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Caso F, Fatica M, Ferraioli M, Megna M, Potestio L, Ruggiero A, Tommasino N, Maione F, Scarpa R, Chimenti MS, Costa L. The role of bDMARDs in the prevention and treatment of inflammatory-related comorbidities in Psoriatic Arthritis. Expert Opin Biol Ther 2024:1-13. [PMID: 39037828 DOI: 10.1080/14712598.2024.2384090] [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: 05/26/2024] [Accepted: 07/21/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Psoriatic arthritis (PsA) is an immune-inflammatory disease that affects both joints and entheses, and with diverse extra-articular manifestations (psoriasis, inflammatory bowel disease (IBD), and uveitis). A wide range of comorbid conditions, including cardiovascular diseases, obesity, metabolic syndrome (MetS), nonalcoholic fatty liver disease (NAFLD), mental health disorders (depression/anxiety), and osteoporosis are highly prevalent in course of PsA.Biological DMARDs (bDMARD), including TNF-inhibitors (TNFi), Interleukin (IL-17i) and IL-23i represent the cornerstone of the management of active disease. The use of these therapies obviously requires considering comorbidities presence, safety aspects and contraindications. AREAS COVERED The aim of this review is to describe the inflammatory mechanisms behind PsA comorbidities, and the role of bDMARDs in the prevention and treatment of these conditions in course of PsA. EXPERT OPINION Tailoring therapeutic strategies to the individual characteristics of each PsA patient can be an effective approach to manage comorbidities, maximizing the efficacy of bDMARDs, and reducing the incidence of AEs. Identifying targets within disease pathways can guide research into therapeutics that address both PsA and comorbidities simultaneously, but more studies are advocated for clarifying the potential prevention and management of bDMARDs used for PsA.
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Affiliation(s)
- Francesco Caso
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Mauro Fatica
- U.O.C. Reumatologia, Dipartimento di Medicina dei Sistemi, Universitá di Roma "Tor Vergata", Roma, Italy
| | - Mario Ferraioli
- U.O.C. Reumatologia, Dipartimento di Medicina dei Sistemi, Universitá di Roma "Tor Vergata", Roma, Italy
| | - Matteo Megna
- Section of Dermatology - Department of Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Luca Potestio
- Section of Dermatology - Department of Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Angelo Ruggiero
- Section of Dermatology - Department of Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Nello Tommasino
- Section of Dermatology - Department of Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Francesco Maione
- ImmunoPharmaLab, Department of Pharmacy, School of Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Raffaele Scarpa
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Maria Sole Chimenti
- U.O.C. Reumatologia, Dipartimento di Medicina dei Sistemi, Universitá di Roma "Tor Vergata", Roma, Italy
| | - Luisa Costa
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
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Chandwar K, Prasanna Misra D. What does artificial intelligence mean in rheumatology? Arch Rheumatol 2024; 39:1-9. [PMID: 38774703 PMCID: PMC11104749 DOI: 10.46497/archrheumatol.2024.10664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 05/24/2024] Open
Abstract
Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.
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Affiliation(s)
- Kunal Chandwar
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India
| | - Durga Prasanna Misra
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, India
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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Grovu R, Huo Y, Nguyen A, Mourad O, Pan Z, El-Gharib K, Wei C, Mustafa A, Quan T, Slobodnick A. Machine learning: Predicting hospital length of stay in patients admitted for lupus flares. Lupus 2023; 32:1418-1429. [PMID: 37831499 DOI: 10.1177/09612033231206830] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
BACKGROUND Although rare, severe systemic lupus erythematosus (SLE) flares requiring hospitalization account for most of the direct costs of SLE care. New machine learning (ML) methods may optimize lupus care by predicting which patients will have a prolonged hospital length of stay (LOS). Our study uses a machine learning approach to predict the LOS in patients admitted for lupus flares and assesses which features prolong LOS. METHODS Our study sampled 5831 patients admitted for lupus flares from the National Inpatient Sample Database 2016-2018 and collected 90 demographics and comorbidity features. Four machine learning (ML) models were built (XGBoost, Linear Support Vector Machines, K Nearest Neighbors, and Logistic Regression) to predict LOS, and their performance was evaluated using multiple metrics, including accuracy, receiver operator area under the curve (ROC-AUC), precision-recall area under the curve (PR- AUC), and F1-score. Using the highest-performing model (XGBoost), we assessed the feature importance of our input features using Shapley value explanations (SHAP) to rank their impact on LOS. RESULTS Our XGB model performed the best with a ROC-AUC of 0.87, PR-AUC of 0.61, an F1 score of 0.56, and an accuracy of 95%. The features with the most significant impact on the model were "the need for a central line," "acute dialysis," and "acute renal failure." Other top features include those related to renal and infectious comorbidities. CONCLUSION Our results were consistent with the established literature and showed promise in ML over traditional methods of predictive analyses, even with rare rheumatic events such as lupus flare hospitalizations.
