A73 SURVIVAL PREDICTORS IN PATIENTS UNDERGOING LIVER TRANSPLANT FOR NON-ALCOHOLIC STEATOHEPATITIS: A POPULATION-BASED COHORT STUDY
Bibliographic record
Abstract
Non-alcoholic steatohepatitis is a recognized cause of cirrhosis affecting 12% of word population. The proportion of orthotopic liver transplants (OLT) for NASH cirrhosis increased 8-fold (1.2 to 9.7%) between 2001–2009 and is anticipated to become the leading indication for OLT in the next 20 years. Surprisingly, disease-specific indications and mortality predictors for OLT in NASH are lacking. To develop a 5-years mortality prediction model applicable during the pre-surgical assessment of patients with NASH cirrhosis eligible for OLT. This single center retrospective cohort study included subjects undergoing OLT at the University of Alberta between 2002–2012 for NASH/cryptogenic cirrhosis. Clinical information was extracted from a dedicated computerized database (OTTR) and audited. The primary outcome was all-cause mortality at 5 years. Prediction models were constructed using Cox proportional hazard regression techniques. Model assumptions and discrimination capacity were tested. Ethics approval was obtained from the local ethics board. Of 524 OLT patients, NASH cirrhosis was the main indication in 41 (8%) patients (NASH-OLT). Compared to those receiving an OLT for other indications, the NASH-OLT cohort had comparable follow-up times (2.7 vs. 3.2y. p=0.4), 5yrs-mortality rates (11/41; 27% vs. 106/483; 22%, p=0.4) and MELD scores (18 vs. 20, p=0.1); a greater BMI (28 vs. 25 p=0.0006), higher prevalence of diabetes (39 vs. 20%, p=0.004) and renal insufficiency (32 vs. 16%, p=0.004), donors with lower Donor Risk Indexes (1.37 vs. 1.49, p=0.05) longer hospital stays (34 vs. 30d, p=0.01) and received two times more blood products during transplant (p<0.03). Previously published mortality estimation tools had poor discriminatory capacity in the NASH-OLT cohort: Charlson index for OLT [C-index = 0.54, p=0.6], United Network for Organ Sharing (UNOS) [C-index = 0.58, p=0.4] and Cardoso 2014 [C-index = 0.43, p=0.9]. We developed a 5yr mortality prediction model that included: extreme (<600 or >1200) modified-BMI (BMI kg/m2 * albumin g/L) [coef=1.62; p=0.02], pre-operative INR [coef=-2.76; p=0.02) and receiving a non-local transplant [coef=2.49; p=0.02). This new model had better mortality prediction capacity than any previously reported model (X2= 16.4, C-index=0.84, p=0.0008). Previously validated survival predictors for OLT perform very poorly in the NASH-OLT population. In this small cohort, 3 simple preoperative parameters (modified BMI, INR and receiving a non-local transplant) can be used to predict 5-yrs mortality in NASH-OLT recipients with excellent discriminatory capacity. Further validation of the proposed model needs to be done in a larger cohort. CAGAlberta Innovates Health Solutions (AIHS)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".