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Background <p>Acute kidney injury (AKI) following lung transplantation (LTx) is correlated with high mortality rates. We aimed to establish a risk-score model for AKI prediction in LTx.</p> Methods <p>We retrospectively reviewed data from the Institutional Lung Transplant Database from 2016 to 2022. The primary endpoint was to establish a risk-score model to predict AKI. The secondary endpoint was the impact of AKI on postoperative rehabilitation and survival incidence at the 1-year follow-up.</p> Results <p>Of 415 patients, 27% (<i>n</i> = 112) developed AKI within 48&#xa0;h after LTx. Multivariable analysis revealed that body mass index, diabetes, plasma infusion, surgical time, and postoperative extracorporeal membrane oxygenation (ECMO) assistance were risk factors for AKI. This risk score was created and calibrated based on these five factors, ranging from 0 to 16 points, with the associated prediction of postoperative AKI ranging from 3 to 99% (Hosmer–Lemeshow χ<sup>2</sup> = 7.502; <i>P</i> = 0.484). Good discrimination was verified by developing and validating the datasets [Area Under the Curve (AUC) = 0.813 and 0.782, respectively]. Based on score distribution, patients were classified into three risk levels: low risk (0–3), moderate risk (3–7), and high risk (7–16). AKI is associated with prolonged stay length of intensive care unit and postoperative hospital (<i>P</i> &lt; 0.001 and <i>P</i> = 0.003), and has an impact in the 3-to-6-month survival (<i>P</i> = 0.008 and <i>P</i> = 0.006).</p> Conclusions <p>A risk-score model based on perioperative variables effectively predicted the risk of AKI within 48&#xa0;h after LTx. This model may be useful in early decision-making regarding AKI treatment.</p>

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A risk-score prediction of postoperative acute kidney injury following lung transplantation: a retrospective cohort study

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  • 发表日期：

    2025年12月30日

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  • Furong Lin,
  • Yahong Liu,
  • Bu Long,
  • Yingfen Li,
  • Xin Xu,
  • Chao Yang,
  • Yaoliang Zhang,
  • Lan Lan

摘要

Background

Acute kidney injury (AKI) following lung transplantation (LTx) is correlated with high mortality rates. We aimed to establish a risk-score model for AKI prediction in LTx.

Methods

We retrospectively reviewed data from the Institutional Lung Transplant Database from 2016 to 2022. The primary endpoint was to establish a risk-score model to predict AKI. The secondary endpoint was the impact of AKI on postoperative rehabilitation and survival incidence at the 1-year follow-up.

Results

Of 415 patients, 27% (n = 112) developed AKI within 48 h after LTx. Multivariable analysis revealed that body mass index, diabetes, plasma infusion, surgical time, and postoperative extracorporeal membrane oxygenation (ECMO) assistance were risk factors for AKI. This risk score was created and calibrated based on these five factors, ranging from 0 to 16 points, with the associated prediction of postoperative AKI ranging from 3 to 99% (Hosmer–Lemeshow χ2 = 7.502; P = 0.484). Good discrimination was verified by developing and validating the datasets [Area Under the Curve (AUC) = 0.813 and 0.782, respectively]. Based on score distribution, patients were classified into three risk levels: low risk (0–3), moderate risk (3–7), and high risk (7–16). AKI is associated with prolonged stay length of intensive care unit and postoperative hospital (P < 0.001 and P = 0.003), and has an impact in the 3-to-6-month survival (P = 0.008 and P = 0.006).

Conclusions

A risk-score model based on perioperative variables effectively predicted the risk of AKI within 48 h after LTx. This model may be useful in early decision-making regarding AKI treatment.