ArticleFrontiers in endocrinology2026
A novel nomogram based on HALP score for predicting time to glycemic stability in hospitalized type 2 diabetes patients.
Article in Frontiers in endocrinology, 2026. 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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7 authors.
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Abstract
Background: Achieving rapid glycemic stabilization is a critical goal in the inpatient management of type 2 diabetes mellitus(T2DM). This study aimed to develop and validate a nomogram incorporating the hemoglobin, albumin, lymphocyte, and platelet (HALP) score and key clinical parameters to predict the time to glycemic stability in hospitalized T2DM patients. Methods: We conducted a retrospective analysis of 356 hospitalized T2DM patients. Baseline demographic, clinical, and laboratory data, including the HALP score, were collected. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent predictors for the time to glycemic stability. The model's discriminative ability was assessed using the concordance index, and its calibration was evaluated with calibration curves. Decision curve analysis (DCA) was used to estimate clinical utility. Results: Multivariate Cox regression analysis identified older age, lower hemoglobin level, higher hemoglobin A1c (HbA1c), and a lower HALP score as independent risk factors associated with a longer time to glycemic stability. These four variables were integrated into a prognostic nomogram, which demonstrated good predictive accuracy, with a C-index of 0.81(95% CI:0.78 - 0.84) in the training cohort. The calibration curves showed satisfactory agreement between predicted and observed probabilities. Decision curve analysis (DCA) indicated favorable clinical net benefit across a reasonable range of threshold probabilities. Conclusions: We developed and validated a practical nomogram that effectively predicts the time to glycemic stability in hospitalized T2DM patients, that may assist clinicians in early identification of patients at risk for delayed stabilization, thereby facilitating personalized management strategies and optimizing inpatient diabetes care.
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