P Cluitmans | PubMed Health

PubMed1998

Long-term postoperative cognitive dysfunction in the elderly ISPOCD1 study. ISPOCD investigators. International Study of Post-Operative Cognitive Dysfunction.

BACKGROUND: Long-term postoperative cognitive dysfunction may occur in the elderly. Age may be a risk factor and hypoxaemia and arterial hypotension causative factors. We investigated these hypotheses in an international multicentre study. METHODS: 1218 patients aged at least 60 years completed neuropsychological tests before and 1 week and 3 months after major non-cardiac surgery. We measured oxygen saturation by continuous pulse oximetry before surgery and throughout the day of and the first 3 nights after surgery. We recorded blood pressure every 3 min by oscillometry during the operation and every 15-30 min for the rest of that day and night. We identified postoperative cognitive dysfunction with neuropsychological tests compared with controls recruited from the UK (n=176) and the same countries as study centres (n=145). FINDINGS: Postoperative cognitive dysfunction was present in 266 (25.8% [95% CI 23.1-28.5]) of patients 1 week after surgery and in 94 (9.9% [8.1-12.0]) 3 months after surgery, compared with 3.4% and 2.8%, respectively, of UK controls (p<0.0001 and p=0.0037, respectively). Increasing age and duration of anaesthesia, little education, a second operation, postoperative infections, and respiratory complications were risk factors for early postoperative cognitive dysfunction, but only age was a risk factor for late postoperative cognitive dysfunction. Hypoxaemia and hypotension were not significant risk factors at any time. INTERPRETATION: Our findings have implications for studies of the causes of cognitive decline and, in clinical practice, for the information given to patients before surgery.

Abdomen↗

PubMed1996

Modelling techniques and their application for monitoring in high dependency environments--learning models.

This paper reviews the use of learning models including Bayesian classifiers and artificial neural networks in monitoring and interpreting biosignals. Generally learning models applied for analysis of biosignals are "black-box' types trained on the basis of measured signals. It is illustrated that the training and application of learning models more or less follow the same sequences. The main focus is the interpretation of electrical signals from the brain (electroencephalogram (EEG) and evoked potentials (EP)). Current analysis of these signals often reveals sudden changes in the EEG or evoked potentials to be the earliest discernible signs of inadequate perfusion of the brain. They may reflect problems such as systemic arterial oxygen desaturation or hypotension arising from other body system failures during critical illness. It is suggested that these brain signals should be recorded in the critical care unit, and that they should form part of the annotated database of biosignals established during the IMPROVE project. This would allow for the development of new methods for on-line warning of impending damage to the central nervous system, such that corrective actions could be taken before permanent damage occurred.

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