Risk Assessment Tool Research Articles

Mass gathering events and COVID-19; lessons learnt from the 2020 European football championship.

Evidence about the impact of mass gatherings during the coronavirus disease 2019 (COVID-19) pandemic on the number of disease cases and on the health-care systems of host countries is limited. Additionally, there have been few publications on the lessons identified from the adaptation of mass gatherings held during the pandemic, including the implementation of comprehensive public health and social measures aimed at reducing viral transmission. This article describes preparations made for the 2020 Union of European Football Associations (UEFA) European Football Championship (UEFA Euro 2020) by the World Health Organization's (WHO) Regional Office for Europe, UEFA and other stakeholders after the championship had been rescheduled because of the COVID-19 pandemic. Technical guidance on preparations for the football tournament and risk assessment tools were provided by WHO. A task force established by the WHO Regional Office for Europe conducted traditional and event-based disease surveillance before and during UEFA Euro 2020, monitored public health and social measures in the 11 host countries, and developed a risk communication and community engagement strategy that involved multimedia campaigns targeting news and social media, fans, athletes, event organizers and other stakeholders. The lessons and good practices identified during UEFA Euro 2020 are described to help guide preparations for future mass gatherings in health emergencies. Sharing data and recommendations on best practice from previous mass gatherings with the organizers and countries involved in planning for a major event is particularly important.

Assessment of Lifetime Risk for Cardiovascular Disease: Time to Move Forward.

Over the past decades, there has been a notable increase in the risk of Cardiovascular Disease (CVD), even among younger individuals. Policymakers and the health community have revised CVD prevention programs to include younger people in order to take these new circumstances into account. A variety of CVD risk assessment tools have been developed in the past years with the aim of identifying potential CVD candidates at the population level; however, they can hardly discriminate against younger individuals at high risk of CVD.Therefore, in addition to the traditional 10-year CVD risk assessment, lifetime CVD risk assessment has recently been recommended by the American Heart Association/American College of Cardiology and the European Society of Cardiology prevention guidelines, particularly for young individuals. Methodologically, the benefits of these lifetime prediction models are the incorporation of left truncation observed in survival curves and the risk of competing events which are not considered equivalent in the common survival analysis. Thus, lifetime risk data are easily understandable and can be utilized as a risk communication tool for Public Health surveillance. However, given the peculiarities behind these estimates, structural harmonization should be conducted in order to create a sex-, race-specific tool that is sensitive to accurately identifying individuals who are at high risk of CVD. In this review manuscript, we present the most commonly used lifetime CVD risk tools, elucidate several methodological and critical points, their limitations, and the rationale behind their integration into everyday clinical practice.

Assessing the impact of systemic conditions on periodontal health in Malaysian population: a retrospective study

BackgroundPeriodontal diseases, including gingivitis and periodontitis, are among the most prevalent oral health issues globally. They compromise the supportive structures of teeth and are influenced by both local and systemic factors. This study aimed to systematically assess the impact of systemic conditions on periodontal health in Malaysian population, addressing the gap in understanding these factors as potential risk factors.MethodsA retrospective, non-interventional study was conducted using medical records from Hospital Universiti Sains Malaysia, Kelantan, from September 2019 to December 2022. The study included patients with periodontitis and systemic conditions. Data were analyzed using descriptive statistics, chi-squared tests, Fisher’s exact tests, Mann-Whitney U tests, Kruskal-Wallis tests, Spearman’s correlation, and logistic regression.ResultsOut of 600 records, 274 patients were included. The cohort was 51.8% male, with a median age of 51 years. Malays accounted for 92.3% of the sample. Hypertension and diabetes mellitus were the most common comorbid conditions. Severe periodontitis was significantly associated with age (45–64 years, p = 0.018) and Malay ethnicity (p = 0.011). Logistic regression revealed that age and ethnicity were significant predictors of periodontitis severity, with Malays being 12.5 times more likely to develop severe periodontitis.ConclusionSystemic conditions significantly influence periodontitis development and progression. Age and ethnicity are crucial predictors of periodontitis severity in the Malaysian population. Comprehensive risk assessment tools are necessary to incorporate a broader spectrum of risk factors for better management and prevention.

