Development and validation of a predictive nomogram for sepsis-associated severe anemia

Area of Science:

• Critical Care Medicine
• Hematology

Background:

• Sepsis-associated severe anemia (SASA) is a critical complication.
• Early identification of patients at risk for SASA is crucial for timely intervention.

Purpose of the Study:

• To develop and validate a predictive risk nomogram for sepsis-associated severe anemia.
• To identify key predictors of severe anemia in sepsis patients.

Main Methods:

• A predictive model was constructed using data from 252 sepsis patients.
• Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression were employed.
• Model performance was evaluated using C-index, calibration plots, and decision curve analysis with internal validation via bootstrapping.

Main Results:

• Key predictors identified were age, length of intensive care unit (ICU) stay, nutritional method, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score.
• The nomogram demonstrated good discrimination (C-index: 0.8848) and calibration.
• Optimal clinical utility was observed at risk thresholds between 5% and 75%.

Conclusions:

• A validated nomogram incorporating age, ICU stay, nutritional method, and APACHE II score can predict sepsis-associated severe anemia.
• This tool facilitates early, individualized patient care.
• The nomogram offers practical clinical utility for risk stratification in sepsis patients.