Herb-drug interactions: a literature review

Herbs are often administered in combination with therapeutic drugs, raising the potential of herb-drug interactions. An extensive review of the literature identified reported herb-drug interactions with clinical significance, many of which are from case reports and limited clinical observations.
Cases have been published reporting enhanced anticoagulation and bleeding when patients on long-term warfarin therapy also took Salvia miltiorrhiza (danshen). Allium sativum (garlic) decreased the area under the plasma concentration-time curve (AUC) and maximum plasma concentration of saquinavir, but not ritonavir and paracetamol (acetaminophen), in volunteers. A. sativum increased the clotting time and international normalised ratio of warfarin and caused hypoglycaemia when taken with chlorpropamide. Ginkgo biloba (ginkgo) caused bleeding when combined with warfarin or aspirin (acetylsalicylic acid), raised blood pressure when combined with a thiazide diuretic and even caused coma when combined with trazodone in patients. Panax ginseng (ginseng) reduced the blood concentrations of alcohol (ethanol) and warfarin, and induced mania when used concomitantly with phenelzine, but ginseng increased the efficacy of influenza vaccination. Scutellaria baicalensis (huangqin) ameliorated irinotecan-induced gastrointestinal toxicity in cancer patients.
Piper methysticum (kava) increased the 'off' periods in patients with parkinsonism taking levodopa and induced a semicomatose state when given concomitantly with alprazolam. Kava enhanced the hypnotic effect of alcohol in mice, but this was not observed in humans. Silybum marianum (milk thistle) decreased the trough concentrations of indinavir in humans. Piperine from black (Piper nigrum Linn) and long (P. longum Linn) peppers increased the AUC of phenytoin, propranolol and theophylline in healthy volunteers and plasma concentrations of rifamipicin (rifampin) in patients with pulmonary tuberculosis. Eleutheroccus senticosus (Siberian ginseng) increased the serum concentration of digoxin, but did not alter the pharmacokinetics of dextromethorphan and alprazolam in humans. Hypericum perforatum (hypericum; St John's wort) decreased the blood concentrations of ciclosporin (cyclosporin), midazolam, tacrolimus, amitriptyline, digoxin, indinavir, warfarin, phenprocoumon and theophylline, but did not alter the pharmacokinetics of carbamazepine, pravastatin, mycophenolate mofetil and dextromethorphan. Cases have been reported where decreased ciclosporin concentrations led to organ rejection. Hypericum also caused breakthrough bleeding and unplanned pregnancies when used concomitantly with oral contraceptives. It also caused serotonin syndrome when used in combination with selective serotonin reuptake inhibitors (e.g. sertraline and paroxetine).
In conclusion, interactions between herbal medicines and prescribed drugs can occur and may lead to serious clinical consequences. There are other theoretical interactions indicated by preclinical data. Both pharmacokinetic and/or pharmacodynamic mechanisms have been considered to play a role in these interactions, although the underlying mechanisms for the altered drug effects and/or concentrations by concomitant herbal medicines are yet to be determined. The clinical importance of herb-drug interactions depends on many factors associated with the particular herb, drug and patient. Herbs should be appropriately labeled to alert consumers to potential interactions when concomitantly used with drugs, and to recommend a consultation with their general practitioners and other medical carers.

The impact of obesity on drug prescribing in primary care

Background

Healthcare costs attributable to obesity have previously involved estimations based on costs of diseases commonly considered as having obesity as an underlying factor.

Aim

To quantify the impact of obesity on total primary care drug prescribing.

Design of study

Review of computer generated and handwritten prescriptions to determine total prescribing volume for all drug classes.

Setting

Twenty-three general practice surgeries in the UK.

Method

Stratified random selection of 1150 patients who were obese (body mass index [BMI]>30 kg/m2) and 1150 age- and sex-matched controls of normal weight (BMI 18.5–<25 kg/m2). Retrospective review of medical records over an 18-month period.

Results

A higher percentage of patients who were obese, compared with those of normal weight, were prescribed at least one drug in the following disease categories: cardiovascular (36% versus 20%), central nervous system (46% versus 35%), endocrine (26% versus 18%), and musculoskeletal and joint disease (30% versus 22%). All of these categories had a P-value of <0.001. Other categories, such as gastrointestinal (24% versus 18%), infections (42% versus 35%), skin (24% versus 19%) had a P-value of <0.01, while respiratory diseases (18% versus 21%) had a P-value of <0.05. Total prescribing volume was significantly higher for the group with obesity and was increased in the region of two- to fourfold in a wide range of prescribing categories: ulcer healing drugs, lipid regulators, β-adrenoreceptor drugs, drugs affecting the rennin angiotensin system, calcium channel blockers, antibacterial drugs, sulphonylureas, biguanides, non-steroidal anti-inflammatories (NSAIDs) (P<0.001) and fibrates, angiotensin II antagonists, and thyroid drugs (P<0.05). The main impact on prescribing volumes is from numbers of patients treated, although in some areas there is an effect from greater dosage or longer treatment in those who are obese including calcium channel blockers, antihistamines, hypnotics, drugs used in the treatment of nausea and vertigo, biguanides, and NSAIDs (P<0.05) reflected in significantly increased defined daily dose prescribing.

