Standard Risk Scores Research Articles

Multi-modality deep learning model for prediction of chronic obstructive coronary artery disease

Abstract Background/Introduction The incidence of coronary artery disease (CAD) is on the rise with an increasing burden of stable ischemic heart disease (SIHD) and high prevalence of obesity, diabetes, hyperlipidemia, and other cardiovascular risk factors. Pre-test probability estimation for CAD based on demographic features and characterization of chest pain is insufficient to accurately assess whether further evaluation for CAD is warranted. Purpose To develop and validate a deep learning (DL) algorithm utilizing electrocardiogram (ECG) waveforms and clinical features to predict obstructive CAD. Methods Clinical data from subjects undergoing invasive angiography over a 4-year period at a quaternary care center exclusive of subjects who had concern for acute coronary syndromes were used for model training. ECGs obtained up to 5 years prior to angiography were included; ECGs were partitioned 8:1:1 for training, validation, and test datasets. Obstructive CAD was defined as receiving percutaneous coronary intervention during elective angiography. Convolutional neural network models were developed for ECG waveforms alone (DL-ECG), clinical features from standard risk scores (DL-Clinical), and the combination of ECG waveforms and clinical features (DL-ECG+Clinical); a commonly used pre-test probability estimation tool derived from the CAD Consortium study was used as a comparison (PTP). The models were evaluated based on area under the curve receiver operating characteristics (AUC). Shapley additive explanations (SHAP) analysis was performed to understand feature importance in the DL models. Results A total of 40,602 ECGs and 8,361 coronary angiograms from 7,201 patients were included in the analysis. The median age was 66 (IQR 56-75), 71.9% were men, 34.9% had hypertension, 30.6% had diabetes mellitus, and 39.0% had dyslipidemia. The PTP model (AUC 0.733 [0.717-0.750]) had similar performance as the DL-Clinical model (AUC 0.762 [0.746-0.778]). The DL-ECG model (AUC 0.741 [0.726-0.758]) had similar performance as both the clinical feature models. The DL-ECG+Clinical model (AUC 0.807 [0.793-0.822]) trained on ECG waveforms and clinical features had a superior performance (Figure 1). SHAP analysis for the DL-ECG+Clinical model revealed that ECG waveforms had the largest influence on the model’s output followed by age and chest pain type (Figure 2). Conclusions Multi-modality DL model utilizing ECG and commonly used features can improve prediction of obstructive CAD in SIHD compared to traditional pre-test probability estimates. Prospective research is warranted to determine whether incorporation of DL methods to ECG analysis effectively improves diagnosis and outcomes in obstructive CAD.Figure 1.Figure 2.

Circulating ketone bodies, genetic predisposition, and incident atrial fibrillation

Abstract Background Ketone bodies (KB) are essential alternative fuel sources for the myocardium. However, the evidence on the level of circulating KB and heart disorders is inconsistent. Whether the total and individual KB are associated with risk of onset atrial fibrillation (AF) in the general population and in individuals with different genetic susceptibilities to AF are still unclear. Purpose To examine the association between circulating KB and AF risk and to further test whether genetic risk could modify the above association. Methods In the UK Biobank, 247,304 participants (mean age 56.5, 45.3% men) with available data on KB and without baseline AF were included. The concentrations of KB, including β-hydroxybutyrate (β-OHB), acetoacetate, and acetone, were measured using nuclear magnetic resonance, and the values of KB were increased by one and then log10-transformed for analyses. The genetic risk of AF was based on the standard polygenic risk score. The definitions of AF were based on the ICD-10 code I48, which was linked to primary care, hospital admission electronic health records and death records. Cox proportional hazard model was used to estimate the hazard ratio (HR) with 95% confidence interval (CI). Results During a median follow-up of 13.8 years, 16,936 cases of onset of AF were recorded. After adjusting for age, sex, race, Townsend index, education, alcohol intake frequency, smoking status, healthy diet, metabolic equivalent, body mass index, systolic blood pressure (SBP), total cholesterol, estimated glomerular filtration rate, cardiovascular diseases, and diabetes, elevated total KB, β-OHB, acetoacetate, and acetone were all with the risk of AF (HR, 95% CI per 10-fold increase: 1.20, 1.13-1.28; 1.13, 1.07-1.19; 1.12, 1.06-1.18; and 1.78, 1.57-2.01, respectively). The highest risk of AF was observed among individuals at high genetic risk to AF and had the highest quartile of total KB (HR 2.91, 95% CI 2.66-3.19). Participants who had low genetic risk were more susceptible to high total KB for incident AF (p for interaction = 0.036). The associations of total KB, β-OHB, and acetone with AF risk were more prominent for men (HR, 95% CI per 10-fold increase: 1.32, 1.22-1.44; 1.22, 1.14-1.31; 2.01, 1.72-2.36) than for women (p for interaction <0.05). The association between total KB and AF risk was strengthened in individuals <60 years, current smokers, and those with SBP<130 mmHg (p for interaction = 0.033, 0.021, and 0.017, respectively). Conclusions Circulating KB concentrations were associated with AF risk, with acetone showing the strongest relationship with AF. Individuals at low genetic risk were more susceptible to the level of total KB for incident AF. Our findings suggest that KB may be a biomarker, particularly for identifying individuals without AF at baseline who are at low genetic risk. The additive value of plasma KB to a risk prediction score warrants further investigation.

Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration.

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.

Associations of sleep behaviors and genetic risk with risk of incident osteoporosis: A prospective cohort study of 293,164 participants

BackgroundUnhealthy sleep behaviors are associated with higher risks of osteoporosis (OP), while prospective evidence is limited. This study aimed to prospectively investigate this association, quantify the attributable burden of OP incidence reduction due to unhealthy sleep behaviors, and explore potential modifications by genetic risk factors. MethodsThis longitudinal cohort study was conducted utilizing data from the UK Biobank, comprising 293,164 participants initially free of OP and with requisite sleep behaviors data at baseline. We followed the participants after recruitment until November 30, 2022, to ascertain incident OP. We assessed the associations of five sleep behaviors including sleep duration, chronotype, insomnia, daytime napping, and morning wake-up difficulties, as well as sleep behavior patterns identified based on the above sleep behaviors, with the risk of OP, using Cox models adjusted for multiple confounders. The analyses were then performed separately among individuals with different OP susceptibility, indexed by standard polygenetic risk scores(PRS) for OP. Our secondary outcome was OP with pathologic fracture. Subgroup and sensitivity analyses were performed. Additionally, attributable risk percent in the exposed population (AR%) and population attributable fraction (PAF) of sleep behaviors were calculated. ResultsOver a median follow-up of 13.7 years, 8253 new-onset OP cases were documented. Unhealthy sleep behaviors, such as long or short sleep duration, insomnia, daytime napping, morning wake-up difficulties, and unhealthy sleep patterns, were associated with elevated risks of OP (HRs ranging from 1.14 to 1.46, all P-value <0.001) compared to healthy sleep behaviors. Similar associations were observed for OP with pathologic fractures. Insomnia exhibited the largest AR% of 39.98 % (95%CI: 36.46, 43.31) and PAF of 33.25 % (95%CI: 30.00, 36.34) among healthy sleep patterns and components. A statistically significant multiplicative interaction was noted between sleep behaviors and OP PRS on OP risk (all P-interaction <0.001). ConclusionsFour unhealthy sleep behaviors and sleep behavior patterns were associated to increased OP risk, with insomnia contributing the most to OP incidence, while genetic risk for OP modified this association. These findings underscore the crucial role of adhering to healthy sleep behaviors for effective OP prevention.

Predicting cardiovascular events with fluoropyrimidine chemotherapy using a standard cardiovascular risk calculator.

