Evidence map›Paper›PMID 40655498›Full record

ReviewIranian journal of public health2025

Predictive Value of Infection Related Critical Illness Scores on the Risk of Death in Infected Patients: A Systematic Review and Meta-Analysis.

Laiqing Luo, Rongliu Cen

Abstract readReview
In one paragraph

Review in Iranian journal of public health, 2025. 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

2 authors.

Laiqing LuoRehabilitation Department, Sandun district of Zhejiang Hospital, Hangzhou, 310030, China.
Rongliu CenRehabilitation Department, Sandun district of Zhejiang Hospital, Hangzhou, 310030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This article aimed to compare the value of infection related critical illness scores in predicting the risk of death in infected patients, and evaluate the predictive accuracy of three scoring indicators: SOFA score, APACHE II score, and NEWS score. Methods: Through the established retrieval strategy, the relevant literature from January 2013 to December 2023 were searched on platforms such as CNKI, Wanfang, PubMed, Embase, and Cochrane Library, eight relevant literature were included for meta-analysis. Literature screening and data extraction were conducted according to predetermined standards, using a fixed effects model for data analysis. Results: Among the 8 included literature (References (5-12)), the ratio of mortality to survival and 95% confidence interval for SOFA scores were 1.33 and (0.98, 1.75), respectively; The APACHE II score is 2.24 and (1.58, 2.97); The NEWS score is 1.64 and (1.45, 1.85). All three scoring indicators had significant value in predicting the risk of death in infected patients. In addition, comparing the AUC of the three scoring indicators, the SOFA score had the highest AUC, followed by the APACHE II score, and showed significant differences compared to the NEWS score, with Conclusion: The SOFA score has higher accuracy and predictive value in predicting the condition and risk of death of infected patients. However, further attention needs to be paid to the selection of scoring methods to comprehensively consider the clinical situation and research objectives. The results of this study are helpful in guiding the evaluation and prediction of infected patients in clinical practice, and providing a basis for optimizing treatment strategies.

Indexed as

Critical illness scoreDeathInfectionMeta-analysisRisk prediction

Identifiers

PMID40655498
PMCPMC12241729

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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.