Risk Estimates Research Articles

Digital-twin-based multi-scale simulation supports urban emergency management: a case study of urban epidemic transmission

ABSTRACT Improving emergency management capabilities is vital for sustainable cities. However, urban emergency management is a complex, giant system that interacts between urban virtual and real spaces, requiring simulation in the virtual space and feedback to the real space. The digital twin model can potentially play an important role in this process, but still faces significant challenges in terms of modeling, simulation, and visualization. In this study, we propose a digital-twin-based multi-scale simulation method and take epidemic transmission as a case study. First, an entity-oriented unified urban database integrating multi-source urban data was constructed as the digital twins of urban human settlements for emergency modeling. Then, a multi-scale urban emergency simulation method in 3D geographic environment was proposed to support emergency simulation for multi-level urban management units. Moreover, an adaptive fusion rendering of emergency information and 3D scene was proposed to support the refinement of emergency visualization. An experimental case study on epidemic risk estimation was conducted through thousands of outbreak scenarios. The results demonstrate that the proposed method can establish the digital twins of urban human settlements, where modeling, simulation, and visualization of emergency at multi-scales can be carried out, thus enhancing the efficacy and efficiency of emergency management.

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients. AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome. AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation. British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

Burden of Ischemic Heart Diseases among US States from 1990-2019.

Ischemic Heart Disease (IHD) is a leading cause of global mortality, including in the United States. Understanding the burden of IHD in the United States is crucial for informed decision-making and targeted interventions aimed at reducing morbidity and mortality associated with this leading cause of death. This study aimed to understand the burden of IHD, identify gender disparities and risk factors, explore the relationship between socioeconomic growth and IHD, and analyze risk factor distribution across the states of the United States. This study utilized data from the Global Burden of Diseases Study 2019, which provided comprehensive information on IHD from 1990 to 2019. Data related to IHD from these years were extracted using a query tool from the Institute for Health Metrics and Evaluation (IHME) website. The study assessed the relationship between IHD and socioeconomic development using the Socio-demographic Index (SDI) and measured the overall impact of IHD using Disability-adjusted Life Years (DALYs), considering premature death and disability. Additionally, the study analyzed the burden of IHD attributed to six main risk factors. Data analysis involved comparing prevalence, mortality, SDI, DALYs, attributable burden, and risk estimation among the states. Between 1990 and 2019, there was an improvement in socioeconomic development in all states. Age-standardized rates of disease burden for IHD decreased by 50% [ASDR 3278.3 to 1629.4 (95% UI: 1539.9-1712.3) per 100,000] with the most significant decline observed in Minnesota. Males had higher burden rates than females in all states, and the southeast region had the highest mortality rates. The prevalence of IHD showed a declining trend, with approximately 8.9 million cases (95% UI: 8.0 million to 9.8 million) in 2019, representing a 37.1% decrease in the Age-standardized Prevalence Rate (ASPR) from 1990. Metabolic risks were the leading contributors to the disease burden, accounting for 50% of cases, with Mississippi having the highest attributable risk. Arkansas had the highest attributable risk for high cholesterol and smoking. Conversely, Minnesota had the lowest burden of IHD among all the states. This study highlights variations in the burden of IHD across US states and emphasizes the need for tailored prevention programs to address specific risk factors and gender differences. Understanding the trend in IHD may inform policymakers and healthcare professionals in effectively allocating resources to reduce the burden of IHD and improve national health outcomes.

Type 2 diabetes complications in ethnic minority compared with European host populations: a systematic review and meta-analysis

This systematic review and meta-analysis aimed to quantify differences in type 2 diabetes (T2D) complications between ethnic minority populations and European host populations, in both cross-sectional and prospective studies. Following...

Seal and reservoir risk evaluation using hierarchical clustering analysis with seismic attributes in Northwestern Australia

Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO2 sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.

