Summary
The intensive care units (ICUs) that provide comprehensive life-saving care for critically ill patients, face multiple challenges in day-to-day operations and management. One important example is the increasing demand for critical care for patients with severe conditions, which limits the capacity of the ICU. This means, for example, lack of available beds for patients or excessive workloads for medical staff and hospital personnel, which leads to delays in ICU admission and ultimately increased morbidity and mortality. The COVID-19 pandemic since the early of 2020 and in subsequent months has made this even more evident, with creating urgent need for space, supplies, and medical personnel, and placing significant strain on healthcare systems worldwide.
The rapid increase in critical care data volumes, driven by the digitization of healthcare in recent years, has created numerous opportunities to address those challenges. Data analytics has proven beneficial in various areas of medicine. Utilizing advanced data science methods, we are conducting research in several projects using available critical care data to gain valuable information from them and potentially improve the quality of care. This is achieved through providing optimal care for patients with critical illnesses and better planning of resources in the ICU.
Predictive Modeling of ICU Length of Stay
Patient length of stay in the ICU is an important process indicator that measures the quality of care in the ICU. While a longer ICU stay is associated with higher care costs and resource utilization, early ICU discharge potentially causes medical complications, increases the risk of readmission to the ICU, or even leads to a higher mortality rate. A proper estimation of patient length of stay in the ICU assists the healthcare management in allocating appropriate resources and better planning for the future.
The goal is to develop a predictive model for length of stay and readmission of patients admitted to the ICU. The model incorporates diagnostic data from the patient's initial conditions, observations, and medical measurements. It utilizes machine learning methods to predict patient length of stay in the ICU and estimate the likelihood of readmission to the ICU in the event of poor clinical care.
Involved Team Members: Lars Hempel, Ulrike Klotz, Sina Sadeghi, Toralf Kirsten
Cooperation Partners: Sven Bercker (Leipzig University Medical Center)
Heart Failure Predictions using NT-proBNP
Heart failure is a prevalent health problem associated with high morbidity and mortality and consequently rising healthcare costs. Predictive models for heart disease are therefore of great importance to the healthcare system, as they assist physicians to diagnose such life-threatening conditions at earlier stages and adapt their treatment accordingly. The models estimate the likelihood of heart disease occurrence in individuals based on laboratory measurements as risk indicators as well as demographic data. The model also provides insight to physicians and can suggest further measures for patients as needed, such as electrocardiography.
A correlation between NT-proBNP protein levels and heart failure and atrial fibrillation has been demonstrated in the literature. The goal of this project is to employ machine learning to model this correlation between the NT-proBNP protein levels and the likelihood of heart failure and atrial fibrillation.
Involved Team Members: Navid Shekarchizadeh, Masoud Abedi, Sina Sadeghi, Toralf Kirsten
Cooperation Partners: Samira Zeynalova (IMISE, Leipzig University), Frank Meineke (IMISE, Leipzig University), Markus Löffler (IMISE, Leipzig University)
