Photoplethysmography and intracardiac pressures: early insights from a pilot study.

Aims: Invasive haemodynamic monitoring of heart failure (HF) is used to detect deterioration in an early phase thereby preventing hospitalizations. However, this invasive approach is costly and presently lacks widespread accessibility. Hence, there is a pressing need to identify an alternative non-invasive method that is reliable and more readily available. In this pilot study, we investigated the relation between wrist-derived photoplethysmography (PPG) signals and the invasively measured pulmonary capillary wedge pressure (PCWP). Methods and results: Fourteen patients with aortic valve stenosis who underwent transcatheter aortic valve replacement with concomitant right heart catheterization and PPG measurements were included. Six unique features of the PPG signals [heart rate, heart rate variability, systolic amplitude (SA), diastolic amplitude, crest time (CT), and large artery stiffness index (LASI)] were extracted. These features were used to estimate the continuous PCWP values and the categorized PCWP (low < 12 mmHg vs. high ≥ 12 mmHg). All PPG features resulted in regression models that showed low correlations with the invasively measured PCWP. Classification models resulted in higher performances: the model based on the SA and the model based on the LASI both resulted in an area under the curve (AUC) of 0.86 and the model based on the CT resulted in an AUC of 0.72. Conclusion: These results demonstrate the capability to non-invasively classify patients into clinically meaningful categories of PCWP using PPG signals from a wrist-worn wearable device. To enhance and fully explore its potential, the relationship between PPG and PCWP should be further investigated in a larger cohort of HF patients.

Risks and benefits of sharing patient information on social media: a digital dilemma.

Social media (SoMe) has witnessed remarkable growth and emerged as a dominant method of communication worldwide. Platforms such as Facebook, X (formerly Twitter), LinkedIn, Instagram, TikTok, and YouTube have become important tools of the digital native generation. In the field of medicine, particularly, cardiology, attitudes towards SoMe have shifted, and professionals increasingly utilize it to share scientific findings, network with experts, and enhance teaching and learning. Notably, SoMe is being leveraged for teaching purposes, including the sharing of challenging and intriguing cases. However, sharing patient data, including photos or images, online carries significant implications and risks, potentially compromising individual privacy both online and offline. Privacy and data protection are fundamental rights within European Union treaties, and the General Data Protection Regulation (GDPR) serves as the cornerstone of data protection legislation. The GDPR outlines crucial requirements, such as obtaining 'consent' and implementing 'anonymization', that must be met before sharing sensitive and patient-identifiable information. Additionally, it is vital to consider the patient's perspective and prioritize ethical and social considerations when addressing challenges associated with sharing patient information on SoMe platforms. Given the absence of a peer-review process and clear guidelines, we present an initial approach, a code of conduct, and recommendations for the ethical use of SoMe. In conclusion, this comprehensive review underscores the importance of a balanced approach that ensures patient privacy and upholds ethical standards while harnessing the immense potential of SoMe to advance cardiology practice and facilitate knowledge dissemination.

Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures.

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.

Contemporary guideline-directed medical therapy in de novo, chronic, and worsening heart failure patients: First data from the TITRATE-HF study.

AIMS: Despite clear guideline recommendations for initiating four drug classes in all patients with heart failure (HF) with reduced ejection fraction (HFrEF) and the availability of rapid titration schemes, information on real-world implementation lags behind. Closely following the 2021 ESC HF guidelines and 2023 focused update, the TITRATE-HF study started to prospectively investigate the use, sequencing, and titration of guideline-directed medical therapy (GDMT) in HF patients, including the identification of implementation barriers. METHODS AND RESULTS: TITRATE-HF is an ongoing long-term HF registry conducted in the Netherlands. Overall, 4288 patients from 48 hospitals were included. Among these patients, 1732 presented with de novo, 2240 with chronic, and 316 with worsening HF. The median age was 71 years (interquartile range [IQR] 63-78), 29% were female, and median ejection fraction was 35% (IQR 25-40). In total, 44% of chronic and worsening HFrEF patients were prescribed quadruple therapy. However, only 1% of HFrEF patients achieved target dose for all drug classes. In addition, quadruple therapy was more often prescribed to patients treated in a dedicated HF outpatient clinic as compared to a general cardiology outpatient clinic. In each GDMT drug class, 19% to 36% of non-use in HFrEF patients was related to side-effects, intolerances, or contraindications. In the de novo HF cohort, 49% of patients already used one or more GDMT drug classes for other indications than HF. CONCLUSION: This first analysis of the TITRATE-HF study reports relatively high use of GDMT in a contemporary HF cohort, while still showing room for improvement regarding quadruple therapy. Importantly, the use and dose of GDMT were suboptimal, with the reasons often remaining unclear. This underscores the urgency for further optimization of GDMT and implementation strategies within HF management.