Leveraging Data Science Technology for Advancing Credit Risk Assessment

The evaluation of credit risk (CR) has become prominent in recent years, particularly among banks, as default rates are on the rise and economic insecurity remains persistent. Traditional credit scoring techniques oftentimes are inadequate and provide little means for risk estimation, necessitating the development of new models using data science methodologies. In this study, a novel Intelligent Dwarf Mongoose tuned Light Gradient Boosting Machine (IDM-LGBM) model that boosts the accuracy of CR and improves forecasting performance, is introduced. The Light Gradient Boosting Machine model's hyperparameters were optimized using the Intelligent Dwarf Mongoose technique, improving the model's predictive strength. The CR dataset was gathered from the Kaggle platform. The data is then pre-processed using Z-score normalization. To evaluate the efficiency of the suggested IDM-LGBM technique, which has been implemented employing a Python platform. Results show that the IDM-LGBM model performed significantly better than conventional methods in terms of recall (98.1%), accuracy (97.2%), F1-score (97.4%), and precision (96.5%). Subsequent studies could concentrate on addressing real-time data streams and enhancing models to respond to the changing credit environment.

On the Relationship Among Linear Type Traits and Functional Longevity in the Italian Mediterranean Buffalo Using a Weibull Proportional Hazards Model

Weibull proportional hazards model was used to identify the non-genetic effects that affect length of productive life (LPL) of the Italian Mediterranean Buffalo (IMB). Data were provided by the Italian National Association of Buffalo Breeders (ANASB) and included records of reproductive, productive, and linear type traits from 59,943 buffalo with first calving from 2002 to 2019. Data were divided into 4 geographical regions to determine if the relationship between the investigated effects and LPL varied by region. LPL was defined as the number of days from the first calving to culling for those buffaloes that were culled (uncensored) or to the date of the last test-day for those that are still alive (censored). The Weibull model included time-dependent effects of herd-year-season of calving, parity and stage of lactation, production as within-herd deviations, as well as time-independent effects of age at first calving, year of birth, type of reproduction (natural mating vs artificial insemination), classifier and ten linear type traits. The average duration of productive life was 1,604 days. Both year of birth and production level had a significant effect on culling risk. Moreover, culling risk decreased linearly across parities. The reference point for age at first calving relative to other classes was set at 35 months. A significant relationship between linear type traits and relative culling risk was also observed, being highest for buffaloes with the lowest linear scores. The results show that the Weibull model provides consistent and robust risk estimates. Therefore, this model would be recommended for future implementation of the first genetic evaluation of LPL in the Italian Mediterranean Buffalo.

Generation of preoperative anaesthetic plans by ChatGPT-4.0: a mixed-method study.

Recent advances in artificial intelligence (AI) have enabled development of natural language algorithms capable of generating coherent texts. We evaluated the quality, validity, and safety of this generative AI in preoperative anaesthetic planning. In this exploratory, single-centre, convergent mixed-method study, 10 clinical vignettes were randomly selected, and ChatGPT (OpenAI, 4.0) was prompted to create anaesthetic plans, including cardiopulmonary risk assessment, intraoperative anaesthesia technique, and postoperative management. A quantitative assessment compared these plans with those made by eight senior anaesthesia consultants. A qualitative assessment was performed by an adjudication committee through focus group discussion and thematic analysis. Agreement on cardiopulmonary risk assessment was calculated using weighted Kappa, with descriptive data representation for other outcomes. ChatGPT anaesthetic plans showed variable agreement with consultants' plans. ChatGPT, the survey panel, and adjudication committee frequently disagreed on cardiopulmonary risk estimation. The ChatGPT answers were repetitive and lacked variety, evidenced by the strong preference for general anaesthesia and absence of locoregional techniques. It also showed inconsistent choices regarding airway management, postoperative analgesia, and medication use. While some differences were not deemed clinically significant, subpar postoperative pain management advice and failure to recommend tracheal intubation for patients at high risk for pulmonary aspiration were considered inappropriate recommendations. Preoperative anaesthetic plans generated by ChatGPT did not consistently meet minimum clinical standards and were unlikely the result of clinical reasoning. Therefore, ChatGPT is currently not recommended for preoperative planning. Future large language models trained on anaesthesia-specific datasets might improve performance but should undergo vigorous evaluation before use in clinical practice.

Binary classification from [formula omitted]-Tuple Comparisons data

Pairwise comparison classification (Pcomp) is a recently thriving weakly-supervised method that generates a binary classifier based on feedback information from comparisons between unlabeled data pairs (one is more likely to be positive than the other). However, this approach turns out challenging in more complex scenarios involving comparisons among more than two instances. To overcome this problem, this paper starts with a comprehensive exploration of the triplet comparisons data (the first instance is more likely to be positive than the second instance, and the second instance is more likely to be positive than the third instance). Then the problem is extended to investigate N-Tuple comparisons learning (NT-Comp: the confidence of belonging to the positive class from the first instance to the last instance is in descending order, with the first instance being the biggest). This generalized model accommodates not only pairwise comparisons data but also more than two comparisons data. This paper derives an unbiased risk estimator for N-Tuple comparisons learning. The estimation error bound is also established theoretically. Finally, an experiment is conducted to validate the effectiveness of the proposed method.

Better health-related quality of life is associated with prolonged survival and reduced hospitalization risk among dialysis-dependent chronic kidney disease patients: a historical cohort study

Rationale & objectiveEnd-stage kidney disease (ESKD) negatively affects patients’ physical, emotional, and social functioning. Furthermore, adjustment to dialysis require substantial lifestyle changes that may further impact on patients physical and emotional well-being. However, the relationship between Health-Related Quality of life impairment with future adverse outcomes in dialysis is not well characterized. Our study aims to investigate the relationship between Health-Related Quality of Life (HRQoL) and patients’ survival and hospitalization rates within a large European dialysis network.MethodsA historical cohort study was conducted to evaluate association of HRQoL with hospitalization and mortality rates over a 12-month follow-up period. Patients responded to a self-administered survey as part of a Continuous Quality Improvement Program implemented in clinics affiliated with the Spanish FMC-Nephrocare organization. Health-Related Quality of Life (HRQoL) was measured with the KDQOL-36. Potential confounders included socio-demographic characteristics, comorbidities, biochemical parameters, dialysis treatment. We used Cox’s Proportional Hazard regression to assess the hazard of death and Logistic Regression to assess the likelihood of hospital admissions during 12-month follow-up period.ResultsA total of 2280 (51.5%) completed the self-administrated survey, and 1838 patients met the inclusion/exclusion criteria of the study. Higher HRQoL scores were associated with significantly lower mortality and hospitalization risk. Risk estimates were robust to adjustment for potential confounders.ConclusionsSeveral dimensions of HRQoL are associated with patient-centered outcomes (i.e., mortality and hospitalizations at 1 year). Patient-Reported Outcomes contribute unique pieces of information characterizing patients’ health. Residual confounding cannot be fully ruled out; moreover, the high attrition rate could result in selection bias, which may limit the generalizability of the findings to a broader population.

10-Year Atherosclerotic Cardiovascular Disease (ASCVD) Risk Score Calculation in Out-Patient Department

Objective: To calculate 10-year Atherosclerotic Cardiovascular Disease risk score in outdoor department of a tertiary care hospital and identify individuals at high risk of cardiovascular disease. Study Design: Cross-sectional study. Place and Duration of Study: Combined Military Hospital, Malir, Pakistan, from Oct 2020 to Mar 2021. Methodology: 116 patients presenting to Out-Patient Department of General Medicine of CMH, Malir, Pakistan, without previous evidence of cardiovascular disease were included. Previous history of diabetes, smoking, use of anti-hypertensives, Aspirin and statins was recorded. Systolic and diastolic blood pressures, waist circumference, HbA1c, fasting blood sugar, fasting LDL, HDL and total cholesterol were tested for each patient. 10-year ASCVD risk score was calculated using online ASCVD Risk Estimator calculator. Results: Mean age of the patients was 58.6±9.92 years with 62.1% being males and 37.9% being females. Mean 10-year ASCVD risk score was 13.37±7.1, of which 21 patients (18.1%) had Very High, 66(56.9%) had Intermediate and 29(25%) had Low 10-year ASCVD risk score. Among Very High cases, 80.9% were males, all were in age group 50-79 years, 61.9% were diabetic, 33.3% were smokers and 66.7% were obese. Conclusion: A very high percentage of study group had Intermediate or Very High 10-year ASCVD risk score. Identifying those individuals at high risk early and controlling the modifiable risk factors can help lessen the morbidity and mortality associated with cardiovascular diseases.

