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Clinical Care/Education/Nutrition
Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults
  1. Nestoras Nicolas Mathioudakis1,
  2. Estelle Everett1,
  3. Shuvodra Routh1,
  4. Peter J Pronovost2,
  5. Hsin-Chieh Yeh1,3,
  6. Sherita Hill Golden1,3,
  7. Suchi Saria4
  1. 1Division of Endocrinology, Diabetes and Metabolism, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
  2. 2Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
  3. 3Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
  4. 4Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
  1. Correspondence to Dr Nestoras Nicolas Mathioudakis; nmathio1{at}jhmi.edu

Abstract

Objective To develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.

Research design and methods We collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and <54 mg/dL, respectively) occurring within 24 hours of the index day. Split-sample internal validation was performed, with 70% and 30% of index days used for model development and validation, respectively.

Results Using predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CVBG), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (−LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CVBG, diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and −LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia.

Conclusions Hospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

  • hypoglycemia
  • hospital management
  • prediction
  • insulin

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1136/bmjdrc-2017-000499

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    Footnotes

    • Contributors NNM designed the study, analyzed data, wrote the manuscript, and is the guarantor of this work. EE researched data, contributed to the discussion, and reviewed/edited the manuscript. SR researched data, contributed to the discussion, and reviewed/edited the manuscript. PJP designed the study and reviewed/edited the manuscript. H-CY designed the study and reviewed/edited the manuscript. SHG designed the study and reviewed/edited the manuscript. SS contributed to data analysis, study design, and reviewed/edited the manuscript.

    • Funding This study was supported by several grants from the National Institutes of Health. NNM was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (1K23DK111986-01). EE was supported by the Clinical Research and Epidemiology in Diabetes and Endocrinology Training Grant of the NIDDK through Grant Number T32 DK062707. SR was supported by the NIDDK Summer Medical Student Research Program through the T32 Training Program in Molecular and Cellular Endocrinology (T32 DK007751). H-CY was supported by a Diabetes Research Center’s grant from the NIDDK (P30DK079637).

    • Competing interests None declared.

    • Patient consent Detail has been removed from this case description/these case descriptions to ensure anonymity. The editors and reviewers have seen the detailed information available and are satisfied that the information backs up the case the authors are making.

    • Ethics approval The study was approved by the Johns Hopkins Institutional Review Board.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement No additional data are available.