دورية أكاديمية

Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial.

التفاصيل البيبلوغرافية
العنوان: Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial.
المؤلفون: Janssen Daalen JM; Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands., Doesburg D; Amsterdam Data Collective, Amsterdam, The Netherlands., Hunik L; Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands., Kessel R; Amsterdam Data Collective, Amsterdam, The Netherlands., Herngreen T; Amsterdam Data Collective, Amsterdam, The Netherlands., Knol D; Amsterdam Data Collective, Amsterdam, The Netherlands., Ruys T; Amsterdam Data Collective, Amsterdam, The Netherlands., van den Bemt BJF; Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands.; Department of Pharmacy, Sint Maartenskliniek, Nijmegen, The Netherlands.; Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center+, Maastricht, The Netherlands., Schers HJ; Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands.
المصدر: Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2024 Sep; Vol. 116 (3), pp. 824-833. Date of Electronic Publication: 2024 May 06.
نوع المنشور: Journal Article; Validation Study
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: United States NLM ID: 0372741 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-6535 (Electronic) Linking ISSN: 00099236 NLM ISO Abbreviation: Clin Pharmacol Ther Subsets: MEDLINE
أسماء مطبوعة: Publication: 2015- : Hoboken, NJ : Wiley
Original Publication: St. Louis : C.V. Mosby
مواضيع طبية MeSH: Thyroxine*/administration & dosage , Thyroxine*/therapeutic use , Thyroxine*/pharmacokinetics , Machine Learning* , Hypothyroidism*/drug therapy , General Practice*, Humans ; Middle Aged ; Male ; Female ; Adult ; Aged ; Computer Simulation ; Precision Medicine/methods ; Dose-Response Relationship, Drug ; Models, Biological ; Primary Health Care
مستخلص: Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision-support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty-one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.
(© 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)
References: Rodriguez‐Gutierrez, R., Maraka, S., Ospina, N.S., Montori, V.M. & Brito, J.P. Levothyroxine overuse: time for an about face? Lancet Diabetes Endocrinol 5, 246–248 (2017).
Practitioners, T.D.C.o.G. NHG‐Standaard Schildklieraandoeningen (2013) https://richtlijnen.nhg.org/standaarden/schildklieraandoeningen.
Ernst, F.R. et al. The economic impact of levothyroxine dose adjustments: the CONTROL HE study. Clin Drug Investig 37, 71–83 (2017).
Darwich, A. et al. Why has model‐informed precision dosing not yet become common clinical reality? Lessons from the past and a roadmap for the future. Clin Pharmacol Therap 101, 646–656 (2017).
Darwich, A.S. et al. Model‐informed precision dosing: background, requirements, validation, implementation, and forward trajectory of individualizing drug therapy. Annu Rev Pharmacol Toxicol 61, 225–245 (2021).
Zwart, T.C. et al. Model‐informed precision dosing to optimise immunosuppressive therapy in renal transplantation. Drug Discov Today 26, 2527–2546 (2021).
Abalovich, M. et al. Adequate levothyroxine doses for the treatment of hypothyroidism newly discovered during pregnancy. Thyroid 23, 1479–1483 (2013).
Al‐Dhahri, S.F., Al‐Angari, S.S., Alharbi, J. & AlEssa, M. Optimal levothyroxine dose in post‐total thyroidectomy patients: a prediction model for initial dose titration. Eur Arch Otorrinolaringol 276, 2559–2564 (2019).
Chen, S.S. et al. Optimizing levothyroxine dose adjustment after thyroidectomy with a decision tree. J Surg Res 244, 102–106 (2019).
Elfenbein, D.M., Schaefer, S., Shumway, C., Chen, H., Sippel, R.S. & Schneider, D.F. Prospective intervention of a novel levothyroxine dosing protocol based on body mass index after thyroidectomy. J Am Coll Surg 222, 83–88 (2016).
Singh, R., Tandon, A. & Awasthi, A. Development and prospective validation of the levothyroxine dose prediction model in primary hypothyroidism. Horm Metab Res 53, 264–271 (2021).
Zaborek, N.A. et al. The optimal dosing scheme for levothyroxine after thyroidectomy: a comprehensive comparison and evaluation. Surgery 165, 92–98 (2019).
Trivedi, M.H. et al. Assessing physicians' use of treatment algorithms: project IMPACTS study design and rationale. Contemp Clin Trials 28, 192–212 (2007).
Tanguay‐Sela, M. et al. Evaluating the perceived utility of an artificial intelligence‐powered clinical decision support system for depression treatment using a simulation center. Psychiatry Res 308, 114336 (2022).