DeepSurv landmarking: a deep learning approach for dynamic survival analysis with longitudinal data

Risk prediction models quantify the probability of an individual experiencing specified events, there has significant attention to utilize longitudinal data during follow-up to dynamically update risk estimates. This paper devotes to a dynamic prediction model that combines survival analysis and deep learning techniques, which integrates the deep learning network-DeepSurv into the landmarking framework, to predict time-to-event outcomes with longitudinal covariates. Precisely, at each landmark time point, new information of the dataset is reconstructed, and the DeepSurv network is employed to capture the non-linear relationship between covariates and hazard rates, thereby give the dynamic prediction of survival functions. Furthermore, the importance of different features at each landmark time point is evaluated and ranked to estimate the impact of covariates on predictive outcomes over time. Performances on simulation experiments demonstrate that the proposed DeepSurv landmarking model outperforms state-of-art landmark methods, especially when the dependencies between covariates and the survival process are unknown. Additionally, the model exhibits remarkable dynamic prediction capabilities in the real heart-valve-transplantation-surgery data.

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background settingTraditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.MethodsThe variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.ResultsCompared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.ConclusionsThe proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

The Impact of Comorbidity and Age on the Risk of Hospitalization and Mortality in Patients with Previous COVID-19 Infection-Based on Nationwide Data.

Objectives: The influence of comorbidity on long-term hospitalization and mortality after COVID-19 in adults (40-59 years) and older adults (≥60 years) is yet to be explored. Methods: This is a Danish population-based cohort study of patients with a first-time positive PCR test for COVID-19 from 1 March 2020, to 28 February 2022 (N = 1,034,103). Exposed cohorts were patients with 1) a Charlson Comorbidity Index (CCI) score of 1-2 and 2) a CCI score ≥3, who were compared to patients without comorbidity (CCI of zero) within the groups of adults (67.9%) and older adults (32.1%) for the risk of hospitalization and mortality. Next, within the age groups, each disease category of the CCI was considered as an exposed cohort and compared to patients who did not have the specific disease of interest. Adjusted hazard ratios (HR) for hospitalization and mortality were estimated by Cox regression models adjusted for confounders. Results: The highest HRs were in adult patients with a CCI score of ≥3. The adjusted HR was 4.54 (95%CI: 4.38-4.70) for hospitalization, and among older adults it was 3.05 (95%CI: 2.99-3.11). The adjusted HR for mortality among adults with a CCI score ≥3 was 21.04 (95%CI: 18.86-23.47), and the adjusted HR for mortality among older adults was 4.61 (95%CI: 4.44-4.78). The underlying disease influenced the risk estimates among adults and older adults, and "dementia" had the highest impact on mortality. Conclusion: A CCI score of 1 or above increases the risk of hospitalization and mortality up to 2 years after a positive PCR test of COVID-19 for adults and older adults. Further, the type of underlying disease in older adults highly influences the risk of hospitalization and mortality.