Implementation of an interactive, digital cancer risk assessment tool in a Medicaid-predominant, racially-diverse gynecology clinic in Brooklyn, New York

Implementation of an interactive, digital cancer risk assessment tool in a Medicaid-predominant, racially-diverse gynecology clinic in Brooklyn, New York

Development and Application of a Delivery Infrastructure Collapse Risk Assessment Tool: A Focus on Metropolitan Metropolitan Areas

Purpose: This study aims to develop a tool to systematically assess the risk of collapse in the delivery infrastructure and apply it to 42 districts in Gyeonggi Province. The ultimate goal is to provide data for preventive and improvement strategies tailored to regional needs.Methods: Hospitals performing over 50 deliveries annually were identified. Regions were categorized as 'none,' 'one,' or 'more than two' delivery hospitals, with further subdivision by annual delivery volume. Regions were then classified into 5 categories: 'relatively stable,' 'low-risk,' 'moderate-risk,' 'high-risk,' and 'collapsed.'Results: Of the 42 districts, 23 were classified as relatively stable, 3 as low-risk, 1 as moderate-risk, 4 as highrisk, and 11 as collapsed. Notably, areas where the delivery infrastructure has collapsed or is at high risk of collapse accounted for approximately 36% of the total.Conclusion: Tailored strategies and urgent policy support are required for high-risk regions. While perinatal regionalization may be a future direction, stable infrastructure must be maintained until the maternal care delivery system is established.

Differences in Risk for Sexual Violence Protocol (RSVP) risk factors among older and adult men who have sexually offended

ABSTRACT Ageing has a protective impact on recidivism risk, including for those who offend sexually (Ambroziak, G., Kahn, R. E., Mundt, J. C., Keiser, K. L., & Thornton, D. (2020). Are civilly detained and committed sexually violent persons released after age 60 low risk? Criminal Justice and Behavior, 48(7), 981–998; Doren, D. M. (2006). What do we know about the effect of aging on recidivism risk for sexual offenders? Sexual Abuse: A Journal of Research and Treatment, 18; Harris, G. T., & Rice, M. E. (2007). Adjusting actuarial violence risk assessments based on aging or the passage of time. Criminal Justice and Behavior, 34(3), 297–313). Recent research of the Risk for Sexual Violence Protocol (RSVP) suggests increased age may affect the validity of this guideline, with good predictive performance in samples of men mostly aged under 50 (Davis, M. R., Woods, M., Raymond, B., Ogloff, J. R. P., & McEwan, T. E. (in submission). Does it work? Convergent and predictive validity of the Risk for Sexual Violence Protocol (RSVP) in an Australian Community Forensic Mental Health Setting. Manuscript submitted for publication; Vargen, L. M., Jackson, K. J., & Hart, S. D. (2020). Interrater reliability, concurrent validity, and predictive validity of the Risk for Sexual Violence Protocol. Law and Human Behavior, 44(1), 37–50), but poor predictive performance in a sample aged over 50 (Raymond, B. C., Davis, M. R., Ogloff, J. R. P., & McEwan, T. E. (in submission). Convergent and predictive validity of the sexual violence risk-20 and risk for sexual violence protocol with older sexual offenders. Manuscript submitted for publication). This study investigated possible reasons for these contrasting results by investigating age-related differences in risk factor endorsement and the relationships between risk factors. RSVP risk factors in two samples of men with prior sexual offending, older adults aged between 50 and 78 (n = 95) and adults aged between 18 and 46 (n = 139). Few differences were identified in risk factor endorsement or the relationship between risk factors. The younger adult sample were observed to have more risk factors present. PRACTICE IMPACT STATEMENT This research adds to the literature regarding older men who offend sexually. It is anticipated that further research into factors that are associated with this age group, may encourage future research of gerontological literature in a forensic arena. Until more reliable evidence is available, the current risk assessment tools may require a level of caution when used with older men.

10 TIPS on how to use dynamic risk assessment and alerts for AKI

Abstract Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing length of hospitalization and minimizing resource utilization. Implementation of dynamic risk scores and incorporation of novel biomarkers show promise for early detection and minimizing progression of AKI. Like any risk assessment tools, these require further external validation in a variety of clinical settings prior to widespread implementation. Additionally, alerts which may minimize exposure to a variety of nephrotoxic medications or prompt early nephrology consultation are shown to reduce the incidence and progression of AKI severity and enhance renal recovery. While dynamic risk scores and alerts are valuable, implementation requires thoughtfulness and should be used in conjunction with the overall clinical picture in certain situations, particularly when considering the initiation of fluid and diuretic administration or renal replacement therapy. Despite the contemporary challenges encountered with alert fatigue, implementing an alert-based bundle to improve AKI care is associated with improved outcomes, even when implementation is incomplete. Lastly, all alert-based interventions should be validated at an institutional level and assessed for their ability to improve institutionally relevant, clinical meaningful outcomes, reduce resource utilization and provide cost effective interventions.

