Improving mortality models in the ICU with high-frequency data.

Background: Assessment of the performance of Intensive Care Units (ICU) is of vital importance for an effective healthcare system. Such assessment ensures that the limited resources of the healthcare system are allocated where they are most needed. Severity scoring systems are employed for this purpose and improving these systems is a continuing area of research which has focused on the use of more complex techniques and new variables.

Objectives: This paper investigates whether scoring systems could be improved through use of metrics which better summarise the high frequency data collected by automated systems for patients in the ICU.

Methods And Data: 3128 admissions to the Gold Coast University Hospital ICU are used to construct three logistic regressions based on the most widely used scoring system (APACHE III) to compare performance with and without predictors leveraging available high frequency information. Performance is assessed based on model accuracy, calibration, and discrimination. High frequency information was considered for existing pulse and mean arterial pressure physiology fields and resulting models compared against a baseline logistic regression using only APACHE III physiology variables.

Results: Model discrimination and accuracy were better for models which included high frequency predictors, with calibration remaining good in all cases. The most influential high frequency summaries were the number of turning points in a patient's mean arterial pressure or pulse in the first 24 h of ICU admission.

Conclusions: The findings indicate that scoring systems can be improved by better accounting for high frequency data.