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Zhu J, Shan Y, Li Y, Xu X, Wu X, Xue Y, Gao G. Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury. Intensive Care Med Exp 2024; 12:58. [PMID: 38954280 DOI: 10.1186/s40635-024-00643-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/17/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients. METHODS We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting. RESULTS The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results. CONCLUSIONS The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.
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Affiliation(s)
- Jun Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yihua Li
- Department of Neurosurgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Xuxu Xu
- Department of Neurosurgery, Minhang Hospital Fudan University, Shanghai, 201199, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China
| | - Yajun Xue
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201600, China.
| | - Guoyi Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
- Neurotrauma Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, China.
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Nair SS, Guo A, Boen J, Aggarwal A, Chahal O, Tandon A, Patel M, Sankararaman S, Durr NJ, Azad TD, Pirracchio R, Stevens RD. A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit. Comput Biol Med 2024; 177:108677. [PMID: 38833800 DOI: 10.1016/j.compbiomed.2024.108677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024]
Abstract
Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.
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Affiliation(s)
- Shiker S Nair
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
| | - Alina Guo
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Joseph Boen
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Ataes Aggarwal
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Ojas Chahal
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Arushi Tandon
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Meer Patel
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Sreenidhi Sankararaman
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Nicholas J Durr
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Tej D Azad
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Romain Pirracchio
- Department of Anesthesia and Perioperative Care, UCSF, San Francisco, USA
| | - Robert D Stevens
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Vakitbilir N, Froese L, Gomez A, Sainbhi AS, Stein KY, Islam A, Bergmann TJG, Marquez I, Amenta F, Ibrahim Y, Zeiler FA. Time-Series Modeling and Forecasting of Cerebral Pressure-Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature. SENSORS (BASEL, SWITZERLAND) 2024; 24:1453. [PMID: 38474990 DOI: 10.3390/s24051453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
The modeling and forecasting of cerebral pressure-flow dynamics in the time-frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure-flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.
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Affiliation(s)
- Nuray Vakitbilir
- Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Logan Froese
- Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Alwyn Gomez
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
| | - Amanjyot Singh Sainbhi
- Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Kevin Y Stein
- Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Abrar Islam
- Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Tobias J G Bergmann
- Undergraduate Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Izabella Marquez
- Undergraduate Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Fiorella Amenta
- Undergraduate Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Younis Ibrahim
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada
| | - Frederick A Zeiler
- Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
- Division of Anesthesia, Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
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van Hal ST, van der Jagt M, van Genderen ME, Gommers D, Veenland JF. Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review. Neurocrit Care 2024:10.1007/s12028-023-01910-2. [PMID: 38212559 DOI: 10.1007/s12028-023-01910-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/01/2023] [Indexed: 01/13/2024]
Abstract
Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating "real-time model testing" stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.
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Affiliation(s)
- S T van Hal
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands.
| | - M van der Jagt
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - M E van Genderen
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - D Gommers
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - J F Veenland
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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Fong N, Feng J, Hubbard A, Dang LE, Pirracchio R. IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study. Crit Care Explor 2024; 6:e1024. [PMID: 38161734 PMCID: PMC10756747 DOI: 10.1097/cce.0000000000001024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
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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Affiliation(s)
- Nicholas Fong
- Department of Anesthesia and Perioperative Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, CA
- School of Medicine, University of California San Francisco, San Francisco, CA
| | - Jean Feng
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA
| | - Alan Hubbard
- Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA
| | - Lauren Eyler Dang
- Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA
| | - Romain Pirracchio
- Department of Anesthesia and Perioperative Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA
- Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA
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6
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Fonseca J, Liu X, Oliveira HP, Pereira T. Mortality prediction using medical time series on TBI patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107806. [PMID: 37832428 DOI: 10.1016/j.cmpb.2023.107806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 07/29/2023] [Accepted: 09/08/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences making more complex the medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Artificial intelligence (AI) methods can take advantage of existing data by performing helpful predictions and guiding physicians toward a better prognosis and, consequently, better healthcare. The objective of this work was to develop learning models and evaluate their capability of predicting the mortality of TBI. The predictive model would allow the early assessment of the more serious cases and scarce medical resources can be pointed toward the patients who need them most. METHODS Long Short Term Memory (LSTM) and Transformer architectures were tested and compared in performance, coupled with data imbalance, missing data, and feature selection strategies. From the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, a cohort of TBI patients was selected and an analysis of the first 48 hours of multiple time series sequential variables was done to predict hospital mortality. RESULTS The best performance was obtained with the Transformer architecture, achieving an AUC of 0.907 with the larger group of features and trained with class proportion class weights and binary cross entropy loss. CONCLUSIONS Using the time series sequential data, LSTM and Transformers proved to be both viable options for predicting TBI hospital mortality in 48 hours after admission. Overall, using sequential deep learning models with time series data to predict TBI mortality is viable and can be used as a helpful indicator of the well-being of patients.
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Affiliation(s)
- João Fonseca
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering, University of Porto, Porto, Portugal
| | - Xiuyun Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin, China
| | - Hélder P Oliveira
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FCUP - Faculty of Science, University of Porto, Porto, Portugal
| | - Tania Pereira
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering, University of Porto, Porto, Portugal.
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