A Readmission Risk Assessment Tool Is Not Predictive of 90-Day Readmission After Total Joint Arthroplasty at an Urban Tertiary Referral Hospital

A Readmission Risk Assessment Tool Is Not Predictive of 90-Day Readmission After Total Joint Arthroplasty at an Urban Tertiary Referral Hospital

Comparison of cardiovascular disease risk assessment tools in primary prevention patients with raised lipoprotein(a)

Abstract Background Genetically inherited lipoprotein (a) (lp(a)) is an independent cardiac risk factor. The approach to cardiovascular disease (CVD) risk modification is, however, uncertain. Current CVD risk calculators do not take lp(a) into account and guidance suggests binary treatment decisions based on presentation of lp(a) without considering other risk factors. A new CVD risk assessment tool has been developed which includes lp(a) concentration, Lp(a) Clinical Guidance (lpaclinicalguidance.com). Purpose The aim was to compare the CVD risk estimations and management impact, over both 10 years and lifetime, using the HEART UK consensus statement, QRISK3, Lp(a) Score, Score-2, Score-2 OP, and LIFE-CVD assessment tools in 20 primary prevention patients with raised lp(a). Methods This was a retrospective analysis from an existing database, recruited through lipid and cardiology outpatient services, with raised lp(a), and no history of CVD. A random selection with a broad range of lp(a) concentrations were chosen. The 10-year risk threshold to treat was based on current NICE guidance at 10% and, in the absence of consensus, a lifetime risk threshold of 25%. Lp(a) was measured by a reference clinical laboratory using a Roche platform (Denka Seiken reagent, isoform insensitive). Results The subset (n=20) included patients with lp(a) ranging from 64 nmol/L to >720 nmol/L, aged 20 to 78 (12 females). Risk assessment tool age restrictions prohibited risk estimation in all patients (explaining the variation in denominator below). Figure 1 shows a comparison of 10-year risk assessment tools, figure 2 lifetime risk. HEART UK guidance would indicate 90% of patients (18/20) were eligible to commence lipid lowering therapy, however, all patients would have treatment indicated based on Lp(a) Score (16/16). Using Score-2, no patients would have treatment indicated (0/6). Only 25% of patients (1/4) would have treatment indicated using Score-2 OP despite a minimum age criterion that alone introduces a major CVD risk factor. Of the remaining risk estimation tools: LIFE-CVD, QRISK3 lifetime risk, and QRISK3 10-year risk indicated 85% (11/13), 50% (8/16), and 38% (6/16) of patients, respectively, would have treatment indicated. Conclusions In primary prevention patients with raised lp(a), CVD risk estimation and decision-making is impaired by discrepancies in risk assessment tools. There may be underestimation in CVD risk, and subsequently under-treatment, with principal use of QRISK3 10-year risk to guide lipid lowering therapy. Lifetime CVD risk may be underestimated by QRISK and LIFE-CVD proportionally to the degree of elevation of lp(a). Risk assessment is imperfect; population data can only provide limited information when applied to an individual and the wide discrepancy found here calls into question the validity of risk estimation formulae in this population, or alternatively provides a range of tools for clinicians to select to suit the question asked.

Abstract Versus Concrete Risk Identification in Clinical Research in Japan: Randomized and Prospective Pilot Research on the Effect of Risk Reduction Activities in a Risk-Based Approach.

The risk-based approach (RBA) of clinical trial was first introduced in 2011-2012. RBA necessitates implementing risk reduction activities that are proportionate to risk in order to reduce avoidable quality issues. However, there is no consistent methodology or research for identifying and evaluating risks and planning risk reduction activities. We aimed to evaluate risk reduction activities and their effects by using two risk identification and evaluation methods. Among the risk identification and evaluation methods, we selected one method with the lowest number of categories for identifying risks [risk assessment form (RAF)] and one with the highest number [risk assessment tool (RAT)]. Each method was used to identify and evaluate risks in and plan risk reduction activities for the research on ponatinib blood concentration and treatment outcome in patients with chronic phase chronic myelogenous leukemia. RAF and RAT can identify risk using abstract questions and a list of concrete risks, respectively. The sites were randomized into two groups to implement planned risk reduction activities using RAF and RAT and to compare the mean of errors and protocol deviation per subject visit between the two groups. The mean of errors per subject visit and the mean of protocol deviation per subject visit were lower in the RAF group than in the RAT group. Our study indicates that risk reductions can be successfully implemented by using a method to identify and evaluate risks in a small number of abstract categories that are critical to quality of clinical research.

