Evidence map›Paper›PMID 41896762›Full record

ArticleBMC infectious diseases2026

Construction and validation of a predictive model for mortality risk in patients with Staphylococcus aureus bloodstream infection.

Donghao Cai, Tongjie Chen, Jinhong Jiang, Xiaojing Hong, Hui Li, Junqing Tan, Song Li, Shaoqin Lai, Xiaojun Li, Aiwen Li

Abstract readValidation Study
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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.

Donghao CaiDepartment of Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Tongjie ChenThe Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Jinhong JiangDepartment of Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Xiaojing HongDepartment of Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Hui LiDepartment of Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Junqing TanDepartment of Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
Song LiThe Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Shaoqin LaiDepartment of Clinical Laboratory, Guangzhou Twelfth People's Hospital, Guangzhou, China.
Xiaojun LiAdministration Department of Nosocomial Infection, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China. 314025602@qq.com.
Aiwen LiDepartment of Clinical Laboratory, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, China. 51802953@qq.com.

Funding

Health Commission of Guangdong Province A2024094Health Commission of Guangdong Province A2025064Traditional Chinese Medicine Bureau of Guangdong Province 20232008
6 · The paper itself

Abstract

purposeTo establish and validate a predictive model for the risk of death in patients with Staphylococcus aureus (S. aureus) bloodstream infection (BSI) to support clinical decision-making and patient management.

methodsThis study included demographic and clinical data from 206 patients with S. aureus BSI in China from January 2020 to June 2025. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Independent risk factors were then identified by multivariate Cox regression analysis. A prognostic model and corresponding nomogram were constructed. The models were evaluated using bootstrap, the area under the curve (AUC) of Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and calibration curves. Finally, data from 60 patients with S. aureus BSI from other centers were used for external validation of the model.

resultsBased on the results of LASSO regression, the low red blood cell count (RBC), increased age, elevated C-reactive protein (CRP), elevated blood urea nitrogen (BUN), and low platelet counts (PLT) were used to construct the prognostic model. Among the aforementioned factors, the low RBC (hazard ratio [HR] of 0.41; 95% confidence interval [CI],0.22–0.76) and increased age (HR,1.04; 95%CI,1.00–1.07) were found to be independent risk factors for death in patients with S. aureus BSI. The results of bootstrap showed that the model’s bias and C-index were 0.003 and 0.735, respectively. The ROC curve shows that AUC values across three cohorts ranged from 0.724 to 0.831. These three calibration curves show that at 7, 14, and 28 days, the curves fluctuate around the 45°diagonal line. This indicates a good correlation between the actual risk and the predicted risk, demonstrating a high degree of calibration. The DCA curves showed that the model yielded relatively stable clinical net benefits for 28-day mortality risk prediction within the risk threshold range of 10 ~ 25%.

conclusionRBC and age are independent risk factors for 28-day mortality in patients with S. aureus BSI. When combined with CRP, BUN, and PLT, they show certain prognostic predictive value. In patients with S. aureus BSI, our model could facilitate close clinical monitoring, prompt intervention, and improvement of patient prognosis.

Indexed as

BacteremiaStaphylococcal InfectionsStaphylococcus aureusAdultAgedChinaC-Reactive ProteinFemaleHumansMaleMiddle AgedNomogramsPrognosisProportional Hazards ModelsRisk FactorsROC CurveC-Reactive ProteinBloodstream infectionNomogramPredictive modelStaphylococcus aureus

Identifiers

PMID41896762
PMCPMC13147600

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.