Predictors of Failed Spinal Arachnoid Puncture Procedures: An Artificial Neural Network Analysis

Abstract

Background Knowing the predicting factors for difficult neuraxial blocks might help better plan the procedure. This study aimed to determine the predictors of failed spinal arachnoid puncture procedures using artificial neural network (ANN) analysis. Methodology With approvals, prospectively collected data from 300 spinal arachnoid punctures in the operation theater of an academic institute having postgraduate anesthesia training were retrospectively evaluated. Fifteen variables from anthropo-demographic, spinal surface anatomy, procedure, and performers' experiences were fed as input for the ANN. A failed spinal arachnoid puncture procedure was defined as the requirement of more than three punctures, with three punctures but more than six passes, or if the performer handed over the procedure to another, considering it difficult after the second puncture. STATCRAFT v.2 software (Predictive Analytics Solutions Pvt. Ltd., Bengaluru, India) was used for ANN model generation. Considering the overfitting tendency of the ANN, Pr(>|z|) < 0.01 in the ANN was considered significant. The area under the receiver operating characteristic (AuROC) curve of the ANN model and its sensitivity and specificity were also assessed. Significant factors with multiple gradings were also evaluated for their statistical significance across the grades or classes using INSTAT software (Graphpad Prism, La Jolla, CA, USA); a two-tailed P-value of <0.05 was considered significant. Results Interspinous process-based spine grade, performers' experience, and positioning difficulty were significant determinants of failed spinal arachnoid puncture procedures in the ANN model. The ANN model had an AuROC of 0.907, specificity of 0.976, and sensitivity of 0.385. The interclass comparison showed that increasing spinal grades and decreasing experiences were associated with increased pass and puncture. Conclusions The ANN model found the determinants of the failed spinal arachnoid puncture procedure well with good AuROC and specificity but poor sensitivity.

Keywords: failure; lumber puncture; machine learning; neuraxial anesthesia; procedures; spinal column.

Figure 1. The artificial neural network architecture for failed spinal arachnoid puncture procedures.

Note: The input parameters of the architecture are rewritten in Paint for clarity. ASA-PS, American Society of Anesthesiologists; BMI, body mass index; LA, local anesthesia; TLS, thoraco-lumbosacral, TPM, transverse paravertebral muscle

Figure 2. The receiver operating characteristics and area under the artificial neural network model curve for predicting failed spinal arachnoid puncture procedures.

AUC, area under the curve