scholarly journals MONITORING OF ANTICONVULSANT DRUG SIDE EFFECTS IN OUTPATIENTS WITH EPILEPSY

2018 ◽  
Vol 10 (1) ◽  
pp. 303
Author(s):  
Santi Purna Sari ◽  
Natasha Kurnia Salma S ◽  
Alfina Rianti
Keyword(s):  
Valproic Acid ◽  
Side Effects ◽  
Medical Records ◽  
Side Effect ◽  
Anticonvulsant Drug ◽  
Secondary Data ◽  
Drug Side Effects ◽  
Inclusion Criteria ◽  
Descriptive Data ◽  
Effect Monitoring

Objective: This study aimed to monitor the side effects of carbamazepine, phenytoin, and valproic acid, and combinations of these drugs in adultpatients with epilepsy, to raise awareness of the importance of drug side effect monitoring in hospitals.Methods: In this prospective study, descriptive data were collected from patients who met the inclusion criteria of complete samples. Primary datawere obtained using questionnaires, secondary data were collected from medical records, and analyses were performed using the Naranjo algorithm.Results: Among the 54 included patients, 38 (70.37%) of them experienced drug side effects, and the most frequently observed side effect occurredin 48.15% of study subjects.Conclusion: No correlation was identified between side effects and age (p=0.903) or gender (p=1.000).

The Rationality of Using Diclofenac in Outpatient Cases of Osteoarthritis of RSUP MH Palembang Period January-March 2018

2021 ◽  
Vol 2 (1) ◽  
pp. 43-50
Author(s):  
Dara Prameswari ◽  
Nita Parisa ◽  
Muhammad Totong Kamaluddin
Keyword(s):  
Side Effects ◽  
Medical Records ◽  
Daily Activity ◽  
Sampling Technique ◽  
Synergistic Interaction ◽  
Inclusion Criteria ◽  
Cross Sectional ◽  
Correct Dose ◽  
History Of ◽  
Lowback Pain

Rationality of Diclofenac Use in Osteoarthritis Outpatient Case at RSUP MHPalembang in January-March 2018. Osteoarthritis (OA) is the most common diseasein joints that affects people in their middle until late years. In Indonesia the prevalenceof OA is relatively high and disturbs their daily activity. Diclofenac is one of the drug ofchoice in treating OA. To avoid multiple side effects from Diclofenac use, the usagemust be in accordance to rationality indicators which are correct dose, correctfrequency, and correct length of use. This study is aimed to know the rationality ofDiclofenac use in Osteoarthritis outpatient cases at RSUP Mohammad HoesinPalembang. This study is a descriptive observational with a cross-sectional approachto know the rationality of Diclofenac use in outpatient cases of osteoarthritis at RSUPMohammad Hoesin Palembang. Samples were medical records of OA patients inoutpatient setting from January to March 2018 which fulfilled the inclusion andexclusion criteria. Sampling technique used was total sampling. The amount ofsamples fulfilling the inclusion criteria were 201 patients, with the most were aged 46-65 years (60.2%), female (55.7%), and has a history of comorbidity which includes lowback pain (22.8%). The result of this study shows pattern of Diclofenac use with dosageof 2 x 25mg (73.6%), length of use about <7 days (57.2%). In combination with otherdrugs there were no interaction to be found (84.4%), or synergistic interaction (8.5%)and antagonistic interaction (7.1%). The use of diclofenac in osteoarthritis cases atoutpatient setting in RSUP Dr Mohammad Hoesin Palembang is rational and needs tobe maintained.

