POTENSI INTERAKSI OBAT PADA PASIEN COVID-19 DI SALAH SATU RUMAH SAKIT DI PROVINSI KALIMANTAN SELATAN

Coronavirus 2019 (COVID-19) adalah penyakit menular yang disebabkan oleh virus corona baru yang pertama kali diidentifikasi pada Desember 2019. Hingga saat penelitian ini dilakukan, belum ditemukan obat yang spesifik untuk penyakit COVID-19 sehingga pengobatan masih bersifat suportif dan simptomatik. Hal ini mengakibatkan pasien mendapatkan banyak obat/polifarmasi yang berpotensi berinteraksi. Tujuan dari penelitian ini untuk menganalisis potensi interaksi obat pada pasien Covid-19. Jenis penelitian ini adalah observasional retrospektif dengan metode analisis secara deskriptif. Penelitian dilakukan dengan cara mengumpulkan data dari Pusat Data Elektronik dan Rekam Medis pasien Covid-19 periode maret-agustus 2020. Pemeriksaan interaksi obat dilakukan melalui website Micromedex. Berdasarkan data yang diperoleh dari 114 rekam medis pasien Covid-19 didapatkan sebanyak 231 potensi interaksi obat. Untuk Kategori keparahan interaksi obat Minor (17 kasus), Moderate (151 kasus), Mayor (765 kasus) dan Contraindicated (20 kasus). Sementara untuk kategori onset interaksi obat Delayed (85 kasus), Rapid (71 kasus) dan Not Specified (797 kasus). Lima interaksi obat terbesar yaitu Azitromisin – Hidroksikloroquin (80 kasus), Azitromisin – Levofloxacin (62 kasus), Hidroksikloroquin – Levofloxacin (56 kasus), Aztromisin – Lovinapir dan Ritonavir (26 kasus) dan Azitromisin Moxifloxacilin (26 kasus)

Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments

Adverse drug events due to drug-drug interactions can be prevented by avoiding concomitant use of causative drugs; therefore, it is important to understand drug combinations that cause drug-drug interactions. Although many attempts to identify drug-drug interactions from real-world databases such as spontaneous reporting systems have been performed, little is known about drug-drug interactions caused by three or more drugs in polypharmacy, i.e., multiple drug-drug interactions. Therefore, we attempted to detect multiple drug-drug interactions using decision tree analysis using the Japanese Adverse Drug Event Report (JADER) database, a Japanese spontaneous reporting system. First, we used decision tree analysis to detect drug combinations that increase the risk of rhabdomyolysis in cases registered in the JADER database that used six statins. Next, the risk of three or more drug combinations that significantly increased the risk of rhabdomyolysis was validated with in vivo experiments in rats. The analysis identified a multiple drug-drug interaction signal only for pitavastatin. The reporting rate of rhabdomyolysis for pitavastatin in the JADER database was 0.09, and it increased to 0.16 in combination with allopurinol. Furthermore, the rate was even higher (0.40) in combination with valsartan. Additionally, necrosis of leg muscles was observed in some rats simultaneously treated with these three drugs, and their creatine kinase and myoglobin levels were elevated. The combination of pitavastatin, allopurinol, and valsartan should be treated with caution as a multiple drug-drug interaction. Since multiple drug-drug interactions were detected with decision tree analysis and the increased risk was verified in animal experiments, decision tree analysis is considered to be an effective method for detecting multiple drug-drug interactions.This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions

Implementação de alertas de interações medicamentosas na construção de uma farmácia online

