Use of an electronic decision support tool to reduce polypharmacy in elderly people with chronic diseases: cluster randomised controlled trialBMJ 2020; 369 doi: https://doi.org/10.1136/bmj.m1822 (Published 18 June 2020) Cite this as: BMJ 2020;369:m1822
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In the June 18 issue of the BMJ, Rieckert and colleagues present a multinational randomised controlled trial in primary care, testing the use of a pharmacotherapeutic decision support system (PRIMA-eDS) in 3904 patients (aged 75 years and older) with polypharmacy and multiple chronic diseases . The study shows a reduction of 0.42 drugs per patient (from a median of 10 medications per patient) and a modest risk reduction in mortality and admission to hospital.
We conducted a similar pilot study in 3 nursing homes in Belgium, in equally multi-morbid and poly-medicated patients (mean age 85y), testing the impact of a complex intervention. The intervention is the combination of an electronic decision support tool (for the appraisal of potentially inappropriate medication use, anticholinergic use, or medications that can be de-prescribed) with focused nurse observations. These are based on a questionnaire on potential side-effects, generated from the individual medication charts (Pharmanurse tool). This feedback served as the basis of a multidisciplinary medication review with the input of nurses, pharmacist, and GPs .
Both studies and others in the past confirm that adequate decision support in medication review is feasible in this particular population, even in the particular setting of the nursing home. These types of interventions result in the reduction of polypharmacy, and potentially inappropriateness by discontinuation of needlessly prolonged chronic treatment (e.g. benzodiazepines, antipsychotics, anticholinergics) or deprescribing of preventive medicine in the light of a limited life-expectancy. The effects may be more pronounced if decision support is embedded in a setting with excellent medical documentation, if it is provided in the context of a culture of quality assurance, if it is firmly based on validated decision rules, and if it is applied in a multidisciplinary context with sophisticated and user-friendly software. However, effect evaluation is based on process outcomes (e.g. reduction of the number of drugs, reduction of PIMs). Proving impact on hard and quality of live outcomes other than patient safety seems to be more difficult i poly-morbid oldest old. This will require considerable resources in terms of sample size and data collection efforts, especially in the underfunded setting of nursing homes, often characterized by very poor clinical record keeping.
How much evidence do we need to engage in a comprehensive quality assurance program of polypharmacy in the oldest old, either community-dwelling or in nursing homes? How long will we ignore the use of efficient sophisticated yet user-friendly decision support tools for primary care professionals (GP, pharmacist, nurse).
The COVID-19 pandemic has illustrated how vulnerable the oldest population is and how poor the clinical data is to inform sound epidemiological responses.
In Belgium (a country with 11.5 million inhabitants, 144,000 nursing home beds, and honest reporting of confirmed ànd suspected Covid-deaths), almost 10,000 deaths were recorded during the epidemic. Two thirds of them occurred in nursing homes. Hence, mortality rates in the nursing homes were approx. 5,000 deaths per 100,000 residents, compared to approx.. 30 per 100,000 in the rest of the population.
Sound medical documentation, spurred by and supporting continuous pharmacotherapeutic quality assurance in this setting could help us to be better prepared for such catastrophic events.
1 Rieckert A, Reeves D, Altiner A, et al. Use of an electronic decision support tool to reduce polypharmacy in elderly people with chronic diseases: cluster randomised controlled trial. BMJ 2020;369:m1822. doi:10.1136/bmj.m1822
2 Dilles T, Vander Stichele RH, Van Bortel LM, et al. The Development and Test of an Intervention to Improve ADR Screening in Nursing Homes. J Am Med Dir Assoc 2013;14:379.e1-379.e6. doi:10.1016/j.jamda.2013.02.011
3 Wauters M, Elseviers M, Dilles T, et al. OptiMEDs Pilot Study - Full Text View - ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT04142645 (accessed 29 Jun 2020).
Competing interests: No competing interests
Thank you and congratulations to the authors for undertaking this very difficult study.
It is reassuring in the short term that describing seems superficially not to have had any clinically significant adverse outcomes (or participant/patient perception differences as per the SF12 outcomes). However, the conclusion that a reduction in prescribing was achieved without detriment to patient outcomes depends on how one defines patient outcomes, and perhaps no statistically significant detriment to patient outcomes would be a more accurate conclusion.
I note the differences in CFS (Clinical Frailty Scale) categories between the groups with 66.5% (decision support-DS) vs 62.2% (control) group in the lower risk vulnerable/managing well categories; and yet the trend in the robust and serious outcome measures indicate increased adverse outcomes in the DS group: mortality (19.5% vs 18.8%) and fractures (3% vs 2.3%) where the opposite should have been expected.
I wonder whether using first unplanned hospital admission in combination with mortality is the wrong outcome measure to use. Deprescribing may be resulting in reduced diuretics, ACEI, BBlockers, antiplatelets, bisphosphonates, statins etc so it would not be unsurprising that there would be a reduction in hospital admissions with side-effects from these medications but the trade off of this may be starting to show up in 24 month outcome data being presented.
Although I concur with appropriate deprescribing, I would caution careful consideration of the potential adverse unexpected consequences of deprescribing tolerated medications, especially as patients in the intervention (DS) group did not report significantly better outcomes on the SF12 despite the reduced medication load.
I look forward to your further outcome publications and perhaps data on longer term follow up in mortality (Kaplan-Meier curves -especially the differences in area between the two curves overall), strokes, fractures and MIs.
Competing interests: No competing interests