Outcome prediction for the individual patient in the ICU.
A very difficult clinical problem facing surgeons is knowing when further treatment is futile and no longer appropriate in a patient who has developed severe complications after surgery and is being treated in an intensive care unit. It is now possible to prolong the process of dying among such patients. This results in unnecessary pain and loss of dignity for the patient, anguish and distress for the patient's relatives and is dehumanizing for the clinical and nursing staff. It has also tremendous implications in the use of limited health care resources. A computer model designed to aid this process has to have the following properties: it must reflect the dynamic pathophysiological process and be able to predict death with extreme accuracy and early in the clinical course. The Riyadh algorithm uses computerised dynamic trend analysis of daily organ failure scores (APACHE II score corrected for the number and duration of organ failures), noting the rate of change in score relative to that of the previous day and an absolute threshold to predict death has been developed for this purpose. The algorithm was developed by tracking the daily scores of 200 IUC patients until their death or discharge from the intensive care unit. It was subsequently validated perspectively on 831 patients. During the validation process, the clinicians were blinded to the predictions. There wer 290 deaths and the program predicted 109 deaths (38% of all deaths) with no false-positive predictions. Forty percent of the predictions were made within 48 h in the ICU and 74% within a week.(ABSTRACT TRUNCATED AT 250 WORDS)
Chang
R W
RW
St. Helier Hospital, Carshalton, Surrey, London.
Bihari
D J
DJ
D017281:Cost of Illness; D003362:Cost-Benefit Analysis; D003422:Critical Care / Q000191:economics; D006801:Humans; D008020:Life Support Care / Q000191:economics; D009102:Multiple Organ Failure / Q000145:classification* / Q000401:mortality / Q000628:therapy; D015588:Observer Variation; D011379:Prognosis; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D016019:Survival Analysis