Colin Curtain

A Comparison of Prescribing Criteria When Applied to Older Community-Based Patients

BackgroundStudies have compared prescribing criteria for older people in general terms, reporting the findings without true side-by-side comparisons of the frequency and type of potential drug-related problems (DRPs).ObjectiveThe aim of this study was to compare the frequency and type of DRPs identified by several prescribing criteria. Additionally, original pharmacist DRP findings were compared with DRPs identified using the prescribing criteria.MethodThree prescribing criteria were automated: Beers 2012 (Beers), Screening Tool of Older Person’s Prescriptions/Screening Tool to Alert doctors to Right Treatment (STOPP/START), and Prescribing Indicators in Elderly Australians (PIEA). The criteria were applied to medication reviews of 570 ambulatory older Australian patients. DRPs identified by each set of criteria were recorded. Each DRP was assigned a descriptive term which highlighted mainly drug classes and/or diagnoses to provide a meaningful common language for comparison between recorded DRPs. Descriptive terms were used to compare the frequency and type of DRP identified by each set of criteria, as well as against original pharmacists’ findings.ResultsBeers identified 399 DRPs via 21 different descriptive terms, STOPP/START identified 1,032 DRPs via 42 terms, and PIEA identified 1,492 DRPs via 33 terms. The various types of DRPs identified by all of the three prescribing criteria were represented by 53 different terms. When constrained to the same 53 different terms, pharmacists identified 862 DRPs.ConclusionEach set of criteria displayed relevance through mutual agreement of known high-risk medication classes in older people. The number and scope of DRPs identified by pharmacists was best represented by STOPP/START. The application of STOPP/START may be further augmented with relevant criteria from PIEA and Beers.

Drug-Related Problems Detected in Australian Community Pharmacies: The PROMISe Trial

Background Drug-related problems (DRPs) are a major burden on health care systems. Community pharmacists are ideally placed to detect, prevent, and resolve these DRPs. Objective: To determine the number and nature of DRPs detected and clinical interventions performed by Australian community pharmacists, using an electronic system. Methods: An electronic documentation system was designed and integrated into the existing dispensing software of 186 pharmacies to allow pharmacists to record details about the clinical interventions they performed to prevent or resolve DRPs. Participating pharmacies were randomly allocated to 3 groups: group 1 had documentation software, group 2 had documentation software plus a timed reminder to document interventions, and group 3 had documentation software, a timed reminder, and an electronic decision support prompt. Pharmacists classified DRPs, entered recommendations they made, and estimated the clinical significance of the intervention. An observational substudy that included pharmacies without any documentation software was completed to verify intervention rates. Results: Over 12 weeks, 531 participating pharmacists recorded 6230 clinical interventions from 2,013,923 prescriptions, with a median intervention rate of 0.23% of prescriptions. No significant differences were seen between the 3 groups that used documentation software; as expected, however, the pharmacies that used this software had a significantly higher documentation rate compared to the pharmacies without documentation software. The most common interventions were related to drug selection problems (30.8%) and educational issues (24.4%). Recommendations were often related to a change in therapy (40.0%), and 41.6% of interventions were self-rated as highly significant. Drug groups most commonly subject to an intervention included antibiotics, glucocorticoids, nonsteroidal antiinflammatory drugs, and opioids. Conclusions: The documentation system allowed for the determination of the frequency and types of DRPs, as well as the recommendations made to resolve them In community pharmacy practice. Use of the software, including its electronic prompts, significantly increased the documentation of interventions by pharmacists.

Review of computerized clinical decision support in community pharmacy.

Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes.

