BackgroundChemotherapy prescriptions validation by the oncology pharmacist often require interventions to optimise some aspects of the treatment, usually related to the safety and effectiveness of antineoplastic agents.PurposeOur pharmacy department has developed an initiative to register these interventions, in order to characterise possible areas of improvement in the prescription validation process.Material and methodsDuring a period of 2 months, we created a database collecting data from the interventions made, which included the following information: date of intervention, medical record number, drug involved, reason/type of intervention and result of the intervention (accepted/not accepted). Sociodemographic, clinical and laboratory data were obtained from medical records. Statistical analysis of the results was performed using Microsoft Excel.Results44 interventions (43 accepted) were recorded. The department in which more interventions were recorded was medical oncology (64%), followed by haematology (29%), paediatrics (4.8%) and radiotherapy oncology (2.4%). Median age of the patients included in the database was 58.5 years (2–87), and 72% of patients were women. The most common reasons for intervention were due to ‘prescribing errors’ (47.7%), ‘pharmacotherapeutic recommendations’ (22.7%), ‘consultations/requests for information’ (15.9%), ‘adverse events’ (6.8%) and some minor reasons grouped into the category ‘others’ (6.8%). The most common types of intervention were ‘dose modification due to an adverse event (AE)’ (34%) and ‘resolution of consultations regarding prescription/medication administration’ (18%). The next types of interventions by frequency were ‘treatment recommendations’ (9.1%) and dose adjustments based on renal function’ (6.81%). Less common intervention types (4.5%) were: ‘changes in prescription’, ‘dose adjustments based on an AE’, ‘dose adjustments based on pharmacotherapeutic recommendations’, ‘changes in route of administration’ and ‘changes in dosing schedule’. Finally, type of interventions such as ‘changes in the regimen of administration’, ‘treatment interruption’ or ‘pharmaceutical compounding’ were reported in 2.3% of cases.ConclusionOncology pharmacist participation in the patient care multidisciplinary team is essential, as is clear from the high rate of acceptance of our interventions. One of the most important aspects of pharmaceutical validation is to identify errors in the prescription and medication administration process, as well as participation in the individualisation of patient therapy through pharmacotherapeutic recommendations, ensuring the effectiveness and safety of the treatment.No conflict of interest.
BackgroundBenzodiazepines (BZD) have to be reconciled within 24 h of admission to avoid withdrawal symptoms. On the other hand, it’s important to check the discharge report to avoid treatment discrepancies.PurposeTo find out the accuracy of BZD reconciliation at admission and discharge in our hospital.Material and methodsObservational prospective study carried out in a tertiary care hospital over two months. Patients over 65 years and without a relevant psychiatric condition were selected. The BZD prescribed at admission was compared with patient’s home treatment. Clinical data was obtained from the electronic clinical history and electronic prescription. The discharge report was also checked for BZD indications. Reconciliation was classified as “reconciled” (same BZD and same half-life), “partially reconciled” (change to another BZD with different half-life) or “not reconciled”. At discharge, physicians’ indications were revised.Results110 patients were included. The median age was 81 (65–95). They were 71 women (64%) and 39 men (36%). At admission the results were: 1) “Reconciled”: 63 patients (57.3%); 2) “Partially reconciled”: 9 patients (8.2%) and 3) “Not reconciled”: 38 patients (34.5%). At discharge, physicians indicated the following: 1) “Same treatment”: 69 patients (62.7%); 2) “No mention of BZD”: 25 patients (22.7%); 3) “BZD combination”: 5 patients (4.5%); 4) “Changed to other BZD”: 5 patients (4.5%) and 5) “Withdrawal”: 2 patients (1.8%). 4 patients (3.6%) died during the hospitalisation.ConclusionAlmost a third part of patients don’t have their treatment suitably reconciled. It might occur that treatment was changed because it was not warranted, regardless of reconciliation. At discharge, physicians use phrases like “same treatment” or don’t mention anything about BZD. This can lead to treatment discrepancies with Primary Care.References and/or acknowledgementsNo conflict of interest.
Background Erlotinib, gefitinib and crizotinib are tyrosine kinase inhibitors (TKIs) used in the treatment of non-small cell lung cancer (NSCLC). Their hepatic metabolism involves several cytochrome p450 isoenzymes, making them susceptible to potential interactions. Purpose To identify and analyse potential interactions between TKI and the patients’ home treatment, and to determine the degree of acceptance of recommendations by the clinician. Materials and methods In our prospective study, we selected all patients with NSCLC treated with TKIs using the Pharmacy Landtools software, monitoring them over a period of six months. Sociodemographic, clinical and home treatment (HT) data were obtained through electronic medical histories. HT was confirmed by interviewing patients. The interactions were consulted in the SPC, the Micromedex and Lexicomp databases and related scientific articles. Results 19 patients were studied (31.58% men and 68.42% women) with a median age of 71 years (aged 46–83). 11 patients were treated with gefitinib, 5 with erlotinib and 3 with crizotinib. 18 potential interactions were detected, distributed as follows: 44.44% gefitinib and 27.78% erlotinib and crizotinib, respectively. 75% were due to the use of proton pump inhibitors. Of these interactions, only those considered relevant were reported (72.22%). 92.31% recommendations were accepted, resulting in substitution (83.33%) or withdrawal (16.67%) of the drug. During our intervention period, no side effects related to a drug-drug interaction were detected. Conclusions Although TKI interactions are described in the literature, they are not always detected by the clinician. It is essential to detect, report and improve patients’ drug treatment, preventing these potential interactions which may result in adverse effects or in lack of effectiveness of the antitumor treatment. No conflict of interest.
Background Vancomycin is primarily effective against Gram-positive cocci. However, as it can only penetrate the tissue superficially, it is uncertain if it is really able to achieve concentrations of therapeutic benefit at the site of infection. Suboptimal concentrations have been associated with lack of clinical response and increased resistance. There are no clear criteria on pharmacokinetic parameters associated with a good response, although the most conservative proposals consider an AUC/MIC > 400, in pathological conditions such as pneumonia and meningitis. Some authors have described the failure to achieve these values with the usual doses when the MIC > 2. Purpose Our work evaluates the pharmacokinetic data of vancomycin in a group of 30 inpatients, and individual Bayesian estimates of the dose needed to overcome the described value of AUC/MIC > 400. Materials and MethodsWe estimated the kinetic parameters of a population of 30 patients with a staphylococcal infection through a Bayesian model with application v.1.0 Abbotbase Pharmacokinetic Systems. From each patient we obtained the MIC, and the dose required to obtain an AUC/MIC > 400. We calculated the percentage of patients who reached target values for AUC/MIC with a standard dose of 1 g/12 h and those receiving an individualised dose according to the kinetic parameters obtained by Bayesian setting. Maximum doses of 4 grammes/day were considered. ResultsMean clearance (CI 95%) obtained through Bayesian estimation was 3.91 l/h (3.2–4.6). Median MIC value was 1 mcg/ml. According to these data, 57% of patients would reach therapeutic AUC values with conventional dose. However, if the dose is set individually 90% of patients would reach the target value, with a mean calculated dose of 2300 mg (CI95%: 1550–3000). Conclusions Most patients with staphylococcal infections can be treated with vancomycin, which also contributes to cost reduction. A Bayesian approach shows better pharmacodynamic results than conventional dosing, with a 90% of patients successfully treated in a real setting. No conflict of interest.
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