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Record W4285247808 · doi:10.4103/joacp.joacp_678_20

Post-double lung transplant, emergent cervical spine surgery, and COVID pandemic: A triple threat to perioperative management

2022· article· en· W4285247808 on OpenAlexaff
Alex Sapa, Lashmi Venkatraghavan, Tumul Chowdhury

Bibliographic record

VenueJournal of Anaesthesiology Clinical Pharmacology · 2022
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineSurgeryMyelopathyPast medical historySpinal cord

Abstract

fetched live from OpenAlex

Dear Editor, Recipients of double lung transplants (DLT) have multiple anesthetic considerations for future surgeries. These include, altered physiology (impaired cough and disrupted lymphatics), cor pulmonale, need for aseptic techniques, and multisystem dysfunction due to immunosuppressants.[1] An emergent cervical spine surgery in these patients potentiates further risks due to innate complexities related to positioning, unstable spine, bleeding, and extubation. We present a case of a middle-aged patient who underwent an emergent cervical spine surgery after a recent DLT. To make this more complicated, this case took place during the COVID-19 pandemic further multiplying these risks. A 56-year-old man was admitted for redo cervical spine decompression and fusion (C1-T2) due to progressively worsening cervical myelopathy. His past medical history included scleroderma, chronic kidney disease, thyroid goiter, and chronic pain. His scleroderma caused severely restrictive interstitial lung disease and pulmonary hypertension leading to DLT. A major challenge during this case was induction and intubation. Previous cervical spine fusion limited neck extension, while scleroderma increased aspiration risk. Gentle bronchoscopy while under deep anesthesia was the method of choice to avoid stimulation, to avoid exacerbation of pulmonary hypertension. This was made more difficult by tracheal deviation cause by his large goiter. We were further burdened by COVID-19 precautions. Double gloves made manipulation difficult, while face shields produced glare and obscured views of the screen. A second major challenge was preparing for any disruption in hemodynamic status and blood loss. His history of pulmonary hypertension made this a priority. Extensive bone manipulation often leads to severe bleeding, especially in redo spinal surgeries with hardware. Point-of-care tests, and diligent monitoring assessed need for transfusion to maintain spinal perfusion. In contrast, adequate depth using a total intravenous anesthetic was necessary to avoid sympathetic stimulation. This was vital as neuromonitoring prevented the use of volatile anesthetics and muscle relaxation. We thought this case was important to highlight, as there is a paucity of such cases in the literature. We conducted a literature search to identify case reports of lung transplant recipients undergoing spinal surgery and found only three articles of high relevance [Table 1].[234] This article outlines the risks of patients such as ours, and we hope that further documenting our care can lead to a better understanding of such challenges.Table 1: Summary of literature searchDeclaration of patient consent The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0040.002

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 head0.085
GPT teacher head0.442
Teacher spread0.358 · 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 designCase report
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
Published2022
Admission routes1
Has abstractyes

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