Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI

The Out-of-pocket Expenses of People With Tinnitus in Europe

BACKGROUND

Despite the high frequency of tinnitus and its impact on wellbeing, little is known about its economic burden, and, to our knowledge, no data are available on out-of-pocket (OOP) expenses.

METHODS

In 2022, a survey was conducted on OOP costs of tinnitus. We enrolled 679 participants with slight, moderate, and severe tinnitus in Italy, United Kingdom, Netherlands, Germany, and Spain. We estimated annual OOP expenses for tinnitus-related healthcare visits, treatments, medications, and alternative medicine practices. Prevalence of tinnitus in the general population, obtained from a representative survey we conducted in Europe in 2017-2018, was used to generalize costs for people with any tinnitus at the national level.

RESULTS

OOP expenses were 368€ (95% confidence intervals [CI], 78€-690€), 728€ (95% CI, 316€-1,288€), and 1,492€ (95% CI, 760€-2,688€) for slight, moderate, and severe tinnitus, respectively, with annual expenditure of 565€ for people with any tinnitus: 209€ for healthcare visits; 93€ for treatments; 16€ for drugs; 64€ for hearing supporting systems; and 183€ for acupuncture, homeopathy, and osteopathy. Individuals with slight, moderate, and severe tinnitus expressed a willingness to invest 1.6, 4.3, and 7.0 times their monthly income, respectively, to achieve complete relief from tinnitus.

CONCLUSION

This study offers for the first time insights into the OOP expenses incurred by individuals with tinnitus. OOP expenses exhibited substantial variations based on severity status, accounting for more than 17 billion € in the countries considered. In terms of financial burden, these findings align tinnitus to the recognized leading disabilities, including back pain and migraine.

Current understanding of subjective tinnitus in adults

PURPOSE

An up-to-date overview of epidemiology, etiology and pathophysiological mechanisms, diagnostic and evaluation methods, current treatment status and future directions of subjective tinnitus in adults.

METHODS

Review of current evidence-based literature on subjective tinnitus in adults.

RESULTS

The prevalence of subjective tinnitus in the adult population is estimated to be around 14%, and it tends to increase with age. Subjective tinnitus is a complex condition with multiple factors contributing to its origin. However, the exact causes and underlying mechanisms remain unknown. Potential causes may include hearing loss, dysfunction in the somatosensory system, and auditory cortical dysfunction, although severe underlying pathology is rare. Currently, diagnosis primarily relies on patient self-reported medical history and physician-based clinical assessment due to the lack of objective testing. Various treatment and management options have been proposed, but their effectiveness varies, and there is no universally agreed-upon treatment option.

CONCLUSIONS

Tinnitus is a complex and heterogeneous disease with a high incidence rate and a tendency to increase with age. A holistic perspective is needed to understand the generation, perception, and emotional responses to tinnitus. Diagnosis requires a comprehensive assessment based on medical history and relevant examinations, identification of concurrent psychosomatic comorbidities, and active pursuit of objective diagnostic methods. At the same time, on the basis of existing treatment plans and combining emerging technologies, we will develop new personalized, precise, and combined treatment plans.

Phenotypic spectrum of tinnitus patients bearing rare ANK2 gene variants

PURPOSE

To describe the clinical, audiological, and psychometric features observed in patients with chronic tinnitus and rare variants in the ANK2 gene.

METHODS

We report a case series of 12 patients with chronic tinnitus and heterozygous variants in the ANK2 gene. Tinnitus phenotyping included audiological (standard and high-frequency audiometry, Auditory Brainstem Responses (ABR) and Auditory Middle Latency Responses (AMLR)), psychoacoustic and psychometric assessment by a Visual Analog Scale (VAS) for tinnitus annoyance, the Tinnitus Handicap Inventory (THI), the test on Hypersensitivity to Sound (THS-GÜF), the Patient Health Questionnaire (PHQ-9), the Hospital Anxiety and Depression Scale (HADS) and the Montreal Cognitive Assessment (MoCA).