Risk assessment using DEVI and CARPREG -1 score to predict maternal adverse cardiac cvents in pregnancies with valvular heart disease: external validation study from MPAC registry

Abstract Background Even though many risk assessment tools in pregnant women with valvular heart disease (VHD) are available, validation studies are few that assess their performance in diverse settings, which is needed prior to the application of routine clinical practice. Objectives To validate and establish the clinical utility of two risk stratification tools – DEVI (VHD-specific tool) and CARPREG-I in predicting adverse cardiac events in pregnant patients with VHD. Methods This cohort study involved consecutive pregnancies complicated with VHD enrolled in the prospective MPAC registry from July 2016 to December 2019(1). The ability to discriminate those with and without adverse cardiac outcomes was assessed using the area under the curve for both the DEVI and CARPREG-1 scores. Performance was assessed through discrimination and calibration characteristics. Clinical utility was evaluated with Decision Curve Analysis. Results Among 1029 pregnancies complicated with heart disease in the MPAC registry cohort, 604(52.3%) with valvular heart disease were included in this analysis. One or more composite adverse cardiac events occurred in 70 (11.6%) pregnancies during antenatal or the early postpartum period. Mitral regurgitation was the most common lesion (67.7%). In assessing the pregnancy outcomes, those who continued past 20 weeks were included, with the majority (92.0%) delivered at term, and the cesarean rates were 33.6% (n=203). The most common adverse event was heart failure (n=32, 5.3%), most of which (68.6%) occurred in the postnatal period. There were 11 maternal deaths (1.8%) during the study period, six among those with mechanical heart valves (four prosthetic valve thrombosis), two among those with severe mitral stenosis and pulmonary hypertension and two with severe mitral regurgitation and infective endocarditis. The area under the receiver operating characteristic curve (AUC) was 0.766, with 95% confidence intervals (CI) 0.703- 0.828 for DEVI and 0.703 (95%CI 0.644- 0.763) for CARPREG-I models. Calibration plots suggested that the DEVI score overestimates risk at higher probabilities, whereas the CARPREG-I score underestimates the risk at most probabilities. Decision curve analysis demonstrated that both models were useful across predicted probability thresholds between 10% and 60%. Conclusion In this external validation study in pregnant women with primary rheumatic VHD, DEVI and CARPREG-I scores showed good discrimination ability and clinical utility across various probabilities. Both can be used to classify those who develop adverse cardiac outcomes during pregnancy and childbirth. However, both models need recalibration to improve the agreement between the predicted and observed events prior to routine application in diverse clinical settings.Figure 1.a and b ROC and Decision Curves

Validation of the HFA-ICOS risk assessment tool with real-world data from a prospective cohort of breast cancer patients in treatment with anthracyclines and trastuzumab

Abstract Background The increasing prevalence of cancer therapy-related cardiovascular toxicity (CTRCD) poses a significant challenge to patient management, necessitating accurate risk assessments. Previous methodologies have struggled with precise risk stratification, partly due to varying definitions of CTRCD and the specificity of cancer treatments. Purpose This study aimed to evaluate the assessment tool proposed by the Heart Failure Association and the International Cardio-Oncology Society (HFA-ICOS) in predicting the risk of CTRCD in patients undergoing anthracycline and trastuzumab treatment for breast cancer at a national reference center. Methods A prospective cohort study was conducted, encompassing 172 patients treated from January 2022 to July 2023. Patients were assessed at baseline, three months, and six months post-treatment initiation, with exclusion criteria including baseline left ventricular ejection fraction (LVEF) <50%, history of heart failure, and stage IV breast cancer. The study utilized the HFA-ICOS tool for risk stratification and CTRCD was defined with the current criteria of the European Society of Cardiology guideline. Descriptive statistics were calculated, and incidence rates and Cox proportional-hazards models were developed according to the risk category. Results In our study, 138 (80.2%) patients were classified as low risk, 23 (13.4%) as moderate risk and 11 (6.4%) as high risk. We identified 81 (47.1%) patients that developed CTRCD in follow-up. Descriptive characteristics can be found in Table 1. The global incidence rate for CTRCD in our cohort was of 2.4 new cases per 1000 person-days, however no differences in incidence rates were found when stratifying by risk category (Figure 1A). Moreover, patients with moderate to high risk according to the HFA-ICOS tool did not show a significantly increased hazard ratio for cardiotoxicity (HR 0.41, 95%CI 0.15-1.12, p=0.082 and HR 1.39, 95%CI 0.50-3.86, p=0.5, respectively) as shown in Figure 1B. Conclusion While the HFA-ICOS tool provides a structured framework for assessing cardiotoxicity risk, its predictive value in this cohort was limited. The findings underscore the complexity of cancer therapy-related cardiotoxicity and the need for enhanced, individualized risk assessment tools. Future research should focus on refining risk prediction models, incorporating broader clinical and genetic factors to improve patient outcomes in cardio-oncology.Table 1.Clinical CharacteristicsFigure 1.Incidence rates and Cox models

