An exploratory database study of factors influencing the continuation of brexpiprazole treatment (prescription) in patients with schizophrenia using information from psychiatric electronic medical records processed with natural language processing

被引:0
|
作者
Iyo, Masaomi [1 ]
Akiyoshi, Hisashi [2 ,3 ]
Sekine, Daisuke [2 ]
Shibasaki, Yoshiyuki [2 ]
Mamiya, Noriyuki [2 ]
机构
[1] Chiba Univ, Grad Sch Med, Dept Psychiat, Chiba, Japan
[2] Otsuka Pharmaceut Co Ltd, Med Affairs Dept, Tokyo, Japan
[3] Shinagawa Grand Cent Tower,2-16-4 Konan,Minato Ku, Tokyo 1088242, Japan
关键词
Brexpiprazole; Factors affecting discontinuation; Database research; Text mining; MENTAT & REG; Schizophrenia; LONG-ACTING INJECTION; PREDICTORS;
D O I
10.1016/j.schres.2023.03.008
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Using natural language processing (NLP) technology to analyze and organize textual information in psychiatric electronic medical records can identify undiscovered factors associated with treatment discontinuation. This study aimed to evaluate brexpiprazole treatment continuation rate and factors affecting brexpiprazole discontinuation using a database that employs the MENTAT (R) system with NLP technology. This retrospective observational study evaluated patients with schizophrenia who were newly initiated on brexpiprazole (April 18, 2018May 15, 2020). The first prescriptions of brexpiprazole were followed up for 180 days. Factors associated with brexpiprazole discontinuation were assessed using structured and unstructured patient data (April 18, 2017December 31, 2020). The analysis population comprised 515 patients; mean (standard deviation) age of patients was 48.0 (15.3) years, and 47.8 % were male. Using Kaplan-Meier analysis, the cumulative brexpiprazole continuation rate at 180 days was 29 % (estimate: 0.29; 95 % confidence interval, 0.25-0.33). Univariate Cox proportional hazards analysis identified 16 variables independently associated with brexpiprazole discontinuation. Multivariate analysis identified eight variables associated with treatment discontinuation: variables with hazard ratio <1 were the presence of physical complications, longer hospitalization duration, and maximum chlorpromazine-equivalent dose of antipsychotics of >200 to <400 mg/day vs <200 mg/day in the past year; variables with hazard ratio >1 were previous electroconvulsive therapy, availability of key contact person information, a history of crime committed/reported, increase in brexpiprazole dose to 2 mg in >28 days, and appearance/worsening of symptoms other than positive symptoms. In conclusion, we identified potential new factors that may be associated with brexpiprazole discontinuation, which may improve the treatment strategy and continuation rate in patients with schizophrenia.
引用
收藏
页码:122 / 131
页数:10
相关论文
共 29 条
  • [21] Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing
    Gonzalez-Juanatey, Carlos
    Nchez, Manuel Anguita-Sa
    Barrios, Vivencio
    Nunez-Gil, Ivan
    Gomez-Doblas, Juan Josa
    Garcia-Moll, Xavier
    Lafuente-Gormaz, Carlos
    Rollan-Gomez, Maria Jesus
    Peral-Disdie, Vicente
    Martinez-Dolz, Luis
    Rodriguez-Santamarta, Miguel
    Vinolas-Prat, Xavier
    Soriano-Colome, Toni
    Munoz-Aguilera, Roberto
    Plaza, Ignacio
    Curcio-Ruigomez, Alejandro
    Orts-Soler, Ernesto
    Segovia, Javier
    Mate, Claudia
    Cequier, Angel
    PLOS ONE, 2022, 17 (02):
  • [22] Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?
    Brown, Jeremiah R.
    Ricket, Iben M.
    Reeves, Ruth M.
    Shah, Rashmee U.
    Goodrich, Christine A.
    Gobbel, Glen
    Stabler, Meagan E.
    Perkins, Amy M.
    Minter, Freneka
    Cox, Kevin C.
    Dorn, Chad
    Denton, Jason
    Bray, Bruce E.
    Gouripeddi, Ramkiran
    Higgins, John
    Chapman, Wendy W.
    MacKenzie, Todd
    Matheny, Michael E.
    JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2022, 11 (07):
  • [23] Exploratory analysis of patient characteristics and treatment duration in patients using esketamine for treatmentresistant depression: A large longitudinal study linking commercial claims data to electronic medical records
    Xiong, Xiaomo
    Liu, Xinyue
    DiBello, Julia
    Li, Sam
    Lu, Kevin
    PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2023, 32 : 312 - 313
  • [24] Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study
    Hu, Danqing
    Li, Shaolei
    Zhang, Huanyao
    Wu, Nan
    Lu, Xudong
    JMIR MEDICAL INFORMATICS, 2022, 10 (04) : 153 - 170
  • [25] Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study
    Yang, Zhen
    Pou-Prom, Chloe
    Jones, Ashley
    Banning, Michaelia
    Dai, David
    Mamdani, Muhammad
    Oh, Jiwon
    Antoniou, Tony
    JMIR MEDICAL INFORMATICS, 2022, 10 (01)
  • [26] Systemic inflammation in patients with atherosclerotic cardiovascular and chronic kidney disease in Spain: a population-based study using electronic medical records from a primary care database
    Pesce, G.
    Khachatryan, A.
    Lopez Ledesma, B.
    Gusto, G.
    Vukmirica, J.
    Scheffer Apecechea, N.
    Cases, A.
    EUROPEAN HEART JOURNAL, 2024, 45
  • [27] A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study
    Homburg, Maarten
    Meijer, Eline
    Berends, Matthijs
    Kupers, Thijmen
    Hartman, Tim Olde
    Muris, Jean
    de Schepper, Evelien
    Velek, Premysl
    Kuiper, Jeroen
    Berger, Marjolein
    Peters, Lilian
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2023, 25
  • [28] Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System
    Misra-Hebert, Anita D.
    Milinovich, Alex
    Zajichek, Alex
    Ji, Xinge
    Hobbs, Todd D.
    Weng, Wayne
    Petraro, Paul
    Kong, Sheldon X.
    Mocarski, Michelle
    Ganguly, Rahul
    Bauman, Janine M.
    Pantalone, Kevin M.
    Zimmerman, Robert S.
    Kattan, Michael W.
    DIABETES CARE, 2020, 43 (08) : 1937 - 1940
  • [29] Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data
    Sezgin, Emre
    Hussain, Syed-Amad
    Rust, Steve
    Huang, Yungui
    JMIR FORMATIVE RESEARCH, 2023, 7