Evidence map›Paper›PMID 40996565›Full record

ArticleWorld journal of urology2025

Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine learning: a retrospective cohort study.

Rong Xu, Jia-Jia Wang, Wei-Hong Zhao, Jin Xiong, Zi-Wen Lu, Li-Cai Mo

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Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Rong Xu *Department of Urology, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, No.150, Ximen Street, Linhai, Taizhou, 317000, Zhejiang Province, China.
Jia-Jia Wang *Department of Traditional Chinese Medicine, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, Linhai, Taizhou, 317000, Zhejiang Province, China.
Wei-Hong ZhaoDepartment of Urology, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, No.150, Ximen Street, Linhai, Taizhou, 317000, Zhejiang Province, China.
Jin XiongDepartment of Urology, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, No.150, Ximen Street, Linhai, Taizhou, 317000, Zhejiang Province, China.
Zi-Wen LuDepartment of Urology, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, No.150, Ximen Street, Linhai, Taizhou, 317000, Zhejiang Province, China. luzw@enzemed.com.
Li-Cai MoDepartment of Urology, Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University, No.150, Ximen Street, Linhai, Taizhou, 317000, Zhejiang Province, China. molc8912@enzemed.com.ORCID http://orcid.org/0000-0002-4272-3210

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePostoperative fever is a common complication following percutaneous nephrolithotomy (PCNL) that occurs even in patients with sterile urine cultures. Traditional risk-assessment tools are insufficient in this subset of patients. This study aims to develop a risk prediction model for detecting postoperative fever in patients with sterile preoperative urine cultures by integrating the Mayo Adhesive Probability (MAP) score with machine learning (ML) techniques.

methodsThis retrospective cohort study included 730 patients with sterile urine cultures who underwent mini-percutaneous nephrolithotomy (mini-PCNL) at Taizhou Hospital from March 2022 to March 2025. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify key variables. Ten ML models were built, and Shapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability, and the optimal model was selected. An online tool was deployed for clinical use.

resultsPostoperative fever occurred in 17.4% of the patients. Logistic regression (LR) demonstrated the best predictive performance (AUC = 0.914, accuracy = 92.1%, specificity = 97.3%). Major predictive factors for fever risk included MAP score ≥ 3, diabetes mellitus, female sex, positive urine leukocytes, and low lymphocyte-monocyte ratio (LMR). SHAP analysis confirmed MAP score and urine leukocytes as the most influential variables. Limitations of the study include its single-centre design.

conclusionsThe proposed tool, combining MAP scores with ML, significantly enhances the accuracy of predicting the risk of postoperative fever following mini-PCNL in patients with sterile urine cultures. The LR model demonstrates strong utility and interpretability, offers personalised risk assessments preoperatively and constitutes a practical and accessible online tool for individualised clinical decision-making.

Indexed as

FeverMachine LearningNephrolithotomy, PercutaneousPostoperative ComplicationsAdultAgedCohort StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk AssessmentMachine learningMAP scoreNegative urine culturePercutaneous nephrolithotomyPostoperative fever

Identifiers

PMID40996565
PMCPMC12464075

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.