Distinct Treatment Response and Liver Transplant-Free Survival of Autoimmune Hepatitis in the Elderly
Bibliographic record
Abstract
Introduction: Autoimmune hepatitis has been previously thought to be a disease predominantly of young female patients; therefore, information regarding autoimmune hepatitis in the elderly population is scare. In this study, we aimed to examine the difference in the clinical and biochemical features at the time of presentation and treatment responses between elderly and younger patients with autoimmune hepatitis. Methods: We analyzed 162 patients with autoimmune hepatitis (159 type 1 and 3 type 2). One hundred twenty-seven patients were females (78%) with a mean age of 49±18 years (range: 17-89). Fifty-four patients (33%) were older than 60 years at diagnosis (elderly). Results: Elderly patients were more frequently females (89 vs. 73%, P=0.03). At diagnosis there were no significant differences (P=0.5) in the frequency of concomitant autoimmune disease (i.e., ulcerative colitis, autoimmune thyroiditis) among elderly and younger patients. Also, serum ALT (P=0.3), AST (P=0.5), ALP (P=0.3), bilirubin (P=0.5), INR (P=0.5), and IgG (P=0.3) at diagnosis were similar among elderly and younger patients. There were no difference in the frequency of plasma cell infiltration (P=0.4) and cirrhosis at diagnosis (P=0.2) among elderly and younger patients. All patients received initial treatment with prednisone and azathioprine (106 patients), or prednisone alone (56 patients). Incomplete response was more frequent in elderly patients (42 vs. 25%, P=0.03), but there were no difference in the frequency of treatment failure (P=0.6), or relapse during treatment tapering or withdrawal (P=1.0) among elderly and younger patients. Finally, liver transplant-free survival time was worse in elderly patients than younger patients (17±2 vs. 23±1 years, P=0.04; Figure 1).FigureConclusion: Clinicians should consider autoimmune hepatitis in the differential diagnosis of elderly patients with chronic liver disease as up to one-third of these patients are older than 60 years. Moreover, elderly patient have a higher frequency of suboptimal response to treatment that may explain the worse liver transplant free-survival time. Further studies with aged matched controls to corroborate the impact on survival in this population are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".