Heart最新文献

Background: No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment.

Methods: Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined.

Results: A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds).

Conclusions: CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Background: Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms.

Objective: To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers.

Methods: A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier.

Results: Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input.

Conclusion: Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms.

Severe secondary mitral regurgitation carries a poor prognosis with one in five patients dying within 12 months of diagnosis. Fortunately, there are now a number of safe and effective therapies available to improve outcomes. Here, we summarise the most up-to-date treatments. Optimal guideline-directed medical therapy is the mainstay therapy and has been shown to reduce the severity of mitral regurgitation in 40-45% of patients. Rapid medication titration protocols reduce heart failure hospitalisation and facilitate earlier referral for device therapy. The pursuit of sinus rhythm in patients with atrial fibrillation has been shown to significantly reduce mitral regurgitation severity, as has the use of cardiac resynchronisation devices in patients who meet guideline-directed criteria. Finally, we highlight the key role of mitral valve intervention, particularly transcatheter edge-to-edge repair (TEER) for management of moderate-severe mitral regurgitation in carefully selected patients with poor left ventricular systolic function, with a number needed to treat of 3.1 to reduce heart failure hospitalisation and 5.9 to reduce all-cause death. To slow the rapid accumulation of morbidity and mortality, we advocate a proactive approach with accelerated medical optimisation, followed by management of atrial fibrillation and cardiac resynchronisation therapy if indicated, then, rapid referral to the Heart Team for consideration of mitral valve intervention in patients with ongoing symptoms and at least moderate-severe mitral regurgitation. Mitral TEER has been shown to be 'reasonably cost-effective' (but not cost-saving) in the UK in selected patients, although TEER remains underused with only 6.5 procedures per million population (pmp) compared with Germany (77 pmp), Switzerland (44 pmp) and the USA (32 pmp).

In an era of rapidly expanding use of transcatheter aortic valve implantation (TAVI), the management of patients with bicuspid aortic valve (BAV) disease is far less well established than in those with trileaflet anatomy. Results of isolated surgical aortic valve replacement are excellent in suitable patients, and surgery also allows treatment of concomitant pathology of the aortic root and ascending aorta that is frequently encountered in this cohort. Conversely, TAVI provides an excellent alternative in older patients who may be unsuitable for surgery, although outcomes in BAV disease have only been reported in relatively small non-randomised series. Here, we discuss the pertinent literature on this topic and outline contemporary interventional treatment options in this challenging setting.

The frequent concurrence of elevated blood pressure (BP) and type 2 diabetes markedly elevates the risk of cardiovascular disease and mortality. In this review, we discuss the evidence supporting the role of BP-lowering therapies in preventing cardiovascular events in people with type 2 diabetes and the most appropriate BP treatment target in these individuals. We outline possible reasons for the heterogeneous effect of BP lowering in patients with and without diabetes and consider several pathophysiological mechanisms that could potentially explain such differences. The review introduces a mediation model, delineating the intricate interplay between hypertension and diabetes and their joint contribution to cardiovascular and renal pathologies. Finally, we outline the role of lifestyle changes and other pharmacological options in attenuating cardiometabolic risks in patients with type 2 diabetes. We propose a comprehensive, patient-centred management strategy, integrating various antihypertensive therapeutic approaches and providing clinicians with a systematic framework for better decision-making.