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Mazzolenis ME, Bulat E, Schatman ME, Gumb C, Gilligan CJ, Yong RJ. The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review. Curr Pain Headache Rep 2024; 28:785-792. [PMID: 38809404 DOI: 10.1007/s11916-024-01272-0] [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: 05/04/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE OF REVIEW As artificial intelligence (AI) and machine learning (ML) are becoming more pervasive in medicine, understanding their ethical considerations for chronic pain and headache management is crucial for optimizing their safety. RECENT FINDINGS We reviewed thirty-eight editorial and original research articles published between 2018 and 2023, focusing on the application of AI and ML to chronic pain or headache. The core medical principles of beneficence, non-maleficence, autonomy, and justice constituted the evaluation framework. The AI applications addressed topics such as pain intensity prediction, diagnostic aides, risk assessment for medication misuse, empowering patients to self-manage their conditions, and optimizing access to care. Virtually all AI applications aligned both positively and negatively with specific medical ethics principles. This review highlights the potential of AI to enhance patient outcomes and physicians' experiences in managing chronic pain and headache. We emphasize the importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache, and propose the four core principles of medical ethics as an evaluation framework.
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Affiliation(s)
- Maria Emilia Mazzolenis
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Evgeny Bulat
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, 02115, MA, USA
| | - Michael E Schatman
- Department of Anesthesiology, Perioperative Care, and Pain Medicine, Department of Population Health - Division of Medical Ethics, New York University Grossman School of Medicine, New York, NY, USA
| | - Chris Gumb
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Christopher J Gilligan
- Department of Anesthesiology, Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Robert J Yong
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, 02115, MA, USA.
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Stubberud A, Langseth H, Nachev P, Matharu MS, Tronvik E. Artificial intelligence and headache. Cephalalgia 2024; 44:3331024241268290. [PMID: 39099427 DOI: 10.1177/03331024241268290] [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] [Indexed: 08/06/2024]
Abstract
BACKGROUND AND METHODS In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. RESULTS We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. CONCLUSIONS The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.
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Affiliation(s)
- Anker Stubberud
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Helge Langseth
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Manjit S Matharu
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, Neuroclinic, StOlav University Hospital, Trondheim, Norway
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Ihara K, Dumkrieger G, Zhang P, Takizawa T, Schwedt TJ, Chiang CC. Application of Artificial Intelligence in the Headache Field. Curr Pain Headache Rep 2024:10.1007/s11916-024-01297-5. [PMID: 38976174 DOI: 10.1007/s11916-024-01297-5] [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: 06/27/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE OF REVIEW Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. RECENT FINDINGS We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.
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Affiliation(s)
- Keiko Ihara
- Department of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
- Japanese Red Cross Ashikaga Hospital, Ashikaga, Tochigi, Japan
| | | | - Pengfei Zhang
- Department of Neurology, Rutgers University, New Brunswick, NJ, USA
| | - Tsubasa Takizawa
- Department of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Todd J Schwedt
- Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA
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Okada M, Katsuki M, Shimazu T, Takeshima T, Mitsufuji T, Ito Y, Ohbayashi K, Imai N, Miyahara J, Matsumori Y, Nakazato Y, Fujita K, Hoshino E, Yamamoto T. Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study. Life (Basel) 2024; 14:744. [PMID: 38929727 PMCID: PMC11204521 DOI: 10.3390/life14060744] [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: 05/07/2024] [Revised: 06/05/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients' questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists' diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models' overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21-1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings.
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Affiliation(s)
- Mariko Okada
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | - Masahito Katsuki
- Physical Education and Health Center, Nagaoka University of Technology, Niigata 940-2137, Japan
| | - Tomokazu Shimazu
- Department of Neurology, Saitama Neuropsychiatric Institute, Saitama 338-8577, Japan
| | - Takao Takeshima
- Headache Center and Department of Neurology, Tominaga Hospital, Osaka 556-0017, Japan
| | - Takashi Mitsufuji
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | - Yasuo Ito
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | | | - Noboru Imai
- Department of Neurology, Japanese Red Cross Shizuoka Hospital, Shizuoka 420-0853, Japan
| | - Junichi Miyahara
- Headache Center and Department of Neurology, Tominaga Hospital, Osaka 556-0017, Japan
| | | | - Yoshihiko Nakazato
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
| | - Kazuki Fujita
- Department of Neurology, Jichi Medical University Saitama Medical Center, Saitama 330-8503, Japan
| | - Eri Hoshino
- Department of Neurology, Saitama Neuropsychiatric Institute, Saitama 338-8577, Japan
| | - Toshimasa Yamamoto
- Department of Neurology, Saitama Medical University, 38 Morohongo, Moroyama-machi, Iruma-gun, Saitama 350-0495, Japan; (M.O.)
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Cerda IH, Zhang E, Dominguez M, Ahmed M, Lang M, Ashina S, Schatman ME, Yong RJ, Fonseca ACG. Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management. Curr Pain Headache Rep 2024:10.1007/s11916-024-01279-7. [PMID: 38836996 DOI: 10.1007/s11916-024-01279-7] [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: 05/20/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders. RECENT FINDINGS Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.
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Affiliation(s)
| | - Emily Zhang
- Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Moises Dominguez
- Department of Neurology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA
| | | | - Min Lang
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Sait Ashina
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael E Schatman
- Department of Anesthesiology, Perioperative Care, and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Department of Population Health-Division of Medical Ethics, NYU Grossman School of Medicine, New York, NY, USA
| | - R Jason Yong
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA
| | - Alexandra C G Fonseca
- Harvard Medical School, Boston, MA, USA.
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA.
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Cohen F, Bobker S. Artificial intelligence and social media: (Appropriately) harnessing headache medicine's new arsenal in the 21st century. Headache 2024; 64:607-608. [PMID: 38666603 DOI: 10.1111/head.14724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/07/2024] [Indexed: 06/19/2024]
Affiliation(s)
- Fred Cohen
- Department of Neurology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Medicine, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sarah Bobker
- Department of Neurology, NYU Langone Neurology Associates, New York University School of Medicine, New York, New York, USA
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Torrente A, Maccora S, Prinzi F, Alonge P, Pilati L, Lupica A, Di Stefano V, Camarda C, Vitabile S, Brighina F. The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci 2024; 14:85. [PMID: 38248300 PMCID: PMC10813497 DOI: 10.3390/brainsci14010085] [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: 12/22/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs' management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.
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Affiliation(s)
- Angelo Torrente
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Simona Maccora
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology Unit, ARNAS Civico di Cristina and Benfratelli Hospitals, 90127 Palermo, Italy
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Paolo Alonge
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Laura Pilati
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology and Stroke Unit, P.O. “S. Antonio Abate”, 91016 Trapani, Italy
| | - Antonino Lupica
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Vincenzo Di Stefano
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Cecilia Camarda
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Filippo Brighina
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
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Deng W, Wang D, Wan Y, Lai S, Ding Y, Wang X. Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review. Front Cardiovasc Med 2024; 10:1287434. [PMID: 38259313 PMCID: PMC10800829 DOI: 10.3389/fcvm.2023.1287434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Background The number of models developed for predicting major adverse cardiovascular events (MACE) in patients undergoing percutaneous coronary intervention (PCI) is increasing, but the performance of these models is unknown. The purpose of this systematic review is to evaluate, describe, and compare existing models and analyze the factors that can predict outcomes. Methods We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 during the execution of this review. Databases including Embase, PubMed, The Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, and SINOMED were comprehensively searched for identifying studies published from 1977 to 19 May 2023. Model development studies specifically designed for assessing the occurrence of MACE after PCI with or without external validation were included. Bias and transparency were evaluated by the Prediction Model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariate Individual Prognosis Or Diagnosis (TRIPOD) statement. The key findings were narratively summarized and presented in tables. Results A total of 5,234 articles were retrieved, and after thorough screening, 23 studies that met the predefined inclusion criteria were ultimately included. The models were mainly constructed using data from individuals diagnosed with ST-segment elevation myocardial infarction (STEMI). The discrimination of the models, as measured by the area under the curve (AUC) or C-index, varied between 0.638 and 0.96. The commonly used predictor variables include LVEF, age, Killip classification, diabetes, and various others. All models were determined to have a high risk of bias, and their adherence to the TRIPOD items was reported to be over 60%. Conclusion The existing models show some predictive ability, but all have a high risk of bias due to methodological shortcomings. This suggests that investigators should follow guidelines to develop high-quality models for better clinical service and dissemination. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=400835, Identifier CRD42023400835.
