Net Reclassification Improvement Index Research Articles

Estimated pulse wave velocity as a predictor of all-cause and cardiovascular mortality in patients with hypertension in China: a prospective cohort study.

Whether the estimated pulse wave velocity (ePWV) in Chinese patients with hypertension can serve as an independent predictor of cardiovascular and all-cause mortality remains unknown. Therefore, this study investigated the associations between ePWV and cardiovascular and all-cause mortalities and explored potential effect modifiers influencing these relationships. Finally, we compared the ePWV with the brachial-ankle pulse wave velocity (baPWV) to determine which parameter better predicts mortality. The population of this longitudinal cohort study was selected from the China H-type Hypertension Registry Study. The exposure and outcome variables were ePWV and all-cause and cardiovascular mortalities, respectively. The Cox proportional hazard regression model was applied to assess the associations between ePWV and all-cause and cardiovascular mortalities. The performances of ePWV and baPWV in predicting death were compared using the receiver operating characteristic (ROC) curve area, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). This prospective study enrolled 14,232 patients with hypertension. Following an average follow-up of 48 months, 806 individuals succumbed to all-cause mortality, with 397 cases specifically attributed to cardiovascular diseases. The Cox proportional regression analysis revealed a significant association between a 1 m/s increase in ePWV and a 37% higher risk of all-cause mortality (hazard ratio [HR]: 1.37, 95% confidence interval [CI]: 1.31-1.43) as well as a 52% higher risk of cardiovascular mortality (HR: 1.52, 95% CI: 1.43-1.62) in the fully adjusted model. The findings for ePWV according to quartile demonstrated hazard ratios for all-cause mortality for Q2 (10.25 < ePWV < 11.32), Q3 (11.32 < ePWV < 12.40), and Q4 (ePWV ≥ 12.40) of 1.50 (HR: 1.50, 95% CI: 1.07-2.10), 2.34 (HR: 2.34, 95% CI: 1.73-3.18), and 4.09 (HR: 4.09, 95% CI: 3.05-5.49), respectively, compared with Q1 (ePWV < 10.25). The risk of cardiovascular death also increased in proportion to the rise in ePWV. The results of the area under the ROC curve, NRI, and IDI all indicated that ePWV outperformed baPWV in predicting mortality. The results of the subgroup analysis demonstrated that body mass index (BMI) and hypoglycemic drug use modified the association between ePWV and mortality. The performance of ePWV in predicting all-cause and cardiovascular mortalities was superior to that of baPWV alone. Patients who were overweight or obese with higher ePWV values exhibited a significantly increased risk of all-cause death. The correlation between elevated ePWV and the risk of cardiovascular death was more pronounced in patients who had not received hypoglycemic drugs.

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases

Background and aimsA machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson’s disease (PD), and Alzheimer’s disease (AD). Materials and methodsThe eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. ResultsThe results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039–0.279), 0.113 (0.005–0.176), and 0.201 (−0.021–0.423), respectively; and IDI values of 0.098 (0.073–0.122), 0.070 (0.049–0.090), and 0.085 (0.068–0.101), respectively. ConclusionThe performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study.

Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Diagnostic value comparison of the combination of prostate-specific membrane antigen-body positron emission tomography/magnetic resonance imaging and prostate health index with each alone in early diagnosis of prostate cancer

ObjectiveThis study aimed to figure out whether the combination of prostate health index (PHI) and prostate-specific membrane antigen (PSMA)-positron emission tomography/magnetic resonance imaging (PET/MR) could improve the diagnostic accuracy for prostate cancer (PCa) than that of each individual method used alone. MethodsIn this prospective, observational study, 41 patients who underwent systematic prostate biopsy between June 2019 and September 2022 were enrolled. Both the PHI test and 18F-PSMA-1007-PET/MR were performed prior to biopsies. The diagnostic accuracy of different models was compared by logistic regression, area under the curves (AUC) of the receiver operating characteristic and net reclassification improvement (NRI) index. ResultsAmong the 41 patients, 14 (34.1%) were pathologically diagnosed with PCa. PHI in PCa group was significantly higher than that in benign group (44.4 vs. 35.0, p=0.048). Similarly, all the patients in PCa group received the positive results of 18F-PSMA-1007-PET/MR, of which the positive rate was significantly higher than those in bengin group (100% vs. 62.96%, p<0.05). The 18F-PSMA-1007-PET/MR provided additional diagnostic value to PHI (AUC: 0.802 vs. 0.692, p=0.025). However, there was no significant difference between the combination model and the 18F-PSMA-1007-PET/MR alone (AUC 0.802 vs. 0.685, p=0.071). The optimal PHI cutoff of the combination model is 32, with which the model could significantly reduce unnecessary biopsy (NRI: 22.22%, 95% confidence interval: 6.54–37.90, p=0.005). But among patients with PHI≥43.5, there was no significant difference between the combination model and PHI alone (NRI: 11.11%, 95% confidence interval: −0.74–22.97, p=0.066). ConclusionThe combination of PHI and 18F-PSMA-1007-PET/MR outperforms PHI alone for predicting PCa, especially in avoiding unnecessary biopsy. However, for patients with PHI≥43.5, the addition of 18F-PSMA-1007-PET/MR to PHI does not yield additional benefits.

Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models

IntroductionInsulin resistance and defects in pancreatic beta cells are the two major pathophysiologic abnormalities that underlie type 2 diabetes. In addition, visceral fat area (VFA) is reported to be a...

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor

To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis. A retrospective cohort study. This study was conducted at a tertiary referral center. A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio. Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation. In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor. This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Meta-Analysis Global Group in Chronic Heart Failure score for the prediction of mortality in valvular heart disease.

Valvular heart disease (VHD) is one of the leading causes of heart failure. Clinically significant VHD can induce different patterns of cardiac remodelling, and risk stratification is challenging in patients with various degrees of cardiac dysfunction. The study aimed to investigate the prognostic implications of Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score in patients with VHD. This study used data from the China Valvular Heart Disease (China-VHD) registry, which was a multicentre, prospective, observational cohort study for patients with significant (at least moderate) VHD. In total, 10446 patients with moderate or greater VHD from the China-VHD study were included in the present analysis. The primary outcome of interest was all-cause mortality within 2years. Among 10446 patients with VHD, the mean age was 61.98±13.47years, and 5819 (55.7%) were male. During 2years of follow-up, 895 (8.6%) patients died. The MAGGIC score was monotonically and independently associated with mortality in both total cohort [adjusted hazard ratio: 1.095, 95% confidence interval (CI): 1.084-1.107, P<0.001] and most types of VHD (aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, mixed aortic stenosis and aortic regurgitation, and multiple VHD). The score was also an independent prognostic factor in patients with or without symptoms or preserved left ventricular ejection fraction (LVEF) and exhibited both satisfactory discrimination and calibration properties in predicting mortality. The prognostic value of MAGGIC score was robust in most quartiles of N-terminal pro-brain natriuretic peptide level, with no significant interaction observed (Pinteraction =0.498). Compared with the EuroSCORE II, the MAGGIC score achieved significantly better predictive performance in overall population [C index: 0.769 vs. 0.727; net reclassification improvement index (95% CI): 0.354 (0.313-0.396), P<0.001; integrated discrimination improvement index (95% CI): 0.069 (0.052-0.085), P<0.001] and in subgroups of patients divided by therapeutic strategy, LVEF, symptomatic status, stage of VHD, and aetiology of VHD. The MAGGIC score is a reliable prognostic factor across the range of cardiac dysfunction in VHD and may assist in risk stratification and guide clinical decision-making.

The prognostic value of right ventricular longitudinal strain in predicting incident atrial fibrillation and ischemic stroke in the general population

