OR03-5 Fractures during Bisphosphonate Therapy after Orthotopic Liver Transplantation: Incidence and Predictors
Bibliographic record
Abstract
Introduction: Bone loss is significant within the first 3 to 6 months after orthotopic liver transplantation (OLT) and is associated with high rates of fractures. Incident fractures after OLT are correlated with substantial decreased quality of life. To attempt preventing post-transplant fractures, the cornerstone of therapeutic management is bisphosphonates (BP). Objectives: The main objective of our study was to determine the incidence of fractures during the first three years after a first OLT in patients receiving BP. The secondary objective was to study the predictors of fractures in these patients. Methods: We conducted a retrospective study in a cohort of patients who underwent OLT between January 2012 and September 2016 at our center. All adult recipients who received BP after OLT were included in the study. Clinical, laboratory, bone mineral density (BMD) and fracture data were extracted from electronic medical records. Vertebral and non-vertebral fractures were included and all fractures were confirmed by a radiologist. Results: During the study period, 304 OLT were performed and 158 patients (median age 57 y.o.; 29.8% female) met the inclusion criteria. Prior to OLT, 14 patients (8.9%) met the criteria for osteoporosis (T-score ≤ -2.5) and a total of 24 patients (15.2%) had a past history of fractures (vertebral and non-vertebral). The cumulative incidence of fractures after OLT in patients treated with BP was 19.1% (26 patients) at 36 months, with a median time to first fracture of 6 months (Q1=2, Q3=20 months). Predictive factors of fractures in univariate analyses included: age > 60 y.o. (HR 2.46; 95% CI, 1.14-5.33), new onset diabetes after transplantation (NODAT) (HR 2.38; 95% CI, 1.10-5.15), narcotic use at 6 months after OLT (HR 2.19; 95% CI, 1.00-4.77) and length of hospital stays in the first year (days) (HR 1.01; 95% CI, 1.00-1.01). Previous fragility fracture was not associated with higher risk of post-transplant fracture. Multivariate analysis confirmed that age > 60 y.o. (HR 2.66; 95% CI, 1.23-5.79; p=0.01), narcotic use (HR 2.65; 95% CI, 1.20-5.84; p=0.02) and NODAT (HR 2.68; 95% CI, 1.23-5.84; p=0.01) were significant independent risk factors for fractures. BMD was available before and after transplant in 34 patients and there was a statistically significant decrease in mean absolute BMD (g/cm2) at the femoral neck (-0.07 g/cm2 vs. -0.04 g/cm2; p=0.02) and total hip (-0.07 g/cm2 vs. -0.02 g/cm2; p=0.02) in patients with incident fractures compared to patients without fracture during follow-up. Conclusion: Fractures are a frequent complication after OLT even in patients preemptively treated with BP. Special attention should be paid to BP treated older patients, narcotic users and patients with NODAT in order to prevent fractures after OLT. Further studies are needed to identify the best therapeutic strategy in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".