Decision Curve Analysis Research Articles

Association of estimated glucose disposal rate with incident cardiovascular disease under different metabolic and circadian rhythm states: findings from a national population-based prospective cohort study

BackgroundRecent studies have shown that both metabolic syndrome and circadian rhythm syndrome are firmly associated with the occurrence of cardiovascular disease (CVD), with insulin resistance playing a significant role. The estimated glucose disposal rate (eGDR) is considered to be a reliable surrogate marker for insulin resistance. However, the relationship between eGDR and CVD under different metabolic and circadian rhythm states has not been thoroughly studied, and large-scale prospective cohort studies are needed to clarify this relationship.MethodsThis study is based on the China Health and Retirement Longitudinal Study (CHARLS), recruiting individuals aged 45 and above with complete eGDR data. The eGDR was calculated by the formula: eGDR(mg/kg/min) = 21.158 − (0.09 × WC) − (3.407 × hypertension) − (0.551 × HbA1c) [WC (cm), hypertension (yes = 1/no = 0), and HbA1c (%)] (Zabala et al. in Cardiovasc Diabetol 20(1):202; 2021).Participants were divided into four subgroups based on the quartiles (Q) of eGDR.The cumulative incidence rates and hazard ratios (HR) with 95% confidence intervals (CI) were calculated, with the lowest eGDR quartile (representing the highest degree of insulin resistance) as the reference. Participants were further divided into subgroups based on the diagnosis of Metabolic syndrome (MetS) or circadian syndrome (CircS) to explore the relationship between eGDR and CVD under different metabolic and circadian rhythm conditions. The dose–response relationship between eGDR and CVD incidence was investigated using a restricted cubic spline (RCS) based on a Cox regression model. Receiver operating characteristic (ROC) curves were generated to assess the predictive value of eGDR for CVD incidence. A clinical decision curve analysis (DCA) was also conducted to assess the clinical utility of the basic model.Results6507 participants were included, with a median age of 58 years [52 years, 64 years], and 55% were female. Over a median follow-up duration of 87 months, 679 first-episode CVD events were recorded, including heart disease and stroke. The RCS curves demonstrated a significant dose–response relationship between eGDR and the incidence of first-presentation CVD in different metabolic and circadian rhythm subgroups (all P-values < 0.001, non-linearity P > 0.05). eGDR exhibited a significant linear relationship with all outcomes (non-linearity P < 0.05). The Kaplan–Meier cumulative incidence curves showed that as eGDR levels increased, the cumulative incidence rates of first CVD, heart disease, and stroke gradually decreased from Q1 to Q4 groups. Significant differences were observed across all metabolic and circadian rhythm subgroups (log-rank test P < 0.001). Through the Cox proportional hazards model, we confirmed a significant association between baseline eGDR levels and first-onset CVD, heart disease, and stroke. Subgroup analyses indicated that the predictive ability of eGDR for CVD risk varied across different Body mass index (BMI) (P for interaction = 0.025) and age (P for interaction = 0.045) subgroups. Mediation analysis revealed that CircS partially mediated this association. Furthermore, time-dependent ROC curves demonstrated the potential of eGDR as a predictor of CVD risk, revealing possible differences in the model's application across different cardiovascular conditions.ConclusioneGDR is an independent predictor of CVD risk, with lower eGDR levels being closely associated with a higher risk of CVD (including heart disease and stroke). In populations with MetS or CircS, the association between lower eGDR levels and increased risk is more pronounced.

Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

In this study, we aimed to develop and validate a nomogram to predicting the risk of sepsis-associated acute kidney injury (SA-AKI) in patients admitted to emergency departments (EDs). We randomly divided a retrospective dataset of 391 patients with sepsis into a 294-person training cohort and a 97-person validation cohort, and developed three predictive models using multivariate logistic regression analysis and clinical insight. No difference was observed between the three models using the DeLong test and Model 3 was selected as the risk prediction model based on the principle of least inclusion indicators. The use of vasopressor drugs, patient age, platelet count, procalcitonin, and D-dimer levels were included. The training and validation cohorts had a consistency index of 0.832 and 0.866, respectively, indicating high accuracy and stability in predicting SA-AKI risk. The area under the receiver operating characteristic curve was 0.832, showing excellent discrimination. The calibration curves for the training and validation cohorts showed excellent calibration. The decision curve and clinical impact curve analyses showed that the net clinical benefit of using the nomogram was greatest over a probability threshold of 0.05–0.90. In addition, the model showed moderate validity in predicting the 30-day survival and the incidence of major adverse renal events within 30 days. The nomogram developed for SA-AKI risk assessment in patients in EDs showed good discriminability and clinical utility. It can provide a theoretical basis for emergency physicians to prevent SA-AKI.

