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Background Pulmonary artery wedge pressure (PAWP) during exercise, as a surrogate for left ventricular (LV) end-diastolic pressure (EDP), is used to diagnose heart failure with preserved ejection fraction (HFpEF). However, LVEDP is the gold standard to assess LV filling, end-diastolic PAWP (PAWPED) is supposed to coincide with LVEDP and mean PAWP throughout the cardiac cycle (PAWPM) better reflects the haemodynamic load imposed on the pulmonary circulation. The objective of the present study was to determine precision and accuracy of PAWP estimates for LVEDP during exercise, as well as the rate of agreement between these measures. Methods 46 individuals underwent simultaneous right and left heart catheterisation, at rest and during exercise, to confirm/exclude HFpEF. We evaluated: linear regression between LVEDP and PAWP, Bland–Altman graphs, and the rate of concordance of dichotomised LVEDP and PAWP ≥ or < diagnostic thresholds for HFpEF. Results At peak exercise, PAWPM and LVEDP, as well as PAWPED and LVEDP, were fairly correlated (R2>0.69, p2 mmHg·L−1·min−1. Conclusions During exercise, PAWP is accurate but not precise for the estimation of LVEDP. Despite a good rate of concordance, these two measures might occasionally disagree.

Pulmonary hypertension (PH) is a complex condition that can occur as a result of a wide range of disorders, including left heart disease, lung disease and chronic pulmonary thromboembolism. Contemporary PH patients are older and frequently have a multitude of comorbidities that may contribute to or simply coincide with their PH. Identifying the cause of PH in these complicated patients can be challenging but is essential, given that the aetiology of the disease has a significant impact on the management options available. In this article, we present two cases that highlight the difficulties involved in obtaining a precise diagnosis of the cause of PH within the setting of multiple comorbidities. The importance of performing a comprehensive, multidimensional diagnostic work-up is demonstrated, in addition to the need to specifically consider cardiopulmonary haemodynamic data in the context of the wider clinical picture. The article also illustrates why achieving an accurate diagnosis is necessary for optimal patient management. This may involve treatment of comorbidities as a priority, which can ameliorate the severity of PH, obviating the need to consider PH-targeted medical treatment.

Advances in the diagnosis and management of pulmonary arterial hypertension (PAH) have resulted in significant improvements in outcomes for patients with this devastating and progressive disease. However, because of the non-specific nature of its symptoms, and the low level of suspicion among clinicians, prompt and accurate diagnosis of PAH as a rare disease remains a challenge. This article explains some of the issues that need to be addressed when faced with a patient with suspected PAH and describes how noninvasive and invasive techniques can be used effectively to ensure an accurate diagnosis. The availability of PAH-specific therapy means that once diagnosed, patients have a much greater chance of survival than they would have had in the past. However, despite improved survival, mortality is still high and, therefore, there is still room for improvement. It is currently recommended that patients with an inadequate clinical response to treatment receive sequential combination therapy; however, supportive data are still scarce. Although there is no clear explanation, these findings may be explained by the design and end-points chosen in clinical trials, the changing population of PAH and a need to improve the management strategy in this disease. Indeed, there is a clear need for randomised controlled studies that investigate whether adopting individualised treatment strategies, including upfront combination therapy, could help to optimise long-term management of patients with PAH.

Background and aims Pulmonary hypertension due to left heart disease (PH-LHD) is the most frequent form of PH. As differential diagnosis with pulmonary arterial hypertension (PAH) has therapeutic implications, it is important to accurately and noninvasively differentiate PH-LHD from PAH before referral to PH centres. The aim was to develop and validate a machine learning (ML) model to improve prediction of PH-LHD in a population of PAH and PH-LHD patients. Methods Noninvasive PH-LHD predictors from 172 PAH and 172 PH-LHD patients from the PH centre database at the University Hospitals of Leuven (Leuven, Belgium) were used to develop an ML model. The Jacobs score was used as performance benchmark. The dataset was split into a training and test set (70:30) and the best model was selected after 10-fold cross-validation on the training dataset (n=240). The final model was externally validated using 165 patients (91 PAH, 74 PH-LHD) from Erasme Hospital (Brussels, Belgium). Results In the internal test dataset (n=104), a random forest-based model correctly diagnosed 70% of PH-LHD patients (sensitivity: n=35/50), with 100% positive predicted value, 78% negative predicted value and 100% specificity. The model outperformed the Jacobs score, which identified 18% (n=9/50) of the patients with PH-LHD without false positives. In external validation, the model had 64% sensitivity at 100% specificity, while the Jacobs score had a sensitivity of 3% for no false positives. Conclusions ML significantly improves the sensitivity of PH-LHD prediction at 100% specificity. Such a model may substantially reduce the number of patients referred for invasive diagnostics without missing PAH diagnoses.