Abstract Background Right ventricular (RV) longitudinal strain (RVLS) is a predictor of many cardiovascular outcomes including ventricular arrhythmias. However, the prognostic value of RVLS in predicting incident atrial fibrillation (AF) in participants from the general population is currently unknown. Purpose The aim of this study was to investigate if RVLS can be used to predict AF and ischemic stroke in the general population. Methods A total of 2794 participants (mean age 54±17 years, 42% male) from the general population underwent a health examination including two-dimensional speckle tracking echocardiography. RVLS was measured in the apical four-chamber projection optimized for a view of the RV. The RV free wall was divided into three segments, and the strain values of the three segments were averaged to obtain RVLS. The primary endpoint was incident AF at follow-up. All participants with known AF and prior stroke at baseline were excluded (n=175). The secondary endpoint consisted of the composite of AF and ischemic stroke. Results During a median follow-up of 5 years (interquartile range, 4.5-6.2 years), 110 participants (4%) were diagnosed with incident AF and 59 (2%) experienced an ischemic stroke, resulting in 169 (6%) experiencing the composite outcome. The risk of AF increased incrementally with decreasing tertile of RVLS, being approximately 3-fold higher in the 1st tertile as compared to the 3rd tertile (HR 2.71; 95% CI (1.64-4.46), p&amp;lt;0.001; figure). RVLS was a univariable predictor of AF with 9% increased risk per 1% decrease in RVLS (per 1% decrease: HR 1.09; 95% CI (1.06-1.13), p&amp;lt;0.001). The association remained significant even after multivariable adjustment for clinical and echocardiographic parameters (per 1% decrease: HR 1.06; 95% CI (1.01-1.11), p=0.015). RVLS was also a univariable predictor of the composite outcome of AF and ischemic stroke (per 1% decrease: HR 1.08; 95% CI (1.05-1.12), p&amp;lt;0.001). After multivariable adjustment for the same clinical and echocardiographic parameters, RVLS remained an independent predictor of the composite outcome (per 1% decrease: HR 1.04; 95% CI (1.00-1.09), p=0.032) Additionally, RVLS provided incremental prognostic information with regard to predicting AF as assessed by a significant increase in the net reclassification improvement (NRI) index beyond the CHARGE-AF score (continuous NRI, 0.232; 95% CI, 0.011-0.570). Conclusion In a low-risk general population, RVLS provides novel prognostic information on the long-term risk of AF and ischemic stroke. In addition, RVLS provides incremental prognostic information beyond the CHARGE-AF score in predicting AF in the general population.

Utility of coronary artery calcium in refining 10-year ASCVD risk prediction using a Thai CV risk score.

Coronary artery calcium (CAC) scanning is a valuable additional tool for calculating the risk of cardiovascular (CV) events. We aimed to determine if a CAC score could improve performance of a Thai CV risk score in prediction of 10-year atherosclerotic cardiovascular disease (ASCVD) risk for asymptomatic patients with CV risk factors. This was a retrospective cohort study that enrolled asymptomatic patients with CV risk factors who underwent CAC scans between 2005 and 2013. The patients were classified as low-, intermediate-, or high-risk (<10%, 10%-<20%, and ≥20%, respectively) of having ASCVD within 10-years based on a Thai CV risk score. In each patient, CAC score was considered as a categorical variable (0, 1-99, and ≥100) and natural-log variable to assess the risk of developing CV events (CV death, non-fatal MI, or non-fatal stroke). The C statistic and the net reclassification improvement (NRI) index were applied to assess whether CAC improved ASCVD risk prediction. A total of 6,964 patients were analyzed (mean age: 59.0 ± 8.4 years; 63.3% women). The majority of patients were classified as low- or intermediate-risk (75.3% and 20.5%, respectively), whereas only 4.2% were classified as high-risk. Nearly half (49.7%) of patients had a CAC score of zero (no calcifications detected), while 32.0% had scores of 1-99, and 18.3% of ≥100. In the low- and intermediate-risk groups, patients with a CAC ≥100 experienced higher rates of CV events, with hazard ratios (95% CI) of 1.95 (1.35, 2.81) and 3.04 (2.26, 4.10), respectively. Incorporation of ln(CAC + 1) into their Thai CV risk scores improved the C statistic from 0.703 (0.68, 0.72) to 0.716 (0.69, 0.74), and resulted in an NRI index of 0.06 (0.02, 0.10). To enhance the performance of the Thai CV risk score, a revision of the CV risk model was performed, incorporating ln(CAC + 1), which further increased the C statistic to 0.771 (0.755, 0.788). The addition of CAC to traditional risk factors improved CV risk stratification and ASCVD prediction. Whether this adjustment leads to a reduction in CV events and is cost-effective will require further assessment.

