Clinical trial NCT04259021: The Voice Analysis as a Preoperative Prediction Method of a Difficult Airway
Official title: The Voice Analysis as a Preoperative Prediction Method of a Difficult Airway
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Difficult or Failed Intubation
Voice
Laryngoscopy
Keywords: difficult air way, pre-anesthetic evaluation, voice analysis
Lead sponsor: Fundacion Dexeus
Sponsor class: OTHER
First posted: 2020-02-06
Enrollment: 722
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients who will undergo general anesthesia by orotracheal or nasotracheal intubation

Brief summary: Before an anesthetic procedure, airway management is essential to ensure adequate ventilation and breathing of the patient during the entire surgical process.

The preanesthetic evaluation of the airway allows for proper planning, facilitates the anticipation of human resources and necessary means to face the possible challenges in a safe and efficient way. Orofacial mask ventilation and endotracheal intubation are a crucial step in general anesthesia. Most of the time, management is not complicated, but when an unpredicted difficult airway occurs, it is currently one of the most important challenges to face as an anesthesiologist. These situations are rare as the prevalence of a difficult airway is approximately 2.2% of the general population.

When there is a case of a difficult airway and adequate management is not achieved, very serious complications may occur including brain damage, cardio-respiratory arrest, aspiration of gastric content, traumatic airway injuries, tooth damage, unnecessary surgical access to keep the airway permeable or death. For these reasons, in anesthesia, an unforeseen difficult airway is considered a crisis situation. Therefore, a preoperative airway assessment is paramount.

Traditional predictive tests evaluate multiple anthropometric characteristics in which the physical presence of the patient is mandatory. However, no test can currently predict a difficult airway based on a single characteristic nor in the patient's absence. Nowadays, the optimization of resources and new technologies have increased interest in developing new tests or methods for preoperatively assessing the difficulty of the airway and new methods of airway evaluation have been proposed. As recently demonstrated, the detection of a difficult airway depends not only on the morphology but also on functional traits of the airway. Some studies propose the analysis of voice parameters as a reflection of anatomical and functional features of the superior airway.

The investigators propose that the analysis of voice characteristics could reflect the airway's anatomy and therefore the investigators will be able to predict a difficult airway, and this would enable the development of a voice-based assessment method which could have an promising role in facilitating telematic airway evaluation.

Detailed description: Proper airway management is essential for safe anesthesia practice. In particular, ventilation with an orofacial mask and endotracheal intubation are a crucial step in general anesthesia. The prevalence of a difficult airway is approximately 2.2% although it can vary greatly according to different groups of patients. It is defined, according to the American Society of Anesthesiology, as the clinical situation in which an experienced anesthesiologist has difficulty with ventilation using a face mask, during endotracheal intubation or both. Although other definitions also exist and include difficulty in ventilation or placement of supraglottic devices, difficult laryngoscopy defined by a 3 or 4 grade on the Cormack-Lehane scale or multiple attempts at intubation. A case of an unexpected difficult airway can lead to minor complications, such as loss or damage to teeth, pharynx or entail severe injuries, such as aspiration of gastric content, laryngeal lesions, respiratory failure, cardiorespiratory arrest or even death. Furthermore, complications increase considerably when there are no alternative plans and multiple repeated attempts at intubation are made.

Thus, the factors that mostly contribute to complications when an unanticipated difficult airway is found are due to deficiencies in identification, communication, preoperative planning and lack of training. An unpredicted difficult airway is a situation in which decision making is difficult, management is complex, and it is considered a crisis situation.

The importance of evaluation and management of the airway is such that different study groups within the main scientific societies of anesthesiologists are dedicated to studying and implementing protocols with the aim of avoiding these risky situations. A difficult airway should be detected during the pre-anesthetic visit, thus allowing the anesthesiologist to evaluate the patient, identify risk factors, advance to possible complications, and prepare an appropriate anesthetic plan. This means organizing both material and personal help, informing the patient; changing the type of anesthesia or even postponing elective surgeries to schedule them when the patient is correctly optimized making sure that the means in which the surgical procedure is safe.

Regardless of the surgical procedure and the initial anesthetic plan, the evaluation of the airway should always be performed with all patients since any sedation or regional anesthesia can be converted into a general anesthesia and require control of the airway.

In recent years, an increase in the efficiency of health procedures without the loss of safety or quality is expected. An attempt to protocolize a remote preoperative evaluation (telephone call) has been made which patients undergoing low complexity procedures. However, the most important limiting factor in remote pre-anesthetic evaluation is the inability to perform the airway assessment properly. Although there are risk factors that can be detected with a medical history such as a previous record of a difficult airway, obstructive sleep apnea syndrome, hoarseness, dysphonia or obesity, the traditional evaluative predictive tests require the physical presence of the patient and are based on the physical examination of the patient such as thyroid-chin distance, cervical mobility, mouth opening and subluxation capacity, or the assessment of pharyngeal structures by the Mallampati test consisting of the direct visualization of the upper airway. However, no test can predict a difficult airway based on a single characteristic in the patient's absence.