Shah, C., Davtyan, K., Nasrallah, I., Bryan, R.N. & Mohan, S. Artificial intelligence‐powered clinical decision support and simulation platform for radiology trainee education. J Digit Imaging 36, 11–16 (2023).
Hadlow, N.C., Rothacker, K.M., Wardrop, R., Brown, S.J., Lim, E.M. & Walsh, J.P. The relationship between TSH and free T(4) in a large population is complex and nonlinear and differs by age and sex. J Clin Endocrinol Metab 98, 2936–2943 (2013).
Collins, G.S. et al. Protocol for development of a reporting guideline (TRIPOD‐AI) and risk of bias tool (PROBAST‐AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).
Brooke, J. SUS‐A quick and dirty usability scale. Usability Evaluat Industry 189, 4–7 (1996).
Holden, R.J. & Karsh, B.‐T. The technology acceptance model: its past and its future in health care. J Biomed Inform 43, 159–172 (2010).
Krol, M.W., de Boer, D., Delnoij, D.M. & Rademakers, J.J. The net promoter score–an asset to patient experience surveys? Health Expect 18, 3099–3109 (2015).
Guideline on the choice of the non‐inferiority margin. Committee for Medicinal Products for Human Use (European Medicines Agency, London, United Kingdom, 2005).
Flinterman, L.E. et al. Impact of a forced dose‐equivalent levothyroxine brand switch on plasma thyrotropin: a cohort study. Thyroid 30, 821–828 (2020).
Gong, I.Y. et al. Levothyroxine dose and risk of atrial fibrillation: a nested case‐control study. Am Heart J 232, 47–56 (2021).
Selmer, C. et al. The spectrum of thyroid disease and risk of new onset atrial fibrillation: a large population cohort study. BMJ 345, e7895 (2012).
la Cour, J.L. et al. Risk of over‐ and under‐ treatment with levothyroxine in primary care in Copenhagen, Denmark. Eur J Endocrinol 185, 673–679 (2021).
Shah, K., Reyes‐Gastelum, D., Gay, B.L. & Papaleontiou, M. Understanding worry about risks associated with thyroid hormone therapy: a National Survey of endocrinologists, family physicians, and geriatricians. Endocr Pract 28, 25–29 (2022).
Monzani, F. et al. Effect of levothyroxine replacement on lipid profile and intima‐media thickness in subclinical hypothyroidism: a double‐blind, placebo‐ controlled study. J Clin Endocrinol Metab 89, 2099–2106 (2004).
Lambert, S.I. et al. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med 6, 111 (2023).
Rahimi, B., Nadri, H., Lotfnezhad Afshar, H. & Timpka, T. A systematic review of the technology acceptance model in health informatics. Appl Clin Inform 9, 604–634 (2018).
DiStefano, J. 3rd & Jonklaas, J. Predicting optimal combination LT4 + LT3 therapy for hypothyroidism based on residual thyroid function. Front Endocrinol (Lausanne) 10, 746 (2019).
Cruz‐Loya, M., Chu, B.B., Jonklaas, J., Schneider, D.F. & DiStefano, J. 3rd. Optimized replacement T4 and T4+T3 dosing in male and female hypothyroid patients with different BMIs using a personalized mechanistic model of thyroid hormone regulation dynamics. Front Endocrinol (Lausanne) 13, 888429 (2022).
Marston, L., Carpenter, J.R., Walters, K.R., Morris, R.W., Nazareth, I. & Petersen, I. Issues in multiple imputation of missing data for large general practice clinical databases. Pharmacoepidemiol Drug Saf 19, 618–626 (2010).
Evron, J.M., Hummel, S.L., Reyes‐Gastelum, D., Haymart, M.R., Banerjee, M. & Papaleontiou, M. Association of thyroid hormone treatment intensity with cardiovascular mortality among US veterans. JAMA Netw Open 5, e2211863 (2022).
Choi, B.C. & Pak, A.W. A catalog of biases in questionnaires. Prev Chronic Dis 2, A13 (2005).
Keane, P.A. & Topol, E.J. With an eye to AI and autonomous diagnosis. NPJ Digit Med 1, 40 (2018).
Benjamens, S., Dhunnoo, P. & Mesko, B. The state of artificial intelligence‐based FDA‐approved medical devices and algorithms: an online database. NPJ Digit Med 3, 118 (2020).
المشرفين على المادة: Q51BO43MG4 (Thyroxine)
تواريخ الأحداث: Date Created: 20240507 Date Completed: 20240821 Latest Revision: 20240821
رمز التحديث: 20240821
DOI: 10.1002/cpt.3293
PMID: 38711388
قاعدة البيانات: MEDLINE
الوصف
تدمد:1532-6535
DOI:10.1002/cpt.3293