Sex-related differences in functional capacity and its implications in risk stratification before major non-cardiac surgery: a post hoc analysis of the international METS study

Poor functional capacity has been identified as an important modifiable risk factor for postoperative complications. Cardiopulmonary exercise testing (CPET) provides objective parameters of functional capacity (e.g., oxygen consumption at peak exercise, peak VO2), with significant prognostication for postoperative complications. However, sex-specific thresholds for functional capacity to predict surgical risk are yet to be established. Therefore, we performed a post hoc analysis of the international, multicentre, prospective observational METS (Measurement of Exercise Tolerance before Surgery) study to evaluate if sex-specific thresholds of peak VO2 improve risk prediction of postoperative complications. We undertook a post hoc analysis (HREC/71824/PMCC) of the METS study, which was performed between March 2013 and March 2016. We investigated whether sex-specific differences exist for CPET-derived parameters and associated thresholds for predicting postoperative complications in this large cohort of patients that had major non-cardiac surgery (n=1266). Logistic regression models were analyzed for the association of low peak VO2 with moderate-to-severe in-hospital postoperative complications. Optimal sex-specific peak VO2 thresholds were obtained by maximizing the Youden index of receiver operating characteristic (ROC) curves. Finally, multivariable logistic regression models tested the resulting sex-specific thresholds against the established non-sex-specific peak VO2 threshold (14mLkg-1 min-1) adjusted for clinically relevant features such as comorbidities and surgical complexity. Models were evaluated by bootstrapping optimism-corrected area under the ROC curve and the net reclassification improvement index (NRI). Female patients (n=480) had a lower mean (SD) peak VO2 than males (16.7 (4.9) mL kg-1 min-1 versus 21.2 (6.5) mL kg-1 min-1, p<0.001) and a lower postoperative complication rate (10.4% versus 15.3%; p=0.018) than males (n=786). The optimal peak VO2 threshold for predicting postoperative complications was 12.4mLkg-1 min-1 for females and 22.3mLkg-1 min-1 for males, respectively. In the multivariable regression model, low non-sex-specific peak VO2 did not independently predict postoperative complications. In contrast, low sex-specific peak VO2 was an independent predictor of postoperative complications (OR 2.29; 95% CI: 1.60, 3.30; p<0.001). The optimism-corrected AUC-ROC of the sex-specific model was higher compared with the non-sex-specific model (0.73 versus 0.7; DeLong's test: p=0.021). The sex-specific model classified 39% of the patients more correctly than the baseline model (NRI=0.39; 95% CI: 0.24, 0.55). In contrast, the non-sex-specific model only classified 9% of the patients more correctly when compared against the baseline model (NRI=0.09; 95% CI:-0.04, 0.22). Our data report sex-specific differences in preoperative CPET-derived functional capacity parameters. Sex-specific peak VO2 thresholds identify patients at increased risk for postoperative complications with a higher discriminatory ability than a sex-unspecific threshold. As such, sex-specific threshold values should be considered in preoperative CPET to potentially improve risk stratification and to guide surgical decision-making, including eligibility for surgery, preoperative optimization strategies (prehabilitation) or seeking non-surgical options. There was no funding for the present study. The original METS study was funded by Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, Ontario Ministry of Health and Long-Term Care, Ontario Ministry of Research, Innovation and Science, UK National Institute of Academic Anaesthesia, UK Clinical Research Collaboration, Australian and New Zealand College of Anaesthetists, and Monash University.

The Prognostic Potential of End-Stage Knee Osteoarthritis and Its Components to Predict Knee Replacement: Data From the Osteoarthritis Initiative.

We aimed to determine how 2 definitions of end-stage knee osteoarthritis (esKOA) and each component (knee symptoms, persistent knee pain, radiographic severity, and presence of limited mobility or instability) related to future knee replacement (KR). We performed knee-based analyses of Osteoarthritis Initiative data from baseline to the first 4 annual follow-up visits, and data on KR from baseline until the fifth yearly contact. We calculated a base model using common risk factors for KR in logistic regression models with generalized estimating equations. We assessed model performance with area under the receiver-operating characteristic curve (AUC) and Hosmer-Lemeshow test. We then added esKOA or each component from the visit (< 12 months) before a KR and change in the year before a KR. We calculated the net reclassification improvement (NRI) index and the integrated discrimination improvement (IDI) index. Our sample was mostly female (58%), ≥ 65 years old, White (82%), and without radiographic knee osteoarthritis (50%). At the visit before a KR, Kellgren-Lawrence (KL) grades (ordinal scale; AUC 0.88, NRI 1.12, IDI 0.11), the alternate definition of esKOA (AUC 0.84, NRI 1.16, IDI 0.12), and a model with every component of esKOA (AUC 0.91, NRI 1.30, IDI 0.17) had the best performances. During the year before a KR, change in esKOA status (alternate definition) had the best performance (AUC 0.86, NRI 1.24, IDI 0.12). Radiographic severity may be a screening tool to find a knee that will likely receive a KR. However, esKOA may be an ideal outcome in clinical trials because a change in esKOA state predicts future KR.

