Personalising Care: Machine learning from pressure waves (ICP)
Soojin Park, Associate Prof. Neurology
Division of Neurocritical Care, Columbia University, NYC, USA
Watch on demand: https://lives2020.e-lives.org/media/machine-learning-pressure-waves
❗ Motivation
- Acute hydrocephalus affects ~37k pts/yr in USA
- Rx = EVD, but 1/5th develop infection ventriculitis
- Risk of ventriculitis ↑ with duration and frequency of CSF sampling (by which diagnosis made..)
❓ Question
Can we find a way of using physiological information contained in ICP waveform to develop a method for detecting ventriculitis, without having to sample CSF?
Park reminds us of the normal ICP waveform (exam revision déjà vu..)
And how it’s morphology changes with ↑ICP
This alteration in waveform morphology with ↑ICP has a biologically plausible mechanism in ventriculitis
⭐ Goal 1
Examine changes in ICP waveform morphologies prior to ventriculitis
- Dataset = only patients WITH ventriculitis
- Collaboration with group experienced in ICP waveform big data, however their pre-processing identified abnormal waveforms as artefactual!
- ⚠Problem = vague definition of ventriculitis
- Used ‘gold standard’ of limiting it to those with culture-positive CSF
- n = 19 pts
- ❗ Park mentions that CSF is cultured 3/w at this institution, perhaps not usual practice – CT: worth considering this in the context of their motivation
- ⚠ EVDs left open to drainage most of the time, typical practice across other institutions, thus waveform only intermittently present when EVD clamped by nurse
- ❓ Challenge = automating identification of waveforms (CT: I note solution was not to get desperate medical student to manually sift data in exchange for ‘research experience on their CV..)
Methods
- Dominant pulses extracted using Morphological Clustering Analysis of ICP Pulse
- Before / During / After ventriculitis (i.e culture-positive CSF)
- Morphologically similar groups obtained by hierarchical k-means clustering
- Dynamic Time Warping used as a ‘distance’ metric to correct for speed (HR), see below
- Meta-clusters determined by clinicians, see figure B below.
- Bi/triphasic (green)
- Monophasic/tombstone (yellow)
- Artefactual (red)
- = supervised learning
Results
- Prior to ventriculitis majority of pulses had physiological tri/biphasic appearance
- During ventriculitis this dropped from 61.8 > 22.6%, a statistically significant change, which persisted
- ✨ Most importantly this change occurred a full day before the ventriculitis was clinically detectable
⭐ Goal 2
Leverage time-varying dominant pulses of ICP from hourly EVD clamping data into a detection model of ventriculitis
- Collaboration:
- Columbia Vangelos College of Physicians & Surgeons
- R Adams Cowley Shock Trauma Center, University of Maryland
- Aims:
- Improve performance and generalisability of model to other institutions data
- Work in submission therefore not shown
- Collaborators sought, see email below:
Concluding Remarks
- Example presented for ICP but process generalisable to other waveforms, of which there are many in ICU!
My thoughts:
- I’ve often been disappointed at how little waveform data is actually stored from ICU monitors
- Perhaps I shouldn’t be given the general lack of high-quality ICU data (see data sharing session) and huge storage requirements
- Most of the ‘high resolution/granular/insert other buzzword here’ EHRs I’ve come across sample at a frequency ~ 1 hz (c.f. 125-250 hZ in this study)
- Starting point for those interested in waveform data in ICU = MIMIC-III Waveform Database
- Be warned this is truly big data
Blog by Chris Tomlinson:
Anaesthetist & Critical Care Registrar
PhD Candidate at UCL UKRI Centre for AI-enabled Healthcare