1061 Gender Differences in Characteristics of Hospitalized Patients With Cirrhosis: A Prospective Multicenter Inpatient Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Women with cirrhosis listed for liver transplant have increased waitlist mortality compared with men, but the reasons for this disparity is not well-established. We hypothesized that women with cirrhosis have different comorbidities and reasons for hospitalizations than men - and explored this hypothesis in a large, multi-center cohort of inpatients with cirrhosis. METHODS: Our cohort included patients with cirrhosis hospitalized non-electively, prospectively enrolled at 4 academic medical centers 1/2013-2/2017. Data collected included comorbidities, reasons for admission, cirrhosis complications, medications, hospital course, and outcomes. Readmission and mortality rates were compared between women and men using logistic regression. RESULTS: Of the 674 hospitalized patients with cirrhosis, 256 (38%) were women. Median age was 56 years; 75% were non-Hispanic White. Women were less likely than men to have cirrhosis from alcohol (34% vs 26%, P < 0.001), and more likely to have NASH (22% vs 15%, P < 0.001). Women and men had similar cirrhosis complications, liver-related medications, MELD (20 vs 20, P = 0.36), and Child-Pugh (9 vs 10, P = 0.36) scores. Women were more likely to have certain non-liver related comorbidities, including insulin-dependent diabetes (23% vs 14%, P = 0.002) and connective tissue disease (4% vs 1%, P = 0.004), as well as to require non-opiate pain medications (14% vs 7%, P = 0.008) or antidepressant/sleep aids (38% vs 28%, P = 0.005). Overall infection rates on admission were similar between women and men (27% vs 22%, P = 0.19), but women were more likely than men to have UTIs (14% vs 4%, P < 0.001) and less likely to have SBP (6% vs 11%, P = 0.04). Rates of other decompensating events were similar between women and men, including GI bleeding, renal dysfunction, and volume overload ( P for all > 0.05). There were no differences between women and men in 30d (11% vs 13%, P = 0.60) or 90d (22% vs 27%, P = 0.18) mortality. In logistic regression, female gender was not associated with increased 30d mortality (OR 0.88, 95% CI 0.54-1.43, P = 0.60) or 90d readmission (OR 1.06, 95% CI 0.75-1.48, P = 0.76). CONCLUSION: Although women and men hospitalized with cirrhosis have similar severity of liver disease, they differ by their etiologies of cirrhosis, non-liver related comorbidities, infectious complications, and patterns of medication use. Future studies should further explore the role that these non-liver-related factors play in contributing to well-established gender disparities in waitlist outcomes.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".