Rapid Diagnostic Model for Critical Illness Polyneuropathy Based on Electrophysiological Data

Critical illness polyneuropathy (CIP) is a common cause of weakness in critically ill patients, and early diagnosis and treatment are essential. Conventional comprehensive electrophysiological testing requires 60-90 min and can be invasive, limiting its utility in the intensive care setting. This study aimed to develop a rapid, efficient, and minimally invasive diagnostic model for CIP.

Patients who met the inclusion criteria were recruited from the intensive care units (ICU) of the First Medical Center and Fifth Medical Center of the PLA General Hospital and Nanfang Hospital. To identify the optimal diagnostic model, we compared several machine learning approaches, including K-nearest neighbor, support vector machine with radial basis function (SVM-RBF), SVM with Gaussian kernel, random forest, and extreme gradient boosting (XGB), using all electrophysiological features. These were also compared with nerve conduction studies of the peroneal and sural nerves. To construct a rapid diagnostic model, different feature combinations were assessed across the machine learning methods. Model performance was evaluated using cross-validated areas under the receiver operating characteristic curve (AUCs).

Of 14,768 admissions screened, 134 patients with CIP and 135 matched controls were included. In total, 41 electrophysiological features were analyzed. Feature ranking revealed that the distal compound muscle action potential (CMAP) of the peroneal nerve contributed most to diagnostic accuracy. After comparison, the SVM-RBF method was selected to establish the final diagnostic model. The optimal model, based on seven electrophysiological features of the peroneal and ulnar nerves (proximal latency, distal latency, and CMAP amplitude), achieved an AUC of 0.93, which was comparable to the all-feature XGB model (AUC = 0.95). In the independent validation set, the rapid diagnostic model maintained strong performance (AUC = 0.88), similar to the all-feature model (AUC = 0.90).

We developed a rapid diagnostic model for CIP using the SVM-RBF method and seven electrophysiological features from the peroneal and ulnar nerves. This model enables efficient, minimally invasive, and timely diagnosis of CIP in critically ill patients. The source code and related scripts are available at [https://github.com/PLAGH-Neuro-Yang/RDM-CIP].