Evidence map›Paper›PMID 39146357›Full record

ArticlePloS one2024

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Hiroshi Hoshijima, Tomo Miyazaki, Yuto Mitsui, Shinichiro Omachi, Masanori Yamauchi, Kentaro Mizuta

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Review
  15. 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.

Hiroshi HoshijimaDivision of Dento-Oral Anesthesiology, Tohoku University Graduate School of Dentistry, Miyagi, Japan.ORCID 0000-0003-1466-6228
Tomo MiyazakiGraduate School of Engineering, Tohoku University, Miyagi, Japan.ORCID 0000-0001-5205-0542
Yuto MitsuiGraduate School of Engineering, Tohoku University, Miyagi, Japan.ORCID 0009-0005-2525-4837
Shinichiro OmachiGraduate School of Engineering, Tohoku University, Miyagi, Japan.
Masanori YamauchiDepartment of Anesthesiology, Tohoku University Hospital, Miyagi, Japan.
Kentaro MizutaDivision of Dento-Oral Anesthesiology, Tohoku University Graduate School of Dentistry, Miyagi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associated with a longer stay in the post-anesthesia care unit, readmissions, and perioperative costs. This retrospective study used artificial intelligence to analyze data of 37,548 adult patients (aged ≥20 years) who underwent surgery under general anesthesia at Tohoku University Hospital from January 1, 2010 to December 31, 2019. To evaluate PONV, patients who experienced nausea and/or vomiting or used antiemetics within 24 hours after surgery were extracted from postoperative medical and nursing records. We create a model that predicts probability of PONV using the gradient tree boosting model, which is a widely used machine learning algorithm in many applications due to its efficiency and accuracy. The model implementation used the LightGBM framework. Data were available for 33,676 patients. Total blood loss was identified as the strongest contributor to PONV, followed by sex, total infusion volume, and patient's age. Other identified risk factors were duration of surgery (60-400 min), no blood transfusion, use of desflurane for maintenance of anesthesia, laparoscopic surgery, lateral positioning during surgery, propofol not used for maintenance of anesthesia, and epidural anesthesia at the lumbar level. The duration of anesthesia and the use of either sevoflurane or fentanyl were not identified as risk factors for PONV. We used artificial intelligence to evaluate the extent to which risk factors for PONV contribute to the development of PONV. Intraoperative total blood loss was identified as the potential risk factor most strongly associated with PONV, although it may correlate with duration of surgery, and insufficient circulating blood volume. The use of sevoflurane and fentanyl and the anesthesia time were not identified as risk factors for PONV in this study.

Indexed as

Machine LearningPostoperative Nausea and VomitingAdultAgedAnesthesia, GeneralAntiemeticsFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsAntiemetics

Identifiers

PMID39146357
PMCPMC11326632

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.