Roundtable Discussion: The ALLHAT Trial

Abstract

, the angiotensin-converting enzyme [ACE] inhibitors, the calcium channel blockers [CCBs], and the α blockers (because they had some positive effects on different metabolic aspects) were any more effective in reducing cardiovascular events than a diuretic-in this case, chlorthalidone.There were data over many decades regarding the benefits of diuretic-based therapy in preventing both cerebrovascular as well as cardiovascular events in hypertension. Meta-analyses had also reported a decrease in all-cause mortality with these agents. There were no placebo-controlled hypertension morbidity trials with CCBs or ACE inhibitors when ALLHAT was planned and initiated and there has been only one placebo-controlled study with a CCB and no placebocontrolled studies with ACE inhibitors to demonstrate a reduction of cardiovascular events in hypertensive patients since then. We believed they were likely to be beneficial, but we had no data. Physicians were, however, using the newer classes of drugs more frequently while using fewer and fewer diuretics and β blockers.It was decided to determine how these newer drugs compared with a diuretic. The primary purpose of ALLHAT was to determine whether amlodipine (a CCB), lisinopril (an ACE inhibitor), or doxazosin (an α blocker) were better than a diuretic at preventing coronary events and other cardiovascular events.DR. MOSER: OK, but why did the ALLHAT investigators confine the study to an elderly population and choose to have more than 35% black patients? I understand that it is important to include these particular populations because hypertension is more common and may be more serious in these groups, but didn't you concern yourselves with the fact that this might skew the results in some way, and not be able to be generalized to the population at large? DR. CUSHMAN: No, we thought it was very important to get the answer in different age and racial groups, and the only way to do this was to have a fairly sizable representation. Black individuals have been underrepresented in many other hypertension trials except for a few in the United States; there also was very little likelihood that we would have much information about ACE inhibitors or calcium blockers in African Americans in other studies. So we selectively wanted to recruit more African Americans and to analyze them as a subgroup separately. The age group of ALLHAT is also representative of most hypertensives.

Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine

“…Experienced in a variety of healthcare settings, both procedural and technological innovations can measure and optimize their impact in complex, dynamically changing interactions within the healthcare system. A review of the six largest bibliometric databases yielded 134,012 publications (1857-2024) with the keyword "preventive medicine", including only 110 publications with the keywords "preventive medicine" + "heart attack" (1979-2023) and only 3 "preventive medicine" + "heart attack" + "artificial intelligence" or "preventive medicine" + "heart attack" + "machine learning" (2003-2023) [22][23][24]. The results from the perspective of research to date show that AI-based heart attack prediction systems offer a number of benefits that can significantly improve healthcare outcomes, but more are needed.…”

“…AI algorithms analyzing large datasets (patient records, medical images, and genetic information) identify rules, mechanisms, patterns, and risk factors associated with heart attacks, and this allows for the early detection of potential problems, enabling quick intervention and taking preventive actions. AI provides a personalized risk assessment by taking into account a wide range of factors, including medical history, lifestyle, genetics, and environmental variables, allowing for more accurate predictions tailored to an individual's specific risk profile [1,[22][23][24][25]. Continuous patient monitoring provides the real-time analysis of health parameters and can detect subtle changes in health that may indicate an increased risk of heart attack.…”

“…This relieves some of the burden of manual data analysis, allowing focus on patient care and clinical decision-making. By supporting decision-making, AI helps in developing personalized prevention, treatment, rehabilitation, and care plans [22][23][24][25][26][27][28]. AI ensures data integration and analyzes data from various sources, which enables a more comprehensive understanding of the patient's health condition and increases the accuracy of predictions and the effectiveness of preventive/therapeutic actions taken.…”