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Multi-parametric cardiovascular magnetic resonance imaging detects subclinical myocardial involvement in patients diagnosed with phaeochromocytoma

2015· article· en· W5450118 on OpenAlexfundno aff
Vanessa M. Ferreira, Mafalda Marcelino, Stefan K. Piechnik, Claudia Marini, Theodoros D. Karamitsos, Jane M Francis, Jayanth R. Arnold, Radu Mihai, Julia Thomas, Maria Herincs, Márta Korbonits, Zaki Hassan‐Smith, Wiebke Arlt, Niki Karavitaki, Ashley Grossman, John Wass, Stefan Neubauer

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2015
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsnot available
FundersClarendon FundUniversity of OxfordBritish Heart FoundationNational Institute for Health and Care ResearchAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsMedicineCardiologyEjection fractionInternal medicineMagnetic resonance imagingMyocardial infarctionHeart failureSubclinical infectionPheochromocytomaProspective cohort studyCardiac magnetic resonance imagingAngiologyCardiomyopathyRadiology

Abstract

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Background In patients with phaeochromocytoma, acute or chronic exposure to catecholamines may lead to cardiac pathology, including left ventricular (LV) hypertrophy, myocardial infarction, stress-induced cardiomyopathy and heart failure. The burden of myocardial involvement in this disease with systemic effects is unknown. In this prospective, multicentre study, we sought to describe the variety and incidence of cardiac abnormalities in patients diagnosed with phaeochromocytoma using multi-parametric cardiovascular magnetic resonance (CMR) imaging. Methods We studied 50 patients diagnosed with phaeochromocytoma. Twenty patients (n=20, age 51±14 yrs) newly-diagnosed with confirmed phaeochromocytoma prospectively underwent CMR before and after curative surgical resection of the phaeochromocytoma (median follow-up 1 year). In addition, 30 patients (n=30, age 52±14 yrs) previously diagnosed with phaeochromocytoma who had curative surgery were also recruited for cardiac characterisation. Patients with known cardiac conditions were excluded. CMR included cine imaging for global and regional LV function, dark-blood T2-weighted imaging for oedema and late gadolinium enhancement imaging to detect the presence and patterns of any scarring. Results In patients with newly-diagnosed phaeochromocytoma, the mean LV ejection fraction was 67±10% (EF range 4788%); of these patients, 20% (n=4/20) had mild global LV dysfunction (EF 47-56%). A significant proportion (65%, n=13/20) demonstrated scarring, all with a non-ischaemic pattern, but these areas were small (<10% myocardium); no patient had evidence of myocardial infarction (isolated subendocardial scarring). One patient demonstrated global myocardial oedema with normal EF. All LV dysfunction or oedema were reversible and normalised at postoperative follow-up. In patients previously-diagnosed who already had had curative surgery, the mean LVEF was essentially normal (73±7%) with only one patient (3%) who had mild global LV dysfunction (EF=56%). Compared to the newlydiagnosed patients, a significantly smaller proportion of previously-diagnosed patients (17% vs. 65%; p<0.001) demonstrated areas of scarring, which were also small in areas with a non-ischaemic pattern, except for one patient who suffered a small myocardial infarction. Conclusions Subclinical cardiac abnormalities are frequent findings on CMR in patients newly-diagnosed with phaeochromocytoma, including mild LV dysfunction, myocardial oedema and small areas of non-ischaemic scarring, with the former two demonstrating normalization after surgical resection of the phaeochromocytoma. In patients who had previously undergone curative surgical resection of their phaeochromocytomas, the incidence of cardiac

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.247
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2015
Admission routes1
Has abstractyes

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