiovascular events (MACE), and to assess their added prognostic value compared with serum total-cholesterol. The study population comprised 109,509 individuals (53% men) from the MORGAM-Project, aged 19–97 years, without established cardiovascular disease, and not on antihypertensive treatment. While BMI was reported in all, WHR and EFM were reported in ∼52,000 participants. Prognostic importance of anthropometric measurements and total-cholesterol was evaluated using adjusted Cox proportional-hazards regression, logistic regression, area under the receiver-operating-characteristic curve (AUCROC), and net reclassification improvement (NRI). The primary endpoint was MACE, a composite of stroke, myocardial infarction, or death from coronary heart disease. Age interacted significantly with anthropometric measures and total-cholesterol on MACE (P ≤ 0.003), and therefore age-stratified analyses (<50 versus ≥ 50 years) were performed. BMI, WHR, EFM, and total-cholesterol were independently associated with MACE (P ≤ 0.003) and resulted in significantly positive NRI when added to age, sex, smoking status, and systolic blood pressure. Only total-cholesterol increased discrimination ability (AUCROC difference; P < 0.001). In subjects < 50 years, the prediction model with total-cholesterol was superior to the model including BMI, but not superior to models containing WHR or EFM, while in those ≥ 50 years, the model with total-cholesterol was superior to all models containing anthropometric variables, whether assessed individually or combined. We found a potential role for replacing total-cholesterol with anthropometric measures for MACE-prediction among individuals < 50 years when laboratory measurements are unavailable, but not among those ≥ 50 years.

Comparison of Risk Assessment Strategies for Patients with Diabetes Mellitus and Stable Chest Pain: A Coronary Computed Tomography Angiography Study.

Background To compare two risk assessment strategies to identify individuals likely to benefit from further imaging testing in patients with diabetes mellitus (DM) and stable chest pain (SCP) suspected of obstructive coronary artery disease (CAD). Methods 602 DM patients referred to coronary computed tomography angiography (CCTA) for SCP were included. They were divided into high- and low-risk groups according to the 2016 National Institute of Health and Care Excellence guideline-determined strategy (NICE strategy) which focused on symptom evaluation and 2019 European Society of Cardiology guideline-determined strategy (ESC strategy) which was based on pretest probability (PTP) sequentially determined by the ESC-PTP estimator and risk factor-weighted clinical likelihood (RF-CL) model, respectively. The associations of clinical outcomes with risk groups and net reclassification improvement (NRI) were evaluated. Results The NICE and ESC strategy classified 44% and 39% patients into the low-risk group, respectively. Compared to the NICE strategy, the ESC strategy indicated stronger associations between risk groups and events (hazard ratios: 4.24 versus 1.91), intensive clinical management, and a positive NRI (27.71%, p < 0.0001). The application of the RF-CL model ameliorated the underestimation of risk in patients with borderline ESC-PTP, which principally account for the improvement of the ESC strategy. Conclusion Compared to the NICE strategy, the ESC strategy seemed to be associated with greater efficiency in identifying high risk individuals in patients with DM and SCP.

Comparison of NICE and ESC strategy for risk assessment in women with stable chest pain: a coronary computed tomography angiography study.

