Aims Dysglycemia, including the three domains hyperglycemia, hypoglycemia, and increased glycemic variability (GV), is associated with high mortality among critically ill patients. However, this association differs by diabetes status, and reports in this regard are limited. This study aimed to evaluate the associations between the three dysglycemia domains and mortality in critically ill patients by diabetes status and determined the contributing factors for dysglycemia. Methods This retrospective study included 958 critically ill patients (admitted to the ICU) with or without DM. Dysglycemia was defined as abnormality of any of the three dimensions. We evaluated the effects of the three domains of glucose control on mortality using binary logistic regression and then adjusted for confounders. The associations between dysglycemia and other variables were investigated using cumulative logistic regression analysis. Result GV independently and similarly affected mortality in both groups after adjustment for confounders (DM: odds ratio [OR], 1.05; 95% confidence interval [CI]: 1.03-1.08; p <0.001; non-DM: OR, 1.07; 95% CI, 1.03-1.11; p = 0.002). Hypoglycemia was strongly associated with ICU mortality among patients without DM (3.12; 1.76-5.53; p <0.001) and less so among those with DM (1.18; 0.49-2.83; p = 0.72). Hyperglycemia was non-significantly associated with mortality in both groups. However, the effects of dysglycemia seemed cumulative. The factors contributing to dysglycemia included disease severity, insulin treatment, glucocorticoid use, serum albumin level, total parenteral nutrition, duration of diabetes, elevated procalcitonin level, and need for mechanical ventilation and renal replacement therapy. Conclusion The association between the three dimensions of dysglycemia and mortality varied by diabetes status. Dysglycemia in critical patients is associated with excess mortality; however, glucose management in patients should be specific to the patient’s need considering the diabetes status and broader dimensions. The identified factors for dysglycemia could be used for risk assessment in glucose management requirement in critically ill patients, which may improve clinical outcomes.

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metformin and oral contraceptives (OCP) and four themes were identified using ICA: limitations of reported adherence data, adherence to lifestyle interventions, adherence to medication interventions, and outcomes assessed concomitantly with adherence. Conclusions: Diverse methods of adherence assessment are utilized. Adequate adherence data related to PCOS can be crucial for healthcare professionals as they seek to provide the highest quality of care to their patients. Pharmacists specifically can play an integral part in improving patient adherence for those with PCOS having complicated treatment plans. Future studies of PCOS treatments should effectively include and report at least one measure of adherence to every treatment evaluated. It is essential to make clinicians aware of treatment adherence as a significant factor in clinical outcomes for PCOS patients.

Authors would like to update below as article note which was missed out in original publication. The original article has been corrected.Haoming Ma and Guo Yu contributed equally to this manuscript.

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