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Finkelstein J, Gabriel A, Schmer S, Truong TT, Dunn A. Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System. J Med Syst 2024; 48:89. [PMID: 39292314 PMCID: PMC11410896 DOI: 10.1007/s10916-024-02104-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 08/29/2024] [Indexed: 09/19/2024]
Abstract
Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.
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Affiliation(s)
- Joseph Finkelstein
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA.
| | - Aileen Gabriel
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA
| | - Susanna Schmer
- Department of Case Management, Mount Sinai Health System, New York, NY, USA
| | - Tuyet-Trinh Truong
- Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Dunn
- Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Briglia M, Allia F, Avola R, Signorini C, Cardile V, Romano GL, Giurdanella G, Malaguarnera R, Bellomo M, Graziano ACE. Diet and Nutrients in Rare Neurological Disorders: Biological, Biochemical, and Pathophysiological Evidence. Nutrients 2024; 16:3114. [PMID: 39339713 PMCID: PMC11435074 DOI: 10.3390/nu16183114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 09/12/2024] [Accepted: 09/12/2024] [Indexed: 09/30/2024] Open
Abstract
Background/Objectives: Rare diseases are a wide and heterogeneous group of multisystem life-threatening or chronically debilitating clinical conditions with reduced life expectancy and a relevant mortality rate in childhood. Some of these disorders have typical neurological symptoms, presenting from birth to adulthood. Dietary patterns and nutritional compounds play key roles in the onset and progression of neurological disorders, and the impact of alimentary needs must be enlightened especially in rare neurological diseases. This work aims to collect the in vitro, in vivo, and clinical evidence on the effects of diet and of nutrient intake on some rare neurological disorders, including some genetic diseases, and rare brain tumors. Herein, those aspects are critically linked to the genetic, biological, biochemical, and pathophysiological hallmarks typical of each disorder. Methods: By searching the major web-based databases (PubMed, Web of Science Core Collection, DynaMed, and Clinicaltrials.gov), we try to sum up and improve our understanding of the emerging role of nutrition as both first-line therapy and risk factors in rare neurological diseases. Results: In line with the increasing number of consensus opinions suggesting that nutrients should receive the same attention as pharmacological treatments, the results of this work pointed out that a standard dietary recommendation in a specific rare disease is often limited by the heterogeneity of occurrent genetic mutations and by the variability of pathophysiological manifestation. Conclusions: In conclusion, we hope that the knowledge gaps identified here may inspire further research for a better evaluation of molecular mechanisms and long-term effects.
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Affiliation(s)
- Marilena Briglia
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Fabio Allia
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Rosanna Avola
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Cinzia Signorini
- Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy;
| | - Venera Cardile
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy;
| | - Giovanni Luca Romano
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Giovanni Giurdanella
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Roberta Malaguarnera
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Maria Bellomo
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
| | - Adriana Carol Eleonora Graziano
- Department of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy; (M.B.); (F.A.); (R.A.); (G.L.R.); (R.M.); (M.B.)
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Brauner LE, Yao Y, Grigull L, Klawonn F. Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review. J Clin Med 2024; 13:5132. [PMID: 39274347 PMCID: PMC11396573 DOI: 10.3390/jcm13175132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/16/2024] [Accepted: 08/26/2024] [Indexed: 09/16/2024] Open
Abstract
Background: A major challenge faced by patients with rare diseases (RDs) often stems from delays in diagnosis, typically due to nonspecific clinical symptoms or doctors' limited experience in connecting symptoms to the underlying RD. Using patient-oriented questionnaires (POQs) as a data source for machine learning (ML) techniques can serve as a potential solution. These questionnaires enable patients to portray their day-to-day experiences living with their condition, irrespective of clinical symptoms. This systematic review-registered at PROSPERO with the Registration-ID: CRD42023490838-aims to present the current state of research in this domain by conducting a systematic literature search and identifying the potentials and limitations of this methodology. Methods: The review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was primarily funded by the German Federal Ministry of Education and Research under grant no. 16DHBKI056 (ki4all). The methodology involved a systematic search across the databases PubMed, Semantic Scholar and Google Scholar, covering articles published until June 2023. The inclusion criteria encompass examining the use of POQs in diagnosing rare and common diseases. Additionally, studies that focused on applying ML techniques to the resulting datasets were considered for inclusion. The primary objective was to include English as well as German research that involved the generation of predictions regarding the underlying disease based on the information gathered from POQs. Furthermore, studies exploring identifying predictive indicators associated with the underlying disease were also included in the literature review. The following data were extracted from the selected studies: year of publication, number of questions in the POQs, answer scale in the questionnaires, the ML algorithms used, the input data for the ML algorithms, the performance of these algorithms and how the performance was measured. In addition, information on the development of the questionnaires was recorded. Results: This search retrieved 421 results in total. After one superficial and two comprehensive screening runs performed by two authors independently, we ended up with 26 studies for further consideration. Sixteen of these studies deal with diseases and ML algorithms to analyse data; the other ten studies provide contributing research in this field. We discuss several potentials and limitations of the evaluated approach. Conclusions: Overall, the results show that the full potential has not yet been exploited and that further research in this direction is worthwhile, because the study results show that ML algorithms can achieve promising results on POQ data; however, their use in everyday medical practice has not yet been investigated.
