Risk Evaluation Research Articles

Chemical characteristics and health risk evaluation of natural waters in the Du River Source National Nature Reserve: A case study in Zhushan County, Hubei Province, China

To study the distribution of trace elements in natural water of the Du River Source National Nature Reserve and to assess the water quality and health risks, Zhushan County in Hubei Province was selected as the study area. Element content in 361 natural water samples collected from Zhushan County were measured by ICP-MS, ICP-OES, and HG-AFS. The main anions and cations present in water samples from Zhushan County are Ca2+ and HCO3−. The water chemistry is predominantly influenced by the weathering of carbonate rocks. The water samples with high content of selenium (Se) (0∼82.9 μg/L, mean 4.6 μg/L) in natural water in Zhushan County are mainly distributed in the northern part of Zhushan. The strontium (Sr) content of 49.6% of the water samples (0.001–2.177 mg/L, mean 0.234 mg/L) reached the criteria of natural mineral water for drinking in China (Sr ≥ 0.2 mg/L), which is distributed throughout the county. The high content of metasilicic acid (H2SiO3) (0.026–35.910 mg/L, mean 12.598 mg/L) and zinc (Zn) (0∼407.218 μg/L, mean 12.406 μg/L) are concentrated in northern Zhushan County. 99.7% water samples were freshwater and 98.9% meet the criteria of “good” water quality. All of the natural water samples have low health risk and low heavy metal pollution. 6.1% water samples meet the criteria of Se-type mineral water, while 45.4% meet the criteria of Sr-type mineral water, and 4.4% water samples meet the criteria of “low sodium, high Se, and high Sr” mineral water. Zhushan County has the potential for Se-type mineral water and Sr-type mineral water development. The findings of this study hold immense significance for the public health implications of drinking water in Du River Source, thereby offering valuable insights for effective water resources management.

Coronavirus risk factor by Sugeno fuzzy logic

<span>World recently faced big challenges with the pandemic of coronavirus disease 2019 (COVID-19). Governments suffer from the problem of appropriately identifying the risk factor of this virus and establishing their safety procedures accordingly. This paper concentrates on designing a coronavirus risk factor (CRF) by the power of Sugeno fuzzy logic (SFL). The main advantage of the CRF is that it can provides a quick and suitable risk evaluation. According to the degree of severity, three essential parameters are considered: number of infected cases, number of people in intensive care units (ICU) and number of deaths. All of these parameters are provided per population. Such interesting and promising outcomes are attained, where the total effect is found equal to 95.3%.</span>

Artificial intelligence and machine learning for early cancer prediction and response

Worldwide, cancer claims more lives than any other disease. Although cancer detection, prognosis, and treatment have all advanced, one major obstacle is the lack of personalized, data-driven care. Artificial intelligence (AI), used to forecast and automate numerous malignancies, has emerged as a viable solution for increasing healthcare accuracy and patient outcomes. Artificial intelligence applications in cancer include risk evaluation, early detection, patient prognosis prediction, and treatment selection based on comprehensive data. Machine learning (ML), a form of artificial intelligence that allows computers to learn from training data, is very successful in predicting breast, brain, lung, liver, and prostate cancers. Indeed, AI and machine learning have outperformed physicians in predicting cancer. These technologies can potentially enhance the diagnosis, prediction, and quality of life of patients with a wide range of disorders, not just cancer. As a result, it is critical to enhance existing AI and ML technologies and create new programs to help patients. This article discusses the use of AI and machine learning algorithms in cancer prediction, including its present uses, future possibilities, and limitations.

Ethical Considerations of Including Minors in Clinical Trials Using the Example of the Indicated Prevention of Psychotic Disorders

Ethical Considerations of Including Minors in Clinical Trials Using the Example of the Indicated Prevention of Psychotic Disorders Abstract: As a vulnerable group, minors require special protection in studies. For this reason, researchers are often reluctant to initiate studies, and ethics committees are reluctant to authorize such studies. This often excludes minors from participating in clinical studies. This exclusion can lead to researchers and clinicians receiving only incomplete data or having to rely on adult-based findings in the treatment of minors. Using the example of the study "Computer-Assisted Risk Evaluation in the Early Detection of Psychotic Disorders" (CARE), which was conducted as an 'other clinical investigation' according to the Medical Device Regulation, we present a line of argumentation for the inclusion of minors which weighs the ethical principles of nonmaleficence (especially regarding possible stigmatization), beneficence, autonomy, and fairness. We show the necessity of including minors based on the development-specific differences in diagnostics and early intervention. Further, we present specific protective measures. This argumentation can also be transferred to other disorders with the onset in childhood and adolescence and thus help to avoid excluding minors from appropriate evidence-based care because of insufficient studies.

