Adverse Drug Event, Clinical Pharmacy, Drug-Drug Interaction, Drug-Related Problems, Machine Learning, Medication Errors, Neonatal Intensive Care Unit
Conditions
Brief summary
Drug-related problems in newborn babies have been reported with a rate of 4-30%. It is estimated that the higher rates of these problems in hospitalized children under the age of two are related to the variety of drugs used and the differences in the age, weight and diagnosis of the patients. In this context, with the clinical parameters and demographic data obtained in the first 24 hours of the patients hospitalized in the neonatal intensive care unit, machine learning algorithms are used to predict the risks that may arise from possible drug-related problems (prescribing and administration errors, side effects and drug-drug interactions) that may occur during hospitalization. The algorithm, which will be created by modeling with a high number of big data pool, is planned to be transformed into a clinical decision support system software that can be used easily in clinical practice with online and mobile applications. By processing the data of the patients to be included in the model, it is aimed to prevent and manage drug-related problems before they occur, as well as to provide cost-effective medşcation treatment to patients hospitalized in the neonatal intensive care unit, together with a reduction in the risk of drug-related mortality and morbidity.
Interventions
Prevention of drug-related problems by clinical pharmacist in neonatal intensive care unit.
Sponsors
Hacettepe University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
1 Hours to 28 Days
Healthy volunteers
No
Inclusion criteria
* Newborns aged 0-28 days, * Consent form taken by the parents to participate in the study, * Patients admitted to neonatal intensive care unit or surgical wards
Exclusion criteria
* Have a postnatal age greater than 28 days, * Patients who will not be given any medication, * Patients who took part in any drug research within the last 28 days
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Neonatal Nutrition Screening Tool | Through study completion, an average of 1 year. | It could be used on all infants in the neonatal intensive care on a weekly basis by nursing staff to identify those at high risk of poor growth and in need of additional nutrition support during their stay. |
| Score for Neonatal Acute Physiology and Perinatal Extension Score | Through study completion, an average of 1 year. | Score for Neonatal Acute Physiology and Perinatal Extension Score is predictor of mortality in neonates. |
| Neonatal Therapeutic Intervention Scoring System | Through study completion, an average of 1 year. | It is a therapy-based severity of illness (morbidity) assessment index. |
| Neonatal Early-Onset Sepsis Risk Score | Through study completion, an average of 1 year. | It is use first week of life for determined sepsis risk with gestational age, highest maternal antepartum temperature, duration of rupture of membranes, etc. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Adverse Drug Reactions Algorithm for Infants | Through study completion, an average of 1 year. | The new algorithm developed using actual patient data is more valid and reliable than the Naranjo algorithm for identifying adverse drug reactions in the neonatal intensive care unit population. |
| Neonatal Adverse Event Severity Scale | Through study completion, an average of 1 year. | It describes a consensus process that led to the development of standard severity criteria for neonatal adverse events. The use of this tool could improve the quality of drug and device safety evaluations and facilitate the conduct of neonatal clinical trials. |
| The Drug Interaction Probability Scale | Through study completion, an average of 1 year. | This scale uses a series of questions relating to the potential drug interaction to estimate a probability score. |
| National Aeronautics and Space Administration Task Load Index | Through study completion, an average of 1 year. | NASA Task Load Index (NASA-TLX) is a widely used, subjective, multidimensional assessment tool that rates perceived workload in order to assess a task, system, or team's effectiveness or other aspects of performance. |
Countries
Turkey (Türkiye)
Contacts
Primary ContactNadir Yalçın, MSc
Outcome results
None listed