Drugs and Adverse Drug ReactionsHow Worried Should We Be?

JAMA The Journal of the American Medical Association (Impact Factor: 30.39). 05/1998; 279(15):1216-1217. DOI: 10.1001/jama.279.15.1216

ABSTRACT Physicians can hardly pick up a medical journal or a newspaper today
without reading about some new medication, and how it promises to completely
change the course of a disease or relieve some troublesome symptom. Indeed,
the wonders of pharmacology are numerous. It is clear, for example, that after
a myocardial infarction patients will live longer if they take β-blockers 1 and that patients with congestive heart failure live
longer and feel better when they take angiotensin-converting enzyme inhibitors.2 However, medications are a double-edged sword.


Available from: David W Bates, Apr 22, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Drug-related problems (DRPs) frequently occur in modern medical practice, increasing the morbidity and mortality as well as increasing cost of care. Objective: The study is to evaluate the incidence of DRPs in patients admitted to a psychiatric department. Materials and Methods: A prospective observational study was conducted for a period of 4 months at Baliga psychiatric hospital. All prescriptions of the study population were screened for DRPs such as adverse drug reactions (ADRs) and potential drug-drug interactions (pDDIs) by using computerized database system. Results: Out of 120 patients, 19 patients had observed 26 DRPs. Out of 33 patients, 19 patients had observed 26 ADRs and 14 patients had observed 24 pDDIs. The overall incidence of DRPs was 15.83%. Female patients outnumbered the male patients, in which 12 women constitute 10% followed by men 7 (5.83%). The common ADRs observed were hyponatremia and headache. Considering the outcomes, 20 (76.9%) cases recovered from ADRs and 20 (76.9%) of the ADRs were definitely preventable. Majority of ADRs were probable and were found to be mild to moderately severe. Conclusions: Age, female gender and polypharmacy were the risk factors for the developing DRPs.
    Perspectives in clinical research 01/2015; 6(1):58-61. DOI:10.4103/2229-3485.148820
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics-techniques for analyzing large quantities of data and gleaning new insights from that analysis-which is part of what is known as big data. As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases-that is, key examples-where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient's condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure-analytics, algorithms, registries, assessment scores, monitoring devices, and so forth-that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.
    Health Affairs 07/2014; 33(7):1123-31. DOI:10.1377/hlthaff.2014.0041 · 4.32 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: What is known and objectiveStudies in a number of countries have shown that adverse drug events (ADE) occur frequently among hospital inpatients. The objective of this study was to conduct a systematic review of observational studies of the frequency of ADE in adult inpatients and to examine factors associated with observed heterogeneity in the reported results.Methods The systematic review included observational studies, which identified and analysed ADE during hospitalization of adult inpatients. The literature search was conducted on MEDLINE, Embase, Lilacs and Google Scholar (January of 2000 to June of 2013). Article selection, quality assessment and information extraction were performed by two of the authors, working independently. Using the random-effects model, the proportion of patients with adverse events was used as an outcome measure. Proportion was estimated for subgroups based on event identification method: stimulated reporting (SR), retrospective monitoring (RM) and prospective monitoring (PM). For the latter group, meta-regression was used to identify sources of heterogeneity in the estimates.Results and discussionTwenty-eight articles from the 7550 identified met our inclusion criteria. The articles were heterogeneous in terms of quality, outcome definition and event identification method and in the corresponding descriptions. Of the 28 articles selected, 25 were included in the corresponding quantitative summary: four used SR, six RM and 15 PM, returning incidences of 2·3% (CI 95%: 1·6–4·5), 8·7% (CI 95%: 4·8–15·3) and 21·3% (CI 95%: 15·7–28·3), respectively, and I2 greater than 95%. There were other sources of heterogeneity, including the use of combined strategies within each subgroup. In the PM subgroup, using multivariate meta-regression model, no variables were found to associate with proportion.What is new and conclusionEvent frequency seems to associate with the event identification method. PM returned the highest estimates. This subgroup used a greater diversity of approaches for event identification and more diverse data sources. Improved recording of information on the event identification method, the characteristics of the events and the conduct of the study would enable more reliable and precise estimates of the frequency of ADE among hospital inpatients.
    Journal of Clinical Pharmacy and Therapeutics 09/2014; 39(6). DOI:10.1111/jcpt.12204 · 1.53 Impact Factor