Drug-Related Problems in Neonatal Patients

NCT ID: NCT04899960

Last Updated: 2021-09-02

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

512 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-02-01

Study Completion Date

2021-11-01

Brief Summary

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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.

Detailed Description

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Conditions

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Neonatal Intensive Care Unit Medication Errors Drug-Related Problems Adverse Drug Event Drug-Drug Interaction Clinical Pharmacy Machine Learning

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

PREVENTION

Blinding Strategy

NONE

Study Groups

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Observational Group

Group Type NO_INTERVENTION

No interventions assigned to this group

Control (Validation) Group

Group Type NO_INTERVENTION

No interventions assigned to this group

İnterventional Group

Group Type EXPERIMENTAL

Clinical Pharmacist Intervention

Intervention Type DRUG

Prevention of drug-related problems by clinical pharmacist in neonatal intensive care unit.

Interventions

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Clinical Pharmacist Intervention

Prevention of drug-related problems by clinical pharmacist in neonatal intensive care unit.

Intervention Type DRUG

Eligibility Criteria

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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
Minimum Eligible Age

1 Hour

Maximum Eligible Age

28 Days

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Hacettepe University

OTHER

Sponsor Role lead

Responsible Party

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Nadir Yalçın

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Nadir Yalçın, MSc

Role: PRINCIPAL_INVESTIGATOR

Hacettepe University

Locations

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Nadir Yalçın

Ankara, TR, Turkey (Türkiye)

Site Status RECRUITING

Countries

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Turkey (Türkiye)

Central Contacts

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Nadir Yalçın, MSc

Role: CONTACT

+905356849300

Facility Contacts

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Nadir Yalçın, MSc

Role: primary

+903123052043

References

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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.

Reference Type DERIVED
PMID: 37645440 (View on PubMed)

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.

Reference Type DERIVED
PMID: 37124199 (View on PubMed)

Other Identifiers

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KA-20004

Identifier Type: -

Identifier Source: org_study_id

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