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Record W4381846219 · doi:10.1093/jbcr/irad045.035

61 Effect of Pre-Existing Hypertension on Cardiovascular Support and Mortality Among Major Burn Patients

2023· article· en· W4381846219 on OpenAlexaff
Trina Stephens, Anthony Papp, Bettina Papp, Sara Sheikh‐Oleslami, Donald Griesdale, Fagun Jain

Bibliographic record

VenueJournal of Burn Care & Research · 2023
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineEmergency medicineIntensive care unitTotal body surface areaComorbidityIntensive care medicineUnivariate analysisCohortInternal medicineMultivariate analysis

Abstract

fetched live from OpenAlex

Abstract Introduction Hypertension (HTN) is the most common comorbidity seen in patients who sustain a major burn. HTN causes increased responsiveness of the autonomic nervous system to stressful stimuli, activation of RAAS, arterial and myocardial hypertrophy, and impaired vasodilation. Changes to these pathways are also seen in response to major burns. Given this common pathophysiology, patients with pre-existing HTN may demonstrate different mortality risk after major burn than those without HTN. The objective of this study is to compare the risk of in-hospital mortality among adult patients with and without pre-existing HTN admitted to the intensive care unit (ICU) with a major burn. In addition, we sought to determine the association of pre-existing HTN with the need for vasoactive agents, fluid resuscitation and urine output in the first 48-hours of ICU admission Methods Single-centre, historical cohort study using data from a Burn Registry, Intensive Care Database, and medical chart review. Variables of interest were demographic and injury characteristics, use of vasoactive agents, 12-, 24-, and 48-hour fluid requirements and urine output, and in-hospital mortality. We included adult (≥18 years) patients presenting with ≥20% total body surface area (TBSA) burns who were admitted to the ICU from January 2010 to August 2021. Pre-existing HTN was defined by pharmacy records showing the patient had filled a prescription for an anti-HTN medication within the 12-months prior to their burn. We compared variables of interest by HTN exposure and conducted univariate analysis to assess mortality risk Results 199 patients met inclusion criteria. Of these, 145 records were complete. The mean (standard deviation, SD) age was 47 (7) years and TBSA was 37 (16) percent. Most patients were males (83.5%) who sustained a flame burn (87.6%) without an inhalation injury (28.5%). Pharmacy records revealed that 28 patients (19.3%) had pre-existing HTN. Mean (SD) 24-hour fluid requirements were 7.1 (5.6) L and 5.2 (3.2) L for HTN and non-HTN patients, respectively (p=0.17). Mean (SD) 24-hour urine output was 1024 (747) mL and 1314 (780) mL for HTN and non-HTN patients, respectively (p=0.12). Sixty-one percent of HTN patients required vasoactive agents in the first 24-hours compared to 46.2% of non-HTN patients (p=0.18). Pre-existing HTN was associated with a 5-fold increase in in-hospital mortality (OR 5.5, CI 2.2-14.0) Conclusions Preliminary results suggest differences in fluid requirements, urine output and mortality risk between HTN and non-HTN patients with major burns. An external registry has been queried to identify pharmacy records for the 54 patients with missing data Applicability of Research to Practice Identifying differences in mortality risk and need for cardiovascular support among burn patients with HTN changes our approach to counselling and caring for these patients

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.385
Teacher spread0.318 · 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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Citations0
Published2023
Admission routes1
Has abstractyes

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