Improved Model Discrimination Research Articles

Prognostic Value of Stress CMR in Symptomatic Patients With Coronary Stenosis on CCTA

BackgroundNoninvasive functional imaging is often performed in patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA). However, the prognostic value of stress cardiac magnetic resonance (CMR) is unknown in patients with coronary stenosis of unknown significance on coronary CTA. ObjectivesThis study assessed the prognostic value of stress CMR in symptomatic patients with obstructive CAD of unknown significance on coronary CTA. MethodsBetween 2008 and 2020, consecutive symptomatic patients without known CAD referred for coronary CTA were screened. Patients with obstructive CAD (at least 1 ≥50% stenosis on coronary CTA) were further referred for stress CMR and followed for the occurrence of major adverse cardiovascular events (MACEs), defined as cardiovascular death or nonfatal myocardial infarction. ResultsOf 2,210 patients who completed CMR, 2,038 (46.5% men; mean age 69.8 ± 12.2 years) completed follow-up (median 6.8 years; IQR: 5.9-9.2 years); 281 experienced a MACE (13.8%). Inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with MACEs (HR: 4.51 [95% CI: 3.55-5.74], and HR: 3.32 [95% CI: 2.55-4.32], respectively; P < 0.001). In multivariable Cox regression, the number of segments with >70% stenosis, with noncalcified plaques and the number of vessels with obstructive CAD were prognosticators (P < 0.001). The presence of inducible ischemia and LGE were independent predictors of MACEs (HR: 3.97 [95% CI: 3.43-5.13]; HR: 2.30 [95% CI: 1.52-3.33]; P < 0.001). After adjustment, stress CMR showed the best improvement in model discrimination and reclassification above traditional risk factors and coronary CTA (C-statistic improvement: 0.04; net reclassification improvement = 0.421; integrative discrimination index = 0.047). ConclusionsIn symptomatic patients with obstructive CAD of unknown significance on coronary CTA, stress CMR had incremental prognostic value to predict MACEs.

External Validation of the Passive Surveillance Stroke Severity Indicator.

The Passive Surveillance Stroke Severity (PaSSV) Indicator was derived to estimate stroke severity from variables in administrative datasets but has not been externally validated. We used linked administrative datasets to identify patients with first hospitalization for acute stroke between 2007-2018 in Alberta, Canada. We used the PaSSV indicator to estimate stroke severity. We used Cox proportional hazard models and evaluated the change in hazard ratios and model discrimination for 30-day and 1-year case fatality with and without PaSSV. Similar comparisons were made for 90-day home time thresholds using logistic regression. We also linked with a clinical registry to obtain National Institutes of Health Stroke Scale (NIHSS) and compared estimates from models without stroke severity, with PaSSV, and with NIHSS. There were 28,672 patients with acute stroke in the full sample. In comparison to no stroke severity, addition of PaSSV to the 30-day case fatality models resulted in improvement in model discrimination (C-statistic 0.72 [95%CI 0.71-0.73] to 0.80 [0.79-0.80]). After adjustment for PaSSV, admission to a comprehensive stroke center was associated with lower 30-day case fatality (adjusted hazard ratio changed from 1.03 [0.96-1.10] to 0.72 [0.67-0.77]). In the registry sample (N = 1328), model discrimination for 30-day case fatality improved with the inclusion of stroke severity. Results were similar for 1-year case fatality and home time outcomes. Addition of PaSSV improved model discrimination for case fatality and home time outcomes. The validity of PASSV in two Canadian provinces suggests that it is a useful tool for baseline risk adjustment in acute stroke.

Derivation and validation of a simple multidimensional index incorporating exercise capacity parameters for survival prediction in idiopathic pulmonary fibrosis

IntroductionThe gender-age-physiology (GAP) index is an easy-to-use baseline mortality prediction model in idiopathic pulmonary fibrosis (IPF). The GAP index does not incorporate exercise capacity parameters such as 6 min walk...

Echocardiography Nomogram for Predicting Survival among Chronic Lung Disease Patients with Severe Pulmonary Hypertension.

