Use of Peak Glucose Level and Peak Glycemic Gap in Mortality Risk Stratification in Critically Ill Patients with Sepsis and Prior Diabetes Mellitus of Different Body Mass Indexes.
Yi-Hsuan TsaiKai-Yin HungWen-Feng FangPublished in: Nutrients (2023)
Sepsis remains a critical concern in healthcare, and its management is complicated when patients have pre-existing diabetes and varying body mass indexes (BMIs). This retrospective multicenter observational study, encompassing data from 15,884 sepsis patients admitted between 2012 and 2017, investigates the relationship between peak glucose levels and peak glycemic gap in the first 3 days of ICU admission, and their impact on mortality. The study reveals that maintaining peak glucose levels between 141-220 mg/dL is associated with improved survival rates in sepsis patients with diabetes. Conversely, peak glycemic gaps exceeding 146 mg/dL are linked to poorer survival outcomes. Patients with peak glycemic gaps below -73 mg/dL also experience inferior survival rates. In terms of predicting mortality, modified Sequential Organ Failure Assessment-Peak Glycemic Gap (mSOFA-pgg) scores outperform traditional SOFA scores by 6.8% for 90-day mortality in overweight patients. Similarly, the modified SOFA-Peak Glucose (mSOFA-pg) score demonstrates a 17.2% improvement over the SOFA score for predicting 28-day mortality in underweight patients. Importantly, both mSOFA-pg and mSOFA-pgg scores exhibit superior predictive power compared to traditional SOFA scores for patients at high nutritional risk. These findings underscore the importance of glycemic control in sepsis management and highlight the potential utility of the mSOFA-pg and mSOFA-pgg scores in predicting mortality risk, especially in patients with diabetes and varying nutritional statuses.
Keyphrases
- glycemic control
- type diabetes
- end stage renal disease
- intensive care unit
- healthcare
- acute kidney injury
- ejection fraction
- cardiovascular events
- chronic kidney disease
- newly diagnosed
- septic shock
- cardiovascular disease
- prognostic factors
- emergency department
- patient reported outcomes
- clinical trial
- weight loss
- adipose tissue
- peritoneal dialysis
- cross sectional
- insulin resistance
- coronary artery disease
- deep learning
- machine learning
- risk assessment
- electronic health record
- climate change
- weight gain
- human health