A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis

A future of AI-driven personalized care for people with multiple sclerosis

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.

Effects of a Digital Care Pathway for Multiple Sclerosis: Observational Study

BACKGROUND

Helsinki University Hospital has developed a digital care pathway (DCP) for people with multiple sclerosis (MS) to improve the care quality. DCP was designed for especially newly diagnosed patients to support adaptation to a chronic disease.

OBJECTIVE

This study investigated the MS DCP user behavior and its impact on patient education-mediated changes in health care use, patient-perceived impact of MS on psychological and physical functional health, and patient satisfaction.

METHODS

We collected data from the service launch in March 2020 until the end of 2022 (observation period). The number of users, user logins, and their timing and messages sent were collected. The association of the DCP on health care use was studied in a case-control setting in which patients were allowed to freely select whether they wanted to use the service (DCP group n=63) or not (control group n=112). The number of physical and remote appointments either to a doctor, nurse, or other services were considered in addition to emergency department visits and inpatient days. The follow-up time was 1 year (study period). Furthermore, a subgroup of 36 patients was recruited to fill out surveys on net promoter score (NPS) at 3, 6, and 12 months, and their physical and psychological functional health (Multiple Sclerosis Impact Scale) at 0, 3, 6, and 12 months.

RESULTS

During the observation period, a total of 225 patients had the option to use the service, out of whom 79.1% (178/225) logged into the service. On average, a user of the DCP sent 6.8 messages and logged on 7.4 times, with 72.29% (1182/1635) of logins taking place within 1 year of initiating the service. In case-control cohorts, no statistically significant differences between the groups were found for physical doctors' appointments, remote doctors' contacts, physical nurse appointments, remote nurse contacts, emergency department visits, or inpatient days. However, the MS DCP was associated with a 2.05 (SD 0.48) visit increase in other services, within 1 year from diagnosis. In the prospective DCP-cohort, no clinically significant change was observed in the physical functional health between the 0 and 12-month marks, but psychological functional health was improved between 3 and 6 months. Patient satisfaction improved from the NPS index of 21 (favorable) at the 3-month mark to the NPS index of 63 (excellent) at the 12-month mark.

CONCLUSIONS

The MS DCP has been used by a majority of the people with MS as a complementary service to regular operations, and we find high satisfaction with the service. Psychological health was enhanced during the use of MS DCP. Our results indicate that DCPs hold great promise for managing chronic conditions such as MS. Future studies should explore the potential of DCPs in different health care settings and patient subgroups.

The global patient-reported outcomes for multiple sclerosis initiative: bridging the gap between clinical research and care - updates at the 2023 plenary event

Significant advancements have been achieved in delineating the progress of the Global PROMS (PROMS) Initiative. The PROMS Initiative, a collaborative endeavor by the European Charcot Foundation and the Multiple Sclerosis International Federation, strives to amplify the influence of patient input on MS care and establish a cohesive perspective on Patient-Reported Outcomes (PROs) for diverse stakeholders. This initiative has established an expansive, participatory governance framework launching four dedicated working groups that have made substantive contributions to research, clinical management, eHealth, and healthcare system reform. The initiative prioritizes the global integration of patient (For the purposes of the Global PROMS Initiative, the term "patient" refers to the people with the disease (aka People with Multiple Sclerosis - pwMS): any individual with lived experience of the disease. People affected by the disease/Multiple Sclerosis: any individual or group that is affected by the disease: E.g., family members, caregivers will be also engaged as the other stakeholders in the initiative). insights into the management of MS care. It merges subjective PROs with objective clinical metrics, thereby addressing the complex variability of disease presentation and progression. Following the completion of its second phase, the initiative aims to help increasing the uptake of eHealth tools and passive PROs within research and clinical settings, affirming its unwavering dedication to the progressive refinement of MS care. Looking forward, the initiative is poised to continue enhancing global surveys, rethinking to the relevant statistical approaches in clinical trials, and cultivating a unified stance among 'industry', regulatory bodies and health policy making regarding the application of PROs in MS healthcare strategies.

Mobile Applications to Support Multiple Sclerosis Communities: The Post-COVID-19 Scenario

Introduction: The increase in the use of mobile apps since the COVID-19 pandemic, even among people with multiple sclerosis (PwMS) and health care providers (HCPs), has enabled access to reliable information, symptoms monitoring and management, and social connections. The pandemic has undoubtedly contributed to the acceleration of the "digital revolution." But how far has it progressed for the MS communities? Methods: Italian Google Play and App Store were queried, selecting MS-specific apps in English or Italian language and usable by a wide public. Results: Fifty-four (n = 54) MS-specific apps were identified; most were PwMS-oriented (83%), free of charge (94%), and in English language (76%). The 45 PwMS-oriented apps focused on increasing MS knowledge (71%), tracking symptoms (33%), and promoting networking with peers or HCPs (38%). The 13 HCPs-oriented tools addressed education and updates on MS (62%), disease assessment and management (54%), and research (15%). Google Search tool was also queried to find non-MS-specific apps to fulfill some unmet domains (as sleep, pain, sexual or mental health). Twenty-four additional apps were listed to provide a valuable contribution. Conclusion: The "digital revolution" led to increasingly customized tools for PwMS, especially as m-health or social-networking apps. However, apps to support other specific MS-relevant domains, appealing HCPs-oriented apps, and specific mobile tools for MS caregivers are still lacking. The absence of data assessing the usability and quality of MS apps in ecologically contexts leads to not reliable conclusions about potential benefits. A strong dialogue between MS communities and the digital industry is encouraged to fill this gap.

Changes in MRI Workflow of Multiple Sclerosis after Introduction of an AI-Software: A Qualitative Study

The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis.

The Finger Dexterity Test: Validation study of a smartphone-based manual dexterity assessment

BACKGROUND

The Nine-Hole Peg Test (9HPT) is the golden standard to measure manual dexterity in people with multiple sclerosis (MS). However, administration requires trained personnel and dedicated time during a clinical visit.

OBJECTIVES

The objective of this study is to validate a smartphone-based test for remote manual dexterity assessment, the icompanion Finger Dexterity Test (FDT), to be included into the icompanion application.

METHODS

A total of 65 MS and 81 healthy subjects were tested, and 20 healthy subjects were retested 2 weeks later.

RESULTS

The FDT significantly correlated with the 9HPT (dominant: ρ = 0.62, p < 0.001; non-dominant: ρ = 0.52, p < 0.001). MS subjects had significantly higher FDT scores than healthy subjects (dominant: p = 0.015; non-dominant: p = 0.013), which was not the case for the 9HPT. A significant correlation with age (dominant: ρ = 0.46, p < 0.001; non-dominant: ρ = 0.40, p = 0.002), Expanded Disability Status Scale (EDSS, dominant: ρ = 0.36, p = 0.005; non-dominant: ρ = 0.31, p = 0.024), and disease duration for the non-dominant hand (ρ = 0.31, p = 0.016) was observed. There was a good test-retest reliability in healthy subjects (dominant: r = 0.69, p = 0.001; non-dominant: r = 0.87, p < 0.001).

CONCLUSIONS

The icompanion FDT shows a moderate-to-good concurrent validity and test-retest reliability, differentiates between the MS subjects and healthy controls, and correlates with clinical parameters. This test can be implemented into routine MS care for remote follow-up of manual dexterity.

Management of multiple sclerosis fatigue in the digital age: from assessment to treatment

Fatigue is one of the most disabling symptoms of Multiple Sclerosis (MS), affecting more than 80% of patients over the disease course. Nevertheless, it has a multi-faceted and complex nature, making its diagnosis, evaluation, and treatment extremely challenging in clinical practice. In the last years, digital supporting tools have emerged to support the care of people with MS. These include not only smartphone or table-based apps, but also wearable devices or novel techniques such as virtual reality. Furthermore, an additional effective and cost-efficient tool for the therapeutic management of people with fatigue is becoming increasingly available. Virtual reality and e-Health are viable and modern tools to both assess and treat fatigue, with a variety of applications and adaptability to patient needs and disability levels. Most importantly, they can be employed in the patient's home setting and can not only bridge clinic visits but also be complementary to the monitoring and treatment means for those MS patients who live far away from healthcare structures. In this narrative review, we discuss the current knowledge and future perspectives in the digital management of fatigue in MS. These may also serve as sources for research of novel digital biomarkers in the identification of disease activity and progression.

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

INTRODUCTION

Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI).

AIMS

Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow.

METHODS

Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration.

RESULTS

We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth.

CONCLUSIONS

To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges

INTRODUCTION

Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential.

AREAS COVERED

The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential.

EXPERT OPINION

There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

Mobile apps used for people living with multiple sclerosis: A scoping review

BACKGROUND

Multiple Sclerosis (MS) is a chronic neurodegenerative disorder. People living with MS (plwMS) require long-term, multidisciplinary care in both clinical and community settings. MS-specific mHealth interventions have advanced in the form of clinical treatments, rehabilitation, disease monitoring and self-management of disease. However, mHealth interventions for plwMS appear to have limited proof of clinical efficacy. As native mobile apps target specific mobile operating systems, they tend to have better interactive designs leveraging platform-specific guidelines. Thus, to improve such efficacy, it is pivotal to explore the design characteristics of native mobile apps used for plwMS.

OBJECTIVES

This study aimed to explore the design characteristics of native mobile apps used for adults living with MS in academic settings.

