Evidence map›Paper›PMID 42741588›Full record

ArticleInterventional cardiology (London, England)2026

Assessing Care of Patients with Severe Mental Illness in the Era of Primary Angioplasty Pathways for ST-elevation MI Using Artificial Intelligence Methods.

Eoin Gogarty, Isabelle Sweeney, Brian Tam To, Jack Wu, Ajay M Shah, Fiona Gaughran, Kevin O'Gallagher

Abstract read
In one paragraph

Article in Interventional cardiology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Eoin GogartyDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.ORCID https://orcid.org/0000-0003-4137-4220
Isabelle SweeneyDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.ORCID https://orcid.org/0000-0002-7694-2593
Brian Tam ToCardiovascular Department, King's College Hospital NHS Foundation Trust London, UK.
Jack WuSchool of Cardiovascular and Metabolic Medicine & Sciences, King's College London, British Heart Foundation Centre of Research Excellence London, UK.ORCID https://orcid.org/0000-0001-6043-5450
Ajay M ShahCardiovascular Department, King's College Hospital NHS Foundation Trust London, UK.ORCID https://orcid.org/0000-0002-6547-0631
Fiona GaughranDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK.ORCID https://orcid.org/0000-0001-7414-5569
Kevin O'GallagherCardiovascular Department, King's College Hospital NHS Foundation Trust London, UK.ORCID https://orcid.org/0000-0003-2218-4763

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: People with severe mental illness (SMI) experience significant health disparities, including in cardiovascular care. This study used natural language processing (NLP), an artificial intelligence method, to investigate care pathways for SMI patients within a highly protocolised urban primary angioplasty network for ST-elevation MI (STEMI). Methods: This cohort study used NLP to extract SMI diagnoses and clinical data from free-text electronic health records, including STEMI and non-STEMI audit data. Primary outcomes included 28-day and 1-year mortality, with secondary outcomes examining pathway performance metrics and procedural data. Exploratory outcomes assessed presenting symptoms and comorbidities. Results: A total of 2,292 adults presented with first-time STEMI from 2012-2019, of whom 51 (2.2%) had recorded SMI diagnoses. No significant differences were found in mortality between SMI and non-SMI patients at 28-day and 1-year, although a non-significant trend toward higher crude mortality in SMI patients emerged at 3 years (23.5% versus 16.1%; p=0.152). Procedure metrics and timings were similar between groups. SMI patients were more likely to have presented with cardiac arrest (adjusted OR 2.48; 95% CI [1.18-5.22]; p=0.017) and had higher rates of chronic obstructive pulmonary disease (adjusted OR 2.20; 95% CI [1.00-4.83]; p=0.049). Percutaneous coronary intervention rates in SMI patients with a non-STEMI were less than their representation in the overall non-STEMI population (2.2% versus 3.3%; p=0.042). Conclusion: Highly protocolised STEMI pathways appear to provide equitable acute care for patients with SMI without excess short-term mortality. However, potential disparities emerge in longer-term outcomes and in non-STEMI treatment pathways, suggesting a need for improved secondary prevention and targeted interventions for this vulnerable population.

Indexed as

acute coronary syndromeartificial intelligencecardiovascular diseasenatural language processingSevere mental illnessST-elevation MI

Identifiers

PMID42741588
PMCPMC13573014

What OpenQuestion holds

Textmetadata
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.