Patients admitted to the intensive care unit (ICU) suffer from various diseases and comorbidities. Only critically ill patients that require continuous care, organ support, or monitoring are admitted to the ICU.
Frequent clinical examination and evaluation plays a crucial role in in the ICU, as the severity of disease can rapidly change. Numerous variables are being collected continuously to inform healthcare providers about the disease state of patients in this dynamic environment. Consequently, the ICU generates an enormous amount of variables which, besides opportunities, brings challenges in handling and utilizing this data to its full potential. Two challenges, or opportunities for improvement, arise; 1) “How can these variables be measured in the most optimal way?” and 2) “How can these variables be used to inform and guide healthcare providers, patients, and their families about disease state and prognosis?”
This thesis of Eline Cox takes a first step in addressing the above challenges by exploring the current state of outcome prediction and improving the beside measurements of used variables. It became clear that, despite the efforts of many researchers and healthcare providers, predicting outcomes in ICU patients is challenging. Numerous prognostic models have been developed to enhance prediction accuracy, yet their integration into clinical practice remains limited.
Moreover, innovative approaches for optimizing bedside data using ultrasonography and other non-invasive techniques, were presented. The potential of new measurements and effective data collection at the bedside may help identify patients at risk, guide decision-making, and improve predictions about disease course or patient outcomes.
