ArticleJournal of diabetes investigation2025
Construction and validation of nomogram prediction model for ketoacidosis in elderly diabetic patients based on baseline data and glycolipid metabolism.
Article in Journal of diabetes investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Critical methodological concerns regarding the nomogram prediction model for DKA in elderly diabetic patients.Journal of diabetes investigation · 2025Article
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4 authors.
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Abstract
objectiveTo explore the risk factors of ketoacidosis (DKA) in elderly patients with diabetes and to construct a nomogram prediction model to guide clinical practice.
methodsBaseline, glycolipid metabolism, and related data were collected. Risk factors were screened by multifactor logistic regression analysis to construct a model. The effectiveness of the model was evaluated by Receiver Operating Characteristiv (ROC) curve, calibration curve analysis, and decision curve analysis (DCA).
resultsLogistic regression analysis showed that age, duration of diabetes, FBG, 2hPG, HbA1c, TG, TC, and C peptide level were the independent risk factors for DKA in elderly diabetic patients (P < 0.05). The nomogram prediction model constructed based on these factors showed good prediction performance in both the training set and the verification set, with the C-index indexes being 0.880 and 0.918, respectively, and the average absolute errors of coincidence between the predicted value and the true value being 0.102 and 0.075, respectively. The results of the Hosmer-Lemeshow test were χ
conclusionNomogram prediction model based on baseline data and glucose and lipid metabolism indicators showed good prediction efficiency in both the training set and the verification set. Age, diabetes duration, FBG, 2hPG, HbA1c, TG, TC, and C peptide levels were independent risk factors for DKA in elderly diabetic patients.
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