Fluoropyrimidine chemotherapy is important for treatment of many solid tumours but is associated with cardiotoxicity. The relationship of fluoropyrimidine-associated cardiotoxicity (FAC) with conventional cardiovascular (CV) risk factors is poorly understood, and standard cardiovascular risk scores are not validated in this context. Single-centre retrospective study of patients treated with fluoropyrimidine chemotherapy using electronic health records for cardiovascular risk factors (and calculation of QRISK3 score), cancer treatment, and clinical outcomes. FAC was defined by cardiovascular events during or within 3months of fluoropyrimidine treatment, and Cox regression was used to assess associations of CV risk and cancer treatment with FAC. One thousand eight hundred ninety-eight patients were included (45% male; median age 64years), with median follow up 24.5 (11.5-48.3months); 52.7% of patients were at moderate or high baseline CV risk (QRISK3 score >10%) Cardiovascular events occurred in 3.1% (59/1898)-most commonly angina (64.4%, 38/59) and atrial fibrillation (13.6%, 8/59), with 39% events during cycle one of treatment. In univariable analysis, QRISK3 score >20% was significantly associated with incident FAC (HR 2.25, 95% CI 1.11-4.93, P=0.03). On multivariable analysis, beta-blocker use (HR 1.04, 95% CI 1.00-1.08, P=0.04) and higher BMI (HR 2.33, 95% CI 1.04-5.19, P=0.04) were independently associated with incident CV events. Thirty-two of the 59 patients with FAC were subsequently rechallenged with fluoropyrimidine chemotherapy, with repeat CV events in 6% (2/32). Incident FAC did not affect overall survival (P=0.50). High BMI and use of beta-blockers are associated with risk of CV events during fluoropyrimidine chemotherapy. QRISK3 score may also play a role in identifying patients at high risk of CV events during fluoropyrimidine chemotherapy. Re-challenge with further fluoropyrimidine chemotherapy can be considered in patients following CV events during prior treatment.

Integrating Genome-Wide Polygenic Risk Scores With Nongenetic Models to Predict Surgical Site Infection After Total Knee Arthroplasty Using United Kingdom Biobank Data

BackgroundPrediction of the risk of developing surgical site infection (SSI) in patients following total knee arthroplasty (TKA) is of clinical importance. Genetic susceptibility is involved in developing TKA-related SSI. Previously reported models for predicting SSI were constructed using nongenetic risk factors without incorporating genetic risk factors. To address this issue, we performed a genome-wide association study (GWAS) using the UK Biobank database. MethodsAdult patients who underwent primary TKA (n = 19,767) were analyzed and divided into SSI (n = 269) and non-SSI (n = 19,498) cohorts. Nongenetic covariates, including demographic data and preoperative comorbidities, were recorded. Genetic variants associated with SSI were identified by GWAS and included to obtain standardized polygenic risk scores (zPRS, an estimate of genetic risk). Prediction models were established through analyses of multivariable logistic regression and the receiver operating characteristic curve. ResultsThere were 4 variants (rs117896641, rs111686424, rs8101598, and rs74648298) achieving genome-wide significance that were identified. The logistic regression analysis revealed 7 significant risk factors: increasing zPRS, decreasing age, men, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, and peripheral vascular disease. The areas under the receiver operating characteristic curve were 0.628 and 0.708 when zPRS (model 1) and nongenetic covariates (model 2) were used as predictors, respectively. The areas under the receiver operating characteristic curve increased to 0.76 when both zPRS and nongenetic covariates (model 3) were used as predictors. A risk-prediction nomogram was constructed based on model 3 to visualize the relative effect of statistically significant covariates on the risk of SSI and predict the probability of developing SSI. Age and zPRS were the top 2 covariates that contributed to the risk, with younger age and higher zPRS associated with higher risks. ConclusionsOur GWAS identified 4 novel variants that were significantly associated with susceptibility to SSI following TKA. Integrating genome-wide zPRS with nongenetic risk factors improved the performance of the model in predicting SSI.

Prognostic scores for ursodeoxycholic acid-treated patients predict graft loss and mortality in recurrent primary biliary cholangitis after liver transplantation