Emotional and physical support during labor for primiparouse women

2011 ◽  
Vol 26 (S2) ◽  
pp. 1102-1102
Author(s):  
L.H. Nasab ◽  
R. Shahoie ◽  
F. Zaheri ◽  
F. Ranaie
Keyword(s):  
Clinical Trial ◽  
Side Effects ◽  
Side Effect ◽  
Labor Pain ◽  
Inclusion Criteria ◽  
Control Data ◽  
Convenience Sample ◽  
Physical Support ◽  
And Control ◽  
Primiparous Women

BackgroundThe experience of labor varies markedly from women to Women. While medications can help women cope with the pain of labor, they usually come with side effects that women did not expect or want. There are many non medical ways to cope with the pain which has not side effect.The purpose of this study was to evaluate the effect of emotional and physical support during labor for primiparouse women.MethodIn this clinical trial study, convenience sample of 80 primiparous women who fulfilled the inclusion criteria was recruited from one hospital in Sanandaj (Center of Kurdistan provenice), Iran. They were randomly divided in Two groups (Case and Control). Data were collected from May to October 2008 using a demographic form and Visual Analoge Scale(VAS).ResultThe study revealed that emotional and physical support during labor cause women has better tolerance of pain. Furthermore, comparison pain intensity among two groups in dillatation 8 and 10 cm was statically significant (p < 0/03& p = 0/000).ConclusionEmotional and physical support during labor can be considered as a non pharmacological therapeutic method to reduce labor pain and decrease side effect of drugs. In addition, education and using of this method increases women satisfying from labor and natural delivery and to make better experience of motherhood.

scholarly journals A cross sectional survey of side effects of antiretroviral (ARV) in outpatients HIV by Naranjo Algorithm

2021 ◽  
Vol 17 (1) ◽  
pp. 34-45
Author(s):  
Oki Nugraha Putra ◽  
Keyword(s):  
Antiretroviral Therapy ◽  
Side Effects ◽  
Side Effect ◽  
Opportunistic Infections ◽  
Inclusion Criteria ◽  
Hiv Patients ◽  
Cross Sectional ◽  
Naranjo Algorithm ◽  
Keywords Hiv ◽  
Probable Category

Background: The main modality in HIV patients is the administration of long-treatment antiretroviral therapy (ARV). One of the problems from the use of ARV therapy is the side effects that can reduce patient compliance in taking medication, which has the potential to cause treatment failure. Objective: This study aims to examine the side effects and their causality in the use of ARVs in outpatient HIV patients at the VCT Clinic, Bhayangkara H.S. Hospital. Samsoeri Mertojoso Surabaya. Methods: This research was a prospective observational study with a cross-sectional design. Side effect data were taken from HIV patients by interview using the Naranjo algorithm. HIV patients who met the inclusion criteria were included in the study sample using consecutive sampling. This research was conducted from January to March 2020. Results: There were 72 outpatient HIV patients who met the inclusion criteria. The most opportunistic infections found in HIV patients are tuberculosis and Pneumocystis pneumonia. The results showed that the most common side effects experienced by patients were dizziness (43%), nausea and vomiting (31%), and rash (11%) with the highest Naranjo score being in the probable category of 86%. The Naranjo score in HIV patients with opportunistic infections and with comorbidities was significantly smaller than those in HIV patients without opportunistic infections or without comorbidities with independent t-test (P <0.05). Conclusion: The side effects in HIV patients while undergoing treatment with antiretroviral therapy are classified as a minor side effect and the cause of the side effects that occur is thought to be due to the probable category of ARV therapy. Keywords: HIV Patients, Antiretroviral, Side Effects, Naranjo's Algorithm.

The Rationality of Using Diclofenac in Outpatient Cases of Osteoarthritis of RSUP MH Palembang Period January-March 2018

2021 ◽  
Vol 2 (1) ◽  
pp. 43-50
Author(s):  
Dara Prameswari ◽  
Nita Parisa ◽  
Muhammad Totong Kamaluddin
Keyword(s):  
Side Effects ◽  
Medical Records ◽  
Daily Activity ◽  
Sampling Technique ◽  
Synergistic Interaction ◽  
Inclusion Criteria ◽  
Cross Sectional ◽  
Correct Dose ◽  
History Of ◽  
Lowback Pain

