First report of the clinical characteristics and outcomes of cardiac amyloidosis in Saudi Arabia

Aims: Cardiac amyloidosis (CA) is a potentially fatal multisystemic disease that remains significantly underdiagnosed, particularly in the Middle East. This study aims to evaluate the prevalence and clinical characteristics of CA in a high‐risk population at a tertiary centre in Saudi Arabia. Methods: This cross‐sectional, retrospective, single‐centre study was conducted at a tertiary hospital in Riyadh, Saudi Arabia. We reviewed the medical records of heart failure patients seen between August 2018 and July 2022 who exhibited red flags for CA and subsequently underwent CA screening. Red flags that prompted the workup included at least two of the following factors: the presence of unilateral or bilateral carpal tunnel syndrome, a family history of transthyretin amyloid (ATTR) amyloidosis and specific electrocardiographic features (relative/absolute low QRS voltage, pseudoinfarct pattern and atrioventricular/interventricular conduction abnormalities). Echocardiographic red flags included mainly increased wall thickness (≥12 mm), significant diastolic dysfunction, reduced left ventricular (LV) longitudinal function, right ventricular (RV) dysfunction and elevated right atrial (RA)/pulmonary artery (PA) pressure. Cardiac magnetic resonance (CMR) red flags included aspects similar to those in an echocardiogram as well as a subendocardial or transmural late gadolinium enhancement (LGE) pattern. These patients were assessed for CA through technetium‐99m pyrophosphate ([99mTc]Tc‐PYP) bone scintigraphy, serum and urine protein electrophoresis with immunofixation and a serum‐free light chain assay. Results: A total of 177 patients were screened, of which 21.0 (11.9%) patients were diagnosed with transthyretin amyloid CA (ATTR‐CA) and 13 (7.3%) patients were diagnosed with light chain CA (AL‐CA). Compared with patients with negative/equivocal [99mTc]Tc‐PYP scans (grades 0–1), patients with positive [99mTc]Tc‐PYP scans (grades 2–3) were older (78.0 vs. 68.0 years, P < 0.001), had higher levels of troponin (P = 0.003) and N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) (P < 0.001), had a higher LV mass index (P < 0.001), displayed a more depressed global longitudinal strain (GLS) (P < 0.001) with a greater prevalence of a relative apical sparing pattern (P < 0.001) and demonstrated a higher incidence of first‐degree atrioventricular block (P = 0.008) and low voltage patterns on electrocardiography (P < 0.001). Patients with ATTR‐CA and AL‐CA were more likely to have a subendocardial or transmural LGE pattern on CMR (P < 0.001) and had a significantly lower overall survival (P < 0.001) when compared with other heart failure aetiologies. Conclusions: This is the first study to describe the clinical characteristics and outcomes of CA in the Middle East and Saudi Arabia. The prevalence of CA among screened heart failure patients here aligns with major international studies, suggesting significant underdiagnosis in the region. Therefore, larger multicentric studies and regional screening programmes are urgently needed to accurately characterize the epidemiology and outcomes of CA in the Middle East

Transcatheter aortic valve implantation: Single center experience with mid-term follow-up

BackgroundTranscatheter aortic-valve implantation (TAVI) is an emerging intervention for the treatment of high-risk patients with severe aortic stenosis and coexisting illnesses.MethodsBetween August 2009 and May 2012, we have conducted a retrospective study underwent a balloon expandable transcatheter xenograft (Edwards SAPIEN®) and evaluated the intermediate-term all cause mortality. Average STS and EuroSCORE II predicted risk for mortality was 18.4±8.9% and 7.6±5.5%, respectively. The end points included feasibility, safety, efficacy, and durability.ResultsA total of 27 consecutive patients underwent TAVI. Nine (33.3%) patients among them had it through the trans-apical approach. The mean age was 76±8 years; 41% of the patients were females. There was 100% successful implantation. Hospital and one-year mortality were 2 (7.4%) and 4 (14.8%), respectively. At 1 year, the incidence of stroke was 1 (3.7%), infection 6 (22%), AV block 2 (7.4%), severe bleeding 2 (7.4%), vascular complication (14.8%), and the incidence of periprosthetic aortic regurgitation (<2) was 5 (18.5%). No patient been converted to open surgery. There was a significant improvement of patient left ventricular function post TAVI (p=0.001), and of aortic valve area (AVA) with mean 0.4±01cm2 ranging from 0.2 to 0.9 cm2 pre-and 1±0.3cm2 ranging from 1 to1.9cm2 post-TAVI (p=0.0001). The aortic pressure gradients have improved significantly from 45±11mmHg pre-to 8.5±5.5mmHg post-implantation (p=0.0001). For all patients the average length of follow-up by echocardiography was 346 days and was 100% completed.ConclusionsTAVI has provided good results in the initial 27 patients. However, The use of transcatheter aortic valve implantation should be restricted to the inoperable high-risk patients

Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care

Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate staffing, and facilitating informed discharge planning. While conventional methods rely on statistical models and clinical expertise, recent advances in machine learning (ML) present promising avenues for enhancing LOS prediction. This research focuses on developing an ML-based LOS prediction model trained on a comprehensive real-world dataset and discussing the important factors towards practical deployment of trained ML models in clinical settings. This research involves the development of a comprehensive adult cardiac patient dataset (SaudiCardioStay (SCS)) from the King Faisal Specialist Hospital & Research Centre (KFSH&RC) hospital in Saudi Arabia, comprising 4930 patient encounters for 3611 unique patients collected from 2019 to 2022 (excluding 2020). A diverse range of classical ML models (i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), artificial neural networks (ANNs), Average Voting Regression (AvgVotReg)) are implemented for the SCS dataset to explore the potential of existing ML models in LOS prediction. In addition, this study introduces a novel approach for LOS prediction by incorporating a dedicated LOS classifier within a sophisticated ensemble methodology (i.e., Two-Level Sequential Cascade Generalization (2LSCG), Three-Level Sequential Cascade Generalization (3LSCG), Parallel Cascade Generalization (PCG)), aiming to enhance prediction accuracy and capture nuanced patterns in healthcare data. The experimental results indicate the best mean absolute error (MAE) of 0.1700 for the 3LSCG model. Relatively comparable performance was observed for the AvgVotReg model, with a MAE of 0.1703. In the end, a detailed analysis of the practical implications, limitations, and recommendations concerning the deployment of ML approaches in actual clinical settings is presented

Imaging Cardiovascular Emergencies: Real World Clinical Cases

: Cardiovascular emergencies represent life-threatening conditions requiring a high index of clinical suspicion. In an emergency scenario, a simple stepwise biomarker/imaging diagnostic algorithm may help prompt diagnosis and timely treatment along with related improved outcomes. This article describes several clinical cases of cardiovascular emergencies, such as coronary stent thrombosis-restenosis, takotsubo syndrome, acute myocarditis, massive pulmonary embolism, type A acute aortic dissection, cardiac tamponade, and endocarditis

Preoperative Assessment and Management of Cardiovascular Risk in Patients Undergoing Non-Cardiac Surgery: Implementing a Systematic Stepwise Approach during the COVID-19 Pandemic Era

Major adverse cardiac events, defined as death or myocardial infarction, are common causes of perioperative mortality and major morbidity in patients undergoing non-cardiac surgery. Reduction of perioperative cardiovascular risk in relation to non-cardiac surgery requires a stepwise patient evaluation that integrates clinical risk factors, functional status and the estimated stress of the planned surgical procedure. Major guidelines on preoperative cardiovascular risk assessment recommend to establish, firstly, the risk of surgery per se (low, moderate, high) and the related timing (elective vs. urgent/emergent), evaluate the presence of unstable cardiac conditions or a recent coronary revascularization (percutaneous coronary intervention or coronary artery bypass grafting), assess the functional capacity of the patient (usually expressed in metabolic equivalents), determine the value of non-invasive and/or invasive cardiovascular testing and then combine these data in estimating perioperative risk for major cardiac adverse events using validated scores (Revised Cardiac Risk Index (RCRI) or National Surgical Quality Improvement Program (NSQIP)). This stepwise approach has the potential to guide clinicians in determining which patients could benefit from cardiovascular therapy and/or coronary artery revascularization before non-cardiac surgery towards decreasing the incidence of perioperative morbidity and mortality. Finally, it should be highlighted that there is a need to implement specific strategies in the 2019 Coronavirus disease (COVID-19) pandemic to minimize the risk of transmission of COVID-19 infection during the preoperative risk assessment process