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Record W4386271964 · doi:10.1111/jgh.16334

How can we improve surveillance system for alcohol‐associated hepatocellular carcinoma?

2023· letter· en· W4386271964 on OpenAlexaboutno aff
K. Kim, Hye Won Lee, Sang Hoon Ahn

Bibliographic record

VenueJournal of Gastroenterology and Hepatology · 2023
Typeletter
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatocellular carcinomaAlcoholic liver diseaseCirrhosisLiver diseaseInternal medicineLiver transplantationFatty liverGastroenterologySteatosisAlcoholic hepatitisDiseaseTransplantation

Abstract

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Promoting compliance with hepatocellular carcinoma (HCC) surveillance in alcoholic patients is challenging. As Jacob et al.1 stated, alcohol is one of the leading causes of chronic liver diseases, which range from the initial stages of steatosis to hepatitis, cirrhosis, and HCC. With the rise in global adult per-capital alcohol consumption, reducing HCC mortality is an important. Currently, the American Association for the Study of Liver Diseases (AASLD) and European Association for the Study of Liver Diseases (EASL) recommend screening for HCC in patients with liver cirrhosis of Child-Pugh A, B, or C who are candiates for transplantation.2, 3 The recommended screening frequency is every 6 months using ultrasound with or without analysis of the alpha-fetoprotein level. Surveillance can lead to earlier detection of HCC, thereby lowering the mortality rate. At the time of diagnosis, most patients with alcohol-associated HCC have advanced disease, and they are frequently identified outside the surveillance program. This might be associated with late referrals, suboptimal compliance with surveillance, insufficient disease awareness, provider perceptinos of the likelihood of patient complaince, and alcohol dependence. It is important to estimate the risk of HCC in patients with alcohol-associated liver diseases. The risk factors for alcohol-associated HCC include the level of alcohol consumption, female sex, smoking, concomitant liver disease, obesity, diabetes, and genetics. The genetic polymorphisms associated with alcohol-associated HCC include those in patatin-like phospholipase domain containing 3 variants in the transmembrane 6 superfamily 2, and membrane-bound O-acyltransferase domain containing 7.4 Incorporating these clinical and genetic risk factors in risk scores would benefit patients undergoing screening for HCC. As reviewed by Jacob et al.,1 several risk scores for HCC are available for use in cirrhotic patients. The ADRESS-HCC model predicts the 1-year probability of HCC in cirrhotic patients. This model was developed using a national liver transplant waitlist cohort (n = 17,124 patients); its c-index was 0.691 in the validation set.5 The Toronto HCC risk index (THRI) in cirrhotic patients showed good c-index of 0.77 in the external validation set.6 The aMAP risk score was developed based on 11 cohorts, and showed strong performance (c-incex of 0.82–0.87).7 However, the cohorts were composed mainly of patients with chronic viral hepatitis: only one included patients with non-viral hepatitis. Furthermore, the non-viral hepatitis cohort was composed, primarily, of patients with nonalcoholic fatty liver disease, and excessive alcohol was considered an additional risk factor in only 11% of the patients. The aMAP score yielded a c-index of 0.85 in the non-viral hepatitis cohort, which was validated in a study involving of 269 patients with alcohol-assocaited cirrhosis (c-index 0.82–0.83). Further validation in a greater number of patients with alcohol-associated cirrhosis is needed.8 The above-mentioned scores are useful for assessing the HCC risk in cirrhotic patients. However, these scores were developed for mixed etiologies, and do not incorporate genetic risk factors. Further studies should focus on developing alcohol-associated HCC risk scores, which could potentially include genetic risk factors. According to a systematic review, a small proportion of patients with cirrhosis undergo the recommended surveillance (pooled surveillance rate 18.4%).9 Pharmacological psychosocial, and combined strategies for maintaining alcohol abstinence are used in primary care clinics. Mail-based outreach has been shown to be effective in multiple randomized clinical trials (RCT). In a recent RCT by Singal et al.,10 1436 patients in the mail-based outreach arm and 1436 in the visit-based surveillance arm were compared, and all etiologies of liver disease were included. In the mail-based outreach arm, reminder telephone calls were made to patients who did not respond within 2 weeks. The mail-based outreach arm had a significantly higher recommended surveillance rate (35.1% vs. 21.9%), a lower no surveillance rate (29.8% vs. 43.5%) and a greater proportion of time covered by surveillance (41.3% vs. 31.0%) than the visit-based survellance arm. Other RCTs using the mail-based outreach method revealed higher surveillance rates in the mailed outreach arm, compared to the usual-care arm.11, 12 Other types of interventions include patient education, clinical reminders for providers, HCC surveillance compliance reporting, provider education, chronic disease management software, and outreach nurses. Although the mail-based outreach method has been demonstrated to be effective in several RCTs, the surveillance rate needs to be increased further. Patient education and chronic disease management software have been evaluated in RCTs, but the results are controversial; therefore, more RCTs are needed. Additionally, all the above-mentioned methods were developed and validated for chronic liver disease of any cause. Therefore, they need to be validated in patients with alcohol-associated cirrhosis. Implementation of multiple strategies to enhance the surveillance rate will be challenging. For instance, a digital intervention using a smartphone application is a possibility, because such a system used in the management of other chronic liver diseases.13 In conclusion, effective management alcohol-associated liver diseases is vital. It is important to identify patients at high risk for alcohol-associated HCC and to assess the HCC risk continuously in these patients. Moreover, increasing the rate of compliance with surveillance is an important task, which will require the development of novel methods as well as further research studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.010
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0020.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0200.008

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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