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Incorporating susceptibility genetic variants of risk factors has been reported to enhance the risk prediction of polygenic risk score (PRS). However, it remains unclear whether this approach is effective for lung cancer. Hence, we aimed to construct a meta polygenic risk score (metaPRS) of lung cancer and assess its prediction of lung cancer risk and implication for risk stratification. Here, a total of 2180 genetic variants were used to develop nine PRSs for lung cancer, three PRSs for different histopathologic subtypes, and 17 PRSs for lung cancer‐related risk factors, respectively. These PRSs were then integrated into a metaPRS for lung cancer using the elastic‐net Cox regression model in the UK Biobank (N = 442,508). Furthermore, the predictive effects of the metaPRS were assessed in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial (N = 108,665). The metaPRS was associated with lung cancer risk with a hazard ratio of 1.33 (95% confidence interval: 1.27–1.39) per standard deviation increased. The metaPRS showed the highest C‐index (0.580) compared with the previous nine PRSs (C‐index: 0.513–0.564) in PLCO. Besides, smokers in the intermediate risk group predicted by the clinical risk model (1.34%–1.51%) with the intermediate‐high genetic risk had a 6‐year average absolute lung cancer risk that exceeded the clinical risk model threshold (≥1.51%). The addition of metaPRS to the clinical risk model showed continuous net reclassification improvement (continuous NRI = 6.50%) in PLCO. These findings suggest the metaPRS can improve the predictive efficiency of lung cancer compared with the previous PRSs and refine risk stratification for lung cancer. [ABSTRACT FROM AUTHOR]

Background: Non‐laboratory‐based cardiovascular risk prediction tools are feasible alternatives to laboratory‐based tools in low‐ and middle‐income countries. However, their effectiveness compared to their laboratory‐based counterparts has not been adequately tested. Aim: We compared estimates from laboratory‐based and non‐laboratory‐based risk prediction tools in a low‐ and middle‐income country setting. Methods: Using a cross‐sectional design, residents of the Rishi Valley region, Andhra Pradesh, India, were surveyed from 2012 to 2015. Ten‐year absolute risk was compared for laboratory‐based and non‐laboratory‐based Framingham Risk Score (FRS), World Health Organization‐Risk Score (WHO‐RS) and risk prediction tool for global populations (Globorisk). An agreement was assessed using ordinary least‐products (OLP) regression (for RS) and quadratic weighted kappa (κw, for risk band). Results: Among 2847 participants aged 40–74 years, the mean age was 54.0 years. Cardiovascular RS increased with age and was greater in men than women in each age group. For all tools, regardless of whether laboratory or non‐laboratory‐based, over 80% of the participants were classified in the same risk band. There was strong agreement between laboratory‐based and non‐laboratory‐based tools, greatest for the WHO‐RS tools (OLP slope = 0.96, κw = 0.93) and least for the FRS (OLP slope = 0.84, κw = 0.88). The level of agreement was greater among women than men, less in those with hypercholesterolaemia or hypertension than those without, and was particularly poor among those with diabetes. Conclusions: Non‐laboratory‐based Framingham, WHO‐RS and Globorisk tools performed relatively well compared with their laboratory‐based counterparts in rural India. However, they may be less useful for risk stratification when applied to individuals with diabetes. [ABSTRACT FROM AUTHOR]

Malignant melanoma is the most severe skin cancer with a rising incidence rate. Several noninvasive image techniques and computer‐aided diagnosis systems have been developed to help find melanoma in its early stages. However, most previous research utilized dermoscopic images to build a diagnosis model, and only a few used prospective datasets. This study develops and evaluates a convolutional neural network (CNN) for melanoma identification and risk prediction using optical coherence tomography (OCT) imaging of mice skin. Longitudinal tests are performed on four animal models: melanoma mice, dysplastic nevus mice, and their respective controls. The CNN classifies melanoma and healthy tissues with high sensitivity (0.99) and specificity (0.98) and also assigns a risk score to each image based on the probability of melanoma presence, which may facilitate early diagnosis and management of melanoma in clinical settings. [ABSTRACT FROM AUTHOR]

This study introduces an approach to predicting cardiovascular diseases (CVDs) by leveraging neural networks and evaluates its performance against the widely recognized SCORE2 risk assessment tool. Given the significant global health impact of CVDs, our research aims to assess the effectiveness of deep learning‐based models compared with traditional methods. Our methodology involves developing and deploying a neural network model capable of learning intricate patterns and relationships from a comprehensive dataset. Leveraging a large population cohort with extended follow‐up enables precise and long‐term risk estimation. We utilize the T‐paired test to compare risk predictions between our neural network model and the SCORE2 tool. Our results indicate a notable accuracy of 0.77 for our neural network‐based model in predicting CVDs, with the T‐paired test revealing no significant variations in risk levels between the two methods. These findings underscore the effectiveness of neural networks as a robust tool for CVD risk prediction and advocate for further exploration and integration of these technologies to enhance cardiovascular risk assessment, thereby advancing predictive modeling in healthcare. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Both chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) confer a high risk of cardiovascular disease and mortality. These entities frequently coincide. The separate and joint impact of CKD and T2DM on the risk of major cardiovascular events (MACE) and survival is unclear.In this prospective cohort study, patients with angiographically proven coronary artery disease were investigated according to their CKD and T2DM status (T2DM−/CKD−, T2DM+/CKD−, T2DM−/CKD+, T2DM+/CKD+) and followed for up to 18 years.A total of 1441 patients were included in the study of whom 39% experienced MACE (T2DM−/CKD−: 31%, T2DM+/CKD−: 43%, T2DM−/CKD+: 53%, T2DM+/CKD+: 61%) and 53% died. A log‐rank test revealed significant differences between the event‐free time period of the four groups (χ2 (3) = 112.57, p < 0.001). The presence of T2DM and CKD was associated with a 2.72‐fold increase [1.98–3.73] in MACE compared to patients who suffered from neither condition (p < 0.001). T2DM alone led to a 1.37‐fold increase [1.1–1.7], (p = 0.004), CKD alone to a 1.71‐fold increase [1.31–2.25], (p < 0.001).T2DM and CKD in patients with coronary artery disease are mutually independent predictors of cardiovascular events. Patients with both CKD and T2DM are at an extremely high risk for cardiovascular events. [ABSTRACT FROM AUTHOR]

