Prediction of hyperkalemia in dogs from electrocardiographic parameters using an artificial neural network.
Acad Emerg Med
; 8(6): 599-603, 2001 Jun.
Article
em En
| MEDLINE
| ID: mdl-11388932
ABSTRACT
OBJECTIVE:
To predict severe hyperkalemia from single electrocardiogram (ECG) tracings.METHODS:
Ten conditioned dogs each underwent this protocol three times Under isoflurane anesthesia, 2 mEq/kg/hr of potassium chloride was given intravenously until P-waves were absent from the ECG and ventricular rates decreased > or =20% in < or =5 minutes. Serum potassium levels (K(+)) were measured at regular intervals with concurrent digital storage of lead II of the surface ECG. A three-layer artificial neural network with four hidden nodes was trained to predict K(+) from 15 separate elements of corresponding ECG data. Data were divided into a training set and a test set. Sensitivity, specificity, and diagnostic accuracy for recognizing hyperkalemia were calculated for the test set based on a prospectively defined K(+) = 7.5.RESULTS:
The model produced data for 189 events; 139 were placed in the training set and 50 in the test set. The test set had 37 potassium levels at or above 7.5 mmol/L. The neural network had a sensitivity of 89% (95% CI = 75% to 97%) and a specificity of 77% (95% CI = 46% to 95%) in recognizing these. The positive likelihood ratio was 3.87. Overall accuracy of this model was 86% (95% CI = 73% to 94%). Mean (+/-SD) difference between predicted and actual K(+) values was 0.4 +/- 2.0 (95% CI = -0.2 to 1.0).CONCLUSIONS:
An artificial neural network can accurately diagnose experimental hyperkalemia using ECG parameters. Further work could potentially demonstrate its usefulness in bedside diagnosis of human subjects.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Eletrocardiografia
/
Hiperpotassemia
Tipo de estudo:
Diagnostic_studies
/
Guideline
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Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Acad Emerg Med
Ano de publicação:
2001
Tipo de documento:
Article