Non-invasive assessment of programmed cell death ligand-1 expression using 18F-FDG PET-CT imaging in esophageal squamous cell carcinoma

Programmed cell death ligand-1 (PD-L1) is an ideal checkpoint for immunohistochemical detection. The method of obtaining PD-L1 expression through biopsy can impact the accurate assessment of PD-L1 expression due to the spatial and temporal heterogeneity of tumors. Because of the limited sample size, biopsies often give only a localized picture of the tumor. In this retrospective study, a total of 2,386 metabolic tumor volume (MTV) features were extracted from 18F-FDG PET-CT images. A radiomics model was developed to holistically and non-invasively assess PD-L1 expression in patients with esophageal squamous cell carcinoma by identifying seven independent factors through feature screening. The radiomics model shows effective discrimination, with an area under the receiver operating characteristic curve of 0.888 [95% confidence interval (CI): 0.831–0.945] and 0.889 (95% CI: 0.706–1.000) for the training and validation cohorts, respectively. The results of the decision curve analysis demonstrated that utilizing the radiation model to forecast PD-L1 expression levels yielded more net benefits at threshold probabilities below 0.669. The clinical impact curves demonstrate that when the threshold probability is less than 0.501, the loss-to-benefit ratio is less than one in all cases.

Identifying Prostatic Utricle Translucent Membrane in Hematospermia Patients Using a Novel Nomogram.

Aims/Background Hematospermia, characterized by blood in the ejaculate, is a common and distressing condition in urology. Identifying the underlying causes, including translucent membranes in the prostatic utricle, is crucial for effective management. Despite advancements in diagnostic techniques, reliable predictive tools are needed to enhance preoperative planning and patient outcomes. This study aimed to develop a novel nomogram to predict the presence of translucent membranes in the prostatic utricle of hematospermia patients. Methods In total, 284 patients were selected from The Second People's Hospital of Hefei database based on inclusion and exclusion criteria. The cohort was divided into a training set (198 patients) and a validation set (86 patients). To identify risk factors associated with the prostatic utricle translucent membrane, multivariable logistic regression analysis was employed. The identified risk factors were then used to construct a predictive nomogram model. The performance of the nomogram was evaluated using several statistical tools: receiver operating characteristic (ROC) curves to assess discriminative ability, calibration curves to evaluate prediction accuracy, and decision curve analysis (DCA) to determine clinical utility. Results The findings revealed that age, duration of disease, history of seminal vesiculitis, and seminal vesicle dimensions (width, length, and thickness) were independent risk factors for the presence of a prostatic utricle translucent membrane in patients with hematospermia. Using these variables, a nomogram was developed. The nomogram demonstrated strong predictive capability, as evidenced by its performance in ROC and calibration curve analyses. Furthermore, the DCA indicated that the nomogram offered significant clinical net benefits in predicting the presence of a translucent membrane. Conclusion Clinical use of the developed nomogram can assist clinicians in identifying patients with hematospermia who have translucent membrane in the prostatic utricle and in developing individualized treatment.

The association of gene polymorphisms of adenosine and dopamine receptors with the response to caffeine citrate treatment in infants with apnea of prematurity: a prospective nested case-control study

BackgroundTo investigate the potential influence of adenosine and dopamine receptor genes polymorphisms in combination with clinical factors on the response of preterm infants to caffeine citrate treatment in apnea of prematurity (AOP).MethodsA prospective nested case-control study enrolled 221 preterm infants with gestational age < 34 weeks. These infants were divided into the response (n = 160) and the non-response groups (n = 61). 22 single-nucleotide polymorphisms in adenosine and dopamine receptor genes were genotyped. The basic characteristics and clinical outcomes of the two groups were compared. Univariate logistic regression analysis was performed to evaluate the differences in genotype distribution between the groups. Multivariable logistic regression analysis was performed to identify independent risk and protective factors and develop a nomogram to predict caffeine citrate response in preterm infants.ResultsPreterm infants in the non-response group had lower gestational age, lower birth weight, longer periods of oxygen supplementation and caffeine citrate use, and higher incidence of patent ductus arteriosus (PDA), bronchopulmonary dysplasia (BPD), neonatal respiratory distress syndrome (NRDS), retinopathy of prematurity (ROP), and brain injury (P < 0.05 for all). The ADORA1 rs10920573, ADORA2B rs2015353, ADORA3 rs10776728, DRD3 rs7625282, and DRD3 rs6280 gene polymorphisms were associated with caffeine citrate response in preterm infants (PFDR < 0.05 for all). The ADORA1 rs10920573 CC (aOR, 3.51; 95% CI, 1.34–9.25) and DRD3 rs6280 CT genotypes (aOR, 3.19; 95% CI, 1.53–6.65) were independent risk factors for non-response, whereas greater gestational age (aOR, 0.631; 95% CI, 0.53–0.75) was an independent protective factor for response. The concordance index of the nomogram was 0.764 (95% CI, 0.687–0.842), and the calibration and decision curve analysis indicated the nomogram had excellent predict performance.ConclusionsAdenosine receptor gene and dopamine receptor gene polymorphisms influence caffeine citrate treatment response in AOP. By combining genetic and clinical variables, it is possible to predict the response to caffeine citrate treatment in preterm infants.