METHODS

A scoping review of studies was conducted. A literature search was performed through PubMed, CINAHL, MEDLINE and Cochrane Library. Per native mobile apps, characteristics, persuasive technology elements and evaluations were summarized.

RESULTS

A total of 14 native mobile apps were identified and 43% of the identified apps were used for data collection (n=6). Approximately 70% of the included apps involved users (plwMS) whilst developing (n=10). A total of three apps utilized embedded sensors. Videos or photos were used for physical activity interventions (n=2) and gamification principles were applied for cognitive and/or motor rehabilitation interventions (n=3). Behavior change theories were integrated into the design of the apps for fatigue management and physical activity. Regarding persuasive technology, the design principles of primary support were applied across all identified apps. The elements of dialogue support and social support were the least applied. The methods for evaluating the identified apps were varied.

CONCLUSION

The findings suggest that the identified apps were in the early stages of development and had a user-centered design. By applying the persuasive systems design model, interaction design qualities and features of the identified mobile apps in academic settings were systematically evaluated at a deeper level. Identifying the digital functionality and interface design of mobile apps for plwMS will help researchers to better understand interactive design and how to incorporate these concepts in mHealth interventions for improvement of clinical efficacy.

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

PURPOSE

MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation.

METHODS

We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment.

RESULTS

We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation.

CONCLUSION

We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.

An IT Platform Supporting Rectal Cancer Tumor Board Activities: Implementation Process and Impact Analysis

Colorectal cancer (RC) is the third most common cancer, with an increasing incidence in recent years. Digital health solutions supporting multidisciplinary tumor boards (MTBs) could improve positive outcomes for RC patients. This paper describes the implementation process of a digital solution within the RC-MTB and its impact analysis in the context of the Fondazione Policlinico 'A. Gemelli' in Italy. Adopting a two-phase methodological approach, the first phase qualitatively describes each phase of the implementation of the IT platform, while the second phase quantitatively describes the analysis of the impact of the IT platform. Descriptive and inferential analyses were performed for all variables, with a p-value < 0.05 being considered statistically significant. The implementation of the platform allowed more healthcare professionals to attend meetings and resulted in a decrease in patients sent to the RC-MTB for re-staging and further diagnostic investigations and an increase in patients sent to the RC-MTB for treatment strategies. The results could be attributed to the facilitated access to the platform remotely for specialists, partly compensating for the restrictions imposed by the COVID-19 pandemic, as well as to the integration of the platform into the hospital's IT system. Furthermore, the early involvement of healthcare professionals in the process of customizing the platform to the specific needs of the RC-MTB may have facilitated its use and contributed to the encouraging quantitative results.

Health Economic Impact of Software-Assisted Brain MRI on Therapeutic Decision-Making and Outcomes of Relapsing-Remitting Multiple Sclerosis Patients-A Microsimulation Study

Aim: To develop a microsimulation model to assess the potential health economic impact of software-assisted MRI in detecting disease activity or progression in relapsing-remitting multiple sclerosis (RRMS) patients. Methods: We develop a simulated decision analytical model based on a hypothetical cohort of RRMS patients to compare a baseline decision-making strategy in which only clinical evolution (relapses and disability progression) factors are used for therapy decisions in MS follow-up, with decision-making strategies involving MRI. In this context, we include comparisons with a visual radiologic assessment of lesion evolution, software-assisted lesion detection, and software-assisted brain volume loss estimation. The model simulates clinical (EDSS transitions, number of relapses) and subclinical (new lesions and brain volume loss) disease progression and activity, modulated by the efficacy profiles of different disease-modifying therapies (DMTs). The simulated decision-making process includes the possibility to escalate from a low efficacy DMT to a high efficacy DMT or to switch between high efficacy DMTs when disease activity is detected. We also consider potential error factors that may occur during decision making, such as incomplete detection of new lesions, or inexact computation of brain volume loss. Finally, differences between strategies in terms of the time spent on treatment while having undetected disease progression/activity, the impact on the patient’s quality of life, and costs associated with health status from a US perspective, are reported. Results: The average time with undetected disease progression while on low efficacy treatment is shortened significantly when using MRI, from around 3 years based on clinical criteria alone, to 2 when adding visual examination of MRI, and down to only 1 year with assistive software. Hence, faster escalation to a high efficacy DMT can be performed when MRI software is added to the radiological reading, which has positive effects in terms of health outcomes. The incremental utility shows average gains of 0.23 to 0.37 QALYs over 10 and 15 years, respectively, when using software-assisted MRI compared to clinical parameters only. Due to long-term health benefits, the average annual costs associated with health status are lower by $1500–$2200 per patient when employing MRI and assistive software. Conclusions: The health economic burden of MS is high. Using assistive MRI software to detect and quantify lesions and/or brain atrophy has a significant impact on the detection of disease activity, treatment decisions, health outcomes, utilities, and costs in patients with MS.