A study published on bmj.com yesterday revealed a new risk prediction tool that can identify patients at high risk of serious blood clots (known as venous thromboembolism) who might require preventative treatment.
Based on simple variables, which patients are likely to know, the tool, which can be found here, could be easily integrated into GP computer systems to assess patients’ risk prior to hospital admission, long haul flights, or starting medications that carry an increased clotting risk.
Venous thromboembolism is preventable yet remains a common and potentially lethal disease, claiming over 25,000 lives yearly in England alone. Of those who survive, almost a third experience long-term effects.
The National Institute for Health and Clinical Excellence (NICE) provided guidance to encourage detecting high-risk patients and effective use of preventative measures in 2010; however, validated risk prevention algorithms suitable for use in primary care do not exist.
Researchers from the University of Nottingham decided to develop and validate a new clinical risk prediction algorithm (QThrombosis) designed to predict a person’s risk of developing a potentially fatal blood clot.
The researchers utilized data from 563 general practices in England and Wales, studying over 3.5 million patients between the ages of 25 to 84 years with no previous history of blood clots. Patient’s medical records or death certificates identified first cases of venous thromboembolism (either deep vein thrombosis or pulmonary embolism) at one and five years.
The rate of venous thromboembolism was approximately 15 cases per 10,000 person years of observations.
The study reveals, that the risk of venous thromboembolism in both, men and women, increased with increasing age, body mass index and daily cigarette consumption. Elevated risks were also found among those with varicose veins, congestive heart failure, chronic kidney disease, chronic lung disease, inflammatory bowel disease, and any cancer. Other risk factors include patients who have been admitted to hospital within the last six months and those taking antipsychotic drugs, oral contraceptives, HRT or tamoxifen.
In a concluding statement, the authors write,
“We have developed and validated a new risk prediction model which identifies patients at high risk of venous thromboembolism. The algorithm is based on simple clinical variables that the patient is likely to know or which are routinely recorded in GP computer systems. The algorithm could be integrated into GP computer systems and used to risk assess patients prior to hospital admission or prior to the initiation of medication which might increase risk of venous thromboembolism.”
They point out the need for further research to assess how best to use the algorithm and whether, upon implementation, it has any impact on health outcomes.
Written by Petra Rattue