Quick fixes, drug culture and management culture : excellence and ecstasy, BPR and Brown

This paper critiques the prescriptions of management gurus by charting parallels between management culture and drug culture over the last two decades. I argue that the ‘quick fixes’ peddled by gurus mirror the illicit drug fix of choice popularised in the same era. I suggest that, in terms of the specific nature of their promised highs, Excellence mirrored Ecstasy, and Business Process Reengineering mirrored Heroin. By tracing resonance with drug fixes, I introduce another way to understand why particular corporate fixes are found so attractive, locating this in patterns of addiction and in the gurus’ ability to exploit wider shared cultural contexts. The paper ends by suggesting that the comparison with the world of illicit drugs has lessons not only for our understandings of management and management gurus, but also for critical management academics engagement with both the gurus and our wider audiences.

Exploring in-hospital adverse drug events using ICD-10 codes

 Abstract
Objective Adverse drug events (ADEs) during hospital admissions are a widespread problem associated with adverse patient outcomes. The ‘external cause’ codes in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) provide opportunities for identifying the incidence of ADEs acquired during hospital stays that may assist in targeting interventions to decrease their occurrence. The aim of the present study was to use routine administrative data to identify ADEs acquired during hospital admissions in a suburban healthcare network in Melbourne, Australia.

Methods Thirty-nine secondary diagnosis fields of hospital discharge data for a 1-year period were reviewed for ‘diagnoses not present on admission’ and assigned to the Classification of Hospital Acquired Diagnoses (CHADx) subclasses. Discharges with one or more ADE subclass were extracted for retrospective analysis.

Results From 57 205 hospital discharges, 7891 discharges (13.8%) had at least one CHADx, and 402 discharges (0.7%) had an ADE recorded. The highest proportion of ADEs was due to administration of analgesics (27%) and systemic antibiotics (23%). Other major contributors were anticoagulation (13%), anaesthesia (9%) and medications with cardiovascular side-effects (9%).

Conclusion Hospital data coded in ICD-10 can be used to identify ADEs that occur during hospital stays and also clinical conditions, therapeutic drug classes and treating units where these occur. Using the CHADx algorithm on administrative datasets provides a consistent and economical method for such ADE monitoring.

What is known about the topic? Adverse drug events (ADEs) can result in several different physical consequences, ranging from allergic reactions to death, thereby posing a significant burden on patients and the health system. Numerous studies have compared manual, written incident reporting systems used by hospital staff with computerised automated systems to identify ADEs acquired during hospital admissions. Despite various approaches aimed at improving the detection of ADEs, they remain under-reported, as a result of which interventions to mitigate the effect of ADEs cannot be initiated effectively.

What does this paper add? This research article demonstrates major methodological advances over comparable published studies looking at the effectiveness of using routine administrative data to monitor rates of ADEs that occur during a hospital stay and reviews the type of ADEs and their frequency patterns during patient admission. It also provides an insight into the effect of ADEs that occur within different hospital treating units. The method implemented in this study is unique because it uses a grouping algorithm developed for the Australian Commission on Safety and Quality in Health Care (ACSQHC) to identify ADEs not present on admission from patient data coded in ICD-10. This algorithm links the coded external causes of ADEs with their consequences or manifestations. ADEs identified through the use of programmed code based on this algorithm have not been studied in the past and therefore this paper adds to previous knowledge in this subject area.

What are the implications for health professionals? Although not all ADEs can be prevented with current medical knowledge, this study can assist health professionals in targeting interventions that can efficiently reduce the rate of ADEs that occur during a hospital stay, and improve information available for future medication management decisions.

Is cost-related non-collection of prescriptions associated with a reduction in health? Findings from a large-scale longitudinal study of New Zealand adults

OBJECTIVE: To investigate whether cost-related non-collection of prescription medication is associated with a decline in health. SETTINGS: New Zealand Survey of Family, Income and Employment (SoFIE)-Health. PARTICIPANTS: Data from 17 363 participants with at least two observations in three waves (2004-2005, 2006-2007, 2008-2009) of a panel study were analysed using fixed effects regression modelling. PRIMARY OUTCOME MEASURES: Self-rated health (SRH), physical health (PCS) and mental health scores (MCS) were the health measures used in this study. RESULTS: After adjusting for time-varying confounders, non-collection of prescription items was associated with a 0.11 (95% CI 0.07 to 0.15) unit worsening in SRH, a 1.00 (95% CI 0.61 to 1.40) unit decline in PCS and a 1.69 (95% CI 1.19 to 2.18) unit decline in MCS. The interaction of the main exposure with gender was significant for SRH and MCS. Non-collection of prescription items was associated with a decline in SRH of 0.18 (95% CI 0.11 to 0.25) units for males and 0.08 (95% CI 0.03 to 0.13) units for females, and a decrease in MCS of 2.55 (95% CI 1.67 to 3.42) and 1.29 (95% CI 0.70 to 1.89) units for males and females, respectively. The interaction of the main exposure with age was significant for SRH. For respondents aged 15-24 and 25-64 years, non-collection of prescription items was associated with a decline in SRH of 0.12 (95% CI 0.03 to 0.21) and 0.12 (95% CI 0.07 to 0.17) units, respectively, but for respondents aged 65 years and over, non-collection of prescription items had no significant effect on SRH. CONCLUSION: Our results show that those who do not collect prescription medications because of cost have an increased risk of a subsequent decline in health.