Outcomes of a decision support prompt in community pharmacy dispensing software to promote step-down of proton pump inhibitor therapy

AIM To evaluate the effect of a computerized decision support prompt regarding high-dose proton pump inhibitor (PPI) therapy on prescribing and medication costs. METHODS A prompt activated on dispensing high-dose esomeprazole or pantoprazole was implemented in 73 of 185 pharmacies. Anonymized prescription data and a patient survey were used to determine changes in prescribing and associated medication costs. RESULTS The pharmacist-recorded PPI intervention rate per 100 high-dose PPI prescriptions was 1.67 for the PPI prompt group and 0.17 for the control group (P < 0.001). During the first 28 days of the trial, 196 interventions resulted in 34 instances of PPI step-down, with 28 of these occurring in PPI prompt pharmacies. Cost savings attributable to the prompt were AUD 7.98 (£4.95) per month per PPI prompt pharmacy compared with AUD 1.05 (£0.65) per control pharmacy. CONCLUSION The use of electronic decision support prompts in community pharmacy practice can promote the quality use of medicines.

Prediction of Hospitalization due to Adverse Drug Reactions in Elderly Community-Dwelling Patients (The PADR-EC Score).

Background Adverse drug reactions (ADRs) are the major cause of medication-related hospital admissions in older patients living in the community. This study aimed to develop and validate a score to predict ADR-related hospitalization in people aged ≥65 years. Methods ADR-related hospitalization and its risk factors were determined using a prospective, cross-sectional study in patients aged ≥65 years admitted to two hospitals. A predictive model was developed in the derivation cohort (n = 768) and the model was applied in the validation cohort (n = 240). ADR-related hospital admission was determined through expert consensus from comprehensive reviews of medical records and patient interviews. The causality and preventability of the ADR were assessed based on the Naranjo algorithm and modified Schumock and Thornton criteria, respectively. Results In the derivation sample (mean [±SD] age, 80.1±7.7 years), 115 (15%) patients were admitted due to a definite or probable ADR; 92.2% of these admissions were deemed preventable. The number of antihypertensives was the strongest predictor of an ADR followed by presence of dementia, renal failure, drug changes in the preceding 3 months and use of anticholinergic medications; these variables were used to derive the ADR prediction score. The predictive ability of the score, assessed from calculation of the area under the receiver operator characteristic (ROC) curve, was 0.70 (95% confidence interval (CI) 0.65–0.75). In the validation sample (mean [±SD] age, 79.6±7.6 years), 30 (12.5%) patients’ admissions were related to definite or probable ADRs; 80% of these admissions were deemed preventable. The area under the ROC curve in this sample was 0.67 (95% CI 0.56–0.78). Conclusions This study proposes a practical and simple tool to identify elderly patients who are at an increased risk of preventable ADR-related hospital admission. Further refinement and testing of this tool is necessary to implement the score in clinical practice.

Complementary and Alternative Medicine Use in Cancer Patients in Rural Australia

Aim. Numerous studies have demonstrated the high prevalence of complementary and alternative medicine (CAM) use in metropolitan cancer cohorts but few have been conducted in regional and remote populations. This study aimed to investigate the trends and regional variations in CAM use by cancer patients at a regional cancer care center in Toowoomba, South East Queensland, Australia. Methods. All English-speaking adult cancer patients attending the regional cancer care center were invited to participate. Eligible patients were provided a self-administered questionnaire that was developed based on published surveys. Ethics approval was obtained. Results. Overall 142 patients completed the questionnaire and 68% were currently or had previously used at least one form of CAM. CAM users and nonusers did not differ significantly by region, age, gender, time since diagnosis, income, town size, treatment intent, or metastases. CAM users were more likely to have a higher level of education. Concurrent CAM use with conventional treatment was reported by approximately half of respondents. The most common reason for CAM use was “to improve general physical well-being.” The most common sources of CAM information were family (31%) and friends (29%). Disclosure of CAM use to either the general practitioner or specialist was reported by 46% and 33% of patients, respectively. The most common reason for nondisclosure was “doctor never asked.” Conclusion. This study supports previous research that CAM use is as common in regional and remote areas as metropolitan areas. Nondisclosure of CAM use to health professionals was common. Future research needs to focus on strategies to improve communication between patients and health professionals about the use of CAM.