Myocarditis prognostic score: a new risk assessment tool in patients with myocarditis

Abstract Background Myocarditis is an inflammatory disease of the myocardium with multiple causes and evolutions. The aim of our study was to design a prognostic multiparametric score in patients with myocarditis, to identify those at higher risk of adverse cardiovascular events (AE). Methods A prospective study was performed enrolling 98 patients with myocarditis: 72 M, 26 F; median age 27 [IQR 20–40]. Patients were divided into 2 groups: complicated (CM) and uncomplicated myocarditis (UM). Six months after hospital admission, cardiac magnetic resonance (CMR) and cardiological consultation were repeated. AE (death, hospitalization for heart failure, heart transplant, ICD implantation, heart failure development) were evaluated at 6 months and after 3 years. Results We found 67 UM and 31 CM. AE were significantly higher in patients with complicated myocarditis. We found a significant correlation between AE and reduced left ventricular ejection fraction (LVEF) at hospital admission, increased GLS, septal LGE at CMR, longer persistence time of increased troponin, LGE extension progression or persistence 6-month CMR. A myocarditis prognostic score was developed on the basis of these parameters. A score ≥ 5 showed higher sensitivity (100%) and specificity (87%) - AUC 1, to identify AE in patients with myocarditis. A score between 3 and 4 showed high sensitivity but low specificity. A score ≤ 2 was associated with low probability of events. Conclusion Our study confirms the high probability of AE in patients with CM and it suggests a myocarditis prognostic score to identify patients at higher risk of AE.

Comparison of pulmonary arterial risk assessment tools to predict mortality or morbidity in treatment naive and previously diagnosed patients

Abstract Background Pulmonary arterial hypertension (PAH) risk stratification (1-year prediction of risk) is crucial and should be the most accurate to ensure appropriate aggressiveness of the treatment choices. According to the European guidelines, several risk tools can be used both at baseline and/or follow-up assessment. Yet, a comparison of risk tools performances only in treatment-naïve or only previously diagnosed patients is still missing. Aims To compare the available risk assessment tools performances to predict 1-year mortality but also 1-year clinical worsening in treatment-naïve and previously diagnosed PAH patients. Methods Eight PAH trials (AMBITION, ARIES1, ARIES2, FREEDOM, GRIPHON, PATTENT, PHIRST and SERAPHIN) provided by the FDA were harmonized and treatment-naïve patients were separated from previously diagnosed patients. Outcomes were 1-year mortality and 1-year clinical worsening (i.e., morbidity) either from baseline assessment or at follow-up. Kaplan-Meier and log-rank tests were performed for each of the following risk tools: European 4-strata, REVEAL2.0, REVEAL Lite, noninvasive FRENCH. In each subgroup, their C-indices were calculated and compared using a total of 100 bootstrap samples from the original data, providing a robust method for comparing across the different tools. Results Among the 4,122 patients included, 1,701 (41%) were treatment-naïve and 2,411 (59%) were previously diagnosed patients. Compared to treatment naïve patients, previously diagnosed patients were younger (p<0.001), with lower level of natriuretic peptide (p=0.013), higher 6MWD (p<0.001) and higher mPAP (p<0.001). At 1-year, the mortality rate was the same in the 2 subgroups (5.6% 5.1%, p=0.05) but clinical worsening occurred more often among previously diagnosed patients (19% vs 8.4%, p<0.001). Each of the risk tools were able to predict both 1-year mortality or morbidity in each subgroup, especially among previously diagnosed or at follow-up, with fair to good C-index (Figure 1). Forrest plots of 95%CI for the C-index differences among the 100 bootstrap samples showed that REVEAL 2.0 was the best performer, followed by REVEAL Lite (Figure 2). Conclusions In this large dataset, we showed that all PAH risk tools were able to predict not only 1-year mortality but also 1-year morbidity, both in treatment naïve and previously diagnosed patients. REVEAL 2.0 depicted the higher C-index.Figure 1.C-index for PAH risk toolsFigure 2.Forrest plots of 95%CI