Os sistemas de apoio à decisão clinica têm-se revelado essenciais no dia-a-dia da população, nomeadamente dos profissionais e dos pacientes. Estes sistemas podem ser aplicados com diferentes objetivos: como sistemas de alerta; de prevenção de doenças; sistemas para dosagem de medicação e prescrição; entre outras. Atualmente é notório o aumento de interesse por parte da população em entender e em possuir um papel ativo nas decisões médicas. Para conseguirem fazê-lo necessitam de procurar informação. O meio mais utilizado para obter essa informação é a internet, onde a informação se encontra em grande quantidade e muito dispersa. Para além da quantidade é imprescindível encontrar informação credível, para que não haja indução da pessoa em erro. Para ajudar a solucionar estes problemas surgiram os sistemas de recomendação na saúde. Estes sistemas foram idealizados para fornecer informações às quais os utilizadores podem recorrer para tomar decisões conscientes e seguras sobre a sua saúde. Também os sistemas de alerta se têm revelado importantes na área da saúde. Estes sistemas podem ser usados em diferentes contextos e sobre diferentes assuntos, como por exemplo, a alteração do estado clínico de um paciente monitorizado, em tempo real, ou em interações medicamentosas. As interações medicamentosas podem advir da automedicação do utente ou da larga quantidade de medicação que, a partir de determinada idade, os utentes ingerem. Pode ter como causa medicação que administrem regularmente, ou até mesmo diariamente, ou doenças/estados que o utente possua que, em simultâneo com determinada medicação pode causar reações adversas. Neste trabalho foi desenvolvido um protótipo de uma farmácia online (FoAM) que fornece, ao utilizador, alertas quando há possibilidade de interações. As causas de interações consideradas foram os medicamentos que o utilizador consuma e/ou doenças/estados que possua. O objetivo é alertar para o caso das causas que o utilizador possui interagirem com o(s) medicamento(s) que este deseja adquirir. Para alcançar esse objetivo foi necessário selecionar os medicamentos a disponibilizar assim como as suas interações. Essa seleção foi baseada no prontuário terapêutico 2013 disponibilizado pelo INFARMED. Depois de recolhida e analisada a informação, foi possível compreender que informações clínicas o sistema necessita para que consiga identificar os medicamentos que não são aconselháveis adquirir. Para isso, é necessário que o utilizador forneça essas informações clínicas pessoais, necessidade que vai de encontro à posição defendida por diversos autores que apontam o uso de registos eletrónicos de saúde muito benéfico para conseguir alertas mais personalizados suprindo assim as necessidades do utilizador. É também preponderante que o utilizador perceba o porquê de determinado medicamento não ser aconselhável, por isso, ao ser emitido o alerta é também apresentada a justificação do mesmo, ou seja, é disponibilizado ao utilizador qual a causa que indicou no formulário responsável pela interação.Systems to support clinical decision have becoming essential in a daily basis to professionals and patients. These systems can be applied with different purposes: as warning systems; disease prevention; systems for medication dosage and prescription; among many others. Currently it is evident the increase of interest by the population to understand and have an active role in medical decisions. To be able to do so they need to seek information. The most common way to get it is the internet, where there is in large quantity and very scattered. In addition to the amount of information it is essential to find credible one, to avoid leading someone on error. To help solve these problems the recommendation systems in health have emerged. These systems were designed to give credible information and make possible to the users get informed and make safe decisions about their health. Also alert systems have becoming important in healthcare. These alert systems can be used in different contexts and on different issues, such as the modification of a monitored patient's clinical condition in real time, or in drugs interactions. Drugs interactions can result from self medication or large amount of drugs that users take after a certain age. Can be caused by the medication that the user takes regularly, or even in a daily basis, or diseases/conditions that the patient has, which simultaneously with certain medication can cause adverse reactions. In this work it was developed a prototype of an online pharmacy that provides to patients alerts when there is a possibility of interactions. The conditions of interactions considered in this work were the medications that the patient take and/or diseases/conditions that the patient have. The goal is to alert in case of these conditions interact with the medication that patient want to purchase. To achieve this goal it was necessary to select the available drugs as well as their interactions. This selection was based on 2013 therapeutic chart provided by INFARMED. After collecting and analyzing this information, it was possible to understand which clinical data the system needs to identify the drugs that weren’t advisable to purchase. For that, it is necessary that the user provides that personal clinical data. That need is advocated by several authors, which point out that the use of electronic health records is very beneficial to get more customized alerts thus supplying the needs of the user. It is also primary for the user to realize why a particular drug is not advisable, so when the alert is emitted also a justification is presented, which means, it is provided to the user which condition given by the user is responsible for the interaction

Physicians’ responses to computerized drug–drug interaction alerts for outpatients

[[abstract]]Introduction Adverse drug reactions (ADR) increase morbidity and mortality; potential drug–drug interactions (DDI) increase the probability of ADR. Studies have proven that computerized drug-interaction alert systems (DIAS) might reduce medication errors and potential adverse events. However, the relatively high override rates obscure the benefits of alert systems, which result in barriers for availability. It is important to understand the frequency at which physicians override DIAS and the reasons for overriding reminders. Method All the DDI records of outpatient prescriptions from a tertiary university hospital from 2005 and 2006 detections by the DIAS are included in the study. The DIAS is a JAVA language software that was integrated into the computerized physician order entry system. The alert window is displayed when DDIs occur during order entries, and physicians choose the appropriate action according to the DDI alerts. There are seven response choices are obligated in representing overriding and acceptance: (1) necessary order and override; (2) expected DDI and override; (3) expected DDI with modified dosage and override; (4) no DDI and override; (5) too busy to respond and override; (6) unaware of the DDI and accept; and (7) unexpected DDI and accept. The responses were collected for analysis. Results A total of 11,084 DDI alerts of 1,243,464 outpatient prescriptions were present, 0.89% of all computerized prescriptions. The overall rate for accepting was 8.5%, but most of the alerts were overridden (91.5%). Physicians of family medicine and gynecology-obstetrics were more willing to accept the alerts with acceptance rates of 20.8% and 20.0% respectively (p < 0.001). Information regarding the recognition of DDIs indicated that 82.0% of the DDIs were aware by physicians, 15.9% of DDIs were unaware by physicians, and 2.1% of alerts were ignored. The percentage of total alerts declined from 1.12% to 0.79% during 24 months’ study period, and total overridden alerts also declined (from 1.04% to 0.73%). Conclusion We explored the physicians’ behavior by analyzing responses to the DDI alerts. Although the override rate is still high, the reasons why physicians may override DDI alerts were well analyzed and most DDI were recognized by physicians. Nonetheless, the trend of total overrides is in decline, which indicates a learning curve effect from exposure to DIAS. By analyzing the computerized responses provided by physicians, efforts should be made to improve the efficiency of the DIAS, and pharmacists, as well as patient safety staffs, can catch physicians’ appropriate reasons for overriding DDI alerts, improving patient safety

Approaches to resolve multidrug therapy related adverse drug events using the Japanese Adverse Drug Event Report (JADER) database.

学位授与機関：城西大学 学位記番号：博乙第86号，学位の種別：博士（薬学）， 学位授与年月日： 令和4年(2022年)9月17日 (94p.)博士（薬学）城西大