Qualitative and quantitative PET/MRI in the evaluation of axillary lymph node metastasis in patients with breast cancer

Objective: To investigate the value of qualitative and quantitative PET/MRI in the evaluation of axillary lymph node metastasis in patients with breast cancer. Methods: A total of 33 patients with breast cancer underwent preoperative PET/MRI examinations in Jingling Hospital from February to August in 2022 were retrospectively collected. All these patients were female, aged from 34 to 73 (51.4±11.3) years. Histopathological results and follow-up data were deemed as the referent standard, and the images were independently evaluated by two experienced breast imaging radiologists. The qualitative PET/MRI evaluation procedures were designed to evaluated the MRI alone to classify the axillary lymph nodes firstly, and then, the axillary lymph nodes status was reclassified by combining MRI and PET images. The net reclassification improvement index (NRI) was calculated using the R Programming Language (RStudio). The quantitative PET/MRI evaluation of the maximum standard uptake value (SUVmax) of axillary lymph nodes were measured by two radiologists, respectively, and the average value was compared with the referent standard to conduct a receiver operating characteristic (ROC) curve to select the optimal cutoff value of SUVmax. Based on the cutoff value and MRI classification results, axillary lymph nodes status was divided into quantitative PET/MRI positive or negative. The sensitivity, specificity and accuracy of MRI and quantitative PET/MRI in evaluating axillary lymph node metastasis were compared, and the area under the ROC curve (AUC) was compared. Results: There was no significant difference in sensitivity, specificity and accuracy between MRI and quantitative PET/MRI in evaluating lymph node metastasis of breast cancer (81.82% vs 95.46%; 81.82% vs 100%; 81.82% vs 96.97%) (all P>0.05). The AUC had a statistically significant difference [0.82 (0.65 to 0.93) vs 0.98 (0.85 to 1.00), P=0.026)]. According to the referent standard, in the 11 cases without ipsilateral axillary lymph node metastasis, the SUVmax was 0.83±0.18, while in the 22 cases with ipsilateral axillary lymph node metastasis, the SUVmax was [4.36 (1.77, 5.85)]. Compared with MRI alone, the NRI of qualitative PET/MRI in evaluating lymph node metastasis was 36.36% (P=0.021). Conclusion: Compared with MRI alone, quantitative PET/MRI has a higher AUC for evaluating axillary lymph node metastasis in patients with breast cancer, and qualitative PET/MRI had a better reclassification power in the evaluation of axillary lymph node metastasis.

Assessment of cardio-renal-hepatic function in patients with valvular heart disease: a multi-biomarker approach—the cardio-renal-hepatic score

BackgroundValvular heart disease (VHD) can cause damage to extra-cardiac organs, and lead to multi-organ dysfunction. However, little is known about the cardio-renal-hepatic co-dysfunction, as well as its prognostic implications in patients with VHD. The study sought to develop a multi-biomarker index to assess heart, kidney, and liver function in an integrative fashion, and investigate the prognostic role of cardio-renal-hepatic function in VHD.MethodsUsing a large, contemporary, prospective cohort of 6004 patients with VHD, the study developed a multi-biomarker score for predicting all-cause mortality based on biomarkers reflecting heart, kidney, and liver function (N-terminal pro-B-type natriuretic peptide [NT-proBNP], creatinine, and albumin). The score was externally validated in another contemporary, prospective cohort of 3156 patients with VHD.ResultsDuring a median follow up of 731 (704–748) days, 594 (9.9%) deaths occurred. Increasing levels of NT-proBNP, creatinine, and albumin were independently and monotonically associated with mortality, and a weighted multi-biomarker index, named the cardio-renal-hepatic (CRH) score, was developed based on Cox regression coefficients of these biomarkers. The CRH score was a strong and independent predictor of mortality, with 1-point increase carrying over two times of mortality risk (overall adjusted hazard ratio [95% confidence interval]: 2.095 [1.891–2.320], P < 0.001). The score provided complementary prognostic information beyond conventional risk factors (C index: 0.78 vs 0.81; overall net reclassification improvement index [95% confidence interval]: 0.255 [0.204–0.299]; likelihood ratio test P < 0.001), and was identified as the most important predictor of mortality by the proportion of explainable log-likelihood ratio χ2 statistics, the best subset analysis, as well as the random survival forest analysis in most types of VHD. The predictive performance of the score was also demonstrated in patients under conservative treatment, with normal left ventricular systolic function, or with primary VHD. It achieved satisfactory discrimination (C index: 0.78 and 0.72) and calibration in both derivation and validation cohorts.ConclusionsA multi-biomarker index was developed to assess cardio-renal-hepatic function in patients with VHD. The cardio-renal-hepatic co-dysfunction is a powerful predictor of mortality and should be considered in clinical management decisions.