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Affiliation(s)
- Radu Grovu
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Yanran Huo
- Department of Engineering, University of Massachusetts, Dartmouth, MA, USA
| | - Andrew Nguyen
- Medicine Department, Harvard Medical School, Boston, MA, USA
| | - Omar Mourad
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Zihang Pan
- Medicine Department, Duke-NUS Medical School, Singapore
| | - Khalil El-Gharib
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Chapman Wei
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Ahmad Mustafa
- Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA
| | - Theodore Quan
- Medicine Department, George Washington University School of Medicine, Washington, DC, USA
| | - Anastasia Slobodnick
- Rheumatology Department, Staten Island University Hospital, Staten Island, NY, USA
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Motta F, Morandini P, Maffia F, Vecellio M, Tonutti A, De Santis M, Costanzo A, Puggioni F, Savevski V, Selmi C. Connecting the use of innovative treatments and glucocorticoids with the multidisciplinary evaluation through rule-based natural-language processing: a real-world study on patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis. Front Med (Lausanne) 2023; 10:1179240. [PMID: 37387783 PMCID: PMC10301822 DOI: 10.3389/fmed.2023.1179240] [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] [Received: 03/03/2023] [Accepted: 05/15/2023] [Indexed: 07/01/2023] Open
Abstract
Background The impact of a multidisciplinary management of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis on systemic glucocorticoids or innovative treatments remains unknown. Rule-based natural language processing and text extraction help to manage large datasets of unstructured information and provide insights into the profile of treatment choices. Methods We obtained structured information from text data of outpatient visits between 2017 and 2022 using regular expressions (RegEx) to define elastic search patterns and to consider only affirmative citation of diseases or prescribed therapy by detecting negations. Care processes were described by binary flags which express the presence of RA, PsA and psoriasis and the prescription of glucocorticoids and biologics or small molecules in each cases. Logistic regression analyses were used to train the classifier to predict outcomes using the number of visits and the other specialist visits as the main variables. Results We identified 1743 patients with RA, 1359 with PsA and 2,287 with psoriasis, accounting for 5,677, 4,468 and 7,770 outpatient visits, respectively. Among these, 25% of RA, 32% of PsA and 25% of psoriasis cases received biologics or small molecules, while 49% of RA, 28% of PsA, and 40% of psoriasis cases received glucocorticoids. Patients evaluated also by other specialists were treated more frequently with glucocorticoids (70% vs. 49% for RA, 60% vs. 28% for PsA, 51% vs. 40% for psoriasis; p < 0.001) as well as with biologics/small molecules (49% vs. 25% for RA, 64% vs. 32% in PsA; 51% vs. 25% for psoriasis; p < 0.001) compared to cases seen only by the main specialist. Conclusion Patients with RA, PsA, or psoriasis undergoing multiple evaluations are more likely to receive innovative treatments or glucocorticoids, possibly reflecting more complex cases.
