Yapay Zekâ ile Zor Trakeal Entübasyon Tahmini: Prospektif Gözlemsel Bir Çalışma

Amaç: Zor hava yolu riski olan hastaların preoperatif tespiti için birçok prediktif klinik test birlikte kullanılmaktadır. Bu çalışmada, çeşitli klinik testler ve antropometrik ölçümler kullanarak farklı yapay zekâ algoritmaları ile zor entübasyonun tahmin edilmesi, ayrıca Cormack ve Lehane (C-L) sınıflandırmasının doğruluk performansının yapay zekâ ile değerlendirilmesi amaçlanmıştır. Gereç ve Yöntemler: Bu çalışma, 2016 ve 2019 yılları arasında tek kör prospektif gözlemsel bir çalışma olarak gerçekleştirildi. Elektif cerrahi planlanan ve endotrakeal entübasyon gerektiren, Amerikan Anesteziyologlar Derneği fiziksel durumu I-III olan toplam 1486 hasta dahil edildi. Hastaların demografik değişkenleri, klinik testleri ve antropometrik ölçümleri kaydedildi. Zor entübasyon 4 dereceli C-L sistemi ile kolay ve zor entübasyon kriterlerine göre değerlendirildi. Zor entübasyon, 16 farklı yapay zekâ algoritması kullanılarak tahmin edilmeye çalışıldı. Bulgular: Yapay zekâ algoritmaları arasında en yüksek başarı oranı RandomForest yöntemi ile elde edilmiştir. Bu yöntemle zor entübasyon %92,85 duyarlılık, %96,94 özgüllük, %93,69 pozitif öngörü değeri ve%96,52 negatif öngörü değeri ile tahmin edildi. C-L sınıflandırması doğruluk performansı ise %95,60 olarak belirlendi. Sonuç: Yapay zekâ, zor entübasyonu tahmin etmede oldukça başarılı olmuştur. Ayrıca yapay zekâ algoritmaları ile kolay ve zor entübe hastaların C-L sınıflandırmaları başarıyla tahmin edilmiştir. Laringeal görünüm için 6 dereceli modifiye C-L sınıflandırması kullanmak, daha güçlü zor entübasyon tahmini sağlayabilir. Yapay zekâ eğitiminde daha güvenli ve daha güçlü bir tahmin, zor entübasyon tanımını destekleyen bireysel farklılıklar ve klinik özellikler eklenerek elde edilebilir.

Anahtar Kelimeler:

Trakeal entübasyon tahmini, zor entübasyon, yapay zeka, Cormack-Lehane, entübasyon, anestezi

Prediction of Difficult Tracheal Intubation by Artificial Intelligence: A Prospective Observational Study

Aim: Many predictive clinical tests are used together for preoperative detection of patients with difficult airway risk. In this study, we aimed to predict difficult intubation with different artificial intelligence algorithms using various clinical tests and anthropometric measurements, besides, to evaluate the accuracy performance of Cormack and Lehane (C-L) classification with artificial intelligence. Material and Methods: This study was conducted as a single-blind prospective observational study between 2016 and 2019. A total of 1486 patients with American Society of Anesthesiologists physical status I-III, scheduled to undergo elective surgery and requiring endotracheal intubation, were included. Demographic variables, clinical tests and anthropometric measurements of the patients were recorded. Difficult intubation was evaluated using the 4-grade C-L system according to the easy and difficult intubation criteria. Difficult intubation was tried to predict using 16 different artificial intelligence algorithms. Results: The highest success rate among artificial intelligence algorithms was obtained by the RandomForest method. With this method, difficult intubation was predicted with 92.85% sensitivity, 96.94% specificity, 93.69% positive predictive value and 96.52% negative predictive value. C-L classification accuracy performance also determined as 95.60%. Conclusion: Artificial intelligence has been considerably successful in predicting difficult intubation. Besides, C-L classifications of easy and difficult intubated patients were successfully predicted with artificial intelligence algorithms. Using a 6-grade modified C-L classification for laryngeal view may provide stronger difficult intubation prediction. A safer and more potent prediction in training artificial intelligence can be achieved by adding individual differences and clinical features that support the definition of difficult intubation.

Keywords:

Tracheal intubation prediction, difficult intubation, artificial intelligence, intubation, anesthesia, Cormack-Lehane,

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