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Petch J, Nelson W, Wu M, Ghassemi M, Benz A, Fatemi M, Di S, Carnicelli A, Granger C, Giugliano R, Hong H, Patel M, Wallentin L, Eikelboom J, Connolly SJ. Optimizing warfarin dosing for patients with atrial fibrillation using machine learning. Sci Rep 2024; 14:4516. [PMID: 38402362 PMCID: PMC10894214 DOI: 10.1038/s41598-024-55110-9] [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: 05/02/2023] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.
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Affiliation(s)
- Jeremy Petch
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.
- Population Health Research Institute, Hamilton, ON, Canada.
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada.
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Mary Wu
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Vector Institute, Toronto, ON, Canada
| | - Alexander Benz
- Population Health Research Institute, Hamilton, ON, Canada
- Department of Cardiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anthony Carnicelli
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Christopher Granger
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Robert Giugliano
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hwanhee Hong
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Manesh Patel
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Lars Wallentin
- Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - John Eikelboom
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Hematology and Thromboembolism, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Stuart J Connolly
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada
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Lyu L, Cheng Y, Wahed AS. Imputation-based Q-learning for optimizing dynamic treatment regimes with right-censored survival outcome. Biometrics 2023; 79:3676-3689. [PMID: 37129942 DOI: 10.1111/biom.13872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
Q-learning has been one of the most commonly used methods for optimizing dynamic treatment regimes (DTRs) in multistage decision-making. Right-censored survival outcome poses a significant challenge to Q-Learning due to its reliance on parametric models for counterfactual estimation which are subject to misspecification and sensitive to missing covariates. In this paper, we propose an imputation-based Q-learning (IQ-learning) where flexible nonparametric or semiparametric models are employed to estimate optimal treatment rules for each stage and then weighted hot-deck multiple imputation (MI) and direct-draw MI are used to predict optimal potential survival times. Missing data are handled using inverse probability weighting and MI, and the nonrandom treatment assignment among the observed is accounted for using a propensity-score approach. We investigate the performance of IQ-learning via extensive simulations and show that it is more robust to model misspecification than existing Q-Learning methods, imputes only plausible potential survival times contrary to parametric models and provides more flexibility in terms of baseline hazard shape. Using IQ-learning, we developed an optimal DTR for leukemia treatment based on a randomized trial with observational follow-up that motivated this study.
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Affiliation(s)
- Lingyun Lyu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yu Cheng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Abdus S Wahed
- Departments of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
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Pickles A, Edwards D, Horvath L, Emsley R. Research Reviews: Advances in methods for evaluating child and adolescent mental health interventions. J Child Psychol Psychiatry 2023; 64:1765-1775. [PMID: 37793673 DOI: 10.1111/jcpp.13892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/06/2023]
Abstract
BACKROUND The evidence base for interventions for child mental health and neurodevelopment is weak and the current capacity for rigorous evaluation limited. We describe some of the challenges that make this field particularly difficult and expensive for evaluation studies. METHODS We describe and review the use of novel study designs and analysis methodology for their potential to improve this situation. RESULTS While several novel designs appeared ill-suited to our field, systematic review found others that offered potential but had yet to be widely adopted, some not at all. CONCLUSIONS While funding is inevitably a constraint, we argue that improvements in the evidence base of both current and new treatments will only be achieved by the adoption of a number of these new technologies and study designs, the consistent application of rigorous constructive but demanding standards, and the engagement of the public, patients, clinical and research services to build a design, recruitment, and analysis infrastructure.
