Evaluation of the prevalence and economic burden of adverse drug reactions presenting to the medical emergency department of a tertiary referral centre: a prospective study

Background  

Adverse drug reactions (ADRs) are now recognized as an important cause of hospital admissions, with a proportion ranging from 0.9–7.9%. They also constitute a significant economic burden. We thus aimed at determining the prevalence and the economic burden of ADRs presenting to Medical Emergency Department (ED) of a tertiary referral center in India     

Trends in hospital admissions for adverse drug reactions in England: analysis of national hospital episode statistics 1998–2005

Background  

Adverse drug reactions (ADRs) are a frequent cause of mortality and morbidity to patients worldwide, with great associated costs to the healthcare providers including the NHS in England. We examined trends in hospital admissions associated with adverse drug reaction in English hospitals and the accuracy of national reporting.     

Predicting adverse drug reactions through interpretable deep learning framework

Background

Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.

Methods

In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.

Results

We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.

Conclusions

The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.

     

The frequency of adverse drug reaction related admissions according to method of detection, admission urgency and medical department specialty

Background  

Adverse Drug Reactions (ADRs) have been regarded as a major public health problem since they represent a sizable percentage of admissions. Unfortunately, there is a wide variation of ADR related admissions among different studies. The aim of this study was to evaluate the frequency of ADR related admissions and its dependency on reporting and method of detection, urgency of admissions and included medical departments reflecting department/hospital type within one study.     

Adverse Reactions Due to Directly Observed Treatment Strategy Therapy in Chinese Tuberculosis Patients: A Prospective Study

Background

More than 1 million tuberculosis (TB) patients are receiving directly observed treatment strategy (DOTS) therapy in China every year. As to the profile of adverse drug reactions (ADRs) due to DOTS therapy, no consensus has been reached. There is no report regarding ADRs due to DOTS therapy with a large Chinese TB population. This study aimed to determine the incidence and prognosis of ADRs due to DOTS therapy, and to evaluate their impact on anti-TB treatment in China.

Methods

A prospective population-based cohort study was performed during 2007–2008. Sputum smear positive pulmonary TB patients who received DOTS therapy were included and followed up for six to nine months in 52 counties of four regions in China. The suspected ADRs were recorded and reviewed by Chinese State Food and Drug Administration.

Results

A total of 4304 TB patients were included in this study. 649 patients (15.08%) showed at least one ADR and 766 cases in total were detected. The incidence (count) of ADR based on affected organ was: liver dysfunction 6.34% (273), gastrointestinal disorders 3.74% (161), arthralgia 2.51% (108), allergic reactions 2.35% (101), neurological system disorders 2.04% (88), renal impairment 0.07% (3) and others 0.05% (2). Most cases of ADRs (95%) had a good clinical outcome, while two with hepatotoxicity and one with renal impairment died. Compared with patients without ADRs, patients with ADRs were more likely to have positive smear test results at the end of the intensive phase (adjusted OR, 2.00; 95%CI, 1.44–2.78) and unsuccessful anti-TB outcomes (adjusted OR, 2.58; 95%CI, 1.43–4.68).

Conclusions

The incidence of ADRs due to DOTS therapy was 15.08%. Those ADRs had a substantial impact on TB control in China. This highlighted the importance of developing strategies to ameliorate ADRs both to improve the quality of patient care and to control TB safely.     

Errors in medication history at hospital admission: prevalence and predicting factors

Background

Data on adverse drug reactions (ADRs) related to antiretroviral (ARV) use in public health practice are few indicating the need for ART safety surveillance in clinical care.

Objectives

To evaluate the incidence, type and risk factors associated with adverse drug reactions (ADRs) among patients on antiretroviral drugs (ARV).

Methods

Patients initiated on ARVs between May 2006 and May 2009 were evaluated in a retrospective cohort analysis in three health facilities in Nigeria. Regimens prescribed include nucleoside backbone of zidovudine (AZT)/lamivudine (3TC), stavudine (d4T)/3TC, or tenofovir (TDF)/3TC in combination with either nevirapine (NVP) or efavirenz (EFV). Generalized Estimating Equation (GEE) model was used to identify risk factors associated with occurrence of ADR.

