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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Observational Group | No Intervention | ||
| Control (Validation) Group | No Intervention | ||
| İnterventional Group | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Clinical Pharmacist Intervention | Drug | Prevention of drug-related problems by clinical pharmacist in neonatal intensive care unit. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Score for Neonatal Acute Physiology and Perinatal Extension Score | Score for Neonatal Acute Physiology and Perinatal Extension Score is predictor of mortality in neonates. | Through study completion, an average of 1 year. |
| Neonatal Therapeutic Intervention Scoring System | It is a therapy-based severity of illness (morbidity) assessment index. | Through study completion, an average of 1 year. |
| Neonatal Early-Onset Sepsis Risk Score | It is use first week of life for determined sepsis risk with gestational age, highest maternal antepartum temperature, duration of rupture of membranes, etc. | Through study completion, an average of 1 year. |
| Neonatal Nutrition Screening Tool | 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. | Through study completion, an average of 1 year. |
| Measure | Description | Time Frame |
|---|---|---|
| Neonatal Adverse Event Severity Scale | 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. | Through study completion, an average of 1 year. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Nadir Yalçın, MSc | Contact | +905356849300 | nadir.yalcin@hotmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Nadir Yalçın, MSc | Hacettepe University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Nadir Yalçın | Recruiting | Ankara | TR | 06100 | Turkey (Türkiye) |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37645440 | Derived | Yalcin N, Kasikci M, Celik HT, Allegaert K, Demirkan K, Yigit S. Impact of clinical pharmacist-led intervention for drug-related problems in neonatal intensive care unit a randomized controlled trial. Front Pharmacol. 2023 Aug 14;14:1242779. doi: 10.3389/fphar.2023.1242779. eCollection 2023. | |
| 37124199 | Derived |
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| ID | Term |
|---|---|
| D064420 | Drug-Related Side Effects and Adverse Reactions |
| ID | Term |
|---|---|
| D064419 | Chemically-Induced Disorders |
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| The Drug Interaction Probability Scale | This scale uses a series of questions relating to the potential drug interaction to estimate a probability score. | Through study completion, an average of 1 year. |
| Adverse Drug Reactions Algorithm for Infants | 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. | Through study completion, an average of 1 year. |
| National Aeronautics and Space Administration Task Load Index | 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. | Through study completion, an average of 1 year. |
| Yalcin N, Kasikci M, Celik HT, Allegaert K, Demirkan K, Yigit S, Yurdakok M. Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit. Front Pharmacol. 2023 Apr 14;14:1151560. doi: 10.3389/fphar.2023.1151560. eCollection 2023. |