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  • Inflammation Markers...

Inflammation Markers Help Predict AKI After Bladder Cancer Surgery, reports study

Written By : Dr Riya Dave |Medically Reviewed By : Dr. Kamal Kant Kohli Published On 2025-10-05T20:30:41+05:30  |  Updated On 5 Oct 2025 8:31 PM IST
bladder cancer
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Researchers have identified in a new study that perioperative dynamic alterations in systemic inflammatory markers are highly correlated with acute kidney injury (AKI) following radical cystectomy. The research also illustrated that machine learning–based prediction models, particularly those incorporating perioperative inflammatory measures, can efficiently classify patients at increased risk. The study was published in the Journal of Inflammation Research by Liu Z. and colleagues.

The research sought to establish if dynamic perioperative variations in systemic inflammatory markers were linked to AKI after radical cystectomy and if such changes would enhance prediction performance when integrated into machine learning models.

It was a multi-center retrospective cohort study between 2013 and 2022 in three university-affiliated tertiary care hospitals. A total of 727 patients undergoing radical cystectomy with urinary diversion for bladder cancer were included in the study. Inflammatory markers were based on pre- and post-operative blood counts, and trends were classified with restricted cubic splines (RCS).

A perioperative inflammation index was estimated from the number of positive trends in these markers. Multivariable logistic regression was employed to find independent risk factors for AKI. Machine learning models, such as XGBoost, were used to derive predictive models. Predictive performance was assessed with the area under the receiver operating characteristic curve (AUROC).

Results

  • Of 727 enrolled patients, 151 (20.8%) developed AKI after surgery.

  • Postoperative hemoglobin (p = 0.003; OR, 0.977; 95% CI, 0.962–0.992)

  • Albumin level (p = 0.007; OR, 0.906; 95% CI, 0.843–0.974)

  • Intraoperative fluid infusion rate (p < 0.001; OR, 0.769; 95% CI, 0.665–0.890)

  • Perioperative inflammation index (p < 0.001; OR, 1.507; 95% CI, 1.209–1.877)

  • Among all predictive models, XGBoost reported the highest accuracy with an AUROC of 0.801 (95% CI, 0.735–0.867), which reflected better performance in comparison to other algorithms.

This study shows that dynamic perioperative changes in systemic inflammation markers are strongly associated with AKI risk after radical cystectomy. Integrating perioperative inflammatory assessment into routine practice can potentially optimize care and avert AKI among patients undergoing bladder cancer surgery.

Reference:

Liu Z, Fan P, Lu Y, Cao M, Yao W, Chen D, Ji F. Integrating Pre- and Postoperative Systemic Inflammatory Markers for Acute Kidney Injury Prediction Following Radical Cystectomy: A Multi-Center Retrospective Study. J Inflamm Res. 2025;18:13335-13345

https://doi.org/10.2147/JIR.S529335



Journal of Inflammation ResearchAcute kidney injuryradical cystectomybladder cancersystemic inflammationperioperative managementhemoglobinalbuminfluid infusioninflammation indexmachine learningXGBoostAUROC
Source : Journal of Inflammation Research
Dr Riya Dave
Dr Riya Dave

    Dr Riya Dave has completed dentistry from Gujarat University in 2022. She is a dentist and accomplished medical and scientific writer known for her commitment to bridging the gap between clinical expertise and accessible healthcare information. She has been actively involved in writing blogs related to health and wellness.

    Dr. Kamal Kant Kohli
    Dr. Kamal Kant Kohli

    Dr Kamal Kant Kohli-MBBS, DTCD- a chest specialist with more than 30 years of practice and a flair for writing clinical articles, Dr Kamal Kant Kohli joined Medical Dialogues as a Chief Editor of Medical News. Besides writing articles, as an editor, he proofreads and verifies all the medical content published on Medical Dialogues including those coming from journals, studies,medical conferences,guidelines etc. Email: drkohli@medicaldialogues.in. Contact no. 011-43720751

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