Intraoperative Near-infrared Spectroscopy Can Predict Skin Flap Necrosis
Notice bibliographique
Résumé
Dear sir, We have carefully reviewed with interest the article titled “Intraoperative Near-infrared Spectroscopy Can Predict Skin Flap Necrosis” by Hill et al,1 published in Plastic and Reconstructive Surgery Global Open on March 26, 2024. We acknowledge the importance of investigating a new noninvasive optical technology for early detection of skin flap necrosis, supported by a substantial sample size. We also appreciate the complexities involved in conducting such a study, as we are familiar with the optical technology used in this study and its utilization for the intraoperative assessment of tissue oxygenation.2 Although we found this study engaging and important, we would like to offer some comments and observations. We hope that these observations can be addressed, as the article has the potential to make a valuable contribution to the literature. The technology used in this study has been misinterpreted. Specifically, Snapshot-NIR should be classified as a near-infrared imaging (NIRI) technique rather than a near-infrared spectroscopy (NIRS) technique. Although both methodologies are based on the same principles of tissue optics, they are different techniques. NIRI, also known as diffuse optical imaging or topography, is a noncontact technique that assesses regional tissue oxygenation (StO2) in superficial layers (up to 2 mm) by calculating NIR light reflection and reconstructing two-dimensional images of the chromophore concentrations in tissue. In contrast, NIRS is a contact technique that evaluates changes in tissue StO2 in deeper layers (up to 20 mm) by calculating NIR light absorption by tissue chromophores.3 Therefore, although we concur with the article’s title indicating that intraoperative NIRS can predict skin flap necrosis, the study utilizes NIRI as the technique.4 The article describes Snapshot as an NIRS device that measures tissue perfusion, which is not an accurate definition. NIRI and NIRS techniques can only measure changes in tissue oxygenation, not tissue perfusion. Tissue perfusion refers to the delivery of blood to a capillary bed in tissue, whereas tissue oxygenation indicates the status of oxygen consumption by tissue cells.5 Although tissue oxygenation is often a good surrogate of tissue perfusion, it may not always hold true, especially in pathological conditions and injured tissues where the correlation between the two parameters may be inconsistent. For example, a flap may have good microvascular blood supply, but the cells within the flap may not be able to efficiently utilize oxygen due to cell damage. In scenarios like these, which are not uncommon in transplant surgery, local perfusion may be sufficient, but tissue oxygenation could be inadequate, potentially compromising tissue viability. Understanding the differences and relationship between tissue perfusion and oxygenation in pathological conditions is highly important, especially in reconstructive surgery, where the use of NIRS and NIRI techniques is increasingly improving the monitoring of flap vitality and function. In reconstructive surgery, postsurgical care requires reliable techniques for monitoring the hemodynamics and metabolic conditions of reconstructed tissue. This necessitates real-time, continuous monitoring of both local tissue perfusion and oxygenation independently. We hope addressing these points will enhance the precision and coherence of this valuable article. Thank you for considering our letter. DISCLOSURE The authors received a Research Project Grant from the Canadian Institute of Health Research to conduct research titled “Advanced optical monitoring of free tissue transfer hemodynamics” (Application Number: 497249) in 2023.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».