Dataset Information

Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring.

ABSTRACT: OBJECTIVES:The purpose of this study was to develop and validate a simple-to-use nomogram for prediction of 5-, 10-, and 15-year survival among asymptomatic adults. BACKGROUND:Simple-to-use prognostication tools that incorporate robust methods such as coronary artery calcium scoring (CACS) for predicting near-, intermediate- and long-term mortality are warranted. METHODS:In a consecutive series of 9,715 persons (mean age: 53.4 ± 10.5 years; 59.3% male) undergoing CACS, we developed a nomogram using Cox proportional hazards regression modeling that included: age, sex, smoking, hypertension, dyslipidemia, diabetes, family history of coronary artery disease, and CACS. We developed a prognostic index (PI) summing the number of risk points corresponding to weighted covariates, which was used to configure the nomogram. Validation of the nomogram was assessed by discrimination and calibration applied to a separate cohort of 7,824 adults who also underwent CACS. RESULTS:A total of 936 and 294 deaths occurred in the derivation and validation sets at a median follow-up of 14.6 years (interquartile range: 13.7 to 15.5 years) and 9.4 years (interquartile range: 6.8 to 11.5 years), respectively. The developed model effectively predicted 5-, 10-, and 15-year probability of survival. The PI displayed high discrimination in the derivation and validation sets (C-index 0.74 and 0.76, respectively), indicating suitable external performance of our nomogram model. The predicted and actual estimates of survival in each dataset according to PI quartiles were similar (though not identical), demonstrating improved model calibration. CONCLUSIONS:A simple-to-use nomogram effectively predicts 5-, 10- and 15-year survival for asymptomatic adults undergoing screening for cardiac risk factors. This nomogram may be considered for use in clinical care.

SUBMITTER: O Hartaigh B

PROVIDER: S-EPMC5723248 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring.

Ó Hartaigh Bríain B Gransar Heidi H Callister Tracy T Shaw Leslee J LJ Schulman-Marcus Joshua J Stuijfzand Wijnand J WJ Valenti Valentina V Cho Iksung I Szymonifka Jackie J Lin Fay Y FY Berman Daniel S DS Chang Hyuk-Jae HJ Min James K JK

JACC. Cardiovascular imaging 20170614 3

<h4>Objectives</h4>The purpose of this study was to develop and validate a simple-to-use nomogram for prediction of 5-, 10-, and 15-year survival among asymptomatic adults.<h4>Background</h4>Simple-to-use prognostication tools that incorporate robust methods such as coronary artery calcium scoring (CACS) for predicting near-, intermediate- and long-term mortality are warranted.<h4>Methods</h4>In a consecutive series of 9,715 persons (mean age: 53.4 ± 10.5 years; 59.3% male) undergoing CACS, we d ...[more]

PMID: 28624402

Similar Datasets

Project description:ObjectivesThe aim of this study was to examine the long-term prognosis in asymptomatic individuals with a coronary artery calcium (CAC) score of 0 and its associated warranty period.BackgroundEmerging evidence supports a CAC score of 0 as a favorable cardiovascular short-to intermediate-term prognostic factor.MethodsA total of 9,715 individuals undergoing CAC imaging were stratified by age, Framingham risk score (FRS), and National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) categories and followed for a mean of 14.6 years (range 12.9 to 16.8 years). Cox regression, area under the receiver-operating characteristic curve, and net reclassification information were used to assess all-cause mortality, discrimination, and reclassification of a CAC score of 0 compared with the FRS and NCEP ATP III, respectively. A warranty period was pre-defined as <1% annual mortality rate. Vascular age was estimated by linear regression.ResultsIn 4,864 individuals with a baseline CAC score of 0 (mean age, 52.1 ± 10.8 years; 57.9% male), 229 deaths occurred. The warranty period of a CAC score of 0 was almost 15 years for individuals at low and intermediate risk with no significant differences regarding age and sex. A CAC score of 0 was associated with a vascular age of 1, 10, 20, and 30 years less than the chronological age of individuals between 50 and 59, 60 and 69, 70 and 79, and 80 years of age and older, respectively. The CAC score was the strongest predictor of death (hazard ratio: 2.67, 95% confidence interval: 2.29 to 3.11) that enabled discrimination and consistent reclassification beyond the FRS (area under the receiver-operating characteristic curve: 0.71 vs. 0.64, p < 0.001) and NCEP ATP III (area under the receiver-operating characteristic curve: 0.72 vs. 0.64, p < 0.001).ConclusionsA CAC score of 0 confers a 15-year warranty period against mortality in individuals at low to intermediate risk that is unaffected by age or sex. Furthermore, in individuals considered at high risk by clinical risk scores, a CAC score of 0 confers better survival than in individuals at low to intermediate risk but with any CAC score.

