Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients

Ye, Guochang; Balasubramanian, Vignesh; Li, John K-J.; Kaya, Mehmet

doi:10.1109/jtehm.2022.3179874

Cited by 13 publications

(11 citation statements)

References 28 publications

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“…Most studies thus far were performed in pediatric patients (16). Available studies in adult patients present some limitations (17)(18)(19)(20). The algorithm developed by Güiza et al (20) was trained in a relatively small population of 178 neurocritical care patients, only 61% of which presenting an episode of elevated ICP.…”

Section: Discussionmentioning

confidence: 99%

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

Fong,

Feng,

Hubbard

et al. 2023

Critical Care Explorations

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OBJECTIVES: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes. DESIGN: Retrospective study. SETTING: Three hundred thirty-five ICUs at 208 hospitals in the United States. SUBJECTS: Adults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Predictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature). Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontime-varying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in the eICU dataset were extracted (46,207 timeblocks). The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients’ temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters. CONCLUSIONS: IntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.

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Section: Discussionmentioning

confidence: 99%

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

Fong,

Feng,

Hubbard

et al. 2023

Critical Care Explorations

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show abstract

“… 53 In 2022, Ye and colleagues published results of a RNN long short-term memory (LSTM) algorithm trained to forecast ICP values with a 10-min horizon. 54 The algorithm was trained using 50 Hz ICP wave data from 13 patients with TBI. Overall the LSTM model average accuracy, sensitivity, specificity, and RMSE was noted to be 94.62%, 74.91%, 94.83%, and 2.18 mm Hg, respectively.…”

Section: Icp Forecasting Algorithmsmentioning

confidence: 99%

“…Overall the LSTM model average accuracy, sensitivity, specificity, and RMSE was noted to be 94.62%, 74.91%, 94.83%, and 2.18 mm Hg, respectively. 54 …”

Section: Icp Forecasting Algorithmsmentioning

confidence: 99%

Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review

McNamara

Meka

Anstey

et al. 2023

Journal of Neurotrauma

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Traumatic intracranial hypertension (tIH) is a common and potentially lethal complication of moderate to severe traumatic brain injury (m-sTBI). It often develops with little warning and is managed reactively with the tiered application of intracranial pressure (ICP)-lowering interventions administered in response to an ICP rising above a set threshold. For over 45 years, a variety of research groups have worked toward the development of technology to allow for the preemptive management of tIH in the hope of improving patient outcomes. In 2022, the first operationalizable tIH prediction system became a reality. With such a system, ICP lowering interventions could be administered prior to the rise in ICP, thus protecting the patient from potentially damaging tIH episodes and limiting the overall ICP burden experienced. In this review, we discuss related approaches to ICP forecasting and IH prediction algorithms, which collectively provide the foundation for the successful development of an operational tIH prediction system. We also discuss operationalization and the statistical assessment of tIH algorithms. This review will be of relevance to clinicians and researchers interested in development of this technology as well as those with a general interest in the bedside application of machine learning (ML) technology.

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“…A study by Ye, G et al, applied artificial recurrent neural network using preprocessed ICP data for continuous ICP evaluation on traumatic brain injury patients [ 201 , 202 ]. The accuracy of this model was 94.62% with average sensitivity of 74.91%.…”

Section: Noninvasive and Artificial Intelligence Based Pressure Measu...mentioning

confidence: 99%

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

Manga

Muthavarapu

Redij

et al. 2023

Sensors

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The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

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Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients

Cited by 13 publications

References 28 publications

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

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