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Affiliation(s)
- Francesca Motta
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Pierandrea Morandini
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Fiore Maffia
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Matteo Vecellio
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Centro Ricerche Fondazione Italiana Ricerca Sull'Artrite (FIRA), Fondazione Pisana per la Scienza ONLUS, San Giuliano Terme (Pisa), Milan, Italy
| | - Antonio Tonutti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Maria De Santis
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Antonio Costanzo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Dermatology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Francesca Puggioni
- Division of Asthma, Allergy and Personalized Medicine, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Carlo Selmi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
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Caso F, Costa L, Triggianese P, Maione F, Bertolini N, Vastarella M, Chimenti MS, Tasso M. Recent developments for new investigational JAK inhibitors in psoriatic arthritis. Expert Opin Investig Drugs 2023:1-11. [PMID: 37096862 DOI: 10.1080/13543784.2023.2207737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
INTRODUCTION Psoriatic arthritis (PsA) is a chronic inflammatory disease affecting about one-third of subjects with psoriasis. Several treatment modalities targeting Janus Kinase pathways and intracellular inflammatory cascade are now available and under clinical investigation to treat this disease. AREAS COVERED This review describes ongoing and recently completed phase 2 and 3 Randomized Clinical Trials (RCTs) evaluating the efficacy and safety of approved JAK (Tofacitinib and Upadacitinib) and investigational JAK inhibitors (JAK1 inhibitors: Filgotinib and Ivarmacitinib (SHR0302); TYK2 inhibitors: Brepocitinib (PF-06700841) Deucravacitinib (BMS-986165), and NDI-034858) in PsA through February 2023. EXPERT OPINION Current standard of care has significantly improved the quality of life in PsA. Recently approved JAK inhibitors for PsA have addressed many of the unmet needs of PsA, particularly of those with severe phenotypes. Preliminary results from several RCTs have reported good and fast efficacy and an acceptable safety profile of investigational JAK inhibitors in PsA. Additional clinical trials and long-term outcome data on these agents are necessary for increasing available therapeutic options for PsA.
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Affiliation(s)
- Francesco Caso
- Rheumatology Unit, Department of Clinical Medicine and Surgery, University of Naples Federico II
| | - Luisa Costa
- Rheumatology Unit, Department of Clinical Medicine and Surgery, University of Naples Federico II
| | - Paola Triggianese
- Rheumatology, allergology and clinical immunology, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Francesco Maione
- ImmunoPharmaLab, Department of Pharmacy, School of Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Nicoletta Bertolini
- Rheumatology Unit, Department of Clinical Medicine and Surgery, University of Naples Federico II
| | - Maria Vastarella
- Dermatology Unit, Department of Clinical Medicine and Surgery, University of Naples Federico II
| | - Maria Sole Chimenti
- Rheumatology, allergology and clinical immunology, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Marco Tasso
- Rheumatology Unit, Department of Clinical Medicine and Surgery, University of Naples Federico II
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Misra DP, Hauge EM, Crowson CS, Kitas GD, Ormseth SR, Karpouzas GA. Atherosclerotic Cardiovascular Risk Stratification in the Rheumatic Diseases:: An Integrative, Multiparametric Approach. Rheum Dis Clin North Am 2023; 49:19-43. [PMID: 36424025 DOI: 10.1016/j.rdc.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Cardiovascular disease (CVD) risk is increased in most inflammatory rheumatic diseases (IRDs), reiterating the role of inflammation in the initiation and progression of atherosclerosis. An inverse association of CVD risk with body weight and lipid levels has been described in IRDs. Coronary artery calcium scores, plaque burden and characteristics, and carotid plaques on ultrasound optimize CVD risk estimate in IRDs. Biomarkers of cardiac injury, autoantibodies, lipid biomarkers, and cytokines also improve risk assessment in IRDs. Machine learning and deep learning algorithms for phenotype and image analysis hold promise to improve CVD risk stratification in IRDs.
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Affiliation(s)
- Durga Prasanna Misra
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Rae Bareli Road, Lucknow 226014, India
| | - Ellen M Hauge
- Division of Rheumatology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99 DK-8200, Aarhus, Denmark
| | - Cynthia S Crowson
- Department of Quantitative Health Sciences and Division of Rheumatology, Mayo Clinic, 200 first St SW, Rochester, MN 55905, USA
| | | | - Sarah R Ormseth
- The Lundquist Institute and Harbor-UCLA Medical Center, 1124 West Carson Street, Building E4-R17, Torrance, CA 90502, USA
| | - George A Karpouzas
- The Lundquist Institute and Harbor-UCLA Medical Center, 1124 West Carson Street, Building E4-R17, Torrance, CA 90502, USA.