Recurrent primary biliary cholangitis (rPBC) develops in approximately 30% of patients and negatively impacts graft and overall patient survival after liver transplantation (LT). There is a lack of data regarding the response rate to ursodeoxycholic acid (UDCA) in rPBC. We evaluated a large, international, multi-center cohort to assess the performance of PBC scores in predicting the risk of graft and overall survival after LT in patients with rPBC. A total of 332 patients with rPBC after LT were evaluated from 28 centers across Europe, North and South America. The median age at the time of rPBC was 58.0 years [IQR 53.2-62.6], and 298 patients (90%) were female. The biochemical response was measured with serum levels of alkaline phosphatase (ALP) and bilirubin, and Paris-2, GLOBE and UK-PBC scores at 1 year after UDCA initiation. During a median follow-up of 8.7 years [IQR 4.3-12.9] after rPBC diagnosis, 52 patients (16%) had graft loss and 103 (31%) died. After 1 year of UDCA initiation the histological stage at rPBC (hazard ratio [HR] 3.97, 95% CI 1.36-11.55, p= 0.01), use of prednisone (HR 3.18, 95% CI 1.04-9.73, p= 0.04), ALP xULN (HR 1.59, 95% CI 1.26-2.01, p <0.001), Paris-2 criteria (HR 4.14, 95% CI 1.57-10.92, p= 0.004), GLOBE score (HR 2.82, 95% CI 1.71-4.66, p <0.001), and the UK-PBC score (HR 1.06, 95% CI 1.03-1.09, p <0.001) were associated with graft survival in the multivariate analysis. Similar results were observed for overall survival. Patients with rPBC and disease activity, as indicated by standard PBC risk scores, have impaired outcomes, supporting efforts to treat recurrent disease in similar ways to pre-transplant PBC. One in three people who undergo liver transplantation for primary biliary cholangitis develop recurrent disease in their new liver. Patients with recurrent primary biliary cholangitis and incomplete response to ursodeoxycholic acid, according to conventional prognostic scores, have worse clinical outcomes, with higher risk of graft loss and mortality in similar ways to the disease before liver transplantation. Our results supportsupport efforts to treat recurrent disease in similar ways to pre-transplant primary biliary cholangitis.

Long-term night shift work, genetic predisposition and risk of incident asthma: a prospective cohort study.

Evidence about a potential link between current and lifetime night shift work and risk of incident asthma is insufficient. To investigate the association of current and lifetime night shift work with risk of incident asthma, and the modified effect of genetic susceptibility on this association. We included 253773 individuals with complete night shift work information in the UK biobank. We calculated the standard polygenetic risk score (PRS) for asthma. The Cox proportional hazard models were conducted to estimate hazard ratios and 95% CIs. After multivariable adjustments, we found that current night shift work was associated with an increased risk of incident asthma in a dose-response fashion (P for trend < 0.001). Compared with day workers, those working usual/permanent night shifts had a 17% (95% CI: 1.04-1.33) higher risk of asthma incidence. In addition, we observed significant dose-dependent relationships of longer lifetime duration or frequency of night shift work with elevated risk of asthma incidence (all P for trend < 0.05). Compared with never night shift workers, those with a duration (≥5 years) or frequency (≥8 nights/month) of night shift work exhibited a 20% (95% CI: 1.03-1.39) or 22% (95% CI: 1.03-1.44) higher risk of incident asthma, respectively. Moreover, the elevated risk of incident asthma related to current and lifetime night shift work exposure was strengthened by high PRS, although no significant shift work-PRS interactions were detected. Both current and lifetime night shift work may increase the risk of incident asthma, regardless of genetic predisposition to asthma.

Age-Adjusted Charlson Comorbidity Index Is a Significant Factor for Predicting Thrombosis Development and Survival in Polycythemia Vera