Age-Related Trends in the Predictive Value of Carotid Intima-Media Thickness for Cardiovascular Death: A Prospective Population-Based Cohort Study.

Background The age-related trends in the predictive ability of carotid intima-media thickness (CIMT) for cardiovascular risk remain unclear. We aimed to identify the age-related trends in the predictive value of CIMT for cardiovascular death. Methods and Results In a prospective cohort of adults aged 35 to 75 years without history of cardiovascular disease who were enrolled between 2014 and 2020, we measured CIMT at baseline and collected the vital status and cause of death. We divided the study population into 4 age groups (35-44, 45-54, 55-64, and 65-75 years). Competing risk models were fitted to estimate the associations between CIMT and cardiovascular death. The added values of CIMT in prediction were assessed by the differences of the Harrell's concordance index and the net reclassification improvement index. We included 369 478 adults and followed them for a median of 4.7 years. A total of 4723 (1.28%) cardiovascular deaths occurred. After adjusting for the traditional risk factors, the hazard ratios for CIMTmean per SD decreased with age, from 1.27 (95% CI, 1.17-1.37) in the 35 to 44 years age group to 1.14 (95% CI, 1.10-1.19) in the 65 to 75 years age group (P for interaction <0.01). Meanwhile, the net reclassification improvement indexes for CIMTmean were attenuated with age, from 22.60% (95% CI, 15.56%-29.64%) in the 35 to 44 years age group to 7.00% (95% CI, -6.82% to 20.83%) in the 65 to 75 years age group. Similar results were found for maximum CIMT in all age groups. Conclusions CIMT may improve cardiovascular risk prediction in the young and middle-aged populations, rather than those aged ≥55 years.

Prognostic significance of troponin I in assessing cardiovascular risk in the Russian population. Data from ESSE-RF1 and ESSE-RF2 multicenter studies

Aim. To assess the significance of troponin I (hs-cTnI) for cardio­vascular risk stratification in a population of Russian working-age men and women of.Material and methods. The data of 13976 men and women aged 25-64 from the ESSE-RF1 and ESSE-RF2 multicenter studies were analyzed. The examination included following points: standard questionnaire, measurements of height, weight, waist circumference, body mass index, blood pressure, pulse rate, biochemical tests. The level of hs-cTnI in blood serum samples stored at -70оC in the Biobank of the National Medical Research Center for Therapy and Preventive Medicine was determined by chemiluminescent immunoassay analysis. Individual cardiovascular risk was calculated using the SCORE scale: values &lt;1% — low risk; 1-4% — moderate, 5-9% — high and ≥10% very high. Similar to the BiomarCaRE study, participants were divided into 3 sex-adjusted cTnI levels: men &lt;6, 6 to 12, and &gt;12 pg/ml; women &lt;4, 4 to 10, and &gt;10 pg/ml. We analyzed following hard endpoints: cardiovascular death or non-fatal myocardial infarction, and composite endpoint: the same and stroke.Results. The prospective analysis of the relationship between troponin levels and endpoints and composite endpoints showed that in participants without prior CVD, hs-cTnI levels &gt;12 pg/ml in men and &gt;10 pg/ml in women are associated with an increased risk of endpoints and composite endpoints by 3,39 (1,91-6,03) and 2,69 (1,6-4,52) times (p&lt;0,001), respectively. Similarly to BiomarCaRE, the SCORE-calculated risk reclassification was validated by adding the hs-cTnI value. The net reclassification improvement index (NRI) for endpoints and composite endpoints was 13% and 11% (p&lt;0,001), respectively. Among men, the reclassification for NRI composite endpoints was more accurate than among women: 19% and 11%, respectively.Conclusion. The hs-cTnI level is an independent predictor of myo­cardial infarction, stroke, and death from CVD in people aged 35-64 years without prior CVD. Adding the hs-cTnI level to the SCORE model makes the risk prediction more accurate.

