Reply to Potentially inappropriate medication use in older adults: a reply to Amorim <i>et al.</i>
Bibliographic record
Abstract
Dear Sir/Madam, We thank Amorim et al. for their comments and considerable interest in our article on the prevalence of potentially inappropriate medication (PIM) use among community-dwelling older adults in the province of Quebec, Canada (1). As mentioned in our study, we assessed PIM use in a large administrative database, the Quebec Integrated Chronic Disease Surveillance System (QICDSS) (2). We used the 2015 Beers criteria to identify PIMs and we specified that only the subset of PIMs that should be avoided generally in older adults was considered (3). However, for some medications of this subset, clinical conditions were required to qualify them as PIMs. Thus, as described in our study, we did not include all PIMs since the QICDSS does not contain all validated clinical data necessary for the inclusion of these drugs. As discussed in the limitations section of our study, we agree that not including some PIMs may have led to potential underestimation of PIM use. However, we submit that it is more appropriate to underestimate PIM use, knowing which medications were not considered, than to introduce potential information bias by identifying clinical conditions without a validation process, which could lead to mischaracterization of PIM use. Indeed, for example, there is no validated case definition in our database to precisely identify the cases of confirmed gastroparesis or hypogonadism, which are necessary to define potentially inappropriate prescribing of metoclopramide and androgens, respectively (3). In addition, the QICDSS does not contain laboratory results; nitrofurantoin is potentially inappropriate according to the Beers criteria if used ‘in individuals with creatinine clearance <30 ml/min or for long term use’ (3). Therefore, as with metoclopramide and androgens, we did not include this criterion to avoid mischaracterizing the use of nitrofurantoin. Regarding the comments of Amorim et al., on the definition of non-steroidal anti-inflammatory drug (NSAID) exposure used in our study, we agree that NSAIDs may be considered inappropriate for long-term use due to the associated serious adverse effects, including increased risk of dyspepsia, gastrointestinal bleeding and ulcers, perforation, acute myocardial infarction and acute renal failure (3,4). Thus, the 2015 Beers criterion states that chronic oral use of NSAIDs should be avoided in older adults, except if other alternatives are not effective and/or gastroprotective agents are taken concomitantly (3). However, in the recommendation section, it does not specify how many days the term chronic refers to. As there is no consensus definition for chronic exposure to NSAIDs, we used the 90-day cut-off in our study to define chronic exposure (i.e. continuous use >3 months), as has been done in previous research (4,5). This duration of treatment has also been used to characterize the chronic use of other PIMs such as benzodiazepines or sulfonylureas (6). We agree that the prevalence of NSAID use may have been potentially underestimated using this exposure definition in our study. However, as mentioned previously, we argue that it is more appropriate to underestimate prevalence than to overestimate it. Finally, Amorim et al. suggested to characterize the deprescribing of some frequent PIMs including proton pump inhibitors, benzodiazepines, antipsychotics and sulfonylureas. We agree that it would be interesting to identify dose reductions in order to better characterize deprescribing; however, it was not the purpose of the study. We aimed to provide a concrete picture of PIM use at the population level in order to identify the most common PIMs used, which could be targeted in further interventions, such as well-designed deprescribing interventions. Moreover, Amorim et al. have mentioned that characterizing deprescribing of PIMs may help to determine the appropriateness of these PIMs. However, a medication could have been appropriate and then deprescribed, when the circumstances justifying its use have passed. Thus, the identification of a deprescribing process alone could not validate that the medication was a PIM. To conclude, we agree that the prevalence of PIM use may have been underestimated in our study. However, we argue that the methodology used in our study is the most adequate to provide a valid population-based picture of PIM use. Funding: CS reports grants from the Fonds de recherche du Québec-Santé and from the Centre de recherche sur les soins et les service de première ligne de l’Université Laval. M-EG receives a scholarship from the Fonds de recherche du Québec-Santé. Ethical approval: not applicable. Conflict of interest: BR, MS and M-LL declare no conflict of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.051 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".