Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence

McCarron RM, Shapiro B, Rawles J, Luo J. Depression. Ann Intern Med. 2021;174:ITC65–80.

Article  PubMed  Google Scholar 

Malhi GS, Mann JJ. Depression. Lancet. 2018;392(10161):2299–312.

Article  PubMed  Google Scholar 

Greenberg PE, Fournier A, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 2015;76(2):155–62.

Article  PubMed  Google Scholar 

Holvast F, Massoudi B, Voshaar RC, Verhaak PFM. Non-pharmacological treatment for depressed older patients in primary care: a systematic review and meta-analysis. PLoS ONE. 2017;12(9):e0184666.

Article  PubMed  PubMed Central  Google Scholar 

Davydow DS, Fenger-Grøn M, Ribe A, Pedersen H, Prior A, Vedsted P, et al. Depression and risk of hospitalisations and rehospitalisations for ambulatory care-sensitive conditions in Denmark: a population-based cohort study. BMJ Open. 2015;5:e009878.

Article  PubMed  PubMed Central  Google Scholar 

Park SC, Oh HS, Oh DH, Jung SA, Na KS, Lee HY, et al. Evidence-Based, non-pharmacological treatment guideline for depression in Korea. J Korean Med Sci. 2014;29:12–22.

Article  PubMed  CAS  Google Scholar 

Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, Grant BF. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiat. 2018;75:336–46.

Article  Google Scholar 

Kleine-Budde K, Müller R, Kawohl W, Bramesfeld A, Moock J, Rössler W. The cost of depression—A cost analysis from a large database. J Affect Disord. 2013;147:137–43.

Article  PubMed  Google Scholar 

Okumura Y, Higuchi T. Cost of depression among adults in Japan. Prim Care Companion CNS Disord. 2011;13(3):26159.

Google Scholar 

Zhang L, Chen Y, Yue L, Liu Q, Montgomery W, Zhi L, et al. Medication use patterns, health care resource utilization, and economic burden for patients with major depressive disorder in Beijing, People’s Republic of China. Neuropsychiatr Dis Trea. 2016;20(12):941–9.

Google Scholar 

Han C, Wang SM, Lee SJ, Patkar AA, Masand PS, Pae CU. Second-generation antipsychotics in the treatment of major depressive disorder: current evidence. Expert Rev Neurother. 2013;13(7):851–70.

Article  PubMed  CAS  Google Scholar 

Valenstein M. Keeping our eyes on STAR*D. AJP. 2006;163:1484–6.

Article  Google Scholar 

Wiles N, Taylor A, Turner N, Barnes M, Campbell J, Lewis G, Morrison J, Peters TJ, Thomas L, Turner K, et al. Management of treatment-resistant depression in primary care: a mixed-methods study. Br J Gen Pr. 2018;68:e673–81.

Article  Google Scholar 

Rege S, Sura S, Aparasu RR. Atypical antipsychotic prescribing in elderly patients with depression. Res Social Adm Pharm. 2018;14:645–52.

Article  PubMed  Google Scholar 

EMA, Questions and answers on Seroquel XR and associated names (50, 150, 200, 300 and 400 mg prolonged-release tablets containing quetiapine), EMA Website. 2010. https://www.ema.europa.eu/en/documents/referral/questions-answers-seroquel-xr-associated-names-50-150-200-300-400-mg-prolonged-release-tablets_en.pdf. Accessed 27 Sep 2019.

Cleare A, Pariante CM, Young AH, et al. Evidence-based guidelines for treating depressive disorders with antidepressants: a revision of the 2008 British Association for Psychopharmacology guidelines. J Psychopharmacol. 2015;29:459–525.

Article  PubMed  CAS  Google Scholar 

Kennedy SH, Lam RW, McIntyre RS, et al. Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: section 3 Pharmacological treatments. Can J Psychiatry. 2016;61:540–60.

Article  PubMed  PubMed Central  Google Scholar 

Hiemke C, Bergemann N, Clement HW, Conca A, Deckert J, Domschke K, Eckermann G, Egberts K, Gerlach M, Greiner C, Gründer G, Haen E, Havemann-Reinecke U, Hefner G, Helmer R, Janssen G, Jaquenoud E, Laux G, Messer T, Mössner R, Müller MJ, Paulzen M, Pfuhlmann B, Riederer P, Saria A, Schoppek B, Schoretsanitis G, Schwarz M, Gracia MS, Stegmann B, Steimer W, Stingl JC, Uhr M, Ulrich S, Unterecker S, Waschgler R, Zernig G, Zurek G, Baumann P. Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology: update 2017. Pharmacopsychiatry. 2018;51(1–02):e1. https://doi.org/10.1055/s-0037-1600991. (Epub 2018 Feb 1. Erratum for: Pharmacopsychiatry. 2018 Jan;51(1–02):9–62).