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Affiliation(s)
- Lea Eileen Brauner
- Department of Computer Science, Ostfalia University of Applied Sciences, 38302 Wolfenbuettel, Germany
| | - Yao Yao
- Department of Computer Science, Ostfalia University of Applied Sciences, 38302 Wolfenbuettel, Germany
| | - Lorenz Grigull
- Center for Rare Diseases Bonn (ZSEB), University Hospital of Bonn, 53127 Bonn, Germany
| | - Frank Klawonn
- Department of Computer Science, Ostfalia University of Applied Sciences, 38302 Wolfenbuettel, Germany
- Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany
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Ma K, Gauthier LO, Cheung F, Huang S, Lek M. High-throughput assays to assess variant effects on disease. Dis Model Mech 2024; 17:dmm050573. [PMID: 38940340 PMCID: PMC11225591 DOI: 10.1242/dmm.050573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.
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Affiliation(s)
- Kaiyue Ma
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Logan O. Gauthier
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Frances Cheung
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Shushu Huang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Monkol Lek
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
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Faviez C, Chen X, Garcelon N, Zaidan M, Billot K, Petzold F, Faour H, Douillet M, Rozet JM, Cormier-Daire V, Attié-Bitach T, Lyonnet S, Saunier S, Burgun A. Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies. BMC Med Inform Decis Mak 2024; 24:134. [PMID: 38789985 PMCID: PMC11127295 DOI: 10.1186/s12911-024-02538-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. METHODS Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. RESULTS A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. CONCLUSION Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.
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Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France.
- HeKA, Inria Paris, Paris, F-75012, France.
- Universite Paris Cite, Paris, France.
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France
- HeKA, Inria Paris, Paris, F-75012, France
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France
- HeKA, Inria Paris, Paris, F-75012, France
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Mohamad Zaidan
- Service de Néphrologie, Dialyse et Transplantation, Hôpital Universitaire Bicêtre, Assistance Publique-Hôpitaux de Paris (AP-HP), Kremlin Bicêtre, F-94270, France
| | - Katy Billot
- Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
| | - Friederike Petzold
- Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
- Division of Nephrology, University of Leipzig Medical Center, Leipzig, Germany
| | - Hassan Faour
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Maxime Douillet
- Data Science Platform, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, F-75015, France
| | - Jean-Michel Rozet
- Laboratory of Genetics in Ophthalmology, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
| | - Valérie Cormier-Daire
- Reference Centre for Constitutional Bone Diseases, laboratory of Osteochondrodysplasia, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
| | - Tania Attié-Bitach
- Service d'Histologie-Embryologie-Cytogénétique, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
| | - Stanislas Lyonnet
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
- Laboratory of Embryology and Genetics of Congenital Malformations, INSERM UMR 1163, Imagine Institute, Paris Cité, Paris, F-75015, France
| | - Sophie Saunier
- Laboratory of Renal Hereditary Diseases, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, F-75015, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France
- HeKA, Inria Paris, Paris, F-75012, France
- Department of Medical Informatics, Hôpital Necker-Enfants Malades, AP-HP, Paris, F-75015, France
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Kapoor S, Kalmegh V, Kumar H, Mandoli A, Shard A. Rare diseases and pyruvate kinase M2: a promising therapeutic connection. Drug Discov Today 2024; 29:103949. [PMID: 38492882 DOI: 10.1016/j.drudis.2024.103949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
Pyruvate kinase M2 (PKM2) is a key glycolytic enzyme that regulates proliferating cell metabolism. The role of PKM2 in common diseases has been well established, but its role in rare diseases (RDs) is less understood. Over the past few years, PKM2 has emerged as a crucial player in RDs, including, neoplastic, respiratory, metabolic, and neurological disorders. Herein, we summarize recent findings and developments highlighting PKM2 as an emerging key player in RDs. We also discuss the current status of PKM2 modulation in RDs with particular emphasis on preclinical and clinical studies in addition to current challenges in the field.
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Affiliation(s)
- Saumya Kapoor
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Ahmedabad (NIPER-A), Gandhinagar, Gujarat, India
| | - Vaishnavi Kalmegh
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Ahmedabad (NIPER-A), Gandhinagar, Gujarat, India
| | - Hemant Kumar
- Department of Pharmacology and Toxicology, NIPER-A, Gandhinagar, Gujarat, India.
| | - Amit Mandoli
- Department of Biotechnology, NIPER-A, Gandhinagar, Gujarat, India.
| | - Amit Shard
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Ahmedabad (NIPER-A), Gandhinagar, Gujarat, India.
