Evidence map›Paper›PMID 37720840›Full record

ArticleClinical interventions in aging2023

Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke.

Jingyi Li, Haowen Luo, Yongsen Chen, Bin Wu, Mengqi Han, Weijie Jia, Yifan Wu, Rui Cheng, Xiaoman Wang, Jingyao Ke and 5 more

Abstract read
In one paragraph

Article in Clinical interventions in aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 pooled it
–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

31 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

15 authors.

Jingyi LiDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Haowen LuoDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Yongsen ChenDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Bin WuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Mengqi HanDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Weijie JiaDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Yifan WuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Rui ChengDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Xiaoman WangDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Jingyao KeDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Hongfei XianDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
JianMo LiuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Pengfei YuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Jianglong TuDepartment of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.
Yingping YiDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.ORCID 0000-0002-6603-7442

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index. Patients and Methods: We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR. Results: NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, Conclusion: NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.

Indexed as

Ischemic StrokePneumoniaStrokeBiomarkersC-Reactive ProteinHumansInflammationBiomarkersC-Reactive Proteinacute ischemic strokeinflammationpredictionstroke-associated pneumonia

Identifiers

PMID37720840
PMCPMC10503514

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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.