Background: The worldwide-recognized prognostic role of age and previous thrombosis as part of standard risk score in polycythemia vera (PV) deserves some deeper insight . Aimof our study was to identify prediction factors for thrombosis development and overall survival (OS) in PV patients. Methods: Study included 816 PV patients diagnosed according to WHO classification and treated between 2005-2022 at University center. The analysis included: demographic, clinical and laboratory parameters with neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), thrombosis data, age-adjusted Charlson comorbidity index (ACCI) and presence of cardiovascular (CV) risk factors (arterial hypertension, diabetes, smoking, hyperlipidaemia). JAK2 positivity had 97.8% of patients. During follow up 29.8% of patients died. Results: Mean age was 62 years (range 18-87), 51.7% male and 61.3% older &amp;gt;60 years. According to standard risk score, 26.5% of patients belonged to low-risk group while 73.5% to high-risk group. According to ACCI: 11.8% were without comorbidities, mild grade had 34.4%, moderate 36% and severe 17.8% of patients. Distribution according to CV risk factor presence: without 25.6%, one 38.8%, two 27.5%, three 7.5%, four 0.6% of patients with highest incidence of arterial hypertension&amp;gt;smoking&amp;gt;diabetes &amp;gt;hyperlipidaemia. Previous thrombosis was reported in 182 patients (22.3%) with 222 thrombosis (160 arterial, 62 venous). During follow up 111 patients (13.6%) had 120 thrombosis (82 arterial, 38 venous). Previous thrombosis (p=0.029), ACCI groups (p&amp;lt;0.001), presence of CV factor ≥1 (p=0.001) or secondary malignancy (p&amp;lt;0.001) predicted venous thrombosis development. In comparison, previous thrombosis (p = 0.03), male gender (p=0.006) standard risk score (p=0.001), ACCI (p&amp;lt;0.001), CV factor ≥1 (p&amp;lt;0.001), NLR ≥5 (p=0.008) and PLR ≥500 (p&amp;lt;0.001), were predictors of arterial thrombosis development. In multivariate analysis, risk of venous thrombosis development was independently associated with only ACCI (HR 3.75; p&amp;lt;0.001) while arterial thrombosis with ACCI (HR2 2.12; p&amp;lt;0.01) and PLR ≥500 (HR 1.886, p=0.028). Patients with thrombosis during the follow-up had significantly shorter survival (p=0.048). Patients in low risk standard group had median survival of 221 months v.s high risk group patients with 119months (p&amp;lt;0.001). Median survival of patients according ACCI groups: without comorbidities 233 months; mild grade 171 months; moderate grade 120 months and severe grade 84 months (p&amp;lt;0.001). Univariate analysis identified factors associated with shorter OS: age ≥65y(p&amp;lt;0.001), Le ≥15x10 9/L (p&amp;lt;0.001), ANC ≥10x10 9/L (p&amp;lt;0.001), Plt ≥1000x10 9/L (p=0.027), presence of secondary malignancy (p&amp;lt;0.001), previous thrombosis (p=0.03), development of arterial (p&amp;lt;0.001) and venous (p&amp;lt;0.001) thrombosis during follow up. Of CV factors only presence of arterial hypertension showed negative survival effect (p=0.003). Multivariate Cox regression model revealed age ≥65y (HR=3.3; p&amp;lt;0.001), ACCI group (HR=1.7; p&amp;lt;0.001) and ANC ≥10x10 9/L (HR=1.6; p=0.001) as most important survival predictors. Patients with ACCI score ≥4 significantly received hydroxyurea (p&amp;lt;0.001). The mean OS was 12.5 years while 5-years and 10-years OS was 81% and 62% respectively. The cause of death was as the follows: heart failure (37%), second malignancy (15.2%), thrombosis (14.8%), progression of disease (8.6%), COVID infection (4.2%) and unknown cause in 20.2% of patients. Conclusion: Age-adjusted CCI was found to be the most reliable predictor of thrombosis development in PV. The application of ACCI allows better PV survival assessment by delineating ACCI moderate grade as intermediate risk survival group and improves identification of high-risk PV patients incorporating ACCI severe grade group with survival less than 7 years.

Multifractal foundations of biomarker discovery for heart disease and stroke

Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

BackgroundFinding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.MethodsWe trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.ResultsTabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman’s ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).ConclusionsOur findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

Artificial intelligence for dementia-Applied models and digital health.

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Association of Unhealthy Lifestyle and Genetic Risk Factors With Mild Cognitive Impairment in Chinese Older Adults

