Predictive Capability of an iPad-Based Medical Device (medx) for the Diagnosis of Vertigo and Dizziness

Needs and supporting tools for primary care physicians to improve care of patients with vertigo and dizziness: a national survey

Background

The diagnostic workup and treatment decisions for vertigo or dizziness in primary care can be challenging due to the broad range of possible causes and limited time and expertise of physicians. This can lead to delays in treatment and unnecessary tests. We aimed to identify the unmet needs of primary care physicians (PCPs) and strategies to improve care for dizzy patients.

Materials and methods

An online survey was conducted among board-certified PCPs in Switzerland to explore needs in caring for dizzy patients and potential educational approaches.

Results

Based on responses from 152 participating PCPs, satisfaction and confidence were higher in diagnosing (82%) and treating (76%) acute dizziness compared to episodic/chronic cases (63 and 59%, respectively). Younger PCPs had lower diagnostic yield and confidence. Areas for improvement in specialist interactions included communication between physicians (23%/36%; always/often true), shorter waiting times for consultations (19%/40%), more detailed feedback (36%/35%), and consistent patient back referrals (31%/30%). PCPs expressed interest in hands-on courses, workshops, practical guidelines, web-based algorithms, and digital tools such as printed dizzy diaries and apps for follow-up.

Conclusion

Enhanced dialog between PCPs and specialists is crucial to address the most common unmet needs. Reducing waiting times for referrals and providing clear instructions to specialists for triage are essential. The findings from this survey will guide the development of tools to improve the diagnosis and treatment of dizzy patients. Younger PCPs, who face higher diagnostic uncertainty, should be prioritized for educational approaches such as hands-on courses, workshops, and practical recommendations.

Diagnosing the benign paroxysmal positional vertigo via 1D and deep-learning composite model

BACKGROUND

Benign Paroxysmal Positional Vertigo (BPPV) is the leading cause of vertigo, and its characteristic nystagmus induced by positional maneuvers makes it a good model for Artificial Intelligence (AI) diagnosis. However, during the testing procedure, up to 10 min of indivisible long-range temporal correlation data are produced, making the AI-informed real-time diagnosing unlikely in clinical practice.

METHODS

A combined 1D and Deep-Learning (DL) composite model was proposed. Two separate cohorts were recruited, with one for model generation and the other for evaluation of model's real-world generalizability. Eight features, including two head traces and three eye traces and their corresponding slow phase velocity (SPV) value, were served as the inputs. Three candidate models were tested, and a sensitivity study was conducted to determine the saliently important features.

RESULTS

The study included 2671 patients in the training cohort and 703 in the test cohort. A hybrid DL model achieved a micro-area under the receiver operating curve (AUROC) of 0.982 (95% CI 0.965, 0.994) and macro-AUROC of 0.965 (95% CI 0.898, 0.999) for overall classification. The highest accuracy was observed for right posterior BPPV, with an AUROC of 0.991 (95% CI 0.972, 1.000), followed by left posterior BPPV, with an AUROC of 0.979 (95% CI 0.940, 0.998), the lowest AUROC was 0.928 (95% CI 0.878, 0.966) for lateral BPPV. The SPV was consistently identified as the most predictive feature in the models. If the model process is carried out 100 times for a 10-min data, one single running takes 0.79 ± 0.06 s.

CONCLUSION

This study designed DL models which can accurately detect and categorize the subtype of BPPV, enabling a quick and straightforward diagnosis of BPPV in clinical setting. The critical feature identified in the model helps expand our understanding of this disorder.

A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study

Background

Questionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders.

Objective

This study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo.

Methods

In this multicenter prospective study, patients presenting with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation.

Results

A total of 1693 patients were enrolled, with a response rate of 96.2% (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5%) females; 1041 (61.5%) patients received the final diagnosis during the study period. Among them, 928 (54.8%) patients were included in model development and validation, and 113 (6.7%) patients who enrolled later were used as a test set for external validation. They were classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% CI 0.917-0.962) in cross-validation and 0.954 (95% CI 0.944-0.967) in external validation.

Conclusions

The questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision-making in ENT and vertigo clinics. Further studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method.

