Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data
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arXiv
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| Format: | Preprint |
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2024
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| author | Nagaraj, Sujay Goodwin, Andrew J. Lopushanskyy, Dmytro Eytan, Danny Greer, Robert W. Goodfellow, Sebastian D. Assadi, Azadeh Jayarajan, Anand Goldenberg, Anna Mazwi, Mjaye L. |
| author_facet | Nagaraj, Sujay Goodwin, Andrew J. Lopushanskyy, Dmytro Eytan, Danny Greer, Robert W. Goodfellow, Sebastian D. Assadi, Azadeh Jayarajan, Anand Goldenberg, Anna Mazwi, Mjaye L. |
| contents | Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a distinct artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children's hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data Nagaraj, Sujay Goodwin, Andrew J. Lopushanskyy, Dmytro Eytan, Danny Greer, Robert W. Goodfellow, Sebastian D. Assadi, Azadeh Jayarajan, Anand Goldenberg, Anna Mazwi, Mjaye L. Signal Processing Artificial Intelligence Machine Learning Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a distinct artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children's hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety. |
| title | Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.00041 |