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De Cock D, Myasoedova E, Aletaha D, Studenic P. Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs). Ther Adv Musculoskelet Dis 2022; 14:1759720X221105978. [PMID: 35794905 PMCID: PMC9251966 DOI: 10.1177/1759720x221105978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/22/2022] [Indexed: 11/17/2022] Open
Abstract
Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.
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Affiliation(s)
- Diederik De Cock
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Elena Myasoedova
- Division of Rheumatology, Department of Internal Medicine and Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Daniel Aletaha
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Vienna, Austria
| | - Paul Studenic
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
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Persistence of C-reactive protein increased levels and high disease activity are predictors of cardiovascular disease in patients with axial spondyloarthritis. Sci Rep 2022; 12:7498. [PMID: 35525861 PMCID: PMC9079083 DOI: 10.1038/s41598-022-11640-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 04/13/2022] [Indexed: 01/03/2023] Open
Abstract
An accurate prediction of cardiovascular (CV) risk in patients with Axial Spondyloarthritis (axSpA) is a strong unmet need, as CV risk algorithms poorly perform in these subjects. The aim of this study was to establish whether the persistence of high C-reactive protein (CRP) and high disease activity may be considered predictive factors of CVD in axSpA. 295 patients without personal history of CVD, were consecutively enrolled in this study. To evaluate the relationship between CV events occurrence (fatal and non-fatal) and the persistence of increased CRP levels, ASDAS (Ankylosing Spondylitis Disease Activity Score) > 2.1, and BASDAI (Bath Ankylosing Spondylitis Disease Activity) > 4 during the follow-up, univariable and multivariable Cox Proportional Hazard Models have been performed. During follow-up (we analyzed 10-years retrospective data), 23 patients had a CV event. Multivariable Cox Proportional Hazard Models showed a strong association between CV event and the persistency of increased CRP levels (namely, percentage of visits in which CRP levels were increased) (HR = 1.03; 95%CI 1.015-1.045; p < 0.001), of ASDAS > 2.1 (HR = 1.014, 95%CI 1.000-1.028, p = 0.047), and of BASDAI > 4 (HR 1.019, 95%CI 1.006-1.033, p = 0.006) during follow-up, after adjustment for age, sex, and diabetes. This study suggests that persistence of increased CRP levels and high disease activity may be considered biomarkers to identify those axSpA patients at higher risk of CVD. Innovative axSpA-specific CV risk score, including these variables, have to be developed.
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Bragazzi NL, Bridgewood C, Watad A, Damiani G, Kong JD, McGonagle D. Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature. Front Immunol 2022; 13:847312. [PMID: 35359924 PMCID: PMC8960164 DOI: 10.3389/fimmu.2022.847312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 01/19/2022] [Indexed: 01/17/2023] Open
Abstract
Background Rheumatological and dermatological disorders contribute to a significant portion of the global burden of disease. Big Data are increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including dermatology and rheumatology. Rheumatology and dermatology can potentially benefit from Big Data. Methods A systematic review of the literature was conducted according to the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines, mining “Uno per tutti”, a highly integrated and automated tool/meta-database developed at the University of Genoa, Genoa, Italy, and consisting of 20 major scholarly electronic databases, including PubMed/MEDLINE. Big Data- or artificial intelligence-based studies were judged based on the modified Qiao’s critical appraisal tool for critical methodological quality assessment of Big Data/machine learning-based studies. Other studies designed as cross-sectional, longitudinal, or randomized investigations, reviews/overviews or expert opinions/commentaries were evaluated by means of the relevant “Joanna Briggs Institute” (JBI)’s critical appraisal tool for the critical methodological quality assessment. Results Fourteen papers were included in the present systematic review of the literature. Most of the studies included concerned molecular applications of Big Data, especially in the fields of genomics and post-genomics. Other studies concerned epidemiological applications, with a practical dearth of studies assessing smart and digital applications for psoriatic arthritis patients. Conclusions Big Data can be a real paradigm shift that revolutionizes rheumatological and dermatological practice and clinical research, helping to early intercept psoriatic arthritis patients. However, there are some methodological issues that should be properly addressed (like recording and association biases) and some ethical issues that should be considered (such as privacy). Therefore, further research in the field is warranted. Systematic Review Registration Registration code 10.17605/OSF.IO/4KCU2.