Predictors of adverse drug reaction-related hospitalisation in Southwest Ethiopia: A prospective cross-sectional study

Background Adverse drug reactions (ADRs) are important causes of morbidity and mortality in the healthcare system; however, there are no studies reporting on the magnitude and risk factors associated with ADR-related hospitalisation in Ethiopia. Objectives To characterise the reaction types and the drugs implicated in admission to Jimma University Specialized Hospital, Southwest Ethiopia, and to identify risk factors associated with ADR-related hospitalisation. Methods A prospective cross-sectional study was conducted from May 2015 to August 2016 among consenting patients aged ≥18 years consecutively admitted to medical wards taking at least one medication prior to admission. ADR-related hospitalisations were determined through expert review of medical records, laboratory tests, patient interviews and physical observation. ADR causality was assessed by the Naranjo algorithm followed by consensus review with internal medicine specialist. ADR preventability was assessed using Schumock and Thornton’s criteria. Only definite and probable ADRs that provoked hospitalisation were considered. Binary logistic regression was used to identify independent predictors of ADR-related hospitalisation. Results Of 1,001 patients, 103 (10.3%) had ADR-related admissions. Common ADRs responsible for hospitalisation were hepatotoxicity (35, 29.4%) and acute kidney injury (27, 22.7%). The drug classes most frequently implicated were antitubercular agents (45, 25.0%) followed by antivirals (22, 12.2%) and diuretics (19, 10.6%). Independent predictors of ADR-related hospitalisation were body mass index (BMI) <18.5 kg/m2 (adjusted odd ratio [AOR] = 1.69; 95% confidence interval [CI] = 1.10–2.62; p = 0.047), pre-existing renal disease (AOR = 2.84; 95%CI = 1.38–5.85, p = 0.004), pre-existing liver disease (AOR = 2.61; 95%CI = 1.38–4.96; p = 0.003), number of comorbidities ≥4 (AOR = 2.09; 95%CI = 1.27–3.44; p = 0.004), number of drugs ≥6 (AOR = 2.02; 95%CI = 1.26–3.25; p = 0.004) and history of previous ADRs (AOR = 24.27; 95%CI = 11.29–52.17; p<0.001). Most ADRs (106, 89.1%) were preventable. Conclusions ADRs were a common cause of hospitalisation. The majority of ADRs were preventable, highlighting the need for monitoring and review of patients with lower BMI, ADR history, renal and liver diseases, multiple comorbidities and medications. ADR predictors should be integrated into clinical pathways and pharmacovigilance systems.

Impact of residential medication management reviews on anticholinergic burden in aged care residents.

Abstract Objectives: The primary objective of this study was to investigate the impact of Residential Medication Management Reviews (RMMRs) on anticholinergic burden quantified by seven anticholinergic risk scales. Design: Retrospective analysis. Setting: Accredited pharmacists conducted RMMRs in aged-care facilities (ACFs) in Sydney, Australia. Participants: RMMRs pertained to 814 residents aged 65 years or older. Measurements: Anticholinergic burden was quantified using seven scales at baseline, after pharmacists’ recommendations and after the actual GP uptake of pharmacists’ recommendations. Change in the anticholinergic burden was measured using the Wilcoxon sign rank test. Results: At baseline, depending on the scale used to estimate the anticholinergic burden, between 36% and 67% of patients were prescribed at least one regular anticholinergic medication (ACM). Anticholinergic burden scores were significantly (p < 0.001) lower after pharmacists’ recommendations as determined by each of the seven scales. The reduction in anticholinergic burden was also significant (p < 0.001) after GPs’ acceptance of the pharmacists’ recommendations according to all scales with the exception of one scale which reached borderline significance (p = 0.052). Conclusion: Despite the limitations of the retrospective design and differences in the estimation of anticholinergic burden, this is the first study to demonstrate that RMMRs are effective in reducing ACM prescribing in ACF residents, using a range of measures of anticholinergic burden. Future studies should focus on whether a decrease in anticholinergic burden will translate into improvement in clinical outcomes.