Survival risk stratification based on prognosis nomogram to identify patients with esophageal squamous cell carcinoma who may benefit from postoperative adjuvant therapy

ObjectiveThe purpose of the study is to develop a prognosis nomogram for esophageal squamous cell carcinoma (ESCC) patients with radical resection and to identify patients who may benefit from postoperative adjuvant radiotherapy/chemoradiotherapy through survival risk stratification.MethodsWe retrospectively enrolled patients who underwent esophagectomy in the First Affiliated Hospital of Nanjing Medical University from July 2015 to June 2017. Patients with stage I-III esophageal squamous cell carcinoma who received radical R0 resection with or without postoperative adjuvant radiotherapy/chemoradiotherapy were included. Further, patients were randomly allocated into two groups (training and validation cohorts) with a distribution ratio of 7:3. The prognosis nomogram was constructed based on independent factors determined by univariate and multivariate Cox analyses. The area under the receiver operating characteristic curve (AUC) and calibration curve were adopted to evaluate the discriminative ability and reliability of the nomogram. The accuracy and clinical practicability were respectively assessed by C-index values and decision curve analysis (DCA), and further contrasted the nomogram model and the eighth edition of the American Joint Committee on Cancer (AJCC) TNM staging system. In addition, survival risk stratification was further performed according to the nomogram, and the effect of postoperative adjuvant therapy on each risk group was appraised by the Kaplan-Meier survival analysis.ResultsA total of 399 patients with esophageal squamous cell carcinoma were recruited in this study, including the training cohort (n = 280) and the validation cohort (n = 119). The nomogram-related AUC values ​​for 1, 3, and 5-year OS were 0.900, 0.795, and 0.802, respectively, and 0.800, 0.865, 0.829 in the validation cohort, respectively. The slope of the calibration curve for both cohorts was close to 1, indicating good consistency. The C-index value of the nomogram was 0.769, which was higher than that of the AJCC 8th TNM staging system by 0.061 (p < 0.001). Based on the prognosis nomogram, patients were stratified into three risk groups (low, medium, and high), and there were obvious differences in prognosis among the groups (p < 0.001). Furthermore, postoperative adjuvant therapy has been shown to enhance the 5-year survival rate by over 15% among patients classified as medium- and high-risk.ConclusionThe constructed nomogram as developed resulted in accurate and effective prediction performance in survival outcomes for patients with stage I-III esophageal squamous cell carcinoma who underwent radical R0 resection, which is superior to the AJCC 8th TNM staging system. The survival risk stratification had potential clinical application to guide further personalized adjuvant therapy.

CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.

To develop and validate a CT-based radiomics model to predict pathologic complete response (pCR) after neoadjuvant immunotherapy plus chemoradiotherapy (NICRT) in locally advanced esophageal squamous cell carcinoma (ESCC). A total of 105 patients with locally advanced ESCC receiving NICRT from February 2019 to December 2023 were enrolled. Patients were randomly divided into the training cohort and the test cohort at a 3:1 ratio. Enhanced CT scans were obtained before NICRT treatment. The 2D and 3D regions of interest were segmented, and features were extracted, followed by feature selection. Six algorithms were applied to construct the radiomics and clinical models. These models were evaluated by area under curve (AUC), accuracy, sensitivity, and specificity, and their respective optimal algorithms were further compared. Forty-eight patients (45.75%) achieved pCR after NICRT. The AUC values of three algorithms in 2D radiomics models were higher than those in the 3D radiomics model and clinical model. Among these, the 2D radiomics model based on eXtreme Gradient Boosting (XGBoost) exhibited the best performance, with an AUC of 0.89 (95% CI, 0.81-0.97), accuracy of 0.85, sensitivity of 0.86, and specificity of 0.84 in the training cohort, and an AUC of 0.80 (95% CI, 0.64-0.97), accuracy of 0.77, sensitivity of 0.84, and specificity of 0.69 in the test cohort. Calibration curves also showed good agreement between predicted and actual response, and the decision curve analysis further confirmed its clinical applicability. The 2D radiomics model can effectively predict pCR to NICRT in locally advanced ESCC. Question Can CT-based radiomics predict pathologic complete response (pCR) after neoadjuvant immunotherapy plus chemoradiotherapy (NICRT) in locally advanced esophageal squamous cell carcinoma (ESCC)? Findings The model based on eXtreme Gradient Boosting (XGBoost) performed best, with an AUC of 0.89 in the training and 0.80 in the test cohort. Clinical relevance This CT-based radiomics model exhibits promising performance for predicting pCR to NICRT in locally advanced ESCC, which may be valuable in personalized treatment plan optimization.