Proenkephalin improves risk prediction in acute coronary syndromes: derivation and external validation of the KID-ACS risk score

Abstract Background Early assessment of renal and mortality risk in patients with acute coronary syndromes (ACS) is essential to guide treatment decisions according to international guidelines. Circulating proenkephalin (PENK) is a stable opioid metabolite with fast response to changes in kidney function. Purpose This study examined the predictive value of PENK for acute kidney injury (AKI) and mortality in patients presenting with acute coronary syndromes (ACS). Methods Plasma PENK levels were measured using a validated sandwich immunoassay in a prospective multicentre ACS cohort from Switzerland (n = 4 787) and in validation cohorts from the UK (n = 1 141) and Czechia (n = 920). A biomarker-enhanced risk score for simultaneous prediction of both in-hospital acute kidney injury (AKI) and 30-day death (KID-ACS score) with model coefficients adjusted to the outcome was derived and externally validated. In the development of the in-hospital AKI model, patients from one participating study centre were reserved for external validation (Swiss validation cohort). Results Circulating PENK ranked among the top predictors of renal outcomes and mortality in patients with ACS. Accounting for established risk factors and risk models, circulating PENK levels at presentation were independently associated with in-hospital AKI (per log2 increase: adjusted odds ratio [OR] 1.56, 95% CI 1.14 – 2.12, P = 0.005) and with 30-day mortality (per log2 increase: adjusted OR 2.69, 95% CI 1.89 – 3.84, P < 0.001). Dynamic changes in PENK levels within the first 12-24 hours after presentation were strongly and independently associated with in-hospital AKI (increase vs. decrease: adjusted OR 2.83, 95% CI 1.98 – 4.05, P < 0.001). The simple 6-item KID-ACS risk score for in-hospital AKI and 30-day mortality integrates PENK levels at presentation and showed an AUC of 0.72 (95% CI 0.68 – 0.76) for in-hospital AKI, and of 0.91 (95% CI 0.87 – 0.95) for 30-day mortality in the derivation cohort. Upon external validation, KID-ACS had similarly high performance for in-hospital AKI (Swiss validation cohort: AUC 0.73, 95% CI 0.70 – 0.77; Czech validation cohort: 0.74, 95% CI 0.68 – 0.80) and 30-day mortality (UK validation cohort: AUC of 0.87, 95% CI 0.82 – 0.91; Czech validation cohort: 0.91, 95% CI 0.87 – 0.94). The simple 6-item KID-ACS score was non-inferior to established risk models and significantly outperformed the CA-AKI risk score in predicting in-hospital AKI (Zurich cohort: delta AUC 0.11, 95% CI 0.07 – 0.15, P = 0.001) and the GRACE 2.0 in predicting 30-day mortality (UK cohort: delta AUC 0.05, 95% CI 0.01 – 0.09, P = 0.027). Conclusions Circulating PENK was identified as independent predictor of in-hospital AKI and 30-day mortality in patients with ACS. The simple 6-item KID-ACS risk score integrates circulating PENK levels and provides a novel tool for simultaneous assessment of renal and mortality risk in patients presenting with ACS.Independent Predictive Value PENKPerformance and validation of KID-ACS

Using EEA data to calculate health impacts of air pollution via the AirQ+ model: a practical example

Abstract The AirQ+ software tool for health risk assessment of air pollution, developed by the World Health Organization, helps quantify the effects of exposure to air pollution. Specifically, AirQ+ can estimate the effects of short-term changes in air pollution (based on risk estimates from time-series studies) and the effects of long-term exposures (using life-tables approach and based on risk estimates from cohort studies). AirQ+ is available in English, French, German and Russian. In this presentation, AirQ+ will be loaded with air pollution data from the European Environment Agency datasets for a quick evaluation of health effects from air pollution in an European city.

Adjustment of the SMART risk score by bioactive adrenomedullin enables a more accurate prediction of mortality in patients with ASCVD

Abstract Background Bioactive adrenomedullin 1-52 (bio-ADM) is a dynamic blood biomarker for real-time assessment of endothelial function. Bio-ADM was recently shown to be a prognostic marker in patients with acute heart failure and cardiogenic shock. The SMART (Second Manifestations of Arterial Disease) score is a validated tool for risk assessment in patients with established atherosclerotic cardiovascular disease (ASCVD). The aim of this study was to assess whether measurement of bio-ADM adds incremental value to the SMART score in stable patients with ASCVD. Methods Circulating bio-ADM levels were assessed in n=695 stable patients with ASCVD in an all-comer cohort. Endpoints evaluated were all-cause mortality and cardiovascular mortality; follow up was 3 years. Results Bio-ADM was higher in non-survivors (all-cause death: n=54, median 33.1 pg/mL) compared to survivors (n=641, median 17.9 pg/mL; p<0.0001). Univariable Cox regression analyses showed bio-ADM to be associated with adverse outcome [standardized hazard ratio (HR) of bio-ADM values: All-cause death: 2.4, 95% confidence interval (CI): 2.0-2.9; p<0.001, cardiovascular death: 2.5, 95% CI: 1.9-3.3; p<0.001]. This association remained significant in various multivariable Cox regression models. Bio-ADM was found to be a strong marker for mortality (c-index: 0.80, Chi²: 54.1) and proved to be superior to other markers including hs-Troponin T (c-index: 0.61, Chi²: 2.0) and eGFR CKD-EPI 2021 (c-index: 0.687, Chi²: 33.5). Addition of bio-ADM to the SMART score significantly improved model performance in predicting mortality (SMART score: c-index: 0.717, Chi²: 24.73; SMART score + bio-ADM: c-index: 0.832, Chi²: 63.24; Delta c-index: 0.115; Delta Chi²: 38.51; all p<0.001) and showed net reclassification improvement in risk categorization with 11.6% in the group of survivors and 9.8% for non-survivors. Conclusion Bio-ADM provides incremental added value (improved discrimination, calibration and reclassification) on top of the SMART risk score in patients with ASCVD.

Using deep learning to detect atherosclerotic plaques on carotid ultrasound images in the UK Biobank

Abstract Background Atherosclerosis is the main underlying cause of cardiovascular disease (CVD). Existing CVD risk assessment tools do not consider the burden of subclinical atherosclerosis. The presence of carotid plaques on carotid ultrasound is a well-known marker of subclinical atherosclerosis. The accumulation of population-scale data on the presence of atherosclerotic plaques, along with deep phenotyping, can allow not only to address the effectiveness of carotid ultrasound in routine clinical practice, but to shed light on the biology of atherosclerosis development. Purpose To develop an effective deep learning model for plaque detection in carotid ultrasound images in the UK Biobank. Methods We used 680 carotid ultrasound images with manually annotated plaques to train a deep learning model employing the YOLOv8 architecture. Different augmentation techniques were used to increase the generalizability of the model. The developed model was applied to automatically detect plaques in raw ultrasound images from 19,507 UK Biobank participants. Logistic and Cox regression were used to explore the associations of plaque presence and number as predicted by the model with conventional CVD risk factors and the risk of future CVD events over follow-up. To explore the genetic architecture of subclinical atherosclerosis, we conducted a genome-wide association study (GWAS) on plaque presence, followed by meta-analysis with data from the CHARGE Consortium. Results Our plaque detection model achieved high classification metrics of accuracy, sensitivity, and specificity (89.3%, 89.5%, and 89.2%, respectively) and detected atherosclerotic plaques in 44% of UK Biobank participants. As expected, plaques were more common among men than women and their prevalence increased linearly with age. Both plaque presence and number of plaques were correlated with conventional CVD risk factors including diabetes, hypertension, and hyperlipidemia, and showed strong associations with future risk of incident CVD events (Hazard Ratio for plaque presence: 1.48 [95%CI: 1.21-1.82], for 2 plaques or more: 1.65, [95% CI: 1.28-2.13]). Incorporating plaque-derived phenotypes minimally altered the C-index of the time-to-event model. GWAS meta-analysis of carotid plaque presence revealed 5 previously known loci, as well as a significant locus including the LPA gene that had not previously been associated with carotid plaque. Conclusion We have developed and implemented an efficient plaque detection model to data from the UK Biobank, which holds significant promise for studying atherosclerosis at a population-wide scale through integration with multiomics data and electronic health records.

Enhancing primary prevention: the incremental predictive value of NTproBNP beyond ASCVD risk assessment

