Evidence map›Paper›PMID 41619022›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Multiclass classification of infections after cervical spine surgery in the elderly: a machine learning approach based on preoperative and perioperative data.

Yuezhang Ying, Ziwei Fan, Lequn Fu, Chenyu He, Zhangheng Huang, Honglin Teng

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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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.

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2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuezhang Ying *the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Ziwei Fan *the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Lequn Fu *Huazhong University of Science and Technology, Wuhan, China.
Chenyu Hethe First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Zhangheng Huangthe First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Honglin Tengthe First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. tenghonglin@wzhospital.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn recent years, cervical spine surgeries performed in the elderly population have led to some postoperative complications after cervical spine procedures, particularly infections. While current research focuses predominantly on surgical site infections (SSIs), other types, including respiratory tract infections (RTIs) and urinary tract infections (UTIs), remain understudied. Additionally, comprehensive multiclass machine learning models for predicting diverse postoperative infections are lacking. This study aims to develop an optimal ML-based predictive model for identifying multiple infection risks in elderly patients undergoing cervical spine procedures.

methodsThis retrospective study included patients aged 60 years and older who underwent open cervical spine surgery at our institution between March 2011 and January 2024. We developed six ML algorithms [logistic regression, Gaussian naive Bayes, decision tree, random forest, K-nearest neighbors, and Light Gradient Boosting Machine (LightGBM)]. These algorithms were comprehensively evaluated in terms of accuracy, precision, recall, F1-score, and AUC.

resultsThis study included a total of 1,151 patients, among whom 40 were diagnosed with postoperative SSIs, 30 with RTIs, and 35 with UTIs. In developing the ML models, we incorporated 54 variables, and among the various algorithms evaluated, LightGBM demonstrated the best performance in terms of accuracy, precision, recall, F1-score, and AUC for predicting different classes of outcomes. SHAP analysis highlighted the significant factors that were utilized to predict various types of infections, including actual days of hospitalization, absolute monocyte count, total cholesterol level, glucose level, APTT ratio, albumin concentration, AST, and so forth.

conclusionsML algorithms demonstrated exceptional performance in predicting different infections that may arise following cervical spine surgery in elderly patients. These algorithms successfully identified the key predictors of postoperative infections, enabling clinicians to effectively identify patients who are at heightened risk of developing infections after surgery in clinical practice.

Indexed as

Cervical VertebraeMachine LearningPostoperative ComplicationsRespiratory Tract InfectionsSurgical Wound InfectionUrinary Tract InfectionsAgedAged, 80 and overBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsArtificial intelligenceCervical spineElderlyMachine learningPostoperative infectionSpine surgery

Identifiers

PMID41619022

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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.