The clinical and Practice Dilemma of Frequent Switching Among Generic Medications: Magnitude and Patient Safety Prospective.

Objective: To assess the magnitude of generic-generic and brand-generic medication switching and its impact on patients' understanding and the potential risk of medication errors. Methods: The study composed of 2 parts. The first part is a retrospective study to measure the frequency of medications switching in King Faisal Specialist Hospital and Research Centre (KFSH&RC), from 1st of January 2015 to the 31st of December, 2020. Brand medications that were switched to generic medications, generic medications that were switched to brand medications and generic medications that were switched to other generic medications were included. Medications that were switched before or after the study period were excluded. The primary outcomes are the total percentage of switching from brand to generic of all medications in KFSH&RC drug formulary, frequency of generic-generic medications switching and percentage of switching back from generic to brand medications. The second part is a cross sectional survey-based study to assess patients' understanding of their medications and the potential for medication errors by assessing their ability to identify the discrepancies and duplications utilizing a validated questionnaire. Results: Over 5 years, the number of generic medications increased from 553 (35.5%) to 640 (41.1%) out of the 1554 formulary items. Percentage of switching from brand to generic over that 5-year period was 15.9%. Percentage of switching back from generic to brand was 12.8%. Total number of generic medications that had been switched to other generic medications was 256 (16.5%). Out of the 218 patients who had been switched from generic to generic medication, only 43 patients (19.7%) knew the indication for the generic medication they were taking. One hundred forty-six patients (67%) knew the indication of generic medications exclusively by their physical "trademark" characteristics, with 11.5% unrecognized duplication. Conclusion: There is a clear tendency to switch brand medications to generics and to switch between generics. We found the percentage of subsequent switching back to branded products is alarming. Frequent switching between medications negatively impacted patient comprehension and resulted in medication duplication. There is a crucial need for appropriate medication counseling and medication use ecosystem redesign.

A complex unit for a complex disease: the HCM-Family Unit.

Hypertrophic cardiomyopathy (HCM) is a group of heterogeneous disorders that are most commonly passed on in a heritable manner. It is a relatively rare disease around the globe, but due to increased rates of consanguinity within the Kingdom of Saudi Arabia, we speculate a high incidence of undiagnosed cases. The aim of this paper is to elucidate a systematic approach in dealing with HCM patients and since HCM has variable presentation, we have summarized differentials for diagnosis and how different subtypes and genes can have an impact on the clinical picture, management and prognosis. Moreover, we propose a referral multi-disciplinary team HCM-Family Unit in Saudi Arabia and an integrated role in a network between King Faisal Hospital and Inherited and Rare Cardiovascular Disease Unit-Monaldi Hospital, Italy (among the 24 excellence centers of the European Reference Network (ERN) GUARD-Heart).   Graphical Abstract.

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.

A rare case of takotsubo syndrome within the first day after heart transplantation.

A 39-year-old woman underwent heart transplantation (HTx) for advanced heart failure. The donor was a 36-year-old young woman without past medical history. The first day after HTx, T-waves changes were noted. Echocardiography revealed akinesia/dyskinesia of all basal segments of the two ventricles. Coronary catheterization plus biopsy were done 7 days later showing no coronary obstruction, no rejection and complete recovery of wall motion abnormalities on echocardiogram, suggesting biventricular inverted takotsubo syndrome (TTS). This is a case of TTS during the first day after HTx, with completely denervated heart but because of the inotropic drug support it still represents a target for catecholamine-induced cardiac dysfunction.

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.

Augmentation index predicts mortality in patients with aortic stenosis: an echo-tracking study.