Results

2650 patients were followed-up for 2456 person-years and reported 114 ADRs (incidence rate = 4.6/100 person-years).There were more females 1706(64%) and 73(64%) of the ADRs were reported by women. Overall, 61(54%) of ADRs were reported by patients on AZT with 54(47%) of these occurring in patients on AZT/NVP. The commonest ADRs reported were pain 25(30%) and skinrash 10(18%). Most ADRs were grade 1(39%) with only 1% being life threatening (grade 4). Adjusted GEE analysis showed that ADR was less likely to occur in patients on longer duration of ART compared to the first six months on treatment; 6-12 months AOR 0.38(95% CI:0.16-0.91) and 12-24 months AOR 0.34(95% CI:0.16-0.73) respectively. Compared to patients on TDF, ADR was less likely to occur in patients on d4T and AZT AOR 0.18(95% CI 0.05-0.64) and AOR 0.24(95% CI:0.7-0.9) respectively. Age, gender and CD4 count were not significantly associated with ADRs.

Conclusion

ADRs are more likely to occur within the first six months on treatment. Close monitoring within this period is required to prevent occurrence of severe ADR and improve ART adherence. Further research on the tolerability of tenofovir in this environment is recommended.     

A Systematic Investigation of Computation Models for Predicting Adverse Drug Reactions (ADRs)

Background

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.

Principal Findings

In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.

Conclusion

Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.     

Development of an Adverse Drug Reaction Risk Assessment Score among Hospitalized Patients with Chronic Kidney Disease

Background

Adverse drug reactions (ADRs) represent a major burden on the healthcare system. Chronic kidney disease (CKD) patients are particularly vulnerable to ADRs because they are usually on multiple drug regimens, have multiple comorbidities, and because of alteration in their pharmacokinetics and pharmacodynamic parameters. Therefore, one step towards reducing this burden is to identify patients who are at increased risk of an ADR.

Objective

To develop a method of identifying CKD patients who are at increased risk for experiencing ADRs during hospitalisation.

Materials and Methods

Factors associated with ADRs were identified by using demographic, clinical and laboratory variables of patients with CKD stages 3 to 5 (estimated glomerular filtration rate, 10–59 ml/min/1.73 m²) who were admitted between January 1, 2012, and December 31, 2012, to the renal unit of Dubai Hospital. An ADR risk score was developed by constructing a series of logistic regression models. The overall model performance for sequential models was evaluated using Akaike Information Criterion for goodness of fit. Odd ratios of the variables retained in the best model were used to compute the risk scores.

Results

Of 512 patients (mean [SD] age, 60 [16] years), 62 (12.1%) experienced an ADR during their hospitalisation. An ADR risk score included age 65 years or more, female sex, conservatively managed end-stage renal disease, vascular disease, serum level of C-reactive protein more than 10 mg/L, serum level of albumin less than 3.5 g/dL, and the use of 8 medications or more during hospitalization. The C statistic, which assesses the ability of the risk score to predict ADRs, was 0.838; 95% CI, 0.784–0.892).

Conclusion

A score using routinely available patient data can be used to identify CKD patients who are at increased risk of ADRs.     

Transposable element derived DNaseI-hypersensitive sites in the human genome

Background  

Transposable elements (TEs) are abundant genomic sequences that have been found to contribute to genome evolution in unexpected ways. Here, we characterize the evolutionary and functional characteristics of TE-derived human genome regulatory sequences uncovered by the high throughput mapping of DNaseI-hypersensitive (HS) sites.     

Association between Thiopurine S-Methyltransferase Polymorphisms and Azathioprine-Induced Adverse Drug Reactions in Patients with Autoimmune Diseases: A Meta-Analysis

Purpose

Azathioprine (AZA) is widely used as an immunosuppressive drug in autoimmune diseases, but its use is limited by significant adverse drug reactions (ADRs). Thiopurine S-methyltransferase (TPMT) is an important enzyme involved in AZA metabolism. Several clinical guidelines recommend determining TPMT genotype or phenotype before initiating AZA therapy. Although several studies have investigated the association between TPMT polymorphisms and AZA-induced ADRs, the results are inconsistent. The purpose of this study is to evaluate whether there is an association between TPMT polymorphisms and AZA-induced ADRs using meta-analysis.