Discrimination Model Construction for Non-Lactational Mastitis and Breast Cancer Based on Imaging Features.

Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. Methods This study is a retrospective analysis. Clinical data of 108 patients suspected of NLM based on imaging diagnosis, admitted to The First Affiliated Hospital with Nanjing Medical University between May 2018 and August 2023, were collected. Among them, 94 cases were pathologically confirmed as NLM and 14 cases as BC. Univariate and multivariate logistic regression analyses were performed on the patients' clinical data, ultrasound features, and MRI features to select the risk factors for discriminating NLM and BC, and construct a discrimination model. The discrimination performance of the model was analyzed with the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results In the NLM group, there were 24 cases of granulomatous lobular mastitis (25.53%) and 70 cases of plasma cell mastitis (74.47%). In the BC group, there were 2 cases of infiltrating ductal carcinoma, 2 cases of atypical hyperplasia, 3 cases of papillary carcinoma, and 7 cases of ductal carcinoma in situ. Age, internal blood flow, calcification, edge, enhancement characteristics, apparent diffusion coefficient (ADC) values, and time-intensity curve (TIC) type were independent factors for differentiating NLM and BC (p < 0.05). The ROC analysis showed that the area under the curve of the model for discriminating NLM and BC was 0.920. The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.

Performance of an automated echocardiographic artificial intelligence model. to detect subclinical heart failure with preserved ejection fraction (HFpEF) in community-dwelling older adults

Abstract Background Heart failure (HF) with preserved Ejection Fraction (HFpEF) is common among older adults and associated with a high burden of morbidity and mortality. Early identification of subclinical HFpEF, defined by abnormalities in cardiac structure and function without symptoms of HF, can prompt early initiation of evidence-based therapies to prevent HFpEF. However, scalable strategies for the identification of subclinical HFpEF are lacking. Purpose To evaluate the performance of a validated, automated echocardiographic artificial Intelligence algorithm (Ultromics, UK) to identify subclinical HFpEF among community-dwelling individuals without HF. Methods The study included participants of the Dallas Hearts and Minds Study without HF who underwent cardiovascular phenotyping with resting and exercise echocardiography and maximal cardiopulmonary exercise testing. The resting apical 4C echocardiographic images were analyzed by the AI echo algorithm to identify the HFpEF phenotype (AI-HFpEF). Subclinical HFpEF was defined by E/e’ &amp;gt;14 and Peak exercise oxygen uptake (VO2peak) &amp;lt;25th percentile for the cohort. The performance of the AI algorithm to detect subclinical HFpEF was determined using the area under the receiver operating curve and decision curve analysis and was compared with the previously validated H2FpEF score. The association between the AI-HFpEF phenotype and the presence of subclinical HFpEF, VO2peak, exercise E/e’, left ventricular strain, and left atrial strain was assessed using multivariable-adjusted logistic and linear regression models. Results The study included 511 participants (mean age: 61 y, 57% women, mean VO2peak = 16.9 ml/kg/min). Using the AI echo algorithm, subclinical HFpEF was detected in 10% of participants (n=76), and 10% were non-diagnostic. The participants with (vs. without) AI-HFpEF phenotype were older and had a higher burden of CVD risk factors, left ventricular hypertrophy, and left atrial dilation. Subclinical HFpEF, as defined by the gold standard criteria, was present in 5.3% of participants. The AUROC for diagnosis of subclinical HFpEF using the AI-HFpEF algorithm was 0.85 vs. 0.78 by the H2FpEF score (see Figure). In the adjusted analysis, the AI-HFpEF phenotype was significantly associated with greater odds of subclinical HFpEF by exercise and echocardiographic phenotyping criteria, higher E/e’ with exercise, lower VO2 peak, and worse LV and LA strain (see Table). In decision curve analysis, the AI-HFpEF algorithm identified 23 additional cases of subclinical HFpEF per 1000 screened participants compared with the H2FpEF score. Conclusion The AI-HFpEF algorithm can reliably identify community-dwelling individuals with subclinical HFpEF characterized by diastolic dysfunction at rest or exercise and impaired exercise capacity. Future studies are needed to assess the utility of the AI-HFpEF algorithm in screening for subclinical HFpEF in health systems.