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Affiliation(s)
- Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics, York University, Toronto, ON, Canada.,Department of Health Sciences (DISSAL), Postgraduate School of Public Health, University of Genoa, Genoa, Italy.,Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, United Kingdom
| | - Charlie Bridgewood
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, United Kingdom
| | - Abdulla Watad
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, United Kingdom.,Department of Medicine B, Rheumatology Unit and Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Ramat-Gan, Israel.,Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Giovanni Damiani
- Clinical Dermatology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Galeazzi Orthopaedic Institute, Milan, Italy
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics, York University, Toronto, ON, Canada
| | - Dennis McGonagle
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, United Kingdom.,National Institute for Health Research Leeds Biomedical Research Centre, Leeds Teaching Hospitals, Leeds, United Kingdom
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Sperti M, Malavolta M, Staunovo Polacco F, Dellavalle A, Ruggieri R, Bergia S, Fazio A, Santoro C, Deriu MA. Cardiovascular risk prediction: from classical statistical methods to machine learning approaches. Minerva Cardiol Angiol 2022; 70:102-122. [PMID: 35261223 DOI: 10.23736/s2724-5683.21.05868-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Nowadays, cardiovascular risk prediction scores are commonly used in primary prevention settings. Estimating the cardiovascular individual risk is of crucial importance for effective patient management and optimal therapy identification, with relevant consequences on secondary prevention settings. To reach this goal, a plethora of risk scores have been developed in the past, most of them assuming that each cardiovascular risk factor is linearly dependent on the outcome. However, the overall accuracy of these methods often remains insufficient to solve the problem at hand. In this scenario, machine learning techniques have repeatedly proved successful in improving cardiovascular risk predictions, being able to capture the non-linearity present in the data. In this concern, we present a detailed discussion concerning the application of classical versus machine learning-based cardiovascular risk scores in the clinical setting. This review aimed to give an overview of the current risk scores based on classical statistical approaches and machine learning techniques applied to predict the risk of several cardiovascular diseases, comparing them, discussing their similarities and differences, and highlighting their main drawbacks to aid the physician having a more critical understanding of these tools.
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Affiliation(s)
- Michela Sperti
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Marta Malavolta
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Federica Staunovo Polacco
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Annalisa Dellavalle
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Rossella Ruggieri
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Sara Bergia
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Alice Fazio
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Carmine Santoro
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy
| | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy -
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12
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Folle L, Simon D, Tascilar K, Krönke G, Liphardt AM, Maier A, Schett G, Kleyer A. Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns—How Neural Networks Can Tell Us Where to “Deep Dive” Clinically. Front Med (Lausanne) 2022; 9:850552. [PMID: 35360728 PMCID: PMC8960274 DOI: 10.3389/fmed.2022.850552] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/15/2022] [Indexed: 12/29/2022] Open
Abstract
Objective: We investigated whether a neural network based on the shape of joints can differentiate between rheumatoid arthritis (RA), psoriatic arthritis (PsA), and healthy controls (HC), which class patients with undifferentiated arthritis (UA) are assigned to, and whether this neural network is able to identify disease-specific regions in joints. Methods We trained a novel neural network on 3D articular bone shapes of hand joints of RA and PsA patients as well as HC. Bone shapes were created from high-resolution peripheral-computed-tomography (HR-pQCT) data of the second metacarpal bone head. Heat maps of critical spots were generated using GradCAM. After training, we fed shape patterns of UA into the neural network to classify them into RA, PsA, or HC. Results Hand bone shapes from 932 HR-pQCT scans of 617 patients were available. The network could differentiate the classes with an area-under-receiver-operator-curve of 82% for HC, 75% for RA, and 68% for PsA. Heat maps identified anatomical regions such as bare area or ligament attachments prone to erosions and bony spurs. When feeding UA data into the neural network, 86% were classified as “RA,” 11% as “PsA,” and 3% as “HC” based on the joint shape. Conclusion We investigated neural networks to differentiate the shape of joints of RA, PsA, and HC and extracted disease-specific characteristics as heat maps on 3D joint shapes that can be utilized in clinical routine examination using ultrasound. Finally, unspecific diseases such as UA could be grouped using the trained network based on joint shape.
