Association of Potentially Inappropriate Medications With All-Cause Mortality in the Elderly Acute Decompensated Heart Failure Patients: Importance of Nonsteroidal Anti-Inflammatory Drug Prescription
Bibliographic record
Abstract
BACKGROUND: Acute decompensated heart failure (ADHF) is the most common cause of readmissions in the hospital. ADHF patients are associated with polypharmacy. It is a common problem among elderly patients due to frequently occurring multiple morbidities and is associated with the use of potentially inappropriate medications (PIMs). The aim of this study was to examine the association between PIMs and all-cause mortality in elderly ADHF patients. METHODS: This retrospective study included ADHF patients who were admitted to the Showa University Fujigaoka Hospital between January 2015 and August 2016. We investigated the proportion of patients taking at least one PIM at admission and the characteristics of patients at admission. PIMs were defined based on the Screening Tool of Older People's potentially inappropriate Prescriptions (STOPP). Multiple Cox regression analysis was performed to examine the association between PIM use and all-cause mortality. RESULTS: A total of 193 elderly patients (median age 81 years, interquartile range (IQR) 65 - 99 years) were included in the study. All-cause death occurred in 30 patients. The median number of medications at admission was 7 (IQR 0 - 18). The number of medications (greater than or equal to six) at admission was associated with mortality. Multivariate Cox regression analysis revealed that systolic blood pressure (SBP) < 100 mm Hg at admission, chronic obstructive pulmonary disease (COPD), and use of non-steroidal anti-inflammatory drugs (NSAIDs) at admission were independent predictors for all-cause mortality. CONCLUSIONS: The medical staff should attempt to stop unnecessary medications that are prone to be inappropriate prescribing. In particular, prescription of NSAIDs should be carefully assessed and monitored.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".