For women presenting with stable chest pain (SCP), the appropriate risk assessment strategy to identify individuals unlikely to benefit from further cardiovascular imaging testing (CIT) is debatable. Thus, the present study intended to compare two risk assessment strategies in these individuals. 2592 women with SCP who underwent coronary computed tomography angiography (CCTA) were divided into low and high risk group according to 2016 National Institute of Health and Care Excellence guideline-determined strategy (NICE strategy) and 2019 European Society of Cardiology guideline-determined strategy (ESC strategy), respectively. The associations of coronary artery disease (CAD), major adverse cardiovascular event (MACE) and other subsequent clinical outcomes with risk groups and net reclassification improvement (NRI) were evaluated to compare different strategies. Both NICE strategy which focused on symptom evaluation and ESC strategy which was based on pretest probability (PTP) determined by ESC-PTP model and coronary artery calcium score-weighted clinical likelihood (CACS-CL) model classified a proportion (34.49% and 63.97%, respectively) of individuals into the low risk group. Compared to NICE strategy, ESC strategy indicated stronger associations between risk groups and obstructive CAD (odds ratio: 27.63 versus 3.57), MACE (hazard ratios: 4.24 versus 1.91), more intensive clinical management as well as a positive NRI (27.71%, p < 0.0001). Compared to NICE strategy, ESC strategy which sequentially incorporated ESC-PTP model with CACS-CL model seemed to be associated with greater effectiveness in identifying individuals who may derive maximum benefit from further CIT in women presenting with SCP.

Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer

To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC). We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n=164) and validation (n=70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation. The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index ≥0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05). The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC.

Comparison of Different Investigation Strategies to Defer Cardiac Testing in Patients With Stable Chest Pain

ObjectivesThis study aimed to compare the current 5 investigation strategies to defer cardiac testing in patients with stable chest pain. BackgroundFor the clinical management of stable chest pain, the identification of patients unlikely to benefit from further cardiac testing is important, but the most appropriate investigation strategy is unknown. MethodsA total of 4,207 patients referred to coronary computed tomography angiography for stable chest pain were classified into low- and high-risk groups according to the 2016 National Institute of Health and Care Excellence (NICE) guideline-determined strategy; PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk tool–based strategy; 2019 European Society of Cardiology (ESC) guideline–determined strategy; and coronary artery calcium score (CACS), either in isolation (the CACS strategy) or as part of a weighted clinical likelihood model–based strategy (the CACS-CL strategy). The associations of obstructive coronary artery disease on coronary computed tomography angiography, major adverse cardiovascular events, and subsequent clinical management with risk groups according to different strategies were evaluated and compared. ResultsThe NICE, PROMISE, ESC, CACS, and CACS-CL strategies classified a proportion (22.63%, 29.21%, 41.84%, 46.76%, and 51.41%, respectively) of patients into low-risk groups. Compared with the NICE, PROMISE, ESC, and CACS strategies, the CACS-CL strategy had a stronger association between risk groups and obstructive coronary artery disease (odd ratios: 16.00 vs 2.93, 5.53, 7.94, and 10.39, respectively), major adverse cardiovascular events (HRs: 6.83 vs 1.90, 2.94, 4.23, and 5.13, respectively) and intensive subsequent clinical management as well as better metrics of diagnostic accuracy and positive net reclassification improvement. ConclusionsAmong contemporary strategies used to identify patients with stable chest pain at low risk, the use of CACS, especially when combined with clinical risk features, showed the strongest potential to effectively defer cardiac testing.

External validation of the 4C Mortality Score for patients with COVID-19 and pre-existing cardiovascular diseases/risk factors

ObjectivesPredictive algorithms to inform risk management decisions are needed for patients with COVID-19, although the traditional risk scores have not been adequately assessed in Asian patients. We aimed to evaluate...

Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer

BackgroundAccurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction.MethodsWe identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness.ResultsThe radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful.ConclusionUS radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.

Kidney Function as Risk Factor and Predictor of Cardiovascular Outcomes and Mortality Among Older Adults

Estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (UACR) are associated with cardiovascular events in the general population but their utility among older adults is unclear. We investigated the associations of eGFR and UACR with stroke, myocardial infarction (MI), and death among older adults. Population-based cohort study. 1,581 participants (aged≥70 years) in the Berlin Initiative Study (BIS) without prior stroke or MI. Serum creatinine- and cystatin C-based eGFR, UACR categories, and measured GFR (n=436). Stroke, MI, and all-cause mortality. HRs and 95% CIs derived from multivariable-adjusted Cox proportional hazards models for association analyses. Net reclassification improvement (NRI) and C statistic differences comparing the predictive benefit of kidney measures with a traditional cardiovascular risk model. During a median follow-up of 8.2 years, 193 strokes, 125 MIs, and 531 deaths occurred.Independent of UACR, when GFR was estimated using the creatinine- and cystatinC-based BIS equation, eGFR of 45 to 59mL/min/1.73m2 (vs eGFR>60mL/min/1.73m2) was associated with stroke (HR, 2.23; 95% CI, 1.55-3.21) but not MI or all-cause mortality. For those with eGFR<45mL/min/1.73m2, the HRs were 1.99 (95% CI, 1.23-3.20) for stroke, 1.38 (95% CI, 0.81-2.36) for MI, and 1.57 (95% CI, 1.20-2.06) for mortality. Compared with UACR<30mg/g, UACR of 30 to 300mg/g was not associated with stroke (HR, 0.91; 95% CI, 0.63-1.33) but was associated with MI (HR, 1.65; 95% CI, 1.09-2.51) and all-cause mortality (HR, 1.63; 95% CI, 1.34-1.98). Prediction analysis for stroke showed significant positive NRI for eGFR calculated using the cystatin C-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and the creatinine- and cystatin C-based BIS and Full Age Spectrum equations. UACR demonstrated significant positive NRIs for MI and mortality. eGFR and UACR categorization based on single assessments; lack of cause-specific death data. eGFR of 45 to 59mL/min/1.73m2 without albuminuria was associated with stroke but not MI or all-cause mortality in older adults. In contrast, UACR of 30 to 300mg/g was associated with MI and all-cause mortality but not with stroke. Furthermore, cystatin C-based eGFR improved risk prediction for stroke in this cohort of older adults.

Comparison of the CAMI-NSTEMI and GRACE Risk Model for Predicting In-Hospital Mortality in Chinese Non-ST-Segment Elevation Myocardial Infarction Patients.

Introduction The ability of risk models to predict in-hospital mortality and the influence on downstream therapeutic strategy has not been fully investigated in Chinese Non-ST-segment elevation myocardial infarction (NSTEMI) patients. Thus, we sought to validate and compare the performance of the Global Registry of Acute Coronary Events risk model (GRM) and China Acute Myocardial Infarction risk model (CRM) and investigate impacts of the two models on the selection of downstream therapeutic strategies among these patients. Methods We identified 2587 consecutive patients with NSTEMI. The primary endpoint was in-hospital death. For each patient, the predicted mortality was calculated according to GRM and CRM, respectively. The area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow (H–L) test, and net reclassification improvement (NRI) were used to assess the performance of models. Results In-hospital death occurred in 4.89% (126/2587) patients. Compared to GRM, CRM demonstrated a larger AUC (0.809 versus 0.752, p < 0.0001), less discrepancy between observed and predicted mortality (H–L χ2: 22.71 for GRM, p=0.0038 and 10.25 for CRM, p=0.2479), and positive NRI (0.3311, p < 0.0001), resulting in a significant change of downstream therapeutic strategy. Conclusion In Chinese NSTEMI patients, the CRM provided a more accurate estimation for in-hospital mortality, and application of the CRM instead of the GRM changes the downstream therapeutic strategy remarkably.

Incorporation of shear wave elastography into a prediction model in the assessment of cervical lymph nodes

Rationale and objectivesTo assess the performance of shear wave elastography (SWE) and an extended model in predicting malignant cervical lymph nodes (LNs).Materials and methods109 patients who underwent ultrasound (US) and SWE before needle biopsy were enrolled. The optimal cutoff value of elasticity indices (EIs) was determined by receiver operating characteristic (ROC) curves. The c-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare extended model and traditional one.ResultsMalignant LNs had higher EIs than benign nodes (p < 0.001). The optimal cutoff point was 42 kilopascal, corresponding to 83.3% sensitivity, 64.7% specificity, and 68.8% overall accuracy. A multivariable logistic regression analysis confirmed that EI was an independent predictor for malignancy. The new extended prediction model had a positive NRI (0.96) and IDI (0.10) for predicting malignant neck LNs. Nevertheless, the c-statistic was not significantly different between the two models.ConclusionThe parameter of SWE theoretically improve the model performance. However, its real clinical impact is minor, as the parameters of US-based model is already very robust. SWE can be considered as an adjunctive quantitative tool beyond conventional US examination.