RESULTS

All patients reported a persistent, unilateral noise-type tinnitus, mainly described as white noise or narrowband noise. Seven patients (58%) were considered to have extreme phenotype (THI score > 76), and all patients reported some degree of hyperacusis (THS-GÜF score > 18 in 75% of patients). Seven patients scored MoCA < 26, regardless of the age reported, suggesting a mild cognitive disorder. ABR showed no significant differences in latencies and amplitudes between ears with or without tinnitus. Similarly, the latencies of Pa, Pb waves, and NaPa complex in the AMLR did not differ based on the presence of tinnitus. However, there were statistical differences in the amplitudes of Pa waves in AMLR, with significantly greater amplitudes observed in ears with tinnitus.

CONCLUSION

Patients with ANK2 variants and severe tinnitus exhibit an endophenotype featuring hyperacusis, persistent noise-like tinnitus, high-frequency hearing loss, and decreased amplitudes in AMLR. However, anxiety, depression, and cognitive symptoms vary among individuals.

Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

BACKGROUND

Machine learning (ML) models are evaluated in a test set to estimate model performance after deployment. The design of the test set is therefore of importance because if the data distribution after deployment differs too much, the model performance decreases. At the same time, the data often contains undetected groups. For example, multiple assessments from one user may constitute a group, which is usually the case in mHealth scenarios.

METHODS

In this work, we evaluate a model's performance using several cross-validation train-test-split approaches, in some cases deliberately ignoring the groups. By sorting the groups (in our case: Users) by time, we additionally simulate a concept drift scenario for better external validity. For this evaluation, we use 7 longitudinal mHealth datasets, all containing Ecological Momentary Assessments (EMA). Further, we compared the model performance with baseline heuristics, questioning the essential utility of a complex ML model.

RESULTS

Hidden groups in the dataset leads to overestimation of ML performance after deployment. For prediction, a user's last completed questionnaire is a reasonable heuristic for the next response, and potentially outperforms a complex ML model. Because we included 7 studies, low variance appears to be a more fundamental phenomenon of mHealth datasets.

CONCLUSIONS

The way mHealth-based data are generated by EMA leads to questions of user and assessment level and appropriate validation of ML models. Our analysis shows that further research needs to follow to obtain robust ML models. In addition, simple heuristics can be considered as an alternative for ML. Domain experts should be consulted to find potentially hidden groups in the data.

Screening for Circulating Inflammatory Proteins Does Not Reveal Plasma Biomarkers of Constant Tinnitus

BACKGROUND AND OBJECTIVE

Tinnitus would benefit from an objective biomarker. The goal of this study is to identify plasma biomarkers of constant and chronic tinnitus among selected circulating inflammatory proteins.

METHODS

A case-control retrospective study on 548 cases with constant tinnitus and 548 matched controls from the Swedish Tinnitus Outreach Project (STOP), whose plasma samples were examined using Olink's Inflammatory panel. Replication and meta-analysis were performed using the same method on samples from the TwinsUK cohort. Participants from LifeGene, whose blood was collected in Stockholm and Umeå, were recruited to STOP for a tinnitus subtyping study. An age and sex matching was performed at the individual level. TwinsUK participants (n = 928) were selected based on self-reported tinnitus status over 2 to 10 years. Primary outcomes include normalized levels for 96 circulating proteins, which were used as an index test. No reference standard was available in this study.

RESULTS

After adjustment for age, sex, BMI, smoking, hearing loss, and laboratory site, the top proteins identified were FGF-21, MCP4, GDNF, CXCL9, and MCP-1; however, these were no longer statistically significant after correction for multiple testing. Stratification by sex did not yield any significant associations. Similarly, associations with hearing loss or other tinnitus-related comorbidities such as stress, anxiety, depression, hyperacusis, temporomandibular joint disorders, and headache did not yield any significant associations. Analysis in the TwinsUK failed in replicating the top candidates. Meta-analysis of STOP and TwinsUK did not reveal any significant association. Using elastic net regularization, models exhibited poor predictive capacity tinnitus based on inflammatory markers [sensitivity = 0.52 (95% CI 0.47-0.57), specificity = 0.53 (0.48-0.58), positive predictive value = 0.52 (0.47-0.56), negative predictive values = 0.53 (0.49-0.58), and AUC = 0.53 (0.49-0.56)].

DISCUSSION

Our results did not identify significant associations of the selected inflammatory proteins with constant tinnitus. Future studies examining longitudinal relations among those with more severe tinnitus and using more recent expanded proteomics platforms and sampling of cerebrospinal fluid could increase the likelihood of identifying relevant molecular biomarkers.

Candidate Key Proteins in Tinnitus: A Bioinformatic Study of Synaptic Transmission in Spiral Ganglion Neurons

To study key proteins associated with changes in synaptic transmission in the spiral ganglion in tinnitus, we build three gene lists from the GeneCard database: 1. Perception of sound (PoS), 2. Acoustic stimulation (AcouStim), and 3. Tinnitus (Tin). Enrichment analysis by the DAVID database resulted in similar Gene Ontology (GO) terms for cellular components in all gene lists, reflecting synaptic structures known to be involved in auditory processing. The STRING protein-protein interaction (PPI) network and the Cytoscape data analyzer were used to identify the top two high-degree proteins (HDPs) and their high-score interaction proteins (HSIPs) identified by the combined score (CS) of the corresponding edges. The top two protein pairs (key proteins) for the PoS are BDNF-GDNF and OTOF-CACNA1D and for the AcouStim process BDNF-NTRK2 and TH-CALB1. The Tin process showed BDNF and NGF as HDPs, with high-score interactions with NTRK1 and NGFR at a comparable level. Compared to the PoS and AcouStim process, the number of HSIPs of key proteins (CS > 90. percentile) increases strongly in Tin. In the PoS and AcouStim networks, BDNF receptor signaling is the dominant pathway, and in the Tin network, the NGF-signaling pathway is of similar importance. Key proteins and their HSIPs are good indicators of biological processes and of signaling pathways characteristic for the normal hearing on the one hand and tinnitus on the other.

Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data

Tinnitus is a highly prevalent condition, affecting more than 1 in 7 adults in the EU and causing negative effects on sufferers' quality of life. In this study, we utilised data collected within the "UNITI" project, the largest EU tinnitus-related research programme. Initially, we extracted characteristics from both auditory brainstem response (ABR) and auditory middle latency response (AMLR) signals, which were derived from tinnitus patients. We then combined these features with the patients' clinical data, and integrated them to build machine learning models for the classification of individuals and their ears according to their level of tinnitus-related distress. Several models were developed and tested on different datasets to determine the most relevant features and achieve high performances. Specifically, seven widely used classifiers were utilised on all generated datasets: random forest (RF), linear, radial, and polynomial support vector machines (SVM), naive bayes (NB), neural networks (NN), and linear discriminant analysis (LDA). Results showed that features extracted from the wavelet-scattering transformed AMLR signals were the most informative data. In combination with the 15 LASSO-selected clinical features, the SVM classifier achieved optimal performance with an AUC value, sensitivity, and specificity of 92.53%, 84.84%, and 83.04%, respectively, indicating high discrimination performance between the two groups.

Tinnitus and Influencing Comorbidities

Numerous studies show that impairments in chronic tinnitus are closely connected with psychosomatic and other concomitant symptoms. This overview summarizes some of these studies. Beyond hearing loss, individual interactions of medical and psychosocial stress factors as well as resources are of central importance. Tinnitus related distress reflects a large number of intercorrelated, psychosomatic influences - such as personality traits, stress reactivity and depression or anxiety - which can be accompanied by cognitive difficulties and should be conceptualized and assessed within a vulnerability-stress-reaction model. Superordinate factors such as age, gender or education level can increase vulnerability to stress. Therefore, diagnosis and therapy of chronic tinnitus be individualised, multidimensional and interdisciplinary. Multimodal psychosomatic therapy approaches aim to address individually constellated medical, audiological and psychological influences in order to sustainably increase the quality of life of those affected. Counselling in the first contact is also indispensable for diagnosis and therapy.

Tinnitus at the Junction of Traditional Medicine and Modern Technology

The WHO estimated that 430 million people worldwide suffer from moderate-to-severe hearing loss [...].

Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation

Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools' detection and annotation results, regarding the waves of interest, were then compared to the clinicians' manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals.

Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season

Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment.