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Devoy C, Flores Bueso Y, Buckley S, Walker S, Tangney M. Synthetic protein protease sensor platform. Front Bioeng Biotechnol 2024; 12:1347953. [PMID: 38646011 PMCID: PMC11026627 DOI: 10.3389/fbioe.2024.1347953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/21/2024] [Indexed: 04/23/2024] Open
Abstract
Introduction: Protease activity can serve as a highly specific biomarker for application in health, biotech, and beyond. The aim of this study was to develop a protease cleavable synthetic protein platform to detect protease activity in a rapid cell-free setting. Methods: The protease sensor is modular, with orthogonal peptide tags at the N and C terminal ends, which can be uncoupled via a protease responsive module located in between. The sensor design allows for several different readouts of cleavage signal. A protein 'backbone' [Green fluorescent protein (GFP)] was designed in silico to have both a C-terminal Flag-tag and N-Terminal 6x histidine tag (HIS) for antibody detection. A protease cleavage site, which can be adapted for any known protease cleavage sequence, enables the uncoupling of the peptide tags. Three different proteases-Tobacco, Etch Virus (TEV), the main protease from coronavirus SARS-COV-2 (Mpro) and Matrix Metallopeptidase 9 (MMP9)-a cancer-selective human protease-were examined. A sandwich Enzyme-Linked Immunosorbent Assay (ELISA) was developed based on antibodies against the HIS and Flag tags. As an alternative readout, a C-terminal quencher peptide separable by protease cleavage from the GFP was also included. Purified proteins were deployed in cell-free cleavage assays with their respective protease. Western blots, fluorescence assays and immunoassay were performed on samples. Results: Following the design, build and validation of protein constructs, specific protease cleavage was initially demonstrated by Western blot. The novel ELISA proved to afford highly sensitive detection of protease activity in all cases. By way of alternative readout, activation of fluorescence signal upon protease cleavage was also demonstrated but did not match the sensitivity provided by the ELISA method. Discussion: This platform, comprising a protease-responsive synthetic protein device and accompanying readout, is suitable for future deployment in a rapid, low-cost, lateral flow setting. The modular protein device can readily accommodate any desired protease-response module (target protease cleavage site). This study validates the concept with three disparate proteases and applications-human infectious disease, cancer and agricultural crop infection.
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Affiliation(s)
- Ciaran Devoy
- Cancer Research@UCC, University College Cork, Cork, Ireland
| | - Yensi Flores Bueso
- Cancer Research@UCC, University College Cork, Cork, Ireland
- APC Microbiome Ireland, University College Cork, Cork, Ireland
| | | | - Sidney Walker
- Cancer Research@UCC, University College Cork, Cork, Ireland
| | - Mark Tangney
- Cancer Research@UCC, University College Cork, Cork, Ireland
- IEd Hub, University College Cork, Cork, Ireland
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Lutsyk K, Gicquel T, Cortial L, Forget S, Braun S, Boyer PO, Laugel V, Blin O. Does gene therapies clinical research in rare diseases reflects the competitivity of the country: Example of France. Therapie 2024:S0040-5957(24)00028-3. [PMID: 38458946 DOI: 10.1016/j.therap.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Rare diseases are chronic, serious and generally genetic conditions affecting a small number of people, and their therapeutic management is a real challenge. They represent a considerable burden for patients, caregivers and society alike. Compared with existing symptomatic treatments, gene therapies represent a promising new approach aimed at treating these diseases by replacing a defective gene, or by abolishing or reviving a gene-derived function. France is considered one of the leading countries in the research and development of drugs for rare diseases, yet the position of French public and private stakeholders in the research and development of gene therapies for rare diseases at global and European level remains unclear. To answer this question, we used the GENOTRIAL FR database developed by OrphanDev to clarify France's involvement and competitiveness in this field. The results show that France is actively involved in gene therapy clinical trials, with a dense international collaboration network and solid expertise. However, the French medical infrastructure is mainly involved in clinical research on gene therapy candidates sponsored by several foreign countries. To a lesser extent, French public and private entities are also developing their own gene therapy candidates for various rare diseases, some of which have already reached advanced clinical phases. In conclusion, a number of technical and financial challenges need to be overcome if France is to maintain its position as a European and world leader and increase its contribution to reducing the economic and social burden of rare diseases by developing revolutionary and effective new therapies.
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Affiliation(s)
- Karyna Lutsyk
- Pharmacologie clinique et pharmacosurveillance, Aix Marseille University, OrphanDev, UMR1106, Assistance publique-Hôpitaux de Marseille, 13005 Marseille, France
| | - Tristan Gicquel
- Pharmacologie clinique et pharmacosurveillance, Aix Marseille University, OrphanDev, UMR1106, Assistance publique-Hôpitaux de Marseille, 13005 Marseille, France
| | - Lucas Cortial
- Pharmacologie clinique et pharmacosurveillance, Aix Marseille University, OrphanDev, UMR1106, Assistance publique-Hôpitaux de Marseille, 13005 Marseille, France
| | | | | | | | | | - Olivier Blin
- Pharmacologie clinique et pharmacosurveillance, Aix Marseille University, OrphanDev, UMR1106, Assistance publique-Hôpitaux de Marseille, 13005 Marseille, France.
