# Nomogram for anemia risk prediction and validation in sepsis patients

## TL;DR

This study created a reliable tool to predict anemia risk in sepsis patients, improving clinical decision-making.

## Contribution

The first validated nomogram for anemia risk prediction in Chinese Han sepsis patients is developed.

## Key findings

- Age, gender, APACHE Ⅱ scores, albumin, procalcitonin, and cholinesterase are independent predictors of anemia in sepsis patients.
- The nomogram demonstrated excellent discriminative performance with an AUC of 0.911 in the training set.
- The model showed robust calibration and can enable early anemia risk stratification.

## Abstract

Anemia is a common sepsis complication with poor long-term outcomes, and accurate risk stratification tools are critical for clinical management. We conducted a multicenter retrospective observational study to develop and validate a nomogram for anemia in Chinese Han sepsis patients, enrolling 723 eligible patients from two tertiary hospitals. Lasso regression for parameter screening and binary logistic regression identified age, gender, APACHE Ⅱ scores, albumin (Alb), procalcitonin (PCT), and cholinesterase (ChE) as independent predictors. Based on multifactorial results of the binary logistic regression analysis in the training cohort, a nomogram was established to predict the anemia risk in sepsis patients. The nomogram exhibited excellent discriminative performance (AUC: 0.911 [95% CI, 0.880–0.936] in the training set, 0.896 [95% CI, 0.843–0.936] internally, 0.876 [95% CI, 0.801–0.931] externally) and good calibration. These findings provide a highly accurate nomogram for anemia risk prediction in this population, offering a reliable tool for clinical decision-making.

•First validated nomogram for anemia risk prediction in Chinese Han sepsis patients•Key independent predictors of anemia occurrence in sepsis are identified•The model shows excellent discriminative ability and robust calibration•Enables early anemia risk stratification and targeted interventions to improve outcomes

First validated nomogram for anemia risk prediction in Chinese Han sepsis patients

Key independent predictors of anemia occurrence in sepsis are identified

The model shows excellent discriminative ability and robust calibration

Enables early anemia risk stratification and targeted interventions to improve outcomes

Health sciences