Pathology-Related Influences on the VEM: Three Years’ Experience since Implementation of a New Parameter in Phoniatric Voice Diagnostics

Abstract

The vocal extent measure (VEM) represents a new diagnostic tool to express vocal capacity by quantifying the dynamic performance and frequency range of voice range profiles (VRPs). For VEM calculation, the VRP area is multiplied by the quotient of the theoretical perimeter of a circle with equal VRP area and the actual VRP perimeter. Since different diseases affect voice function to varying degrees, pathology-related influences on the VEM should be investigated more detailed in this retrospective study, three years after VEM implementation. Data was obtained in a standardized voice assessment comprising videolaryngostroboscopy, voice handicap index (VHI-9i), and acoustic-aerodynamic analysis with automatic calculation of VEM and dysphonia severity index (DSI). The complete dataset comprised 1030 subjects, from which 994 adults (376 male, 618 female; 18-86 years) were analyzed more detailed. The VEM differed significantly between pathology subgroups ( p < 0.001 ) and correlated with the corresponding DSI values. Regarding VHI-9i, the VEM reflected the subjective impairment better than the DSI. We conclude that the VEM proved to be a comprehensible and easy-to-use interval-scaled parameter for objective VRP evaluation in all pathology subgroups. As expected, exclusive consideration of the measured pathology-related influences on the VEM does not allow conclusions regarding the specific underlying diagnosis.

Validation and Classification of the 9-Item Voice Handicap Index (VHI-9i)

“…However, the VHI-9i did not correlate well with the two objective parameters DSI and VEM, and had only slightly higher correlations with GRB. This supports recent studies that all these vocal parameters measure different aspects of a patient’s voice and are neither mutually interchangeable nor redundant [ 31 , 36 , 41 , 43 ]. Due to the weak correlations, poor discriminating performance and sometimes impossible cut-off points, DSI, VEM and G ultimately had no part in our recommendation for the revised VHI-9i cut-off points.…”

“…Calculating the median between Max J and Max CCC is not a commonly used method for solving these problems. However, based on the frequent use of the VHI-9i in clinical investigations [ 18 , 19 , 20 , 21 , 22 , 31 , 36 , 41 ], it appears that the new classification will be a practical option for clinical settings.…”

“…VRP measurements were performed to show the functional interactions of different components of voice generation regarding vocal frequency and intensity [ 33 , 34 ]. The detailed procedure of VRP recordings is described in previous publications [ 35 , 36 ].…”

“…The VEM emphasizes the vocal abilities and enables a classification of voice performance as a positive criterion [ 21 , 31 , 41 ]. Based on Müller’s investigation of 994 subjects [ 36 ], the resulting VEM scores were divided into percentiles, distinguishing a normal vocal capacity (≥108) from mildly reduced (<108 to ≥93), moderately (<93 to ≥69) and severely reduced (<69) vocal capacities.…”

Effect of Sleep Quality and Duration on Jitter, Shimmer, and Harmonics-to-Noise Ratio among Colombian University Professors

“…Therefore, our results suggest that teachers with good sleep (Norlander et al, 2005;Torsvall & Åkerstedt, 1980) have suggested that a high level of energy, optimism, and sleep quality are positively intercorrelated. However, a lower HNR usually is related to more pathological voices (Montero Benavides et al, 2014;Müller et al, 2020). Thus, it seems likely that after having a good night´s sleep, professors wake up feeling more energized, making them lower their vocal effort self-monitoring and underestimating their voice use throughout the day.…”