Apolipoprotein E polymorphism ε4 (APOE ε4) and methylenetetrahydrofolate reductase (MTHFR) TT genotype are genetic risk factors of mild cognitive impairment (MCI), but whether this risk can be changed by modifiable lifestyle factors is unknown. To explore whether unhealthy lifestyle (unhealthy dietary intake, current smoking, nonlimited alcohol consumption, and irregular physical activities) is associated with a higher risk of age-related MCI considering genetic risk. This population-based cohort study used data from Tianjin Elderly Nutrition and Cognition (TENC) study participants, recruited from March 1, 2018, through June 30, 2021, and followed up until November 30, 2022. Participants were Chinese adults aged 60 years or older who completed the neuropsychological assessments, general physical examinations, and a personal interview. Healthy lifestyle was defined according to the Chinese Dietary Guidelines 2022, including healthy diet, regular physical activity, limited alcohol consumption, and no current smoking, categorized into healthy and unhealthy lifestyles according to weighted standardized lifestyle score. Genetic risk was defined by MTHFR TT genotype and APOE ε4, categorized into low and high genetic risk according to weighted standardized genetic risk score. The main outcome was newly diagnosed MCI as identified using a modified version of Petersen criteria. Hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazard regression models. A total of 4665 participants were included (mean [SD] age, 67.9 [4.9] years; 2546 female [54.6%] and 2119 male [45.4%]); 653 participants with new-onset MCI (mean [SD] age, 68.4 [5.4] years; 267 female [40.9%] and 386 male [59.1%]) were identified after a median follow-up of 3.11 years (range, 0.82-4.61 years). Individuals with a low genetic risk and an unhealthy lifestyle (HR, 3.01; 95% CI, 2.38-3.79), a high genetic risk and a healthy lifestyle (HR, 2.65; 95% CI, 2.03-3.44), and a high genetic risk and an unhealthy lifestyle (HR, 3.58; 95% CI, 2.73-4.69) had a higher risk of MCI compared with participants with a low genetic risk and a healthy lifestyle. There was a synergistic interaction between lifestyle categories and genetic risk (β = 3.58; 95% CI, 2.73-4.69). In this cohort study of TENC participants, the findings show that unhealthy lifestyle and high genetic risk were significantly associated with a higher risk of MCI among Chinese older adults. Unhealthy lifestyle factors were associated with a higher risk of MCI regardless of genetic risk, and lifestyle and genetic risk had synergistic interactions. These findings could contribute to the development of dietary guidelines and the prevention of early-stage dementia.

Estimated Atherosclerotic Cardiovascular Disease Risk Score: An Automated Decision Aid for Statin Therapy

Estimation of atherosclerotic cardiovascular disease (ASCVD) risk is a key step in cardiovascular disease (CVD) prevention, but it requires entering additional risk factor information into a computer. We developed a simplified ASCVD risk score that can be automatically calculated by the clinical laboratory when a fasting standard lipid panel is reported. Equations for an estimated ASCVD (eASCVD) risk score were developed for 4 race/sex groups (non-Hispanic White/Black, men/women), using the following variables: total cholesterol, high-density lipoprotein cholesterol, triglycerides, and age. The eASCVD score was derived using regression analysis to yield similar risk estimates as the standard ASCVD risk equations for non-diabetic individuals not on lipid-lowering therapy in the National Health and Nutrition Examination Survey (NHANES) (n = 6027). At a cutpoint of 7.5%/10-year, the eASCVD risk score had an overall sensitivity of 69.1% and a specificity of 97.5% for identifying statin-eligible patients with at least intermediate risk based on the standard risk score. By using the sum of other risk factors present (systolic blood pressure >130 mmHg, blood pressure medication use, and cigarette use), the overall sensitivity of the eASCVD score improved to 93.7%, with a specificity of 92.3%. Furthermore, it showed 90% concordance with the standard risk score in predicting cardiovascular events in the Atherosclerosis Risk in Communities (ARIC) study (n = 14 742). Because the automated eASCVD risk score can be computed for all patients with a fasting standard lipid panel, it could be used as an adjunctive tool for the primary prevention of ASCVD and as a decision aid for statin therapy.

Anesthetic Management in Parturients With Fontan Physiology

Anesthetic Management in Parturients With Fontan Physiology

Abstract 424: An Automated Estimated Atherosclerotic Cardiovascular Disease Risk Score

