Risk Prediction Model Research Articles

Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms.

Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model. Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage II-III gastric cancer. A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies-Least absolute shrinkage and selection operator (LASSO) and random forest (RF)-we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output. Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy. In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.

Social Determinants of Health and Risk-Adjusted Sepsis Mortality in the Nationwide Veterans Affairs Healthcare System.

Traditional risk prediction and risk adjustment models have focused on clinical characteristics, but accounting for social determinants of health (SDOH) and complex health conditions could improve understanding of sepsis outcomes and our ability to predict outcomes, treat patients, and assess quality of care. To evaluate the impact of SDOH and health scales in sepsis mortality risk prediction and hospital performance assessment. Observational cohort study. One hundred twenty-nine hospitals in the nationwide Veterans Affairs (VA) Healthcare System between 2017 and 2021. Veterans admitted through emergency departments with community-acquired sepsis. Individual- and community-level SDOH (race, housing instability, marital status, Area Deprivation Index [ADI], and rural residence) and two health scales (the Care Assessment Need [CAN] score and Claims-Based Frailty Index [CFI]). The primary outcome was 90-day mortality from emergency department arrival; secondary outcomes included 30-day mortality and in-hospital mortality. Among 144,889 patients admitted to the hospital with community-acquired sepsis, 139,080 were men (96.0%), median (IQR) age was 71 (64-77) years, and median (IQR) ADI was 60 (38-81). Multivariable regression models had good calibration and discrimination across models that adjusted for different sets of variables (e.g., AUROC, 0.782; Brier score, 1.33; and standardized mortality rate, 1.00). Risk-adjusted hospital performance was similar across all models. Among 129 VA hospitals, three hospitals shifted from the lowest or highest quintile of performance when comparing models that excluded SDOH to models that adjusted for all variables. Models that adjusted for ADI reported odds ratios (CI) of 1.00 (1.00-1.00), indicating that ADI does not significantly predict sepsis mortality in this cohort of patients. In patients with community-acquired sepsis, adjusting for community SDOH variables such as ADI did not improve 90-day sepsis mortality predictions in mortality models and did not substantively alter hospital performance within the VA Healthcare System. Understanding the role of SDOH in risk prediction and risk adjustment models is vital because it could prevent hospitals from being negatively evaluated for treating less advantaged patients. However, we found that in VA hospitals, the potential impact of SDOH on 90-day sepsis mortality was minimal.

Machine learning in the prevention of heart failure.

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions.

Risk prediction model for linezolid-induced anaemia in older adults

Risk prediction model for linezolid-induced anaemia in older adults

Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework

Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (NP1 = 10, NP2 = 17, NP3 = 23). Usability metrics consistently improved, indicating SERDIF’s effectiveness in linking complex environmental and health datasets. Furthermore, SERDIF-enabled epidemiologists to study environmental factors in a pregnancy cohort in Lombardy, showcasing its versatility beyond rare diseases. This framework offers for the first time a user-friendly, FAIR-compliant design for environment-health data linkage with export capabilities enabling data analysis to mitigate health risks posed by climate change.

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.

Construction of a Risk Prediction Model for Ureteral Stricture after Ureteroscopic Holmium Laser Lithotripsy.

Introduction: To analyze the influencing factors of ureteral stenosis after ureteroscopic holmium laser lithotripsy. Methods: The clinical data of 427 patients treated with ureteroscopic holmium laser lithotripsy were selected, and the patients were divided into two groups based on the presence or absence of ureteral stenosis after the operation. Univariate and multivariate logistic regression were used to analyze the independent risk factors for postoperative ureteral stenosis, and R software and regression coefficients were used to construct a predictive model. Results: After a 1-year follow-up of 427 patients, 28 patients (6.56%) developed ureteral stenosis; univariate analysis showed that the occurrence of ureteral stenosis after subureteral holmium laser lithotripsy was related to stone diameter, stone incarceration, degree of hydronephrosis, holmium laser injury of mucosa, and operation time (p < 0.05); further logistic regression analysis showed that a large stone diameter, stone incarceration, and moderate to severe hydronephrosis were independent risk factors for ureteral stenosis after ureteroscopic holmium laser lithotripsy (p < 0.05); According to H-L deviation degree and area under receiver operating characteristic curve test, the results show that the model has high accuracy (χ2 = 2.475, p = 0.613) and differentiation (0.875 [95% confidence interval or CI: 0.817-0.919]), and the external verification of the nomogram prediction model was carried out by the verification group. The results showed that the prediction probability of the calibration curve was close to the actual probability and had a good consistency (area under the curve: 0.873 [95 CI: 0.822-0.914]). Conclusion: The established nomogram model exhibits high accuracy and discriminative ability. It can effectively identify high-risk groups, enabling timely prevention of ureteral stenosis and minimizing the risk of postoperative ureteral stenosis.

Computed tomography and coronary artery calcium score for screening of coronary artery disease and cardiovascular risk management in asymptomatic individuals.

Several risk prediction models exist to predict atherosclerotic cardiovascular disease in asymptomatic individuals, but systematic reviews have generally found these models to be of limited utility. The coronary artery calcium score (CACS) offers an improvement in risk prediction, yet its role remains contentious. Notably, its negative predictive value has ahigh ability to rule out clinically relevant atherosclerotic cardiovascular disease. Nonetheless, CACS 0does not permanently reclassify to alower cardiovascular risk and periodic reassessment every 5to 10years remains necessary. Conversely, elevated CACS (> 100 or > 75th percentile adjusted for age, sex and ethnicity) can reclassify intermediate-risk individuals to ahigh risk, benefiting from preventive medication. The forthcoming update to the Dutch cardiovascular risk management guideline intends to re-position CACS for cardiovascular risk assessment as such in asymptomatic individuals. Beyond CACS as asingle number, several guidelines recommend coronary CT angiography (CCTA), which provides additional information about luminal stenosis and (high-risk) plaque composition, as the first choice of test in symptomatic patients and high-risk patients. Ongoing randomised studies will have to determine the value of atherosclerosis evaluation with CCTA for primary prevention in asymptomatic individuals.

Semi-Supervised Triply Robust Inductive Transfer Learning

In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce target population and uses a large amount of unlabeled data simultaneously to improve the learning accuracy in the target population. Specifically, we consider a high dimensional covariate shift setting and employ two nuisance models, a density ratio model and an imputation model, to combine transfer learning and surrogate-assisted semi-supervised learning strategies effectively and achieve triple robustness. While the STRIFLE approach assumes the target and source populations to share the same conditional distribution of outcome Y given both the surrogate features S and predictors X , it allows the true underlying model of Y⏧ X to differ between the two populations due to the potential covariate shift in S and X . Different from double robustness, even if both nuisance models are misspecified or the distribution of Y⏧ S , X is not the same between the two populations when the shifted source population and the target population share enough similarities, the triply robust STRIFLE estimator can still partially use the source population when the shifted source population and the target population share enough similarities. Moreover, it is guaranteed to be no worse than the target-only surrogate-assisted semi-supervised estimator with an additional error term from transferability detection. These desirable properties of our estimator are established theoretically and verified in finite samples via extensive simulation studies. We use the STRIFLE estimator to train a Type II diabetes polygenic risk prediction model for the African American target population by transferring knowledge from electronic health records linked genomic data observed in a larger European source population. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Are women's breast cancer risk appraisals in line with updated clinical risk estimates communicated? Results from a UK Family History Risk and Prevention Clinic.

The incorporation of breast density and a polygenic risk score (PRS) into breast cancer risk prediction models can alter previously communicated risk estimates. Previous research finds that risk communication does not usually change personal risk appraisals. This study aimed to examine how women from the Family History Risk (FH-Risk) study appraise their breast cancer risk following communication of an updated risk estimate. In the FH-Risk study 323 women attended a consultation to receive an updated breast cancer risk estimate. A subset (n=190) completed a questionnaire, assessing their subjective breast cancer risk appraisals, satisfaction with the information provided and cancer related worry. One hundred and three were notified of a decrease risk, 34 an increase and 53 an unchanged risk. Women's subjective risk appraisals were in line with the updated risk estimates provided, with age, a PRS and breast density explaining most of the variance in these appraisals. Those notified of an increased risk demonstrated higher subjective risk perceptions compared to those whose risk remained unchanged or decreased. Women's subjective breast cancer risk appraisals are amenable to change following updated risk feedback, with new information breast density and a PRS accepted and integrated into existing risk appraisals. Trust in the service, the analogies and visual communication strategies used may have positively influenced the integration of this new information. Further research is warranted to assess whether similar patterns emerge for other illnesses and in different clinical contexts to determine the best strategies for communicating updated risk estimates.

A-201 The Impact of Implementing the CKD-EPI 2021 Equation for Calculation of Estimated Glomerular Filtration Rate (eGFR) and the Kidney Failure Risk Equation (KFRE) on a Large Community Laboratory Population

Abstract Background Community laboratories in Ontario, Canada have reported estimated glomerular filtration rate (eGFR) using the CKD-EPI 2009 equation without race adjustment since 2020. In 2023, the Canadian Society of Nephrology recommended implementation of the CKD-EPI 2021 equation for eGFR, which does not incorporate a race-based modifier. Community laboratories will also report the 5-year Kidney Failure Risk Equation (KFRE), a risk prediction model to estimate the risk of renal failure in people with chronic kidney disease (CKD) Stages 3 to 5 (eGFR &amp;lt;60 mL/min/1.73m2), which is more accurate than judging by eGFR alone. This study assessed the impact of implementing the CKD-EPI 2021 equation for eGFR and the 5-year KFRE in a large community laboratory population. Methods Laboratory results for serum creatinine and urine albumin to creatinine ratio (ACR), patient sex and age were extracted from Dynacare’s laboratory information system for November 2023. The CKD-EPI 2009 without race adjustment and the CKD-EPI 2021 were calculated. Reclassification of CKD stage using the CKD-EPI 2021 equation was assessed. The 4-variable 5-year KFRE was calculated for patients in CKD Stage 3-5 (eGFR &amp;lt;60 mL/min/1.73m2) when all parameters including, patient age, sex, creatinine and ACR, were available. Results Data was extracted for 242,843 patients. The median age was 61 years old and 47% of the population was female. The calculated median eGFR was 83 and 87 mL/min/1.73m2 when the CKD-EPI 2009 with no race adjustment, and CKD-EPI 2021 equation, respectively, were applied. Linear regression analysis showed a correlation of 0.997 with a slope of 0.990 and y-intercept of 4.513. A total of 13.0% (n=31,679) of patients were reclassified into a less progressive CKD stage when the CKD-EPI 2021 was applied compared to the CKD-EPI 2009 without race adjustment. Notable, was the reclassification of 21.8% (n=6899) of those patients from Stage 3a to 2, i.e. patients no longer have CKD (eGFR &amp;gt;60 mL/min/1.73m2) based on the new calculation. 64.8% (n=20513) of patients were reclassified from Stage 2 to 1, 9.4% (n=2977) were reclassified from Stage 3b to 3a, 3.5% (n=1102) were classified from Stage 4 to 3b, and 0.6% (n=188) were reclassified from Stage 5 to 4. The 5-year KFRE was calculated when creatinine and ACR were included on the same order and the patient was classified as Stage 3 to 5 (n=12,737 patients with eGFR &amp;lt;60 mL/min/1.73m2). The 5-year KFRE showed increasing median risk, 1%, 4%, 27%, 95%, as CKD stage increased from 3a, 3b, 4 and 5, respectively. 1.1% (n=58) of patients in Stage 3a, 43.3% (n=1584) of patients in Stage 3b, 96.6% (n=1994) of patients in Stage 4, and 100% (n=401) of patients in Stage 5 met the KFRE threshold of ≥5% for referral to nephrology. Conclusions Implementation of the CKD-EPI 2021 resulted in reclassification to a less progressive CKD stage. Use of the KFRE as a clinical decision tool will increase patient referral to nephrology. This study provided insight into the impact of the implementation of the CKD-EPI 2021 and KFRE and highlights the underutilization of ACR in a community population.

Systematic review of clinical prediction models for psychosis in individuals meeting At Risk Mental State criteria.

This study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field. Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST). Studies in any setting were included. The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias. Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias. Two of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries. https://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.

Construction and Evaluation of a Risk Prediction Model for Septic Cardiomyopathy Based on MIMIC-Ⅳ Database

Objective To analyze the risk factors of septic cardiomyopathy (SC),and to construct and evaluate the risk prediction model of SC. Methods The clinical data of patients with sepsis were extracted from MIMIC-Ⅳ database and randomized into a training set and a validation set at a ratio of 7 to 3.According to the presence or absence of SC,the patients were assigned into SC and non-SC groups.The independent risk factors of SC were determined by univariate and multivariate Logistic regression analysis,and a risk prediction model and a nomogram were established.The area under the receiver operating characteristic curve (AUC),calibration curve,and decision curve analysis (DCA) were employed to evaluate the distinguishing degree,calibration,and clinical applicability of the model,respectively. Results A total of 2628 sepsis patients were enrolled in this study,including 1865 patients in the training set and 763 patients in the validation set.There was no significant difference in the incidence of SC between the training set and the validation set (58.98% vs. 62.25%,P=0.120).Except chronic obstructive pulmonary disease (P=0.015) and length of stay in intensive care unit (P=0.016),there was no significant difference in other clinical indicators between the two groups (all P>0.05).Logistic regression analysis showed that coronary heart disease (P=0.028),heart failure (P<0.001),increased neutrophil count (P=0.001),decreased lymphocyte count (P=0.036),increased creatine kinase isoenzyme (P<0.001),and increased blood urea nitrogen (P=0.042) were independent risk factors for SC.The AUC of the nomogram prediction model in the training set and validation set was 0.759 (95% CI=0.732-0.785) and 0.765 (95% CI=0.723-0.807),respectively.The established model showcased good fitting degrees in both data sets (training set:P=0.075;validation set:P=0.067).The DCA results showed that the nomogram prediction model had good clinical applicability. Conclusion The nomogram prediction model based on basic diseases and clinical biochemical indicators can effectively predict the risk of SC occurrence.

Hepatocellular Carcinoma risk scores for non-viral liver disease: a systematic review and meta-analysis

BackgroundHepatocellular carcinoma (HCC) risk prediction models may provide a more personalised approach to surveillance for HCC among patients with cirrhosis. This systematic review aims to summarise the performance of HCC prediction models in patients with non-viral chronic liver disease. MethodThe study was prospectively registered with PROSPERO (ID: CRD42022370078) and reported in accordance with PRISMA guidelines. MEDLINE and EMBASE databases were searched using a validated search filter for prediction model studies. Two reviewers independently assessed studies for inclusion and risk of bias. Model performance (discrimination and calibration) were identified to assess the risk of HCC at specified time points. A random effects meta-analysis was done on a subset of studies that reported performance of the same model. Results7,854 studies were identified. After review, 14 studies with a total of 94,014 participants were included. 45% of patients had viral hepatitis, 27% alcohol related liver disease (ArLD) and 19% metabolic associated steatotic liver disease (MASLD). Follow-up ranged from 15.1 - 138 months. Only one model was developed using a competing risk approach. Age (7 models) and sex (6 models) were the most frequently included predictors. Model discrimination (AUROC or c-statistic) ranged from 0.61 - 0.947. Only the ‘aMAP’ score (age, male sex, albumin,bilirubin, and platelets) had sufficient external validation for quantitative analysis, with a pooled c-statistic of 0.81 (95% CI 0.80-0.83). Calibration was reported in only 9 of 14 studies. All studies were rated high risk of bias. ConclusionStudies describing risk prediction of HCC in non-viral chronic liver disease are poorly reported, have a high risk of bias and do not account for competing risk events. Patients with ArLD and MASLD are underrepresented in development and validation cohorts. These factors remain barriers to the clinical utility and uptake of HCC risk models in persons with the commonest liver diseases. Impact and implicationsThe recent EASL policy statement emphasises the potential of risk-based surveillance to reduce both HCC-related deaths and surveillance costs. This study addresses the gap in understanding the performance of current HCC risk models in patients with non-viral liver diseases, reflecting the epidemiological landscape of liver disease in Western countries.In our review of these models, we identified several key concerns regarding reporting standards and risk of bias and confirmed that patients with alcohol-related liver disease (ArLD) and metabolic-associated steatotic liver disease (MASLD) are underrepresented in model development and validation cohorts. Additionally, most models fail to account for the significant risk of competing events, leading to potential overestimation of true HCC risk.This study highlights these critical issues that may hinder the implementation of risk models in clinical practice and offers key recommendations for future model development studies.

Predictors of left atrial appendage emptying velocity: Derivation and validation of CHIRP3(M-1) score

Atrial fibrillation (AF) leads to impaired left atrial appendage contractility, increasing the risk of thromboembolic stroke. The left atrial appendage emptying velocity (LAAev) measured on transesophageal echocardiogram (TEE) is a marker of increased thromboembolic risk. To evaluate predictors of reduced LAAev for identifying individuals at increased risk of cardiogenic stroke. This was a single-center retrospective review of TEEs and clinical charts. Predictors of LAAev <30 cm/s were identified using logistic regression. A risk prediction model was created using stepwise selection in a derivation set (n=695) and separately tested in a validated set (n=300). We included TEEs on 995 patients (age 71.3±12.7 years, female 38.1%, history of AF 82.1%, in AF at evaluation 27.7%, CHA2DS2-VASc score 4.1±1.9, LAAev 41.6±21.0 cm/s). Significant multivariable predictors of LAAev <30 cm/s in derivation set were used to create the CHIRP3M-1 score containing 8 variables: Coronary artery disease (1), congestive Heart failure (1), Increased left atrial volume index ≥42 mL/m2 (1), current Rhythm AF (1), Paroxysmal AF (2), Persistent AF (3), long standing Persistent/permanent AF (4), and >moderate Mitral regurgitation (-1). In the validation set, as compared to intermediate scores (3-4), those with a low scores (≤2) and high scores (≥5) had odds ratios for LAAev <30 cm/s of 0.41 (0.21, 0.78, p=0.007) and 2.58 (95% CI 1.45, 4.61, p=0.001) respectively. We developed and validated a novel risk stratification system to predict reduced LAAev using clinical and echocardiographic variables. This may help refine the stratification of cardiogenic stroke risk.

Development of a CVD mortality risk score using nutritional predictors: A risk prediction model in the Golestan Cohort Study

Background and AimWe aimed to develop a dietary score using prediction model method for evaluating the risk of cardiovascular disease (CVD) mortality and suggesting a simple and practical scoring system within the healthcare context. Method and ResultsA total of 43878 adult participants (aged 37-80 years) from the Golestan Cohort Study (GCS) were included in analysis. A random split of the subjects into the derivation (n=28930) and the validation sets (n=14948) was done. The Cox proportional hazard model was used to develop prediction model for the 8-year risk of CVD mortality. The model’s discrimination and calibration were assessed by C-statistic and calibration plot, respectively. To enhance clinical utility, we devised a point-based scoring system derived from our model. This prediction model was developed by nine predictors including age, physical activity level (MET minutes/week), waist-to-hip ratio, tea intake (cup/day), vegetable intake (gr/1000 kcal/day), white meat intake (gr/1000 kcal/day), salt intake (gr/1000 kcal/day), dairy intake (Cup/1000 kcal/day), and percentage of protein intake. The model had an acceptable discrimination in both derivation (C-statistic: 0.76, p<0.001) and validation (C- statistic: 0.77, p<0.001) samples. Also, the calibration of model in both derivation and validation datasets was 0.81. ConclusionThis is the first attempt to develop a risk prediction model of CVD mortality and the risk scoring system by the majority of nutritional predictors in a large cohort study. This nutritional risk assessment tool is suitable for motivating at-risk individuals to make lifestyle and dietary pattern changes to reduce future risk to prevent health problems.

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

ObjectiveDespite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can...

Predictors of ischemic stroke in Chagas disease: Insights into mechanisms beyond cardiomyopathy severity

BackgroundChagas disease is a risk factor for ischemic stroke, which causes high mortality rates and significant disability. This study aims to determine the incidence and risk factors for ischemic strokes in a large cohort of Chagas cardiomyopathy patients, with a particular focus on the mechanisms involved in the pathophysiology of stroke in this condition. MethodsThe study enrolled 517 patients with Chagas cardiomyopathy who were referred to our institution from March 2000 to December 2021. All patients underwent systematic cardiological and neurological assessments. The primary outcome was the occurrence of ischemic stroke during the follow-up period, classified based on the SSS-TOAST and CCS criteria. Natural cubic splines functions were applied to examine the potential nonlinear association between continuous variables and stroke risk. ResultsThe mean age of the cohort was 52 ± 13 years, and 299 (58 %) were men. During a mean follow-up period of 4.8 years (interquartile range-IQR 1.1 to 7.1 years), a total of 72 patients (14.8 %) had an ischemic stroke, being fatal in 10. The overall incidence rate of ischemic stroke was 3.0/100 patient-years (95 % confidence interval 2.4 to 3.8). The stroke subtypes were cardioembolic (n = 41), undetermined (n = 11), and other subtypes (n = 20). The predictors of stroke were age, left atrial volume, left ventricular ejection fraction (LVEF), LV thrombus and prior stroke with thrombus. There was a nonlinear relationship between stroke risk, LVEF, and left atrial volume. A bimodal distribution of stroke occurrences was observed according to the severity of LV dysfunction, with a threshold for LVEF of 45 %. The final model for stroke risk prediction showed good discrimination, with a C statistic of 0.775. ConclusionsIn a contemporary cohort of Chagas disease patients with a broad spectrum of disease severity, stroke incidence remains high despite anticoagulation. Stroke risk shows a nonlinear association with ventricular dysfunction and left atrial size, highlighting a distinct bimodal pattern of stroke occurrence in Chagas disease.

The MOST (Mortality Score for TBI): A Novel Prediction Model Beyond CRASH-Basic and IMPACT-Core for Isolated Traumatic Brain Injury

BackgroundDue to significant injury heterogeneity, outcome prediction following traumatic brain injury (TBI) is challenging. This study aimed to develop a simple model for high-accuracy mortality risk prediction after TBI. Study DesignData from the American College of Surgeons (ACS) Trauma Quality Program (TQP) from 2019 to 2021 was used to develop a summary score based on age, the Glasgow Coma Scale (GCS) component subscores, and pupillary reactivity data. We then compared the predictive accuracy to that of the Corticosteroid Randomisation After Significant Head Injury Trial (CRASH)-Basic and International Mission for Prognosis and Analysis of Clinical Trial in TBI (IMPACT)-Core models. Two separate series of sensitivity analyses were conducted to further assess our model's generalizability. We evaluated predictive performance of the models with discrimination [the area under the receiver-operating characteristic curves (AUC), sensitivity, specificity] and calibration (Brier score). Discriminative ability was compared with DeLong tests. Results259,404 patients were included in the present study (mean age, 60 years; 93,495 (36%) female). The mortality score after TBI (MOST) model (AUC = 0.875) had better discrimination (DeLong test p values < 0.00001) than CRASH-Basic (AUC = 0.837) and IMPACT-Core (AUC = 0.821) models, and superior calibration (MOST = 0.02729, CRASH-Basic = 0.02962, IMPACT-Core = 0.02962) in predicting in-hospital mortality. The MOST model similarly outperformed in predicting 3-, 7-, 14-, and 30-day mortality. ConclusionThe MOST model can be rapidly calculated and outperforms two widely used models for predicting mortality in TBI patients. It utilizes a larger, contemporaneous dataset that reflects modern trauma care.

Predicting recurrence of instability after a primary traumatic anterior shoulder dislocation.

The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a clinical prediction model. A total of 1,293 patients with PTASD managed nonoperatively were identified from a trauma database (mean age 23.3 years (15 to 35); 14.3% female). We assessed the prevalence of RI, and used multivariate regression modelling to evaluate which demographic- and injury-related factors were independently predictive for its occurrence. The overall rate of RI at a mean follow-up of 34.4 months (SD 47.0) was 62.8% (n = 812), with 81.0% (n = 658) experiencing their first recurrence within two years of PTASD. The median time for recurrence was 9.8 months (IQR 3.9 to 19.4). Independent predictors increasing risk of RI included male sex (p < 0.001), younger age at PTASD (p < 0.001), participation in contact sport (p < 0.001), and the presence of a bony Bankart (BB) lesion (p = 0.028). Greater tuberosity fracture (GTF) was protective (p < 0.001). However, the discriminative ability of the resulting predictive model for two-year risk of RI was poor (area under the curve (AUC) 0.672). A subset analysis excluding identifiable radiological predictors of BB and GTF worsened the predictive ability (AUC 0.646). This study clarifies the prevalence and risk factors for RI following PTASD in a large, unselected patient cohort. Although these data permitted the development of a predictive tool for RI, its discriminative ability was poor. Predicting RI remains challenging, and as-yet-undetermined risk factors may be important in determining the risk.