Prescription of medicines by medical students of Karachi, Pakistan: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Prescription of medicines by non-doctors is an issue with serious global implications. To our knowledge prescription of drugs by medical and non-medical students has not been studied before. We aimed to determine the practice and attitudes of drug prescription by medical students and: a) how non-medical students respond to this practice, b) How this compares with the attitudes and practices of non-medical students.</p> <p>Methods</p> <p>A cross-sectional study was conducted on a sample of 600 students randomly selected from 2 medical and 2 non-medical universities. Ethical requirements were ensured and data was collected using self administered questionnaires. The Chi square tests and logistic univariate regression analyses were performed using SPSS v 14 to identify associations and differences.</p> <p>Results</p> <p>A total of 572 forms were completed and the sample consisted of 295 medical students and 277 non-medical students with no significant difference in their demographic profile. Of the 295 medical students 163 (55.3%) had prescribed a medicine independently and most (48.5%) said that they did this 2–3 times a year. The commonest reasons for this were 'previous experience' (68.7%), 'problem too trivial' (34.4%) and 'we knew everything about the condition' (31.3%). One-third (33.6%) of the undergraduate medical students thought that it was alright to independently diagnose an illness while a vast majority (78.3%) thought that it was alright for them to prescribe medicines to others. Common prescriptions were pain-killers, antipyretics, antiallergics and antibiotics. Medical students who prescribed medicines were of lesser age (CI = 1.366–1.887) and more likely to belong to the 1^st(CI = 3.588–21.731), 2^nd(CI = 2.059– 10.869) or 3^rd(CI = 4.331–26.374) year of medical college. One-third (33.9%) of the non-medical students reported that a medical student had prescribed medicines to them and 21.3% said that they trusted medical students and would follow their advice blindly. Many students thought it alright for medical students to diagnose and treat illnesses. A similar proportion of non-medical students (58.5%) reported prescribing medicines to others.</p> <p>Conclusion</p> <p>Prescription of medicines by non-doctors is rampant and urgent corrective measures are warranted. We have highlighted areas for future research and intervention and have given a few recommendations.</p

Native New Zealand plants with inhibitory activity towards Mycobacterium tuberculosis

<p>Abstract</p> <p>Background</p> <p>Plants have long been investigated as a source of antibiotics and other bioactives for the treatment of human disease. New Zealand contains a diverse and unique flora, however, few of its endemic plants have been used to treat tuberculosis. One plant, <it>Laurelia novae-zelandiae</it>, was reportedly used by indigenous Maori for the treatment of tubercular lesions.</p> <p>Methods</p> <p><it>Laurelia novae-zelandiae </it>and 44 other native plants were tested for direct anti-bacterial activity. Plants were extracted with different solvents and extracts screened for inhibition of the surrogate species, <it>Mycobacterium smegmatis</it>. Active plant samples were then tested for bacteriostatic activity towards <it>M. tuberculosis </it>and other clinically-important species.</p> <p>Results</p> <p>Extracts of six native plants were active against <it>M. smegmatis</it>. Many of these were also inhibitory towards <it>M. tuberculosis </it>including <it>Laurelia novae-zelandiae </it>(Pukatea). <it>M. excelsa </it>(Pohutukawa) was the only plant extract tested that was active against <it>Staphylococcus aureus</it>.</p> <p>Conclusions</p> <p>Our data provide support for the traditional use of Pukatea in treating tuberculosis. In addition, our analyses indicate that other native plant species possess antibiotic activity.</p

Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy

<p>Abstract</p> <p>Background</p> <p>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</p> <p>Methods</p> <p>Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.</p> <p>Results</p> <p>The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.</p> <p>Conclusion</p> <p>The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.</p

Dialysis-associated peritonitis in children

Peritonitis remains a frequent complication of peritoneal dialysis in children and is the most common reason for technique failure. The microbiology is characterized by a predominance of Gram-positive organisms, with fungi responsible for less than 5% of episodes. Data collected by the International Pediatric Peritonitis Registry have revealed a worldwide variation in the bacterial etiology of peritonitis, as well as in the rate of culture-negative peritonitis. Risk factors for infection include young age, the absence of prophylactic antibiotics at catheter placement, spiking of dialysis bags, and the presence of a catheter exit-site or tunnel infection. Clinical symptoms at presentation are somewhat organism specific and can be objectively assessed with a Disease Severity Score. Whereas recommendations for empiric antibiotic therapy in children have been published by the International Society of Peritoneal Dialysis, epidemiologic data and antibiotic susceptibility data suggest that it may be desirable to take the patient- and center-specific history of microorganisms and their sensitivity patterns into account when prescribing initial therapy. The vast majority of patients are treated successfully and continue peritoneal dialysis, with the poorest outcome noted in patients with peritonitis secondary to Gram-negative organisms or fungi and in those with a relapsing infection