Assessment of a simple artificial neural network for predicting residual neuromuscular block.

Background: Postoperative residual curarization (PORC) after surgery is common and its detection has a high error rate. Artificial neural networks are being used increasingly to examine complex data. We hypothesized that a neural network would enhance prediction of PORC.
Methods: In 40 previously reported patients, neuromuscular function, neuromuscular block/antagonist usage and time intervals were recorded throughout anaesthesia until tracheal extubation by an observer uninvolved in patient care. PORC was defined as significant 'fade' (train of four <0.7) at extubation. Neuromuscular function was classified as PORC (value=1) or no PORC (value=0). A back-propagation neural network was trained to assign similar values (0, 1) for prediction of PORC, by examining the impact of (i) the degree of spontaneous recovery at reversal, and (ii) the time since pharmacological reversal, using the jackknife method. Successful prediction was defined as attainment of a predicted value within 0.2 of the target value.
Results: Twenty-six patients (65%) had PORC at tracheal extubation. Clinical detection of PORC had a sensitivity of 0 and specificity of 1, with an indeterminate positive predictive value and a negative predictive value of 0.35. Using the artificial neural network, one patient with residual block and one with adequate neuromuscular function were incorrectly classified during the test phase, with no indeterminate predictions, giving an artificial neural network sensitivity of 0.96 (chi(2)=44, P<0.001) and specificity of 0.92 (P=1), with a positive predictive value of 0.96 and a negative predictive value of 0.93 (chi(2)=12, P<0.001).
Conclusions: Neural network-based prediction, using readily available clinical measurements, is significantly better than human judgement in predicting recovery of neuromuscular function.