Validation of a comprehensive diagnostic algorithm for patients with acute vertigo and dizziness

BACKGROUND

Vertigo and dizziness are common complaints in emergency departments and primary care, which pose major diagnostic challenges due to various underlying etiologies. Most supportive diagnostic algorithms concentrate on either identifying cerebrovascular events or diagnosing specific vestibular disorders or are restricted to specific patient subgroups.

METHODS

The study was conducted in the scope of the 'PoiSe' project (prevention, online feedback, and interdisciplinary therapy of acute vestibular syndromes by e-health). A three-level algorithm was developed according to international guidelines and scientific evidence addressing both, the detection of cerebrovascular events and the classification to non-vascular vestibular disorders (unilateral vestibulopathy, benign paroxysmal positional vertigo, vestibular paroxysmia, Menière's disease, vestibular migraine, functional dizziness). The algorithm was validated on a prospectively collected dataset of 407 patients with acute vertigo and dizziness presenting to the emergency department at LMU Munich.

RESULTS

The algorithm assigned 287 of 407 patients to the correct diagnosis, corresponding to an overall accuracy of 71%. Cerebrovascular events were identified with high sensitivity of 94%. The six most common vestibular disorders were classified with high specificity above 95%. Random forest identified the presence of a paresis, sensory loss, central ocular motor and vestibular signs (HINTS), and older age as the most important variables indicating a cerebrovascular event.

CONCLUSIONS

The proposed diagnostic algorithm can correctly classify the most common vestibular disorders based on a comprehensive set of key questions and clinical examinations. It is easily applied, not limited to subgroups, and might therefore be transferred to broad clinical settings such as primary health care.

Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results

BACKGROUND

Dizziness and imbalance are common symptoms that are often inadequately diagnosed or managed, due to a lack of dedicated specialists. Decision Support Systems (DSS) may support first-line physicians to diagnose and manage these patients based on personalised data.

AIM

To examine the diagnostic accuracy and application of the EMBalance DSS for diagnosis and management of common vestibular disorders in primary care.

METHODS

Patients with persistent dizziness were recruited from primary care in Germany, Greece, Belgium and the UK and randomised to primary care clinicians assessing the patients with (+ DSS) versus assessment without (- DSS) the EMBalance DSS. Subsequently, specialists in neuro-otology/audiovestibular medicine performed clinical evaluation of each patient in a blinded way to provide the "gold standard" against which the + DSS, - DSS and the DSS as a standalone tool (i.e. without the final decision made by the clinician) were validated.

RESULTS

One hundred ninety-four participants (age range 25-85, mean = 57.7, SD = 16.7 years) were assigned to the + DSS (N = 100) and to the - DSS group (N = 94). The diagnosis suggested by the + DSS primary care physician agreed with the expert diagnosis in 54%, compared to 41.5% of cases in the - DSS group (odds ratio 1.35). Similar positive trends were observed for management and further referral in the + DSS vs. the - DSS group. The standalone DSS had better diagnostic and management accuracy than the + DSS group.

CONCLUSION

There were trends for improved vestibular diagnosis and management when using the EMBalance DSS. The tool requires further development to improve its diagnostic accuracy, but holds promise for timely and effective diagnosis and management of dizzy patients in primary care.

TRIAL REGISTRATION NUMBER

NCT02704819 (clinicaltrials.gov).

Curriculum for vestibular medicine (vestmed) proposed by the barany society

This document presents the initiative of the Bárány Society to improve diagnosis and care of patients presenting with vestibular symptoms worldwide.

The Vestibular Medicine (VestMed) concept embraces a wide approach to the potential causes of vestibular symptoms, acknowledging that vertigo, dizziness, and unsteadiness are non-specific symptoms that may arise from a broad spectrum of disorders, spanning from the inner ear to the brainstem, cerebellum and supratentorial cerebral networks, to many disorders beyond these structures.

