Evidence map›Paper›PMID 42201512›Full record

ArticleInfection2026

Risk factors and a diagnostic nomogram for early prediction of gram-positive coccal etiology in spontaneous spinal infection.

Changshun Huang, Xi Chen, Shouxiang Kuang, Rongpan Dang, Yang Li, Guodong Wang, Jianmin Sun, Fengge Zhou, Hongdong Tan, Chenggui Zhang

Abstract read
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In one paragraph

Article in Infection, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing 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.

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

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Changshun Huang *Department of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Xi Chen *Department of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Shouxiang KuangDepartment of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Rongpan DangDepartment of Spinal Infection Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, 250013, Shandong, China.
Yang LiDepartment of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Guodong WangDepartment of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Jianmin SunDepartment of Spinal Infection Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, 250013, Shandong, China.
Fengge ZhouTumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Hongdong TanDepartment of Spinal Infection Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, 250013, Shandong, China. Tanhd_1218@163.com.
Chenggui ZhangDepartment of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China. chenggui1214@pku.edu.cn.

Funding

National Natural Science Foundation of China No. 82202701Natural Science Foundation of Shandong Province ZR2022QH184
6 · The paper itself

Abstract

purposeThis study aimed to identify risk factors associated with Gram-positive (G+) cocci infection in spontaneous spinal infection (SSI) and to develop a practical diagnostic nomogram to support early pathogen-oriented risk stratification before microbiological confirmation.

methodsA retrospective cohort study was conducted on 223 patients with SSI. To ensure robust feature selection while minimizing overfitting, candidate predictors identified from preliminary screening were entered into a LASSO logistic regression model for further selection. The selected predictors were then incorporated into a multivariate logistic regression model to construct the diagnostic nomogram. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves, respectively. Internal validation was performed via bootstrap resampling (500 iterations). Clinical utility was further assessed using decision curve analysis (DCA) and the clinical impact curve (CIC).

resultsPatients were classified into G+ (n = 101) and non-G+ (n = 122) groups. Fever, history of spinal surgery, C-reactive protein (CRP), albumin (ALB), and the degree of intervertebral space height loss were identified as five independent predictors of G+ infection. The resulting nomogram demonstrated excellent discrimination and good calibration, achieving an AUC of 0.884 (95% CI: 0.841-0.926). DCA and CIC suggested potential clinical value of the model within the present cohort, showing a favorable net benefit across a range of threshold probabilities.

conclusionThe proposed nomogram provides a potentially reliable tool for predicting G+ cocci infection in patients with SSI. By integrating clinical biomarkers and radiologic features, the model enables early prediction of G+ infection and may support preliminary pathogen-oriented risk stratification before microbiological confirmation.

Indexed as

Gram-Positive Bacterial InfectionsGram-Positive CocciNomogramsAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsROC CurveDiagnostic nomogramGram-positive cocciLASSO regressionPrediction modelPyogenic spondylodiscitisSpontaneous spinal infection

Identifiers

PMID42201512

What Socratic holds

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Registered trials

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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 Socratic graph.