Nomogram-based prediction of MACE in older adults with STEMI

Abstract Introduction Despite the significant progress made in diagnosing and treating patients with ST elevation myocardial infarction (STEMI), high-risk subgroups such as older adults (those over 65 years-old) still face considerable residual risk of major adverse cardiovascular events (MACE) following treatment for their index event. Thus, it is crucial to create accurate, validated, and actionable tools to stratify patients according to their risk of MACE after admission for STEMI. Purpose Utilizing clinical data obtained from a registry of patients diagnosed with STEMI, this study aims to develop a nomogram that can reliably predict the likelihood of MACE incidence. Methods Using a large electronic single-center registry, all consecutive patients with STEMI, undergoing primary percutaneous coronary intervention (PCI), and aged 65 and above were identified. Demographic, laboratory, clinical and intra-procedural variables were examined. Univariate and multivariate logistic regression analyses were utilized to identify those factors which were independently associated with MACE incidence in the post PCI follow-up period. A nomogram was developed in a training set, and performance metrics such as receiver operating curves (ROC), calibration plots, and decision curve analysis were used for validation in a testing set. Analyses were performed using tidyverse and rms packages in R programming language. Results 1946 patients were included in the study and split into 70% training and 30% testing sets. Both groups were similar in terms of baseline demographic and clinical characteristics. Mean follow-up period was 17 months. Total MACE incidence in the post PCI follow-up period was 18%. Thirty-eight factors with limited missing information (&amp;lt;5%) were examined for their relationship with MACE incidence and after performing univariate and multivariate analysis 8 were selected for construction of the nomogram: left-ventricular ejection fraction (LVEF), serum creatinine, hemoglobin and fasting blood glucose levels, presence of valvular heart disease, post PCI TIMI flow grade, diameter of the stent placed in the culprit lesion, and presence of shock in the post PCI setting. The area under the curve (AUC) for MACE prediction in post PCI follow-up period showed acceptable discrimination (c-statistic=73%, 95%CI 71.2% to 76.3%). Calibration plots demonstrated that the nomogram model was well-calibrated, and its predictions were closely aligned with observed outcomes. Additionally, decision curve analysis indicated that the model exhibited strong discriminatory ability in predicting MACE. Conclusions A nomogram developed using a combination of laboratory, clinical, and procedural parameters reliably predicts future risk for MACE in older adults following STEMI. Future implementation of this model may identify the highest risk individuals to target more aggressive preventive efforts in a vulnerable cohort of patients.Plot 1Plot 2

A Comprehensive Nomogram Integrating Phonocardiogram and Echocardiogram Features for the Diagnosis of Heart Failure With Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) is associated with high hospitalization and mortality rates, representing a significant healthcare burden. This study aims to utilize various information including echocardiogram and phonocardiogram to construct and validate a nomogram, assisting in clinical decision-making. This study analyzed 204 patients (68 HFpEF and 136 non-HFpEF) from the First Affiliated Hospital of Chongqing Medical University. A total of 49 features were integrated and used, including phonocardiogram, echocardiogram features, and clinical parameters. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal matching factors, and a stepwise logistic regression was employed to determine independent risk factors and develop a nomogram. Model performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). The nomogram was constructed using five significant indicators, including NT-proBNP (OR = 4.689, p = 0.015), E/e' (OR = 1.219, p = 0.032), LAVI (OR = 1.088, p < 0.01), D/S (OR = 0.014, p < 0.01), and QM1 (OR = 1.058, p < 0.01), and showed a better AUC of 0.945 (95% CI = 0.908-0.982) in the training set and 0.933 (95% CI = 0.873-0.992) in the testing set compared to conventional nomogram without phonocardiogram features. The calibration curve and Hosmer-Lemeshow test demonstrated no statistical significance in the training and testing sets (p = 0.814 and p = 0.736), indicating the nomogram was well-calibrated. The DCA and CIC results confirmed favorable clinical usefulness. The nomogram, integrating phonocardiogram and echocardiogram features, enhances HFpEF diagnostic efficiency, offering a valuable tool for clinical decision-making.