Background: Calculating Atherosclerotic Cardiovascular Disease (ASCVD) risk is a key step in CVD prevention, but that standard approach requires risk factor data and use of a computer. Objective: To develop a simplified ASCVD risk score automatically calculated by the clinical laboratory from a standard lipid panel. Methods: Using data from 6,027 participants (excluding those with diabetes or on lipid lowering drugs) from the National Health and Nutrition Examination Survey (NHANES), we developed regression equations to generate an eASCVD risk score that provides estimates similar to standard ASCVD score in 4 race/sex groups: Non-Hispanic Whites men (NHWM) or women (NHWW) and Black men (BM) or women (BW), using TC, HDL-C, TG, and age. The new eASCVD score was externally validated in the Atherosclerosis Risk In Communities (ARIC) (N=14,742) study and compared to the standard ASCVD risk score for its association with clinical ASCVD events (N=14,742). Results: The eASCVD risk score had a sensitivity of 69% and a specificity of 97.5% for identifying statin-eligible patients with at least intermediate risk by the standard risk score at a cutpoint of 7.5%/10-year. By using the sum of the presence of non-lipid risk factors (SBP&gt;130 mmHg, blood pressure medications and smoking) to adjust the threshold of the eASCVD risk score, its overall sensitivity increased to 94%, with a specificity of 92%. For each race/gender group, the eASCVD risk score performed similarly, but the positive predictive value (PPV) for men was better (88.6% for NHWM and 88.7% for BM) than for women (79% for NHWM and 78.7% for BW). Finally, eASCVD risk score showed 90% agreement with the standard risk score in predicting CVD events in the ARIC study. Conclusions: The eASCVD risk score can be automatically calculated by the clinical laboratory from a standard lipid panel. It identifies the majority of statin-eligible patients and can be used as a decision aid for statin therapy.

Concurrent IgA Nephropathy and Membranous Nephropathy, Is It an Overlap Syndrome?

IgA nephropathy (IgAN) and membranous nephropathy (MN) are common glomerulonephritis, the presence of which in the same patient– concurrent of IgAN and MN (cIgAN/MN) has been described occasionally. This study aims to show clinical-pathological features of cIgAN/MN and attempts to suggest underlying pathogenesis using disease-specific biomarkers and a genomics approach. This retrospective cohort study described the clinical and pathological data from 137 patients with cIgAN/MN diagnosed in Peking University First Hospital from 2005 to 2019. One hundred primary IgAN and 100 MN cases were randomly selected as disease controls between the same time interval. Moreover, disease-specific biomarkers and polygenic risk score models were conducted to reveal the underlying pathogenesis. The median age of the cIgAN/MN cases was 45-year-old, and 46% were women. Compared to IgAN, patients with cIgAN/MN had a higher level of 24-hour proteinuria excretion but lower microscopic hematuria. They had a lower median level of galactose-deficient IgA1 (Gd-IgA1, 4.00 versus 5.45 μg/ml, P=0.002) as well as the standardized genetic risk scores of developing IgAN (GRSs: 0.05 versus 0.68, P<0.001). Compared to MN, patients with cIgAN/MN had a lower proportion of nephrotic syndrome and a lower level of albumin-to-creatinine ratio. However, the 24-hour proteinuria levels, serum lipid profiles, proportion of hypertension, and pathology classification were similar. Patients with cIgAN/MN had lower levels of plasma autoantibodies against the M-type transmembrane phospholipase A2 receptor (PLA2R) (11.23 versus 36.59 U/ml, P=0.005). Intriguingly, there were no statistical differences in standardized GRSs of developing MN between them (2.77 versus 3.02, P=0.326). Compared to IgAN, cIgAN/MN may lean towards MN more according to clinical-pathological features, disease-specific biomarker levels, and disease-specific genetic risk scores.

Classical risk factors for primary coronary artery disease from an aging perspective through Mendelian Randomization.

The significance of classical risk factors in coronary artery disease (CAD) remains unclear in older age due to possible changes in underlying disease pathologies. Therefore, we conducted Mendelian Randomization approaches to investigate the causal relationship between classical risk factors and primary CAD in different age groups. A Mendelian Randomization study was conducted in European-ethnicity individuals from the UK Biobank population. Analyses were performed using data of 22,313 CAD cases (71.6% men) and 407,920 controls (44.5% men). Using logistic regression analyses, we investigated the associations between standardized genetic risk score and primary CAD stratified by age of diagnosis. In addition, feature importance and model accuracy were assessed in different age groups to evaluate predictive power of the genetic risk scores with increasing age. We found age-dependent associations for all classical CAD risk factors. Notably, body mass index (OR 1.22 diagnosis < 50years; OR 1.02 diagnosis > 70years), blood pressure (OR 1.12 < 50years; OR 1.04 > 70years), LDL cholesterol (OR 1.16 < 50years; OR 1.02 > 70years), and triglyceride levels (OR 1.11 < 50years; 1.04 > 70years). In line with the Mendelian Randomization analyses, model accuracy and feature importance of the classical risk factors decreased with increasing age of diagnosis. Causal determinants for primary CAD are age dependent with classical CAD risk factors attenuating in relation with primary CAD with increasing age. These results question the need for (some) currently applied cardiovascular disease risk reducing interventions at older age.

