Davis, S.

de Wit, K.

Goodacre, S.

Horner, D.

Hunt, B.J.

Pandor, A.

Stevens, J.W.

English

White Rose Research Online

This is a repository copy of Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury..White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/142015/Version: Accepted VersionArticle:Davis, S. orcid.org/0000-0002-6609-4287, Goodacre, S. orcid.org/0000-0003-0803-8444, Pandor, A. orcid.org/0000-0003-2552-5260 et al. (4 more authors) (2019) Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limbimmobilisation for injury. British Journal of Haematology. ISSN 0007-1048 https://doi.org/10.1111/bjh.15748This is the peer reviewed version of the following article: Davis, S. , Goodacre, S. , Pandor,A. , Horner, D. , Stevens, J. W., Wit, K. and Hunt, B. J. (2019), Decision analysis modelling‐of the effects of thromboprophylaxis for people with lower limb immobilisation for injury. Br J Haematol., which has been published in final form at https://doi.org/10.1111/bjh.15748. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury  Running short title: Thromboprophylaxis for lower limb immobilisation following injury  Authors: Sarah Davis1, Steve Goodacre1, Abdullah Pandor1, Daniel Horner2, John W Stevens1, Kerstin de Wit3, Beverley J Hunt4  Author addresses: 1 ScHARR, University of Sheffield, UK 2 Emergency Department, Salford Royal NHS Foundation Trust, UK  3 Department of Medicine, Hamilton General Hospital, Canada 4 Haemostasis Research Unit, Kings College London, UK Corresponding author: Sarah Davis ScHARR, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK Email: s.davis@sheffield.ac.uk Tel: (+44) 0114 2225209  ORCiD: 0000-0002-6609-4287  Keywords: Thromboprophylaxis, lower limb immobilisation, venous thromboembolism    Pharmacological thromboprophylaxis reduces the risk of symptomatic venous thromboembolism (VTE) in people with lower limb immobilisation due to injury (Zee et al, 2017) but can increase the risk of bleeding. Clinicians therefore need to weigh the risks and benefits of thromboprophylaxis to determine the overall benefit of treatment. Decision-analytic modelling can inform this process by simulating patient management according to alternative strategies to determine the probability of different outcomes with each strategy. Outcomes can then be valued as quality-adjusted life years (QALYs) to determine which strategy is associated with the greatest quality-adjusted life expectancy. We developed a decision-analytic model to compare the management of a cohort of patients with lower limb immobilisation following injury receiving pharmacological thromboprophylaxis to management without this treatment, in terms of 6-month and 5-year outcomes, and lifetime QALYs.  Full details of the methods and data sources are provided in the online appendix. Briefly, a six-month decision tree model was used to estimate for each strategy; the number of patients receiving thromboprophylaxis, the impact of thromboprophylaxis on VTE outcomes (pulmonary emboli (PE) and deep vein thrombosis (DVT)), and the incidence of major bleeds during either thromboprophylaxis or VTE treatment with anticoagulants. Major bleeds were divided into fatal bleeds, non-fatal intracranial haemorrhage (ICH) and other major bleeds. PEs were divided into fatal and non-fatal events. DVTs were divided first into symptomatic and asymptomatic DVTs and then into proximal and distal DVTs. Symptomatic DVTs and non-fatal PEs are assumed to result in 3 months of anticoagulant treatment. A Markov model was then used to extrapolate life-time outcomes including overall survival and ongoing morbidity related to either bleeds or VTE. The health states included within the Markov model capture the risk of post-thrombotic syndrome (PTS) following VTE and the risk of chronic thromboembolic pulmonary hypertension (CTEPH) following PE. The risk of PTS is dependent on whether the DVT is symptomatic and treated or asymptomatic and untreated and also whether the DVT is proximal or distal. The CTEPH state is divided according to whether patients receive medical or surgical management to allow for differential costs and survival between these groups. There is also a post ICH state to capture ongoing morbidity following ICHs. The effectiveness of thromboprophylaxis and the risk of VTE in patients not receiving thromboprophylaxis were estimated from a systematic review of thromboprophylaxis in lower limb immobilisation (Pandor et al, in press). The relative risk of bleeding was estimated from a systematic review of thromboprophylaxis across multiple conditions (National Clinical Guideline Centre に Acute and Chronic Conditions (UK), 2010) and applied to a baseline risk of bleeding from a large primary care database with 16.4 million person years of follow-up (Hippisley-Cox and Coupland 2014). The data sources used to determine the probabilites of subsequent events in the decision tree and Markov models are described in the online appendix. QALYs were estimated by applying estimates of health utility (a measure of health-related quality of life on a scale of zero to one) to life expectancy after each of the events in the model. During the decision tree phase, absolute utility values were applied based on the events occurring, with age dependent general population values applied to those not having any events. A disutility (i.e. a reduction in quality of life) was applied to patients receiving prophylaxis with LMWH to account for the impact of regular injections and a disutility was ;ヮヮﾉｷWS S┌ヴｷﾐｪ VTE デヴW;デﾏWﾐデ デﾗ ヴWaﾉWIデ ヮ;デｷWﾐデゲげ preferences to avoid long-term treatment. During the Markov model phase, patients without long-term sequelae or ongoing symptoms have general population levels of utility which vary with age and those with sequelae or ongoing symptoms