The Bárány Society Vestibular Medicine Curriculum (BS-VestMed-Cur) is based on the concept that VestMed is practiced by different physician specialties and non-physician allied health professionals. Each profession has its characteristic disciplinary role and profile, but all work in overlapping areas. Each discipline requires good awareness of the variety of disorders that can present with vestibular symptoms, their underlying mechanisms and etiologies, diagnostic criteria and treatment options. Similarly, all disciplines require an understanding of their own limitations, the contribution to patient care from other professionals and when to involve other members of the VestMed community. Therefore, the BS-VestMed-Cur is the same for all health professionals involved, the overlaps and differences of the various relevant professions being defined by different levels of detail and depth of knowledge and skills.

The BS-VestMed-Cur defines a Basic and an Expert Level Curriculum. The Basic Level Curriculum covers the VestMed topics in less detail and depth, yet still conveys the concept of the wide net approach. It is designed for health professionals as an introduction to, and first step toward, VestMed expertise. The Expert Level Curriculum defines a Focused and Broad Expert. It covers the VestMed spectrum in high detail and requires a high level of understanding. In the Basic and Expert Level Curricula, the range of topics is the same and runs from anatomy, physiology and physics of the vestibular system, to vestibular symptoms, history taking, bedside examination, ancillary testing, the various vestibular disorders, their treatment and professional attitudes. Additionally, research topics relevant to clinical practice are included in the Expert Level Curriculum. For Focused Expert proficiency, the Basic Level Curriculum is required to ensure a broad overview and additionally requires an expansion of knowledge and skills in one or a few specific topics related to the focused expertise, e.g. inner ear surgery. Broad Expert proficiency targets professionals who deal with all sorts of patients presenting with vestibular symptoms (e.g. otorhinolaryngologists, neurologists, audiovestibular physicians, physical therapists), requiring a high level of VestMed expertise across the whole spectrum. For the Broad Expert, the Expert Level Curriculum is required in which the minimum attainment targets for all the topics go beyond the Basic Level Curriculum. The minimum requirements regarding knowledge and skills vary between Broad Experts, since they are tuned to the activity profile and underlying specialty of the expert.

  The BS-VestMed-Cur aims to provide a basis for current and future teaching and training programs for physicians and non-physicians. The Basic Level Curriculum could also serve as a resource for inspiration for teaching VestMed to students, postgraduate generalists such as primary care physicians and undergraduate health professionals, or anybody wishing to enter VestMed.

  VestMed is considered a set of competences related to an area of practice of established physician specialties and non-physician health professions rather than a separate clinical specialty. This curriculum does not aim to define a new single clinical specialty.

  The BS-VestMed-Cur should also integrate with, facilitate and encourage translational research in the vestibular field.

A Systematic Review of Clinical Vestibular Symptom Triage, Tools, and Algorithms

OBJECTIVE

The evaluation of peripheral vestibular disorders in clinical practice is an especially difficult endeavor, particularly for the inexperienced clinician. The goal of this systematic review is thus to evaluate the design, approaches, and outcomes for clinical vestibular symptom triage and decision support tools reported in contemporary published literature.

DATA SOURCES

A comprehensive search of existing literature in August 2020 was conducted using MEDLINE, CINAHL, and EMBASE using terms of desired diagnostic tools such as algorithm, protocol, and questionnaire as well as an exhaustive set of terms to encompass vestibular disorders.

REVIEW METHODS

Study characteristics, tool metrics, and performance were extracted using a standardized form. Quality assessment was conducted using a modified version of the Quality of Diagnostic Accuracy Studies 2 (QUADAS-2) assessment tool.

RESULTS

A total of 18 articles each reporting a novel tool for the evaluation of vestibular disorders were identified. Tools were organized into 3 discrete categories, including self-administered questionnaires, health care professional administered tools, and decision support systems. Most tools could differentiate between specific vestibular pathologies, with outcome measures including sensitivity, specificity, and accuracy.

CONCLUSION

A multitude of tools have been published to aid with the evaluation of vertiginous patients. Our systematic review identified several low-evidence reports of triage and decision support tools for the evaluation of vestibular disorders.

Update on benign paroxysmal positional vertigo

Benign paroxysmal positional vertigo (BPPV) is the most common cause of vertigo worldwide. This review considers recent advances in the diagnosis and management of BPPV including the use of web-based technology and artificial intelligence as well as the evidence supporting the use of vitamin D supplements for patients with BPPV and subnormal serum vitamin D.

Differences between primary care physicians and specialised neurotologists in the diagnosis of dizziness and vertigo in Japan

OBJECTIVE

Vertigo and dizziness are frequent symptoms in patients at out-patient services. An accurate diagnosis for vertigo or dizziness is essential for symptom relief; however, it is often challenging. This study aimed to identify differences in diagnoses between primary-care physicians and specialised neurotologists.

METHOD

In total, 217 patients were enrolled. To compare diagnoses, data was collected from the reference letters of primary-care physicians, medical questionnaires completed by patients and medical records.

RESULTS

In total, 62.2 per cent and 29.5 per cent of the patients were referred by otorhinolaryngologists and internists, respectively. The cause of vertigo or dizziness and diagnosis was missing in 47.0 per cent of the reference letters. In addition, 67.3 per cent of the diagnoses by previous physicians differed from those reported by specialised neurotologists.

CONCLUSION

To ensure patient satisfaction and high quality of life, an accurate diagnosis for vertigo or dizziness is required; therefore, methods or materials to improve the diagnostic accuracy are needed.

Vestibular Disorders

BACKGROUND

Recent research findings have improved the understanding of the diagnosis, pathophysiology, genetics, etiology, and treatment of peripheral, central, and functional vestibular vertigo syndromes.

METHOD

A literature search, with special attention to the current classification, treatment trials, Cochrane analyses, and other meta-analyses.

RESULTS

There are internationally accepted diagnostic criteria for benign positional paroxysmal vertigo, Menière's disease, bilateral vestibulopathy, vestibular paroxysmia, and functional dizziness. Whether an acute vestibular syndrome is central or peripheral can usually be determined rapidly on the basis of the history and the clinical examination. "Cere - bellar vertigo" is a clinically important entity. For bilateral vestibulopathy, balance training is an effective treatment. For Menière's disease, preventive treatment with betahistine (48 mg and 144 mg per day) is not superior to placebo. For vestibular paroxysmia, oxcarbazepine has been shown to be effective. Treatments that are probably effective for functional dizziness include vestibular rehabilitation, cognitive behavioral therapy, and serotonin reuptake inhibitors.

CONCLUSION

The diagnostic assessment of vestibular syndromes is much easier for clinicians now that it has been internationally standardized. There is still a lack of randomized, controlled trials on the treatment of, for example, Menière's disease, vestibular migraine, and "cerebellar vertigo."

A Novel Diagnostic Prediction Model for Vestibular Migraine

BACKGROUND

Increasing morbidity and misdiagnosis of vestibular migraine (VM) gravely affect the treatment of the disease as well as the patients' quality of life. A powerful diagnostic prediction model is of great importance for management of the disease in the clinical setting.

MATERIALS AND METHODS

Patients with a main complaint of dizziness were invited to join this prospective study. The diagnosis of VM was made according to the International Classification of Headache Disorders. Study variables were collected from a rigorous questionnaire survey, clinical evaluation, and laboratory tests for the development of a novel predictive diagnosis model for VM.

RESULTS

A total of 235 patients were included in this study: 73 were diagnosed with VM and 162 were diagnosed with non-VM vertigo. Compared with non-VM vertigo patients, serum magnesium levels in VM patients were lower. Following the logistic regression analysis of risk factors, a predictive model was developed based on 6 variables: age, sex, autonomic symptoms, hypertension, cognitive impairment, and serum Mg²⁺ concentration. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.856, which was better than some of the reported predictive models.

CONCLUSION

With high sensitivity and specificity, the proposed logistic model has a very good predictive capability for the diagnosis of VM. It can be used as a screening tool as well as a complementary diagnostic tool for primary care providers and other clinicians who are non-experts of VM.

Benign Positional Paroxysmal Vertigo Treatment: a Practical Update

PURPOSE OF THE REVIEW

To define the best up-to-date practical approach to treat benign paroxysmal positional vertigo (BPPV).