Rationality of Diclofenac Use in Osteoarthritis Outpatient Case at RSUP MHPalembang in January-March 2018. Osteoarthritis (OA) is the most common diseasein joints that affects people in their middle until late years. In Indonesia the prevalenceof OA is relatively high and disturbs their daily activity. Diclofenac is one of the drug ofchoice in treating OA. To avoid multiple side effects from Diclofenac use, the usagemust be in accordance to rationality indicators which are correct dose, correctfrequency, and correct length of use. This study is aimed to know the rationality ofDiclofenac use in Osteoarthritis outpatient cases at RSUP Mohammad HoesinPalembang. This study is a descriptive observational with a cross-sectional approachto know the rationality of Diclofenac use in outpatient cases of osteoarthritis at RSUPMohammad Hoesin Palembang. Samples were medical records of OA patients inoutpatient setting from January to March 2018 which fulfilled the inclusion andexclusion criteria. Sampling technique used was total sampling. The amount ofsamples fulfilling the inclusion criteria were 201 patients, with the most were aged 46-65 years (60.2%), female (55.7%), and has a history of comorbidity which includes lowback pain (22.8%). The result of this study shows pattern of Diclofenac use with dosageof 2 x 25mg (73.6%), length of use about <7 days (57.2%). In combination with otherdrugs there were no interaction to be found (84.4%), or synergistic interaction (8.5%)and antagonistic interaction (7.1%). The use of diclofenac in osteoarthritis cases atoutpatient setting in RSUP Dr Mohammad Hoesin Palembang is rational and needs tobe maintained.

scholarly journals The prevalence of trauma from occlusion as the aggravating factors of periodontitis

2021 ◽  
Vol 5 (1) ◽  
pp. 7-11
Author(s):  
Sunnati , ◽  
Zulfan M ALIBASYAH ◽  
Sri Rezeki ◽  
Nurul Mustabsyirah Rafi'i
Keyword(s):  
Medical Records ◽  
Periodontal Diseases ◽  
Dominant Factor ◽  
Inclusion Criteria ◽  
Elderly Age ◽  
Descriptive Data ◽  
Local Factor ◽  
Occlusal Adjustment ◽  
Dental Hospital ◽  
Banda Aceh

Trauma from occlusion (TFO) is a local factor that can exacerbate tissue inflammation in periodontitis cases. There have been many clinical studies on TFO, but the prevalence of patients is currently unknown. The purpose of this study was to determine the majority of TFO cases as a factor that aggravates periodontitis based on the medic records at Oral and Dental Hospital, Universitas Syiah Kuala, Banda Aceh, Indonesia in 2017-2019. This study used a comprehensive sampling approach to evaluate 10,532 medical records associated with periodontal disease, including factors such as age, sex, causes, and TFO treatment. Descriptive data analysis showed that from a total of 10,532 medical records, there were 391 medical records related to periodontia. A total of 194 samples matched the inclusion criteria. There were 79 TFO patients (40.7%), 3 TFO cases (1.5%), and 112 cases (57.7%) other periodontal diseases without TFO. The prevalence of TFO as a dominant factor aggravating chronic periodontitis at the Oral and Dental Hospital, Syiah Kuala University, Banda Aceh Indonesia in 2017-2019. Patients with elderly age and female sex predominantly experience periodontitis which is aggravated by TFO.KEYWORDS: Occlusal adjustment blocking method, periodontitis, Trauma from occlusion

scholarly journals Quantitative Antibiotic Use Profile in Typhoid

2020 ◽  
Vol 3 (3) ◽  
pp. 113-118
Author(s):  
Hidayatul Kurniawati ◽  
Marianti
Keyword(s):  
Typhoid Fever ◽  
Medical Records ◽  
Bacterial Resistance ◽  
Antibiotic Use ◽  
Drug Side Effects ◽  
Inclusion Criteria ◽  
Adult Age ◽  
Retrospective Data Collection ◽  
The Right ◽  
Age Range