Hereditary Neuromuscular Disorders in Reproductive Medicine

Neuromuscular disorders (NMDs) encompass a broad range of hereditary and acquired conditions that affect motor units, significantly impacting patients’ quality of life and reproductive health. This narrative review aims to explore in detail the reproductive challenges associated with major hereditary NMDs, including Charcot–Marie–Tooth disease (CMT), dystrophinopathies, Myotonic Dystrophy (DM), Facioscapulohumeral Muscular Dystrophy (FSHD), Spinal Muscular Atrophy (SMA), Limb–Girdle Muscular Dystrophy (LGMD), and Amyotrophic Lateral Sclerosis (ALS). Specifically, it discusses the stages of diagnosis and genetic testing, recurrence risk estimation, options for preimplantation genetic testing (PGT) and prenatal diagnosis (PND), the reciprocal influence between pregnancy and disease, potential obstetric complications, and risks to the newborn.

Polycystic ovary syndrome and gestational diabetes mellitus association to pregnancy outcomes: A national register-based cohort study.

It is well known that both women with polycystic ovary syndrome (PCOS) and women with gestational diabetes mellitus (GDM) have increased risks of adverse pregnancy outcomes, but little is known whether the combination of these two conditions exacerbates the risks. We explored risk estimates for adverse pregnancy outcomes in women with either PCOS or GDM and the combination of both PCOS and GDM. Nationwide register-based historical cohort study in Sweden including women who gave birth to singleton infants during 1997-2015 (N = 281 806). The risks of adverse pregnancy outcomes were estimated for women exposed for PCOS-only (n = 40 272), GDM-only (n = 2236), both PCOS and GDM (n = 1036) using multivariable logistic regression analyses. Risks were expressed as odds ratios with 95% confidence intervals (CIs) and adjusted for maternal characteristics, including maternal BMI. Women with neither PCOS nor GDM served as control group. Maternal outcomes were gestational hypertension, preeclampsia, postpartum hemorrhage, and obstetric anal sphincter injury. Neonatal outcomes were preterm birth, stillbirth, shoulder dystocia, born small or large for gestational age, macrosomia, low Apgar score, infant birth trauma, cerebral impact of the infant, neonatal hypoglycemia, meconium aspiration syndrome and respiratory distress. Based on non-significant PCOS by GDM interaction analyses, we found no evidence that having PCOS adds any extra risk beyond that of having GDM for maternal and neonatal outcomes. For example, the adjusted odds ratio for preeclampsia in women with PCOS-only were 1.18 (95% CI 1.11-1.26), for GDM-only 1.77 (95% CI 1.45-2.15), and for women with PCOS and GDM 1.86 (95% CI 1.46-2.36). Corresponding adjusted odds ratio for preterm birth in women with PCOS-only were 1.34 (95% CI 1.28-1.41), GDM-only 1.64 (95% CI 1.39-1.93), and for women with PCOS and GDM 2.08 (95% CI 1.67-2.58). Women with PCOS had an increased risk of stillbirth compared with the control group (aOR 1.52, 95% CI 1.29-1.80), whereas no increased risk was noted in women with GDM (aOR 0.58, 95% CI 0.24-1.39). The combination of PCOS and GDM adds no extra risk beyond that of having GDM alone, for a number of maternal and neonatal outcomes. Nevertheless, PCOS is still an unrecognized risk factor in pregnancy, exemplified by the increased risk of stillbirth.

An overview of genetic mutations in Aicardi-Goutières syndrome in Iranian population.

Aicardi-Goutières syndrome (AGS) is a rare neurodevelopmental disorder that can be misdiagnosed with infectious disorders. Molecular genetics tools and subsequent counseling have an important role in the estimation of recurrence risk and prevention of additional cases in the family. The current study provides an overview of genetic mutations in AGS in Iranian population. In a time period of 3 years, we assessed nine AGS cases and identified the underlying mutations using whole exome sequencing. Mutations were located in TREX1, IFIH1, RNASEH2B, RNASEH2A and SAMHD1 genes and inherited in either autosomal dominant or autosomal recessive manner. Since both modes of inheritance have been previously reported for AGS, appropriate genetic counseling is needed for estimation of recurrence risk in families.