Severe pulmonary hypertension in chronic lung diseases (severe CLD-PH) differs significantly from other types of PH in physiology and prognosis. We aimed to assess whether echocardiography helps predict long-term survival in patients with severe CLD-PH. This single-centre, observational cohort study enrolled 100 patients with severe CLD-PH (mean pulmonary arterial pressure ≥35 mm Hg or ≥25 mm Hg with cardiac index <2.0 L/min/m2 or pulmonary vascular resistance ≥6 Wood units) between 2009 and 2014. The population was randomly divided into a derivation and validation cohort in a 2:1 ratio. To construct a nomogram, a multivariable logistic regression model was applied, and scores were assigned based on the hazard ratio of independent echocardiographic predictors. Multivariate Cox hazards analysis identified the strongest predictors of mortality as pulmonary arterial systolic pressure (PASP), tricuspid annular plane systolic excursion, and right ventricular end-diastolic transverse dimension. The three independent predictors were entered into the nomogram. Compared with PASP alone, the nomogram resulted in an integrated discrimination improvement of 15.5% (95% confidence interval, 5.52–25.5%, p = 0.002) with a net improvement in model discrimination (C-statistic from 0.591 to 0.746). Using echocardiographic parameters, we established and validated a novel nomogram to predict all-cause death for patients with severe CLD-PH.

Change in Left Atrioventricular Coupling Index to Predict Incident Atrial Fibrillation: The Multi-Ethnic Study of Atherosclerosis (MESA).

Background Left atrial (LA) and left ventricular (LV) structural and functional parameters have independent prognostic values as predictors of atrial fibrillation (AF). Purpose To investigate the prognostic value of a left atrioventricular coupling index (LACI) and average annualized change in LACI (hereafter, ΔLACI) measured by cardiac MRI to predict incident AF in a population-based sample from the Multi-Ethnic Study of Atherosclerosis (MESA). Materials and Methods In a secondary analysis of the prospective MESA, 1911 study participants without clinically recognized AF and cardiovascular disease at baseline had LACI assessed with cardiac MRI at baseline (examination 1, 2000-2002) and 10 years later (examination 5, 2010-2012). LACI was defined as the ratio of LA to LV end-diastolic volumes. Univariable and multivariable Cox proportional hazard models were used to evaluate the associations of LACI and average ΔLACI with incident AF. Results Among the 1911 participants (mean age, 59 years ± 9 [standard deviation]; 907 men), 87 incident AF events occurred over 3.9 years ± 0.9 after the second imaging (examination 5). After adjustment for traditional risk factors, greater LACI and ΔLACI were independently associated with AF (hazard ratio, 1.69 [95% CI: 1.46, 1.96] and 1.71 [95% CI: 1.50, 1.94], respectively; both P < .001). Adjusted models for LACI and ΔLACI showed improvement in model discrimination compared with currently used AF risk score (Cohort for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation, or CHARGE-AF, score) model (area under receiver operating characteristic curve [AUC], 0.78 vs 0.74; and AUC, 0.80 vs 0.74, respectively; both P < .001); and to the final model including individual LA or LV parameters for predicting AF incidence (AUC, 0.78 vs 0.76; and AUC, 0.80 vs 0.78, respectively; both P < .001). Conclusion Atrioventricular coupling (left atrioventricular coupling index [LACI]) and coupling change (annual change in LACI) were strong predictors for atrial fibrillation (AF) in a multiethnic population. Both had incremental prognostic value for predicting AF over traditional risk factors, and superior discrimination compared with the Cohort for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation, or CHARGE-AF, score and to individual left atrial or left ventricular parameters. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Leiner in this issue.

PDAY risk score predicts cardiovascular events in young adults: the CARDIA study.

Atherosclerotic cardiovascular disease (ASCVD) risk prediction equations apply to older adults. For this study, the Pathobiologic Determinants of Atherosclerosis in Youth (PDAY) risk score, based on post-mortem measurements of atherosclerosis in 15-34-year olds dying accidentally, was used to predict ASCVD events, specifically myocardial infarction and revascularization, in middle age, from risk measured at ≤40 years of age. The Coronary Artery Risk Development in Young Adults Study (CARDIA) collected longitudinal cardiovascular risk data, coronary artery calcium (CAC) scores, and ASCVD data beginning at age 18 and 30 years with 30-year follow-up. Predictive accuracy for ASCVD of the PDAY risk score, calculated at baseline (mean age 24) and at all six CARDIA examinations up until year 15, was examined. We also examined whether the presence of CAC improved model discrimination. The cohort for this study comprised 5004 Black and White men and women, at baseline and 3558 with data at year 15. Each standard deviation increase in PDAY score, at each examination, was significantly associated with future ASCVD. Hazard ratios (per standard deviation) increased from 1.74 to 2.04 from year 0 to year 15. C-statistics ranged from 0.771 to 0.794. Coronary artery calcium measurement at age 33-45 years improved risk prediction only if the score was 0. Cumulative risk exposure over the first 15 years of the CARDIA study also had high-predictive value (c-statistic 0.798, 95% confidence interval 0.762-0.835). The PDAY risk score may be used in young adults, prior to age 40 years to predict ASCVD events.

Prognostic value of left atrioventricular coupling index (LACI) in pre- and post-menopausal women : from the multi-ethnic study of atherosclerosis (MESA)

Abstract Funding Acknowledgements Type of funding sources: None. BACKGROUND Although endogenous sex hormones influence both left atrial (LA) and left ventricular (LV) structure in peri-menopausal women, no study has ever evaluated the interaction between sex hormones levels and the left atrioventricular coupling. PURPOSE This study aimed to assess the prognostic value of a left atrioventricular coupling index (LACI) in pre- and post-menopausal women without history of CVD at baseline. METHODS In all women participating in the Multi-Ethnic Study of Atherosclerosis (MESA) with baseline cardiovascular magnetic resonance (CMR) study, LACI was measured as the ratio of the LA end-diastolic volume divided by the LV end-diastolic volume. Cox proportional hazard models were used to assess the association between LACI and the outcomes of atrial fibrillation (AF), heart failure (HF), coronary heart disease (CHD) death, and hard CVD defined by myocardial infarction, resuscitated cardiac arrest, stroke, or CHD death. In multivariable analyses, the associations between LACI and the time-to-event were evaluated, adjusting for demographics, traditional cardiovascular risk factors, menopausal status and sex hormone levels. RESULTS Among the 2,087 women (61.2 ± 10.1 years), 485 cardiovascular events were observed during mean follow-up period of 13.2 ± 3.3 years. Greater LACI was independently associated with AF (HR 1.70; 95% CI [1.51-1.90]), HF (HR 1.62; 95% CI [1.33-1.97]), CHD death (HR 1.36; 95% CI [1.10-1.68]), and hard CVD (1.30; 95% CI [1.13-1.51], all p &amp;lt; 0.001). Adjusted models with LACI showed significant improvement in model discrimination and reclassification compared to currently used standard models to predict the incidences of AF (C-statistic: 0.82 vs. 0.79; NRI = 0.325; IDI = 0.036), HF (C-statistic: 0.84 vs. 0.81; NRI = 0.571; IDI = 0.023), CHD death (C-statistic: 0.87 vs. 0.85; NRI = 0.506; IDI = 0.012), and hard CVD (C-statistic: 0.78 vs. 0.76; NRI = 0.229; IDI = 0.012). The prognostic value of LACI was homogeneous in both pre- and post-menopausal women with a better discrimination and reclassification compared to individual LA or LV parameters. CONCLUSIONS In a multi-ethnic population of pre- and post-menopausal women, LACI is an independent predictor of HF, AF, CHD death and hard CVD. In both pre- and post-menopausal women, LACI has an incremental prognostic value to predict cardiovascular events over traditional risk factors and sex hormone levels. ClinicalTrials.gov Identifier: NCT00005487 Abstract Figure. Kaplan-Meier curves by LACI terciles Abstract Figure. Kaplan-Meier curves by LACI and Menop.

Incremental prognostic value of vasodilator stress cardiovascular magnetic resonance over coronary computed tomography angiography in symptomatic patients

Abstract Funding Acknowledgements Type of funding sources: None. BACKGROUND Current guidelines recommend to perform noninvasive functional imaging in patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CCTA). However, the prognostic value of stress cardiovascular magnetic resonance (CMR) over traditional risk factors and CCTA is not known. PURPOSE To assess the incremental prognostic value of stress CMR beyond traditional risk factors and CCTA in patients with obstructive CAD on CCTA. METHODS Between 2008 and 2020, consecutive symptomatic patients without known CAD referred for CCTA were screened. Among those, patients with obstructive CAD (at least 1 ≥50% stenosis on CCTA) and referred for functional stress CMR were included and followed for the occurrence of major adverse cardiovascular events (MACE), defined by cardiovascular death or nonfatal myocardial infarction. Univariable and multivariable Cox regressions were performed to determine the prognostic value of CCTA and CMR findings. RESULTS Of 2,210 patients who completed the CMR protocol, 2,038 patients (46.5% male, mean age 69.8 ± 12.2 years) completed the follow-up (median 6.8 [IQR 5.9-9.2] years); 281 experienced a MACE (13.8%). Stress CMR was well tolerated without severe adverse events. Using Kaplan-Meier analysis, inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of MACE (hazard ratio, HR: 4.51, [95%CI: 3.55-5.74]; and HR: 3.32, [95%CI: 2.55-4.32], respectively; both p &amp;lt; 0.001). In multivariable Cox regression, the presence of inducible ischemia and LGE were independent predictors of a higher incidence of MACE (HR: 3.97, [95%CI: 3.43-5.13]; HR: 2.30, [95%CI: 1.52-3.33]; respectively, both p &amp;lt; 0.001). After adjustment, stress CMR findings showed the best improvement in model discrimination and reclassification above traditional risk factors and CCTA findings (C statistic improvement: 0.08; NRI = 0.421; IDI = 0.047). CONCLUSIONS In symptomatic patients with obstructive CAD of unknown significance on CCTA, stress CMR has an incremental prognostic value to predict MACE over traditional risk factors and CCTA findings. Abstract Figure. Kaplan-Meier curves for MACE Abstract Figure. Competitive risk analysis