Epicardial Fat Volume Improves the Prediction of Obstructive Coronary Artery Disease Above Traditional Risk Factors and Coronary Calcium Score.

Recent studies have demonstrated the tremendous potential of epicardial fat volume (EFV) to predict obstructive coronary artery disease. We aimed to develop a new model to estimate pretest probability of obstructive coronary artery disease using traditional risk factors with coronary calcium score and EFV and compare it with proposed models in Chinese patients who underwent coronary computed tomography angiography. The new models were derived from 5743 consecutive patients using multivariate logistic regression and validated in an internal cohort using invasive coronary angiography as the outcome and an external cohort with clinical outcome data. Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve, integrated discrimination improvement and net reclassification improvement were calculated to validate and compare the performance of models. EFV improved prediction above conventional risk factors and coronary calcium score (area under the receiver operating characteristic curve increased from 0.856 to 0.874, integrated discrimination improvement 0.0487, net reclassification improvement 0.1181, P<0.0001 for all). The final model included 5 predictors: age, sex, symptom, coronary calcium score, and EFV. Good internal validation and external validation of the new model were achieved, with positive net reclassification improvement and integrated discrimination improvement, excellent area under the receiver operating characteristic curve and favorable calibration. Further, the new model demonstrated a better prediction of clinical outcome, resulting in a more cost-effective risk stratification to optimize decision-making of downstream diagnosis and treatment. Addition of EFV to conventional risk factors and coronary calcium score offered a more accurate and effective estimation for pretest probability of obstructive coronary artery disease, which may help to improve initial management of stable chest pain.

Coronary calcium score improves the estimation for pretest probability of obstructive coronary artery disease and avoids unnecessary testing in individuals at low extreme of traditional risk factor burden: validation and comparison of CONFIRM score and genders extended model

BackgroundReliability of models for estimating pretest probability (PTP) of obstructive coronary artery disease (CAD) has not been investigated in individuals at low extreme of traditional risk factor (RF) burden. Thus, we sought to validate and compare CONFIRM score and Genders extended model (GEM) among these individuals.MethodsWe identified symptomatic individuals with 0 or 1 RF who underwent coronary calcium scan and coronary computed tomographic angiography (CCTA). Follow-up clinical data were also recorded. PTP of obstructive CAD for every individual was estimated according to CONFIRM score and GEM, respectively. Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer–Lemeshow (H-L) test were used to assess the performance of models.ResultsThere were 1201 individuals with 0 RF and 2415 with 1 RF. The AUC for GEM was significantly larger than that for CONFIRM score, no matter in individuals with 0 (0.843 v.s. 0.762, p < 0.0001) or 1 (0.823 v.s. 0.752, p < 0.0001) RF. Compared to CONFIRM score, GEM demonstrated positive IDI (5% in individuals with 0 RF and 8% in individuals with 1 RF), positive NRI (41.50% in individuals with 0 RF and 40.19% in individuals with 1 RF), better prediction of clinical events and less discrepancy between observed and predicted probabilities, resulting in a significant decrease of unnecessary testing, especially in negative individuals.ConclusionIn individuals at low extreme of traditional RF burden of CAD, the addition of coronary calcium score provided a more accurate estimation for PTP and application of GEM instead of CONFIRM score could avoid unnecessary testing.

