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Aims: Heart failure with preserved ejection fraction (HFpEF) continues to be a diagnostic challenge. Cardiac magnetic resonance atrial measurement, feature tracking (CMR-FT), tagging has long been suggested to diagnose HFpEF and potentially complement echocardiography especially when echocardiography is indeterminate. Data supporting the use of CMR atrial measurements, CMR-FT or tagging, are absent. Our aim is to conduct a prospective case-control study assessing the diagnostic accuracy of CMR atrial volume/area, CMR-FT, and tagging to diagnose HFpEF amongst patients suspected of having HFpEF. Methods and results: One hundred and twenty-one suspected HFpEF patients were prospectively recruited from four centres. Patients underwent echocardiography, CMR, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurements within 24â h to diagnose HFpEF. Patients without HFpEF diagnosis underwent catheter pressure measurements or stress echocardiography to confirm HFpEF or non-HFpEF. Area under the curve (AUC) was determined by comparing HFpEF with non-HFpEF patients. Fifty-three HFpEF (median age 78 years, interquartile range 74-82 years) and thirty-eight non-HFpEF (median age 70 years, interquartile range 64-76 years) were recruited. Cardiac magnetic resonance left atrial (LA) reservoir strain (ResS), LA area index (LAAi), and LA volume index (LAVi) had the highest diagnostic accuracy (AUCs 0.803, 0.815, and 0.776, respectively). Left atrial ResS, LAAi, and LAVi had significantly better diagnostic accuracy than CMR-FT left ventricle (LV)/right ventricle (RV) parameters and tagging (P < 0.01). Tagging circumferential and radial strain had poor diagnostic accuracy (AUC 0.644 and 0.541, respectively). Conclusion: Cardiac magnetic resonance LA ResS, LAAi, and LAVi have the highest diagnostic accuracy to identify HFpEF patients from non-HFpEF patients amongst clinically suspected HFpEF patients. Cardiac magnetic resonance feature tracking LV/RV parameters and tagging had low diagnostic accuracy to diagnose HFpEF.
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Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging. Intraventricular four-dimensional flow (4D flow) phase-contrast cardiovascular magnetic resonance (CMR) can assess different components of left ventricular (LV) flow including direct flow, delayed ejection, retained inflow and residual volume. This could be utilised to identify HFpEF. This study investigated if intraventricular 4D flow CMR could differentiate HFpEF patients from non-HFpEF and asymptomatic controls. Suspected HFpEF patients and asymptomatic controls were recruited prospectively. HFpEF patients were confirmed using European Society of Cardiology (ESC) 2021 expert recommendations. Non-HFpEF patients were diagnosed if suspected HFpEF patients did not fulfil ESC 2021 criteria. LV direct flow, delayed ejection, retained inflow and residual volume were obtained from 4D flow CMR images. Receiver operating characteristic (ROC) curves were plotted. 63 subjects (25 HFpEF patients, 22 non-HFpEF patients and 16 asymptomatic controls) were included in this study. 46% were male, mean age 69.8 ± 9.1 years. CMR 4D flow derived LV direct flow and residual volume could differentiate HFpEF vs combined group of non-HFpEF and asymptomatic controls (p < 0.001 for both) as well as HFpEF vs non-HFpEF patients (p = 0.021 and p = 0.005, respectively). Among the 4 parameters, direct flow had the largest area under curve (AUC) of 0.781 when comparing HFpEF vs combined group of non-HFpEF and asymptomatic controls, while residual volume had the largest AUC of 0.740 when comparing HFpEF and non-HFpEF patients. CMR 4D flow derived LV direct flow and residual volume show promise in differentiating HFpEF patients from non-HFpEF patients.
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OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.