Diagnostic precision in thyroid-associated ophthalmopathy using multi-center radiomics with 99mTc-DTPA SPECT/CT

To explore the performance of 99mTc-diethylene triaminepentaacetic acid (DTPA) SPECT/CT texture analysis in evaluating the activity of thyroid-associated ophthalmopathy (TAO) . This retrospective study examined 115 TAO patients from a single institution as an internal cohort and 58 TAO patients from another institution as an external validation set. Patients in the internal cohort were randomly divided into training (n = 81) and internal validation sets (n = 34). Radiomics signatures were constructed with the minimal redundancy maximal relevance and least absolute shrinkage and selection operator algorithms in training set. Multivariate logistic regression analysis was used to develop a clinical model and a combined clinical–radiomics model. Diagnostic performance of models was evaluated using receiver operating characteristic curve analysis, calibration curves and decision curve analysis. Compared with CT and SPECT radiomics models, Rad-scoreSPECT/CT demonstrated the best performance with areas under the receiver operating characteristic curve of 0.94 and 0.91 in the training and test sets, respectively. The combined clinical-radiomics model exhibited significantly better performance in evaluating TAO activity. Our results demonstrate the validity of a multimodal radiomic model of 99mTc-DTPA-SPECT/CT to assess TAO activity. The combined clinical-radiomics model exhibited significantly better diagnostic performance than the clinical model.

ABLE-SCORE: a simplified risk score for major adverse cardiovascular outcomes in patients with left ventricular noncompaction

Abstract Background Left ventricular noncompaction (LVNC) is a heterogeneous entity with life-threatening complications and variable prognosis [1,2]. However, there are limited prediction models available to identify individuals at high risk of adverse outcomes, and the current risk score in LVNC is comparatively complex for clinical practice [3,4]. Purpose To develop and validate a simplified risk score to predict major adverse cardiovascular events (MACE) in LVNC. Methods This multicenter longitudinal cohort study consecutively enrolled morphologically diagnosed LVNC patients between January 2009 and December 2020 at a single national-level medical center (derivation cohort n=300; internal validation cohort n=129), and between January 2014 and December 2022 at two national-level medical centers (external validation cohort n=95). The derivation/internal validation cohorts and the multicenter external validation cohort were followed annually until December 2022 and December 2023, respectively. MACE was defined as a composite of all-cause mortality, heart transplantation/left ventricular assist device implantation, cardiac resynchronization therapy, malignant ventricular arrhythmia, and thromboembolism. A simplified risk score, the ABLE-SCORE, was developed based on independent risk factors for MACE in the multivariable Cox regression predictive model, and underwent both internal and external validation to confirm its discrimination (Harrell’s C-index), calibration (calibration plots), and clinical applicability (decision curve analysis). Results A total of 524 LVNC patients (43.5 ± 16.6 years, 65.8% male) were included in the study. At the end of the follow-up, 97 (32.3%), 38 (29.5%), and 20 (21.1%) individuals in the derivation, internal validation, and external validation cohort experienced at least one endpoint of MACE. The ABLE-SCORE was established using four easily accessible clinical variables: age at diagnosis, N-terminal pro-brain natriuretic peptide levels, left atrium enlargement and left ventricular ejection fraction≤40% measured by echocardiography. The risk score showed excellent performance in discrimination, with Harrell’s C-index of 0.821 (95%CI 0.772-0.869), 0.786 (95%CI 0.703-0.869), and 0.750 (95%CI 0.644-0.856) in the derivation, internal validation, and external validation cohort, respectively. Calibration plots of the three datasets suggested accurate agreement between the predicted and observed 5-year risk of MACE in LVNC. According to decision curve analysis, the ABLE-SCORE displayed greater net benefit than the currently existing risk score for MACE in LVNC [3], indicating its strength in clinical applicability. Conclusions Derived from readily available laboratory and echocardiographic variables, a simplified and efficient risk score for MACE was developed and validated using a large LVNC cohort, making it a reliable and convenient tool for the risk stratification and clinical management of patients with LVNC.Development of the ABLE-SCOREValidation of the ABLE-SCORE

Radiomics-based multimodality prediction of left atrial appendage thrombus using coronary computed tomography angiography

