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Year : 2023  |  Volume : 6  |  Issue : 1  |  Page : 4-9

Predictive machine learning algorithms in anticipating problems with airway management

Department of Anaesthesiology and Critical Care, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India

Correspondence Address:
Dr. Pankaj Kundra
Department of Anaesthesiology and Critical Care, Jawaharlal Institute of Postgraduate Medical Education and Research, Second Floor, Institute Block, Puducherry - 605 006
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/arwy.arwy_3_23

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Machine learning is artificial intelligence (AI) which can predict the output variable with the fed input features. This allows computers to learn from experience without being programmed. The outcome variable in machine learning algorithm may be continuous variable or categorical variable. Supervised machine learning is commonly applied artificial intelligence (AI) in medical field. Decision tree, gradient boost machine (GBM) learning, extreme GBM (XGBM), Support vector machine, K nearest neighbour and multi-layer perceptron are few machine learning algorithms which are being utilised to address the classification and regression problems. Though the incidence of difficult intubation (DI) is rare, occurrence of such event in an unanticipated situation can result in development of arrhythmias due to desaturation and cardiac arrest if not intervened on time. It is preferred to choose the physical parameters that can predict the difficult airway more accurately in clinical scenario and train the algorithm rather than including all the non-specific parameters. Body mass index (BMI) [>30 kg.m-2: anticipated difficult mask ventilation (DMV), direct laryngoscopy (DL) and DI], inter-insicor distance (IID) (<2 cm: anticipated DL), modified Mallampati (MMP) (Grade 1 and 2: Ease of intubation; Grade 3 and 4: anticipated DI), temporomandibular distance (TMD) (<6.5 cm - anticipated DI), restriction of neck extension (if present: anticipated DL and DI), receded mandible (if present: anticipated DL and DI), and poor submandibular space compliance (if present: anticipated DL and DI) parameters which are used to predict DA by clinical assessment, can be used to feed to train the machine learning algorithm. Despite using these sophisticated tools, extubation may fail and patients require reintubation in ICU. It is very challenging to assess the lung compliance in spontaneously breathing patients as compliance will be overestimated due to generation of negative pressure. Cause for which patient has been placed on mechanical ventilation is resolved/resolving, BMI (>30 kg.m-2), intact sensorium (absence of delirium), absence of consolidation, absence of copious secretions, oxygenation status (PaO2/FiO2: >250), ventilation status (paCO2: 30-45 mmHg), measure of work of breathing (respiratory rate, rapid shallow breathing index), heart rate and blood pressure during spontaneous breathing trial (SBT) and diaphragmatic thickness fraction can be used as input features to predict the success of extubation in critically ill patients. With widespread utility of applications in medical fraternity, applications for prediction of difficult airway (or for weaning success) can be programmed which can be accessed by the clinicians to predict DA, thereby all the preparations for managing DA may be done to prevent adverse consequences of unanticipated difficult airway.

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