Informing patients about potential adverse drug reactions after percutaneous coronary intervention reduces the occurrence of self-reported adverse drug reactions
Bibliographic record
Abstract
Abstract Background Healthcare providers are commonly reluctant to inform patients about potential adverse drug reactions (ADRs) from prescribed therapy to avoid increasing the incidence of ADRs through the nocebo effect. However, patients need information on all aspects of prescribed therapy to facilitate informed decision-making and take ownership of their care. Purpose To describe wether patients received information about potential ADRs from prescribed therapy before hospital discharge after percutaneous coronary intervention (PCI). Furthermore, to determine whether receiving information about ADRs is associated with incidence of self-reported ADRs. Methods CONCARDPCI is a prospective multicentre cohort study including 3417 patients after PCI. The study was conducted between June 2017 and May 2020 at seven large referral PCI centres in Norway and Denmark. Clinical data were collected from patients' medical records. Socio-demographic characteristics were obtained by self-report during index hospitalization after PCI. Two questions from the Heart Continuity of Care Questionnaire were used to determine if information about potential ADRs was received before hospital discharge. De novo created questions were used to determine if patients reported ADRs from prescribed therapy. Questionnaires were distributed two (T1), six (T2), and twelve months (T3) after hospital discharge to assess the incidence of self-reported ADRs in a longitudinal perspective. Logistic regression was utilised to scrutinize the aims, reported as odds ratios (ORs) with 95% confidence intervals (CIs). Results Patients were predominantly men (78%), with a mean age of 66 years (SD 11, range 20–96 years), and married or living with a partner (75%). Stable coronary artery disease was the most frequent cause of admission for PCI (30%). Before hospital discharge, 59% were informed of potential ADRs from prescribed therapy and 50% were informed what to do if ADRs occurred. The incidence of self-reported ADRs were significantly lower for those who were informed of potential ADRs compared to those who were not informed at T1 (OR 0.54, CI: 0.45–0.65, p<0.001), T2 (OR 0.56, CI: 0.46–0.70, p<0.001) and T3 (OR 0.51, CI: 0.42–0.67, p<0.001) (Figure 1). After controlling for socio-demographic and clinical variables, the incidence of self-reported ADRs were similar at T1 (OR 0.58, CI: 0.47–0.71, p<0.001), T2 (OR 0.56, CI: 0.46–0.70, p<0.001) and T3 (OR 0.53, CI: 0.42–0.66, p<0.001). Those less likely to receive information about ADRs were living alone (OR 0.75, CI: 0.59–0.97; p=0.029), women (OR 0.58, CI: 0.45–0.75, p<0.001) and those with three or more comorbidities (OR 0.68, CI: 0.47–0.98, p=0.029) compared to their counterparts. Conclusion Our study showed that informing patients about potential ADRs after PCI reduces the occurrence of self-reported ADRs. Information about potential ADRs should be provided regardless of cohabitation status, sex and comorbidities. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Western Norway Health Authority
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".