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Objective <p>To develop and validate a predictive model for acute kidney injury (AKI) after aortic dissection (AD) repair by integrating the dynamic neutrophil-to-lymphocyte ratio (ΔNLR) with key clinical variables.</p> Methods <p>This retrospective cohort study included 720 patients who underwent AD surgery. Patients were randomly split into training (70%) and validation (30%) cohorts. AKI was defined per RIFLE criteria. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection from demographics, medical history, imaging, surgical data, and inflammatory ratios (including preoperative, postoperative, and Δ values). Multivariable logistic regression built the final model, evaluated by discrimination (C-statistic), calibration (plots, Hosmer-Lemeshow test), and clinical utility (decision curve analysis).</p> Results <p>The incidence of postoperative AKI was 17.2%. The final model incorporated four independent predictors: ΔNLR (Odds Ratio [OR]: 2.23), preoperative platelet-to-fibrinogen ratio (PFR) (OR: 1.95), open surgery (OR: 5.37), and drinking history (OR: 1.72). The model demonstrated good and consistent discrimination, with a C-statistic of 0.751 (95% CI: 0.708–0.794, <i>p</i> &lt; 0.001) in the training cohort and 0.732 (95% CI: 0.673–0.791, <i>p</i> &lt; 0.001) in the validation cohort. Calibration curves showed excellent agreement between predicted and observed probabilities (Hosmer-Lemeshow test <i>p</i> = 0.172). Decision curve analysis confirmed significant clinical net benefit across a clinically relevant range of risk thresholds (approximately 5% to 80%).</p> Conclusion <p>We developed a robust predictive model for AKI after AD surgery, highlighting the critical value of dynamic inflammation monitoring via ΔNLR. This practical tool facilitates early identification of high-risk patients, potentially enabling timely preventive strategies to improve postoperative outcomes. External validation is warranted to confirm generalizability.</p> Clinical trial number <p>Not applicable.</p>

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Machine learning prediction of postoperative acute kidney injury in aortic dissection patients using dynamic inflammatory markers and clinical features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Yansong Xu,
  • Chunyan Huang,
  • Yuewu Wang,
  • Chanyu Huang,
  • Yuan Xie,
  • Guanbiao Liang,
  • Caiying Li,
  • Ruiying Wei,
  • Junting Liu

摘要

Objective

To develop and validate a predictive model for acute kidney injury (AKI) after aortic dissection (AD) repair by integrating the dynamic neutrophil-to-lymphocyte ratio (ΔNLR) with key clinical variables.

Methods

This retrospective cohort study included 720 patients who underwent AD surgery. Patients were randomly split into training (70%) and validation (30%) cohorts. AKI was defined per RIFLE criteria. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection from demographics, medical history, imaging, surgical data, and inflammatory ratios (including preoperative, postoperative, and Δ values). Multivariable logistic regression built the final model, evaluated by discrimination (C-statistic), calibration (plots, Hosmer-Lemeshow test), and clinical utility (decision curve analysis).

Results

The incidence of postoperative AKI was 17.2%. The final model incorporated four independent predictors: ΔNLR (Odds Ratio [OR]: 2.23), preoperative platelet-to-fibrinogen ratio (PFR) (OR: 1.95), open surgery (OR: 5.37), and drinking history (OR: 1.72). The model demonstrated good and consistent discrimination, with a C-statistic of 0.751 (95% CI: 0.708–0.794, p < 0.001) in the training cohort and 0.732 (95% CI: 0.673–0.791, p < 0.001) in the validation cohort. Calibration curves showed excellent agreement between predicted and observed probabilities (Hosmer-Lemeshow test p = 0.172). Decision curve analysis confirmed significant clinical net benefit across a clinically relevant range of risk thresholds (approximately 5% to 80%).

Conclusion

We developed a robust predictive model for AKI after AD surgery, highlighting the critical value of dynamic inflammation monitoring via ΔNLR. This practical tool facilitates early identification of high-risk patients, potentially enabling timely preventive strategies to improve postoperative outcomes. External validation is warranted to confirm generalizability.

Clinical trial number

Not applicable.