Evidence map›Paper›PMID 41259453›Full record

ArticleBrazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica2025

Retrospective evaluation of the PRE-DELIRIC score in a Chinese mixed ICU: implications for nursing practice.

Tao Yuan, Yu-Xia Wang

Abstract read
In one paragraph

Article in Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Tao YuanChangzhou Cancer (Fourth People's) Hospital, Xinbei District, Changzhou, China.ORCID http://orcid.org/0009-0004-5428-8107
Yu-Xia WangChangzhou Cancer (Fourth People's) Hospital, Xinbei District, Changzhou, China.ORCID http://orcid.org/0009-0006-8567-5980

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Delirium is a common complication in intensive care units (ICU). The PRE-DELIRIC model has shown promise in early delirium prediction, but its performance in Chinese ICU settings remains unclear. The objective of this study was to validate the PRE-DELIRIC model in a Chinese mixed medical-surgical ICU and evaluate its utility in guiding nursing interventions for delirium prevention. In this single-center retrospective cohort study, adult patients admitted to the ICU between January 2023 and October 2024 were included. The PRE-DELIRIC score was calculated within 24 h of admission. Delirium was assessed using Confusion Assessment Method for the ICU (CAM-ICU) every 8 h. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC). Among 580 patients, 176 (30.4%) developed delirium. The model showed good discrimination (AUROC 0.84; 95%CI: 0.81-0.87) and calibration (Hosmer-Lemeshow χ2=8.96, P=0.34). At the optimal cut-off point of 30%, sensitivity was 81.8% and specificity 78.2%, with 90.8% negative predictive value. Performance remained consistent across surgical (AUROC 0.84), medical (AUROC 0.86), and trauma patients (AUROC 0.85). Delirious patients had longer ICU stays (median 11.2 vs 7.1 days, P<0.001) and higher mortality (15.9 vs 10.4%, P=0.028). The PRE-DELIRIC model demonstrated reliable predictive performance in Chinese ICU settings. Integration into routine nursing assessment could guide individualized preventive interventions and optimize resource utilization.

Indexed as

DeliriumIntensive Care UnitsAdultAgedChinaEast Asian PeopleFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesROC CurveSensitivity and Specificity

Identifiers

PMID41259453
PMCPMC12645441

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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 OpenQuestion graph.