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Risk factors for hospitalization in youth with type 1 diabetes: Development and validation of a multivariable prediction model
Author(s) -
MejiaOtero Juan D,
Adhikari Soumya,
White Perrin C
Publication year - 2020
Publication title -
pediatric diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.678
H-Index - 75
eISSN - 1399-5448
pISSN - 1399-543X
DOI - 10.1111/pedi.13090
Subject(s) - medicine , diabetes mellitus , multivariable calculus , diabetic ketoacidosis , type 1 diabetes , receiver operating characteristic , type 2 diabetes , univariate , emergency medicine , pediatrics , multivariate statistics , statistics , control engineering , engineering , endocrinology , mathematics
Objective To develop a multivariable prediction model to identify patients with type 1 diabetes at increased risk of hospitalization for diabetic ketoacidosis or hyperglycemia with ketosis in the 12 months following assessment. Methods Retrospective review of clinical data from patients with type 1 diabetes less than 17 years old at a large academic children's hospital (5732 patient years, 652 admissions). Data from the previous 12 months were assessed on October 15, 2015, 2016, 2017, and 2018, and used to predict hospitalization in the following 12 months using generalized estimating equations. Variables that were significant predictors of hospitalization in univariate analyses were entered into a multivariable model. 2014 to 2016 data were used as a training dataset, and 2017 to 2019 data for validation. Discrimination of the model was assessed with receiver operator characteristic curves. Results Admission in the preceding year, hemoglobin (Hb)A1c, non‐commercial insurance, female sex, and non‐White race were all individual predictors of hospitalization, but age, duration of diabetes and number of office visits in the preceding year were not. In multivariable analysis with threshold P < .0033, admissions in the previous 12 months, HbA1c, and non‐commercial insurance remained as significant predictors. The model identified a subset of ~8% of the patients with a collective 42% risk of hospitalization, thus increased 5‐fold compared with the 8% risk of hospitalization in the remaining 93% of patients. Similar results were obtained with the validation dataset. Conclusion Our multivariable prediction model identified patients at increased risk of admission in the 12 months following assessment.