Data fusion models for detection of vital-sign deterioration in acutely ill patients - Khalid, S (2014)
Vital signs can indicate patient deterioration prior to adverse events such as cardiac arrest, emergency admission to the intensive care unit (ICU), or death. However, many adverse events occur in wards outside the ICU where the level of care and the frequency of patient monitoring are lower than in the ICU. This thesis describes models for detection of deterioration in acutely ill patients in two environments: a step-down unit in which patients recovering from an ICU stay are continuously monitored, and a general ward where patients are intermittently monitored following upper gastrointestinal cancer surgery. Existing data fusion models for classification of vital signs depend on a threshold which defines a “region of normality”. Bradypnoea (low breathing rate) and bradycardia (low heart rate) are relatively rare, and so these two types of abnormalities tend to be misclassified by existing methods. In this thesis, techniques for selecting a threshold are described, such that the classification of vital-sign data is improved. In particular, the proposed approach reduces the misclassification of bradycardia and bradypnoea events, and indicates the type of abnormality associated with the deterioration in a patient’s vital signs. Patients recovering from upper gastrointestinal (GI) surgery have a high risk of emergency admission to the ICU. At present in the UK, most intermediate and general wards outside the ICU depend on intermittent, manual monitoring using track-and-trigger systems. Both manual and automated patient monitoring systems are reported to have high false alert rates. The models described in this thesis take into account the low monitoring frequency in the upper GI ward, such that the false alert rate is reduced. In addition to accuracy, early detection of deterioration is a highly desirable feature in patient monitoring systems. The models proposed in this thesis generate alerts for patients earlier than the early warning systems which are currently in use in hospitals in the UK. The improvements to existing models proposed in this thesis could be applied to continuous and intermittently acquired vital-sign data from other clinical environments.