PMID- 30596210
OWN - NLM
STAT- MEDLINE
DCOM- 20200403
LR  - 20200403
IS  - 1179-2027 (Electronic)
IS  - 1170-7690 (Print)
IS  - 1170-7690 (Linking)
VI  - 37
IP  - 3
DP  - 2019 Mar
TI  - Evaluating Cost-Effectiveness Models for Pharmacologic Interventions in Adults 
      with Heart Failure: A Systematic Literature Review.
PG  - 359-389
LID - 10.1007/s40273-018-0755-x [doi]
AB  - BACKGROUND: Heart failure (HF) is a well-recognized public health concern and 
      imposes high economic and societal costs. Decision analytic models exist for 
      evaluating the economic ramifications associated with HF. Despite this, studies 
      that appraise these modelling approaches for augmenting best-practice decisions 
      remain scarce. OBJECTIVE: Our objective was to conduct a systematic literature 
      review (SLR) of published economic models for the management of HF and describe 
      their general and methodological features. METHODS: This SLR employed a 
      combination of relevant search terms associated with HF, which were used in a 
      number of databases, including MEDLINE, Embase, the National Health Service 
      Economic Evaluation Database, Cost-Effectiveness Analysis Registry, ScHARR Health 
      Utilities Database and Cochrane Library Database. A number of model features 
      (i.e. model structure, specification, outcomes assessed, scenario and sensitivity 
      analysis, key model drivers) were extracted and subsequently summarized. RESULTS: 
      Of 64 publications retained, a selection of modelling approaches were identified, 
      including Markov (n = 28), trial-based analytic (n = 22), discrete-event 
      simulation (n = 6), survival analytic (n = 7) and decision-tree modelling (n = 1) 
      approaches. The bulk of publications employed either a cost-utility (n = 27) or 
      cost-effectiveness (n = 36) analysis and evaluated more than one study outcome, 
      which typically included overall costs (n = 59), incremental cost-effectiveness 
      ratios (n = 55), life-years gained (n = 48) and willingness-to-pay thresholds 
      (n = 37). Most publications focused on patients with chronic HF (n = 40) and used 
      New York Heart Association (NYHA) disease classifications to categorize patients 
      and determine disease severity. Few (n = 19) publications documented the use of 
      hospitalization states for modelling patient outcomes and associated costs. A 
      quality assessment of the included publications revealed most articles 
      demonstrated reasonable methodological value. CONCLUSIONS: We identified numerous 
      decision analytic modelling approaches for evaluating the cost effectiveness of 
      pharmacologic treatments in HF. A Markov cohort model approach was most commonly 
      used, and most models relied on NYHA classes as a proxy of HF severity, disease 
      progression and prognosis.
FAU - Di Tanna, Gian Luca
AU  - Di Tanna GL
AD  - Economic Modelling Centre of Excellence, Amgen (Europe) GmbH, Rotkreuz, 
      Switzerland.
FAU - Bychenkova, Anna
AU  - Bychenkova A
AD  - Global Health Economics, Amgen Inc, Uxbridge, UK.
FAU - O'Neill, Frank
AU  - O'Neill F
AD  - Global Health Economics, Amgen Inc, Uxbridge, UK.
FAU - Wirtz, Heidi S
AU  - Wirtz HS
AD  - Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320-1799, USA.
FAU - Miller, Paul
AU  - Miller P
AD  - Miller Economics Ltd, Biohub Alderley Park, Alderley Edge, UK.
FAU - O Hartaigh, Briain
AU  - O Hartaigh B
AD  - Envision Pharma Group, Southport, CT, USA.
FAU - Globe, Gary
AU  - Globe G
AD  - Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320-1799, USA. 
      gglobe@amgen.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Systematic Review
PL  - New Zealand
TA  - Pharmacoeconomics
JT  - PharmacoEconomics
JID - 9212404
MH  - Adult
MH  - Cost-Benefit Analysis
MH  - *Decision Support Techniques
MH  - Decision Trees
MH  - Disease Progression
MH  - Heart Failure/*drug therapy/economics
MH  - Humans
MH  - Markov Chains
MH  - *Models, Economic
PMC - PMC6386015
COIS- CONFLICT OF INTEREST: GL Di Tanna, A Bychenkova, H Wirtz, and G Globe are 
      employees of Amgen and may hold corporate stock in Amgen. H Wirtz also holds 
      corporate stock in Teva Pharmaceutical Industries Ltd. F O'Neill was an employee 
      of Amgen until April 2018. P Miller has previously consulted for AstraZeneca, 
      GSK, Pfizer, Novartis, Roche, Chiesi, and Bayer. B O Hartaigh was an employee of 
      Curo, part of the Envision Pharma Group, when the study was conducted, who was 
      contracted by Amgen to provide editorial support in the preparation of this 
      manuscript. STATEMENT OF HUMAN RIGHTS AND/OR ANIMALS: For this type of study, 
      formal consent is not required. DATA AVAILABILITY: Data sharing is not applicable 
      to this article as no datasets were generated or analysed during the current 
      review.
EDAT- 2019/01/01 06:00
MHDA- 2020/04/04 06:00
PMCR- 2018/12/31
CRDT- 2019/01/01 06:00
PHST- 2019/01/01 06:00 [pubmed]
PHST- 2020/04/04 06:00 [medline]
PHST- 2019/01/01 06:00 [entrez]
PHST- 2018/12/31 00:00 [pmc-release]
AID - 10.1007/s40273-018-0755-x [pii]
AID - 755 [pii]
AID - 10.1007/s40273-018-0755-x [doi]
PST - ppublish
SO  - Pharmacoeconomics. 2019 Mar;37(3):359-389. doi: 10.1007/s40273-018-0755-x.