Contraceptive counseling for iPLEDGE and its burden on dermatologists.

Since 2006, iPLEDGE, a risk evaluation and mitigation strategy (REMS), has attempted to prevent fetal exposures in people taking isotretinoin through contraceptive requirements and regular pregnancy testing. There has been criticism of iPLEDGE's requirements, results, and accessibility. iPLEDGE has placed significant burdens on physicians, patients, and administrative staff. Some level of burden is acceptable to prevent fetal exposures, but iPLEDGE burdens are so strenuous that physicians may choose not to prescribe isotretinoin because of them. There are several evidence-based adaptations that iPLEDGE and physicians can enact to improve the isotretinoin experience. First, physicians can practice shared-decision making in contraceptive counseling and educate patients on long-acting reversible contraceptives (LARCs) to improve the counseling process and outcomes. Second, physicians can take advantage of the reimbursed iPLEDGE contraceptive counseling sessions and refer patients accordingly. Finally, iPLEDGE should recognize the variation in efficacy among contraceptives. Specifically, LARCs and permanent surgical sterilization should be exempt from certain iPLEDGE requirements such as monthly pregnancy testing and attestations. iPLEDGE should work with dermatologists for the continual improvement of iPLEDGE. Communication, repetitive reassessment, and subsequent adaptations will result in better care for patients requiring isotretinoin.

Prediction and evaluation of wireless network data transmission security risk based on machine learning

Prediction and evaluation of wireless network data transmission security risk based on machine learning

The Environmental Impact of Exposure to Electromagnetic Fields (EMF) on Health

We have methodically scrutinized the pervasive electromagnetic fields (EMF) prevalent in modern society for their potential health hazards. The aim of this study was to investigate the levels of exposure to electromagnetic fields (EMF), resulting biological effects, present recommendations, population characteristics, and public opinion. The main aim of this research is to give a comprehensive explanation of the subject. The data collection process encompassed gathering EMF exposure levels in different settings, analyzing experimental studies on biological impacts, consolidating existing standards, investigating demographic discrepancies, and evaluating public perception through surveys. We analyzed the data using descriptive statistics to summarize the findings and inferential statistics to evaluate the relationships between variables. EMF exposure levels differed among various locations, with metropolitan areas and industrial facilities demonstrating elevated amounts. Studies in real life have shown that electromagnetic fields (EMF) can have many effects on living things, including increased oxidative stress, changed gene expression, and messed up biological cycles. Different organizations had varying guidelines, which were based on different risk assessment approaches. There were differences in the demographic groups regarding their exposure to electromagnetic fields (EMF) and their levels of awareness. Children and teenagers had more exposure to EMF, whereas younger individuals showed higher levels of awareness but variable degrees of concern. Public opinion differed among age groups, with younger people relying more on online sources for information. This study offers significant insights into the complex correlation between electromagnetic field (EMF) exposure and human health. The results emphasize the necessity of focused evaluation of risks and implementation of measures to reduce them, standardization of standards, and customized communication campaigns to tackle demographic differences in awareness and apprehension. This research contributes new findings to the existing body of knowledge, facilitating public discussions, influencing governmental decisions, and fostering a healthier and more informed society.

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco.

Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control.

Evaluation of Metal Contamination and Ecological Risk in Surface Sediments within an Industrialized Catchment

Evaluation of Metal Contamination and Ecological Risk in Surface Sediments within an Industrialized Catchment

Alcohol use disorder as a moderator of the relationship between posttraumatic stress disorder and suicidality among military personnel.

