Evidence map›Paper›PMID 25621078›Full record

Observational studyEuropean journal of trauma and emergency surgery : official publication of the European Trauma Society2015

Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease.

K Søreide, K Thorsen, J A Søreide

Open access · hybridAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in European journal of trauma and emergency surgery : official publication of the European Trauma Society, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
10.8field-weighted citation impact, top 2% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Perforated and bleeding peptic ulcer: WSES guidelines.World journal of emergency surgery : WJES · 2020
    Guideline
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Abdominal Sepsis: An Update.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2018
    Review
  9. Article
  10. Article
  11. Review
  12. Perforated peptic ulcer.Lancet (London, England) · 2015
    Review
  13. Emergency surgery in the elderly: the balance between function, frailty, fatality and futility.Scandinavian journal of trauma, resuscitation and emergency medicine · 2015
    Article
  14. 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

3 authors at 1 institution in 1 country.

K SøreideDepartment of Gastrointestinal Surgery, Stavanger University Hospital, P.O. Box 8100, 4068 Stavanger, Norway ; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
K ThorsenDepartment of Gastrointestinal Surgery, Stavanger University Hospital, P.O. Box 8100, 4068 Stavanger, Norway ; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
J A SøreideDepartment of Gastrointestinal Surgery, Stavanger University Hospital, P.O. Box 8100, 4068 Stavanger, Norway ; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Stavanger University Hospital · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition.

methodsANN modelling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer and an output layer) was used to predict the 30-day mortality of consecutive patients from a population-based cohort undergoing surgery for PPU. A receiver-operating characteristic (ROC) analysis was used to assess model accuracy.

resultsOf the 172 patients, 168 had their data included in the model; the data of 117 (70%) were used for the training set, and the data of 51 (39%) were used for the test set. The accuracy, as evaluated by area under the ROC curve (AUC), was best for an inclusive, multifactorial ANN model (AUC 0.90, 95% CIs 0.85-0.95; p < 0.001). This model outperformed standard predictive scores, including Boey and PULP. The importance of each variable decreased as the number of factors included in the ANN model increased.

conclusionsThe prediction of death was most accurate when using an ANN model with several univariate influences on the outcome. This finding demonstrates that PPU is a highly complex disease for which clinical prognoses are likely difficult. The incorporation of computerised learning systems might enhance clinical judgments to improve decision making and outcome prediction.

Indexed as

Neural Networks, ComputerAdultAgedAged, 80 and overArea Under CurveComorbidityFemaleHumansMaleMiddle AgedOutcome Assessment, Health CarePeptic Ulcer PerforationPredictive Value of TestsPrognosisReproducibility of ResultsRisk AssessmentComputer simulationGastroduodenal ulcersMortalityOutcome assessmentPeptic ulcer perforationPredictionPrognosis

Identifiers

PMID25621078
PMCPMC4298653
OpenAlexW2035628141

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.