The combined evaluation of several risk factors obtains greater sensitivity than when analyzed in isolation. This is the case of the Arné Test, which consists of a multifactorial index that offers a sensitivity and specificity greater than 90% to predict a difficult airway when a score greater than or equal to 11 is obtained. In this context, new evaluation methods have appeared, such as ultrasound or other imaging tests, which intend to correlate the anatomical images of the airway with the presence of a difficult airway. But, the detection of a difficult airway depends not only on the morphology, but also on its functional characteristics. With the introduction of new technologies many attempts to develop other methods to predict a difficult airway such as facial recognition based on image processing have been made, however they have not succeeded yet. Other investigators propose the analysis of voice parameters as a reflection of anatomical and functional characteristics of the superior airway. Specifically, studies carried out in the field of maxillofacial surgery describe how the expansion of the maxilla affects the widening of the upper airway and, consequently, in the formation of the vowels and how this translates into a variation in the properties of the voice, such as frequency or amplitude. Accordingly, the investigators propose that voice characteristic analysis could reflect the airway's anatomy and be able to predict a difficult airway.

Building on this, the investigators aim to develop a voice airway assessment method that replaces anthropometric parameters evaluated in traditional tests to predict a difficult airway, facilitating a remote airway evaluation.

Voice recording will be made through a smart phone application to patients who are going to undergo general anesthesia and require orotracheal or nasotracheal intubation, by direct laryngoscopy, at the pre-anesthetic visit. The day of the surgical procedure, the result of the intubation - whether or not a difficult airway exists- will be registered.

The records of the mobile application database will be downloaded, the voice signal will be processed, and parameters related to frequency, morphology and perturbation will be extracted employing Matlab® as these are considered continuous variables, to determine the statistical significance of the differences within parameters. The non-parametric test of Kolmogorov-Smirnov will be used for an easy and a difficult independent airway groups. Thus, these variables will be introduced into several classification algorithms obtained by combined methods using machine learning in order to predict the classification of patients according to the Cormack scale grade and sensitivity and specificity will be determined to assess their ability to predict a difficult airway. The area under the receiver operating characteristic curve will be used to assess the ability of the method to predict difficulty.

The recruitment period will take place over a period of 12 months or until the estimated sample size is reached. The total duration of the study will be approximately 6 months years, ending with a total of 800 patients.

Eligibility criteria: Inclusion Criteria:

* American Society of Anesthesiologists classification I-III
* Adults over 18 years
* Scheduled for intervention or surgical procedure in need of orotracheal or nasotracheal intubation by direct laryngoscopy
* Patients who have given their informed consent

Exclusion Criteria:

* American Society of Anesthesiologists classification \> III
* Minors
* Emergency procedures
* Patients who refuse to participate in the study.

Primary outcomes:
measure: Difficult Airway Criteria (through Arne Test)
timeFrame: Baseline
measure: Voice power spectrum
timeFrame: Baseline
measure: Voice pitch frequency
timeFrame: Baseline
measure: Voice formants
timeFrame: Baseline
measure: Voice harmonics
timeFrame: Baseline
measure: Jitter measurements
timeFrame: Baseline
measure: Shimmer measurements
timeFrame: Baseline
measure: Harmonic to noise ratio
timeFrame: Baseline
measure: Voice Turbulence Index
timeFrame: Baseline
measure: Normalized Noise Energy
timeFrame: Baseline
measure: Intubation process
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Barcelona
country: Spain
facility: Hospital Universitario Dexeus
Study officials:
affiliation: Fundacion Dexeus
name: Claudia Rodiera, M.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: de Carvalho CC, da Silva DM, de Carvalho Junior AD, Santos Neto JM, Rio BR, Neto CN, de Orange FA. Pre-operative voice evaluation as a hypothetical predictor of difficult laryngoscopy. Anaesthesia. 2019 Sep;74(9):1147-1152. doi: 10.1111/anae.14732. Epub 2019 Jun 11.
citation: Apfelbaum JL, Hagberg CA, Caplan RA, Blitt CD, Connis RT, Nickinovich DG, Hagberg CA, Caplan RA, Benumof JL, Berry FA, Blitt CD, Bode RH, Cheney FW, Connis RT, Guidry OF, Nickinovich DG, Ovassapian A; American Society of Anesthesiologists Task Force on Management of the Difficult Airway. Practice guidelines for management of the difficult airway: an updated report by the American Society of Anesthesiologists Task Force on Management of the Difficult Airway. Anesthesiology. 2013 Feb;118(2):251-70. doi: 10.1097/ALN.0b013e31827773b2. No abstract available.
citation: Lebacq J, Schoentgen J, Cantarella G, Bruss FT, Manfredi C, DeJonckere P. Maximal Ambient Noise Levels and Type of Voice Material Required for Valid Use of Smartphones in Clinical Voice Research. J Voice. 2017 Sep;31(5):550-556. doi: 10.1016/j.jvoice.2017.02.017. Epub 2017 Mar 18.
citation: Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Trans Biomed Eng. 2012 May;59(5):1264-71. doi: 10.1109/TBME.2012.2183367. Epub 2012 Jan 9.