Prediction of outcome from intensive care after gastroenterologic emergency.
Prognostic factors determining the outcome from intensive care were studied in 952 patients admitted to 25 Finnish ICUs after gastroenterologic emergency. Logistic regression analysis was used to create predictive models based on the APACHE II-system. The models were constructed by using data from a random two-thirds of the study population and validated in the remaining independent one-third together with the original APACHE II-index. The Acute Physiology Score, age, and a pre-existing liver disease were the three most important determinants of outcome. The inclusion of the TISS score describing the intensity of treatment into a model did not enhance the accuracy of the prediction. Our models were better calibrated than the original APACHE II-equation when tested by the goodness-of-fit -statistics. These statistical models may help the clinicians to predict the outcome for an individual patient by providing them information about the relative impacts of predictive factors or about the probability of death. These probabilities should be interpreted cautiously, taking into account the limitations of statistical methods. This is especially important when assessing the highrisk patients. Their number in our study was too low for accurate outcome prediction.
Niskanen
M
M
Department of Intensive Care, Kuopio University Hospital, Finland.
Kari
A
A
Nikki
P
P
Iisalo
E
E
Kaukinen
L
L
Rauhala
V
V
Saarela
E
E
D018806:APACHE; D000367:Age Factors; D003422:Critical Care / Q000706:statistics & numerical data*; D004630:Emergencies; D005260:Female; D005387:Finland / Q000453:epidemiology; D005544:Forecasting; D005767:Gastrointestinal Diseases / Q000401:mortality / Q000628:therapy*; D015600:Glasgow Coma Scale; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D015203:Reproducibility of Results; D012307:Risk Factors; D016896:Treatment Outcome