Evidence map›Paper›PMID 40490840›Full record

ArticleEuropean journal of medical research2025

Prediction model for unplanned extubation of thoracoabdominal drainage tube in postoperative inpatients: a retrospective study.

Yushu Sun, Xiuping Li, Jia Xu, Xiaojie Zhang, Fanglei Gu, Hongying Pan

Abstract read
In one paragraph

Article in European journal of medical research, 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

6 authors.

Yushu SunNursing Department, Sir Run Run Shaw Hospital & Shanghai Artificial Intelligence Laboratory, Zhejiang University School of Medicine, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, China.
Xiuping LiNursing Department, Sir Run Run Shaw Hospital & Shanghai Artificial Intelligence Laboratory, Zhejiang University School of Medicine, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, China.
Jia XuNursing Department, Sir Run Run Shaw Hospital & Shanghai Artificial Intelligence Laboratory, Zhejiang University School of Medicine, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, China.
Xiaojie ZhangNursing Department, Sir Run Run Shaw Hospital & Shanghai Artificial Intelligence Laboratory, Zhejiang University School of Medicine, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, China.
Fanglei GuNursing Department, Sir Run Run Shaw Hospital & Shanghai Artificial Intelligence Laboratory, Zhejiang University School of Medicine, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, China.
Hongying PanNursing Department, Sir Run Run Shaw Hospital & Shanghai Artificial Intelligence Laboratory, Zhejiang University School of Medicine, Qingchun Road, Shangcheng District, Hangzhou, Zhejiang Province, China. 3191016@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is crucial to identify the risk factors for unplanned extubation (UEX) in thoracoabdominal drainage tubes as early as possible and establish applicable risk prediction model to reduce the incidence of UEX.

methodsA retrospective survey of patients who underwent Thoracoabdominal drainage tubes placement at a tertiary hospital was conducted in Zhejiang Province, China, between January 2020 and January 2023. A training set was established to build the predictive model and conduct internal validation, which was assessed for discrimination using ROC curves and for Calibration using the Hosmer-Lemeshow test and Calibration curves. A nomogram was constructed to visually present the results of the logistic regression analysis. An external validation dataset was created for assessing the external validation of the model.

resultsa total of 2220 patients were enrolled. Multiple logistic regression analysis showed that negative pressure ball drainage, adhesive fixation method, self-care ability (self-care vs. complete dependence), self-care ability (partial dependence vs. complete dependence), and Thoracoabdominal drainage tubes were statistically significant factors associated with UEX (P < 0.05).The predictive model equation was as follows: a = 0.95-1.66 × drainage method + 2.45 × fixation method -4.17 × self-care ability (self-care vs. complete dependence) -2.79 × self- care ability (partial dependence vs. complete dependence).In the internal validation, the AUC was 0.897 (95% CI = 0.87-0.92; P < 0.001), with a sensitivity of 0.75 and specificity of 0.93, indicating a high level of discrimination for the model. The Hosmer-Lemeshow test yielded a chi-square (χ

conclusionThe predictive model for the risk of unplanned extubation of thoracoabdominal drainage tubes in postoperative patients demonstrates good discrimination and Calibration. It can provide reference for clinical nursing staff in predicting the risk and early development of personalized preventive strategies for drainage tube UEX.

Indexed as

Airway ExtubationChest TubesDrainagePostoperative ComplicationsAdultAgedChinaFemaleHumansInpatientsMaleMiddle AgedNomogramsRetrospective StudiesRisk FactorsPrediction modelRisk factorsThoracoabdominal drainage tubeUnplanned extubation

Identifiers

PMID40490840
PMCPMC12147375

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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.