Background. Typhoid fever is a common health problem in developing countries. Antibiotics are used to treat typhoidfever which is caused by a bacterial infection. Selection and use of appropriate and rational antibiotic therapy candetermine a success in treatment to avoid bacterial resistance and minimize drug side effects. This study aims todetermine the rationality of the use of antibiotics in adult patients diagnosed with typhoid fever in the InpatientInstallation of X Hospital in Yogyakarta. Method. Non-experimental research with descriptive observational researchdesign and retrospective data collection. The sample of this study was inpatients with a diagnosis of typhoid fever andwas recorded at the X Hospital Medical Records Installation in Yogyakarta for the period January 2016 - December2017 which was included in the inclusion criteria. Result. Data taken came from 75 medical records that were includedin the inclusion criteria. Patients were dominated by female patients as many as 64% and the adult age range was 18-30 years. The single most widely used antibiotic was levofloxacin in 27 cases (36%). The use of antibiotics with theright indication was 75 patients (100%), the right type was 75 patients (100%), the exact duration of administrationwas 64 patients (85.33%), the right dose was 73 patients (97.33%), the right interval was 73 patients (97.33%) and theright route of administration were 75 patients (100%). Conclusion. The rationality of using antibiotics is good withaccuracy> 75%.

scholarly journals The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

2019 ◽  
Author(s):  
Diego Galeano ◽  
Alberto Paccanaro
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Side Effect ◽  
Large Scale ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Matrix Completion ◽  
Optimal Solution ◽  
Drug Side Effects ◽  
Chemical Structures

AbstractPair-input associations for drug-side effects are obtained through expensive placebo-controlled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

scholarly journals Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions

2020 ◽  
Vol 23 (4) ◽  
pp. 285-294 ◽  
Author(s):  
Bo Zhou ◽  
Xian Zhao ◽  
Jing Lu ◽  
Zuntao Sun ◽  
Min Liu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Computational Methods ◽  
Side Effect ◽  
Permutation Test ◽  
Human Life ◽  
Statistical Significance ◽  
Drug Side Effects ◽  
Computational Investigation ◽  
The Relationship

Background:Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.Method:In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.Results and Conclusion:A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.

scholarly journals Gambaran Faktor Risiko Pasien Kanker Serviks di RSUP Dr. M. Djamil Padang Tahun 2019

2021 ◽  
Vol 1 (3) ◽  
pp. 425-430
Author(s):  
Astri Nadia Hidayat ◽  
Novita Ariani ◽  
Ida Rahman Burhan
Keyword(s):  
Cervical Cancer ◽  
Developing Countries ◽  
Medical Records ◽  
Secondary Data ◽  
Age Group ◽  
Inclusion Criteria ◽  
Middle Income ◽  
Middle Income Countries ◽  
Incidence And Mortality ◽  
High Incidence

   Cervical cancer was one of the most common malignancies in women and was the leading cause of death from cancer, especially in low and middle-income countries (developing countries). The high incidence and mortality rate in developing countries was caused by the lack of knowledge about cervical cancer and limited access to early detection, so that patients come late for treatment and were diagnosed when their condition were severe and the disease had progressed to an advanced stage. This study was conducted in the Medical Record Installation section of Dr. M. Djamil Padang Hospital on 11 August - 2 September 2020. The results of the study were obtained from secondary data from medical records, and data collection was taken by total sampling. Samples that have met the inclusion criteria in this study were 84 patients diagnosed with cervical cancer at Dr. M. Djamil Padang Hospital in 2019. The results showed cervical cancer patients at Dr. M. Djamil Padang Hospital in 2019 were mostly in the ≥50 year age group (51.2%), multiparous category (77.4%), and High School/ equivalent category (70.2 %). Keywords : Risk Factor, Cervical Cancer, Age, Parity, Education Level

Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks

2019 ◽  
Vol 14 (8) ◽  
pp. 709-720 ◽  
Author(s):  
Xian Zhao ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Tao Liu
Keyword(s):  
Side Effects ◽  
Network Model ◽  
Heterogeneous Networks ◽  
Side Effect ◽  
Correlation Coefficients ◽  
Machine Learning Algorithms ◽  
Drug Side Effects ◽  
Essential Information ◽  
Approved Drugs ◽  
Feature Dimension

Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model. Results: The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively. Conclusion: The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

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