A mathematical model for predicting XXXX levels in critically-ill @entity1 : the PIGnOLI model.

@entity1 @entity712 @entity409 @entity2690 @entity177 @entity880 @entity413
Background and Objectives. Glycemic control is of paramount importance in the intensive care unit. Presently, several BG control algorithms have been developed for clinical trials, but they are mostly based on experts' opinion and consensus. There are no validated models predicting how @entity413 levels will change after initiating of @entity2690 infusion in critically ill @entity1 . The study aimed to develop an equation for initial @entity2690 dose setting. Methods. A large critical care database was employed for the study. Linear regression model fitting was employed. Retested blood @entity413 was used as the independent variable. @entity2690 rate was forced into the model. Multivariable fractional polynomials and interaction terms were used to explore the complex relationships among covariates. The overall fit of the model was examined by using residuals and adjusted R-squared values. Regression diagnostics were used to explore the influence of outliers on the model. Main Results. A total of 6,487 ICU admissions requiring @entity2690 pump therapy were identified. The dataset was randomly split into two subsets at 7 to 3 ratio. The initial model comprised fractional polynomials and interactions terms. However, this model was not stable by excluding several outliers. I fitted a simple linear model without interaction. The selected prediction model (Predicting @entity413 Levels in ICU, PIGnOLI) included variables of initial blood @entity413 , @entity2690 rate, PO volume, total parental nutrition, body mass index (BMI), @entity880 , @entity712 , @entity177 , @entity409 , time interval of BS recheck, dextrose rate. @entity2690 rate was significantly associated with blood @entity413 reduction (coefficient: -0.52, 95% CI [-1.03, -0.01]). The parsimonious model was well validated with the validation subset, with an adjusted R-squared value of 0.8259. Conclusion. The study developed the PIGnOLI model for the initial @entity2690 dose setting. Furthermore, experimental study is mandatory to examine whether adjustment of the @entity2690 infusion rate based on PIGnOLI will benefit @entity1 ' outcomes.
