Evidence map›Paper›PMID 39583539›Full record

ArticleCureus2024

Postoperative Pain at Discharge From the Post-anesthesia Care Unit: A Case-Control Study.

Ximena M Aladro Larenas, Maribel Castillo Cuadros, Irving E Miguel Aranda, Cristian I Ham Armenta, Horacio Olivares Mendoza, Mariana Freyre Alcántara, Irina Vázquez Villaseñor, Gabriel Villafuerte Jiménez

Abstract read
In one paragraph

Article in Cureus, 2024. 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

8 authors.

Ximena M Aladro LarenasAnesthesiology, Hospital Ángeles Lomas, Mexico City, MEX.
Maribel Castillo CuadrosAnesthesiology, Hospital Ángeles Lomas, Mexico City, MEX.
Irving E Miguel ArandaAnesthesiology, Hospital Ángeles Lomas, Mexico City, MEX.
Cristian I Ham ArmentaAnesthesiology, Hospital Ángeles Lomas, Mexico City, MEX.
Horacio Olivares MendozaAnesthesiology, Hospital Ángeles Lomas, Mexico City, MEX.
Mariana Freyre AlcántaraAnesthesiology, Hospital Ángeles Lomas, Mexico City, MEX.
Irina Vázquez VillaseñorResearch and Development, Actipulse Neuroscience, Cambridge, USA.
Gabriel Villafuerte JiménezResearch and Development, Actipulse Neuroscience, Cambridge, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDespite advancements in postoperative pain management, approximately 20% of patients still experience severe pain within the first 24 hours post-surgery. Previous studies utilizing machine learning have shown promise in predicting postoperative pain with various models. This study investigates postoperative pain predictors using a machine learning approach based on physiological indicators and demographic factors in a Mexican cohort.

methodsWe conducted a retrospective case-control study to assess pain determinants at Post-anesthesia Care Unit (PACU) discharge at Hospital Ángeles Lomas in Mexico City. Data were collected from 550 patients discharged from the PACU, including 292 cases and 258 controls, covering a range of surgical procedures and illnesses. Machine learning techniques were employed to develop a predictive model for postoperative pain. Physiological responses, such as blood pressure, heart rate, respiratory rate, and anesthesia type, were recorded prior to PACU admission.

resultsSignificant differences were found between cases and controls, with factors such as sex, anesthesia type, and physiological responses influencing postoperative pain. Visual analog scale (VAS) scores at PACU admission were predictive of pain at discharge.

conclusionsOur findings reinforce existing literature by highlighting sex-based disparities in pain experiences and the influence of anesthesia type on pain levels. The logistic regression model developed, incorporating physiological responses and sex, shows potential for refining pain management strategies. Limitations include the lack of detailed surgical data and psychological factors, and validation in a prospective cohort. Future research should focus on more comprehensive predictive models and longitudinal studies to further improve postoperative pain management.

Indexed as

anesthesialogistic regression modelmachine learningpain predictorspostoperative pain management

Identifiers

PMID39583539
PMCPMC11585308

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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.