Discriminative Role of Invasive Left Heart Catheterization in Patients Suspected of Heart Failure With Preserved Ejection Fraction

A nomogram for predicting the risk of heart failure with preserved ejection fraction

BACKGROUND

This study purposed to design and establish a nomogram to predict the risk of having heart failure with preserved ejection fraction.

METHOD

The clinical data of 1031 patients diagnosed with heart failure (HF) in the First Affiliated Hospital of Jinan University from January 2018 to December 2022 were retrospectively analyzed, among which 618 patients were diagnosed with heart failure with preserved ejection fraction (HFpEF). Patients were randomly divided into a training set (70%, n = 722) and a validation set (30%, n = 309). The prediction model of HFpEF was established by using clinical characteristic data parameters, and the risk of having HFpEF was predicted by using a nomogram. Single-factor analysis was used to select independent risk factors (P < 0.05), and then binary logistic regression was used to screen predictive variables (P < 0.05). The discrimination ability of the model was evaluated by the ROC curve and calculating the area under the curve (AUC). In addition, the predictive ability of the established nomogram was evaluated using calibration curves and the Hosmer-Lemeshow goodness of fit test (HL test), and the clinical net benefit was evaluated using decision curve analysis (DCA).

RESULTS

The results of binary logistic regression analysis showed that age, gender, hypertension, coronary heart disease, glycosylated hemoglobin, serum creatinine, E/e' septal, relative wall thickness (RWT), left ventricular mass index (LVMI) and pulmonary hypertension (PH) were independent influencing factors for the risk of having HFpEF (P < 0.05). Based on the results of logistic regression analysis, a nomogram was established and calibration curves were made. The prediction model showed that the AUC of the training dataset was 0.876 (95%CI, 0.851-0.902), and 0.837 (95%CI, 0.791-0.883) in the validation set. According to the calibration curves and HL test, the nomogram shows good calibration, and DCA shows that our model is clinically useful.

CONCLUSION

A nomogram prediction model was constructed to predict the patient's risk of having HFpEF. This prediction model indicated that the combination of creatinine, E/e', RWT, LVMI and PH may be valuable in the diagnosis of HFpEF.

Diagnosis of heart failure with preserved ejection fraction: a systematic narrative review of the evidence

Heart failure (HF) with preserved ejection fraction (HFpEF) is a common condition in clinical practice, affecting more than half of patients with HF. HFpEF is associated with morbidity and mortality and with considerable healthcare resource utilization and costs. Therefore, early diagnosis is crucial to facilitate prompt management, particularly initiation of sodium-glucose co-transporter 2 inhibitors. Although European guidelines define HFpEF as the presence of symptoms with or without signs of HF, left ventricular EF ≥ 50%, and objective evidence of cardiac structural and/or functional abnormalities, together with elevated natriuretic peptide levels, the diagnosis of HFpEF remains challenging. First, there is no clear consensus on how HFpEF should be defined. Furthermore, diagnostic tools, such as natriuretic peptide levels and resting echocardiogram findings, are significantly limited in the diagnosis of HFpEF. As a result, some patients are overdiagnosed (i.e., elderly people with comorbidities that mimic HF), although in other cases, HFpEF is overlooked. In this manuscript, we perform a systematic narrative review of the diagnostic approach to patients with HFpEF. We also propose a comprehensible algorithm that can be easily applied in daily clinical practice and could prove useful for confirming or ruling out a diagnosis of HFpEF.

Coronary microvascular dysfunction is a hallmark of all subtypes of MINOCA

INTRODUCTION

Myocardial infarction without obstructive coronary artery disease (MINOCA) is a heterogeneous clinical condition presenting with myocardial necrosis not due to an obstruction of a major coronary artery. Recently, a relevant role of coronary microvascular dysfunction (CMD) in the pathogenesis of MINOCA has been suggested; however, data on this are scarce. Particularly, it is unclear if CMD is equally present in all subtypes of MINOCA or differentially identifies one or more of these conditions. Therefore, the aim of this study was to assess CMD in all three coronary vessels of MINOCA patients, relating it with the clinical subtype.

METHODS

We retrospectively assessed coronary microvascular function in all three coronary territories by means of angiography-based index of microvascular resistance (aIMR) in 92 patients (64 with working diagnosis of MINOCA, 28 control patients). To further assess the association of CMD with MINOCA subtypes, MINOCA patients were subdivided according to clinical data in coronary cause (n = 13), takotsubo (n = 13), infiltrative or inflammatory cardiomyopathy (n = 9) or unclear (n = 29).

RESULTS

Patients with working diagnosis of MINOCA showed a significantly elevated average aIMR compared to control patients (30.5 ± 7.6 vs. 22.1 ± 5.9, p < 0.001) as a marker of a relevant CMD; these data were consistent in all vessels. Among MINOCA subtypes, no significant difference in average aIMR could be detected between patients with coronary cause (33.2 ± 6.6), takotsubo cardiomyopathy (29.2 ± 6.9), infiltrative or inflammatory cardiomyopathy (28.1 ± 6.8) or unclear cause (30.6 ± 8.5; p = 0.412). Interestingly, aIMR was significantly elevated in the coronary vessel supplying the diseased myocardium compared with other vessels (31.9 ± 11.4 vs. 27.8 ± 8.2, p = 0.049).

CONCLUSION

Coronary microvascular dysfunction is a hallmark of all MINOCA subtypes. This study adds to the pathophysiological understanding of MINOCA and sheds light into the role of CMD in MINOCA.