An investigation into drug-related problems identifiable by commercial medication review software

BACKGROUND Accredited pharmacists conduct home medicines reviews (HMRs) to detect and resolve potential drug-related problems (DRPs). A commercial expert system, Medscope Review Mentor (MRM), has been developed to assist pharmacists in the detection and resolution of potential DRPs. AIMS This study compares types of DRPs identified with the commercial system which uses multiple classification ripple down rules (MCRDR) with the findings of pharmacists. METHOD HMR data from 570 reviews collected from accredited pharmacists was entered into MRM and the DRPs were identified. A list of themes describing the main concept of each DRP identified by MRM was developed to allow comparison with pharmacists. Theme types, frequencies, similarity and dissimilarity were explored. RESULTS The expert system was capable of detecting a wide range of potential DRPs: 2854 themes; compared to pharmacists: 1680 themes. The system identified the same problems as pharmacists in many patient cases. Ninety of 119 types of themes identifiable by pharmacists were also identifiable by software. MRM could identify the same problems in the same patients as pharmacists for 389 problems, resulting in a low overlap of similarity with an averaged Jaccard Index of 0.09. CONCLUSION MRM found significantly more potential DRPs than pharmacists. MRM identified a wide scope of DRPs approaching the range of DRPs that were identified by pharmacists. Differences may be associated with system consistency and perhaps human oversight or human selective prioritisation. DRPs identified by the system were still considered relevant even though the system identified a larger number of problems.

Computer system to support medication reviews: a good but not new concept

In their recent article, de Wit and colleagues put forward a solid argument for the use of clinical decision support systems to support medication reviews for the elderly [1]. They point out that medication reviews are routinely performed, and they have positive effects in terms of reducing medication-related problems. They also rightly note that the quality of the findings and recommendations contained within these medication reviews are only going to be as good as the knowledge and skills of the professionals performing them. They then argue that a clinical decision support system using clinical rules that combine the available pharmacy, clinical and laboratory data may be able to improve the delivery of medication reviews. This we also accept. The authors then assert that doing this would be a ‘‘new approach’’, and outline a simple plan for how they intend to develop this innovative system. In their conclusions, they state ‘‘In our opinion the development of a system, which is able to reduce the time-consuming process of a traditional medication review and create high-quality suggestions for therapy optimisation, will be unique.’’ We wish to highlight that this approach is not unique. Over the past 8 years, we have developed and evaluated systems that can provide intelligent clinical decision support for medication reviewers, and have presented the results at a range of conferences and in international publications [2–4]. In these publications we described the rationale for and the implementation of an advanced system that allows expert medication reviewers to seamlessly and naturally build complex rules based on a patient’s biographical details, medications, medical history, and laboratory results, while performing their routine medication review duties. The approach we used for doing this is based on recent advances in the field of knowledge acquisition (a sub-field of artificial intelligence). This approach allows the expert to iteratively build up and refine a large and complex set of rules that are automatically used to identify medication-related problems. If the system ever identifies a problem that the expert disagrees with, or otherwise fails to identify a problem the expert has detected, the expert is asked to indicate what the problem should have been and why. In this way the system learns over time and the recommendations are highly individualised. Importantly, with this approach, very few false positives are identified, as the system’s knowledge is gradually refined such that problems are only identified when it is definitely appropriate to do so in the particular case. Even when best practice suddenly changes, the system is easily updated to reflect the newest information [3]. With relatively little training our system was capable of identifying approximately 90 % of all medication-related problems for a patient. Importantly, the system was capable of identifying problems that the expert had missed or incorrectly classified (errors). On average, we found that the system prevented the expert from making 2.02 errors per patient [2]. Furthermore, our approach was incorporated into a commercially available system to support medication reviewers. This product has since been used by pharmacists to perform more than 50,000 medication reviews. We have also compared the medication-related problems found by this product against original pharmacist’s findings, and even against the findings found by applying the STOPP/ START guidelines [4, 5]. A 12 member expert panel consisting of accredited medication review pharmacists, I. K. Bindoff (&) G. M. Peterson C. Curtain School of Pharmacy, University of Tasmania, Hobart, Tasmania, Australia e-mail: Ivan.Bindoff@utas.edu.au