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Affiliation(s)
- Lukas Folle
- Pattern Recognition Lab—Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - David Simon
- Department of Internal Medicine 3—Rheumatology and Immunology, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Koray Tascilar
- Department of Internal Medicine 3—Rheumatology and Immunology, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Gerhard Krönke
- Department of Internal Medicine 3—Rheumatology and Immunology, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Anna-Maria Liphardt
- Department of Internal Medicine 3—Rheumatology and Immunology, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab—Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Georg Schett
- Department of Internal Medicine 3—Rheumatology and Immunology, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3—Rheumatology and Immunology, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, FAU Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- *Correspondence: Arnd Kleyer
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13
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Venerito V, Angelini O, Fornaro M, Cacciapaglia F, Lopalco G, Iannone F. A Machine Learning Approach for Predicting Sustained Remission in Rheumatoid Arthritis Patients on Biologic Agents. J Clin Rheumatol 2022; 28:e334-e339. [PMID: 34542990 DOI: 10.1097/rhu.0000000000001720] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
METHODS In this longitudinal study, patients with RA who started a biological disease-modifying antirheumatic drug (bDMARD) in a tertiary care center were analyzed. Demographic and clinical characteristics were collected at treatment baseline, 12-month, and 24-month follow-up. A wrapper feature selection algorithm was used to determine an attribute core set. Four different ML algorithms, namely, LR, random forest, K-nearest neighbors, and extreme gradient boosting, were then trained and validated with 10-fold cross-validation to predict 24-month sustained DAS28 (Disease Activity Score on 28 joints) remission. The performances of the algorithms were then compared assessing accuracy, precision, and recall. RESULTS Our analysis included 367 patients (female 323/367, 88%) with mean age ± SD of 53.7 ± 12.5 years at bDMARD baseline. Sustained DAS28 remission was achieved by 175 (47.2%) of 367 patients. The attribute core set used to train algorithms included acute phase reactant levels, Clinical Disease Activity Index, Health Assessment Questionnaire-Disability Index, as well as several clinical characteristics. Extreme gradient boosting showed the best performance (accuracy, 72.7%; precision, 73.2%; recall, 68.1%), outperforming random forest (accuracy, 65.9%; precision, 65.6%; recall, 59.3%), LR (accuracy, 64.9%; precision, 62.6%; recall, 61.9%), and K-nearest neighbors (accuracy, 63%; precision, 61.5%; recall, 54.8%). CONCLUSIONS We showed that ML models can be used to predict sustained remission in RA patients on bDMARDs. Furthermore, our method only relies on a few easy-to-collect patient attributes. Our results are promising but need to be tested on longitudinal cohort studies.
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Affiliation(s)
- Vincenzo Venerito
- From the Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari "Aldo Moro," Bari, Italy
| | | | - Marco Fornaro
- From the Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari "Aldo Moro," Bari, Italy
| | - Fabio Cacciapaglia
- From the Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari "Aldo Moro," Bari, Italy
| | - Giuseppe Lopalco
- From the Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari "Aldo Moro," Bari, Italy
| | - Florenzo Iannone
- From the Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari "Aldo Moro," Bari, Italy
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14
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Kingsmore KM, Puglisi CE, Grammer AC, Lipsky PE. An introduction to machine learning and analysis of its use in rheumatic diseases. Nat Rev Rheumatol 2021; 17:710-730. [PMID: 34728818 DOI: 10.1038/s41584-021-00708-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2021] [Indexed: 02/07/2023]
Abstract
Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.
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Affiliation(s)
| | | | - Amrie C Grammer
- AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA
| | - Peter E Lipsky
- AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA
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15
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Abstract
With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.
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16
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D'Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M, Ariza-Solé A, Liebetrau C, Manzano-Fernández S, Quadri G, Kinnaird T, Campo G, Simao Henriques JP, Hughes JM, Dominguez-Rodriguez A, Aldinucci M, Morbiducci U, Patti G, Raposeiras-Roubin S, Abu-Assi E, De Ferrari GM. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet 2021; 397:199-207. [PMID: 33453782 DOI: 10.1016/s0140-6736(20)32519-8] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/16/2020] [Accepted: 11/09/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. METHODS Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). FINDINGS The PRAISE score showed an AUC of 0·82 (95% CI 0·78-0·85) in the internal validation cohort and 0·92 (0·90-0·93) in the external validation cohort for 1-year all-cause death; an AUC of 0·74 (0·70-0·78) in the internal validation cohort and 0·81 (0·76-0·85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0·70 (0·66-0·75) in the internal validation cohort and 0·86 (0·82-0·89) in the external validation cohort for 1-year major bleeding. INTERPRETATION A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. FUNDING None.
