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Background: The inherent complexities of conditions such as tinnitus with cause and mechanism not known, presents a lack of high-level evidence to make an inference on the treatment approaches. A comprehensive review cannot be performed owing to an insufficient number of trials. Hence a scoping review approach was adopted to provide a broad overview of approaches for tinnitus management. Objective: To review the current evidence on prevalent treatment strategies used for tinnitus management and determine barriers and facilitators for the adoption of treatment approaches in developed and developing countries. Methods: The present scoping review was performed in compliance with the PRISMA ScR checklist. A literature search was carried out in PubMed, Scopus, Google Scholar, and Cochrane databases. Articles published between 2010-2021 mentioning treatment strategies were included. However, therapies concentrating on symptomatic management were excluded. Evidence with the highest scientific hierarchy such as systematic reviews (SRs) and randomized-controlled trials (RCTs) was considered. The context for the ScR was study-region based and included studies from high-income and low and middle-income countries (HICs, LMICs) Results: A total of 20 records were included with 11 SRs and 9 RCTs. Non-pharmacological interventions demonstrated moderate efficacy, including transcranial magnetic- (TMS) and direct current stimulation (tDCS), hearing aids (combined with a sound generator or alone). No specific drug was found to have a mode of action about the target root cause due to a lack of clinical knowledge. Most of the trials used the same tools for tinnitus severity. Conclusion: Although several scoping reviews are published with an exclusive focus on individual therapies, authors recommend an umbrella review of systematic reviews to generate evidence-based practice consensus for tinnitus.
Background: The inherent complexities of conditions such as tinnitus with cause and mechanism not known, presents a lack of high-level evidence to make an inference on the treatment approaches. A comprehensive review cannot be performed owing to an insufficient number of trials. Hence a scoping review approach was adopted to provide a broad overview of approaches for tinnitus management. Objective: To review the current evidence on prevalent treatment strategies used for tinnitus management and determine barriers and facilitators for the adoption of treatment approaches in developed and developing countries. Methods: The present scoping review was performed in compliance with the PRISMA ScR checklist. A literature search was carried out in PubMed, Scopus, Google Scholar, and Cochrane databases. Articles published between 2010-2021 mentioning treatment strategies were included. However, therapies concentrating on symptomatic management were excluded. Evidence with the highest scientific hierarchy such as systematic reviews (SRs) and randomized-controlled trials (RCTs) was considered. The context for the ScR was study-region based and included studies from high-income and low and middle-income countries (HICs, LMICs) Results: A total of 20 records were included with 11 SRs and 9 RCTs. Non-pharmacological interventions demonstrated moderate efficacy, including transcranial magnetic- (TMS) and direct current stimulation (tDCS), hearing aids (combined with a sound generator or alone). No specific drug was found to have a mode of action about the target root cause due to a lack of clinical knowledge. Most of the trials used the same tools for tinnitus severity. Conclusion: Although several scoping reviews are published with an exclusive focus on individual therapies, authors recommend an umbrella review of systematic reviews to generate evidence-based practice consensus for tinnitus.
Background There is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment success are, however, lacking. Objective This study aimed to use exploratory data mining techniques (ie, decision tree models) to identify the variables associated with the treatment success of internet-based cognitive behavioral therapy (ICBT) for tinnitus. Methods Individuals (N=228) who underwent ICBT in 3 separate clinical trials were included in this analysis. The primary outcome variable was a reduction of 13 points in tinnitus severity, which was measured by using the Tinnitus Functional Index following the intervention. The predictor variables included demographic characteristics, tinnitus and hearing-related variables, and clinical factors (ie, anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Analyses were undertaken by using various exploratory machine learning algorithms to identify the most influencing variables. In total, 6 decision tree models were implemented, namely the classification and regression tree (CART), C5.0, GB, XGBoost, AdaBoost algorithm and random forest models. The Shapley additive explanations framework was applied to the two optimal decision tree models to determine relative predictor importance. Results Among the six decision tree models, the CART (accuracy: mean 70.7%, SD 2.4%; sensitivity: mean 74%, SD 5.5%; specificity: mean 64%, SD 3.7%; area under the receiver operating characteristic curve [AUC]: mean 0.69, SD 0.001) and gradient boosting (accuracy: mean 71.8%, SD 1.5%; sensitivity: mean 78.3%, SD 2.8%; specificity: 58.7%, SD 4.2%; AUC: mean 0.68, SD 0.02) models were found to be the best predictive models. Although the other models had acceptable accuracy (range 56.3%-66.7%) and sensitivity (range 68.6%-77.9%), they all had relatively weak specificity (range 31.1%-50%) and AUCs (range 0.52-0.62). A higher education level was the most influencing factor for ICBT outcomes. The CART decision tree model identified 3 participant groups who had at least an 85% success probability following the undertaking of ICBT. Conclusions Decision tree models, especially the CART and gradient boosting models, appeared to be promising in predicting ICBT outcomes. Their predictive power may be improved by using larger sample sizes and including a wider range of predictive factors in future studies.
BACKGROUND There is a huge variability in the way individuals with tinnitus respond to interventions. These experiential variations together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy (CBT) have the most evidence-base. OBJECTIVE Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment success. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment success are, however, lacking. The current study aimed to used exploratory data mining techniques (i.e., decision tree models) to identify the variables associated with treatment success for an Internet-based cognitive behavioral therapy (ICBT) for tinnitus. METHODS Individuals (n = 228) who underwent ICBT in three separate clinical trials were included in this analysis. The primary outcome variable was reducing 13 points in tinnitus severity as measured by the Tinnitus Functional Index following the intervention. Predictor variables included demographic characteristics, tinnitus, and hearing-related variables, and clinical factors (i.e., anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Analyses were undertaken using various exploratory machine learning algorithms to identify the most suitable variable. Five decision tree models were implemented, namely CART, C5.0, Gradient Boosting, AdaBoost algorithm, and Random Forest. The SHapley Additive exPlanations (SHAP) framework was applied to the two best models to identify the relative predictor importance. RESULTS Of the five decision tree models, CART (accuracy of 74%, sensitivity of 74%, specificity of 64%, and AUC .69) and Gradient boosting (accuracy of 72%, sensitivity of 78%, specificity of 59%, and area under the curve .68) were found to be the best predictive models. Although the other models had an acceptable accuracy (ranged between 56 to 66%) and sensitivity (varied between 69 to 75%), they all had relatively weak specificity (varied between 31 to 50%) and area under the curve (varied between .52 to .6). Higher baseline tinnitus severity and higher education level were the most influencing factors in the ICBT outcome. The CART decision tree model identified three participant groups who had at least 85% success probability following undertaking ICBT. CONCLUSIONS In this study, decision tree models, especially the CART and Gradient boosting models, appear to be promising in predicting the ICBT outcomes. Their predictive power may be improved by using larger sample sizes and including a wider range of predictive factors in future studies.
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