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Record W2548657015 · doi:10.1371/journal.pone.0164863

Cerebral Hemodynamics in Patients with Hemolytic Uremic Syndrome Assessed by Susceptibility Weighted Imaging and Four-Dimensional Non-Contrast MR Angiography

2016· article· en· W2548657015 on OpenAlexaff
Ulrike Löbel, Nils D. Forkert, Peter Schmitt, Torsten Dohrmann, Maria Schroeder, Tim Magnus, Stefan Kluge, Christina Weiler‐Normann, Xiaoming Bi, Jens Fiehler, Jan Sedlacik

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineHematocritMagnetic resonance imagingCreatinineMagnetic resonance angiographyHemodynamicsCardiologyInternal medicineSusceptibility weighted imagingRadiology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Conventional magnetic resonance imaging (MRI) of patients with hemolytic uremic syndrome (HUS) and neurological symptoms performed during an epidemic outbreak of Escherichia coli O104:H4 in Northern Europe has previously shown pathological changes in only approximately 50% of patients. In contrast, susceptibility-weighted imaging (SWI) revealed a loss of venous contrast in a large number of patients. We hypothesized that this observation may be due to an increase in cerebral blood flow (CBF) and aimed to identify a plausible cause. MATERIALS AND METHODS: Baseline 1.5T MRI scans of 36 patients (female, 26; male, 10; mean age, 38.2±19.3 years) were evaluated. Venous contrast was rated on standard SWI minimum intensity projections. A prototype four-dimensional (time resolved) magnetic resonance angiography (4D MRA) assessed cerebral hemodynamics by global time-to-peak (TTP), as a surrogate marker for CBF. Clinical parameters studied were hemoglobin, hematocrit, creatinine, urea levels, blood pressure, heart rate, and end-tidal CO2. RESULTS: SWI venous contrast was abnormally low in 33 of 36 patients. TTP ranged from 3.7 to 10.2 frames (mean, 7.9 ± 1.4). Hemoglobin at the time of MRI (n = 35) was decreased in all patients (range, 5.0 to 12.6 g/dL; mean, 8.2 ± 1.4); hematocrit (n = 33) was abnormally low in all but a single patient (range, 14.3 to 37.2%; mean, 23.7 ± 4.2). Creatinine was abnormally high in 30 of 36 patients (83%) (range, 0.8 to 9.7; mean, 3.7 ± 2.2). SWI venous contrast correlated significantly with hemoglobin (r = 0.52, P = 0.0015), hematocrit (r = 0.65, P < 0.001), and TTP (r = 0.35, P = 0.036). No correlation of SWI with blood pressure, heart rate, end-tidal CO2, creatinine, and urea level was observed. Findings suggest that the loss of venous contrast is related to an increase in CBF secondary to severe anemia related to HUS. SWI contrast of patients with pathological conventional MRI findings was significantly lower compared to patients with normal MRI (mean SWI score, 1.41 and 2.05, respectively; P = 0.04). In patients with abnormal conventional MRI, mean TTP (7.45), mean hemoglobin (7.65), and mean hematocrit (22.0) were lower compared to patients with normal conventional MRI scans (mean TTP = 8.28, mean hemoglobin = 8.63, mean hematocrit = 25.23). CONCLUSION: In contrast to conventional MRI, almost all patients showed pathological changes in cerebral hemodynamics assessed by SWI and 4D MRA. Loss of venous contrast on SWI is most likely the result of an increase in CBF and may be related to the acute onset of anemia. Future studies will be needed to assess a possible therapeutic effect of blood transfusions in patients with HUS and neurological symptoms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.182
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations6
Published2016
Admission routes1
Has abstractyes

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