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Background <p>Multiple organ dysfunction syndrome (MODS) is a severe consequence in individuals with acute intracerebral hemorrhage (ICH), resulting in elevated morbidity and fatality rates. Precise risk forecasting is crucial for prompt interventions. This study sought to create and verify a nomogram model for predicting the likelihood of MODS in patients with acute ICH.</p> Methods <p>A retrospective cohort study was conducted at our hospital from January 2022 to December 2024. A total of 159 patients with acute ICH were included, of whom 31 developed MODS. Baseline data, including demographic, clinical, and laboratory parameters, were collected. Multivariate logistic regression was performed to identify independent risk factors for MODS. A nomogram was constructed incorporating significant predictors. The model’s performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and internal validation with bootstrap resampling (1,000 iterations).</p> Results <p>The logistic regression analysis indicated type 2 diabetes, an Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 35 or higher, endotoxemia, and an ICH volume of 30 mL or greater as independent risk factors for MODS (all <i>P</i> &lt; 0.05). The nomogram had outstanding predictive capability, exhibiting an area under the receiver operating characteristic curve (AUC) of 0.861 (95% CI: 0.786–0.928). Internal validation produced a corrected C-index of 0.811 (95% CI: 0.776–0.852), and the calibration curve demonstrated strong concordance between predicted and actual outcomes. DCA exhibited a superior net advantage of the nomogram relative to extreme treatment approaches.</p> Conclusions <p>The nomogram developed in this study effectively predicts the risk of MODS in patients with acute ICH. It offers a practical tool for early risk stratification and may guide clinical decision-making in critical care settings. Further external validation in larger cohorts is needed to confirm its generalizability.</p>

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Risk factors and predictive model for multiple organ dysfunction syndrome in acute intracerebral hemorrhage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Yang Zheng,
  • Tian-Yu Liang,
  • Qi Qiu

摘要

Background

Multiple organ dysfunction syndrome (MODS) is a severe consequence in individuals with acute intracerebral hemorrhage (ICH), resulting in elevated morbidity and fatality rates. Precise risk forecasting is crucial for prompt interventions. This study sought to create and verify a nomogram model for predicting the likelihood of MODS in patients with acute ICH.

Methods

A retrospective cohort study was conducted at our hospital from January 2022 to December 2024. A total of 159 patients with acute ICH were included, of whom 31 developed MODS. Baseline data, including demographic, clinical, and laboratory parameters, were collected. Multivariate logistic regression was performed to identify independent risk factors for MODS. A nomogram was constructed incorporating significant predictors. The model’s performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and internal validation with bootstrap resampling (1,000 iterations).

Results

The logistic regression analysis indicated type 2 diabetes, an Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 35 or higher, endotoxemia, and an ICH volume of 30 mL or greater as independent risk factors for MODS (all P < 0.05). The nomogram had outstanding predictive capability, exhibiting an area under the receiver operating characteristic curve (AUC) of 0.861 (95% CI: 0.786–0.928). Internal validation produced a corrected C-index of 0.811 (95% CI: 0.776–0.852), and the calibration curve demonstrated strong concordance between predicted and actual outcomes. DCA exhibited a superior net advantage of the nomogram relative to extreme treatment approaches.

Conclusions

The nomogram developed in this study effectively predicts the risk of MODS in patients with acute ICH. It offers a practical tool for early risk stratification and may guide clinical decision-making in critical care settings. Further external validation in larger cohorts is needed to confirm its generalizability.