Change in left atrioventricular coupling index to predict incident atrial fibrillation: the multi-ethnic study of atherosclerosis (MESA)

Abstract Funding Acknowledgements Type of funding sources: None. BACKGROUND Left atrial (LA) and left ventricular (LV) structural and functional parameters have independent prognostic values as predictors of atrial fibrillation (AF). PURPOSE To investigate the prognostic value of a left atrioventricular coupling index (LACI) and average annualized change in LACI measured by cardiac MRI to predict incident AF in a population-based sample from the Multi-Ethnic Study of Atherosclerosis (MESA). METHODS In a secondary analysis of the prospective Multi-Ethnic Study of Atherosclerosis (MESA) study, 1,911 study participants, free of clinically recognized AF and cardiovascular disease at baseline, had LACI assessed with cardiac MRI at baseline (Exam 1, 2000-2002), and ten years later (Exam 5, 2010-2012). LACI was defined as the ratio of LA to LV end-diastolic volumes. Univariable and multivariable Cox proportional hazard models were used to evaluate the associations of LACI and average annualized change in LACI (ΔLACI) with incident AF. RESULTS Among the 1,911 participants (mean age 59 ± 9 years and 907 men), 87 incident AF events occurred over 3.9 ± 0.9 years following the second imaging (Exam 5). After adjustment for traditional risk factors, greater LACI and ΔLACI were independently associated with AF (HR 1.69, 95% CI[1.46-1.96] and HR 1.71, 95% CI[1.50-1.94], respectively; both p&amp;lt;.001). Adjusted models for LACI and ΔLACI showed improvement in model discrimination compared to currently used AF risk score (CHARGE-AF score) model (AUC: 0.78 vs. 0.74, and AUC: 0.80 vs. 0.74, both p&amp;lt;.001); and to the final model including individual LA or LV parameters for predicting AF incidence (AUC: 0.78 vs. 0.76, and AUC: 0.80 vs. 0.78, both p&amp;lt;.001). CONCLUSIONS Atrioventricular coupling (LACI) and coupling change (ΔLACI) were strong predictors for AF in a multi-ethnic population. Both had incremental prognostic value for predicting AF over traditional risk factors, and superior discrimination compared to the CHARGE-AF score and to individual LA or LV parameters. ClinicalTrials.gov Identifier: NCT00005487 Abstract Figure. Kaplan-Meier curves by change in LACI Abstract Figure. Kaplan-Meier curves by ΔLACI and LACI

Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19

BackgroundAcute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce.MethodsPatients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit.ResultsOf 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included.ConclusionsProteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas.

Prognostic Value of T1 Mapping and Feature Tracking by Cardiac Magnetic Resonance in Patients With Signs and Symptoms Suspecting Heart Failure and No Clinical Evidence of Coronary Artery Disease