Methods

We explored PubMed, Web of Science and Embase for articles on TPMT polymorphisms and AZA-induced ADRs. Studies that compared TPMT polymorphisms with-ADRs and without-ADRs in patients with autoimmune diseases were included. Relevant outcome data from all the included articles were extracted and the pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated using Revman 5.3 software.

Results

Eleven published studies, with a total of 651 patients with autoimmune diseases, investigated associations between TPMT polymorphisms and AZA-induced ADRs, were included in this meta-analysis. Our meta-analysis demonstrated that TPMT polymorphisms were significantly associated with AZA-induced overall ADRs, bone marrow toxicity and gastric intolerance; pooled ORs were 3.12 (1.48–6.56), 3.76 (1.97–7.17) and 6.43 (2.04–20.25), respectively. TPMT polymorphisms were not associated with the development of hepatotoxicity; the corresponding pooled OR was 2.86 (95%CI: 0.32–25.86). However, the association in GI subset could be driven by one single study. After this study was excluded, the OR was 2.11 (95%CI: 0.36–12.42); namely, the association became negative.

Conclusions

Our meta-analysis demonstrated an association of TPMT polymorphisms with overall AZA-induced ADRs, bone marrow toxicity and gastric intolerance, but not with hepatotoxicity. The presence of the normal TPMT genotypes cannot preclude the development of ADRs during AZA treatment, TPMT genotyping prior to commencing AZA therapy cannot replace, may augment, the current practice of regular monitoring of the white blood cell. Because of small sample sizes, large and extensive exploration was required to validate our findings.     

Association between Thiopurine S-methyltransferase Polymorphisms and Thiopurine-Induced Adverse Drug Reactions in Patients with Inflammatory Bowel Disease: A Meta-Analysis

Purpose

Thiopurine drugs are well established treatments in the management of inflammatory bowel disease (IBD), but their use is limited by significant adverse drug reactions (ADRs). Thiopurine S-methyltransferase (TPMT) is an important enzyme involved in thiopurine metabolism. Several clinical guidelines recommend determining TPMT genotype or phenotype before initiating thiopurine therapy. Although several studies have investigated the association between TPMT polymorphisms and thiopurine-induced ADRs, the results are inconsistent. The purpose of this study is to evaluate whether there is an association between TPMT polymorphisms and thiopurine-induced ADRs using meta-analysis.

Methods

We explored PubMed, Web of Science and Embase for articles on TPMT polymorphisms and thiopurine-induced ADRs. Studies that compared TPMT polymorphisms with-ADRs and without-ADRs in IBD patients were included. Relevant outcome data from all the included articles were extracted and the pooled odds ratio (OR) with corresponding 95% confidence intervals were calculated using Revman 5.3 software.

Results

Fourteen published studies, with a total of 2,206 IBD patients, which investigated associations between TPMT polymorphisms and thiopurine-induced ADRs were included this meta-analysis. Our meta-analysis demonstrated that TPMT polymorphisms were significantly associated with thiopurine-induced overall ADRs and bone marrow toxicity; pooled ORs were 3.36 (95%CI: 1.82–6.19) and 6.67 (95%CI: 3.88–11.47), respectively. TPMT polymorphisms were not associated with the development of other ADRs including hepatotoxicity, pancreatitis, gastric intolerance, flu-like symptoms and skin reactions; the corresponding pooled ORs were 1.27 (95%CI: 0.60–2.71), 0.97 (95%CI: 0.38–2.48), 1.82 (95%CI: 0.93–3.53), 1.28 (95%CI: 0.47–3.46) and 2.32 (95%CI: 0.86–6.25), respectively.

Conclusions

Our meta-analysis demonstrated an association of TPMT polymorphisms with overall thiopurine-induced ADRs and bone marrow toxicity, but not with hepatotoxicity, pancreatitis, flu-like symptoms, gastric intolerance and skin reactions. These findings suggest that pretesting the TPMT genotype could be helpful in clinical practice before initiating thiopurine therapy. However, white blood cell count analysis should be the mainstay for follow-up.     

