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

Objective This study aimed to develop and validate a predictive risk nomogram for sepsis-associated severe anemia. Methods A prediction model was built using data from 252 sepsis patients in a single institution (January 2022 to December 2023). Severe anemia was defined as a hemoglobin level <60 g/L. Least absolute shrinkage and selection operator regression was used to identify key predictors, and multivariable logistic regression was used to construct the nomogram. Model performance was assessed via the receiver operating characteristic curve (C-index), calibration plots, and decision curve analysis. Internal validation was performed using bootstrapping. Results Predictors included age, length of intensive care unit stay, nutritional method, and Acute Physiology and Chronic Health Evaluation II score. The model demonstrated good discrimination (C-index: 0.8848) and calibration, with high internal validation performance. Decision curve analysis indicated optimal clinical utility at risk thresholds between 5% and 75%. Conclusions The constructed nomogram, incorporating age, length of intensive care unit stay, nutritional method, and Acute Physiology and Chronic Health Evaluation II score, provides a practical tool for early individualized care in sepsis patients.