Human health risk of exposure to potentially toxic metals (PTMS) in indoor settled dusts from selected worship centres (WCS)

The study aimed to determine the concentration of potentially toxic metals (PTMs) and the health risk of exposure to indoor dust from worship centres in Abeokuta, Nigeria. Twenty-four (24) dust samples were collected from various corners of selected worship centres. One (1) gram of the sieved dust was digested using aqua regia. PTM analyses were done using an Atomic Absorption Spectrometer (AAS). The result revealed the presence of Iron (Fe)> Zinc (Zn)>Manganese (Mn)> Lead (Pb) > Copper (Cu) >Nickel (Ni) > Cadmium (Cd) (1050; 81.6; 72.5; 3.304; 2.43; 0.516; 0.076 mg/kg) respectively. The PTMs were within the permissible limit for PTMs in dust stated by UK and Dutch Intervention. Pollution indices revealed Zn as the primary pollutant in the dust. A significant association between Zn, Cu, Ni and Cd while that of Mn and Fe suggest a common origin of anthropogenic and lithogenic sources respectively (p<0.01). Health risk assessment revealed inhalation as the major pathway of PTM exposure with the children population being the most susceptible. Hazard index (HI) values were <1, showing a non-significant non-cancer effect on exposure while the cancer risk estimate shows no carcinogenic risk. Conclusively, the selected worship centres have no PTM concentration of health concern at the time of the investigation. However, regular wet cleaning and close monitoring of the worship centres should be considered.

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.

Estimation of individual lifetime cardiovascular disease risk and treatment benefit in patients with established atherosclerotic cardiovascular disease: the updated SMART-REACH2 model

Abstract Background The 2021 ESC Guidelines on Cardiovascular Disease (CVD) Prevention in Clinical Practice recommend using (lifetime) risk to guide preventive treatment intensification. Purpose Updating and systematically recalibrating the SMART-REACH lifetime prediction model for the estimation of lifetime risk of recurrent CVD in patients with established atherosclerotic CVD (ASCVD) to various European and international risk regions. Methods The updated SMART-REACH (i.e., SMART-REACH2) model was derived using data from 9,477 individuals between 40-90 years with coronary artery disease, cerebrovascular disease, peripheral artery disease, or abdominal aortic aneurysms included in the UCC-SMART cohort in whom 2,090 ASCVD events were observed during a median follow-up of 8.9 years (interquartile range 4.5–14.1). Coefficients for type of CVD, estimated glomerular filtration rate, non-high density lipoprotein cholesterol, systolic blood pressure, duration of CVD, C-reactive protein, current smoking, and presence of diabetes were derived using sex-stratified cause-specific Cox models with age as the time axis, adjusted for the competing risk of non-CVD mortality. Lifetables were used to estimate 10-year and lifetime CVD-risk and CVD-free life expectancy. The SMART-REACH2 model was externally validated in 121,701 patients from the NOR-COR, Nor-COAST, HUNT3, SWEDEHEART, Bialystok PLUS/Polaspire, REACH-EU, and REACH non-EU (Asia, Japan, Latin America, North America) covering 43 countries. The model was recalibrated to specific risk regions based on the expected-observed ratios from representative cohorts. Performance was assessed by calibration of predicted and observed risk and Harrel’s C-statistic for discrimination. Results 16,689ASCVD events were observed in the external validation cohorts. The model was visually well calibrated for the various risk regions. The pooled C-statistic across risk regions was 0.665 [95% confidence interval 0.640-0.691], with adequate performance in the separate regions (Figure 1). Using SMART-REACH2 to estimate potential gain in CVD-free life expectancy upon treatment initiation showed varying projections. For example, two comparable 55-year-old males from the low-risk region and very high-risk region would gain an estimated 2.2 and 3.9 CVD-free life years as a result of smoking cessation (Figure 2). Conclusion(s) By using larger and more contemporary data sources, considering geographical differences, and extending the age range, the updated SMART-REACH2 model provides an improved algorithm for the estimation of lifetime risk and CVD-free life expectancy for individuals with established ASCVD, facilitating shared decision-making for cardiovascular prevention as recommended by the 2021 ESC Prevention of CVD guidelines.C-statistics across global risk regionsCase vignette