The aim of this study was to identify the factors associated with antipsychotic polypharmacy (APP), investigate whether APP could affect the risk of rehospitalization, and explore temporal trends in APP use. Schizophrenia patients discharged from the study hospital between 2006 and 2021 (n = 16,722) were included in the analysis. The logistic regression model was employed to determine the predictors significantly associated with APP use. Survival analysis was used to compare time to rehospitalization between APP and antipsychotic monotherapy (AMT). The temporal trend of APP use was analyzed using the Cochran-Armitage Trend test. In comparison with the patients (n = 10,909) who were discharged on AMT, those (n = 5,813) on APP were significantly more likely to be male gender, to receive LAIs, to take clozapine, to take anticholinergic agents, to have a greater number of previous hospitalizations, and to have a higher CPZ equivalent dose of antipsychotic prescription. The prescription rate of APP significantly increased from 18.4 % in 2006 to 44.9 % in 2021. Compared with AMT, APP was associated with more clozapine use, more LAI use, higher doses of antipsychotics, and an increased risk of rehospitalization. In addition, the prescription of APP continued to increase during the study period.
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http://dx.doi.org/10.1016/j.psychres.2023.115575 | DOI Listing |
AIDS Res Ther
January 2025
Digital Health Africa, Abuja, Nigeria.
Alzheimers Res Ther
January 2025
Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Background: PSEN1, PSEN2, and APP mutations cause Alzheimer's disease (AD) with an early age at onset (AAO) and progressive cognitive decline. PSEN1 mutations are more common and generally have an earlier AAO; however, certain PSEN1 mutations cause a later AAO, similar to those observed in PSEN2 and APP.
Methods: We examined whether common disease endotypes exist across these mutations with a later AAO (~ 55 years) using hiPSC-derived neurons from familial Alzheimer's disease (FAD) patients harboring mutations in PSEN1, PSEN2, and APP and mechanistically characterized by integrating RNA-seq and ATAC-seq.
Nutr J
January 2025
Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Eugeniahemmet T2:02, Stockholm, SE-171 76, Sweden.
Background: mHealth, i.e. mobile-health, strategies may be used as a complement to regular care to support healthy dietary habits in primary care patients.
View Article and Find Full Text PDFHPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States. Electronic address:
Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.
Advanced practice providers (APPs) experience limited clinical opportunities to perform neonatal procedures to maintain competency and hospital credentialing, especially high-acuity procedures that are extremely rare but crucial during patient emergencies. Incorporating simulation as part of continuing professional education can help APPs maintain clinical procedural competency and learn new procedural techniques to improve the quality and safety of procedures performed in the clinical setting. In 2013, we successfully developed and implemented an annual didactic and simulation-based neonatal procedural skills program.
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