Evaluation of ICU Risk Models Adapted for Use as Continuous Markers of Severity of Illness Throughout the ICU Stay*

Abstract

Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores all have relatively high discrimination for ICU mortality when used continuously; Discharge Readiness Scores tended to have slightly higher area under the receiver operating characteristic curves for each endpoint. These findings validate the use of these models on a population level for continuous risk adjustment in the ICU, although Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment appear slower to respond to improvements in patient status than Discharge Readiness Scores, and Discharge Readiness Scores may reflect physiologic improvement from interventions, potentially underestimating risk.

Brief introduction of medical database and data mining technology in big data era

“…It is an era product of the development of medical science to a fairly high level. There are many difficult problems involved in critical medicine, including the application and management of noninvasive ventilation, the rational use of antibiotics, the implementation of nutritional assessment and nutritional support, the indications for analgesia and sedatives, and the scope of application of the ICU risk assessment model 83 . Philips Healthcare is a leading provider of ICU equipment and services, offering a teleICU service called the eICU program.…”

“…There are many difficult problems involved in critical medicine, including the application and management of noninvasive ventilation, the rational use of antibiotics, the implementation of nutritional assessment and nutritional support, the indications for analgesia and sedatives, and the scope of application of the ICU risk assessment model. 83 Philips…”

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic health care data from MIMIC-III and Bristol, UK.

“…These tools range from criteria to evaluate discharge readiness [22,42], to guidelines for discharge planning and education [6]. Additionally, a number of risk models have been developed to predict adverse outcomes following ICU discharge [11][12][13]15,43]. In particular Badawi and Breslow demonstrated that mortality and readmission should be modelled independently as separate outcomes [12].…”

“…This work considers the ICU discharge process, which has accessible data from routine collection and requires a simple but important binary decision that could benefit from an evidence-based approach. Indeed, several statistical models have recently been developed to predict the risk of adverse events following intensive care discharge [11][12][13][14][15]. Such risk models are invaluable tools for clinical decision making and, in the context of ICU discharge, can provide information with which to plan complex deescalations of care.…”

Impact of COVID-19 pandemic on severity of illness and resources required during intensive care in the greater New York City area

“…The Philips eICU program aggregates data from the community of eICUs for routine benchmarking, data analytics and collaborative research 3,4 . This system provides unique access to granular, clinical data in real time, including ventilator use and dynamic severity of illness scoring [5][6][7] .…”