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9
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Sellin J, Pantel JT, Börsch N, Conrad R, Mücke M. [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems]. Schmerz 2024; 38:19-27. [PMID: 38165492 DOI: 10.1007/s00482-023-00777-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems. OBJECTIVE This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable. MATERIALS AND METHODS A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized. RESULTS Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies. CONCLUSION DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.
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Affiliation(s)
- Julia Sellin
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
| | - Jean Tori Pantel
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Natalie Börsch
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Rupert Conrad
- Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Münster, Münster, Deutschland
| | - Martin Mücke
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
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Ferri-Rufete D, López-González A, Casas-Alba D, Cuadras D, Palau F, Martínez-Monseny A. Clinical Genetics Assessment Triangle (CGAT): A simple tool to identify patients with genetic conditions. Eur J Med Genet 2023; 66:104858. [PMID: 37758166 DOI: 10.1016/j.ejmg.2023.104858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/04/2023] [Accepted: 09/24/2023] [Indexed: 10/03/2023]
Abstract
OBJECTIVE The objective of this study was to develop a simple tool for general physicians to promptly identify and refer pediatric patients with a higher probability of having a genetic condition. STUDY DESIGN This retrospective, descriptive study was conducted at a tertiary pediatric hospital's Clinical Genetics Unit from June 2019 to January 2020. We included patients under 18 years of age who visited the unit, excluding those without genetic testing. Epidemiological, clinical, and genetic variables were collected from electronic medical records. The primary outcome was the diagnosis of a genetic condition based on genetic testing. RESULTS Among 445 patients, 304 were included; 163 (53.6%) were male, and mean age was 7.4 years (SD 5.1 years). A genetic condition was diagnosed in 139 patients (45.7%). Using a multiple logistic regression model, five variables significantly contributed to reaching a diagnosis: suspected diagnosis at referral (OR 3.45, P < 0.001), short stature (OR 3.11, P < 0.001), global developmental delay/intellectual disability (OR 2.65, P < 0.001), dysmorphic craniofacial features (OR 1.99, P = 0.035), and multiple congenital anomalies (OR 2.54, P = 0.033). The association strength (OR) increased when these variables were paired with each other. The study's findings are presented in the form of a triangle, known as the Clinical Genetics Assessment Triangle (CGAT), which summarizes the results. A decision tree model is applied to guide clinical department referrals based on the affected sides of the triangle. CONCLUSIONS The CGAT has the potential to enable general physicians to promptly identify pediatric patients with an increased probability of having a genetic condition.
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Affiliation(s)
- David Ferri-Rufete
- Pediatrics Department, Hospital Sant Joan de Déu, Esplugues de Llobregat, 08950, Spain.
| | - Aitor López-González
- Pediatrics Department, Hospital Sant Joan de Déu, Esplugues de Llobregat, 08950, Spain.
| | - Dídac Casas-Alba
- Department of Genetic Medicine, Pediatric Institute of Rare Diseases (IPER), Hospital Sant Joan de Déu, Esplugues de Llobregat, 08950, Spain.
| | - Daniel Cuadras
- Statistics Department, Fundació Sant Joan de Déu, Esplugues de Llobregat, 08950, Spain.
| | - Francesc Palau
- Department of Genetic Medicine, Pediatric Institute of Rare Diseases (IPER), Hospital Sant Joan de Déu, Esplugues de Llobregat, 08950, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, 28029, Spain.
| | - Antonio Martínez-Monseny
- Department of Genetic Medicine, Pediatric Institute of Rare Diseases (IPER), Hospital Sant Joan de Déu, Esplugues de Llobregat, 08950, Spain.
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11
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Schütze D, Holtz S, Neff MC, Köhler SM, Schaaf J, Frischen LS, Sedlmayr B, Müller BS. Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process. BMC Med Inform Decis Mak 2023; 23:144. [PMID: 37525175 PMCID: PMC10391889 DOI: 10.1186/s12911-023-02245-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/19/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND As the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool. The objective of the project 'Smart physician portal for patients with unclear disease' (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs. METHODS Requirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements. RESULTS Numerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended. CONCLUSIONS Essential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.
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Affiliation(s)
- Dania Schütze
- Goethe University Frankfurt, Institute of General Practice, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
| | - Svea Holtz
- Goethe University Frankfurt, Institute of General Practice, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany
| | - Michaela C Neff
- Goethe University Frankfurt, University Hospital, Institute of Medical Informatics, Frankfurt, Germany
| | - Susanne M Köhler
- Goethe University Frankfurt, Institute of General Practice, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany
| | - Jannik Schaaf
- Goethe University Frankfurt, University Hospital, Institute of Medical Informatics, Frankfurt, Germany
| | - Lena S Frischen
- Executive Department for Medical IT-Systems and Digitalization, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Brita Sedlmayr
- Technische Universität Dresden, Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Dresden, Germany
| | - Beate S Müller
- Goethe University Frankfurt, Institute of General Practice, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Institute of General Practice, Cologne, Germany
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12
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Emmert D, Szczypien N, Bender TTA, Grigull L, Gass A, Link C, Klawonn F, Conrad R, Mücke M, Sellin J. A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D. Orphanet J Rare Dis 2023; 18:70. [PMID: 36978184 PMCID: PMC10053427 DOI: 10.1186/s13023-023-02663-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 03/11/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND AND OBJECTIVE The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients. METHODS Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach. RESULTS Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%. CONCLUSIONS Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.
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Affiliation(s)
- D Emmert
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
- Institute for Virology, University Hospital Bonn, Bonn, Germany
| | - N Szczypien
- Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Tim T A Bender
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
| | - L Grigull
- Center for Rare Diseases Bonn (ZSEB), University Hospital Bonn, Bonn, Germany
| | - A Gass
- Clinic for Anesthesiology and Operative Intensive Care Medicine, Department of Pain Medicine, University Hospital Bonn, Bonn, Germany
| | - C Link
- Clinic for Anesthesiology and Operative Intensive Care Medicine, Department of Pain Medicine, University Hospital Bonn, Bonn, Germany
| | - F Klawonn
- Institute for Information Engineering, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Biostatistics Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - R Conrad
- Department of Psychosomatic Medicine and Psychotherapy, University Hospital Muenster, Muenster, Germany.
| | - M Mücke
- Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany.
- Center for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, Germany.
| | - J Sellin
- Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, Germany.
- Center for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, Germany.
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13
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Bajgain B, Lorenzetti D, Lee J, Sauro K. Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol. BMJ Open 2023; 13:e068373. [PMID: 36822813 PMCID: PMC9950925 DOI: 10.1136/bmjopen-2022-068373] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
INTRODUCTION Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare. METHODS AND ANALYSIS This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics. ETHICS AND DISSEMINATION Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease.
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Affiliation(s)
- Bishnu Bajgain
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Diane Lorenzetti
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Joon Lee
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Khara Sauro
- Departments of Community Health Sciences, Surgery & Oncology, University of Calgary, Calgary, Alberta, Canada
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14
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Sintila SA, Boziki M, Bakirtzis C, Stardeli T, Smyrni N, Nikolaidis I, Parissis D, Afrantou T, Karapanayiotides T, Koutroulou I, Giantzi V, Theotokis P, Kesidou E, Xiromerisiou G, Dardiotis E, Ioannidis P, Grigoriadis N. The Experience of a Tertiary Reference Hospital in the Study of Rare Neurological Diseases. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59020266. [PMID: 36837468 PMCID: PMC9959728 DOI: 10.3390/medicina59020266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023]
Abstract
Background and Objectives: Rare diseases (RDs) are life-threatening or chronically impairing conditions that affect about 6% of the world's population. RDs are often called 'orphan' diseases, since people suffering from them attract little support from national health systems. Aim: The aim of this study is to describe the clinical characteristics of, and the available laboratory examinations for, patients who were hospitalized in a tertiary referral center and finally received a diagnosis associated with a Rare Neurological Disease (RND). Materials and Methods: Patients that were hospitalized in our clinic from 1 January 2014 to 31 March 2022 and were finally diagnosed with an RND were consecutively included. The RND classification was performed according to the ORPHAcode system. Results: A total of 342 out of 11.850 (2.9%) adult patients admitted to our department during this period received a diagnosis associated with an RND. The most common diagnosis (N = 80, 23%) involved an RND presenting with dementia, followed by a motor neuron disease spectrum disorder (N = 64, 18.7%). Family history indicative of an RND was present in only 21 patients (6.1%). Fifty-five (16%) people had previously been misdiagnosed with another neurological condition. The mean time delay between disease onset and diagnosis was 4.24 ± 0.41 years. Conclusions: Our data indicate that a broad spectrum of RNDs may reach a tertiary Neurological Center after a significant delay. Moreover, our data underline the need for a network of reference centers, both at a national and international level, expected to support research on the diagnosis and treatment of RND.
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Affiliation(s)
- Styliani-Aggeliki Sintila
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Marina Boziki
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Christos Bakirtzis
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Thomai Stardeli
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Nikoletta Smyrni
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Ioannis Nikolaidis
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Dimitrios Parissis
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Theodora Afrantou
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Theodore Karapanayiotides
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Ioanna Koutroulou
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Virginia Giantzi
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Paschalis Theotokis
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Evangelia Kesidou
- Laboratory of Experimental Neurology and Neuroimmunology, 2nd Department of Neurology, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Georgia Xiromerisiou
- Department of Neurology, University Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41110 Larissa, Greece
| | - Efthimios Dardiotis
- Department of Neurology, University Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41110 Larissa, Greece
| | - Panagiotis Ioannidis
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
- Correspondence: (P.I.); (N.G.)
| | - Nikolaos Grigoriadis
- 2nd Department of Neurology, AHEPA University Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
- Correspondence: (P.I.); (N.G.)
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15
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The Power of Clinical Diagnosis for Deciphering Complex Genetic Mechanisms in Rare Diseases. Genes (Basel) 2023; 14:genes14010196. [PMID: 36672937 PMCID: PMC9858967 DOI: 10.3390/genes14010196] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
Complex genetic disease mechanisms, such as structural or non-coding variants, currently pose a substantial difficulty in frontline diagnostic tests. They thus may account for most unsolved rare disease patients regardless of the clinical phenotype. However, the clinical diagnosis can narrow the genetic focus to just a couple of genes for patients with well-established syndromes defined by prominent physical and/or unique biochemical phenotypes, allowing deeper analyses to consider complex genetic origin. Then, clinical-diagnosis-driven genome sequencing strategies may expedite the development of testing and analytical methods to account for complex disease mechanisms as well as to advance functional assays for the confirmation of complex variants, clinical management, and the development of new therapies.
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16
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Nixon A, Fang L, Havrilla JM, Wang K. Termviewer - A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation. Chem Biodivers 2022; 19:e202200805. [PMID: 36328766 DOI: 10.1002/cbdv.202200805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022]
Abstract
Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high-quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi-party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high-quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand-alone tool for analyzing notes or become part of a machine-learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.
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Affiliation(s)
- Anna Nixon
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - James M Havrilla
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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17
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Benito-Lozano J, López-Villalba B, Arias-Merino G, Posada de la Paz M, Alonso-Ferreira V. Diagnostic delay in rare diseases: data from the Spanish rare diseases patient registry. Orphanet J Rare Dis 2022; 17:418. [PMID: 36397119 PMCID: PMC9670379 DOI: 10.1186/s13023-022-02530-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 09/30/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND According to the International Rare Diseases Research Consortium (IRDiRC), a known rare disease (RD) should be diagnosable within a year. This study sought: firstly, to ascertain how long it takes to obtain the diagnosis of a RD in Spain, along with its associated time trend; and secondly, to identify and measure diagnostic delay (defined by the IRDiRC as any period exceeding a year) by reference to the characteristics of RDs and the persons affected by them. METHODS Using data sourced from the Spanish Rare Diseases Patient Registry, we performed a descriptive analysis of the time elapsed between symptom onset and diagnosis of each RD, by sex, age and date of symptom onset, and type of RD. We analysed the time trend across the period 1960-2021 and possible change points, using a Joinpoint regression model and assuming a Poisson distribution. The multivariate analysis was completed with backward stepwise logistic regression. RESULTS Detailed information was obtained on 3304 persons with RDs: 56.4% had experienced delay in diagnosis of their RDs, with the mean time taken being 6.18 years (median = 2; IQR 0.2-7.5). Both the percentage of patients with diagnostic delay and the average time to diagnosis underwent a significant reduction across the study period (p < 0.001). There was a higher percentage of diagnostic delays: in women (OR 1.25; 95% CI 1.07-1.45); in cases with symptom onset at age 30-44 years (OR 1.48; 95% CI 1.19-1.84): and when analysed by type of RD, in mental and behavioural disorders (OR 4.21; 95% CI 2.26-7.85), followed by RDs of the nervous system (OR 1.39; 95% CI 1.02-1.88). CONCLUSIONS This is the first study to quantify time to diagnosis of RDs in Spain, based on data from a national registry open to any RD. Since over half of all persons affected by RDs experience delay in diagnosis, new studies are needed to ascertain the factors associated with this delay and the implications this has on the lives of patients and their families.
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Affiliation(s)
- Juan Benito-Lozano
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain ,grid.10702.340000 0001 2308 8920Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain
| | - Blanca López-Villalba
- grid.411057.60000 0000 9274 367XPreventive Medicine and Public Health, Hospital Clínico Universitario de Valladolid (HCUV), 47003 Valladolid, Spain
| | - Greta Arias-Merino
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Manuel Posada de la Paz
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Verónica Alonso-Ferreira
- grid.413448.e0000 0000 9314 1427Institute of Rare Diseases Research (IIER), Instituto de Salud Carlos III, 28029 Madrid, Spain ,grid.413448.e0000 0000 9314 1427Centre for Biomedical Network Research On Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
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18
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Hansen RS, Ejdesgaard BA, Madsen KSB, Fruekilde PBN, Vinholt PJ. Computerized clinical decision support tool for diagnosing porphyria - improving efficiency in a specialized laboratory. Scandinavian Journal of Clinical and Laboratory Investigation 2022; 82:167-169. [PMID: 35130463 DOI: 10.1080/00365513.2022.2034937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | - Bo Andersen Ejdesgaard
- Section of Health Data Management and Automation, Odense University Hospital, Odense, Denmark
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19
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Gilbert S, Gabriel H, Pankow A, Biskup S, Wagner AD. [What is confirmed in the diagnostics of autoinflammatory fever diseases?]. Internist (Berl) 2021; 62:1290-1294. [PMID: 34878559 DOI: 10.1007/s00108-021-01221-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2021] [Indexed: 11/29/2022]
Abstract
Periodic fever syndromes (PFS) are a group of rare autoinflammatory diseases, which are characterized by disorders of the innate immune reaction and life-long recurrent episodes of inflammatory symptoms. This article describes the diagnostic approach. In addition to the patient medical history, physical examination and laboratory determinations, gene tests are becoming increasingly more important. The panel diagnostics using high throughput sequencing or next generation sequencing (NGS) is the method of choice for the detection of a genetic cause of PFS. This article discusses the diagnostic decision support systems (DDSS) that can play a future role in the diagnosis of rare diseases, especially those with complex patterns of symptoms.
