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Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.
Yalçin, Nadir; Kasikci, Merve; Çelik, Hasan Tolga; Allegaert, Karel; Demirkan, Kutay; Yigit, Sule; Yurdakök, Murat.
Afiliación
  • Yalçin N; Department of Clinical Pharmacy, Faculty of Pharmacy, Hacettepe University, Ankara, Türkiye.
  • Kasikci M; Department of Biostatistics, Faculty of Medicine, Hacettepe University, Ankara, Türkiye.
  • Çelik HT; Division of Neonatology, Department of Child Health and Diseases, Faculty of Medicine, Hacettepe University, Ankara, Türkiye.
  • Allegaert K; Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Belgium.
  • Demirkan K; Department of Development and Regeneration, KU Leuven, Belgium.
  • Yigit S; Department of Hospital Pharmacy, Erasmus Medical Center, Rotterdam, Netherlands.
  • Yurdakök M; Department of Clinical Pharmacy, Faculty of Pharmacy, Hacettepe University, Ankara, Türkiye.
Front Pharmacol ; 14: 1151560, 2023.
Article en En | MEDLINE | ID: mdl-37124199
Aim: To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients. Design: Prospective, observational cohort study randomized with machine learning (ML) algorithms. Setting: A 22-bed capacity NICU in Ankara, Turkey, between February 2020 and July 2021. Results: A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/day) who received 2,280 prescriptions over 32,925 patient days were analyzed. At least one physician-related ME and nurse-related ME were found in 174 (42.2%) and 235 (57.0%) of the patients, respectively. The parameters that had the highest correlation with ME occurrence and subsequently included in the model were: total number of drugs, anti-infective drugs, nervous system drugs, 5-min APGAR score, postnatal age, alimentary tract and metabolism drugs, and respiratory system drugs as patient-related parameters, and weekly working hours of nurses, weekly working hours of physicians, and number of nurses' monthly shifts as care provider-related parameters. The obtained model showed high performance to predict ME (AUC: 0.920; 95% CI: 0.876-0.970) presence and is accessible online (http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/). Conclusion: This is the first developed and validated model to predict the presence of ME using work environment and pharmacotherapy parameters with high-performance ML algorithms in NICU patients. This approach and the current model hold the promise of implementation of targeted/precision screening to prevent MEs in neonates. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT04899960.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Front Pharmacol Año: 2023 Tipo del documento: Article Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Front Pharmacol Año: 2023 Tipo del documento: Article Pais de publicación: Suiza