Difficult Spinal-Arachnoid Puncture (DSP) Score: Development and Performance Analysis
- PMID: 36793807
- PMCID: PMC9923436
- DOI: 10.7759/cureus.33760
Difficult Spinal-Arachnoid Puncture (DSP) Score: Development and Performance Analysis
Abstract
Background: Difficult and traumatic neuraxial blocks and procedures are not uncommon. Although score-based prediction has been attempted, the practical application of those has remained limited for various reasons. The aim of this study was to develop a clinical scoring system from the strong predictors of failed spinal-arachnoid puncture procedures assessed previously using artificial neural network (ANN) analysis and analyze the score's performance on the index cohort.
Methods: The present study is based on the ANN model analyzing 300 spinal-arachnoid punctures (index cohort) performed in an academic institute in India. For the development of the score, i.e., Difficult Spinal-Arachnoid Puncture (DSP) Score, the coefficient estimates of the input variables, which showed a Pr(>|z|) value of <0.01, were considered. The resultant DSP Score was then applied to the index cohort for receiver operating characteristic (ROC) analysis, Youden's J point determination for best sensitivity and specificity, and diagnostic statistical analysis for the cut-off value for predicting the difficulty.
Results: A DSP Score incorporating spine grades, performers' experience, and positioning difficulty was developed; the minimum and maximum scores were 0 and 7, respectively. The area under the ROC curve for the DSP Score was 0.858 (95% confidence interval 0.811-0.905), Youden's J point for cut-off was at 2, which showed a specificity and sensitivity of 98.15% and 56.5%, respectively.
Conclusion: The ANN model-based DSP Score developed for predicting the difficult spinal-arachnoid puncture procedure showed an excellent area under the ROC curve. At the cut-off value 2, the score had a sensitivity plus specificity of approximately 155%, indicating that the tool can be useful as a diagnostic (predictive) tool in clinical practice.
Keywords: interpretable machine learning; lumbar puncture (lp); orthopedic anesthesia; regional anesthesiology; scores; spinal deformiites; spinal puncture.
Copyright © 2023, Karim et al.
Conflict of interest statement
The authors have declared that no competing interests exist.
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