BackgroundThe ability of left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) by cardiac magnetic resonance for risk stratification in suspected heart failure is limited. We aimed to evaluate the incremental prognostic value of cardiac magnetic resonance‐assessed extracellular volume fraction (ECV) and global longitudinal strain (GLS) in patients with signs and symptoms suspecting heart failure and no clinical evidence of coronary artery disease.Methods and ResultsA total of 474 consecutive patients (57±21 years of age, 56% men) with heart failure‐related symptoms and absence of coronary artery disease underwent cardiac magnetic resonance. After median follow‐up of 18 months, 59 (12%) experienced the outcome of all‐cause death or heart failure hospitalization (DeathCHF). In univariate analysis, cardiac magnetic resonance‐assessed LVEF, LGE, GLS, and ECV were all significantly associated with DeathCHF. Adjusted for a multivariable baseline model including age, sex, LVEF and LGE, ECV, and GLS separately maintained a significant association with DeathCHF (ECV, hazard ratio [HR], 1.44 per 1 SD increase; 95% CI 1.13–1.84; P=0.003, and GLS, HR, 1.78 per 1 SD increase; 95% CI, 1.06–2.96; P=0.028 respectively). Adding both GLS and ECV to the baseline model significantly improved model discrimination (C statistic from 0.749 to 0.782, P=0.017) and risk reclassification (integrated discrimination improvement 0.046 [0.015–0.076], P=0.003; continuous net reclassification improvement 0.378 [0.065–0.752], P<0.001) for DeathCHF, beyond LVEF and LGE.ConclusionsIn patients with signs and symptoms suspecting heart failure and no clinical evidence of coronary artery disease, joint assessment of GLS and ECV provides incremental prognostic value for DeathCHF, independent of LVEF and LGE.

A New Imaging Marker to Predict Incident Heart Failure: Annual Change in Left Atrioventricular Coupling Index. A cardiovascular MRI report from the Multi-Ethnic Study of Atherosclerosis (MESA)

Although left atrial (LA) and left ventricular (LV) structural and functional parameters have independent prognostic value as predictors of HF, the close physiological relationship between the LA and LV suggest that the assessment of LA/LV coupling could better reflect left atrioventricular dysfunction and be a better predictor of heart failure (HF). To assess the prognostic value of a new left atrioventricular coupling index (LACI), measured by CMR, as well as annual change in LACI to predict incident HF. In the MESA, 2250 study participants, free of cardiovascular disease at baseline, had LACI assessed by CMR imaging at baseline (2000), and ten years later (2010). LACI was defined as the ratio of LA to LV end-diastolic volumes. Cox proportional hazard models were used to evaluate the associations of LACI and average annualized change in LACI (ΔLACI) with incident HF after adjustment for traditional HF risk factors. The incremental risk prediction was calculated using C-stat, categorical net reclassification index (NRI) and integrative discrimination index (IDI). Among the 2,250 participants (59 ± 9 years; 47.6% male), 50 incident HFs occurred over 6.8 ± 1.3 years after the second CMR exam. After adjustment, greater LACI and ΔLACI were independently associated with HF (HR 1.44, 95%CI [1.25-1.66] and HR 1.55, 95%CI [1.30-1.85], respectively; both P < 0.0001). Adjusted models for LACI showed significant improvement in model discrimination and reclassification compared to currently used HF risk score model for predicting HF incidence (C-stat: 0.81 vs. 0.77; NRI = 0.411; IDI = 0.043). After adjustment, ΔLACI showed also significant improvement in model discrimination compared to the model with traditional HF risk factors (C-stat: 0.82 vs. 0.77; NRI = 0.491; IDI = 0.058) ( Fig. 1 ). LACI and ΔLACI are independently associated with incident HF. Both have incremental prognostic value for predicting HF over traditional risk factors.

Incremental prognostic value of vasodilator stress cardiovascular magnetic resonance over coronary computed tomography angiography in symptomatic patients

Current guidelines recommend to perform noninvasive functional imaging in patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CCTA). However, the prognostic value of stress cardiovascular magnetic resonance (CMR) over traditional risk factors and CCTA is not known. To assess the incremental prognostic value of stress CMR beyond traditional risk factors and CCTA in patients with obstructive CAD on CCTA. Between 2008 and 2020, consecutive symptomatic patients without known CAD referred for CCTA were screened. Among those, patients with obstructive CAD (at least 1 ≥ 50% stenosis on CCTA) and referred for stress CMR were included and followed for the occurrence of MACE, defined by cardiovascular death or nonfatal myocardial infarction. Of 2210 patients who completed the CMR protocol, 2038 patients (46.5% male, mean age 69.8 ± 12.2 years) completed the follow-up (median 6.8 [IQR 5.9–9.2] years); 281 experienced a MACE (13.8%). Stress CMR was well tolerated without severe adverse events. Using Kaplan-Meier analysis, inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of MACE (hazard ratio [HR]: 4.51, [95% CI: 3.55–5.74]; and HR: 3.32, [95% CI: 2.55–4.32], respectively; both P < 0.001). In multivariable Cox regression, the presence of inducible ischemia and LGE were independent predictors of a higher incidence of MACE (HR: 3.97, [95% CI: 3.43–5.13]; HR: 2.30, [95% CI: 1.52–3.33]; respectively, both P < 0.001). After adjustment, stress CMR findings showed the best improvement in model discrimination and reclassification above traditional risk factors and CCTA findings (C statistic improvement: 0.08; NRI = 0.421; IDI = 0.047) ( Fig. 1 ). In symptomatic patients with obstructive CAD of unknown significance on CCTA, stress CMR has an incremental prognostic value to predict MACE over traditional risk factors and CCTA findings.

A new marker to predict incident atrial fibrillation: Change in left atrioventricular coupling index. Report from the Multi-Ethnic Study of Atherosclerosis (MESA)

Left atrial (LA) and left ventricular (LV) structural and functional parameters have independent prognostic values as predictors of atrial fibrillation (AF). To investigate the prognostic value of a left atrioventricular coupling index (LACI) as well as change in LACI to predict incident AF in a multi-ethnic population. In the Multi-Ethnic Study of Atherosclerosis (MESA), 1911 study participants, free of clinically recognized AF and cardiovascular disease at baseline, had LACI assessed with CMR imaging at baseline (Exam 1, 2000–2002), and ten years later (Exam 5, 2010–2012). LACI was defined as the ratio of LA to LV end-diastolic volumes. Univariable and multivariable Cox proportional hazard models were used to evaluate the associations of LACI and average annualized change in LACI (ΔLACI) with incident AF. Among the 1911 participants (mean age 59 ± 9 years and 47.5% male participants), 87 incident AF events occurred over 3.9 ± 0.9 years following the second imaging (Exam 5). After adjustment for traditional risk factors, greater LACI and ΔLACI were independently associated with AF (HR: 1.69, 95% CI [1.46–1.96] and HR: 1.71, 95% CI [1.50–1.94], respectively; both P < 0.0001). Adjusted models for LACI and ΔLACI showed significant improvement in model discrimination compared to currently used AF risk score model for predicting AF incidence (C-statistic: 0.78 vs. 0.74, and C-statistic: 0.80 vs. 0.74, respectively). The LACI and ΔLACI also showed superior discrimination performance for AF compared to the multivariable model including CHARGE-AF score, and individual LA or LV parameters (Fig. 1). Atrioventricular coupling (LACI) and coupling change (ΔLACI) are strong predictors for AF incidence in a multi-ethnic population. Both have incremental prognostic value for predicting AF over traditional risk factors, and superior discrimination power compared to the CHARGE-AF score and to individual LA or LV parameters.

Change in left atrioventricular coupling index to predict hard cardiovascular disease: The Multi-Ethnic Study of Atherosclerosis (MESA)

Both left atrial (LA) and left ventricular (LV) functional parameters influence the prognosis of patients with cardiovascular diseases (CVD). This study aimed to investigate the prognostic value of a novel left atrioventricular coupling index (LACI), as well as annual change in LACI (ΔLACI) for predicting hard CVD in a population without history of CVD at baseline. In the Multi-Ethnic Study of Atherosclerosis (MESA), 2221 study participants, free of CVD at baseline, had LACI assessed with CMR imaging at baseline (Exam 1, 2000–2002), and ten years later (Exam 5, 2010–2012). LACI was defined by the ratio of the LA end-diastolic volume divided by the LV end-diastolic volume. Cox proportional hazard models were used to evaluate the associations of LACI and average annualized change in LACI (ΔLACI) with hard CVD defined by myocardial infarction, resuscitated cardiac arrest, fatal and nonfatal stroke, or CHD death. Among the 2221 participants (62.8 ± 10.1 years, 47.9% men), 223 cardiovascular events were observed (mean follow-up 13.0 ± 3.2 years). After adjustment for traditional risk factors, greater LACI and ΔLACI were independently associated with hard CVD (HR: 1.23; 95% CI [1.13–1.34] and HR: 1.71, 95% CI [1.50–1.94]; both P < 0.0001). Adjusted models for LACI and ΔLACI showed significant improvement in model discrimination compared to traditional risk score model (C-statistic: 0.79 vs. 0.77, and C-statistic: 0.80 vs. 0.78, respectively) ( Fig. 1 ). LACI is a strong predictor for the incidence of hard CVD. LACI has incremental prognostic value to predict hard CVD over traditional risk factors, and better discrimination and reclassification power compared to individual LA or LV parameters.

