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Refined Hepatic Grading System Improves Risk Stratification of Long-Term Outcomes in the Patients Developing Chronic Gvhd

2016· article· en· W2979532806 on OpenAlexaff
Saud Alhayli, Elizabeth Shin, Wilson Lam, Uday Deotare, Fotios V. Michelis, Santhosh Thyagu, Auro Viswabandya, Jeffrey H. Lipton, Hans A. Messner, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInternal medicineGrading (engineering)GastroenterologyOverlap syndromeCommon Terminology Criteria for Adverse EventsBilirubinRetrospective cohort studyOncologyAdverse effectDiseaseBiology

Abstract

fetched live from OpenAlex

Abstract Background: Chronic GVHD (cGVHD) is a syndrome with diverse clinical features resembling autoimmune disorders. cGVHD affects long-term outcomes of allogeneic HCT, resulting in significant morbidity and mortality. Scoring the severity of cGVHD has been proposed with recent changes in the grading system of cGVHD based on the NIH consensus criteria (NCC) in 2015. Grading of liver GVHD is based on the severity of liver enzyme elevation both in NCC in 2005 and 2015. The cutoff of liver enzyme profiles for liver GVHD grading is arbitrarily determined but never been validated. In this study we attempted to evaluate 3 grading systems of hepatic parameters used in 1) NCC 2005, 2) NCC 2015, and 3) the common terminology criteria for adverse events (CTCAE) version 4.0. We also have adopted binary recursive partitioning (rpart) to define the optimal cut-off that provides the best risk stratification to overall survival (OS) after development of cGVHD. Methods: A retrospective review was conducted to compare the hepatic grading systems using liver enzyme parameters used in NCC 2005, NCC2015 and CTCAE v4.0. We reviewed 336 patients who developed cGVHD after allogeneic HCT performed between 2002 and 2014. Long-term outcomes including OS and non-relapse mortality (NRM) after the occurrence of cGVHD were analyzed using the 3 hepatic grading systems. Using rpart, we determined the optimal value for each component of the liver enzyme profile (i.e. AST, ALT, ALP and bilirubin level) was which could identify the best risk stratification of OS. A refined hepatic grading system was generated based on the cut off of ALP and bilirubin level proposed by rpart method, which divided the patients into three groups: low (bilirubin <14 mmol/L and ALP < 146 IU/L), intermediate (bilirubin level ≥14 mmol/L or ALP ≥ 146 IU/L) and high risk (both) . OS and NRM were also compared according to the refined hepatic grading system. Results: Out of 336 patients, 181 had liver involvement of cGVHD. The 3 year OS rate was 74.9% (66.7-81.3%) in the group developing liver GVHD, while that was 67.0% (57.7-74.7%) in the group without liver GVHD (p=0.629). There is no difference of non-relapse mortality (NRM) between patients with or without liver GVHD (14.4% vs. 17.2%; p= 0.661). In the patients developing liver GVHD, 3 hepatic grading systems were evaluated with respect to OS and NRM after onset of cGVHD. None of the 3 grading systems could stratify the patients statistically according to OS (p=0.211 for NCC 2005; p=0.423 for NCC 2015; p=0.461 for CTCAE4.0) or to NRM (p=0.615 for NCC 2005; p=0.327 for NCC 2015; p=0.941 for CTCAE v4.0). Using rpart, we found that 1) bilirubin level ≥14 mmol/L (p=0.01) and 2) ALP ≥ 146 IU/L (p=0.059) are associated with shorter OS, 2) AST and ALT levels were not associated with OS or NRM. A refined hepatic grading system was generated with assignment of a score to each risk factor. A score of 1 was assigned to bilirubin ≥14 mmol/L and ALP ≥ 146 IU/L, each. Total score was calculated with risk score 0 (n=54, 30.0%), risk score 1 (n=85, 57.2%) and risk score 2 (n=41, 22.8%). This hepatic grading system could stratify the patients according to their OS (p=0.015): 89.6% in low vs. 71.8% in intermediate vs. 58.0% in high risk group after onset of cGVHD. Then, we have applied the refined hepatic grading system into all 336 patients developing cGVHD regardless of organ involvement. As expected, the hepatic grading system can stratify 336 patients according to OS: 79.6% in low vs. 65.4% in intermediate vs. 53.9% in high risk group after onset of cGVHD (p=0.001); according to NRM: 11.9% in low vs. 17.2% in intermediate vs. 26.1% in high risk group after onset of cGVHD (p=0.089). Multivariate analysis was performed including 9 covariates including refined hepatic grading system, liver involvement of cGVHD, cGVHD subtype, cGVHD onset < 5 months, age (by decade), platelet counts, HLA match, gender mismatch, and T cell depletion, and confirmed that the refined hepatic grading system is an independent prognostic factor for OS (p=0.003, HR 0.491) in addition to cGVHD onset <5 months and HLA match. Conclusions: None of hepatic grading systems could stratify the patients according to OS/NRM after development of cGVHD. The refined hepatic grading system using bilirubin ≥14 mmol/L and ALP ≥ 146 IU/L at onset of cGVHD defined by the rpart method, could improve risk stratification of the patients developing cGVHD. Disclosures No relevant conflicts of interest to declare.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.271
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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