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Affiliation(s)
- Andrew Pickles
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Danielle Edwards
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Levente Horvath
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, King's College London, London, UK
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Schofield LM, Singh SJ, Yousaf Z, Wild JM, Hind D. Personalising airway clearance in chronic suppurative lung diseases: a scoping review. ERJ Open Res 2023; 9:00010-2023. [PMID: 37342087 PMCID: PMC10277870 DOI: 10.1183/23120541.00010-2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/14/2023] [Indexed: 06/22/2023] Open
Abstract
Background Personalised airway clearance techniques are commonly recommended to augment mucus clearance in chronic suppurative lung diseases. It is unclear what current literature tells us about how airway clearance regimens should be personalised. This scoping review explores current research on airway clearance technique in chronic suppurative lung diseases, to establish the extent and type of guidance in this area, identify knowledge gaps and determine the factors which physiotherapists should consider when personalising airway clearance regimens. Methods Systematic searching of online databases (MEDLINE, EMBASE, CINAHL, PEDro, Cochrane, Web of Science) was used to identify full-text publications in the last 25 years that described methods of personalising airway clearance techniques in chronic suppurative lung diseases. Items from the TIDieR framework provided a priori categories which were modified based on the initial data to develop a "Best-fit" framework for data charting. The findings were subsequently transformed into a personalisation model. Results A broad range of publications were identified, most commonly general review papers (44%). The items identified were grouped into seven personalisation factors: physical, psychosocial, airway clearance technique (ACT) type, procedures, dosage, response and provider. As only two divergent models of ACT personalisation were found, the personalisation factors identified were then used to develop a model for physiotherapists. Conclusions The personalisation of airway clearance regimens is widely discussed in the current literature, which provides a range of factors that should be considered. This review summarises the current literature, organising findings into a proposed airway clearance personalisation model, to provide clarity in this field.
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Affiliation(s)
- Lynne M. Schofield
- Faculty of Medicine Dentistry and Health, IICD, University of Sheffield, Sheffield, UK
- Paediatric Physiotherapy, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Sally J. Singh
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Zarah Yousaf
- Patient and Public Involvement Member, Leeds Teaching Hospitals NHS Trust, UK
| | - Jim M Wild
- Faculty of Medicine Dentistry and Health, IICD, University of Sheffield, Sheffield, UK
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, UK
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Pereira-Salgado A, Anton A, Franchini F, Mahar RK, Kwan EM, Wong S, Shapiro J, Weickhardt A, Azad AA, Spain L, Gunjur A, Torres J, Parente P, Parnis F, Goh J, Steer C, Brown S, Gibbs P, Tran B, IJzerman M. Real-world clinical outcomes and cost estimates of metastatic castration-resistant prostate cancer treatment: does sequencing of taxanes and androgen receptor-targeted agents matter? Expert Rev Pharmacoecon Outcomes Res 2023; 23:231-239. [PMID: 36541133 DOI: 10.1080/14737167.2023.2161048] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Health economic outcomes of real-world treatment sequencing of androgen receptor-targeted agents (ARTA) and docetaxel (DOC) remain unclear. MATERIAL AND METHODS Data from the electronic Castration-resistant Prostate cancer Australian Database (ePAD) were analyzed including median overall survival (mOS) and median time-to-treatment failure (mTTF). Mean total costs (mTC) and incremental cost-effectiveness ratios (ICER) of treatment sequences were estimated using the average sample method and Zhao and Tian estimator. RESULTS Of 752 men, 441 received ARTA, 194 DOC, and 175 both sequentially. Of participants treated with both, first-line DOC followed by ARTA was the more common sequence (n = 125, 71%). mOS for first-line ARTA was 8.38 years (95% CI: 3.48, not-estimated) vs. 3.29 years (95% CI: 2.92, 4.02) for DOC. mTTF was 15.7 months (95% CI: 14.2, 23.7) for the ARTA-DOC sequence and 18.2 months (95% CI: 16.2, 23.2) for DOC-ARTA. In first-line, ARTA cost an additional $13,244 per mTTF month compared to DOC. In second-line, ARTA cost $6726 per mTTF month. The DOC-ARTA sequence saved $2139 per mTTF compared to ARTA-DOC, though not statistically significant. CONCLUSION ICERs show ARTA had improved clinical benefit compared to DOC but at higher cost. There were no significant cost differences between combined sequences.
