The Correlation between the Results of the Sniffin' Sticks Test, Demographic Data, and Questionnaire of Olfactory Disorders in a Hungarian Population after a SARS-CoV-2 Infection

Assessment of olfactory recovery after COVID-19: cross-sectional study

OBJECTIVE

This study aimed to evaluate recovery patterns of olfactory dysfunction among recovered COVID-19 patients, both subjective and objective, and correlate this recovery to the severity of the disease.

METHODS

The study recruited 200 patients and assigned them to two equal groups, one of them was a control group. The olfactory function of the study group was assessed via subjective and objective methods at baseline and then monthly for three months, with changes in smell function reported at each visit. These patients underwent chemosensory testing using the Sniffin' Sticks test and completed the validated Arabic version of the Questionnaire of Olfactory Disorders-Negative Statements (QOD-NS).

RESULTS

Olfactory dysfunction occurred on the first day of COVID-19 symptoms in 37% of participants. Subjective reports suggested smell recovery in 55% after 3 months, but Sniffin' Sticks showed only 1% with normal function, indicating persistent deficits in others. This study revealed smell recovery for 93% of participants (median 14 days), with most (58%) recovering within 2 weeks. No significant links were found between demographics, COVID-smell loss timing, and recovery speed.

CONCLUSION

Three months after COVID-19, many patients perceive smell recovery, but objective tests reveal shockingly high rates of persistent dysfunction. Further follow-up with objective tests is vital to assess the true burden and potential long-term effects of smell loss.

Quantitive Assessment of Gustatory Function and Its Association with Demographics, and Systemic Morbidity

This study aimed to analyze the associations of gustatory dysfunction as measured by validated taste strips with demographics and co-morbidities. This cross-sectional study retrospectively analyzed records of patients who attended the Orofacial Chemosensory Center of Hadassah Medical Center between 2017 and 2020. Taste strips were used as a validated method to determine taste dysfunction. A total of 272 subjects were included, 137 (50.4%) women and 135 (49.6%) men, with a mean age of 53.5 ± 19.3 years and age range of 18-98 years. The total taste score among the study population was 8.53 ± 4.03 (scale range 0-16). Age had a significant negative correlation with the total taste score (p = 0.001), and men exhibited worse total (p < 0.001), salty (p = 0.003), and bitter (p < 0.001) scores. Major trauma was associated with worse total (p < 0.001) and specialized taste assessments (sweet (p = 0.001), sour (p = 0.002), salty (p = 0.016), and bitter (p < 0.001)). Chemotherapy was associated with reduced total (p < 0.001), salty (p = 0.003), and bitter (p = 0.001) taste scores. Zinc deficiency exhibited worse salty (p = 0.027) and total (p = 0.038) taste scores. Patients with burning mouth syndrome (BMS) showed higher salty scores (p = 0.017). Patients who experienced exposure to toxic chemicals exhibited worse salty scores (p = 0.024). We conclude that gustatory dysfunction is associated with older age, male sex, and co-morbidities of major trauma, current chemotherapy, zinc deficiency, BMS, and exposure to toxins. The study highlights the importance of systemic evaluation and quantitive gustatory dysfunction assessment as part of the diagnostic process of patients with subjective complaints of taste disorders.

Development of a Digital Olfactory Function Test: A Preliminary Study

Olfactory dysfunction is associated with conditions such as neurodegenerative diseases, diabetes, obesity, autoimmune diseases, mental illnesses, and upper-airway-related diseases. The COVID-19 pandemic necessitated the development of an examiner-independent olfactory function test. We recently developed a digital olfactory function test called Digitalscent (DIGITAL SCENT), which is a kiosk-type device with an integrated hardware system. The protocol follows conventional psychophysical olfactory function protocols, including threshold, discrimination, and identification test subsets. Eight healthy participants without olfactory dysfunction volunteered for the suitability test and completed both the YSK olfactory function and Digitalscent tests. Pearson correlations were determined between the YSK olfactory function and Digitalscent tests. Digitalscent could be implemented as a conventional olfactory function test, and all participants followed the Digitalscent test protocols. Limitations in the threshold and identification test subsets included unfamiliarity of the patients to the digital test and incompleteness in the sophisticated release of odorants. A strength of the identification test subset was the dual sensory stimulation of vision and olfaction. Digitalscent could-without facilitating viral transmission-enable early diagnosis of olfactory dysfunction during respiratory viral pandemics. Future studies with larger sample sizes are warranted to facilitate wider use of this digital olfactory function test.

An olfactory perceptual fingerprint in people with olfactory dysfunction due to COVID-19

The sense of smell is based on sensory detection of the molecule(s), which is then further perceptually interpreted. A possible measure of olfactory perception is an odor-independent olfactory perceptual fingerprint (OPF) defined by Snitz et al. We aimed to investigate whether OPF can distinguish patients with olfactory dysfunction (OD) due to coronavirus disease (COVID-19) from controls and which perceptual descriptors are important for that separation. Our study included 99 healthy controls and 41 patients. They rated 10 odors using 8 descriptors such as "pleasant," "intense," "familiar," "warm," "cold," "irritating," "edible," and "disgusting." An unsupervised machine learning method, hierarchical cluster analysis, showed that OPF can distinguish patients from controls with an accuracy of 83%, a sensitivity of 51%, and a specificity of 96%. Furthermore, a supervised machine learning method, random forest classifier, showed that OPF can distinguish patients and controls in the testing dataset with an accuracy of 86%, a sensitivity of 64%, and a specificity of 96%. Principal component analysis and random forest classifier showed that familiarity and intensity were the key qualities to explain the variance of the data. In conclusion, people with COVID-19-related OD have a fundamentally different olfactory perception.