Computed tomography-based radiomics nomogram using machine learning for predicting one-year surgical risk after diagnosis of Crohn's disease.

Identifying patients with aggressive Crohn's disease (CD) threatened by a high risk of early onset surgery is challenging. We aimed to establish and validate a radiomics nomogram to predict one-year surgical risk after the diagnosis of CD, thereby facilitating therapeutic strategies making. Patients with CD who had undergone baseline computed tomography enterography (CTE) examination at diagnosis were recruited and randomly divided into training and test cohorts at a ratio of 7:3. Enteric phase CTE images were obtained. Inflamed segments and mesenteric fat were semiautomatically segmented, followed by feature selection and signature building. A nomogram of radiomics was constructed and validated using a multivariate logistic regression algorithm. A total of 268 eligible patients were retrospectively included, 69 of whom underwent surgery one-year after diagnosis. A total of 1218 features from inflamed segments and 1218 features from peripheral mesenteric fat were extracted, and reduced to 10 and 15 potential predictors, respectively, to construct two radiomic signatures. By incorporating the radiomics signatures and clinical factors, the radiomics-clinical nomogram showed favorable calibration and discrimination in the training cohort, with an area under the curve (AUC) of 0.957, which was confirmed in the test set (AUC, 0.898). Decision curve analysis and net reclassification improvement index demonstrated the clinical usefulness of the nomogram. We successfully established and validated a CTE-based radiomic nomogram with both inflamed segment and mesenteric fat simultaneously evaluated to predict one-year surgical risk in CD patients, which assisted in clinical decision-making and individualized management. This article is protected by copyright. All rights reserved.

Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

PurposeTo develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET).Material and methodsConsecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach.ResultsAmong 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature.ConclusionThis is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.

The Combined Effect of Polygenic Risk Score and Prostate Health Index in Chinese Men Undergoing Prostate Biopsy

To date, the combined effect of polygenic risk score (PRS) and prostate health index (phi) on PCa diagnosis in men undergoing prostate biopsy has never been investigated. A total of 3166 patients who underwent initial prostate biopsy in three tertiary medical centers from August 2013 to March 2019 were included. PRS was calculated on the basis of the genotype of 102 reported East-Asian-specific risk variants. It was then evaluated in the univariable or multivariable logistic regression models that were internally validated using repeated 10-fold cross-validation. Discriminative performance was assessed by area under the receiver operating curve (AUC) and net reclassification improvement (NRI) index. Compared with men in the first quintile of age and family history adjusted PRS, those in the second, third, fourth, and fifth quintiles were 1.86 (odds ratio, 95% confidence interval (CI): 1.34-2.56), 2.07 (95%CI: 1.50-2.84), 3.26 (95%CI: 2.36-4.48), and 5.06 (95%CI: 3.68-6.97) times as likely to develop PCa (all p < 0.001). Adjustment for other clinical parameters yielded similar results. Among patients with prostate-specific antigen (PSA) at 2-10 ng/mL or 2-20 ng/mL, PRS still had an observable ability to differentiate PCa in the group of prostate health index (phi) at 27-36 (Ptrend < 0.05) or >36 (Ptrend ≤ 0.001). Notably, men with moderate phi (27-36) but highest PRS (top 20% percentile) would have a comparable risk of PCa (positive rate: 26.7% or 31.3%) than men with high phi (>36) but lowest PRS (bottom 20% percentile positive rate: 27.4% or 34.2%). The combined model of PRS, phi, and other clinical risk factors provided significantly better performance (AUC: 0.904, 95%CI: 0.887-0.921) than models without PRS. Adding PRS to clinical risk models could provide significant net benefit (NRI, from 8.6% to 27.6%), especially in those early onset patients (NRI, from 29.2% to 44.9%). PRS may provide additional predictive value over phi for PCa. The combination of PRS and phi that effectively captured both clinical and genetic PCa risk is clinically practical, even in patients with gray-zone PSA.