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Affiliation(s)
- Wenqi Deng
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Dayang Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Cardiovascular Diseases, Beijing University of Chinese Medicine, Beijing, China
| | - Yandi Wan
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Sijia Lai
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yukun Ding
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xian Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Cardiovascular Diseases, Beijing University of Chinese Medicine, Beijing, China
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Katsuki M, Matsumori Y, Ichihara T, Yamada Y, Kawamura S, Kashiwagi K, Koh A, Goto T, Kaneko K, Wada N, Yamagishi F. Treatment patterns and characteristics of headache in patients in Japan: A retrospective cross-sectional and longitudinal analysis of health insurance claims data. Cephalalgia 2024; 44:3331024231226177. [PMID: 38194504 DOI: 10.1177/03331024231226177] [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] [Indexed: 01/11/2024]
Abstract
BACKGROUND The present study aimed to investigate prescription patterns for patients aged over 17 years with headaches in the REZULT database. METHODS We conducted a cross-sectional study (Study 1) of the proportion of over-prescription of acute medications (≥30 tablets/90 days for triptans, combination non-steroidal anti-inflammatory drugs (NSAIDs) and multiple types; ≥45 tablets/90 days for single NSAIDs) among patients with headache diagnosed in 2020. We longitudinally studied (Study 2) patients for >2 years from initial headache diagnosis (July 2010 to April 2022). The number of prescribed tablets was counted every 90 days. RESULTS In Study 1, headache was diagnosed in 200,055 of 3,638,125 (5.5%) patients: 13,651/200,055 (6.8%) received acute medication. Single NSAIDs were prescribed to 12,297/13,651 (90.1%) patients and triptans to 1710/13,651 (12.5%). Over-prescription was found in 2262/13,651 (16.6%) patients and 1200/13,651 (8.8%) patients received prophylactic medication. In Study 2, 408,183/6,840,618 (6.0%) patients were first diagnosed with headaches, which persisted for ≥2 years. Over time, the proportion of patients over-prescribed acute medications increased. Over 2 years, 37,617/408,183 (9.2%) patients were over-prescribed acute medications and 29,313/408,183 (7.2%) patients were prescribed prophylaxis at least once. CONCLUSIONS According to real-world data, prophylaxis remains poorly prescribed, and both acute and prophylactic treatment rates for headaches have increased over time.
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Affiliation(s)
- Masahito Katsuki
- Department of Neurosurgery, Suwa Red Cross Hospital, Suwa, Nagano, Japan
- Headache Outpatient, Suwa Red Cross Hospital, Suwa, Nagano, Japan
| | | | - Taisuke Ichihara
- Japan System Techniques Co., Ltd (JAST), Minato-ku, Tokyo, Japan
| | - Yuya Yamada
- Japan System Techniques Co., Ltd (JAST), Minato-ku, Tokyo, Japan
| | - Shin Kawamura
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Kenta Kashiwagi
- Department of Neurology, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Akihito Koh
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
| | - Tetsuya Goto
- Department of Neurosurgery, Suwa Red Cross Hospital, Suwa, Nagano, Japan
| | - Kazuma Kaneko
- Headache Outpatient, Suwa Red Cross Hospital, Suwa, Nagano, Japan
- Department of Neurology, Suwa Red Cross Hospital, Suwa, Nagano, Japan
| | - Naomichi Wada
- Department of Neurosurgery, Suwa Red Cross Hospital, Suwa, Nagano, Japan
- Headache Outpatient, Suwa Red Cross Hospital, Suwa, Nagano, Japan
| | - Fuminori Yamagishi
- Department of Surgery, Itoigawa General Hospital, Itoigawa, Niigata, Japan
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Sasaki S, Katsuki M, Kawahara J, Yamagishi C, Koh A, Kawamura S, Kashiwagi K, Ikeda T, Goto T, Kaneko K, Wada N, Yamagishi F. Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model. Cureus 2023; 15:e44415. [PMID: 37791157 PMCID: PMC10543415 DOI: 10.7759/cureus.44415] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction Misdiagnosis of pediatric and adolescent migraine is a significant problem. The first artificial intelligence (AI)-based pediatric migraine diagnosis model was made utilizing a database of questionnaires obtained from a previous epidemiological study, the Itoigawa Benizuwaigani Study. Methods The AI-based headache diagnosis model was created based on the internal validation based on a retrospective investigation of 909 patients (636 training dataset for model development and 273 test dataset for internal validation) aged six to 17 years diagnosed based on the International Classification of Headache Disorders 3rd edition. The diagnostic performance of the AI model was evaluated. Results The dataset included 234/909 (25.7%) pediatric or adolescent patients with migraine. The mean age was 11.3 (standard deviation 3.17) years. The model's accuracy, sensitivity (recall), specificity, precision, and F-values for the test dataset were 94.5%, 88.7%, 96.5%, 90.0%, and 89.4%, respectively. Conclusions The AI model exhibited high diagnostic performance for pediatric and adolescent migraine. It holds great potential as a powerful tool for diagnosing these conditions, especially when secondary headaches are ruled out. Nonetheless, further data collection and external validation are necessary to enhance the model's performance and ensure its applicability in real-world settings.
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Affiliation(s)
- Shiori Sasaki
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Masahito Katsuki
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Junko Kawahara
- Department of Health Promotion, Itoigawa City, Itoigawa, JPN
| | | | - Akihito Koh
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
| | - Shin Kawamura
- Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, JPN
| | - Kenta Kashiwagi
- Department of Neurology, Itoigawa General Hospital, Itoigawa, JPN
| | - Takashi Ikeda
- Department of Health Promotion, Itoigawa City, Itoigawa, JPN
| | - Tetsuya Goto
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Kazuma Kaneko
- Department of Neurology, Japanese Red Cross Suwa Hospital, Suwa, JPN
| | - Naomichi Wada
- Department of Neurosurgery, Japanese Red Cross Suwa Hospital, Suwa, JPN
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