Ena, J.(Hospital Marina Baixa); Gaviria, A.Z.(Hospital de Fuenlabrada); Romero Sánchez, M. (Hospital de Fuenlabrada); Carretero Gómez, J. (Hospital de Zafra, Badajoz); Carrasco Sánchez, F.J. (Hospital Juan Ramón Jiménez); Segura Heras, J.V. (Universidad Miguel Hernández); Porto Pérez, A.B. (Complexo Hospitalario Universitario de A Coruña); Vázquez Rodríguez, P. (Complexo Hospitalario Universitario de A Coruña); González Bezerra, C.(Hospital San Juan de Dios); Gómez Huelgas, R. (Hospital Regional Universitario).
Background An objective and simple prognostic model for hospitalized patients with hypoglycemia could be helpful in guiding initial intensity of treatment. Methods We carried out a derivation rule for hypoglycemia using data from a nationwide retrospective cohort study of patients with diabetes or hyperglycemia carried out in 2014 (n = 839 patients). The rule for hypoglycemia was validated using a second data set from a nationwide retrospective cohort study carried out in 2016 (n = 561 patients). We derived our prediction rule using logistic regression with hypoglycemia (glucose less than 70 mg/dL) as the primary outcome. Results The incidence of hypoglycemia in the derivation cohort was 10.3%. Patient’s characteristics independently associated with hypoglycemia included episodes of hypoglycemia during the previous three months (odds ratio [OR]: 6.29, 95% confidence interval [95%CI]: 3.37–11.79, p < 0.001) estimated glomerular filtration rate lower than 30 mL/min/1.73 m2 (OR: 2.32, 95%CI: 1.23–4.35, p = 0.009), daily insulin dose greater than 0.3 units per Kg (OR: 1.74, 95%CI: 1.06–2.85, p = 0.028), and days of hospitalization (OR: 1.03, 95%CI: 1.01–1.04, p = 0.001). The model showed an area under the curve (AUC): 0.72 (95%CI: 0.66–0.78, p < 0.001). The AUC in the validation cohort was: 0.71 (95%CI: 0.63–0.79, p < 0.001). Conclusions The rule showed fair accuracy to predict hypoglycemia. Implementation of the rule into computer systems could be used in guiding initial insulin therapy. © 2017 European Federation of Internal Medicine.