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Affiliation(s)
- Stephen Gilbert
- Ada Health GmbH, Karl-Liebknecht-Str. 1, 10178, Berlin, Deutschland.,Else Kröner-Fresenius Center for Digital Health, Faculty of Medicine Carl Gustav Carus, Louisenstr. 120, 61348, Bad Homburg, Deutschland.,Technische Universität Dresden, Dresden, Deutschland
| | - Heinz Gabriel
- Praxis für Humangenetik Tübingen, Paul-Ehrlich-Str. 23, 72076, Tübingen, Deutschland
| | - Anne Pankow
- Abt. für Nieren- und Hochdruckerkrankungen, Ambulanz für seltene entzündliche, Systemerkrankungen mit Nierenbeteiligung, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.,Klinik für Rheumatologie und Immunologie, Berlin, Charité, Charitéplatz 1, 10117, Berlin, Deutschland
| | - Saskia Biskup
- Praxis für Humangenetik Tübingen, Paul-Ehrlich-Str. 23, 72076, Tübingen, Deutschland
| | - Annette Doris Wagner
- Abt. für Nieren- und Hochdruckerkrankungen, Ambulanz für seltene entzündliche, Systemerkrankungen mit Nierenbeteiligung, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
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Zoch M, Sedlmayr B, Knapp A, Bathelt F, Helfer S, Schmitt J, Sedlmayr M. [Interdisciplinary care path and potential IT support for people with rare diseases in Germany]. ZEITSCHRIFT FUR EVIDENZ, FORTBILDUNG UND QUALITAT IM GESUNDHEITSWESEN 2021; 165:68-76. [PMID: 34483074 DOI: 10.1016/j.zefq.2021.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/04/2021] [Accepted: 06/09/2021] [Indexed: 06/13/2023]
Abstract
INTRODUCTION Due to the high variability and, at the same time, rare occurrence of rare diseases, the diagnosis of these patients (approx. 4 million people in Germany) can turn into an odyssey. The large time interval between the appearance of symptoms and the final diagnosis of the rare disease leads to a delay in the appropriate treatment. The often long period of uncertainty about the cause of symptoms as well as non-specific or even wrong therapies can have negative effects on both the course of disease and the patients' quality of life. For a better understanding of the current care situation and IT landscape, the interdisciplinary care pathway for people with rare diseases will be modelled and the possible uses of IT applications identified that have the potential to improve diagnosis, treatment and therapy of rare diseases. METHODS In order to achieve these goals, an initial care pathway was modelled on the basis of process descriptions which are commonly used in the literature, discussed in detail, and agreed upon in a first workshop with six experts from outpatient and inpatient care as well as employees of Centers for Rare Diseases. In a second workshop, ten experts analyzed the resulting care pathway with regard to the possible use of IT applications, and the identification was agreed upon. The experts included those involved in the process, in particular physicians, patients / patient representatives, health care researchers, and experts in hospital IT, IT security, and data protection. RESULTS The two workshops resulted in process models including the specification of possible uses for IT applications. The most important steps in the care pathway for people with rare diseases in Germany include: neonatal screening, seeking medical advice, outpatient care by general practitioners, outpatient care by specialists, care by specialist outpatient clinic, care by clinic, care by a Center for Rare Diseases: case review and case conference and treatment and therapy. The discussion of the possible uses of IT applications resulted in a focus on registers (e. g. with regard to experts, treatment and therapy options) as well as on digital tools, such as "digital findings and findings platform" and "digital referral with referral tracking". DISCUSSION Our results show that the care pathway is very heterogeneous and complex. Thus, the sub-processes show different variants with many branches and repetitions. They also illustrate that the care for people with rare diseases requires a high level of interdisciplinary collaboration; diagnosis as well as treatment and therapy often take place across sectors and in cooperation between different medical health care institutions and professions. When analyzing the current IT landscape, it becomes clear that IT applications can be used at many process steps in the care for people with rare diseases and have a high potential. Therefore, they must be used to inform decisions about the adequate diagnosis and treatment as well as communication about the clinical pictures and the patient's case between practitioners and medical care sectors. CONCLUSION The interdisciplinary collaboration highlights the need for cooperation between the various parties involved in the process, which requires the identification and implementation of interfaces between the stakeholders and their systems. However, it is not enough to include the view of the processes; the data perspective is also required. Creating interoperability also enables the use of IT applications. The basis for this is the results obtained.
