Clinical predictors of response to recombinant interferon-alpha treatment in patients with chronic non-A, non-B hepatitis (hepatitis C). The Hepatitis Interventional Therapy Group.

Author: AlbrechtJ, BalartL A, BodenheimerH C, CareyW, DavisG L, DienstagJ L, LindsayK, PerrilloR P, SchiffE R, TamburroC

Paper Details 
Original Abstract of the Article :
Chronic non-A, non-B hepatitis (NANBH) is a common and often progressive liver disease. Based on current serological tests, hepatitis C virus (HCV) infection is responsible for most cases. Interferon-alpha (IFN) treatment at a dose of 3 x 10(6) units given three times per week for 24 weeks has been ...See full text at original site
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引用元:
https://doi.org/10.1111/j.1365-2893.1994.tb00062.x

データ提供:米国国立医学図書館(NLM)

Predicting Response to Hepatitis C Treatment

The world of medicine is a vast desert, full of mysteries waiting to be unearthed. And in the field of [hepatology], one of the most challenging puzzles is predicting how patients will respond to treatment for hepatitis C. This study, like a seasoned explorer venturing into the desert, seeks to map out the terrain of patient characteristics that can influence their response to interferon-alpha therapy. Using a [large multicentre treatment trial] as their guide, the researchers embarked on a journey to uncover the hidden clues that determine treatment success. They carefully analyzed [41 pretreatment historical, clinical, laboratory and histological variables] – think of them as ancient scrolls holding secrets to the past. This meticulous process led them to identify [six key variables] as potential predictors of response, like oases in the vast desert of possibilities. However, through a deeper dive, or a more precise exploration of these variables, the researchers discovered that only the [3 x 10(6) dose of rIFN] stood out as a reliable predictor, like a guiding star in the night sky.

The Role of Body Weight and Chronic Persistent Hepatitis

Delving further into the data, the researchers found that in patients receiving the [3 x 10(6) units of rIFN] dose, [body weight, surface area, dose m-2, current ethanol use, serum albumin and the presence of chronic persistent hepatitis (CPH) on entry liver biopsy] were more prevalent in those who responded well to therapy. This finding suggests a fascinating interplay between these variables and treatment success. In a multivariate model, both [CPH and body weight] emerged as significant predictors, like twin peaks rising from the desert floor, signifying a strong correlation. However, the model lacked sensitivity as only [18% of the study group had CPH on liver biopsy], making it difficult to apply widely in a clinical setting.

Early Response: The Key to Success

Through this journey into the complexities of hepatitis C treatment, one key discovery shines like a beacon in the desert: early response to therapy is the most reliable predictor of success. Just as a traveler in the desert can anticipate a clear path by observing the early signs of an approaching oasis, doctors can use this knowledge to optimize patient care. By carefully monitoring [serum ALT levels] during the first [12-16 weeks] of treatment, clinicians can identify patients who are likely to benefit from continued therapy and those who may be better served with alternative treatment strategies.

Dr.Camel's Conclusion

This study provides valuable insights into the complex world of hepatitis C treatment. Like a skilled navigator guiding a caravan across the vast expanse of the desert, understanding the factors that influence treatment response allows us to optimize patient care and minimize the burden of this challenging disease. The discovery that early response is a key predictor of success provides a practical tool for clinicians to navigate this journey with their patients.

Date :
  1. Date Completed 1996-10-17
  2. Date Revised 2019-11-01
Further Info :

Pubmed ID

8790560

DOI: Digital Object Identifier

10.1111/j.1365-2893.1994.tb00062.x

Related Literature

SNS
PICO Info
in preparation
Languages

English

Positive IndicatorAn AI analysis index that serves as a benchmark for how positive the results of the study are. Note that it is a benchmark and requires careful interpretation and consideration of different perspectives.

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