Glucometer derived glycemic variability as an early spot prognosticator for insulin dependent critically ill patients
Authors: Nour "Mohammad Saeed" Mustafa Batayha  , Shatha Saleh Farhan Al Saqarat  , Lara Khaled Alazzi  , Roaa Abd Al Kareem Mreiheel Albadaineh  , Bayan Mohammad Khair Saleh Alrjoub  , Salem Rafiq Alsalman  
Published: August 2023

Background: Glycemic variability, as mathematically defined by higher fluctuation rate or higher gap between the maximum and minimum values. In critically ill patients, the blood glucose readings are highly susceptible to fluctuation owing to multi-dimensional confounders. Aim: In this study, we primarily aim to investigate the prognostic utility of the glucometer derived glycemic variability regarding the overall mortality. Also, we aim to explore the optimal operating cutoff point and the binary logistic regression correlation for the glycemic variability versus mortality. Methods: A retrospective study was conducted for tested critically ill patients who were admitted in the Intensive Care Unit at King Hussein Medical Center between 1 Jan 2018 and 31 Dec 2022. Patients who had primarily missed data>20% was excluded from our study. Firstly, a receiver operating characteristic and sensitivity analysis was conducted to explore the area under curve and sensitivity indices of the tested mortality prognosticator. A binary logistic regression was also used to explore the association of the glycemic variability with the 28-day overall ICU mortality rate. All statistical analysis will be conducted with p-value <0.05 as a level of significance via SPSS version 25. Results: Actually, 2528 and 3217 cases were processed as positive actual states (Positive OI, Non-Survivors) and as negative actual states (Negative OI, Survivors) respectively. The AUC±SEM (95 C) for the GLK_BG_Var (%) was significantly higher than that of the GLK_BG Avg 0.829±0.006 (95% CI; 0.817-0.841) versus 0.597±0.008 (95% CI; 0.582-0.612) The probabilities of our investigated admitted ICU patients’ mortality were binary logistically correlated to the GLK_BG_Var% and GLK_BG_Avg via the following constructed BLgR models [e (-5.152+10.8×GLK_BG_%Var) /1+ e (-5.152+10.8×GLK_BG_%Var) and e(-5.416+0.025×GLK_BG_Avg) /1+ e (-5.416+0.025×GLK_BG_Avg), respectively]. Also, the probabilities of the critically ill patients’ mortality at the explored 2 tested BG prognosticators; Var% and Avg, were identified at 48.1% and 43.96% at the optimal thresholds of 47% and 206.94 mg/dl, respectively. Indeed, the explained variations in the dependent variable based on the 2 aforementioned adopted independent investigated mortality’s predictors ranged significantly from 26.1%-35% and 3.4%-4.6% depending on whether you reference the Cox & Snell R2 or Nagelkerke R2 methods, respectively, and they correctly classified approximately 80.1% and 48.4% of the cases, χ2(8) = 926.992 and1232.19, respectively. Conclusion: that the first investigated glucometer-based BG mortality’s prognosticator [GLK_BG_Var%] had higher AUROC, predictive utilities than its comparative average related prognosticator. Also, we revealed a strong correlation between the poor critically ill patients’ outcomes and the higher variability in BG values, notably when the variation exceeding 47%.