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Affiliation(s)
- Amanda Pereira-Salgado
- Centre for Cancer Research and Centre for Health Policy, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Melbourne, Australia.,Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Angelyn Anton
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia.,Department of Personalised Medicine, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.,Department of Cancer Services, Eastern Health, Melbourne, Australia
| | - Fanny Franchini
- Centre for Cancer Research and Centre for Health Policy, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Melbourne, Australia.,Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia.,Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Robert K Mahar
- Centre for Cancer Research and Centre for Health Policy, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Melbourne, Australia.,Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia.,Victorian Comprehensive Cancer Centre, Melbourne, Australia
| | - Edmond M Kwan
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia.,Department of Medical Oncology, Monash Health, Melbourne, Australia
| | | | | | - Andrew Weickhardt
- Olivia Newton John Cancer Wellness and Research Centre, Melbourne, Australia
| | - Arun A Azad
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Lavinia Spain
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia.,Department of Cancer Services, Eastern Health, Melbourne, Australia
| | - Ashray Gunjur
- Olivia Newton John Cancer Wellness and Research Centre, Melbourne, Australia
| | | | - Phillip Parente
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia.,Department of Cancer Services, Eastern Health, Melbourne, Australia
| | - Francis Parnis
- Adelaide Cancer Centre, Adelaide, Australia.,University of Adelaide, Adelaide, Australia
| | - Jeffrey Goh
- Royal Brisbane and Women's Hospital, Brisbane, Australia.,University of Queensland, St Lucia, Australia
| | - Christopher Steer
- Border Medical Oncology, Albury Wodonga Regional Cancer Centre, Albury, Australia.,University of New South Wales, Rural Clinical School, Albury Campus, Albury, Australia
| | | | - Peter Gibbs
- Department of Personalised Medicine, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.,Western Health, Melbourne, Australia
| | - Ben Tran
- Department of Personalised Medicine, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.,Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Maarten IJzerman
- Centre for Cancer Research and Centre for Health Policy, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Melbourne, Australia.,Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia.,Peter MacCallum Cancer Centre, Melbourne, Australia
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McGuinness MB, Abbott CJ. Choosing Analysis Methods to Match Estimands When Investigating Interventions for Macular Disease. JAMA Ophthalmol 2023; 141:147-149. [PMID: 36547954 DOI: 10.1001/jamaophthalmol.2022.5687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Myra B McGuinness
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.,Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, Australia
| | - Carla J Abbott
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia.,Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, Australia
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Seewald NJ. Adaptive Interventions for a Dynamic and Responsive Public Health Approach. Am J Public Health 2023; 113:37-39. [PMID: 36516392 PMCID: PMC9755934 DOI: 10.2105/ajph.2022.307157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Nicholas J Seewald
- Nicholas J. Seewald is with the Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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8
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Zhang Z, Yi D, Fan Y. Doubly robust estimation of optimal dynamic treatment regimes with multicategory treatments and survival outcomes. Stat Med 2022; 41:4903-4923. [PMID: 35948279 DOI: 10.1002/sim.9543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 05/31/2022] [Accepted: 07/21/2022] [Indexed: 11/06/2022]
Abstract
Patients with chronic diseases, such as cancer or epilepsy, are often followed through multiple stages of clinical interventions. Dynamic treatment regimes (DTRs) are sequences of decision rules that assign treatments at each stage based on measured covariates for each patient. A DTR is said to be optimal if the expectation of the desirable clinical benefit reaches a maximum when applied to a population. When there are three or more options for treatments at each decision point and the clinical outcome of interest is a time-to-event variable, estimating an optimal DTR can be complicated. We propose a doubly robust method to estimate optimal DTRs with multicategory treatments and survival outcomes. A novel blip function is defined to measure the difference in expected outcomes among treatments, and a doubly robust weighted least squares algorithm is designed for parameter estimation. Simulations using various weight functions and scenarios support the advantages of the proposed method in estimating optimal DTRs over existing approaches. We further illustrate the practical value of our method by applying it to data from the Standard and New Antiepileptic Drugs study. In this analysis, the proposed method supports the use of the new drug lamotrigine over the standard option carbamazepine. When the actual treatments match the estimated optimal treatments, survival outcomes tend to be better. The newly developed method provides a practical approach for clinicians that is not limited to cases of binary treatment options.