Project description:BACKGROUND:Several risk scores have been created to predict long term mortality after coronary artery bypass grafting (CABG). Several studies demonstrated a reduction in long-term mortality following bilateral internal thoracic arteries (BITA) compared to single internal thoracic artery. However, these prediction models usually referred to long term survival as survival of up to 5 years. Moreover, none of these models were built specifically for operation incorporating BITA grafting. METHODS:A historical cohort study of all patients who underwent isolated BITA grafting between 1996 and 2011 at Tel-Aviv Sourasky medical center, a tertiary referral university affiliated medical center with a 24-bed cardio-thoracic surgery department. Study population (N = 2,935) was randomly divided into 2 groups: learning group which was used to build the prediction model and validation group. Cox regression was used to predict death using pre-procedural risk factors (demographic data, patient comorbidities, cardiac characteristics and patient's status). The accuracy (discrimination and calibration) of the prediction model was evaluated. METHODS AND FINDINGS:The learning (1,468 patients) and validation (1,467 patients) groups had similar preoperative characteristics and similar survival. Older age, diabetes mellitus, chronic obstructive lung disease, congestive heart failure, chronic renal failure, old MI, ejection fraction ?30%, pre-operative use of intra-aortic balloon, and peripheral vascular disease, were significant predictors of mortality and were used to build the prediction model. The area under the ROC curves for 5, 10, and 15-year survival ranged between 0.742 and 0.762 for the learning group and between 0.766 and 0.770 for the validation group. The prediction model showed good calibration performance in both groups. A nomogram was built in order to introduce a simple-to-use tool for prediction of 5, 10, and 15-year survival. CONCLUSIONS:A simple-to-use validated model can be used for a prediction of 5, 10, and 15-year mortality after CABG using the BITA grafting technique.

Project description:Background: Patients with acute heart failure (AHF) who require continuous renal replacement therapy (CRRT) have a high risk of in-hospital mortality. It is clinically important to screen high-risk patients using a model or scoring system. This study aimed to develop and validate a simple-to-use nomogram consisting of independent prognostic variables for the prediction of in-hospital mortality in patients with AHF undergoing CRRT. Methods: We collected clinical data for 121 patients with a diagnosis of AHF who underwent CRRT in an AHF unit between September 2011 and August 2020 and from 105 patients in the medical information mart for intensive care III (MIMIC-III) database. The nomogram model was created using a visual processing logistic regression model and verified using the standard method. Results: Patient age, days after admission, lactic acid level, blood glucose concentration, and diastolic blood pressure were the significant prognostic factors in the logistic regression analyses and were included in our model (named D-GLAD) as predictors. The resulting model containing the above-mentioned five factors had good discrimination ability in both the training group (C-index, 0.829) and the validation group (C-index, 0.740). The calibration and clinical effectiveness showed the nomogram to be accurate for the prediction of in-hospital mortality in both the training and validation cohort when compared with other models. The in-hospital mortality rates in the low-risk, moderate-risk, and high-risk groups were 14.46, 40.74, and 71.91%, respectively. Conclusion: The nomogram allowed the optimal prediction of in-hospital mortality in adults with AHF undergoing CRRT. Using this simple-to-use model, the in-hospital mortality risk can be determined for an individual patient and could be useful for the early identification of high-risk patients. An online version of the D-GLAD model can be accessed at https://ahfcrrt-d-glad.shinyapps.io/DynNomapp/. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT0751838.

Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

Project description:Objective: This study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation. Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation group (n = 668). Univariate and multivariate hazards regression analyses were used to identify independent prognostic factors. The Akaike information criterion (AIC) was used to select variables for the nomogram. The performance of the nomogram was validated concerning its ability of discrimination and calibration and its clinical utility. Results: Age, alpha-fetoprotein (AFP), race, the degree of differentiation, and therapy method were significantly associated with the prognosis of early HCC patients. Based on the AIC results, five variables (age, race, AFP, degree of differentiation, and therapy method) were incorporated into the nomogram. The concordance indexes of the simple nomogram in the training and validation groups were 0.707 (95% CI: 0.683-0.731) and 0.733 (95% CI: 0.699-0.767), respectively. The areas under the receiver operating characteristic (ROC) curve of the nomogram in the training and validation groups were 0.744 and 0.764, respectively, for predicting 3-year survival, and 0.786 and 0.794, respectively, for predicting 5-year survival. Calibration plots showed good consistency between the predictions of the nomogram and the actual observations in both the training and validation groups. Decision curve analysis (DCA) showed that the simple nomogram was clinically useful, and the overall survival significantly differed between low- and high-risk groups divided by the median score of the nomogram in the training group (P < 0.001). Conclusion: A simple-to-use nomogram based on a large population-based study is developed and validated, which is a conventional tool for doctors to facilitate the individual consultation of preoperative patients and the postoperative personalized evaluation.

Dataset Information

Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring.

Publications

Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets