Background:

Pharmacological prophylaxis during hospital admission can reduce the risk of acquired blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis and optimise the balance of benefits, risks and costs.

Objectives:

We aimed to identify validated risk assessment models and estimate their prognostic accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for future research.

Design:

We undertook a systematic review, decision-analytic modelling and observational cohort study conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines.

Setting:

NHS hospitals, with primary data collection at four sites.

Participants:

Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy-related admissions.

Interventions:

Prophylaxis for all patients, none and according to selected risk assessment models.

Main outcome measures:

Model accuracy for predicting blood clots, lifetime costs and quality-adjusted life-years associated with alternative strategies, accuracy of efficient methods for identifying key outcomes and proportion of inpatients recommended prophylaxis using different models.

Results:

We identified 24 validated risk assessment models, but low-quality heterogeneous data suggested weak accuracy for prediction of blood clots and generally high risk of bias in all studies. Decision-analytic modelling showed that pharmacological prophylaxis for all eligible is generally more cost-effective than model-based strategies for both medical and surgical inpatients, when valuing a quality-adjusted life-year at £20,000. The findings were more sensitive to uncertainties in the surgical population; strategies using risk assessment models were more cost-effective if the model was assumed to have a very high sensitivity, or the long-term risks of post-thrombotic complications were lower.

Efficient methods using routine data did not accurately identify blood clots or bleeding events and several pre-specified feasibility criteria were not met. Theoretical prophylaxis rates across an inpatient cohort based on existing risk assessment models ranged from 13% to 91%.

Limitations:

Existing studies may underestimate the accuracy of risk assessment models, leading to underestimation of their cost-effectiveness. The cost-effectiveness findings do not apply to patients with an increased risk of bleeding. Mechanical thromboprophylaxis options were excluded from the modelling. Primary data collection was predominately retrospective, risking case ascertainment bias.

Conclusions:

Thromboprophylaxis for all patients appears to be generally more cost-effective than using a risk assessment model, in hospitalised patients at low risk of bleeding. To be cost-effective, any risk assessment model would need to be highly sensitive. Current evidence on risk assessment models is at high risk of bias and our findings should be interpreted in this context. We were unable to demonstrate the feasibility of using efficient methods to accurately detect relevant outcomes for future research.

Future work:

Further research should evaluate routine prophylaxis strategies for all eligible hospitalised patients. Models that could accurately identify individuals at very low risk of blood clots (who could discontinue prophylaxis) warrant further evaluation.

Study registration:

This study is registered as PROSPERO CRD42020165778 and Researchregistry5216.

Funding:

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127454) and will be published in full in Health Technology Assessment; Vol. 28, No. 20. See the NIHR Funding and Awards website for further award information.

Article history

The research reported in this issue of the journal was funded by the HTA programme as award number NIHR127454. The contractual start date was in July 2019. The draft report began editorial review in January 2022 and was accepted for publication in September 2022. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors’ manuscript and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this manuscript.