RECENT FINDINGS

Both posterior and horizontal canal BPPV canalith repositioning maneuvers (Semont, Epley, and Gufoni's maneuvers) are level 1 evidence treatment for evidence-based medicine. The choice of maneuver (since their efficacy is comparable) is up to the clinician's preferences, failure of the previous maneuver, or movement restrictions of the patient. Maneuvers for controversial variants, such as anterior canal and apogeotropic posterior canal BPPV, have weaker evidence of efficacy. Despite this, these variants are increasingly diagnosed and treated. Maneuvers also play a role in the differential diagnosis with central vestibular disorders. Chair-assisted treatment may be of help if available while surgical canal plugging should be indicated in selected same-canal, same-side intractable severe BPPV. The primary evidence-based treatment strategy for BPPV should be physical therapy through maneuvers. Despite the high success rate of liberatory maneuvers, there is a low percentage of subjects who have unsatisfactory outcomes. These patients need to be investigated to identify recurrences, multiple canal involvement, associated comorbidities (migraine, persistent postural perceptual dizziness), or risk factors for recurrences (low vitamin D serum level). Future research should also identify the optimum maneuvers for variants whose diagnosis and treatment are still a matter of some debate.

An algorithm as a diagnostic tool for central ocular motor disorders, also to diagnose rare disorders

Background

Recently an increasing number of digital tools to aid clinical work have been published. This study’s aim was to create an algorithm which can assist physicians as a “digital expert” with the differential diagnosis of central ocular motor disorders, in particular in rare diseases.

Results

The algorithm’s input consists of a maximum of 60 neurological and oculomotor signs and symptoms. The output is a list of the most probable diagnoses out of 14 alternatives and the most likely topographical anatomical localizations out of eight alternatives. Positive points are given for disease-associated symptoms, negative points for symptoms unlikely to occur with a disease. The accuracy of the algorithm was evaluated using the two diagnoses and two brain zones with the highest scores. In a first step, a dataset of 102 patients (56 males, 48.0 ± 22 yrs) with various central ocular motor disorders and underlying diseases, with a particular focus on rare diseases, was used as the basis for developing the algorithm iteratively. In a second step, the algorithm was validated with a dataset of 104 patients (59 males, 46.0 ± 23 yrs). For 12/14 diseases, the algorithm showed a sensitivity of between 80 and 100% and the specificity of 9/14 diseases was between 82 and 95% (e.g., 100% sensitivity and 75.5% specificity for Niemann Pick type C, and 80% specificity and 91.5% sensitivity for Gaucher’s disease). In terms of a topographic anatomical diagnosis, the sensitivity was between 77 and 100% for 4/8 brain zones, and the specificity of 5/8 zones ranged between 79 and 99%.

Conclusion

This algorithm using our knowledge of the functional anatomy of the ocular motor system and possible underlying diseases is a useful tool, in particular for the diagnosis of rare diseases associated with typical central ocular motor disorders, which are often overlooked.

Electronic supplementary material

The online version of this article (10.1186/s13023-019-1164-8) contains supplementary material, which is available to authorized users.

The Interrelations Between Different Causes of Dizziness: A Conceptual Framework for Understanding Vestibular Disorders

Background:

According to population-based studies that estimate disease prevalence, the majority of patients evaluated at dizziness clinics receive a single vestibular diagnosis. However, accumulating literature supports the notion that different vestibular disorders are interrelated and often underdiagnosed.

Objective:

Given the complexity and richness of these interrelations, we propose that a more inclusive conceptual framework to vestibular diagnostics that explicitly acknowledges this web of association will better inform vestibular differential diagnosis.

Methods:

A narrative review was performed using PubMed database. Articles were included if they defined a cohort of patients, who were given specific vestibular diagnosis. The interrelations among vestibular disorders were analyzed and placed within a conceptual framework.

Results:

The frequency of patients currently receiving multiple vestibular diagnoses in dizziness clinic is approximately 3.7% (1263/33 968 patients). The most common vestibular diagnoses encountered in the dizziness clinic include benign paroxysmal positional vertigo (BPPV), vestibular migraine, vestibular neuritis, and Ménière’s disease.

Conclusions:

A review of the literature demonstrates an intricate web of interconnections among different vestibular disorders such as BPPV, vestibular migraine, Ménière’s disease, vestibular neuritis, bilateral vestibulopathy, superior canal dehiscence syndrome, persistent postural perceptual dizziness, anxiety, head trauma, and aging, among others.