Smartphone-Guided Educational Counseling and Self-Help for Chronic Tinnitus

Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educational counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasibility study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone app unsupervised during their daily routine over a period of four months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of 0.408, while the improvements on the Tinnitus Handicap Inventory (THI) were much smaller with an effect size of 0.168. An analysis of user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively reported a stronger improvement in the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.

Alterations in auditory brainstem response distinguish occasional and constant tinnitus

BACKGROUND

The heterogeneity of tinnitus is thought to underlie the lack of objective diagnostic measures.

METHODS

Longitudinal data from 20,349 participants of the Swedish Longitudinal Occupational Survey of Health (SLOSH) cohort from 2008 to 2018 were used to understand the dynamics of transition between occasional and constant tinnitus. The second part of the study included electrophysiological data from 405 participants of the Swedish Tinnitus Outreach Project (STOP) cohort.

RESULTS

We determined that with increasing frequency of the occasional perception of self-reported tinnitus, the odds of reporting constant tinnitus after 2 years increases from 5.62 (95% CI, 4.83–6.55) for previous tinnitus (sometimes) to 29.74 (4.82–6.55) for previous tinnitus (often). When previous tinnitus was reported to be constant, the odds of reporting it as constant after 2 years rose to 603.02 (524.74–692.98), suggesting that once transitioned to constant tinnitus, the likelihood of tinnitus to persist was much greater. Auditory brain stem responses (ABRs) from subjects reporting nontinnitus (controls), occasional tinnitus, and constant tinnitus show that wave V latency increased in constant tinnitus when compared with occasional tinnitus or nontinnitus. The ABR from occasional tinnitus was indistinguishable from that of the nontinnitus controls.

CONCLUSIONS

Our results support the hypothesis that the transition from occasional to constant tinnitus is accompanied by neuronal changes in the midbrain leading to a persisting tinnitus, which is then less likely to remit.

FUNDING

This study was supported by the GENDER-Net Co-Plus Fund (GNP-182), the European Union’s Horizon 2020 grants no. 848261 (Unification of Treatments and Interventions for Tinnitus [UNITI]) and no. 722046 (European School for Interdisciplinary Tinnitus Research [ESIT]).

Unification of Treatments and Interventions for Tinnitus Patients (UNITI): a study protocol for a multi-center randomized clinical trial

BACKGROUND

Tinnitus represents a relatively common condition in the global population accompanied by various comorbidities and severe burden in many cases. Nevertheless, there is currently no general treatment or cure, presumable due to the heterogeneity of tinnitus with its wide variety of etiologies and tinnitus phenotypes. Hence, most treatment studies merely demonstrated improvement in a subgroup of tinnitus patients. The majority of studies are characterized by small sample sizes, unstandardized treatments and assessments, or applications of interventions targeting only a single organ level. Combinatory treatment approaches, potentially targeting multiple systems as well as treatment personalization, might provide remedy and enhance treatment responses. The aim of the present study is to systematically examine established tinnitus therapies both alone and in combination in a large sample of tinnitus patients. Further, it wants to provide the basis for personalized treatment approaches by evaluating a specific decision support system developed as part of an EU-funded collaborative project (Unification of treatments and interventions for tinnitus patients; UNITI project).

METHODS/STUDY DESIGN

This is a multi-center parallel-arm randomized clinical trial conducted at five different clinical sites over the EU. The effect of four different tinnitus therapy approaches (sound therapy, structured counseling, hearing aids, cognitive behavioral therapy) applied over a time period of 12 weeks as a single or rather a combinatory treatment in a total number of 500 chronic tinnitus patients will be investigated. Assessments and interventions are harmonized over the involved clinical sites. The primary outcome measure focuses on the domain tinnitus distress assessed via the Tinnitus Handicap Inventory.

DISCUSSION

Results and conclusions from the current study might not only provide an essential contribution to combinatory and personalized treatment approaches in tinnitus but could also provide more profound insights in the heterogeneity of tinnitus, representing an important step towards a cure for tinnitus.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04663828 . Registered on 11 December 2020.

Differences in characteristics between people with tinnitus that seek help and that do not

Knowledge on characteristics of people that seek help for tinnitus is scarce. The primary objective of this study was to describe differences in characteristics between people with tinnitus that seek help compared to those who do not seek help. Next, we described differences in characteristics between those with and without tinnitus. In this cross-sectional study, we sent a questionnaire on characteristics in different domains; demographic, tinnitus-specific, general- and psychological health, auditory and noise- and substance behaviour. We assessed if participants had sought help or planned to seek help for tinnitus. Tinnitus distress was defined with the Tinnitus Functional Index. Differences between groups (help seeking: yes/no, tinnitus: yes/no) were described. 932 people took part in our survey. Two hundred and sixteen participants were defined as having tinnitus (23.2%). Seventy-three of those sought or planned to seek help. A constant tinnitus pattern, a varying tinnitus loudness, and hearing loss, were described more frequently in help seekers. Help seekers reported higher TFI scores. Differences between help seekers and people not seeking help were mainly identified in tinnitus- and audiological characteristics. These outcomes might function as a foundation to explore the heterogeneity in tinnitus patients.

UNITI Mobile-EMI-Apps for a Large-Scale European Study on Tinnitus

More and more observational studies exploit the achievements of mobile technology to ease the overall implementation procedure. Many strategies like digital phenotyping, ecological momentary assessments or mobile crowdsensing are used in this context. Recently, an increasing number of intervention studies makes use of mobile technology as well. For the chronic disorder tinnitus, only few long-running intervention studies exist, which use mobile technology in a larger setting. Tinnitus is characterized by its heterogeneous patient's symptom profiles, which complicates the development of general treatments. In the UNITI project, researchers from different European countries try to unify existing treatments and interventions to cope with this heterogeneity. One study arm (UNITI Mobile) exploits mobile technology to investigate newly implemented interventions types, especially within the pan-European setting. The goals are to learn more about the validity and usefulness of mobile technology in this context. Furthermore, differences among the countries shall be investigated. Practically, two native intervention apps have been developed for UNITI and the mobile study arm, which pose features not presented so far in other apps of the authors. Along the implementation procedure, it is discussed whether these features might leverage similar types of studies in future. Since instruments like the mHealth evidence reporting and assessment checklist (mERA), developed by the WHO mHealth technical evidence review group, indicate that aspects shown for UNITI Mobile are important in the context of health interventions using mobile phones, our findings may be of a more general interest and are therefore being discussed in the work at hand.

Why a Clinical Decision Support System is needed for Tinnitus?

Tinnitus is the perception of a phantom sound and the individual's reaction to it. Although much progress has been made, tinnitus remains an unresolved scientific and clinical issue, affecting more than 10% of the general population and having a high prevalence and socioeconomic burden. Clinical decision support systems (CDSS) are used to assist clinicians in their complex decision-making processes, having been proved that they improve healthcare delivery. In this paper, we present a CDSS for tinnitus, attempting to address the question which treatment approach is optimal for a particular patient based on specific parameters. The CDSS will be developed in the context of the EU-funded "UNITI" project and, after the project completion, it will be able to determine the suitability and expected attachment of a particular patient to a list of available clinical interventions, utilizing predictive and classification machine learning models.Clinical Relevance - The proposed clinically utilizable CDSS will be able to suggest the optimal treatment strategy for the tinnitus patient based on a set of heterogeneous data.

Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:816-819. [PMID: 34891415 DOI: 10.1109/embc46164.2021.9629964]

Tinnitus is attributed by the perception of a sound without any physical source causing the symptom. Symptom profiles of tinnitus patients are characterized by a large heterogeneity, which is a major obstacle in developing general treatments for this chronic disorder. As tinnitus patients often report severe constraints in their daily life, the lack of general treatments constitutes such a challenge that patients crave for any kind of promising method to cope with their tinnitus, even if it is not based on evidence. Another drawback constitutes the lack of objective measurements to determine the individual symptoms of patients. Many data sources are therefore investigated to learn more about the heterogeneity of tinnitus patients in order to develop methods to measure the individual situation of patients more objectively. As research assumes that tinnitus is caused by processes in the brain, electroencephalography (EEG) data are heavily investigated by researchers. Following this, we address the question whether EEG data can be used to classify tinnitus using a deep neural network. For this purpose, we analyzed 16,780 raw EEG samples from 42 subjects (divided into tinnitus patients and control group), with a duration of one second per sample. Four different procedures (with or without noise reduction and down-sampling or up-sampling) for automated preprocessing were used and compared. Subsequently, a neural network was trained to classify whether a sample refers to a tinnitus patient or the control group. We obtain a maximum accuracy in the test set of 75.6% using noise reduction and down-sampling. Our findings highlight the potential of deep learning approaches to detect EEG patterns for tinnitus patients as they are difficult to be recognized by humans.

Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients' characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient.The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature.The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level.In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection.

Systematic Review on Healthcare and Societal Costs of Tinnitus

Tinnitus disability is a heterogeneous and complex condition, affecting more than 10% and compromising the quality of life of 2% of the population, with multiple contributors, often unknown, and enigmatic pathophysiology. The available treatment options are unsatisfactory, as they can, at best, reduce tinnitus severity, but not eliminate its perception. Given the spread of tinnitus and the lack of a standardized treatment, it is crucial to understand the economic burden of this condition. We conducted a systematic review of the literature on PubMed/MEDLINE, Embase, the Cochrane Database of Systematic Reviews (CDSR) and Google Scholar, in order to identify all the articles published on the economic burden of tinnitus before 1 April 2021 (PROSPERO-International prospective register of systematic reviews-No: CRD42020180438). Out of 273 articles identified through our search strategy, only five articles from studies conducted in the United States of America (USA), the Netherlands and the United Kingdom (UK) provided data on tinnitus's economic costs. Three studies provided mean annual estimates per patient ranging between EUR 1544 and EUR 3429 for healthcare costs, between EUR 69 and EUR 115 for patient and family costs and between EUR 2565 and EUR 3702 for indirect costs, including productivity loss. The other two studies reported an annual mean cost of EUR 564 per patient for tinnitus-related clinical visits, and total costs of EUR 1388 and EUR 3725 for patients treated with a sound generator and Neuromonics Tinnitus Treatment, respectively. Our comprehensive review shows a gap in the knowledge about the economic burden of tinnitus on healthcare systems, patients and society. The few available studies show considerable expenses due to healthcare and indirect costs, while out-of-pocket costs appear to be less financially burdensome. Comprehensive health economic evaluations are needed to fill the gaps in current knowledge, using a unified method with reliable and standardized tools.

Multidisciplinary Tinnitus Research: Challenges and Future Directions From the Perspective of Early Stage Researchers

Tinnitus can be a burdensome condition on both individual and societal levels. Many aspects of this condition remain elusive, including its underlying mechanisms, ultimately hindering the development of a cure. Interdisciplinary approaches are required to overcome long-established research challenges. This review summarizes current knowledge in various tinnitus-relevant research fields including tinnitus generating mechanisms, heterogeneity, epidemiology, assessment, and treatment development, in an effort to highlight the main challenges and provide suggestions for future research to overcome them. Four common themes across different areas were identified as future research direction: (1) Further establishment of multicenter and multidisciplinary collaborations; (2) Systematic reviews and syntheses of existing knowledge; (3) Standardization of research methods including tinnitus assessment, data acquisition, and data analysis protocols; (4) The design of studies with large sample sizes and the creation of large tinnitus-specific databases that would allow in-depth exploration of tinnitus heterogeneity.

Methodological Aspects of Randomized Controlled Trials for Tinnitus: A Systematic Review and How a Decision Support System Could Overcome Barriers

Although a wide range of tinnitus management interventions is currently under research and a variety of therapeutic interventions have already been applied in clinical practice, no optimal and universal tinnitus treatment has been reached yet. This fact is to some extent a consequence of the high heterogeneity of the methodologies used in tinnitus related clinical studies. In this manuscript, we have identified, summarized, and critically appraised tinnitus-related randomized clinical trials since 2010, aiming at systematically mapping the research conducted in this area. The results of our analysis of the 73 included randomized clinical trials provide important insight on the identification of limitations of previous works, methodological pitfalls or gaps in current knowledge, a prerequisite for the adequate interpretation of current literature and execution of future studies.