Using data analytics to proactively treat or even prevent infections in premature babies
Data analytics can support the doctor and nurse to make the best possible decisions. Daniel Vijlbrief Neonatologist UMC Utrecht
Better patient outcomes
UMC Utrecht champions data-driven clinical decision support to provide patients with the optimal care
Around 10 percent of all infants are born premature (<37 weeks of gestational age). Although the survival of premature infants continues to improve, their neurodevelopmental outcome remains a major concern. When a baby is born premature, it is admitted to the Neonatology Department (NICU) of a hospital. During this period, the baby is very vulnerable and prone to infections. To keep track of the health of the baby, it is connected to many devices. No other department in the hospital collects more data than the department of neonatology.
Having collected 10 years of patient data, Universitair Medisch Centrum (UMC) Utrecht, a leading international university medical center, felt the need to put the collected data to good use. Nowadays, machine learning and artificial intelligence (AI) are becoming more and more important for the healthcare sector. That is why UMC Utrecht started the Applied Data Science in Medicine (ADAM) project. With ADAM, the UMC Utrecht uses the well-known approach of large-scale data analysis to ensure that it chooses the treatment that is most beneficial to a specific patient.
The aim is to develop multiple models in SAS in order to better inform parents, provide the best possible healthcare for babies for a better long-term neurodevelopmental outcome and to eventually apply it to other intensive care departments. Manon Benders Professor and Head of Neonatology UMC Utrecht
Preparing the big data for analysis
Big Data for Small Babies was one of four projects within the ADAM program, initiated by the Board of Directors of UMC Utrecht. This particular project proposed the following question: Is it possible to proactively treat or even prevent an infection in premature babies using data analytics? This question needed to be answered within three months.
To ensure that the technical side of the project ran smoothly and timely, UMC Utrecht called in various data and IT specialists. The multidisciplinary Big Data for Small Babies team consisted of two engineers from IT consultant Finaps, SAS consultants and Daniel Vijlbrief, neonatologist and pediatrician at UMC Utrecht. They used SAS Enterprise Guide and SAS Enterprise Miner to make the data accessible (and anonymous). With SAS Visual Analytics they could analyze and visualize the data.
UMC Utrecht – Facts & Figures
of babies had been treated unnecessarily with antibiotics, a study revealed
accuracy in forecasting the presence of the bacteria that causes sepsis, using SAS
An algorithm that prevents unnecessary use of antibiotics
There were many different sources where data was stored. For this purpose, data analysts helped transfer and provide the data to Finaps. This was the first time data was extracted from NICU devices in a retrospective way. As a result, the team developed a smart algorithm, a statistical model with SAS that can support or deny the suspicion of an infection in premature babies, such as sepsis (blood poisoning).
This algorithm can prevent unnecessary use of antibiotics in premature babies. After all, sepsis is an important clinical problem, and UMC Utrecht had limited knowledge of the course of the infection, including the peak moment of the infection. In retrospect, 60 percent of the babies were treated unnecessarily with antibiotics, with all consequences that treatment entails, such as dependence to the medicine and financial implications. Moreover, every baby with sepsis has to be hospitalised one week longer.
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These outcomes required further investigation. If the algorithm-driven model indicated a 75 percent certainty that the result of the test will be negative (or positive), doctors were prepared to consider it. Depending on the error margin, the model that was developed has an accuracy of 90 percent in forecasting the presence of the bacteria that causes sepsis. This is significantly higher than when doctors make predictions based on examination of the patient and present symptoms. In that case, the accuracy is 40 percent.
The new insights give UMC Utrecht the underpinnings it needs to base its decisions on. Prior to the SAS model, it was mainly based on the patient’s examination combined with gut feeling.
“This project shows that analytically driven solutions are capable of solving complex problems in the health sector,” says Nathan van der Lei, Business Engineer at Finaps. “In our opinion, the next step in healthcare is data-driven clinical decision support.”
What is a nice fun fact for data scientists can have huge implications for the sector. Tim Pijl Data Engineer Finaps
본 문서에 나오는 결과는 본 문서에 설명된 특정 상황, 비즈니스 모델, 데이터 입력 및 컴퓨팅 환경에 적합하게 되어 있습니다. 각 SAS 고객의 경험은 고유한 것으로, 비즈니스 및 기술적 변수에 따라 달라집니다. 따라서 모든 서술은 비전형적인 것이라는 점을 고려해야 합니다. 실제 절약, 결과 및 성능 특성은 개별 고객의 구성 및 조건에 따라 달라질 수 있습니다. SAS는 모든 고객이 비슷한 결과를 달성할 수 있다고 보증하거나 진술하지 않습니다. SAS 제품과 서비스에 대한 유일한 보증은 해당 제품 및 서비스에 대한 서면 계약의 보증서에 명시되어 있습니다. 본 문서의 어떠한 내용도 추가 보증을 구성하는 것으로 해석될 수 없습니다. 고객은 SAS 소프트웨어의 성공적인 구현에 따라 합의된 계약적 교환 또는 프로젝트 성공 요약의 일환으로 성공 사례를 SAS와 공유했습니다. 브랜드 및 제품 명칭은 각 기업의 상표입니다.