Evidence map›Paper›PMID 31161497›Full record

ArticleInternational journal of clinical pharmacy2019

Use of non-formulary high-cost medicines in an Australian public hospital.

Joshua M Inglis, Gillian E Caughey, Sepehr Shakib

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Article in International journal of clinical pharmacy, 2019. 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

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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.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Joshua M InglisDepartment of Clinical Pharmacology, Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia. joshua.m.inglis@gmail.com.ORCID http://orcid.org/0000-0003-0486-9013
Gillian E CaugheyDepartment of Clinical Pharmacology, Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.
Sepehr ShakibDepartment of Clinical Pharmacology, Royal Adelaide Hospital, Port Road, Adelaide, SA, 5000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Clinicians prescribe high-cost medicines for rare diseases and nonapproved indications when conventional therapies have failed. Objective To examine the use of non-formulary high-cost medicines at an Australian public hospital. Methods Retrospective audit of individual patient use applications for nonformulary medicines costing more than $5000 AUD per year at a large tertiary referral hospital in Adelaide, South Australia over a 12-month study period from January 2015 to December 2015. Main outcome measures Total cost of non-formulary high-cost medicines, medication class, indications for use, level of supporting evidence and proposed monitoring outcomes. Results Eighty-seven individual patient use applications were examined. All except one were approved, at a total cost of $1,339,203 AUD. The most common drug classes were anti-CD20 (n = 33, 38%), combined antiretrovirals (n = 10, 11%) and TNF-alpha antagonists (n = 10, 11%). There were 56 indications for these medicines with the majority being inflammatory conditions (n = 52, 60%), followed by infections (n = 14, 16%) and malignancies (n = 14, 16%). Of the first-time individual patient use applications (n = 63), there were 25 applications (40%) that provided a case series as supporting evidence. Approximately half of new individual patient use applications (n = 32) proposed an objective monitoring outcome, but few (n = 13, 21%) contained sufficient information to allow a third party to determine efficacy of the medication. Conclusions Non-formulary high-cost medicines are being used for a broad range of indications based largely on low levels of evidence. Prospective definition of an adequate response to treatment and reporting of these outcomes is required to improve the evidence-base and to aid decision-making for subsequent treatment courses.

Indexed as

Costs and Cost AnalysisFormularies, Hospital as TopicHospitals, PublicHumansOff-Label UsePrescription DrugsRetrospective StudiesSouth AustraliaPrescription DrugsAustraliaFormularyHigh-cost medicinesHospital pharmacy

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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.