Article  PubMed  CAS  Google Scholar 

Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, Mattonen SA, El Naga I. Machine and deep learning methods for radiomics. Med Phys. 2020;47(5):e185–202.

Article  PubMed  Google Scholar 

Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: a review. Metabolism. 2021;124:154872.

Article  PubMed  CAS  Google Scholar 

Rani P, Kotwal S, Manhas J, Sharma V, Sharma S. Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and developments. Arch Comput Methods. 2021;29:1–37.

Google Scholar 

Huang X, Yu Z, Wei X, Shi J, Wang Y, Wang Z, Chen J, Bu S, Li L, Gao F, Zhang J, Xu A. Prediction of vancomycin dose on high-dimensional data using machine learning techniques. Expert Rev Clin Pharmacol. 2021;14(6):761–71.

Article  PubMed  CAS  Google Scholar 

Liu Y, Chen J, You Y, Xu A, Li P, Wang Y, Sun J, Yu Z, Gao F, Zhang J. An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set. Comput Biol Med. 2021;131:104242.

Article  PubMed  CAS  Google Scholar 

National Health Commission of the People’s Republic of China. Code for Diagnosis and Treatment of Mental Disorders 2020 [M]. The National Health Commission of the People’s Republic of China. 2020;5:164.

Chen H, Ma Y, Hong N, Wang H, Su L, Liu C, He J, Jiang H, Long Y, Zhu W. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Med Inform Decis Mak. 2021;21(Suppl 2):126.

Article  PubMed  PubMed Central  Google Scholar 

Powers DMW. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv. Preprint posted online. Accessed 11 Oct 2020.

Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, Fava M. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: Implications for clinical practice. Am J Psychiatry. 2006;163(1):28–40.

Article  PubMed  Google Scholar 

National Collaborating Centre for Mental Health (UK). Depression: The Treatment and Management of Depression in Adults (Updated Edition). Leicester (UK): British Psychological Society. 2010

Seshadri A, Wermers ML, Habermann TJ, et al. Long-term efficacy and tolerability of adjunctive aripiprazole for major depressive disorder: systematic review and meta-analysis. Prim Care Companion CNS Disord. 2021;23(4):34898.

Article  Google Scholar 

You W, Widmer N, De Micheli G. Example-based support vector machine for drug concentration analysis. In: Conf Proc IEEE Eng Med Biol Soc. 2011, 153–157.

Ludden TM. Population pharmacokinetics. J Clin Pharmacol. 1988;28:1059–63.

Article  PubMed  CAS  Google Scholar 

Johansson ÅM, Ueckert S, Plan EL, Hooker AC, Karlsson MO. Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7. J Pharmacokinet Pharmacodyn. 2014;41:223–38.

Article  PubMed  CAS  Google Scholar 

Sibieude E, Khandelwal A, Girard P, Hesthaven JS, Terranova N. Population pharmacokinetic model selection assisted by machine learning. J Pharmacokinet Pharmacodyn. 2021. https://doi.org/10.1007/s10928-021-09793-6.

Article  PubMed  PubMed Central  Google Scholar 

Huang X, Yu Z, Bu S, Lin Z, Hao X, He W, et al. An ensemble model for prediction of vancomycin trough concentrations in pediatric patients. Drug Des Devel Ther. 2021;15:1549–59.

Article  PubMed  PubMed Central  Google Scholar 

Poynton MR, Choi BM, Kim YM, Park IS, Noh GJ, Hong SO, et al. Machine learning methods applied to pharmacokinetic modelling of remifentanil in healthy volunteers: a multi-method comparison. J Int Med Res. 2009;37:1680–91.

Article  PubMed  CAS  Google Scholar 

Shatte A, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49:1426–48.

Article  PubMed  Google Scholar 

Meng HY, Jin WL, Yan CK, Yang H. The application of machine learning techniques in clinical drug therapy. Curr Comput Aided Drug Des. 2019;15:111–9.

Article  PubMed  CAS  Google Scholar 

Jovanović M, et al. Application of counter-propagation artificial neural networks in prediction of topiramate concentration in patients with epilepsy. J Pharm Pharm Sci. 2015;18:856–62. https://doi.org/10.18433/j33031.

Article  PubMed  Google Scholar 

Tang J, et al. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients. Sci Rep. 2017;7:42192.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Liu R, Li X, Zhang W, Zhou HH. Comparison of nine statistical model based warfarin pharmacogenetic dosing algorithms using the racially diverse international warfarin pharmacogenetic consortium cohort database. PLoS ONE. 2015;10:e0135784.

Article  PubMed  PubMed Central  Google Scholar 

Ma Z, Wang P, Gao Z, Wang R, Khalighi K. Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLoS ONE. 2018;13:e0205872.

Article  PubMed  PubMed Central  Google Scholar 

Roche-Lima A, et al. Machine learning algorithm for predicting warfarin dose in Caribbean hispanics using pharmacogenetic data. Front Pharmacol. 2020;10:1550.

Article  PubMed  PubMed Central 

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