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Computational modelling shows promise for predicting mortality risk after knee surgery

Some people are at higher risk than others of dying after the common procedure. EHDEN’s modelling was able to identify them 

Physio being performed by healthcare worker on prone patient. Image by ESB Professional via shutterstock
Some people are at higher risk of death following a common knee surgery, and predictive modelling can help identify who might benefit from alternative options. Image by ESB Professional via Shutterstock


The purpose of a recent study by IMI-funded project EHDEN was to predict which patients had a higher risk of death following knee replacement surgery so that they could be offered alternative treatments instead. 

Knee replacement surgery is a safe and cost-effective surgical procedure for treating painful knee osteoarthritis. Although complications following the procedure are rare, some patients are at a higher risk of death and, up to now, there have been little information available to help identify these patients in advance. 

Using the data of nearly 200,000 knee replacement patients, the EHDEN project have developed and validated an easy-to-implement computational model that could help doctors predict which patients are at higher risk, and therefore should be given other treatment options.   

The paper used a combination of data-driven learning and expert knowledge to create a prediction model that uses age, gender, and 10 comorbidities such as heart failure, COPD or gout as inputs. It was externally validated and demonstrated a good, robust performance, suggesting it could be used for evidence-based decision-making prior to surgery and for deciding on prophylaxis for those at high risk. 

Large-scale health data analyses 

EHDEN is already making the large-scale analysis of health data in Europe a reality through a network of over 100 data partners.  Other important results already achieved include investigations into the safety of COVID-19 treatments and vaccines

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Read the full paper here


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