Deprescribing zolpidem reduces falls and fractures in patients with cirrhosis
Bibliographic record
Abstract
Background & Aims Benzodiazepines are associated with an increased risk of harm in patients with cirrhosis. However, stopping benzodiazepines must be done with care to avoid withdrawal or other unintended consequences. The impact of deprescribing on patients with cirrhosis is unknown. Methods We emulated a hypothetical 3-year trial of benzodiazepine deprescription among Medicare enrollees with compensated cirrhosis who lacked other life-limiting diagnoses. All received continuous benzodiazepine prescriptions for the 6-months prior to their diagnosis of cirrhosis. During a 90-day landmark period following their diagnosis of cirrhosis, patients were classified as complete deprescribers (no benzodiazepines dispensed), continuous users, or partial deprescribers. We used inverse probability treatment weighting to compare complete deprescribers to continuous users of traditional benzodiazepines and zolpidem. Outcomes accounted for competing risk of mortality and included incident decompensation (hepatic encephalopathy, ascites, or variceal bleeding), fractures, falls, and alcohol-related hospitalizations. Results There were 1,651 and 1,463 continuous users of traditional benzodiazepines and zolpidem, respectively, and 728 complete deprescribers. Patients were aged a median of 68 years, 24% had alcohol-related cirrhosis. There was no difference in the risk of death or decompensation for continuous users and deprescribers. Among deprescribers of traditional benzodiazepines, there was no improvement in the risk of falls or fractures. However, compared to continuous zolpidem users, deprescribers had a lower risk of falls (23.2% vs. 31%, p = 0.04) and fractures (21% vs. 29%, p = 0.02). Conclusions Deprescribing zolpidem reduces the risk of falls and fractures. However, deprescribing benzodiazepines does not improve the risk of decompensation. Efforts to safely address the indications for benzodiazepines such as insomnia and anxiety are urgently needed. Lay summary Many people with cirrhosis have anxiety, depression, and sleep disorders. Increasingly, patients with cirrhosis are treated with sedating medications called benzodiazepines, including valium, alprazolam (‘Xanax'), clonopin, and the sleep-aid zolpidem (‘Ambien'), which can cause falls, broken bones, and maybe other brain disorders. For this reason, many researchers are interested in trials of ‘deprescribing' (stopping) benzodiazepines. However, no trials have been performed. We used health record data to simulate a trial of deprescribing. We found that stopping benzodiazepines may reduce the chance of falls or broken bones, but it does not improve survival or liver health.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".