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Affiliation(s)
- Michele Zoch
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland.
| | - Brita Sedlmayr
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland; Zentrum für Evidenzbasierte Gesundheitsversorgung, Universitätsklinikum und Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Deutschland
| | - Andreas Knapp
- Zentrum für Evidenzbasierte Gesundheitsversorgung, Universitätsklinikum und Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Deutschland
| | - Franziska Bathelt
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland
| | - Sven Helfer
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland
| | - Jochen Schmitt
- Zentrum für Evidenzbasierte Gesundheitsversorgung, Universitätsklinikum und Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Deutschland
| | - Martin Sedlmayr
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Deutschland
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Hurvitz N, Azmanov H, Kesler A, Ilan Y. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet 2021; 29:1485-1490. [PMID: 34276056 PMCID: PMC8484657 DOI: 10.1038/s41431-021-00928-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/06/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023] Open
Abstract
Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.
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Affiliation(s)
- Noa Hurvitz
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Henny Azmanov
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Asa Kesler
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel
| | - Yaron Ilan
- Faculty of Medicine, Department of Medicine, Hebrew University, Hadassah Medical Center, Jerusalem, Israel.
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Peng C, Dieck S, Schmid A, Ahmad A, Knaus A, Wenzel M, Mehnert L, Zirn B, Haack T, Ossowski S, Wagner M, Brunet T, Ehmke N, Danyel M, Rosnev S, Kamphans T, Nadav G, Fleischer N, Fröhlich H, Krawitz P. CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph. NAR Genom Bioinform 2021; 3:lqab078. [PMID: 34514393 PMCID: PMC8415429 DOI: 10.1093/nargab/lqab078] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 08/16/2021] [Accepted: 08/31/2021] [Indexed: 12/11/2022] Open
Abstract
Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype–genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.
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Affiliation(s)
- Chengyao Peng
- Institute for Genomic Statistics, University Bonn, 53129 Bonn, Germany
| | - Simon Dieck
- Institute for Genomic Statistics, University Bonn, 53129 Bonn, Germany
| | - Alexander Schmid
- Institute for Genomic Statistics, University Bonn, 53129 Bonn, Germany
| | - Ashar Ahmad
- Fraunhofer SCAI, Department of Bioinformatics, 53757 Sankt Augustin, Germany
| | - Alexej Knaus
- Institute for Genomic Statistics, University Bonn, 53129 Bonn, Germany
| | - Maren Wenzel
- Genetikum Counseling Center, 70173 Stuttgart, Germany
| | - Laura Mehnert
- Genetikum Counseling Center, 70173 Stuttgart, Germany
| | - Birgit Zirn
- Genetikum Counseling Center, 70173 Stuttgart, Germany
| | - Tobias Haack
- Institute of Medical Genetics and Applied Genomics, University Tübingen, 72076 Tübingen, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University Tübingen, 72076 Tübingen, Germany
| | - Matias Wagner
- Institute for Human Genetics, Technical University Munich, 81675 Munich, Germany
| | - Theresa Brunet
- Institute for Human Genetics, Technical University Munich, 81675 Munich, Germany
| | - Nadja Ehmke
- Institute for Medical Genetics, Charité University Medicine, 13353 Berlin, Germany
| | - Magdalena Danyel
- Institute for Medical Genetics, Charité University Medicine, 13353 Berlin, Germany
| | | | | | | | | | - Holger Fröhlich
- Fraunhofer SCAI, Department of Bioinformatics, 53757 Sankt Augustin, Germany
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Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115088] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.
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Schaaf J, Sedlmayr M, Sedlmayr B, Prokosch HU, Storf H. Evaluation of a clinical decision support system for rare diseases: a qualitative study. BMC Med Inform Decis Mak 2021; 21:65. [PMID: 33602191 PMCID: PMC7890997 DOI: 10.1186/s12911-021-01435-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/10/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Rare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The Medical Informatics in Research and Medicine (MIRACUM) consortium developed a CDSS for RDs based on distributed clinical data from eight German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis to obtain an indication of a diagnosis. To optimize our CDSS, we conducted a qualitative study to investigate usability and functionality of our designed CDSS. METHODS We performed a Thinking Aloud Test (TA-Test) with RDs experts working in Rare Diseases Centers (RDCs) at MIRACUM locations which are specialized in diagnosis and treatment of RDs. An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. The TA-Test was recorded on audio and video, whereas the resulting transcripts were analysed with a qualitative content analysis, as a ruled-guided fixed procedure to analyse text-based data. Furthermore, a questionnaire was handed out at the end of the study including the System Usability Scale (SUS). RESULTS A total of eight experts from eight MIRACUM locations with an established RDC were included in the study. Results indicate that more detailed information about patients, such as descriptive attributes or findings, can help the system perform better. The system was rated positively in terms of functionality, such as functions that enable the user to obtain an overview of similar patients or medical history of a patient. However, there is a lack of transparency in the results of the CDSS patient similarity analysis. The study participants often stated that the system should present the user with an overview of exact symptoms, diagnosis, and other characteristics that define two patients as similar. In the usability section, the CDSS received a score of 73.21 points, which is ranked as good usability. CONCLUSIONS This qualitative study investigated the usability and functionality of a CDSS of RDs. Despite positive feedback about functionality of system, the CDSS still requires some revisions and improvement in transparency of the patient similarity analysis.
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Affiliation(s)
- Jannik Schaaf
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany.
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Brita Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Hans-Ulrich Prokosch
- Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Holger Storf
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany
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