P276 Development and validation of a computed tomography-based radiomic nomogram to predict early-onset surgery risk in patients with Crohn’s disease

Abstract Background Identifying patients with aggressive Crohn’s disease (CD) threatened by a high risk of early onset surgery is challenging. We aimed to establish and validate a radiomic nomogram to predict one-year surgery risk after the diagnosis of CD, thereby facilitating therapeutic strategies making. Methods Patients with CD who had undergone computed tomography enterography (CTE) examination at diagnosis were recruited and randomly divided into training and test cohorts at a ratio of 7:3. Arterial phase CTE images were obtained. Inflamed segments and mesenteric fat were semiautomatically segmented, followed by feature selection and signature building. A radiomic nomogram was constructed and validated using a multivariate logistic regression algorithm. (Figure 1) Results A total of 268 eligible patients were retrospectively included, 69 of whom underwent surgery one-year after diagnosis. A total of 1208 features from inflamed segments and 1100 features from peripheral mesenteric fat were extracted, and reduced to 11 and 16 potential predictors, respectively, to construct two radiomic signatures. By incorporating the radiomics signatures and clinical factors, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training cohort, with an area under the curve (AUC) of 0.957, which was confirmed in the test set (AUC, 0.917). Decision curve analysis and net reclassification improvement index demonstrated the clinical usefulness of the nomogram. (Figure 2) Conclusion We successfully established and validated a CTE-based radiomic nomogram with both inflamed segment and mesenteric fat simultaneously evaluated to predict one-year surgery risk in CD patients, which assisted in clinical decision-making and individualized management.

S-37-2: CIRCADIAN RHYTHM OF PERIPHERAL AND CENTRAL ARTERIAL BLOOD PRESSURES IN RELATION TO ALL-CAUSE MORTALITY

Objective: Brachial blood pressure (BP) presents a circadian rhythm. Abnormal rhythms are associated with adverse outcomes. However, it is not clear about the circadian rhythm of central BP and its predictive value. Methods: The study participants were patients referred to the outpatient clinic of Ruijin Hospital for 24-hour ambulatory BP monitoring from the year 2017 to 2020. The 24-hour brachial and central ambulatory BP monitoring were performed with the use of the Mobil-O-Graph (Germany) monitors. Dipping, non-dipping, reverse dipping and extreme dipping were defined as a night-to-day systolic BP ratio of 0.8–0.9, 0.9–1.0, &gt; 1.0, and &lt; 0.8, respectively. In cross-classification analysis of brachial and central dipping status, extreme dipping and reverse dipping were grouped with dipping and non-dipping, respectively. The vital status of patients until Dec 2020 was ascertained according to the vital statistics of the Shanghai Center for Disease Prevention and Control. Results: In 26,023 enrolled patients (men 47.4%, average age 53.0 years) followed up for a median of 1.8 years, 120 deaths occurred. The prevalence of non-dipping (46.7% vs. 43.7%, respectively) and reverse dipping (16.0% vs. 13.1%) was significantly (P &lt; 0.001) higher for central than for brachial BP. The hazard ratios (95% confidence intervals, [CI]) for all-cause mortality were statistically significant for reverse dipping (1.95 [1.18–3.23] and 2.37 [1.43–3.94], respectively), but not other dipping status, versus dipping in brachial and central BP, respectively. In the brachial and central cross-classification analysis, the prevalence of consistent dipping, consistent non-dipping, isolated central non-dipping, and isolated brachial non-dipping was 34.7%, 54.2%, 8.6%, and 2.5%, respectively. Taking the consistent dipping as reference, the hazard ratios (95% CI) for all-cause mortality were 1.83 (0.77–4.31), 1.34 (0.46–3.95), and 1.94 (1.20–3.15) for isolated central, isolated brachial and consistent non-dipping, respectively. Furthermore, cross-classification significantly improved the risk prediction of all-cause mortality with a net reclassification improvement index (95% CI) of 0.193 (0.069–0.304). Conclusions: Non-dipping and reverse dipping were more common for central than for brachial BP, and were associated with a higher mortality risk irrespective of brachial or central BP. Cross-classification of brachial and central dipping status further improved risk stratification.