Evidence map›Paper›PMID 39004698›Full record

ArticleBMC cardiovascular disorders2024

Importance of hospital and clinical factors for early mortality in Takotsubo syndrome: Insights from the Swedish Coronary Angiography and Angioplasty Registry.

Thorsteinn Gudmundsson, Björn Redfors, Truls Råmunddal, Oskar Angerås, Petur Petursson, Araz Rawshani, Henrik Hagström, Joakim Alfredsson, Christina Ekenbäck, Loghman Henareh and 7 more

Abstract read
In one paragraph

Article in BMC cardiovascular disorders, 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

17 authors.

Thorsteinn GudmundssonDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Björn RedforsDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Truls RåmunddalDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Oskar AngeråsDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Petur PeturssonDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Araz RawshaniDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Henrik HagströmDepartment of Cardiology, Umeå University Hospital, Umeå, Sweden.
Joakim AlfredssonDepartment of Cardiology, University Hospital, Linköping, Sweden.
Christina EkenbäckDepartment of Cardiology, Danderyd University Hospital, Stockholm, Sweden.
Loghman HenarehDepartment of Cardiology, Karolinska University Hospital, Stockholm, Sweden.
Kristofer SkoglundDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Charlotta LjungmanDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden.
Moman MohammadDepartment of Cardiology, Skåne University Hospital, Lund, Sweden.
Tomas JernbergDepartment of Cardiology, Karolinska University Hospital, Stockholm, Sweden.
Ole FröbertDepartment of Cardiology, Örebro University Hospital, Örebro, Sweden.
David ErlingeDepartment of Cardiology, Skåne University Hospital, Lund, Sweden.
Elmir OmerovicDepartment of Cardiology, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Bruna stråket 16, Gothenburg, 41345, Sweden. elmir@wlab.gu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTakotsubo syndrome (TTS) is an acute heart failure syndrome with symptoms similar to acute myocardial infarction. TTS is often triggered by acute emotional or physical stress and is a significant cause of morbidity and mortality. Predictors of mortality in patients with TS are not well understood, and there is a need to identify high-risk patients and tailor treatment accordingly. This study aimed to assess the importance of various clinical factors in predicting 30-day mortality in TTS patients using a machine learning algorithm.

methodsWe analyzed data from the nationwide Swedish Coronary Angiography and Angioplasty Registry (SCAAR) for all patients with TTS in Sweden between 2015 and 2022. Gradient boosting was used to assess the relative importance of variables in predicting 30-day mortality in TTS patients.

resultsOf 3,180 patients hospitalized with TTS, 76.0% were women. The median age was 71.0 years (interquartile range 62-77). The crude all-cause mortality rate was 3.2% at 30 days. Machine learning algorithms by gradient boosting identified treating hospitals as the most important predictor of 30-day mortality. This factor was followed in significance by the clinical indication for angiography, creatinine level, Killip class, and age. Other less important factors included weight, height, and certain medical conditions such as hyperlipidemia and smoking status.

conclusionsUsing machine learning with gradient boosting, we analyzed all Swedish patients diagnosed with TTS over seven years and found that the treating hospital was the most significant predictor of 30-day mortality.

Indexed as

Coronary AngiographyRegistriesTakotsubo CardiomyopathyAgedAged, 80 and overFemaleHospitalsHumansMachine LearningMaleMiddle AgedPredictive Value of TestsPrognosisRisk AssessmentRisk FactorsSweden30-day mortalityGradient boostingMachine learningPredictors of mortalitySwedish Coronary Angiography and Angioplasty Registry (SCAAR)Takotsubo syndrome

Identifiers

PMID39004698
PMCPMC11247782

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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.