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Enregistrement W3013492647 · doi:10.1097/tp.0000000000003239

Will the Donor Lungs Fit? Just Grab a Ruler

2020· letter· en· W3013492647 sur OpenAlexaboutno aff
David C. Neujahr

Notice bibliographique

RevueTransplantation · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueTransplantation: Methods and Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineLungThorax (insect anatomy)TransplantationSurgeryLung volumesThoracic cavityLung transplantationInternal medicineAnatomy

Résumé

récupéré en direct d'OpenAlex

A major challenge in clinical lung transplantation is determining if a particular donor lung allograft will fit into a recipient. Transplanting lungs that are too small for the chest can lead to complications ranging from a thorax that has an unfilled space to pulmonary torsion. Transplantation of lungs which are too large can lead to major early perioperative complications because lungs which are edematous from ischemia-reperfusion injury can impact venous return to the heart and decrease cardiac output. Indeed, clinical studies have shown that as the predicted total lung capacity (pTLC) of the donor increases, so does the chance of primary graft dysfunction (PGD).1 Given these issues, most programs recommend listing patients based on parameters centered around the donor and recipient pTLC, and effectively hoping that these “predictions” are close-enough. Transplant surgeons face a dilemma of making a quick decision with relatively limited data and the pressure to perform a transplant, despite a less than ideal pTLC match. The use of pTLC for size matching makes both intuitive and practical sense. In principle, normal lungs should fill a normal recipient thoracic cavity and operate optimally. There are several issues with this assumption. First, end-stage lung disease can result in significant distortion of the chest cavity beyond simply the lungs. Such anatomic derangement is not immediately reversed by the transplant procedure. A patient in whom there has been long-standing fibrotic lung disease may have compensatory changes to the thorax, such as kyphosis, which are never reversed even with healthy lungs, and there may be fundamental changes to the diaphragm function that persist after transplantation. Second, factors external to the thoracic cavity may still have dramatic effects on actual TLC. Significant truncal obesity is an example where there may be significant differences between actual and predicted TLC due to upward pressure from visceral adiposity. Third, the pTLC measures are based on nomograms derived from patients across a spectrum of ages and heights. In fact, different pulmonary function laboratories used different predictive nomograms, which can result in variability of the pTLC. We now know that these nomograms have less precision for predicting actual TLC as subject’s age.2 This is particularly relevant in a time where older patients are increasingly being offered lung transplantation.3 Due to the need to improve donor and recipient lung size, there has been a trend toward real-time matching of lungs based on measurable sizes. In this volume of the journal, Li et al4 from the University of Alberta report on their analysis of lung transplant patients and the risk of PGD based on plain film radiographic measures. This is a retrospective study of 206 bilateral lung transplant recipients. The authors used 3 relatively simple measurements in the donors from the last portable CXR obtained: the apex to mid diaphragm length, the apex to costophrenic angle length (ACPA), and the distance between the costophrenic angles (ICPA). These measures were chosen because they are relatively straightforward measurements to obtain. The same measurements were assessed in the recipients, with the caveat being that in the recipients, they used a standard posterior–anterior CXR. The authors assessed the risk of severe grade 3 PGD in recipients in whom the donors were oversized (donor:recipient ACPA ratio >1) and undersized (donor:recipieint ACPA ratio <1). The major findings using CXR sizing ratios were that patients who were oversized had double the risk for grade 3 PGD at 72 hours. Not surprisingly, the oversized patients had longer ICU times, longer ventilation times, and also a higher risk of requiring surgical downsizing in the operating room. Fortunately, the major downside of oversizing appears to be a short-term phenomenon; there was no survival difference or increased CLAD risk at 1 year in the oversized group. The findings from the Alberta group are important because they show the potential for a relatively simple measurement to potentially improve early outcomes in lung transplantation. The calculation of ACPA length should be readily obtainable with the caliper tools found in virtually every radiology information system. Hence, organ donor coordinators and transplant professionals should be able to calculate these ratios with relatively little new training. The study here does have some caveats. First, this is a retrospective study design. Despite the fact that the authors have done an excellent job accounting for many potential sources of bias, the findings will need to be confirmed prospectively in a larger sample size. Second, in areas where the lung allocation score is used to distribute lungs, this sizing approach may be clinically interesting, yet have limited practical role. The new regional allocation rules in the United States, which removed local allocation as the first node in the match-run, could may make size matching only a minor consideration. For example, consider a lung transplant center which is offered a potentially oversized donor. If that center turns down the lungs, the next recipient in the match-run very likely could be at another center.5 Hence, the transplanting surgeon may decide to take the risk of accepting a larger donor, with the knowledge that they may have to downsize the lungs on the back table, or potentially perform a delayed chest closure. Finally, the use of donor:recipient size ratios by plain radiography will ultimately need to compete with emerging technologies in radiology. A decade ago, it was somewhat onerous to convince the donor management team to obtain a computed tomography (CT) of a potential donor; today, in many regions, it is virtually universal. Refinements in radiology information systems have now made it possible to reliably compute CT lung volume on potential recipients. If this becomes more widely practiced, then going forward, it could be hypothetically possible to do full lung volume matching with CT images.6

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,050
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,050
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0040,007
Communication savante0,0070,015
Science ouverte0,0020,005
Intégrité de la recherche0,0080,014
Charge utile insuffisante (le modèle a refusé de juger)0,0180,022

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,057
Tête enseignante GPT0,322
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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