Machine learning to optimize management of acute hydrocephalus

Active Project Grant Awarded Last action

Award ID
R01NS131606
Recipient
Awarding agency
Obligated
$3,312,183

Award facts

Completion date 70% complete
Place of performance
New York, NY 10032, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Acute hydrocephalus frequently complicates brain injury including intracerebral (ICH) and subarachnoid hemorrhage (SAH), requiring emergent placement of an external ventricular drain (EVD). The EVD allows rapidly accumulated blood to exit, immediately relieving dangerous increased pressure on the brain. Most patients do not need the EVD after this, while 18-30% develop chronic hydrocephalus and require permanent cerebrospinal fluid (CSF) shunt placement. There is great variability in the management of evds across centers, particularly about when to wean evds and the approach to surveying and diagnosing EVD-related infection. The longer the EVD is present and the more frequently the EVD is accessed to sample CSF (to test infection), the higher the risk for infection which contributes to high morbidity and mortality. SAH and ICH patients endure evds for 11.5-16 days (max > 30), with typical ventriculitis onset occurring at 9.5 days. This vicious cycle is hidden in the cost: 37,000 patients a year receive an EVD in the setting of acute hydrocephalus in the US annually, generating in- hospital charges of $151,672 per patient, or $5.6 billion dollars a year. There is a great need to optimize EVD management by recognizing EVD-related infection while reducing CSF sampling and accurately determining need for permanent shunting (or ability to liberate from temporary drainage), and to do so as early as possible to minimize duration of drainage and length of stay. Our central hypothesis is that there is temporal information in digitized patient data that is reflective of intracranial dynamics that can be harvested to break the negative cycle of ventriculitis and shunt dependency. In previous work, we discovered that intracranial pressure waveform morphology changes two days prior to the clinical diagnosis of ventriculitis. Additionally, we identified a predictor of future CSF shunt dependency as early as four days after EVD placement, building on the correlation of radiographic hydrocephalus changes with concurrent CSF drainage volume. We aim to develop a multicenter purpose-built dataset for the management of acute hydrocephalus including physiologic data such as intracranial pressure waveform, imaging, and clinical data. Using this dataset, we will be able to improve and validate our machine learning models for detection of ventriculitis and prediction of shunt dependence. We will leverage the diversity of the data inputs for model generalizability while also identifying and reducing bias by using a federated learning framework for model training and validation. Finally, we will survey physicians to evaluate decision making around EVD management and assess openness to adopting computed prediction scores.

Transactions

Action Obligated Date
Amendment $630,988
Amendment $638,971
Amendment $630,178
Amendment $706,023
Original award $706,023

Subawards

Subrecipient Amount Awarded
The Massachusetts General Hospital "Massachusetts General Hospital will operate as a site for this proposal. We will work to obtain relevant IRB consent and obtain all data necessary for the… $98,023
University Of Maryland, Baltimore University of Maryland will operate as a site for this proposal. We will work to obtain relevant IRB consent and obtain all data necessary for the analysis… $51,424
Emory University Emory University will operate as a site for this proposal. We will work to obtain relevant IRB consent and obtain all data necessary for the analysis proposed… $80,063
Emory University $81,076
University Of Maryland, Baltimore University of Maryland will operate as a site for this proposal. We will work to obtain relevant IRB consent and obtain all data necessary for the analysis… $52,075
Subrecipient not on record "Massachusetts General Hospital will operate as a site for this proposal. We will work to obtain relevant IRB consent and obtain all data necessary for the… $99,264
Emory University Statement of Work Machine Learning to Optimize Management of Acute Hydrocephalus Work Description Emory University will operate as a site for this proposal.… $82,089
University Of Maryland, Baltimore University of Maryland will operate as a site for this proposal. We will work to obtain relevant IRB consent and obtain all data necessary for the analysis… $52,726
The General Hospital Corporation Statement of Work Machine Learning to Optimize Management of Acute Hydrocephalus Work Description Massachusetts General Hospital will operate as a site for this… $100,505
The General Hospital Corporation $104,227

Quick answers

Who received award R01NS131606?

R01NS131606 was awarded to The Trustees Of Columbia University In The City Of New York by National Institute of Neurological Disorders and Stroke (National Institutes of Health) on May 15, 2023.

How much is award R01NS131606 worth?

$3,312,183 has been obligated on R01NS131606.

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