Evidence map›Paper›PMID 36844231›Full record

ArticleFrontiers in medicine2023

Decision-making support systems on extended hospital length of stay: Validation and recalibration of a model for patients with AMI.

Joana Xavier, Joana Seringa, Fausto José Pinto, Teresa Magalhães

Abstract read
In one paragraph

Article in Frontiers in medicine, 2023. 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

4 authors.

Joana XavierNOVA National School of Public Health, Nova University Lisbon, Lisbon, Portugal.
Joana SeringaNOVA National School of Public Health, Nova University Lisbon, Lisbon, Portugal.
Fausto José PintoServiço de Cardiologia do Centro Hospitalar de Lisboa Norte, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.
Teresa MagalhãesNOVA National School of Public Health, Nova University Lisbon, Lisbon, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay. Length of stay predictive models is an efficient way to aid decision-making in health. This study aimed to validate a predictive model on the extended length of stay in patients with acute myocardial infarction at the time of admission. Methods: An analysis was conducted to test and recalibrate a previously developed model in the prediction of prolonged length of stay, for a new set of population. The study was conducted based on administrative and laboratory data of patients admitted for acute myocardial infarction events from a public hospital in Portugal from 2013 to 2015. Results: Comparable performance measures were observed upon the validation and recalibration of the predictive model of extended length of stay. Comorbidities such as shock, diabetes with complications, dysrhythmia, pulmonary edema, and respiratory infections were the common variables found between the previous model and the validated and recalibrated model for acute myocardial infarction. Conclusion: Predictive models for the extended length of stay can be applied in clinical practice since they are recalibrated and modeled to the relevant population characteristics.

Indexed as

acute myocardial infarctioncardiovascular diseasesdecision-makinglength of staypredictive models

Identifiers

PMID36844231
PMCPMC9946108

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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.