Serious Treatment Related Adverse Drug Reactions amongst Anti-Retroviral Na?ve MDR-TB Patients

Background

Globally treatment outcomes for multidrug-resistant Mycobacterium tuberculosis (MDR-TB) remain poor and this is compounded by high drug toxicity. Little is known about the influence of adverse drug reactions (ADRs) on treatment outcomes in South Africa.

Methods

We evaluated the impact of severe ADRs among a prospective cohort of MDR-TB patients in South Africa (2000–2004). The HIV-infected study participants were anti-retroviral naïve.

Results

Of 2,079 patients enrolled, 1,390 (66.8%) were included in this analysis based on known HIV test results (39.1% HIV-infected). At least one severe ADR was reported in 83 (6.9%) patients with ototoxicity being the most frequent ADR experienced (38.9%).

Conclusions

We found that being HIV-infected but antiretroviral naïve did not increase occurrence of SADRs in patients on second-line anti-tuberculosis drugs. Early screening and proactive management of ADRs in this patient population is essential, especially given the rollout of decentralized care and the potential for overlapping toxicity of concomitant MDR-TB and HIV treatment.     

Clinical impact of B-cell depletion with the anti-CD20 antibody rituximab in chronic fatigue syndrome: a preliminary case series

Background  

Chronic fatigue syndrome (CFS) is a disease of unknown aetiology. A patient with CFS had unexpected, marked recovery of CFS symptoms lasting for five months during and after cytotoxic chemotherapy for Hodgkin's disease. We reasoned that the transient CFS recovery was related to methotrexate treatment, which induces immunomodulation in part through B-cell depletion.     

Adverse Drug Reactions in Hospitalised Children in Germany Are Decreasing: Results of a Nine Year Cohort-Based Comparison

Background

In recent years, efforts have been made to improve paediatric drug therapy. The aim of this research was to investigate any changes regarding the frequency and nature of adverse drug reactions (ADRs) in hospitalized children in one paediatric general medical ward over a 9-year period.

Methodology

Two prospective observational cohort studies were conducted at a large University hospital in Germany in 1999 and 2008, respectively. Children aged 0–18 years admitted to the study ward during the study periods were included. ADRs were identified using intensive chart review. Uni- and multivariable regression has been used for data analysis.

Results

A total of 520 patients (574 admissions) were included [1999: n = 144 (167); 2008: n = 376 (407)]. Patients received a total of 2053 drugs [median 3, interquartile range (IQR) 2–5]. 19% of patients did not receive any medication. Median length of stay was 4 days (IQR 3–7; range 1–190 days) with a significantly longer length of stay in 1999. The overall ADR incidence was 13.1% (95% CI, 9.8–16.3) varying significantly between the two study cohorts [1999: 21.9%, 95% CI, 14.7–29.0; 2008: 9.2%, 95% CI, 5.9–12.5 (p<0.001)]. Antibacterials and corticosteroids for systemic use caused most of the ADRs in both cohorts (1999; 2008). Exposure to systemic antibacterials decreased from 62.9% to 43.5% whereas exposure to analgesics and anti-inflammatory drugs increased from 17.4% to 45.2%, respectively. The use of high risk drugs decreased from 75% to 62.2%. In 1999, 45.7% and in 2008 96.2% of ADRs were identified by treating clinicians (p<0.001).

Conclusions

Between 1999 and 2008, the incidence of ADRs decreased significantly. Improved treatment strategies and an increased awareness of ADRs by physicians are most likely to be the cause for this positive development. Nevertheless further research on ADRs particularly in primary care and the establishment of prospective pharmacovigilance systems are still needed.     

Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model

Background  

Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series data. Objective criteria that can be used to evaluate dynamical changes in data are therefore important to filter experimental noise and to enable extraction of unexpected, biologically important information.     

Genome-wide survey of post-meiotic segregation during yeast recombination

Background  

When mismatches in heteroduplex DNA formed during meiotic recombination are left unrepaired, post-meiotic segregation of the two mismatched alleles occurs during the ensuing round of mitosis. This gives rise to somatic mosaicism in multicellular organisms and leads to unexpected allelic combinations among progeny. Despite its implications for inheritance, post-meiotic segregation has been studied at only a few loci.     

Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model

Background  

New approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models.