Abstract Background Atherosclerotic cardiovascular disease (ASCVD) is a global health challenge, prompting widespread use of conventional risk assessment tools (SCORE2 in Europe and the ASCVD Risk calculator in the United States) to guide preventive strategies. Emerging evidence indicates that NTproBNP may help refine risk stratification. We investigate whether measuring NTproBNP in primary prevention correlates with mortality and enhances prediction compared to conventional risk score stratification. Methods Utilizing National Health and Nutrition Examination Survey (NHANES) data (1999 to 2004) linked to the National Death Index, our cohort included individuals without a history of CVD at baseline and suitable for assessment of the ASCVD ACC/AHA risk score. Individuals were grouped based on computed 10-year risk (low: <5%; borderline: 5-7.5%; intermediate: 7.5-20%; high: ≥ 20%). NTproBNP levels were measured (Roche assay, cut-off > 125pg/mL considered as positive). Cox regression estimated the association between ASCVD risk categories, with or without NTproBNP, and all-cause and cardiovascular mortality. Results Among 5257 adults, 47% male, mean age 54 ± 9.1 years, the mean 10-year ASCVD risk was 8.17 ± 8.2%, grouped as: low: 53.3%; borderline: 11.5%; intermediate: 23.9%; and high: 11.3%. NTproBNP > 125pg/mL was measured in 12.2%, 16%, 23.2% and 48.6% individuals, respectively (p < 0.001). Over 16.2 ± 3.4 years, all-cause and cardiovascular death was 22.3% and 5.6%, respectively. Compared to low-risk, the hazard ratio (HR) for all-cause death increased to 2.85 (2.2-3.7), 5.6 (4.2-7.5), and 15.3 (11.7-19.9) for borderline, intermediate, and high-risk, respectively. A significant interaction with NTproBNP was observed (low: HR 1.96 (1.33-2.9); borderline: HR 2.5 (1.6-3.9); intermediate: HR 2 (1.6-2.6), high: HR 1.7 (1.4-2.2), p<0.01 for all interactions, see Fig. 1). For cardiovascular death, a significant interaction also occurred across the groups, but of higher magnitude in low and borderline groups (low: HR 3.2 (1.4-7.3); borderline: HR 5.5 (2.5-12.4), p < 0.001 for both). Conclusion Our findings suggest that NTproBNP provides incremental predictive value, approximately doubling the hazard of all-cause death across all risk categories, and more than tripling the hazard of CV death in low/borderline risk groups, compared to ASCVD risk alone. The integration of NTproBNP measurements into risk assessment strategies could refine primary prevention approaches.

Clinical prediction model of main adverse cardiovascular and cerebrovascular events for elderly patients with acute coronary syndrome

Abstract Background Age is an independent factor for adverse outcomes in acute coronary syndrome (ACS). With the accelerated aging process, there is an increasing incidence of ACS in the elderly; however, research on this population is underrepresented. Due to the unique presence of multiple diseases and organ changes in the elderly, specialized risk assessment tools are required. Purpose To explore the risk factors for poor prognosis of elderly patients with ACS and establishing a risk stratification tool. Methods The derivation cohort consisted of 1674 elderly ACS patients consecutively enrolled in a single center from January 2013 to December 2013. The validation cohort included 2333 elderly ACS patients consecutively enrolled in nine medical centers from January 2015 to May 2019. All patients were followed up for 2 years, and the study endpoint was defined as major adverse cardiovascular and cerebrovascular events (MACCE). Model selection was performed using Cox regression and the Akaike Information Criterion (AIC) to construct the final risk assessment model. The performance of the predictive model was evaluated in the derivation and validation cohorts using the c-index, receiver operating characteristic (ROC) curves, and calibration curves. Results A total of 1,674 elderly ACS patients (70.97±4.68 years, 23.24% female) were included in the derivation cohort, and 210 cases (12.54%) of MACCE were recorded during the 2-year follow-up period. In the validation cohort, there were 2,333 elderly ACS patients (72.13±5.82 years, 35.83% female), with 364 cases (15.60%) of MACCE documented during the 2-year follow-up. Initially, 25 variables were preliminarily selected based on the results of univariate Cox regression and the clinical importance of these variables. Subsequently, these variables were included in a multivariable Cox model, and model selection was performed based on the AIC, resulting in the final selection of 10 variables (including age, cardiac insufficiency, current smoker, DAPT use, history of PCI or CABG, IABP use, number of lesions, residual SYNTAX score, preoperative HGB, and preoperative PLT). Then we developed and validated a nomogram to predict the 2-year MACCE in elderly ACS patients based on the assessment of 10 clinically easily obtainable variables during hospitalization. In both the derivation and validation cohorts, the nomogram demonstrated good discriminative ability (AUC of 0.723 and 0.699, c-index of 0.727 and 0.661, respectively) and calibration (calibration curves closely approximating the 45-degree diagonal line). The Kaplan-Meier analysis reveals significant differences in MACCE incidence rates between groups stratified by the median predicted probability (p<0.001). Conclusion Our study developed and validated a practical nomogram for predicting the 2-year risk of MACCE in elderly patients with ACS.