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

      research-article
      1 ,
      ,
      Cureus
      Cureus
      neuraxial anesthesia, lumber puncture, failure, spinal column, procedures, machine learning

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          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.

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          Most cited references13

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          The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

          Much of biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
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            Logistic regression and artificial neural network classification models: a methodology review.

            Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature.
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              Predictors of successful neuraxial block: a prospective study.

              The epidural and subarachnoid spaces should be identified at the first attempt, since multiple punctures increase the risk of postdural puncture headache, epidural haematoma and neural trauma. The study aimed to describe the predictors of successful neuraxial blocks at the first attempt. After institutional Review Board approval, 1481 patients undergoing spinal or epidural anaesthesia were prospectively enrolled. For each block we recorded: gender, age, height, weight, body habitus, anatomical landmarks (palpability of the spinous processes), spinal anatomy, patient positioning, premedication, needle type and gauge, approach, spinal level of the block, and the provider's level of experience. Retrieval of cerebrospinal fluid or loss of resistance to saline or air identified the subarachnoid and epidural spaces, respectively. The outcome variable was the first attempt success or failure (whether or not the needle was correctly located with one skin puncture and produced adequate surgical anaesthesia). Backward stepwise logistic regression tested its association with the other variables. The first-attempt rate of success was 61.51%. Independent predictors (Odds ratio, 95% confidence limits) were the quality of anatomical landmarks (1.92 (1.57; 2.35)), the provider's level of experience (1.24 (1.15; 1.33)) and the adequacy of patient positioning (3.84 (2.84; 5.19)). The successful location of the subarachnoid or the epidural space at the first attempt is influenced by the quality of patients' anatomical landmarks, the adequacy of patient positioning and the provider's level of experience.
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                Author and article information

                Journal
                Cureus
                Cureus
                2168-8184
                Cureus
                Cureus (Palo Alto (CA) )
                2168-8184
                23 December 2022
                December 2022
                : 14
                : 12
                : e32891
                Affiliations
                [1 ] Anesthesia, Critical Care, and Pain Medicine, All India Institute of Medical Sciences, Raipur, IND
                Author notes
                Article
                10.7759/cureus.32891
                9870597
                7a4b4a72-4e81-4728-a3c9-5b18b0c955e9
                Copyright © 2022, Karim et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 December 2022
                Categories
                Anesthesiology
                Pain Management

                neuraxial anesthesia,lumber puncture,failure,spinal column,procedures,machine learning

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