# Machine Learning to Optimize Management of Acute Hydrocephalus Patients

> **NIH NIH R21** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $445,500

## Abstract

37,000 patients a year receive an external ventricular drain (EVD) in the setting of acute hydrocephalus in the
US, generating in-hospital charges of $151,672 per patient, or $5.6 billion dollars a year. There is great
motivation in the neurointensive care unit for the optimization of EVD management to reduce infection rates,
accurately determine need for permanent shunting, and to do so efficiently in order to minimize duration of
drainage and length of stay (LOS). Risk factors for ventriculitis include EVD duration, cerebrospinal fluid
(CSF) sampling frequency, presence of intraventricular hemorrhage (IVH), and insertion technique. Severe
CSF disturbances in patients with IVH and EVDs limit the value of routine CSF analysis for ventriculitis
prediction. And ventriculitis diagnosis is imprecise, with only a minority declaring culture positivity while all still
demanding antibiotic treatment and delay of permanent shunt. This leads to unnecessary empiric antibiotic
treatment and increased LOS (30.8 vs 22.6 days), with the associated cost ($30,335 more) and morbidity
(e.g. Clostridium difficile infection, emergence of drug-resistant pathology). The process of determining
permanent shunt dependence is variable between institutions, particularly around the decision of when to
begin weaning the EVD or predicting delayed resolution. These decisions in the subacute period determine
LOS and associated adverse events, exposure to radiography, and commitment to potentially unnecessary
permanent foreign materials in the CNS, which then carry lifelong risks for infection and blockage. There is
no accurate noninvasive test (that does not further introduce infection) to diagnose ventriculitis nor
is there a timely method to predict need for permanent shunt after acute hydrocephalus. To fill this
gap, we propose developing a quantitative model from intracranial pressure (ICP) waveform analysis to
increase precision in the diagnosis of ventriculitis and accurately predict need for permanent shunt. In
previous work, we were able to predict with good accuracy who would need permanent shunt placement
using ICP waveform analysis collected during a 24 hour clamp trial. However, a complex model can only be
justified if it achieves a diagnosis earlier or more accurately than traditional clinical methods. In preliminary
work, we clustered raw ICP waveforms and found a pattern of waveforms specific for ventriculitis that
appears 1 day before diagnostic cultures are sent. Our central hypothesis is that there is a temporal
quantitative signal in ICP waveform reflective of intracranial dynamics that can be harvested to optimize acute
hydrocephalus management. Impact and Significance: Noninvasive quantitative models based on ICP
waveform analysis that diagnose ventriculitis and accurately predict need for permanent shunt would
decrease the duration of EVD and the frequency of CSF sampling, two of the risk factors for ventriculitis,
while also decreasing LOS, associated adve...

## Key facts

- **NIH application ID:** 10057040
- **Project number:** 1R21NS113055-01A1
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Soojin Park
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $445,500
- **Award type:** 1
- **Project period:** 2020-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057040

## Citation

> US National Institutes of Health, RePORTER application 10057040, Machine Learning to Optimize Management of Acute Hydrocephalus Patients (1R21NS113055-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10057040. Licensed CC0.

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