Dataset Information

Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients.

ABSTRACT:

Background

Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point of needle insertion to improve procedural success, in particular in obesity. We developed an ultrasound-guided automated spinal landmark identification program to assist anesthetists on spinal needle insertion point with a graphical user interface for spinal anesthesia.

Methods

Forty-eight obese patients requiring spinal anesthesia for Cesarean section were recruited in this prospective cohort study. We utilized a developed machine learning algorithm to determine the needle insertion point using automated spinal landmark ultrasound imaging of the lumbar spine identifying the L3/4 interspinous space (longitudinal view) and the posterior complex of dura mater (transverse view). The demographic and clinical characteristics were also recorded.

Results

The first attempt success rate for spinal anesthesia was 79.1% (38/48) (95%CI 65.0 - 89.5%), followed by successful second attempt of 12.5% (6/48), third attempt of 4.2% (2/48) and 4th attempt (4.2% or 2/48). The scanning duration of L3/4 interspinous space and the posterior complex were 21.0 [IQR: 17.0, 32.0] secs and 11.0 [IQR: 5.0, 22.0] secs respectively. There is good correlation between the program recorded depth of the skin to posterior complex and clinician measured depth (r = 0.915).

Conclusions

The automated spinal landmark identification program is able to provide assistance to needle insertion point identification in obese patients. There is good correlation between program recorded and clinician measured depth of the skin to posterior complex of dura mater. Future research may involve imaging algorithm improvement to assist with needle insertion guidance during neuraxial anesthesia.

Trial registration

This study was registered on clinicaltrials.gov registry ( NCT03687411 ) on 22 Aug 2018.

SUBMITTER: In Chan JJ

PROVIDER: S-EPMC8522234 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients.

In Chan Jason Ju JJ Ma Jun J Leng Yusong Y Tan Kok Kiong KK Tan Chin Wen CW Sultana Rehena R Sia Alex Tiong Heng ATH Sng Ban Leong BL

BMC anesthesiology 20211018 1

<h4>Background</h4>Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point of needle insertion to improve procedural success, in particular in obesity. We developed an ultrasound-guided automated spinal landmark identification program to assist anesthetists on spinal needle insertion point with a graphical user interface for spinal anesthesia.<h4>Methods</h4>Forty-eight obese patients requiring spinal anesthesia fo ...[more]

PMID: 34663224

Similar Datasets

Project description:Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0-P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system. NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

Project description:BackgroundA major barrier to optimal pain management is the difficulty in predicting and assessing patients at high risk for significant pain across multiple locations within the institution in a timely manner. This is compounded by the fragmented display of clinical information on enterprise clinical platform, which further hinders delay the reviews and hence the increased risk of untreated pain. We evaluated and compared the predictive performance of six modelling techniques in predicting significant pain, defined as the maximum pain score of 3 or more on movement at the 13th to 24th hour after spinal morphine administration during caesarean delivery.MethodsWe retrieved medical records from women who underwent caesarean delivery and received postoperative spinal morphine in a single specialist maternity hospital in Singapore between Aug 2019 and Aug 2022. We extracted 120 clinical variables from the medical records of eligible patients and further selected 23 to improve algorithm accuracies. The dataset was split randomly, with 80 % of patients (n = 5248) used for training the models, and 20 % (n = 1313) reserved for validation.ResultsThe study cohort comprised 6561 patients with an incidence of significant postoperative pain of 7.9 %. Ridge regression demonstrated the best performance with both the full (AUC: 0.649) and selected (AUC: 0.719) feature sets. By reducing the number of features, Ridge regression, LASSO, Elastic net, and XGBoost showed similar in AUC (0.704-0.719), sensitivity (0.644-0.695), specificity (0.644-0.705), positive predictive value (0.155-0.179), and negative predictive value (0.949-0.955) in predicting significant postoperative pain. These were attributed to the top three variables, mainly the last recorded postoperative pain score (on movement) before the prediction point, mean and standard deviation of the hourly maximum postoperative pain score (at rest) at 0th to 12th hour.ConclusionsFuture research will aim to refine these models and explore their implementation in clinical settings to enhance real-time pain management and risk stratification for women after caesarean delivery.

Dataset Information

Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients.

Background

Methods

Results

Conclusions

Trial registration

Publications

Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets