The impact of substance use disorders on postoperative falls in major noncardiac surgery: A retrospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: Substance use disorders are increasing in incidence yet may be underrecognized in the surgical population. Perioperatively, these substances and/or treatments for these disorders may be acutely stopped, increasing the risk of withdrawal symptoms and accidents, such as falls. However, there have been no studies evaluating the association between substance use disorders and postoperative falls in a broad surgical population. METHODS: A retrospective cohort analysis of adults (≥18 years) undergoing a broad case mix of major elective noncardiac surgeries was conducted using the New York State Inpatient and Emergency Department Databases from 2016 to 2019. The primary exposure was the presence of one or more substance use disorders at the time of admission. The primary outcome was a postoperative fall, defined as either an in-hospital fall after surgery, or an emergency department visit for a fall within 30-days of surgical discharge. Nearest-neighbour propensity score matching was used to match patients with a substance use disorder to those without one. Logistic regression was used to estimate the association of substance use disorders with postoperative falls in the unmatched and then the matched cohorts. RESULTS: 365,797 patients were included in this study, of which 2.12 % had an active substance use disorder. In the unmatched cohort, patients with a substance use disorder had 2.08 times the crude odds of a postoperative fall compared to their counterparts (95 % CI: 1.55 to 2.80, p < 0.001). In the matched cohort of 15,530 patients, a substance use disorder was also associated with an increased risk of 30-day falls (OR 1.71, 95 % CI 1.06 to 2.75, p = 0.028). DISCUSSION: Approximately 2 % of adults undergoing major elective noncardiac surgery had a substance use disorder. Patients with a substance use disorder had increased risks of postoperative falls. This study identified a potentially high-risk group of patients and highlights a continued need for robust screening and management of various substance use disorders in the perioperative period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".