Aortic valve stenosis (AS) shares similarities with the atherosclerotic process but little is known about the effect of the mechanical properties of large arteries on outcome in patients with AS. The aims of this study were (1) to determine the relationship between indexes of carotid stiffness/compliance and the severity of AS and (2) to identify whether local arterial stiffness is independently associated with mortality. 133 patients with moderate to severe isolated AS and preserved LV ejection fraction (LVEF) were included. All underwent transthoracic echocardiography and local carotid stiffness evaluation by means of high-definition echo-tracking ultrasound with the calculation of stiffness/compliance parameters included augmentation index (AIx). None of the carotid stiffness parameters were significantly associated with AS severity parameters. During a mean follow-up of 51.6 ± 39.4 months, 70 patients received aortic valve replacement, 45 died and 18 were alive with no surgery. Who died were older (79.2 ± 6.9 vs. 73 ± 8.8 years, p < 0.0001), had higher carotid AIx (21.3 ± 14 vs. 16 ± 12%, p = 0.028). In multivariate Cox regression analysis AIx was independently associated with mortality (HR 1.048, 95% CI 1.01-1.07, p = 0.001), also after inclusion of age and creatinine. There was a significant association between the level of AIx and mortality in those patients who did not have surgery (p = 0.016). In severe AS and a normal LVEF, carotid AIx measured by echo-tracking system was independently associated with death. No relationship between AS severity and local carotid stiffness was found. These data emphasize the importance of arterial stiffness has a hallmark of long-term atherosclerotic burden and impaired prognosis.

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.

Breast arterial calcification on mammography does not predict coronary artery disease by invasive coronary angiography.

BACKGROUND: The relationship between breast arterial calcification (BAC) and angiographic coronary artery disease (CAD) is uncertain. Some studies have shown a positive association between BAC and angiographically proven CAD, while other studies have shown no association. OBJECTIVE: Examine the association between visually detected BAC on mammography and CAD found on invasive coronary angiography (ICA) in women and compare the frequency of risk factors for CAD between women with normal and abnormal ICA. DESIGN: Retrospective. SETTING: Single tertiary care center. PATIENTS AND METHODS: A review of the radiology databases was performed for female patients who underwent both ICA and mammography within six months of each other. Cases were excluded if there was a history of CAD, such as coronary artery bypass graft or prior percutaneous coronary intervention. MAIN OUTCOME MEASURES: BAC as a predictor of obstructive CAD on ICA. SAMPLE SIZE: 203 Saudi women RESULTS: The association between age at catheterization and ICA was statistically significant ( P=.01). There was no association between BAC and abnormal ICA ( P=.108). Women with abnormal ICA were older than women with a normal ICA ( P=.01). There was a higher frequency of CAD risk factors among the patients with abnormal ICA, except for smoking. In the multiple logistic regression model, ICA was associated with age, a family history of CAD, diabetes mellitus, hypertension and hypercholesterolemia. BAC-positive women were older than BAC-negative women ( P=.0001). BAC was associated with age, diabetes, hypertension, and chronic kidney disease in the multiple logistic regression model. CONCLUSIONS: BAC on mammography did not predict angiographically proven CAD. There was a strong association between BAC and age and many other conventional CAD risk factors. LIMITATIONS: Relatively small sample, single-center retrospective study. CONFLICT OF INTEREST: None.

Diagnostic accuracy of 13N-ammonia myocardial perfusion imaging with PET-CT in the detection of coronary artery disease.

BACKGROUND: 13N-ammonia positron emission tomography-computed tomography (PET-CT) is being increasingly used as a non-invasive imaging modality for evaluating patients with known or suspected coronary artery disease (CAD), but information about the diagnostic accuracy of PET-MPI is sparse. Objectives: Our objective was to determine the accuracy of 13N-ammonia PET-CT myocardial perfusion imaging (MPI) for detecting CAD. METHODS: We retrospectively evaluated 383 patients with suspected CAD who underwent rest-stress 13N- ammonia PET-CT MPI. Invasive coronary angiography (ICA) was performed within 60 days for all patients with abnormal PET-MPI findings and for selected patients with normal PET-MPI findings. RESULTS: The mean age of the patients was 64±11 years, and the mean body mass index was 32±7 kg/m2. Stress perfusion defects were identified in 147 (34%) out of a total of 383 patients. ICA was performed in 213 patients (145 patients with abnormal PET and 68 patients with normal PET). The sensitivity of PET-MPI for detection of obstructive CAD based on ≥50% stenosis was 90%; specificity, 90%; positive predictive value, 96%; negative predictive value, 76%; and diagnostic accuracy, 80%. CONCLUSIONS: PET-MPI with 13N-ammonia affords high sensitivity and overall accuracy for detecting CAD. The addition of coronary artery calcium score (CACS) can improve CAD risk stratification.