Aims Materials and Methods Results Conclusions To evaluate the predictive value of a contemporary type 2 diabetes (T2D) polygenic score (PGS) in detecting incident diabetes across a range of diabetes risk factors.We analysed participants in the Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk (FOURIER) trial (ClinicalTrials.gov, number NCT0176463), which compared the efficacy of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor evolocumab versus placebo in lowering cardiovascular outcomes in participants with stable atherosclerotic cardiovascular disease and LDL cholesterol levels of 70 mg/dL (1.8 mmol/L) or higher who were receiving statin therapy. Genetic risk was characterized using a previously validated T2D PGS based on ~1.2 million single‐nucleotide polymorphisms. PGS was analysed continuously and categorically as high (top 20% of the PGS) and low to intermediate (lower 80% of the PGS). The effect of evolocumab on incident diabetes in patients without diabetes at baseline was also assessed. HbA1c was measured at baseline and every 24 weeks thereafter, while FPG was measured at baseline, week 12, week 24 and every 24 weeks thereafter. Potential cases of incident diabetes were adjudicated centrally. Hazards ratios (HRs) for incident diabetes were adjusted for baseline characteristics and ancestry.Among 9388 participants, the mean age was 63 ± 9 years and 22.7% were women, with median HbA1c 39 mmol/mol (36 mmol/mol – 41 mmol/mol; 5.7% [5.4%–5.9%]) and mean body mass index (BMI) 28.7 ± 5 kg/m2. Diabetes developed in 690 participants (7.3%) during 2.3 years of median follow‐up. T2D PGS predicted incident T2D (HR per 1‐SD 1.22, 95% CI 1.14–1.32, p < 0.001). The rates of incident T2D in the high and low to intermediate genetic risk categories were 12.1% versus 6.8%, respectively (HR 1.43 95% CI 1.20–1.70, p < 0.001). Notably, high T2D genetic risk had greater predictive strength among individuals with lower HbA1c (P‐int = 0.0499) and lower BMI (P‐int = 0.004).The T2D polygenic score serves as an independent predictor of incident diabetes, particularly among individuals with lower distribution of traditional risk factors. [ABSTRACT FROM AUTHOR]

Background: As the primary organ of metabolism and detoxification, the liver may contribute to the pathogenesis of lung cancer. We aimed to illuminate the intricate link between liver function biomarkers and lung cancer risk, as well as delineate the role of smoking behavior within this association. Methods: We investigated the associations of seven liver function biomarkers levels (alkaline phosphatase [ALP], alanine transaminase [ALT], total bilirubin [TBIL], albumin [ALB], gamma‐glutamyltransferase [GGT], aspartate transaminase [AST], and total protein [TP]) with lung cancer risk across the UK Biobank (N = 337 499) through restricted cubic splines and Cox proportional hazards models. Moreover, Mendelian randomization (MR) was utilized to evaluate the causal effect of smoking behavior on these biomarkers. Then a lung cancer risk prediction model was developed among smokers by backward stepwise logistic regression. Results: During a median follow‐up of 13.3 years, 3003 lung cancer cases were identified. We found ALP levels positively associated with lung cancer risk, whereas ALT, TBIL, ALB, and AST were inversely correlated; TP exhibited a U‐shaped association, whereas GGT displayed a mirrored J‐shaped relationship. These associations were amplified among smokers. MR analysis indicated that smoking behavior could increase ALP (odds ratio [OR]: 1.05) and GGT (OR: 1.15) levels while decreasing TBIL (OR: 0.92), ALB (OR: 0.92), and TP (OR: 0.96) levels. The lung cancer risk model incorporating these biomarkers in smokers demonstrated robust discrimination. Conclusion: Our finding provides perspectives and evidences towards the intricate crosstalk between the hepatic and pulmonary systems, as well as the processes through which tobacco catalyzes lung carcinogenesis. [ABSTRACT FROM AUTHOR]

Background: The clinical relevance of cardiac troponin (cTn) elevation in takotsubo syndrome (TTS) remains uncertain. The present study sought to investigate the role of cardiac troponin (cTn) elevations in mortality prediction of patients with Takotsubo syndrome (TTS). Methods: Patients enrolled in the International Takotsubo (InterTAK) Registry from January 2011 to February 2020 with available data on peak cTn levels were included in the analysis. Peak cTn levels during the index hospitalization were used to define clinically relevant myocardial injury. The threshold at which clinically relevant myocardial injury drives mortality at 1 year was identified using restricted cubic spline analysis. Results: Out of 2′938 patients, 222 (7.6%) patients died during 1‐year follow‐up. A more than 28.8‐fold increase of cTn above the upper reference limit was identified as threshold for clinically relevant myocardial injury. The presence of clinically relevant myocardial injury was significantly associated with an increased risk of mortality at 5 years (adjusted HR 1.58, 95% CI 1.18–2.12, p =.002). Clinically relevant myocardial injury was related to an increased 5‐year mortality risk in patients with apical TTS (adjusted HR 1.57, 95% CI 1.21–2.03, p =.001), in presence of physical stressors (adjusted HR 1.60, 95% CI 1.22–2.11, p =.001), and in absence of emotional stressors (adjusted HR 1.49, 95% CI, 1.17–1.89, p =.001). Conclusion: This study for the first time determined a troponin threshold for the identification of TTS patients at excess risk of mortality. These findings advance risk stratification in TTS and assist in identifying patients in need for close monitoring and follow‐up. [ABSTRACT FROM AUTHOR]

Cardiac risk mitigation is a major priority in improving outcomes for cancer survivors as advances in cancer screening and treatments continue to decrease cancer mortality. More than half of adult cancer patients will be treated with radiotherapy (RT); therefore it is crucial to develop a framework for how to assess and predict radiation‐induced cardiac disease (RICD). Historically, RICD was modelled solely using whole heart metrics such as mean heart dose. However, data over the past decade has identified cardiac substructures which outperform whole heart metrics in predicting for significant cardiac events. Additionally, non‐RT factors such as pre‐existing cardiovascular risk factors and toxicity from other therapies contribute to risk of future cardiac events. In this review, we aim to discuss the current evidence and knowledge gaps in predicting RICD and provide a roadmap for the development of comprehensive models based on three interrelated components, (1) baseline CV risk assessment, (2) cardiac substructure radiation dosimetry linked with cardiac‐specific outcomes and (3) novel biomarker development. [ABSTRACT FROM AUTHOR]

Disruptions to the pharmaceutical supply chain (PSC) have negative implications for patients, motivating their prediction to improve risk mitigation. Although data analytics and machine learning methods have been proposed to support the characterization of probabilities to inform decisions and risk mitigation strategies, their application in the PSC has not been previously described. Further, it is unclear how well these models perform in the presence of emergent events representing deep uncertainty such as the COVID‐19 pandemic. This article examines the use of data‐driven models to predict PSC disruptions before and during the COVID‐19 pandemic. Using data on generic drugs from the pharmacy supply chain division of a Fortune 500 pharmacy benefit management firm, we have developed predictive models based on the naïve Bayes algorithm, where the models predict whether a specific supplier or whether a specific product will experience a supply disruption in the next time period. We find statistically significant changes in the relationships of nearly all variables associated with product supply disruptions during the pandemic, despite pre‐pandemic stability. We present results showing how the sensitivity, specificity, and false positive rate of predictive models changed with the onset of the COVID‐19 pandemic and show the beneficial effects of regular model updating. The results show that maintaining model sensitivity is more challenging than maintaining specificity and false positive rates. The results provide unique insight into the pandemic's effect on risk prediction within the PSC and provide insight for risk analysts to better understand how surprise events and deep uncertainty affect predictive models. [ABSTRACT FROM AUTHOR]

Background and purpose Methods Results Conclusions Neuropeptide Y (NPY) has been reported to be involved in the pathophysiology of several cardiovascular disease processes and might contribute to the incidence of stroke, but the prognostic utility of circulating NPY after acute ischaemic stroke is unclear. This study aimed to prospectively examine the association between plasma NPY levels and adverse clinical outcomes after acute ischaemic stroke.Plasma NPY levels were measured in 3250 patients (2066 men and 1184 women) from the China Antihypertensive Trial in Acute Ischaemic Stroke. The primary outcome was the combination of death and major disability (modified Rankin Scale score ≥3) at 12 months after stroke onset, and secondary outcomes included major disability, death and cardiovascular events.During the 12‐month follow‐up, 702 participants (21.6%) experienced major disability or died. After multivariable adjustment, odds ratio (95% confidence interval) for the highest quartile of NPY was 1.56 (1.19–2.04) for the primary outcome, compared to the lowest quartile. Each standard deviation (0.27 ng/mL) higher log‐transformed NPY was associated with an odds ratio (95% confidence interval) of 1.18 (1.07–1.30) for the primary outcome, 1.28 (1.15–1.42) for major disability. The addition of NPY to a conventional risk factors model improved risk prediction of the composite outcome of death and major disability (category‐free net reclassification index 8.82%, p = 0.040; integrated discrimination improvement 0.38%, p = 0.011).Elevated plasma NPY levels in the acute phase of ischaemic stroke were associated with increased risk of poor clinical outcomes after ischaemic stroke, suggesting that plasma NPY may be a potential prognostic biomarker for ischaemic stroke. [ABSTRACT FROM AUTHOR]

Correlation between echocardiographic and pathoanatomic variables and their prognostic value in murine cardiomyopathy models remain unknown. Using echocardiography, morphometrics, and survival monitoring, we characterized transgenic (TG) mice with dilated cardiomyopathy due to cardiac overexpression of β2‐adrenoceptors focusing on predicting heart failure (HF) risk and HF mortality. In 12‐month‐old non‐TG and TG mice, echocardiography was performed to determine left ventricular (LV) dimensions (d), wall thickness (h), and fractional shortening (FS). Animals were monitored for 3 months for survival. Organ weights and pathological events indicating left HF were determined. TG mice (n = 76) had reduced FS and enlarged LV, and 79% died of HF or likely arrhythmias during the follow‐up period while all non‐TG mice (n = 26) survived. These mice with left HF also had pulmonary congestion and hypertrophy/dilatation of the right ventricle (RV). Weights of lungs, RV, and atria were intercorrelated (r = 0.79–0.83) and also negatively correlated with FS × (h/d) index (r = −0.502 to −0.609). By FS × (h/d) tertiles, TG mice of low tertiles were identified with the highest mortality (96%) largely due to HF (76%). In conclusion, in aged cardiomyopathy mice a good correlation existed between echocardiographic and pathoanatomic variables. Echocardiography‐derived LV function and remodeling were useful in identifying a subgroup of TG mice with a high risk of HF and HF fatality. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood‐derived DNA methylation as a promising tool for early dementia risk detection. METHODS: In conjunction with an extensive array of machine learning techniques, we employed whole blood genome‐wide DNA methylation data as a surrogate for 14 modifiable and non‐modifiable factors in the assessment of dementia risk in independent dementia cohorts. RESULTS: We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross‐sectionally, independent of age and sex (P = 2.0 × 10−3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)‐Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia = 2.59). DISCUSSION: Our work shows the potential of employing blood‐derived DNA methylation data in the assessment of dementia risk. Highlights: We used whole blood DNA methylation as a surrogate for 14 dementia risk factors.Created a multivariate methylation risk score for predicting cognitive impairment.Emphasized the role of machine learning and omics data in predicting dementia.The score predicts cognitive impairment development at the population level. [ABSTRACT FROM AUTHOR]

Aims: 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. Methods and results: 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 3 months 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 64 years), with median follow up 24.5 (11.5–48.3 months); 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). Conclusions: 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. [ABSTRACT FROM AUTHOR]

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in‐hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th–75th percentile) was 70 (58–82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41–49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c‐statistic (95% confidence interval) of 0.96 (0.95–0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in‐hospital mortality. Models for those with HFrEF demonstrated a c‐statistic of 0.96 (0.94–0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c‐statistic of 0.95 (0.93–0.97), SN 87.4%, and SP 89.5% for predicting in‐hospital mortality. The random forest model significantly outperformed logistic regression (c‐statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c‐statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines‐Heart Failure risk score (c‐statistic 0.69, SN 67%, and SP 63%); P < 0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in‐hospital mortality among patients hospitalized with a diagnosis of heart failure. [ABSTRACT FROM AUTHOR]

Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of "semi‐competing risks" in which a non‐terminal event of interest is subject to the occurrence of a terminal event of interest. The semi‐competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non‐terminal event, which we call "clinically immediate" and "non‐immediate" terminal events. Though such phenomena arise in many clinical contexts, to‐date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty‐based illness‐death model with a binary submodel to distinguish risk of immediate terminal event following the non‐terminal event. The model admits direct dependence of the terminal event on the non‐terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions. [ABSTRACT FROM AUTHOR]

The analysis of streaming time‐to‐event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch‐specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis. [ABSTRACT FROM AUTHOR]

Background: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. Methods: The IARC‐ARCAGE European case–control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. Results: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74–0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61–0.64). Conclusion: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction. [ABSTRACT FROM AUTHOR]

Severe fever with thrombocytopenia syndrome (SFTS) represents an emerging infectious disease characterized by a substantial mortality risk. Early identification of patients is crucial for effective risk assessment and timely interventions. In the present study, least absolute shrinkage and selection operator (LASSO)‐Cox regression analysis was conducted to identify key risk factors associated with progression to critical illness at 7‐day and 14‐day. A nomogram was constructed and subsequently assessed for its predictive accuracy through evaluation and validation processes. The risk stratification of patients was performed using X‐tile software. The performance of this risk stratification system was assessed using the Kaplan–Meier method. Additionally, a heat map was generated to visualize the results of these analyses. A total of 262 SFTS patients were included in this study, and four predictive factors were included in the nomogram, namely viral copies, aspartate aminotransferase (AST) level, C‐reactive protein (CRP), and neurological symptoms. The AUCs for 7‐day and 14‐day were 0.802 [95% confidence interval (CI): 0.707–0.897] and 0.859 (95% CI: 0.794–0.925), respectively. The nomogram demonstrated good discrimination among low, moderate, and high‐risk groups. The heat map effectively illustrated the relationships between risk groups and predictive factors, providing valuable insights with high predictive and practical significance. [ABSTRACT FROM AUTHOR]

Patients with stable coronary artery disease (CAD) are at an increased risk of acute myocardial infarction (AMI), particularly among older individuals. Developing a reliable model to predict AMI occurrence in these patients holds the potential to expedite early diagnosis and intervention. This study is aimed at establishing a circulating amino acid‐assisted model, incorporating amino acid profiles alongside clinical variables, to predict AMI risk. A cohort of 874 CAD patients from two independent centers was analyzed. Plasma amino acid levels were quantified using liquid chromatography tandem mass spectrometry (LC‐MS/MS) employing a targeted metabolomics approach. This methodology incorporated 13C isotope‐labeled internal standards for precise quantification of 27 amino acids. Univariate logistic regression was applied to identify differentially expressed amino acids that distinguished between stable CAD and AMI patients. To assess prediction performance, receiver operating characteristic (ROC) curve and nomogram analyses were utilized. Five amino acids—lysine, methionine, tryptophan, tyrosine, and N6‐trimethyllysine—emerged as potential biomarkers (p < 0.05), exhibiting significant differences in their expression levels across the two centers when comparing stable CAD with AMI patients. For AMI risk prediction, the base model, utilizing 12 clinical variables, achieved areas under the curve (AUC) of 0.7387 in the discovery phase (n = 623) and 0.8205 in the external validation set (n = 251). Notably, the integration of these five amino acids into the prediction model significantly enhanced its performance, increasing the AUC to 0.7651 in the discovery phase (Delong's test, p = 1.43e‐02) and to 0.8958 in the validation set (Delong's test, p = 8.91e‐03). In conclusion, the circulating amino acid‐assisted model effectively enhances the prediction of AMI risk among CAD patients, indicating its potential clinical utility in facilitating early detection and intervention. [ABSTRACT FROM AUTHOR]

Moderate calibration, the expected event probability among observations with predicted probability z being equal to z, is a desired property of risk prediction models. Current graphical and numerical techniques for evaluating moderate calibration of risk prediction models are mostly based on smoothing or grouping the data. As well, there is no widely accepted inferential method for the null hypothesis that a model is moderately calibrated. In this work, we discuss recently‐developed, and propose novel, methods for the assessment of moderate calibration for binary responses. The methods are based on the limiting distributions of functions of standardized partial sums of prediction errors converging to the corresponding laws of Brownian motion. The novel method relies on well‐known properties of the Brownian bridge which enables joint inference on mean and moderate calibration, leading to a unified "bridge" test for detecting miscalibration. Simulation studies indicate that the bridge test is more powerful, often substantially, than the alternative test. As a case study we consider a prediction model for short‐term mortality after a heart attack, where we provide suggestions on graphical presentation and the interpretation of results. Moderate calibration can be assessed without requiring arbitrary grouping of data or using methods that require tuning of parameters. [ABSTRACT FROM AUTHOR]

A healthy diet is dominant in cardiovascular disease (CVD) prevention. Inflammation is pivotal for CVD development. This study aimed to evaluate the association between the pro‐inflammatory diet and the CVD risk. This cross‐sectional study involved 10,138 Fasa adult cohort study participants. After excluding participants with missing data, the Energy‐Adjusted Dietary Inflammatory Index (E‐DII) was calculated to assess the inflammatory potential of diet using the recorded Food Frequency Questionnaire. Framingham risk score (FRS) was used to predict the 10‐year risk of CVD. The association between E‐DII and high risk for CVD was investigated using multinominal regression. After exclusion, the mean age of studied individuals (n = 10,030) was 48.6 ± 9.6 years, including 4522 men. Most participants were low risk (FRS <10%) for CVD (87.6%), while 2.7% of them were high risk (FRS ≥20%). The median FRS was 2.80 (1.70, 6.30). The E‐DII ranged from −4.22 to 4.49 (mean E‐DII = 0.880 ± 1.127). E‐DII was significantly associated with FRS. This result persisted after adjusting for confounding factors and in both genders. This study revealed that the pro‐inflammatory diet significantly increases the CVD risk. Consequently, reducing the inflammatory potential of diet should be considered an effective dietary intervention in CVD prevention. [ABSTRACT FROM AUTHOR]

Background: Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. Purpose: To enhance the diagnosis of CCH and non‐CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision‐making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. Methods: In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1‐weighted (T1WI) and T2‐weighted (T2WI) sequence images for each patient. We propose CCH‐Network (CCHNet), an end‐to‐end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state‐of‐the‐art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. Results: CCHNet achieved an AUC of 91.6% (95% CI: 86.8–99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6–95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6–95.6) and a specificity (SP) of 90.0% (95% CI: 82.3–97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3–93.0), an ACC of 84.2% (95% CI: 70.2–93.6), a SE of 83.3% (95% CI: 68.3–95.5), and a SP of 84.7% (95% CI: 70.3–96.8). Conclusions: The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH. [ABSTRACT FROM AUTHOR]

Introduction: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. Methods: We studied pseudo‐values based on the Aalen‐Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR‐IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR‐IPCW). The MLR‐IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. Results: The pseudo‐value, BLR‐IPCW, and MLR‐IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low‐density regions of predicted transition probability. Conclusions: We recommend implementing either the pseudo‐value or BLR‐IPCW approaches to produce a calibration curve, combined with the MLR‐IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN. [ABSTRACT FROM AUTHOR]

Background: Conversion of laparoscopic cholecystectomy to open is uncommon, but is associated with longer hospital stay and recovery. Prognosticating conversion may aid service planning and provision. We therefore aimed to assess the external validity of the largest risk score for operative conversion. Methods: CHOLENZ was a multicentre, prospective, national cohort study of cholecystectomy for benign biliary disease conducted by STRATA, a trainee‐led collaborative network. Data were collected from patients undergoing cholecystectomy in New Zealand hospitals between 1 August and 30 October 2021 with 30‐day follow‐up. The Conversion from Laparoscopic to Open Cholecystectomy (CLOC) score from the CholeS study was assessed for external validity by interrogating its accuracy and calibration in the CHOLENZ dataset. Results: Of 1162 cholecystectomies started laparoscopically, 20 (1.7%) were converted to open in the CHOLENZ dataset. The CLOC score predicted 2.9% (IQR 1.3%–8.1%) would be converted. Area under the curve was 0.65 (95% 0.51–0.79) and calibration was acceptable with a Hosmer‐Lemeshow p value of 0.45; with evidence of tendency to overestimate with interrogation of calibration across a continuous risk profile (intercept 1.27, slope 0.4). Sensitivity analysis with imputed data improved accuracy. Recalibration with the addition of body mass index, and preoperative bilirubin also improved accuracy to 0.86 (95% CI 0.78–0.95). Conclusions: The CLOC score in its original form is not generalisable to the Aotearoa New Zealand setting and is therefore not suitable for clinical use in our local setting. [ABSTRACT FROM AUTHOR]

Background: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR‐based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. Methods: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. Results: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83–0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98–12.87). Conclusions: This simple EHR‐based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well‐being of older adults at risk of critical illness and its consequences. See related article by Haimovich and Deardorff. [ABSTRACT FROM AUTHOR]

We recently derived and validated a serum‐based microRNA risk score (miR‐score) that predicted colorectal cancer (CRC) occurrence with very high accuracy within 14 years of follow‐up in a population‐based cohort study from Germany (ESTHER cohort). Here, we aimed to evaluate associations of the CRC‐specific miR‐score with the risk of developing other common cancers, including female breast cancer (BC), lung cancer (LC), and prostate cancer (PC), in the ESTHER cohort. MicroRNAs (miRNAs) were profiled by quantitative real‐time PCR in serum samples collected at baseline from randomly selected incident cases of BC (n = 90), LC (n = 88), and PC (n = 93) and participants without diagnosis of CRC, LC, BC, or PC (controls, n = 181) until the end of the 17‐year follow‐up. Multivariate logistic regression models were used to evaluate the associations of the miR‐score with BC, LC, and PC incidence. The miR‐score showed strong inverse associations with BC and LC incidence [odds ratio per 1 standard deviation increase: 0.60 (95% confidence interval [CI] 0.43–0.82), p = 0.0017, and 0.64 (95% CI 0.48–0.84),p = 0.0015, respectively]. Associations with PC were not statistically significant but pointed in the positive direction. Our study highlights the potential of serum‐based miRNA biomarkers for cancer‐specific risk prediction. Further large cohort studies aiming to investigate, validate, and optimize the use of circulating miRNA signatures for cancer risk assessment are warranted. Circulating microRNAs (miRNAs) could improve cancer risk prediction. Our findings demonstrate that miRNA profiles associated with tumor progression differ across cancer types and could be useful for developing personalized cancer prevention strategies. [ABSTRACT FROM AUTHOR]

Objectives: To explore associations between type and number of abnormal glucose values on antenatal oral glucose tolerance test (OGTT) with postpartum diabetes in South Asian women diagnosed with gestational diabetes (GDM) using International Association of the Diabetes and Pregnancy Study Groups criteria. Methods: This post‐hoc evaluation of the Lifestyle Intervention IN Gestational Diabetes (LIVING) study, a randomized controlled trial, was conducted among women with GDM in the index pregnancy, across 19 centers in Bangladesh, India, and Sri Lanka. Postpartum diabetes (outcome) was defined on OGTT, using American Diabetes Association (ADA) criteria. Results: We report data on 1468 women with GDM, aged 30.9 (5.0) years, and with median (interquartile range) follow‐up period of 1.8 (1.4–2.4) years after childbirth following the index pregnancy. We found diabetes in 213 (14.5%) women with an incidence of 8.7 (7.6–10.0)/100 women‐years. The lowest incidence rate was 3.8/100 women years, in those with an isolated fasting plasma glucose (FPG) abnormality, and highest was 19.0/100 women years in participants with three abnormal values. The adjusted hazard ratios for two and three abnormal values compared to one abnormal value were 1.73 (95% confidence interval [CI], 1.18–2.54; p =.005) and 3.56 (95% CI, 2.46–5.16; p <.001) respectively. The adjusted hazard ratio for the combined (combination of fasting and postglucose load) abnormalities was 2.61 (95% CI, 1.70–4.00; p <.001), compared to isolated abnormal FPG. Conclusions: Risk of diabetes varied significantly depending upon the type and number of abnormal values on antenatal OGTT. These data may inform future precision medicine approaches such as risk prediction models in identifying women at higher risk and may guide future targeted interventions. [ABSTRACT FROM AUTHOR]

Background: Metabolic dysfunction‐associated fatty liver disease (MAFLD) is an emerging risk factor for chronic kidney disease (CKD). N‐terminal propeptide of collagen type 3 (PRO‐C3) is a biomarker of advanced fibrosis in MAFLD and PRO‐C3 may be involved in renal fibrosis. We aimed to use PRO‐C3 measurements to generate a new algorithmic score to test the prediction of MAFLD with chronic kidney disease (MAFLD–CKD). Methods: A derivation and independent validation cohort of 750 and 129 Asian patients with biopsy‐confirmed MAFLD were included. Serum PRO‐C3 concentration was measured and regression analyses were performed to examine associations with MAFLD–CKD. A derivative algorithm for MAFLD–CKD risk prediction was evaluated with receiver operator characteristic (ROC) curve analysis. Results: The study included two Asian cohorts (n = 180 with MAFLD–CKD; mean‐eGFR: 94.93 mL/min/1.73 m2; median‐urinary albumin‐to‐creatinine ratio: 6.58 mg/mmol). PRO‐C3 was associated with the severity of MAFLD‐CKD and independently associated with MAFLD–CKD (adjusted odds ratio = 1.16, 95% confidence interval [CI]: 1.08–1.23, p <.001). A new non‐invasive score (termed PERIOD) including PRO‐C3 efficiently predicted MAFLD‐CKD (AUROC =.842, 95% CI:.805–.875). Accuracy, specificity and negative predictive values were 80.2%, 85.1% and 88.4%, respectively. In the validation cohort, the PERIOD score had good diagnostic performance (AUROC =.807, 95% CI:.691–.893) with similar results in all patient subgroups. In the MAFLD–CKD subgroup, the accuracy for identifying advanced fibrosis was further improved by combining the PRO‐C3‐based ADAPT with the Agile 3+ scores (AUROC =.90, 95% CI:.836–.964). Conclusions: The PERIOD score is helpful for accurately predicting the risk of MAFLD–CKD. PRO‐C3 can also be used to assess liver fibrosis in people with MAFLD–CKD. [ABSTRACT FROM AUTHOR]

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data. [ABSTRACT FROM AUTHOR]

Aims: Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H‐NMR] spectroscopy) for incident HF. Methods and results: Leveraging data of 68 311 individuals and >0.8 million person‐years of follow‐up from the UK Biobank cohort, we (i) fitted per‐metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well‐validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP‐HF]). During a median follow‐up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP‐HF). Performance‐optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP‐HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004–0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200–0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463–27.825]). Models including age, sex and metabolomics performed almost as well as PCP‐HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI −0.004 to 0.027], continuous NRI: 0.097 [95% CI −0.025 to 0.217], relative IDI: 13.445% [95% CI −10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. Conclusion: Serum metabolomics improves incident HF risk prediction over PCP‐HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically‐based models, potentially offering a cost‐effective, standardizable, and scalable single‐domain alternative. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a model to predict the preconception risk of gestational diabetes mellitus (GDM) in nulliparous women. Methods: This was a retrospective cohort study. A total of 1565 women in early pregnancy who underwent preconception health examinations in the Women and Children's Hospital of Chongqing Medical University between January 2020 and June 2021 were invited to participate in a questionnaire survey. Logistic regression analysis was performed to determine the preconception risk factors for GDM. These factors were used to construct a model to predict GDM risk in nulliparous women. Then, the model was used to assess the preconception risk of GDM in 1060 nulliparous women. Results: Independent preconception risk factors for GDM included the following: age 35 years or greater, diastolic blood pressure 80 mm Hg or greater, fasting plasma glucose 5.1 mmol/L or greater, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) 24 or greater, weight gain 10 kg or greater in the year before pregnancy, age of menarche 15 years or greater, three or more previous pregnancies, daily staple food intake 300 g or greater, fondness for sweets, and family history of diabetes. BMI less than 18.5, daily physical activity duration 1 h or greater, and high‐intensity physical activity were protective factors. These factors were used to construct a model to predict GDM risk in nulliparous women, and the incidence of GDM significantly increased as the risk score increased. The area under the curve of the prediction model was 0.82 (95% confidence interval 0.80–0.85). Conclusion: The preconception GDM risk prediction model demonstrated good predictive efficacy and can be used to identify populations at high risk of GDM before pregnancy, which provides the possibility for preconception intervention. Synopsis: A preconception gestational diabetes mellitus risk prediction model for nulliparous women was constructed and demonstrated to have good predictive efficacy. [ABSTRACT FROM AUTHOR]

Aims: Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk‐factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi‐racial populations. Methods and results: Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow‐up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41–1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009–0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27–1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33–1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N‐terminal pro‐brain natriuretic peptide and other 98 proteins were associated with PRSAF. Conclusions: The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation. [ABSTRACT FROM AUTHOR]

A 2013 systematic review and Delphi consensus study identified 12 modifiable risk and protective factors for dementia, which were subsequently merged into the "LIfestyle for BRAin health" (LIBRA) score. We systematically evaluated whether LIBRA requires revision based on new evidence. To identify modifiable risk and protective factors suitable for dementia risk reduction, we combined an umbrella review of systematic reviews and meta‐analyses with a two‐round Delphi consensus study. The review of 608 unique primary studies and opinions of 18 experts prioritized six modifiable factors: hearing impairment, social contact, sleep, life course inequalities, atrial fibrillation, and psychological stress. Based on expert ranking, hearing impairment, social contact, and sleep were considered the most suitable candidates for inclusion in updated dementia risk scores. As such, the current study shows that dementia risk scores need systematic updates based on emerging evidence. Future studies will validate the updated LIBRA score in different cohorts. Highlights: An umbrella review was combined with opinions of 18 dementia experts.Various candidate targets for dementia risk reduction were identified.Experts prioritized hearing impairment, social contact, and sleep.Re‐assessment of dementia risk scores is encouraged.Future work should evaluate the predictive validity of updated risk scores. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR). METHODS: An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD‐ADRC) cohort. RESULTS: Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P < 0.001). Greater thickness in the signature was related to better general cognition (P < 0.01) and episodic memory (P = 0.01). Results replicated in UCD‐ADRC. DISCUSSION: We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker. [ABSTRACT FROM AUTHOR]

When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State‐of‐the‐art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high‐dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility. [ABSTRACT FROM AUTHOR]

Objective: Guidelines suggest considering antiseizure medication (ASM) discontinuation in seizure‐free patients with epilepsy. Past work has poorly explored how discontinuation effects vary between patients. We evaluated (1) what factors modify the influence of discontinuation on seizure risk; and (2) the range of seizure risk increase due to discontinuation across low‐ versus high‐risk patients. Methods: We pooled three datasets including seizure‐free patients who did and did not discontinue ASMs. We conducted time‐to‐first‐seizure analyses. First, we evaluated what individual patient factors modified the relative effect of ASM discontinuation on seizure risk via interaction terms. Then, we assessed the distribution of 2‐year risk increase as predicted by our adjusted logistic regressions. Results: We included 1626 patients, of whom 678 (42%) planned to discontinue all ASMs. The mean predicted 2‐year seizure risk was 43% [95% confidence interval (CI) 39%–46%] for discontinuation versus 21% (95% CI 19%–24%) for continuation. The mean 2‐year absolute seizure risk increase was 21% (95% CI 18%–26%). No individual interaction term was significant after correcting for multiple comparisons. The median [interquartile range (IQR)] risk increase across patients was 19% (IQR 14%–24%; range 7%–37%). Results were unchanged when restricting analyses to only the two RCTs. Significance: No single patient factor significantly modified the influence of discontinuation on seizure risk, although we captured how absolute risk increases change for patients that are at low versus high risk. Patients should likely continue ASMs if even a 7% 2‐year increase in the chance of any more seizures would be too much and should likely discontinue ASMs if even a 37% risk increase would be too little. In between these extremes, individualized risk calculation and a careful understanding of patient preferences are critical. Future work will further develop a two‐armed individualized seizure risk calculator and contextualize seizure risk thresholds below which to consider discontinuation. Plain Language Summary: Understanding how much antiseizure medications (ASMs) decrease seizure risk is an important part of determining which patients with epilepsy should be treated, especially for patients who have not had a seizure in a while. We found that there was a wide range in the amount that ASM discontinuation increases seizure risk—between 7% and 37%. We found that no single patient factor modified that amount. Understanding what a patient's seizure risk might be if they discontinued versus continued ASM treatment is critical to making informed decisions about whether the benefit of treatment outweighs the downsides. [ABSTRACT FROM AUTHOR]

Stroke is a major cause of death and disability worldwide, but predicting its risk remains challenging. This study aimed to evaluate the cerebral blood flow autoregulation function of subjects with different stroke risk levels and predict their stroke risk. The coupling strength between cerebral oxygen and blood pressure signals was calculated by wavelet analysis and dynamic Bayesian inference and used as a quantitative index of cerebral blood flow autoregulation. A stroke prediction model based on the extreme random tree was constructed using the coupling strength and other data as input features. The results showed that the coupling strength was significantly higher in the high‐risk group than the other groups. Moreover, the prediction model achieved an average accuracy of 0.80 across the three groups. The coupling strength of cerebral oxygen and blood pressure can be used as an objective index to predict stroke risk, which has implications for stroke prevention and intervention. [ABSTRACT FROM AUTHOR]

Severe coronavirus disease 2019 (COVID‐19) is a hyperinflammatory syndrome. The biomarkers of inflammation best suited to triage patients with COVID‐19 are unknown. We conducted a prospective multicenter observational study of adult patients hospitalized specifically for COVID‐19 from February 1, 2020 to October 19, 2022. Biomarkers measured included soluble urokinase plasminogen activator receptor (suPAR), C‐reactive protein, interleukin‐6, procalcitonin, ferritin, and D‐dimer. In‐hospital outcomes examined include death and the need for mechanical ventilation. Patients admitted in the United States (US, n = 1962) were used to compute area under the curves (AUCs) and identify biomarker cutoffs. The combined European cohorts (n = 1137) were used to validate the biomarker cutoffs. In the US cohort, 356 patients met the composite outcome of death (n = 197) or need for mechanical ventilation (n = 290). SuPAR was the most important predictor of the composite outcome and had the highest AUC (0.712) followed by CRP (0.642), ferritin (0.619), IL‐6 (0.614), D‐dimer (0.606), and lastly procalcitonin (0.596). Inclusion of other biomarkers did not improve discrimination. A suPAR cutoff of 4.0 ng/mL demonstrated a sensitivity of 95.4% (95% CI: 92.4%–98.0%) and negative predictive value (NPV) of 92.5% (95% CI: 87.5%–96.9%) for the composite outcome. Patients with suPAR < 4.0 ng/mL comprised 10.6% of the cohort and had a 0.8% probability of the composite outcome. Applying this cutoff to the validation cohort yielded a sensitivity of 93.8% (90.4%–96.7%) and NPV of 95.5% (93.1%–97.8%) for the composite outcome. Among commonly measured biomarkers, suPAR offered stronger discriminatory ability and may be useful in triaging low‐risk patients with COVID‐19. [ABSTRACT FROM AUTHOR]

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real‐time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real‐time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low‐ and middle‐income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations. [ABSTRACT FROM AUTHOR]

Background: Risk prediction models are used for many purposes in emergency surgery, including critical care triage and benchmarking. Several risk prediction models have been developed, and some are used for purposes other than those for which they were developed. We aim to provide an overview of the existing literature on risk prediction models used in emergency surgery and highlight knowledge gaps. Methods: We will conduct a scoping review on risk prediction models used for patients undergoing emergency surgery in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR). We will search Medline, EMBASE, and the Cochrane Library and include all study designs. We aim to answer the following questions: (1) What risk prediction models are used in emergency surgery? (2) Which variables are used in these models? (3) Which surgical specialties are the models used for? (4) Have the models been externally validated? (5) Where have the models been externally validated? (6) What purposes were the models developed for? (7) What are the strengths and limitations of the included models? We will summarize the results descriptively. The certainty of evidence will be evaluated using a modified Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Conclusion: The outlined scoping review will summarize the existing literature on risk prediction models used in emergency surgery and highlight knowledge gaps. [ABSTRACT FROM AUTHOR]

Aim: We assessed the impact of tirzepatide on 10‐year predicted risk of developing type 2 diabetes (T2D) among participants in the SURMOUNT‐1 trial. Materials and Methods: In this post hoc analysis of SURMOUNT‐1, the Cardiometabolic Disease Staging risk engine was used to calculate the 10‐year predicted risk of T2D at baseline, week 24 and week 72 among participants randomized to receive 5, 10, or 15 mg tirzepatide or placebo. Mean changes in risk scores from baseline to weeks 24 and 72 were compared between tirzepatide and placebo groups. Subgroup analyses were conducted based on participants' glycaemic status and body mass index at baseline. Results: Mean baseline T2D predicted risk scores did not differ between tirzepatide and placebo groups (range: 22.9%‐24.3%). At week 72, mean absolute T2D predicted risk score reductions were significantly greater in tirzepatide groups (5 mg, 12.4%; 10 mg, 14.4%; 15 mg, 14.7%) versus placebo (0.7%). At week 72, median relative predicted risk reductions following tirzepatide treatment ranged from 60.3% to 69.0%. For participants with and without prediabetes, risk reductions were significantly greater in tirzepatide groups versus placebo. At week 72, participants with prediabetes (range: 16.0%‐20.3%) had greater mean risk score reductions from baseline versus those without prediabetes (range: 10.1%‐11.3%). Across body mass index subgroups, mean reductions from baseline were significantly greater in tirzepatide groups versus placebo. Conclusion: Tirzepatide treatment significantly reduced the 10‐year predicted risk of developing T2D compared with placebo in participants with obesity or overweight, regardless of baseline glycaemic status. [ABSTRACT FROM AUTHOR]

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS: Risk‐profiling tools may help identify high‐risk populations for clinical trials; however, their performance needs improvement. New risk‐profiling and trial‐recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug‐repurposing efforts and prioritization of disease‐modifying therapeutics. DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. Highlights: Artificial intelligence (AI) is not widely used in the dementia prevention field.Risk‐profiling tools are not used in clinical practice.Causal insights are needed to understand risk factors over the lifespan.AI will help personalize risk‐management tools for dementia prevention.AI could target specific patient groups that will benefit most for clinical trials. [ABSTRACT FROM AUTHOR]

Background: Historical cardiac troponin (cTn) elevation is commonly interpreted as lessening the significance of current cTn elevations at presentation for acute heart failure (AHF). Evidence for this practice is lacking. Our objective was to determine the incremental prognostic significance of historical cTn elevation compared to cTn elevation and ischemic heart disease (IHD) history at presentation for AHF. Methods: A total of 341 AHF patients were prospectively enrolled at five sites. The composite primary outcome was death/cardiopulmonary resuscitation, mechanical cardiac support, intubation, new/emergent dialysis, and/or acute myocardial infarction (AMI)/percutaneous coronary intervention (PCI)/coronary artery bypass grafting (CABG) at 90 days. Secondary outcomes were 30‐day AMI/PCI/CABG and in‐hospital AMI. Logistic regression compared outcomes versus initial emergency department (ED) cTn, the most recent electronic medical record cTn, estimated glomerular filtration rate, age, left ventricular ejection fraction, and IHD history (positive, negative by prior coronary workup, or unknown/no prior workup). Results: Elevated cTn occurred in 163 (49%) patients, 80 (23%) experienced the primary outcome, and 29 had AMI (9%). cTn elevation at ED presentation, adjusted for historical cTn and other covariates, was associated with the primary outcome (adjusted odds ratio [aOR] 2.39, 95% confidence interval [CI] 1.30–4.38), 30‐day AMI/PCI/CABG, and in‐hospital AMI. Historical cTn elevation was associated with greater odds of the primary outcome when IHD history was unknown at ED presentation (aOR 5.27, 95% CI 1.24–21.40) and did not alter odds of the outcome with known positive (aOR 0.74, 95% CI 0.33–1.70) or negative IHD history (aOR 0.79, 95% CI 0.26–2.40). Nevertheless, patients with elevated ED cTn were more likely to be discharged if historical cTn was also elevated (78% vs. 32%, p = 0.025). Conclusions: Historical cTn elevation in AHF patients is a harbinger of worse outcomes for patients who have not had a prior IHD workup and should prompt evaluation for underlying ischemia rather than reassurance for discharge. With known IHD history, historical cTn elevation was neither reassuring nor detrimental, failing to add incremental prognostic value to current cTn elevation alone. [ABSTRACT FROM AUTHOR]

We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re‐estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause‐specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995–2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10‐year calibration plots demonstrated that using the standard approach over‐estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis. [ABSTRACT FROM AUTHOR]

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers. [ABSTRACT FROM AUTHOR]

Aims: Although trials have proven the group‐level effectiveness of various therapies for heart failure with reduced ejection fraction (HFrEF), important differences in absolute effectiveness exist between individuals. We developed and validated the LIFEtime‐perspective for Heart Failure (LIFE‐HF) model for the prediction of individual (lifetime) risk and treatment benefit in patients with HFrEF. Methods and results: Cox proportional hazards functions with age as the time scale were developed in the PARADIGM‐HF and ATMOSPHERE trials (n = 15 415). Outcomes were cardiovascular death, heart failure (HF) hospitalization or cardiovascular death, and non‐cardiovascular mortality. Predictors were age, sex, New York Heart Association class, prior HF hospitalization, diabetes mellitus, extracardiac vascular disease, systolic blood pressure, left ventricular ejection fraction, N‐terminal pro‐B‐type natriuretic peptide, and glomerular filtration rate. The functions were combined in life‐tables to predict individual overall and HF hospitalization‐free survival. External validation was performed in the SwedeHF registry, ASIAN‐HF registry, and DAPA‐HF trial (n = 51 286). Calibration of 2‐ to 10‐year risk was adequate, and c‐statistics were 0.65–0.74. An interactive tool was developed combining the model with hazard ratios from trials to allow estimation of an individual's (lifetime) risk and treatment benefit in clinical practice. Applying the tool to the development cohort, combined treatment with a mineralocorticoid receptor antagonist, sodium–glucose cotransporter 2 inhibitor, and angiotensin receptor–neprilysin inhibitor was estimated to afford a median of 2.5 (interquartile range [IQR] 1.7–3.7) and 3.7 (IQR 2.4–5.5) additional years of overall and HF hospitalization‐free survival, respectively. Conclusion: The LIFE‐HF model enables estimation of lifelong overall and HF hospitalization‐free survival, and (lifetime) treatment benefit for individual patients with HFrEF. It could serve as a tool to improve the management of HFrEF by facilitating personalized medicine and shared decision‐making. [ABSTRACT FROM AUTHOR]

In the context that coal remains the main energy source of China now, the safety of coal mine production is still worth our attention. For the factors influencing the roof accident of coal mine roadway have great complexity and uncertainty, a new risk prediction method of coal mine roof accidents integrating T‐S fuzzy fault tree and Bayesian network (BN) is proposed. The constructed method determines the BN and the conditional probability tables according to the established T‐S fuzzy fault tree. The fault state and fuzzy fault rate of root nodes are described according to the fuzzy number. And the forward inference of Bayesian is used to calculate the probability of the accidents on the roadway roof based on the fault probability of the root nodes and actual fault probability during construction respectively, to realize the risk prediction of roof accidents. Finally, practical data are used to verify the new fusion risk evaluation method by taking two mines as cases. The results show that the influencing factors calculated by the new fusion risk prediction method are completely consistent with the actual situation, confirming the effectiveness and reliability of this method, which providing new ideas for the research about the risk prediction of coal mine roof accidents. [ABSTRACT FROM AUTHOR]

Background: Transthyretin amyloid cardiomyopathy (ATTR‐CM) is often found in patients with heart failure with preserved ejection fraction (HFpEF). However, the evidence regarding ATTR‐CM and prognosis in HFpEF remains scarce. This study sought to determine whether the ATTR‐CM burden was associated with clinical outcomes in HFpEF patients. Methods: We evaluated the associations of baseline ATTR‐CM score with adverse outcomes in HFpEF patients from the TOPCAT trial using the Cox proportional hazards model or the competing risk regression model. The discriminatory ability of the ATTR‐CM score was assessed using the area under the time‐dependent receiver operating characteristic curve (AUC). Results: We included 870 HFpEF patients, 18.9% of which had an ATTR‐CM score ≥6. Per 1 increment in the ATTR‐CM score was significantly associated with an increased risk of the primary outcome (adjusted hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.12–1.27) with an AUC of 0.652 (0.594–0.711), whereas patients with ATTR‐CM score ≥6 presented higher risks of the primary outcome (adjusted HR 2.20, 95% CI 1.65–2.95). Similar results were observed toward the secondary outcomes. Conclusions: The simple ATTR‐CM score identified an 18.9% ATTR‐CM burden in HFpEF patients, and a higher ATTR‐CM burden might predict adverse outcomes with moderate discriminatory abilities in HFpEF. [ABSTRACT FROM AUTHOR]

Background: Several early warning scores have been designed to optimize acute care by identifying patients at risk of deterioration. Methods: In this post hoc dual center study, we analyzed the performance of six clinical scores (the Goodacre score, Groarke, Worthing Physiological Score, Rapid Acute Physiology Score, Rapid Emergency Medicine Score, United Kingdom National Early Warning Score. The primary outcome is 30-day all-cause mortality after inclusion and data were obtained from previous studies performed at two different emergency departments on two continents (Denmark, Europe, and Hong Kong, Asia). Results: We included 2952 people; 1482 (50.2%) were male, mean age (standard deviation) was 65.7 (18.3) years, and 109 (3.7%) died within 30 days. Mortality rate increased steadily with increasing scores for all six scoring systems in Hong Kong while this was less obvious in Denmark. In all patients, Rapid Acute Physiology Score had the lowest discriminatory power while National Early Warning Score had the highest. National Early Warning Score performed best in Hong Kong while Worthing performed marginally better in Denmark. Discussion: Surprisingly, the performance of the scoring systems varied considerably, but were largely unaffected by location, and none of them performed close to what clinicians would normally require for predicting 30-day all-cause mortality Conclusion: All scores performed similarly across both centers, with poor prediction of 30-day all-cause mortality. Based on these findings, we believe that clinical scores must be supplemented by either biochemical values or global markers of physiological reserve to reflect reality and to be of true value. [ABSTRACT FROM AUTHOR]