Management of hyperosmolar hyperglycaemic state in adults with diabetes

Machine learning prediction models and nomogram to predict the risk of in-hospital death for severe DKA: A clinical study based on MIMIC-IV, eICU databases, and a college hospital ICU

AIM

To establish a prediction model and assess the risk factors for severe diabetic ketoacidosis (DKA) in adult patients during the ICU.

INTRODUCTION

With DKA hospitalization rates consistently increasing, in-hospital mortality has become a growing concern.

METHODS

DKA patients aged >18 years old in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-IV)) were considered. Independent risk factors for in-hospital mortality were screened using extreme gradient boosting (XGBoost) and the Bayesian information criterion (BIC) optimal subset regression. One predictive model was developed using machine learning extreme gradient boosting (XGBoost), and the other one was a nomogram based on logistic regression to estimate risks of in-hospital mortality with severe DKA. Established models were assessed by using internal validation and external validation. The MIMIC-IV was split into training and testing samples in a 7:3 ratio. The eICU Collaborative Research Database and admissions data from the department of critical care medicine of the first affiliated hospital of Harbin medical university were used for independent validation. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the C-index. Meanwhile, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) was conducted to evaluate the performance of our new build model. Decision curve analysis (DCA) was performed to assess the clinical net benefit. Net Reclassification Improvement (NRI) was used to compare the predictive power of the two models.

RESULTS

A multivariable model that included acute physiology score III (APS III), the highest levels of blood plasma osmolality (osmolarity_max), minimum osmolarity (osmolarity_min)/osmolarity _max, vasopressor, and the highest levels of blood lactate was represented as the nomogram. The C- index of the nomogram model was 0.915 (95% CI: 0.966-0.864) in the training dataset and 0.971 (95% CI: 0.992-0.950) in the internal validation. The nomogram's sensitivity was well according to all data's HL test (P > 0.05). DCA showed that our model was clinically valuable. The XGB (extreme gradient boosting) model achieved an AUC (area under the curve) of 0.950 (95% CI, 0.920-0.980); however, the nomogram model made was more effective than XGB based on NRI.

CONCLUSION

The predictive XGB and nomogram models for predicting in-hospital patient deaths with DKA were effective. The forecast models can help clinical physicians promptly identify patients at high risk of DKA, prevent in-hospital deaths, and promptly intervene.

Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs. Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed. Results: In total, 5,620 pediatric patients were included in this study. An approximately "U"-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010-1.031], while plasma osmolality <290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966-1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38-2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84-1.94). The interactions between plasma osmolality and age or complications were not significant. Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.

Diabetic ketoacidosis and hyperosmolar hyperglycemic syndrome: review of acute decompensated diabetes in adult patients

Diabetic ketoacidosis and hyperosmolar hyperglycemic syndrome (HHS) are life threatening complications that occur in patients with diabetes. In addition to timely identification of the precipitating cause, the first step in acute management of these disorders includes aggressive administration of intravenous fluids with appropriate replacement of electrolytes (primarily potassium). In patients with diabetic ketoacidosis, this is always followed by administration of insulin, usually via an intravenous insulin infusion that is continued until resolution of ketonemia, but potentially via the subcutaneous route in mild cases. Careful monitoring by experienced physicians is needed during treatment for diabetic ketoacidosis and HHS. Common pitfalls in management include premature termination of intravenous insulin therapy and insufficient timing or dosing of subcutaneous insulin before discontinuation of intravenous insulin. This review covers recommendations for acute management of diabetic ketoacidosis and HHS, the complications associated with these disorders, and methods for preventing recurrence. It also discusses why many patients who present with these disorders are at high risk for hospital readmissions, early morbidity, and mortality well beyond the acute presentation.