ArticleBMC medical informatics and decision making2024
Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundThis study aimed to identify the risk factors of acute ischemic stroke (AIS) occurring during hospitalization in patients following off-pump coronary artery bypass grafting (OPCABG) and utilize Bayesian network (BN) methods to establish predictive models for this disease.
methodsData were collected from the electronic health records of adult patients who underwent OPCABG at Beijing Anzhen Hospital from January 2018 to December 2022. Patients were allocated to the training and test sets in an 8:2 ratio according to the principle of randomness. Subsequently, a BN model was established using the training dataset and validated against the testing dataset. The BN model was developed using a tabu search algorithm. Finally, receiver operating characteristic (ROC) and calibration curves were plotted to assess the extent of disparity in predictive performance between the BN and logistic models.
resultsA total of 10,184 patients (mean (SD) age, 62.45 (8.7) years; 2524 (24.7%) females) were enrolled, including 151 (1.5%) with AIS and 10,033 (98.5%) without AIS. Female sex, history of ischemic stroke, severe carotid artery stenosis, high glycated albumin (GA) levels, high D-dimer levels, high erythrocyte distribution width (RDW), and high blood urea nitrogen (BUN) levels were strongly associated with AIS. Type 2 diabetes mellitus (T2DM) was indirectly linked to AIS through GA and BUN. The BN models exhibited superior performance to logistic regression in both the training and testing sets, achieving accuracies of 72.64% and 71.48%, area under the curve (AUC) of 0.899 (95% confidence interval (CI), 0.876-0.921) and 0.852 (95% CI, 0.769-0.935), sensitivities of 91.87% and 89.29%, and specificities of 72.35% and 71.24% (using the optimal cut-off), respectively.
conclusionFemale gender, IS history, carotid stenosis (> 70%), RDW-CV, GA, D-dimer, BUN, and T2DM are potential predictors of IS in our Chinese cohort. The BN model demonstrated greater efficiency than the logistic regression model. Hence, employing BN models could be conducive to the early diagnosis and prevention of AIS after OPCABG.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.