Evidence map›Paper›PMID 37906226›Full record

ArticleJMIR cardio2023

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study.

Enrico de Koning, Yvette van der Haas, Saguna Saguna, Esmee Stoop, Jan Bosch, Saskia Beeres, Martin Schalij, Mark Boogers

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR cardio, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.4field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Enrico de KoningCardiology Department, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-9343-0717
Yvette van der HaasLulea University of Technology, Lulea, Sweden.ORCID https://orcid.org/0000-0002-0887-3979
Saguna SagunaLulea University of Technology, Lulea, Sweden.ORCID https://orcid.org/0000-0001-8561-7963
Esmee StoopClinical AI and Research lab, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0001-5198-6269
Jan BoschResearch and Development, Regional Ambulance Service Hollands-Midden, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-9690-0104
Saskia BeeresCardiology Department, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-1916-078X
Martin SchalijCardiology Department, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0003-3767-5231
Mark BoogersCardiology Department, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-8755-4254
Leiden University Medical Center · NLLuleå University of Technology · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOvercrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital.

objectiveThe aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics.

methodsPatients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model.

resultsThe AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%.

conclusionsThe AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.

Indexed as

acute coronary syndromealgorithmanginaartificial intelligencecardiologyclinical decision-makingemergency departmentemergency medical serviceHollands Midden Acute Regional Triage–cardiologynatural language processingovercrowdingparamedicsprehospitaltriage

Identifiers

PMID37906226
PMCPMC10646678
OpenAlexW4388033323

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.