Evidence map›Paper›PMID 38597440›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024

[Construction and validation of an in-hospital mortality risk prediction model for patients receiving VA-ECMO: a retrospective multi-center case-control study].

Y Ge, J Li, H Liang, L Hou, L Zuo, Z Chen, J Lu, X Zhao, J Liang, L Peng and 7 more

Open access · greenAbstract readMulticenter StudyEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. 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, top 95% of its field
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, 0 citations in OpenAlex.

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

17 authors at 2 institutions in 1 country.

Y GeDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
J LiDepartment of Critical Care Medicine, Zhongshan People's Hospital, Zhongshan 528403, China.
H LiangDepartment of Critical Care Medicine, Zhongshan People's Hospital, Zhongshan 528403, China.
L HouDepartment of Critical Care Medicine, Zhongshan People's Hospital, Zhongshan 528403, China.
L ZuoDepartment of Intensive Care Unit, Shunde Hospital Affiliated to Southern Medical University (Shunde First People's Hospital), Foshan 528308, China.
Z ChenDepartment of Intensive Care Unit, Shunde Hospital Affiliated to Southern Medical University (Shunde First People's Hospital), Foshan 528308, China.
J LuDepartment of Intensive Care Unit, Shunde Hospital Affiliated to Southern Medical University (Shunde First People's Hospital), Foshan 528308, China.
X ZhaoDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
J LiangDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
L PengDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
J BaoDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
J DuanDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
L LiuDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
K MaoDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
Z ZengDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
H HuDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
Z ChenDepartment of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
Nanfang Hospital · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO).

methodsWe retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January, 2015 and January, 2022 using a convenience sampling method. The patients were divided into a derivation cohort (201 cases) and a validation cohort (101 cases). Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients, based on which a risk prediction model was established in the form of a nomogram. The receiver operator characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the discrimination ability, calibration and clinical validity of this model.

resultsThe in-hospital mortality risk prediction model was established based the risk factors including hypertension (OR=3.694, 95%

conclusionThe risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation, calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system, and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Indexed as

Extracorporeal Membrane OxygenationCase-Control StudiesHospital MortalityHumansPrognosisRetrospective StudiesROC Curvemortalityrisk factorsrisk pre-diction modelvenoarterial extracorporeal membrane oxygenation

Identifiers

PMID38597440
PMCPMC11006704
OpenAlexW4394768714

What OpenQuestion holds

Textmetadata
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.