Study launched to assess blood clot prediction model for paediatric patients

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Monroe Carell Jr Children’s Hospital at Vanderbilt (Nashville, USA) has launched a study to determine the impact of a predictive model for identifying paediatric patients at risk for developing venous thromboembolism (VTE).

The study uses advanced predictive analytics to inform medical teams of patients at risk for blood clots before they happen.

“Hospital-associated blood clots are an increasing cause of morbidity in paediatrics,” said the study’s principal investigator, Shannon Walker, clinical fellow of paediatric haematology-oncology at Children’s Hospital.

While these events are more rare among children than they are among adults, Walker noticed that blood clot development was on the rise.

“The reason children get blood clots is very different from adults,” said Walker, who worked with mentors Allison Wheeler, assistant professor of paediatrics and pathology, microbiology, and immunology, and C Buddy Creech, MD, director of the Vanderbilt Vaccine Research Program and associate professor of paediatric infectious diseases.

“There was no standardised protocol for preventing clots in paediatric patients. As we noticed that the rate of blood clots was going up and recognised that the adult strategy was not going to work for our patients, we wanted to look at each patient’s individual risk factors and see how we could focus our attention on targeted blood clot prevention.”

The study, set to be published in Pediatrics, describes how the team built and validated a predictive model that can be automated to run within the electronic health record of each patient admitted to the hospital.

The model includes 11 risk factors and was based on an analysis of more than 110,000 admissions to Children’s Hospital and has been validated on more than 44,000 separate admissions.

Currently the team is studying using this model along with targeted intervention in the clinical setting in the CLOT (Children’s likelihood of thrombosis) trial.

The prediction model is used in this way: every child admitted to the hospital has a risk score calculated. The patients are randomised, so in half of the patients, elevated scores are reviewed by a haematologist, and then discussed with each patient’s medical team and family to determine a personalised prevention plan. All patients, regardless of randomisation, continue to receive the current standard of care.

“We are not utilising a one-size-fits-all plan,” Walker said. “This is an extra level of review allowing for a very personalised recommendation for each patient with an elevated score. Each day the score is updated, so as risk factors change, the scores change accordingly.

“We are, in real time, assessing the use of this model as a clinical support tool. We saw a clinical opportunity of something we could improve and have moved forward with building the model—to identify high-risk patients and are currently performing the CLOT trial, which will run through the end of the year.”

Walker’s study was possible with the help of the Advanced Vanderbilt Artificial Intelligence Laboratory, or AVAIL. Only in its second year, the programme is leading the way supporting artificial intelligence tools at Vanderbilt University Medical Center through project incubation and curation, including facilitating clinical trials to assess their effectiveness.


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