Statistical evaluation of adding multiple risk factors improves Framingham stroke risk score

Abstract

BackgroundFramingham Stroke Risk Score (FSRS) is the most well-regarded risk appraisal tools for evaluating an individual’s absolute risk on stroke onset. However, several widely accepted risk factors for stroke were not included in the original Framingham model. This study proposed a new model which combines an existing risk models with new risk factors using synthesis analysis, and applied it to the longitudinal Atherosclerosis Risk in Communities (ARIC) data set.MethodsRisk factors in original prediction models and new risk factors in proposed model had been discussed. Three measures, like discrimination, calibration and reclassification, were used to evaluate the performance of the original Framingham model and new risk prediction model.ResultsModified C-statistics, Hosmer-Lemeshow Test and classless NRI, class NRI were the statistical indices which, respectively, denoted the performance of discrimination, calibration and reclassification for evaluating the newly developed risk prediction model on stroke onset. It showed that the NEW-STROKE (new stroke risk score prediction model) model had higher modified C-statistics, smaller Hosmer-Lemeshow chi-square values after recalibration than original FSRS model, and the classless NRI and class NRI of the NEW-STROKE model over the original FSRS model were all significantly positive in overall group.ConclusionThe NEW-STROKE integrated with seven literature-derived risk factors outperformed the original FSRS model in predicting the risk score of stroke. It illustrated that seven literature-derived risk factors contributed significantly to stroke risk prediction.

Prediction of long-term absence due to sickness in employees: development and validation of a multifactorial risk score in two cohort studies

“…Calibration plots confirmed high correspondence between predicted and observed risk for this prediction tool. These results suggest that long-term sickness absence lasting ≥90 days can be predicted with accuracy that equals those used in primary prevention of common chronic conditions, such as cardiovascular disease (C-index 0.76 with the Framingham score) (33,34) and type 2 diabetes (C-index 0.80) (35).…”

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

“…This study describes the development of a machine learning algorithm to accurately predict the onset of ischemic stroke from the period of 1 day up to 1 year following the patient encounter using only data automatically collected from the patient EHR. Although there are existing tools for stroke risk assessment over longer windows of prediction ( 34 , 35 ), the goal of this study was to develop an MLA tool to aid in the patient selection process for clinical trials by identifying patients at a high risk for ischemic stroke within the time period of a study. The XGBoost algorithm obtained AUROC, PPV, NPV, sensitivity and specificity of 0.864, 0.188, 0.981, 0.800, and 0.749, respectively, on the external test set, indicating the tool's ability to maintain high performance in stroke predictions up to 1 year after an initial inpatient encounter.…”

“…The MLA developed and validated in this study outperformed the CHA 2 DS 2 -VASc scoring system, which has been shown to be an effective clinical tool in predicting the 1-year risk of stroke and thromboembolism (TE) in patients both with and without AF ( 34 – 36 ). While the gold standard scoring system that is in wide use for stroke risk assessment is the Framingham Stroke Risk Profile (FSRP) ( 34 , 35 ), the FSRP tool predicts stroke risk between 5 and 10 years prior to the occurrence of stroke and partially relies on subjective information received directly from patients by a technician-administered questionnaire and a self-administered questionnaire ( 37 ). The ability to predict stroke within 1 year may identify patients who have a more immediate risk than those identified in the FRPS, making them viable participants for clinical trials, which occur over limited timeframes.…”

Assessment of Cardiovascular Risk in Women: Progress so Far and Progress to Come

“…In addition, we screened reference lists of relevant articles to identify additional publications and found one further article. Among the 91 identified articles, nine [84][85][86][87][88][89][90][91][92] examined the added value of pregnancy-or reproductive-factors to already existing cardiovascular risk scores. An overview of the studies identified by our literature search is provided in Table 3.…”