Abstract Background Timely detection of thrombus in the left atrial appendage (LAA) may help guide decision-making. However, existing models have challenges in extracting comprehensive image information. It has been shown that radiomics-based prediction models can be used for accurate identification. This study investigates potential radiomic markers and constructs a highly efficient multimodality prediction model for LAA thrombus in patients undergoing coronary computed tomography angiography (CCTA). Methods We retrospectively enrolled patients with nonvalvular atrial fibrillation (NVAF) who underwent CCTA and transesophageal echocardiography (TEE) from May 2015 to May 2020. These patients were classified into thrombus or non-thrombus groups based on TEE findings. The data were balanced by resampling and assigned to training or test sets. Radiomics was conducted on the extracted features, and a random forest algorithm was employed for feature selection and importance ranking. We constructed different multimodality thrombus prediction models and compared their performance using the area under the receiver operating characteristic curve (AUC) and other efficacy parameters. Results Among 670 patients (60.17±10.98 years, 70.1% male), 5.2% (n=35) had LAA thrombi. A total of 1232 radiomics features were extracted, with 25 features selected using a random forest for modeling. Two radiomics features (Ibp_3D_m1_firstorder Skewness and original_shape_Flatness) ranked the highest. The radiomics-based multimodality prediction model achieved an AUC of 0.964 (95% confidence interval [CI]: 0.947-0.982), an accuracy of 0.926 (95% CI: 0.925-0.926), and an F1 score of 0.924, outperforming other traditional prediction models. Decision curve analysis also indicated that this model provided the best net clinical benefit. Conclusions Radiomics facilitates a comprehensive exploration of predictors associated with LAA thrombi. A radiomics-based multimodality prediction model can significantly enhance the efficiency of non-invasively detecting thrombi.Receiver operating characteristic curveDecision curve analysis

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

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

A flexible, calibrated transformer-based risk model for prediction of clinical outcomes in patients with heart failure; an open cohort study using linked UK EHR

Abstract Background Numerous risk scores, both uni- and multi-variable, have been developed to conduct prognostication for a range of clinical outcomes in heart failure (HF) patients [1,2]. However, these models provide only modest discrimination and often depend on complex test data (e.g., ejection fraction) collected outside of routine clinical care [2]. Furthermore, transparency in reporting of key individual-level metrics (e.g., precision, recall) is notably lacking. Purpose We developed and validated a novel deep learning model, the second version of the Transformer-based Risk assessment survival model (TRisk2), for 36-month prediction of all-cause mortality, cardiovascular-related mortality, fatal and non-fatal cardiovascular events, and renal outcomes in patients with HF using routine, linked UK electronic health records (EHR) data. Methods An open cohort of 405 thousand patients with HF between 40 and 90 years of age was identified using linked EHR from 1,063 and 355 English general practices, which were used for TRisk2 model development and external validation, respectively. Comparison was conducted against MAGGIC [2], modified for use on routine linked EHR. Additional analyses compared discriminatory performance in other age groups, by sex, and by baseline disease. All analyses were repeated for prediction of cardiovascular-related mortality, fatal and non-fatal cardiovascular events, and renal outcomes. Results TRisk2 demonstrated superior discrimination with Concordance index (C-index) of 0.828; 95% confidence interval (CI): 0.824 to 0.832 for all-cause mortality prediction outperforming MAGGIC. The proposed model’s performance was found to be stable across stratified analyses by sex, age, and baseline disease status. Both models were overall well-calibrated, and TRisk2 demonstrated greater net benefit than MAGGIC across reasonable decision boundaries in decision curve analyses. At exemplar risk threshold of 50% for 36-month prediction, TRisk2 outperformed MAGGIC by 0.08 for both, precision and recall and identified 10% more events. At the same threshold for 12-month prediction, TRisk2 outperformed MAGGIC by 0.14 and 0.26 precision and recall respectively and captured 70% more events. The well-calibrated TRisk2 similarly outperformed MAGGIC in prediction of cardiovascular-related mortality, fatal and non-fatal cardiovascular events, and renal outcomes. Conclusion Utilising solely routine EHR from primary and secondary care, TRisk2 enabled more accurate prediction than MAGGIC with 10-20% gain in C-index across the four outcome investigations. Integration of TRisk2 into routine clinical care could simultaneously eliminate the reliance of complex tests and improve prognostication of key clinical outcomes, thereby refining management of HF.Models' discrimination on four outcomes

Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model

Abstract Background Although statistical models have been commonly used to identify patients at risk of cardiovascular disease (CVD) for preventive therapy, these models tend to over-recommend therapy [1]. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all individuals, as modelling in this context is currently inadequate. Aims To develop and validate a novel deep learning model, the Transformer-based Risk assessment survival model (TRisk) model, for prediction of 10-year risk of CVD in both the general population and individuals with diabetes. Methods An open cohort of 3 million adults aged 25 to 84 years free of CVD and statin use was identified using linked primary and secondary electronic health records from 291 and 98 general practices in England, which were used for TRisk model development and external validation, respectively. Comparison against two commonly recommended statistical models (QRISK3 [2] and SCORE2 [3]) and a deep learning derivation of QRISK3 was conducted. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status. Similar analyses were conducted in a subgroup of 59 thousand patients with diabetes. Results TRisk demonstrated superior discrimination (C index in the general population: 0.910; 95% confidence interval [CI]: 0.906 to 0.913). TRisk’s performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in all stratified analyses. All models were overall well-calibrated. In decision curve analyses, TRisk demonstrated greater net benefit than benchmark models across the range of relevant thresholds. In impact analyses, at the widely recommended 10% risk threshold and the higher 15% threshold for the general population, TRisk reduced both the total number of patients classified at high risk (by 27% and 39% respectively) and the number of false negatives as compared with currently recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24% of individuals with a small fraction of false negatives (0.2% of cohort). Conclusion TRisk enabled a more targeted selection of individuals at risk of CVD compared to benchmark statistical and deep learning models, in both the general population and patients with diabetes. Incorporation of TRisk into routine clinical care would allow a reduction in the number of treatment-eligible patients in general population and subgroup with diabetes by approximately one-third and one-quarter respectively while preventing at least as many events as with currently adopted approaches.Models' discrimination on both cohortsImpact analyses for both cohorts

Machine learning identification of risk factors for ischaemic stroke in patients with heart failure with reduced ejection fraction but without atrial fibrillation: A report from WARCEF trial

Abstract Background The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging and risk stratification based clinical factors alone has limited discrimination. Our aim was to evaluate the performance of machine learning (ML) to identify risk factors for ischaemic stroke in such HFrEF patients. Methods We performed a post-hoc analysis of the WARCEF trial, only including patients without prior history of AF, and censoring those who developed incident AF during follow-up. We evaluated the performance of 9 ML models to identify risk factors for incident stroke, using metrics including area under the curve (AUC), precision, specificity, and decision curve analysis. The importance of each feature in constructing the model was quantified using SHapley Additive exPlanations (SHAP) values. Results We included 2,213 patients with HFrEF but without AF (mean age 59±12 years; 79% male). Of these, 74 (3.3%) had an ischaemic stroke during mean follow-up of 3.5±1.8 years. Out of the 29 patient-demographics variables, 12 were selected for the ML training. All ML models demonstrated high AUC values, outperforming the CHA2DS2-VASc score (Figure 1). The logistic regression model exhibited a slight deviation from this trend. The XGBoost model achieved an AUC of 0.86 (95% confidence interval [CI]: 0.72-0.99), while the Random Forest model showed an AUC of 0.85 (95% CI: 0.74-0.96). Similarly, the Support Vector Machine and Light Gradient Boosting Machine models recorded AUCs of 0.85 (95% CI: 0.72-0.97) and 0.83 (95% CI: 0.70-0.96), respectively. Each of these models attained precision and specificity scores of 1.0, indicating high performance. These models consistently provided significant net clinical benefits. Key risk factors for stroke identified across these models were creatinine clearance and blood urea nitrogen. The use of warfarin appears to be protective. Conclusions Machine-learning models can identify risk factors for ischaemic stroke in patients with HFrEF but without AF. Worsening renal failure was predictive of an incident stroke, while warfarin use was protective against stroke (Figure 2).Figure 1Figure 2

Development and validation of a predicting model for risk of mortality in heart failure patients with mildly reduced ejection fraction post discharge

Abstract Background Prognostic prediction for heart failure with mildly reduced ejection fraction (HFmrEF) patients remains challenging. We aimed to create and validate a mortality risk prediction model for HFmrEF patients at 6-month, 1-year, and 3-year post discharge. Methods Clinical data of 1691 HFmrEF patients registered in the heart failure registry from 2015 to 2020 at the Heart Center of our hospital were analyzed. Patients were assigned into a training (1183 patients) and validation cohort (508 patients) at 7:3 ratio. Predictive variables for mortality at various period post discharge were identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The predicting model was then established based on these variables. Model performance was evaluated with ROC curve and Decision Curve Analysis (DCA). Results Eight predictors were identified including age, NT-proBNP, hemoglobin levels, use of beta-blockers, ACEI/ARB, invasive ventilation, PCI procedures, and pulmonary artery systolic pressure values. The model achieved a C-index of 0.748 (training set) and 0.755 (validation set). Area Under the Curve (AUC) for training set at 6 months, 1 year, and 3 years were 0.813, 0.784, and 0.774, and AUC for validation set were 0.775, 0.744, and 0.770, respectively. The DCA analysis confirmed the favorable net benefit of the established nomogram model. Conclusion This predicting model might be used for post-discharge risk stratification and individual decision-making of post-discharge management in HFmrEF patients.

Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation.

Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention. We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded). Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record-based predictors were based on the 1-year period before a patient's index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses. In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71-0.72) and 0.015 (95% CI 0.012-0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79-0.82) and 0.009 (95% CI 0.007-0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69-0.71) and 0.79 (95% CI 0.78-0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively. An electronic health record-based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension.