Evidence map›Paper›PMID 27777855›Full record

ArticleSpringerPlus2016

Telbivudine for the treatment of chronic hepatitis B in HBeAg-positive patients in China: a health economic analysis.

S Banerjee, P Gunda, R F Drake, K Hamed

Abstract read
In one paragraph

Article in SpringerPlus, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

S BanerjeeNovartis Healthcare Pvt. Ltd., Hyderabad, India.
P GundaNovartis Healthcare Pvt. Ltd., Hyderabad, India.
R F DrakeNovartis Pharma AG, Basel, Switzerland.
K HamedNovartis Pharmaceuticals Corporation, East Hanover, NJ USA.ORCID 0000-0003-1896-9736

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNucleos(t)ide analogs (NUCs) are the standard of care for chronic hepatitis B (CHB). The present analysis aimed to determine the cost effectiveness of NUCs in Chinese healthcare settings.

methodsA Markov model was used to simulate two therapeutic strategies for a hypothetical patient cohort diagnosed with hepatitis B e antigen-positive CHB, unwilling or unable to receive interferon therapy, and about to start treatment with any NUC. The first strategy included NUC monotherapy without sequencing (telbivudine [LDT], entecavir [ETV], tenofovir [TDF], lamivudine [LAM], adefovir dipivoxil [ADV], and combination therapies of either LDT and ADV or LDT and TDF, followed by best supportive care [BSC]). The second strategy included sequential therapies of individual NUCs: LAM → ADV, ADV → LAM, LDT → ADV, and ETV → ADV, followed by BSC. The analysis included two scenarios: with and without costs due to nephrotoxicity. Renal impact was quantified as costs alone, without consideration for quality of life decrements.

resultsWhen renal impact was not considered, without treatment sequencing, LDT was cost effective compared with other NUCs. Amongst the strategies with sequencing, LDT → ADV was cost effective. The results were similar when renal impact was considered. However, LDT strategy demonstrated better cost effectiveness. In probabilistic sensitivity analysis, in both scenarios, LDT → ADV sequence was cost effective with 51 % probability even at willingness to pay of $20,000.

conclusionUse of LDT, as compared with other NUCs, is cost effective in CHB treatment in Chinese healthcare settings. Considering the detrimental renal impact, overall costs for all treatment options were increased. However, the increase for LDT was comparatively small.

Indexed as

Chronic hepatitis BCost effectivenessRenal impairmentTelbivudine

Identifiers

PMID27777855
PMCPMC5052247

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.