Comparison of CT-RECTOR and J-CTO scores to predict chronic total occlusion difficulty for percutaneous coronary intervention

BackgroundWe sought to evaluate the ability of the CT-RECTOR and J-CTO scores to predict time-efficient guidewire (GW) crossing through a chronic total occlusion (CTO) and final procedure success. MethodsData from 191 consecutive CTO lesions with pre-procedural coronary computed tomography angiography (CCTA) from our center were analyzed retrospectively. The difficulty of the procedure was classified as easy, intermediate, difficult, or very difficult according to CT-RECTOR and J-CTO scores. A successful GW crossing within 30min was set as the first endpoint. Final success of the procedure was set as the second endpoint. Receiver operating characteristic curves and net reclassification improvement (NRI) were used to compare the performance of both scores in predicting both endpoints. ResultsThe first and second endpoints were achieved in 55% and 76% of lesions, respectively. The NRI for prediction for both endpoints were 30.21% and 28.94%, respectively. Use of the CT-RECTOR score demonstrated a positive NRI for both the first (p=0.0027) and second (p=0.0190) endpoints. Compared with the J-CTO score (area under the curve: 0.76), the CT-RECTOR score (area under the curve: 0.85) yielded a higher predictive value for successful GW crossing within 30min (p=0.0018). ConclusionsCompared with J-CTO, the CT-RECTOR scoring system provides a more accurate noninvasive tool for predicting time-efficient GW crossing and final procedure success. This scoring system, which is based on CCTA, may aid in the identification of very difficult CTO lesions and downstream management.

Meta-analysis of ATRIA versus CHA2DS2-VASc for predicting stroke and thromboembolism in patients with atrial fibrillation

BackgroundSeveral studies have compared the discriminative performances of CHA2DS2-VASc and ATRIA scores, but the results are still disputed. Therefore, we aimed to explore their predictive abilities regarding stroke and thromboembolism (TE) risk in AF patients. MethodsWe systematically searched the Cochrane Library, PubMed and ScienceDirect databases up to May 2016 for studies regarding CHA2DS2-VASc and ATRIA scores. The data were extracted and then pooled using Review Manager software version 5.30. ResultsSix cohort studies with 363,432 participants were included. Using the published cut-off points, the pooled C-statistics were 0.66 for ATRIA and 0.63 for CHA2DS2-VASc (Pdiff>0.05). Using the optimized cut-off points, the C-statistics were 0.66 for ATRIA and 0.65 for CHA2DS2-VASc (Pdiff>0.05). However, the ATRIA score presented a positive net reclassification improvement (NRI) value compared with the CHA2DS2-VASc score. In contrast, the CHA2DS2-VASc score classified fewer patients as low and moderate risk than the ATRIA score. The CHA2DS2-VASc score had lower event rates (either events per person or events per 100 person-years) in both the low and moderate risk categories compared with the ATRIA score. ConclusionsIn combination with C-statistics and NRI values, the ATRIA score performed better than the CHA2DS2-VASc score for stroke risk prediction. In contrast, the CHA2DS2-VASc score was superior to the ATRIA score for identifying truly “low risk” AF patients.

Net Reclassification Improvement.

When adding new markers to existing prediction models, it is necessary to evaluate the models to determine whether the additional markers are useful. The net reclassification improvement (NRI) has gained popularity in this role because of its simplicity, ease of estimation, and understandability. Although the NRI provides a single-number summary describing the improvement new markers bring to a model, it also has several potential disadvantages. Any improved classification by the new model is weighted equally, regardless of the direction of reclassification. In prediction models that already identify the high- and low-risk groups well, a positive NRI may not mean better classification of those with medium risk, where it could make the most difference. Also, overfitting, or otherwise misspecified training models, produce overly positive NRI results. Because of the unaccounted for uncertainty in the model coefficient estimation, investigators should rely on bootstrapped confidence intervals rather than on tests of significance. Keeping in mind the limitations and drawbacks, the NRI can be helpful when used correctly.