Interpretable Machine Learning for NLR-Based Bleeding Prediction After Renal Biopsy in Young Adults

Corresponding author: Chen Linli, chenlinli2008@126.com

DOI: 10.12201/bmr.202603.00060

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  Abstract: Objective To evaluate the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR) for bleeding after ultrasound-guided percutaneous renal biopsy in young adults (18~44 years) using interpretable machine learning. Methods This retrospective study included 527 young adults who underwent ultrasound-guided renal biopsy. Five prediction models, including eXtreme Gradient Boosting (XGBoost), were constructed. The optimal model was interpreted using the Shapley Additive exPlanations (SHAP) method. The additional predictive value of NLR was assessed through incremental analysis, with sensitivity comparisons made against the Systemic Immune-inflammation Index (SII) and the Platelet-to-Lymphocyte Ratio (PLR). Results Post-biopsy bleeding occurred in 116 patients. NLR was significantly higher in the bleeding group (2.60±1.12 vs 2.33±0.73, P<0.05). The AUC of the five models ranged from 0.665 to 0.771. XGBoost was selected as the final model for its balanced performance and native SHAP compatibility (test AUC=0.737). SHAP revealed a non-linear threshold effect: NLR<2.0 was protective, while NLR>3.0 was associated with sharply increased risk. Adding NLR improved AUC from 0.696 to 0.735 (ΔAUC=0.040, P=0.441); further addition of SII and PLR provided only marginal gain. Conclusion Preoperative NLR is an important predictive feature of post-biopsy bleeding in young adults, with a threshold effect at approximately 3.0 and additive predictive value over traditional models. NLR offers a convenient, zero-cost biomarker derived from routine complete blood count for preoperative risk stratification.

  Key words: percutaneous renal biopsy; neutrophil-to-lymphocyte ratio; interpretable machine learning

  Submit time: 17 March 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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