Cardiovascular diseases (CVD) are undoubtedly the leading cause of morbidity and mortality in the elderly. Population aging is a global phenomenon. In developed countries, by the year 2050 one in four people will be aged 65+ years. This ongoing growth of the aging population leads to an increasing burden of CVD. The management of CVD in geriatric patients requires specific considerations. Aging is associated with complex pathophysiology due to decreased organ reserve, which is clinically described as frailty. Additionally, the aging population is extremely heterogenous and frequently characterized by a combination of unique features, including atypical disease presentation, multimorbidity, polypharmacy, altered pharmacokinetics, cognitive impairment, renal impairment, dysautonomia, elevated risk of falls, sarcopenia, and frailty. Furthermore, significant gaps in evidence exist largely due to the limited representation of the very elderly, and especially frail patients, in randomized controlled trials. When combined with issues related to life expectancy, goals of care, bioethics, and patients' preferences, these factors pose intricate challenges for healthcare providers. This literature review summarizes selected clinical scenarios that often introduce dilemmas in the management of elderly patients in cardiology practice, emphasizing the intersection of geriatric medicine and cardiology. These include blood pressure management, management of dyslipidemia, anticoagulation in atrial fibrillation, medical and device treatment of heart failure, antiplatelet and interventional management of acute coronary syndromes, and peri-procedural considerations in severe aortic stenosis. The above will provide guidance for clinical practice, as well as implications for health policies and future research in the field of geriatric cardiology.

Objectives: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As traditional microbiological susceptibility testing requires at least 24 hours, automated machine learning (AutoML) techniques could be used as clinical decision support tools to predict antimicrobial resistance and select appropriate empirical antibiotic treatment. Methods: An antimicrobial susceptibility dataset of 11,496 instances from 499 patients admitted to the internal medicine wards of a public hospital in Greece was processed by using Microsoft Azure AutoML to evaluate antibiotic susceptibility predictions using patients’ simple demographic characteristics, as well as previous antibiotic susceptibility testing, without any concomitant clinical data. Furthermore, the balanced dataset was also processed using the same procedure. The datasets contained the attributes of sex, age, sample type, Gram stain, 44 antimicrobial substances, and the antibiotic susceptibility results. Results: The stack ensemble technique achieved the best results in the original and balanced dataset with an area under the curve-weighted metric of 0.822 and 0.850, respectively. Conclusions Implementation of AutoML for antimicrobial susceptibility data can provide clinicians useful information regarding possible antibiotic resistance and aid them in selecting appropriate empirical antibiotic therapy by taking into consideration the local antimicrobial resistance ecosystem.

Objectives: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As traditional microbiological susceptibility testing requires at least 24 hours, automated machine learning (AutoML) techniques could be used as clinical decision support tools to predict antimicrobial resistance and select appropriate empirical antibiotic treatment. Methods: An antimicrobial susceptibility dataset of 11,496 instances from 499 patients admitted to the internal medicine wards of a public hospital in Greece was processed by using Microsoft Azure AutoML to evaluate antibiotic susceptibility predictions using patients’ simple demographic characteristics, as well as previous antibiotic susceptibility testing, without any concomitant clinical data. Furthermore, the balanced dataset was also processed using the same procedure. The datasets contained the attributes of sex, age, sample type, Gram stain, 44 antimicrobial substances, and the antibiotic susceptibility results. Results: The stack ensemble technique achieved the best results in the original and balanced dataset with an area under the curve-weighted metric of 0.822 and 0.850, respectively. Conclusions Implementation of AutoML for antimicrobial susceptibility data can provide clinicians useful information regarding possible antibiotic resistance and aid them in selecting appropriate empirical antibiotic therapy by taking into consideration the local antimicrobial resistance ecosystem.