Abstract 14170: Incremental Value of Magnetic Resonance-Based Ventricular and Atrial Strain in Predicting Ventricular Arrhythmia

Introduction: Only a small subset of patients with primary prevention ICDs, for whom left ventricular ejection fraction (LVEF) is emphasized in current guidelines, receive appropriate ICD therapy. Objectives: To study the incremental value of cardiovascular MR (CMR)-derived right and left ventricular and left atrial strain measured using feature tracking in predicting ventricular arrhythmia in patients eligible for implantable cardioverter-defibrillator (ICD) primary prevention of ventricular arrhythmia. Methods: The study included patients who met the current clinical guideline to receive an ICD for primary prevention of SCD. CMR imaging was performed on either a 1.5 or a 3T scanner, including standard evaluation of LV function and structure, LV/RV/LA strains, LGE. Outcomes were comprised of 1) unstable VT/VF &gt;30sesonds, 2) appropriate ICD therapy; shock or anti-tachycardia pacing for VT/VF, or 3) SCD. The primary outcome was a composite endpoint of any of the above three outcomes. Results: A total of 239 patients were included and the average age was 57±14 years old. Patients with outcomes had slightly lower RV global longitudinal strain (GLS). Univariate Cox proportional hazards regression showed that every standard deviation increase in RV GLS was associated with a 73% increase in risk (p&lt; 0.01). The baseline multivariable Cox’s proportional hazards regression model with a standardized clinical risk score, standardized LVEF, LGE had an AUC of 0.64. The best performing model, which included standardized LV and RV GLS, significantly improved prognosis compared with the baseline model, with an AUC of 0.72 and a categorical free NRI of 0.88 (p=0.04). Model 1 indicated an increased risk of 77% for every standard deviation increase in RV GLS (p&lt; 0.01) and a decreased risk of 49% for every standard deviation increase in LV GLS (p= 0.02). Kaplan-Meier curves could be stratified using the selected thresholds; LV GCS ≥ 5.3 and RV GLS ≥ -10.8, respectively (p=0.02, p&lt;0.01).

Performance of the REACH, PARIS, BleeMACS, and PRECISE-DAPT scores for predicting 1-year bleeding events in patients undergoing coronary drug-eluting stent implantation

This study aimed to evaluate the predictive performance of the REACH, PARIS, BleeMACS, and PRECISE-DAPT scores in Chinese patients undergoing coronary drug-eluting stent (DES) implantation. A total of 1911 patients undergoing coronary DES implantation were consecutively recruited and followed up for 1 year. The primary endpoints were BARC type 3 or 5 bleeding and BARC type 2,3, or 5 bleeding. The BleeMACS score and the PRECISE-DAPT score were significantly associated with 1-year incidence of BARC type 3 or 5 bleeding, but not BARC type 2, 3, or 5 bleeding. The discrimination of the PRECISE-DAPT score was moderate for BARC type 3 or 5 bleeding (c-statistic = 0.633), while those of the REACH (c-statistic = 0.533), PARIS (c-statistic = 0.553), and BleeMACS scores (c-statistic = 0.613) were relatively low. However, the analysis of c-statistic, NRI, and IDI detected no significant discrimination improvement of the PRECISE-DAPT score for BARC type 3 or 5 bleeding compared to the other three scores. The calibrations of the PRECISE-DAPT and BleeMACS scores were modest (Hosmer-Lemeshow test p > .05). Decision curve analysis indicated net benefit of the PRECISE-DAPT score in bleeding risk evaluation. In conclusion, the PRECISE-DAPT score performed moderately in predicting BARC type 3 or 5 bleeding, while the discriminative capacities of the REACH, PARIS, BleeMACS scores were relatively low in patients undergoing DES implantation. But no significant discrimination improvement of the PRECISE-DAPT score compared to the other scores could be detected. Further studies are required to develop standardized bleeding risk scores for this population.