Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model.
BACKGROUND
Models based on artificial neural networks (ANN) are useful in predicting outcome of various disorders. There is currently no useful predictive model for risk assessment in acute lower-gastrointestinal haemorrhage. We investigated whether ANN models using information available during triage could predict clinical outcome in patients with this disorder.


METHODS
ANN and multiple-logistic-regression (MLR) models were constructed from non-endoscopic data of patients admitted with acute lower-gastrointestinal haemorrhage. The performance of ANN in classifying patients into high-risk and low-risk groups was compared with that of another validated scoring system (BLEED), with the outcome variables recurrent bleeding, death, and therapeutic interventions for control of haemorrhage. The ANN models were trained with data from patients admitted to the primary institution during the first 12 months (n=120) and then internally validated with data from patients admitted to the same institution during the next 6 months (n=70). The ANN models were then externally validated and direct comparison made with MLR in patients admitted to an independent institution in another US state (n=142).


RESULTS
Clinical features were similar for training and validation groups. The predictive accuracy of ANN was significantly better than that of BLEED (predictive accuracy in internal validation group for death 87% vs 21%; for recurrent bleeding 89% vs 41%; and for intervention 96% vs 46%) and similar to MLR. During external validation, ANN performed well in predicting death (97%), recurrent bleeding (93%), and need for intervention (94%), and it was superior to MLR (70%, 73%, and 70%, respectively).


CONCLUSIONS
ANN can accurately predict the outcome for patients presenting with acute lower-gastrointestinal haemorrhage and may be generally useful for the risk stratification of these patients.
Division of Gastroenterology, Department of Medicine, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, OH 44106-5066, USA. rxw16@po.cwru.edu
Ananya
A
Das
Tamir
T
Ben-Menachem
Gregory S
GS
Cooper
Amitabh
A
Chak
Michael V
MV
Sivak
Judith A
JA
Gonet
Richard C K
RC
Wong
D000208:Acute Disease; D005260:Female; D006471:Gastrointestinal Hemorrhage / Q000145:classification* / Q000175:diagnosis / Q000628:therapy; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D011379:Prognosis; D012008:Recurrence; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000706:statistics & numerical data*; D014218:Triage / Q000706:statistics & numerical data