Alcohol use disorder (AUD), posttraumatic stress disorder (PTSD), and suicide are substantial public health concerns among military service members, yet the nature of their relationships is not well understood. Here, we tested the hypothesis that AUD moderates the relationship between PTSD symptom severity and suicidal ideation. This secondary analysis uses data collected at baseline for a randomized clinical trial. The sample consists of 160 active-duty service members from three service branches (Army, Air Force, and Navy). All participants met diagnostic criteria for PTSD and were not engaged in evidence-based PTSD treatment at the time of enrollment. Zero-inflated Poisson generalized linear regression models were used to test the associations of PTSD and AUD symptom severity with the presence and severity of suicidal ideation. Findings suggest that AUD symptom severity moderates (i.e., amplifies) the relationship between PTSD symptoms and severity of suicidal ideation among military personnel with untreated PTSD. Among service members with mild or absent AUD, we found no significant association between PTSD symptoms and the severity of suicidal ideation. However, when AUD severity was average (i.e., sample mean) or high (mean + 1SD), PTSD symptoms were significantly positively associated with the severity of suicidal ideation. This study highlights the importance of assessing AUD and PTSD as a part of suicide risk evaluations of veterans. The results also provide strong support for the maintenance and further development in the military health system of treatment programs that simultaneously address AUD and PTSD comorbidity.

Exposition of techno-functional components of intelligent food supply chain: a review and future research agenda

Purpose The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps. Design/methodology/approach This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC. Findings This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies. Originality/value The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

Evaluation of health risk experienced by urban taxi drivers to whole-body vibration and multiple shocks

This cross-sectional study was done aiming at determining the exposure of taxi drivers to vibration on 35 safe taxis (19 cars with engine volume of 1.325 L, 9 cars with engine volume of 1.645 L, and 7 cars with engine volume of 1.761 L). Measurements were based on ‘ISO 2631-1’ on seats’ pans, by Castel GA2005 vibrometer. The results through two different evaluative methods indicated moderate exposure levels, corresponding to suggestive Health Guidance Caution Zone (HGCZ) in ‘ISO 2631-1’. Evaluation of exposure to the WBV among taxi drivers, in the taxis with low and high mean acceleration values, using the VDV method is preferred over the R.M.S method. In lower accelerations, in all conditions, the mean of the reckoned values in the VDV method was less than the R.M.S method, and in higher accelerations, where there are more differences, and the CF values were higher (PAIK 1761 cars), again the assessed mean duration in the VDV method is less than R.M.S method. In higher accelerations and with fewer crest factors, this matter didn’t hold, and the allowed time in the R.M.S method became lower.

Sparked Intuition Power: An Audit Risk Activity

This case explores the subjective nature of professional judgment related to audit risk through the lens of a fictitious company, Sparked Intuition Power (SIP). SIP has just been accepted as a new audit client by an international accounting firm. Students play the role of associates on the audit engagement team. As part of their work on the audit, the students identify potential risks present at SIP as part of the initial risk assessment process. Once these risks have been identified, the students decide how to assess the potential severity of each risk and determine the resulting effect on the overall auditor workload. Through interaction and discussion with their instructors, the students should be better able to navigate the decision-making process related to risk evaluation in an audit engagement.

#401 Cardiovascular risk evaluation in lupus nephritis and ANCA-associated vasculitis

Abstract Background and Aims Individuals with lupus nephritis (LN) and renal ANCA-associated vasculitis (AAV) have amplified cardiovascular risks. Early recognition and intervention for metabolic disease may mitigate cardiovascular risk. We aimed to evaluate the prevalence of cardiovascular metabolic risk evaluation among adults with kidney disease due to LN and AAV treated with immunosuppressants. Method Retrospective cohort study of 219 patients with kidney LN (n = 175) and AAV (n = 44) diagnosed between November 2015 and December 2022. The outcomes were the prevalence of cardiovascular metabolic risk evaluation (HbA1c, fasting glucose and lipid) during the first two years of treatment. Results The median age was 48.6 (33.3, 60.9) years. Table 1 showed that the AAV group had lower eGFR and higher prevalence of diabetes mellitus, hypertension, and hyperlipidemia. Glycemic evaluation was more frequent during the first year than the second year (81.0% versus 61.2%, p < 0.001) for the cohort, as well as the LN (79.2% versus 61.7%, p < 0.001) and AAV groups (88.4% versus 58.8%, p = 0.003). Lipid evaluation was more frequent during the first year than the second year for the cohort (60.5% versus 50.3%, p = 0.04) and for AAV (81.4% versus 47.1%, p = 0004), but not the LN group (55.1% versus 51.0%, p = 0.52). Conclusion Cardiovascular metabolic risk evaluation during lupus nephritis (LN) and ANCA-associated vasculitis (AAV) long-term follow-up should be optimized.