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Affiliation(s)
- Zhang Zhang
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China
| | - Danhui Yi
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China
| | - Yiwei Fan
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
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Takeuchi M, Ogura M, Inagaki N, Kawakami K. Initiating SGLT2 inhibitor therapy to improve renal outcomes for persons with diabetes eligible for an intensified glucose-lowering regimen: hypothetical intervention using parametric g-formula modeling. BMJ Open Diabetes Res Care 2022; 10:10/3/e002636. [PMID: 35675951 PMCID: PMC9185419 DOI: 10.1136/bmjdrc-2021-002636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/24/2022] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Sodium-glucose cotransporter 2 (SGLT2) inhibitors are now recommended in guidelines for persons with type 2 diabetes mellitus (T2DM) and at risk of advanced kidney disease as part of the glucose-lowering regimen. RESEARCH DESIGN AND METHODS To explore the optimal threshold at which to initiate SGLT2 inhibitor therapy, we conducted an observational study analyzed under a counterfactual framework. This study used the electronic healthcare database in Japan, comprising data from approximately 20 million patients at approximately 160 medical institutions. Persons with T2DM with an estimated glomerular filtration rate (eGFR) ≥ 30 mL/min/1.73 m2 in April 2014 were eligible. The primary end point was the composite of renal deterioration (>40% decline in eGFR) and the development of eGFR<30 mL/min/1.73 m2. We estimated the risk of the composite end point occurring over 77 months in different scenarios, such as early or delayed intervention with SGLT2 inhibitors for uncontrolled diabetes at different hemoglobin A1c (HbA1c) thresholds. The parametric g-formula was used to estimate the risk of the composite end point, adjusting for time-fixed and time-varying confounders. RESULTS We analyzed data from 36 237 persons (149 346 person-years observation), of whom 4679 started SGLT2 inhibitor therapy (9470 person-years observation). Overall, initiating SGLT2 inhibitor therapy was associated with a 77-month risk reduction in the end point by 1.3-3.7%. The largest risk reduction was observed within 3 months of initiation once the HbA1c level exceeded 6.5% (risk reduction of 3.7% (95% CI 1.6% to 6.7%)) compared with a threshold of 7.0% or higher. CONCLUSIONS Our analyses favored early intervention with SGLT2 inhibitors to reduce the renal end point, even for persons with moderately controlled HbA1c levels. Our findings also suggest caution against clinical inertia in the care of diabetes.