have utility multipliers applied which reduce their utility by a fixed proportion relative to the general population level for their age. Details of utilities and life expectancy after each of the model states are provided in the online appendix. Short and long-term clinical outcomes per 100,000 patients are presented in Table 1. The model predicts that the combined rate of serious acute adverse outcomes (ICH or death from VTE or bleeding) would be very low regardless of whether thromboprophylaxis is used (around 1 in 4000). The short-term benefits of thromboprophylaxis lie in reducing the rates of non-fatal PE (225 versus 415 per 100,000), symptomatic DVT (492 versus 907 per 100,000) and asymptomatic DVT (3820 versus 7052 per 100,000). These lead to longer term benefits in terms of reduced risks of PTS (1007 versus 1859 per 100,000) and CTEPH (6 versus 11 per 100,000), with an additional 4 patients in 100,000 surviving to 5 years compared with no thromboprophylaxis. Overall, thromboprophylaxis is estimated to result in 0.015 additional QALYs per patient (95% credible interval [CrI] 0.004 to 0.029). Our findings suggest that thromboprophylaxis increases quality-adjusted life expectancy for people with lower limb immobilisation due to injury, but this is driven by the prevention of long-term complications, particularly the utility loss attributed to PTS and the prevention of PTS in patients with asymptomatic DVT, rather than the risk of short-term adverse outcomes. This may be at odds with the commonly perceived rationale for providing thromboprophylaxis. The effect of thromboprophylaxis upon PTS is based on a number of assumptions and extrapolations, rather than direct evidence, so further research is required to determine whether this potential benefit is realised in practice. The estimates of adverse outcomes provided by our analysis can be used by clinicians to advise patients on the risks and benefits of thromboprophylaxis and support shared decision-making.  Acknowledgements We thank Tim Nokes, Consultant Haematologist and Jonathan Keenan, Consultant Orthopaedic Surgeon, Plymouth Hospitals NHS Trust, UK for their expert advice as members of the expert clinical group.  Competing interests    S Goodacre is chair of the NIHR Health Technology Assessment Programme Clinical Evaluation and Trials Board and a member of the HTA Funding Boards Policy Group. K de Wit reports grants from Bayer, outside the submitted work. The remaining authors have no competing interests other than the grant support received by their respective institutions to deliver the study, as outlined in sources of funding below.   Sources of funding This work was supported by a grant from the United Kingdom National Institute for Health Research (NIHR) Health Technology Assessment programme (project reference 15/187/06). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.  Contributors SD developed the decision analytic model and conducted the analysis. AP and DH conducted the systematic reviews that informed the modelling and JW undertook meta-analysis that informed the modelling. SG, DH, KW, and BH were members of the expert clinical group that informed development of the decision analytic model. All named authors contributed to management of the project and interpretation of the analysis. All named authors contributed to redrafting and approved the final draft of the paper. SG was Chief Investigator for this NIHR HTA project and is guarantor for the paper.   References Hippisley-Cox, J. & Coupland, C. (2014) Predicting risk of upper gastrointestinal bleed and intracranial bleed with anticoagulants: cohort study to derive and validate the QBleed scores. BMJ, 349, g4606. National Clinical Guideline Centre に Acute and Chronic Conditions (UK) (2010) Venous Thromboembolism: Reducing the Risk of Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism) in Patients Admitted to Hospital (NICE Clinical Guidelines, No. 92.). London: Royal College of Physicians (UK). Pandor, A., Horner, D., Davis, S., Goodacre, S., Stevens, J.W., Clowes, M., Hunt, B., Nokes, T., Keenan, J. & Hogg, K. (in press) Thromboprophylaxis for lower limb immobilisation: Systematic review and economic evaluation. Health Technol Assess. Zee AA, van Lieshout K, van der Heide M, Janssen L & Janzing HM (2017) Low molecular weight heparin for prevention of venous thromboembolism in patients with lower-limb immobilization. Cochrane Database Syst Rev, 8, CD006681.          Table 1: Predicted clinical outcomes per 100,000 patients with lower limb immobilisation due to injury  Outcomes at 6 months per 100,000 patients  Outcomes at 5 years per 100,000 patients   Fatal PE Fatal bleed Non-fatal ICH Other major bleeda Non-fatal PE Symptomatic DVT Asymptomatic DVT PTS  PE survivor with CTEPH PE survivor without CTEPH ICH survivor Dead (any cause) No prophylaxis 12 9 5 26 415 907 7052 1859 11 397 5 1133 Prophylaxis 7 12 8 35 225 492 3820 1007 6 215 7 1129 Abbreviations: CTEPH, chronic thromboembolic pulmonary hypertension, DVT, deep vein thrombosis; ICH, intracranial haemorrhage, PE, pulmonary embolism; PTS, post-thrombotic syndrome. a Patients having other major bleeds could also have a DVT or non-fatal PE  

Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury.

Pandor A.

Zee A.A.

Crossref

Decision‐analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury

Davis, Sarah

Goodacre, Steve

Pandor, Abdullah

Horner, Daniel

Stevens, John W

de Wit, Kerstin

Hunt, Beverley J

The University of Manchester - Institutional Repository

Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury

https://eprints.whiterose.ac.uk/142015/5/DVT%20draft%20for%20BJH%20letter_submit.pdf

Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury.

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