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

#2806 Role of non-HLA antibodies in pre-transplant evaluation of antibody-mediated damage risk

Abstract Background and Aims Donor-specific HLA antibodies (HLA-DSA) contribute to antibody-mediated rejection (ABMR) after kidney transplantation (KT) and worse graft-survival. Non-HLA antibodies may play a role in the development of ABMR in the presence of HLA-DSA or in the development of microvascular inflammation (MVI) in the absence of HLA-DSA. Considering both antibodies in potential recipients before or after KT could enhance the prediction of post-KT ABMR/MVI. Method Retrospective study of a cohort of 169 KT recipients with: a) pre- and post-KT sera; b) follow-up HLA antibody monitoring; and c) follow-up biopsies. We determined the presence of 60 non-HLA antibodies in sera with a multiplex test: the Single Non-HLA Beads kit (LIFECODES®) on a Luminex® platform. We obtained an absolute number of positive antibodies and a sample MFI ratio sum according to which we stratified results into quartiles. Results Pre-KT, we detected a 15.4% of KT recipients with high HLA sensitization (cPRA >50%), being 8.3% HLA-DSA, and non-HLA antibodies in 85% of KT recipients, with a median of 3 (2-5) antibodies and a median MFI ratio sum of 10.1 (2.9-18.5). During a median follow-up of 60 (18.5-89) months 73 patients developed ABMR/MVI. We observed that a high burden of pre-KT non-HLA antibodies was associated with a higher risk of ABMR/MVI, regardless of HLA sensitization and pre-KT HLA-DSA. Notably, 57% of patients with ABMR/MVI had a high non-HLA burden before KT and 24.6% had a HLA cPRA >50%. Altogether, 68.5% of these patients showed either a high HLA sensitization or a high non-HLA burden before KT (Figure). Conclusion A high non-HLA preTR non-HLA antibody burden detected with a multiplex panel allows identification of patients more likely to develop ABMR/MVI, irrespective of the presence of anti-HLA antibodies.

Potential Metabolic Pathways Involved in Osteoporosis and Evaluation of Fracture Risk in Individuals with Diabetes

Diabetes has a significant global prevalence. Chronic hyperglycemia affects multiple organs and tissues, including bones. A large number of diabetic patients develop osteoporosis; however, the precise relationship between diabetes and osteoporosis remains incompletely elucidated. The activation of the AGE-RAGE signaling pathway hinders the differentiation of osteoblasts and weakens the process of bone formation due to the presence of advanced glycation end products. High glucose environment can induce ferroptosis of osteoblasts and then develop osteoporosis. Hyperglycemia also suppresses the secretion of sex hormones, and the reduction of testosterone is difficult to effectively maintain bone mineral density. As diabetes therapy, thiazolidinediones control blood glucose by activating PPAR-γ. Activated PPAR-γ can promote osteoclast differentiation and regulate osteoblast function, triggering osteoporosis. The effects of metformin and insulin on bone are currently controversial. Currently, there are no appropriate tools available for assessing the risk of fractures in diabetic patients, despite the fact that the occurrence of osteoporotic fractures is considerably greater in diabetic individuals compared to those without diabetes. Further improving the inclusion criteria of FRAX risk factors and clarifying the early occurrence of osteoporosis sites unique to diabetic patients may be an effective way to diagnose and treat diabetic osteoporosis and reduce the risk of fracture occurrence.

