Review of a large clinical series: Predicting death for patients with abdominal septic shock.
This paper reports the result of the MEDAN project that analyzes a multicenter septic shock patient data collection. The mortality prognosis based on 4 scores that are often used is compared with the prognosis of a trained neural network. We built an alarm system using the network classification results. Method. We analyzed the data of 382 patients with abdominal septic shock who were admitted to the intensive care unit (ICU) from 1998 to 2002. The analysis includes the calculation of daily sepsis-related organ failure assessment (SOFA), Acute Physiological and Chronic Health Evaluation (APACHE) II, simplified acute physiology score (SAPS) II, multiple-organ dysfunction score (MODS) scores for each patient and the training and testing of an appropriate neural network. Results. For our patients with abdominal septic shock, the analysis shows that it is not possible to predict their individual fate correctly on the day of admission to the ICU on the basis of any current score. However, when the trained network computes a score value below the threshold during the ICU stay, there is a high probability that the patient will die within 3 days. The trained neural network obtains the same outcome prediction performance as the best score, the SOFA score, using narrower confidence intervals and considering three variables only: systolic blood pressure, diastolic blood pressure and the number of thrombocytes. We conclude that the currently best available score for abdominal septic shock may be replaced by the output of a trained neural network with only 3 input variables.
Department of Surgery, Asklepios Klinik Langen, Academic Teaching Hospital at JW Goethe University Frankfurt am Main, Germany. E.Hanisch@Asklepios.com
Ernst
E
Hanisch
Rüdiger
R
Brause
Jürgen
J
Paetz
Björn
B
Arlt
D018806:APACHE; D006801:Humans; D007362:Intensive Care Units; D009102:Multiple Organ Failure / Q000209:etiology; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods; D012720:Severity of Illness Index; D012772:Shock, Septic / Q000145:classification / Q000175:diagnosis* / Q000401:mortality