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Affiliation(s)
- Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy.
| | - Ovidio De Filippo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Guglielmo Gallone
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Gianluca Mittone
- Department of Computer Science, University of Turin, Turin, Italy
| | - Marco Agostino Deriu
- Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | | | - Albert Ariza-Solé
- Department of Cardiology, University Hospital de Bellvitge, Barcelona, Spain
| | | | | | - Giorgio Quadri
- Interventional Cardiology Unit, Degli Infermi Hospital, Turin, Italy
| | - Tim Kinnaird
- Cardiology Department, University Hospital of Wales, Cardiff, UK
| | - Gianluca Campo
- Azienda Ospedaliera Universitaria di Ferrara, Ferrara, Italy
| | | | | | | | - Marco Aldinucci
- Department of Computer Science, University of Turin, Turin, Italy
| | - Umberto Morbiducci
- Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Giuseppe Patti
- Catheterization Laboratory, Maggiore della Carità Hospital, Novara, Italy
| | | | - Emad Abu-Assi
- Department of Cardiology, University Hospital Álvaro Cunqueiro, Vigo, Spain
| | - Gaetano Maria De Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy
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17
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Navarini L, Currado D, Costa L, Tasso M, Chimenti MS, Caso F. Experimental and Investigational Pharmacotherapy for Psoriatic Arthritis: Drugs of the Future. J Exp Pharmacol 2020; 12:487-502. [PMID: 33235521 PMCID: PMC7679354 DOI: 10.2147/jep.s265633] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/24/2020] [Indexed: 12/28/2022] Open
Abstract
In recent years, different studies have shown in psoriatic arthritis (PsA), the pathogenetic role of multiple cytokines other than tumor necrosis factor-α, such as interleukin-17 (IL-17), and IL-23 and dysfunction of Janus kinase (JAK)-signal family pathway. These molecules also represent the target of recently developed biologic (bDMARDs) and targeted synthetic disease modifying antirheumatic drugs (DMARDs) (tsDMARDs) currently investigated in several Phase II and III randomized controlled trials (RCTs). This review examines the therapeutic efficacy and safety of most recent developed IL-17, IL-23 and JAK inhibitors and highlights how these new PsA therapies are going to revolutionize the management of PsA in the next few years. Ongoing RCTs of these molecules in PsA are also described. Available literature on new anti-IL-17 and anti-IL-23 agents and JAK inhibitors demonstrates the potential role of these molecules as effective therapeutic strategies across multiple PsA clinical domains, along with an acceptable tolerability and safety profile, thus expanding the treatment options available for PsA patients. Of note, other molecules are under investigation, and among those, potential therapeutic strategies seem to be represented by single antibodies blocking simultaneously two cytokines, the agents inhibiting mammalian target of rapamycin (mTOR), receptor retinoic acid receptor-related orphan receptor gamma (RORγt), A3 adenosine receptor (A3 AR), and K+ channel voltage channel inhibitors. Remarkable progress has been made in PsA pharmacotherapy, and novel bDMARDs targeting IL17A and tsDMARDs (JAK-inhibitors) represent promising therapies. More clinical trials are needed to better characterize the efficacy and safety profile of these therapeutic agents in PsA treatment.
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Affiliation(s)
- Luca Navarini
- Unit of Rheumatology, Immunology and Clinical Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Damiano Currado
- Unit of Rheumatology, Immunology and Clinical Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Luisa Costa
- Rheumatology Unit, Department of Clinical Medicine and Surgery, School of Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Marco Tasso
- Rheumatology Unit, Department of Clinical Medicine and Surgery, School of Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Maria Sole Chimenti
- Rheumatology, Allergology and Clinical Immunology, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Francesco Caso
- Rheumatology Unit, Department of Clinical Medicine and Surgery, School of Medicine and Surgery, University of Naples Federico II, Naples, Italy
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