Risk assessment of alcohol withdrawal seizures with a Kohonen feature map.
Recently, it has been suggested that alcohol-induced hyperhomocysteinaemia in patients suffering from chronic alcoholism might be a risk factor for alcohol withdrawal seizures. In the present follow-up study 12 patients with chronic alcoholism who suffered from withdrawal seizures had significantly higher levels of homocysteine (Hcy) on admission (71.43 +/- 25.84 mol/l) than patients (n = 37) who did not develop seizures (32.60 +/- 24.87 mol/l; U = 37.50, p = 0.0003). Using a logistic regression analysis, withdrawal seizures were best predicted by a high Hcy level on admission (p < 0.01; odds ratio 2.07). Based on these findings we developed an artificial neural network system (Kohonen feature map, KFM) for an improved prediction of the risk of alcohol withdrawal seizures. Forty-nine patients with chronic alcoholism (12 with alcohol withdrawal seizures and 37 without seizures) were randomized into a training set and a test set. Best results for sensitivity of the KFM was 83.3% (five of six seizure patients were predicted correctly) with a specificity of 94.4% (one false positive prediction of 19 patients). We conclude that in patients with alcohol-induced hyperhomocysteinaemia the KFM is a useful tool to predict alcohol withdrawal seizures.
Kurth
C
C
Department of Clinical Neurophysiology, Georg-August-Universität Göttingen, Robert Koch Str. 40, 37075 Göttingen, Germany.
Wegerer
V
V
Degner
D
D
Sperling
W
W
Kornhuber
J
J
Paulus
W
W
Bleich
S
S
D000328:Adult; D000368:Aged; D020270:Alcohol Withdrawal Seizures / Q000097:blood*; D000437:Alcoholism / Q000097:blood*; D000465:Algorithms; D005260:Female; D005500:Follow-Up Studies; D006710:Homocysteine / Q000097:blood*; D006801:Humans; D020138:Hyperhomocysteinemia / Q000097:blood; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D018570:Risk Assessment / Q000379:methods; D018709:Statistics, Nonparametric
D006710:Homocysteine