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Affiliation(s)
- Masato Takeuchi
- Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan
| | - Masahito Ogura
- Department of Diabetes, Endocrinology and Nutrition, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobuya Inagaki
- Department of Diabetes, Endocrinology and Nutrition, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koji Kawakami
- Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan
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Kip MMA, de Roock S, Currie G, Marshall DA, Grazziotin LR, Twilt M, Yeung RSM, Benseler SM, Vastert SJ, Wulffraat N, Swart JF, IJzerman MJ. Pharmacological treatment patterns in patients with juvenile idiopathic arthritis in the Netherlands: a real-world data analysis. Rheumatology (Oxford) 2022; 62:SI170-SI180. [PMID: 35583252 PMCID: PMC9949706 DOI: 10.1093/rheumatology/keac299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/07/2022] [Accepted: 05/07/2022] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To investigate medication prescription patterns among children with JIA, including duration, sequence and reasons for medication discontinuation. METHODS This study is a single-centre, retrospective analysis of prospective data from the electronic medical records of JIA patients receiving systemic therapy aged 0-18 years between 1 April 2011 and 31 March 2019. Patient characteristics (age, gender, JIA subtype) and medication prescriptions were extracted and analysed using descriptive statistics, Sankey diagrams and Kaplan-Meier survival methods. RESULTS Over a median of 4.2 years follow-up, the 20 different medicines analysed were prescribed as monotherapy (n = 15) or combination therapy (n = 48 unique combinations) among 236 patients. In non-systemic JIA, synthetic DMARDs were prescribed to almost all patients (99.5%), and always included MTX. In contrast, 43.9% of non-systemic JIA patients received a biologic DMARD (mostly adalimumab or etanercept), ranging from 30.9% for oligoarticular persistent ANA-positive JIA, to 90.9% for polyarticular RF-positive JIA. Among systemic JIA, 91.7% received a biologic DMARD (always including anakinra). When analysing medication prescriptions according to their class, 32.6% involved combination therapy. In 56.8% of patients, subsequent treatment lines were initiated after unsuccessful first-line treatment, resulting in 68 unique sequences. Remission was the most common reason for DMARD discontinuation (44.7%), followed by adverse events (28.9%) and ineffectiveness (22.1%). CONCLUSION This paper reveals the complexity of pharmacological treatment in JIA, as indicated by: the variety of mono- and combination therapies prescribed, substantial variation in medication prescriptions between subtypes, most patients receiving two or more treatment lines, and the large number of unique treatment sequences.
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Affiliation(s)
- Michelle M A Kip
- Department of Health Technology and Services Research, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede,Department of Pediatric Rheumatology, Division of Paediatrics, University Medical Center Utrecht, Wilhelmina Children’s Hospital, Utrecht
| | - Sytze de Roock
- Department of Pediatric Rheumatology, Division of Paediatrics, University Medical Center Utrecht, Wilhelmina Children’s Hospital, Utrecht,Faculty of Medicine, Utrecht University, Utrecht, The Netherlands
| | - Gillian Currie
- Department of Community Health Sciences,Department of Paediatrics, Cumming School of Medicine,Alberta Children’s Hospital Research Institute,Department of Medicine
| | - Deborah A Marshall
- Department of Community Health Sciences,Alberta Children’s Hospital Research Institute,Department of Medicine
| | | | - Marinka Twilt
- Alberta Children’s Hospital Research Institute,Division of Rheumatology, Department of Pediatrics, Alberta Children’s Hospital, Cumming School of Medicine, University of Calgary, Calgary, Alberta
| | - Rae S M Yeung
- Division of Rheumatology, The Hospital for Sick Children, Department of Paediatrics, Immunology and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Susanne M Benseler
- Alberta Children’s Hospital Research Institute,Division of Rheumatology, Department of Pediatrics, Alberta Children’s Hospital, Cumming School of Medicine, University of Calgary, Calgary, Alberta
| | - Sebastiaan J Vastert
- Department of Pediatric Rheumatology, Division of Paediatrics, University Medical Center Utrecht, Wilhelmina Children’s Hospital, Utrecht,Faculty of Medicine, Utrecht University, Utrecht, The Netherlands,European Reference Network RITA (rare Immunodeficiency Autoinflammatory and Autoimmune Diseases Network)
| | - Nico Wulffraat
- Department of Pediatric Rheumatology, Division of Paediatrics, University Medical Center Utrecht, Wilhelmina Children’s Hospital, Utrecht,Faculty of Medicine, Utrecht University, Utrecht, The Netherlands,European Reference Network RITA (rare Immunodeficiency Autoinflammatory and Autoimmune Diseases Network)
| | | | - Maarten J IJzerman
- Correspondence to: Maarten J. IJzerman, Department of Health Technology and Services Research, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands. E-mail:
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