#2775 ERCOS multicenter Spanish study: clinical profile of patients with chronic kidney disease G3a-G5D and osteoporosis

Abstract Background and Aims Fracture risk evaluation has recently been integrated into the 2017 KDIGO CKD-MBD (Chronic Kidney Disease- Mineral and Bone Disorder) guideline and its 2021 Spanish endorsement. It is now suggested to test bone mineral density (BMD) to assess fracture risk “if results will impact treatment decisions”. In fact, multiple new prospective studies have now documented that lower BMD predicts incident fractures in patients with CKD G3a–G5D. However, we scarcely know current real clinical practice in this population. Thus, the aim of the ERCOS (“Enfermedad Renal Crónica OSteoporosis”) study was to describe the clinical profile of patients with CKD who had been diagnosed of osteoporosis in outpatient Nephrology and Rheumatology clinics in Spain. Method ERCOS is a descriptive, observational, and multicenter Spanish study. Patients were recruited by consecutive sampling from 15 different Spanish hospitals. Osteoporosis characteristics (history of fragility fracture, BMD and other fracture risk factors), associated comorbidities, biochemical parameters and treatment were analyzed. Results A total of 163 patients (median age 77 years old) were included. Most were women (71.2%) and 98.3% of them were postmenopausal. Hypertension was the most frequent comorbidity (87.1%) and “attributed” cause of CKD (43.2%). Diabetes mellitus and cardiovascular disease were present in 35.6% and 29.4% of patients, respectively. Median estimated glomerular filtration rate was 36 ml/min/1.73 m2 [creatinine 1.48 mg/dl (95% CI 1,2-2)]. 38% of patients were G5D CKD patients (95.2% undergoing hemodialysis). 37.7% of patients had had a fragility fracture after the diagnosis of CKD (vertebral 52.5%, hip 24.6%, wrist 21.3%, humerus 16.4%, among others). Importantly, despite the diagnosis of osteoporosis, only 60.1% of the patients received specific treatment (mainly bisphosphonates or denosumab). Moreover, antiosteoporotic treatment was much more frequently prescribed by rheumatologists (46.9%), internal medicine physicians (23.5%), and only 13.3% was prescribed by nephrologists. 19.4% of patients suffered from fragility fractures even after initiating specific treatment. Conclusion Most patients with moderate and severe CKD with osteoporosis were old postmenopausal women with a high prevalence of diabetes or cardiovascular disease. Although about 40% had even suffered a fragility fracture, there is a high treatment gap, especially relevant among nephrologists. A call to action and to avoid existing therapeutic nihilism seems necessary, expanding the knowledge of the new more proactive guidelines and homogenizing the care and therapeutic approach in these patients.

Association between heme oxygenase-1 and hyperlipidemia in pre-diabetic patients: a cross-sectional study

BackgroundAlthough the importance and benefit of heme oxygenase-1 (HO-1) in diabetes rodent models has been known, the contribution of HO-1 in the pre-diabetic patients with hyperlipidemia risk still remains unclear. This cross-sectional study aims to evaluate whether HO-1 is associated with hyperlipidemia in pre-diabetes.MethodsSerum level of HO-1 was detected using commercially available ELISA kit among 1,425 participants aged 49.3–63.9 with pre-diabetes in a multicenter Risk Evaluation of cAncers in Chinese diabeTic Individuals: A lONgitudinal (REACTION) prospective observational study. Levels of total cholesterol (TC) and triglyceride (TG) were measured and used to defined hyperlipidemia. The association between HO-1 and hyperlipidemia was explored in different subgroups.ResultThe level of HO-1 in pre-diabetic patients with hyperlipidemia (181.72 ± 309.57 pg/ml) was obviously lower than that in pre-diabetic patients without hyperlipidemia (322.95 ± 456.37 pg/ml). High level of HO-1 [(210.18,1,746.18) pg/ml] was negatively associated with hyperlipidemia (OR, 0.60; 95% CI, 0.37–0.97; p = 0.0367) after we adjusted potential confounding factors. In subgroup analysis, high level of HO-1 was negatively associated with hyperlipidemia in overweight pre-diabetic patients (OR, 0.50; 95% CI, 0.3–0.9; p = 0.034), especially in overweight women (OR, 0.42; 95% CI, 0.21–0.84; p = 0.014).ConclusionsIn conclusion, elevated HO-1 level was negatively associated with risk of hyperlipidemia in overweight pre-diabetic patients, especially in female ones. Our findings provide information on the exploratory study of the mechanism of HO-1 in hyperlipidemia, while also suggesting that its mechanism may be influenced by body weight and gender.

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.