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Background <p>Plasma diquat concentration is prognostic in acute poisoning but often unavailable in resource-limited settings. We aimed to develop a bedside model using routine clinical variables for early risk stratification after hospital admission.</p> Methods <p>This retrospective cohort study included 134 patients with acute diquat poisoning (2016–2025). Predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with independent prognostic effects verified by multivariable logistic regression. A nomogram was constructed and developed using a training cohort (<i>n</i> = 81), then validated on a temporally and randomly split validation cohort (<i>n</i> = 53). Model performance was assessed via discrimination [area under the receiver operating characteristic curve (AUC-ROC)], calibration [Hosmer-Lemeshow test], and clinical utility [decision curve analysis (DCA) and clinical impact curves]. Bootstrap optimism correction was performed.</p> Results <p>The final model comprised five predictors: white blood cell count (WBC), plasma lactate (Lac), renal insufficiency, respiratory failure, and myocardial injury. It demonstrated good discrimination in the training set [AUC-ROC 0.839, 95% confidence interval (CI) 0.754–0.925] and the validation set [AUC-ROC 0.874, 95% CI 0.783–0.966], with satisfactory calibration (<i>P</i> = 0.107 and <i>P</i> = 0.824, respectively). The optimism-corrected C-index was 0.832. DCA suggested potential clinical net benefit within threshold probabilities of 5–75% (training) and 4–97% (validation) in this retrospective simulation. Clinical impact curves showed the model effectively stratified high-risk patients and accurately captured actual mortality events within these ranges.</p> Conclusions <p>This study presents a clinically accessible nomogram for mortality risk stratification in acute diquat poisoning. Its promising preliminary performance warrants prospective, multi-center validation to confirm clinical utility.</p> Trial registration <p>Chinese Clinical Trial Registry, ChiCTR2500098079 (retrospectively registered on 3 March 2025).</p>

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Development and validation of a bedside prognostic model for in-hospital mortality in acute diquat poisoning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月16日

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  • Ye Zhang,
  • Xian Chen,
  • Min Zhao,
  • Haike Du,
  • Xiaoming Jiang,
  • Xianglong Cai,
  • Guoqiang Li,
  • Yingmin Ma

摘要

Background

Plasma diquat concentration is prognostic in acute poisoning but often unavailable in resource-limited settings. We aimed to develop a bedside model using routine clinical variables for early risk stratification after hospital admission.

Methods

This retrospective cohort study included 134 patients with acute diquat poisoning (2016–2025). Predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with independent prognostic effects verified by multivariable logistic regression. A nomogram was constructed and developed using a training cohort (n = 81), then validated on a temporally and randomly split validation cohort (n = 53). Model performance was assessed via discrimination [area under the receiver operating characteristic curve (AUC-ROC)], calibration [Hosmer-Lemeshow test], and clinical utility [decision curve analysis (DCA) and clinical impact curves]. Bootstrap optimism correction was performed.

Results

The final model comprised five predictors: white blood cell count (WBC), plasma lactate (Lac), renal insufficiency, respiratory failure, and myocardial injury. It demonstrated good discrimination in the training set [AUC-ROC 0.839, 95% confidence interval (CI) 0.754–0.925] and the validation set [AUC-ROC 0.874, 95% CI 0.783–0.966], with satisfactory calibration (P = 0.107 and P = 0.824, respectively). The optimism-corrected C-index was 0.832. DCA suggested potential clinical net benefit within threshold probabilities of 5–75% (training) and 4–97% (validation) in this retrospective simulation. Clinical impact curves showed the model effectively stratified high-risk patients and accurately captured actual mortality events within these ranges.

Conclusions

This study presents a clinically accessible nomogram for mortality risk stratification in acute diquat poisoning. Its promising preliminary performance warrants prospective, multi-center validation to confirm clinical utility.

Trial registration

Chinese Clinical Trial Registry, ChiCTR2500098079 (retrospectively registered on 3 March 2025).