Optimal design of experiments to improve the characterisation of atrazine degradation pathways in soil

AbstractContamination of soils with pesticides and their metabolites is a global environmental threat. Deciphering the complex process chains involved in pesticide degradation is a prerequisite for finding effective solution strategies. This study applies prospective optimal design (OD) of experiments to identify laboratory sampling strategies that allow model‐based discrimination of atrazine (AT) degradation pathways. We simulated virtual AT degradation experiments with a first‐order model that reflects a simple reaction chain of complete AT degradation. We added a set of Monod‐based model variants that consider more complex AT degradation pathways. Then, we applied an extended constraint‐based parameter search algorithm that produces Monte‐Carlo ensembles of realistic model outputs, in line with published experimental data. Differences between‐model ensembles were quantified with Bayesian model analysis using an energy distance metric. AT degradation pathways following first‐order reaction chains could be clearly distinguished from those predicted with Monod‐based models. As expected, including measurements of specific bacterial guilds improved model discrimination further. However, experimental designs considering measurements of AT metabolites were most informative, highlighting that environmental fate studies should prioritise measuring metabolites for elucidating active AT degradation pathways in soils. Our results suggest that applying model‐based prospective OD will maximise knowledge gains on soil systems from laboratory and field experiments.Highlights Bayesian model analysis can help to distinguish the active degradation pathway of pesticides. Information on degradation metabolites is crucial to understand pesticide fate. Measurements of specific guilds improve the distinction of active pesticide pathways. Prospective optimal design maximizes information gain in soil sciences.

Integrated Passive-Active Model Identification With Tunable Model Discrimination for Affine Discrete-Time Systems

This letter proposes a passive-active model identification algorithm for affine discrete-time systems that integrates active model discrimination (AMD) and model invalidation (MI). A look-up tree consisting of control inputs is constructed offline for this integrated model identification (IMI) technique to discriminate among models in a time-varying model set, which is only known at run time when repeatedly applying MI online. Furthermore, a novel tunable AMD (TAMD), with its mixed-integer linear programming (MILP) formulation, is proposed and combined with the IMI algorithm, which can improve model discrimination performance. The effectiveness of the proposed IMI algorithm is demonstrated through simulations for identifying intention models of human-driven vehicles in a lane changing scenario.

Abstract 11229: Clinical and Genetic Atrial Fibrillation Risk and Discrimination of Cardioembolic from Non-Cardioembolic Stroke

Introduction: Prior studies demonstrate correlation between atrial fibrillation (AF) polygenic risk score (PRS) and ischemic stroke, especially cardioembolic (CE) stroke, suggesting shared genetic components. In this study, we hypothesized that an AF PRS can discriminate CE from non-CE strokes. Methods: We evaluated AF and stroke risk in 26,145 individuals of European descent from the NINDS Stroke Genetics Network (SiGN) with study-adjudicated TOAST subtypes. AF genetic risk was estimated using two newly derived PRS (LDpred-funct and sBayesR) and two previously validated PRS (pruning and thresholding and LDpred). A clinical risk score (CRS) was derived within separate individuals in UK Biobank utilizing components of the Cohorts for Aging and Genomic Research in Epidemiology (CHARGE-AF) model available in SiGN. We regressed each AF PRS on AF status and separately CE stroke in logistic regression models adjusted for CRS, imputation group, and the first 10 principal components. We calculated discrimination of AF and CE stroke, and compared across models using 2000-iteration bootstrapping and pairwise Z-testing. We also assessed category-free reclassification of CE stroke risk with the addition of PRS to a) CRS, and b) a modified CHA 2 DS 2 -VASc score including all available score components. Results: Each AF PRS was significantly associated with AF and CE stroke after adjustment for the CRS. Addition of any AF PRS significantly improved model discrimination as compared to the CRS alone, and LDpred discriminated both AF and CE better than other PRSs ( Figure ; P &lt;0.005). Adding LDpred PRS to CRS resulted in appropriate reclassification of CE stroke risk compared to the CRS or the modified CHA 2 DS 2 -VASc score alone ( Figure ). Conclusions: Addition of AF polygenic risk to clinical risk factors improves discrimination of CE from non-CE strokes, as well as reclassification of stroke subtype. AF polygenic risk may be a useful biomarker for identifying AF-related strokes.

Risk prediction of atrial fibrillation and its complications in the community using high-sensitivity cardiac troponin I: results from the BiomarCaRE Consortium

Abstract Aims Atrial fibrillation (AF) is becoming increasingly common and is associated with serious complications. Traditional cardiovascular risk factors (CVRF) do not explain all AF cases. Blood-based biomarkers reflecting cardiac injury may help close this gap. High-sensitivity troponin I (hsTnI) has emerged as a potential predictor. Methods We investigated the predictive ability of hsTnI for incident AF in 29,227 participants (median age 52.6 years, 51.2% men) across four different European community cohorts of the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) consortium in comparison to CVRF and established biomarkers (high-sensitive C-reactive protein (hsCRP), N-terminal pro B-type natriuretic peptide (NT-proBNP)). Results During a median follow-up of 13.8 (lower and upper quartiles 4.5, 21.3) years, 1,509 (5.2%) participants developed AF. Those in the highest fourth of hsTnI values at baseline (≥5.1 ng/L) had a 2.71-fold (95% confidence interval (CI) 2.31, 3.17; P&amp;lt;0.01) risk for developing AF compared to those in the lowest fourth (≤2.1 ng/L). In multivariable-adjusted Cox proportional hazard models no statistically significant association was seen between hsTnI and AF, whereas NT-proBNP (hazard ratio (HR) per two-fold increase in NT-proBNP 1.64; 95% CI 1.56, 1.72; P&amp;lt;0.001) as well as hsCRP (HR ratio per two-fold increase in hsCRP 1.05; 95% CI 1.01, 1.10; P=0.01) were statistically significantly related to incident AF. Inclusion of hsTnI did not improve model discrimination over CVRFs (C-index CVRF 0.7914 vs. C-index CVRF, hsTnI 0.7927; 95% CI −0.0004, 0.0031; P=0.130). Higher hsTnI concentrations were associated with AF complications such as stroke (HR 1.25; 95% CI 1.03, 1.51; P=0.02), heart failure (HR 1.27; 95% CI 1.12, 1.44; P&amp;lt;0.001) and cardiovascular events (HR 1.24; 95% CI 1.08, 1.42; P&amp;lt;0.001) as well as overall mortality (HR 1.15; 95% CI 1.05, 1.25; P&amp;lt;0.001) in those who were diagnosed with AF. Conclusion hsTnI as a biomarker of myocardial injury does not improve prediction of AF incidence beyond classical CVRFs. However, it is associated with AF complications and mortality after AF onset probably reflecting underlying subclinical cardiovascular impairment. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union Seventh Framework Programme (FP7/2007-2013

Variability in estimated glomerular filtration rate and patients' outcomes in a real-world heart failure population.

AimsThe prognostic significance of renal function variability has not been fully elucidated in heart failure (HF). This multicentre, prospective cohort study aimed to evaluate the usefulness of visit‐to‐visit variability in estimated glomerular filtration rate (eGFR) for predicting patients' outcomes in a real‐world HF population.MethodsA total of 564 patients who had survived HF hospitalization were randomly assigned with a 2:1 ratio to derivation and validation cohorts, and they were then followed after discharge. Using the data for 6 months after discharge, each patient's visit‐to‐visit eGFR variability (EGV) was estimated. In the derivation cohort, Cox regression analyses were performed to assess the association of EGV with a subsequent composite event (death and HF hospitalization). In the validation cohort, the predictive performance was compared among Cox regression models with EGV, those with B‐type natriuretic peptide (BNP) and those with eGFR.ResultsIn the derivation cohort (376 patients), median age, left ventricular ejection fraction (LVEF), BNP and eGFR at discharge were 72 years, 53.3%, 134.8 pg/mL and 58.7 mL/min/1.73 m2, respectively. During a median follow‐up of 2.2 years, higher EGV was associated with an increased risk of the composite event (adjusted hazard ratio [per standard deviation increase in log‐transformed EGV], 1.5; 95% confidence interval, 1.1–2.0). A similar finding was observed in a stratified analysis by LVEF. In the validation cohort (188 patients), better model fit, discrimination, reclassification and calibration were observed for EGV than for 6‐month averaged BNP or eGFR for predicting the composite event when added to HF risk prediction models. Adding EGV to models with BNP or eGFR improved model discrimination and reclassification.ConclusionsEGV predicts HF outcomes regardless of LVEF. Risk prediction models with EGV have good performance in real‐world HF patients. The study findings highlight the clinical importance of observing visit‐to‐visit fluctuations in renal function in this population.