Evidence map›Paper›PMID 35748018›Full record

ArticleThe Journal of international medical research2022

Length of stay and influencing factors of NICU in the Western Hunan, an underdeveloped area of China: A 9-year retrospective study.

Fen Xie, Qingxia Shu, Zheng-Ying Chen, Jinxiu Li

Abstract read
In one paragraph

Article in The Journal of international medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Review
  6. Review
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

4 authors.

Fen XieDepartment of Nursing, Jishou University School of Medicine, Jishou, Hunan Province, China.
Qingxia ShuDepartment of Nursing, Jishou University School of Medicine, Jishou, Hunan Province, China.ORCID https://orcid.org/0000-0003-0584-6622
Zheng-Ying ChenDepartment of Nursing, Jishou University School of Medicine, Jishou, Hunan Province, China.
Jinxiu LiDepartment of Nursing, Jishou University School of Medicine, Jishou, Hunan Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the factors that influence the length of stay (LOS) in a neonatal intensive care unit (NICU).

methodsThis retrospective study analysed clinical data from all newborns hospitalized in the NICU of a tertiary hospital in the Western Hunan area of China over a 9-year period (2012-2020). Factors associated with the LOS were analysed using univariate analysis and Cox regression analysis.

resultsA total of 16 094 newborns were included in the study: of which 9615 were inborn and 6479 were outborn newborns. There were 9482 males and 6612 females. Over the 9-year period, the mean LOS was 11.08 days (median LOS, 9.00 days; range, 1-141 days); and the LOS first increased, then decreased and stabilized. A LOS of 8-28 days was the most common duration (8849 of 16 094; 54.98%). Cox regression analysis demonstrated that sex, patient source, delivery method, gestational age, birth weight and comorbidities were significantly associated with LOS.

conclusionBeing male, low gestational age and low birth weight increased the LOS. Reducing preterm and post-term infants, as well as eliminating comorbidities, could effectively shorten the LOS of newborns.

Indexed as

Intensive Care Units, NeonatalChinaFemaleGestational AgeHumansInfantInfant, NewbornLength of StayMaleRetrospective Studiesinfluencing factorlength of stayneonatal intensive care unitNewborn

Identifiers

PMID35748018
PMCPMC9247289

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.