
==== Front
NPJ Digit Med
NPJ Digit Med
NPJ Digital Medicine
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Nature Publishing Group UK London

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10.1038/s41746-024-01227-0
Article
Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension
http://orcid.org/0000-0002-2973-6594
Gulamali Faris 12
http://orcid.org/0000-0003-1159-8354
Jayaraman Pushkala 12
http://orcid.org/0000-0003-1525-8541
Sawant Ashwin S. 12
http://orcid.org/0000-0002-5411-6637
Desman Jacob 12
Fox Benjamin 12
Chang Annette 12
http://orcid.org/0000-0003-4647-7704
Soong Brian Y. 3
Arivazagan Naveen 1
http://orcid.org/0000-0002-6364-6250
Reynolds Alexandra S. 4
Duong Son Q. 12
http://orcid.org/0000-0002-3343-744X
Vaid Akhil 12
http://orcid.org/0000-0001-8368-1742
Kovatch Patricia 1
http://orcid.org/0000-0003-4946-6533
Freeman Robert 1
Hofer Ira S. 12
Sakhuja Ankit 12
Dangayach Neha S. 4
Reich David S. 1
http://orcid.org/0000-0001-8135-6858
Charney Alexander W. 1
http://orcid.org/0000-0001-6319-4314
Nadkarni Girish N. Girish.Nadkarni@mountsinai.org

12
1 https://ror.org/04a9tmd77 grid.59734.3c 0000 0001 0670 2351 The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
2 https://ror.org/04a9tmd77 grid.59734.3c 0000 0001 0670 2351 The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
3 https://ror.org/04a9tmd77 grid.59734.3c 0000 0001 0670 2351 Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, NY USA
4 https://ror.org/04a9tmd77 grid.59734.3c 0000 0001 0670 2351 Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY USA
5 9 2024
5 9 2024
2024
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13 8 2024
© The Author(s) 2024
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Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000–2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020–2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80–0.80), 73.8% (95% CI, 72.0–75.6%), 73.5% (95% CI 72.5–74.5%), and 73.0% (95% CI, 72.0–74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32), and craniotomy (OR = 1.43; 95% CI, 1.12–1.84; P < 0.05 for all).

Subject terms

Diagnostic markers
Neurological disorders
https://doi.org/10.13039/100006108 U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS) UL1TR004419 Nadkarni Girish N. https://doi.org/10.13039/100000002 U.S. Department of Health & Human Services | National Institutes of Health (NIH) S10OD026880 S10OD030463 Kovatch Patricia U.S. Department of Health & Human Services | National Institutes of Health (NIH)issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Elevation in intracranial pressure (ICP) is common in severe acute brain injuries (SABI), such as stroke and traumatic brain injuries, contributing to secondary neurological damage1–3. The current gold standard for ICP monitoring is an invasive monitor, which carries risks of infection and hemorrhage, limiting its use4.

Non-invasive ICP estimation methods, like transcranial doppler (TCD) and optic nerve sheath diameter (ONSD) show promise for the detection of intracranial hypertension1,4–6. However, their utility is constrained by limited availability of specialized skills and equipment settings7,8 and the requirement of high clinical suspicion to administer these tests, possibly overlooking subclinical ICP abnormalities. Additionally, there is substantial variability in accuracy and clinical relevance for ICP monitoring using these technologies9–11.

Recognizing these limitations, recent research seeks to correlate physiological data with neurological conditions12. Yet, such studies are often restricted by their sample size, stringent data filters limiting real world application, and lack of external validation limiting generalizability12–16.

To address these limitations, we introduce a novel method, artificial intelligence derived intracranial pressure (aICP), an approach developed to predict intracranial hypertension using extracranial waveform data (Fig. 1). We created the largest model (10 million parameters) to-date for intracranial pressure, trained on over 270 hours of waveform data from a publicly available dataset. The model utilizes physiologic extracranial waveforms that are routinely collected in intensive care to generate a second-by-second prediction of whether intracranial pressure is elevated. Next, we used 258 h of waveform data from the Mount Sinai Hospital, to externally validate this approach.Fig. 1 Preprocessing of datasets and development of the aICP model.

a Schema for AICP (i) Initial dataset (ii) Filtering by monitoring modality (iii) Filtering waveforms (iv) Final dataset. b Model Architecture, Training, and Output.

There has been growing interest in the role of neuromonitoring across a wide range of different phenotypes including pregnancy, liver disease, kidney disease, sepsis, myocardial infarction, and acute respiratory distress syndrome17–19. We examined associations of the model’s predictions with clinical phenotypes both a priori and via unbiased phenome-wide scans.

Results

Cohort description

Average ages were 67.7 for the MIMIC-ICP. Females accounted for 40.8% of the MIMIC-ICP cohort, and 60.5% identified as White. In the MIMIC-ICP cohort, 53.3% of patients were on Medicare, 23.3% on private insurance, and 20.0% on Medicaid. The MIMIC-ICP cohort exhibited an average intracranial blood pressure of 8.9 mmHg, and intracranial hypertension values were observed for 9.7% of the total observation period in the MIMIC-ICP cohort. Neurological comorbidities were documented for all patients in both MIMIC-ICP, but only 19% of the MIMIC-ICP had documented cardiovascular comorbidities as defined by phecodes (Table 1)20. Cardiovascular comorbidities in our study encompassed conditions such as ischemic heart disease, valvular disease, and arrhythmias. Additionally, neurological comorbidities were classified into categories including vascular etiologies (e.g., intracerebral hemorrhage), infectious etiologies (e.g., abscesses), and oncological issues (e.g., tumors) (Supplementary Table 1). Patients with intracranial pressure monitors represent a range of different pathologies including traumatic brain injury (40.3%), cancer (25.0%), aneurysm (15.3%), and stroke (11.5%) (Supplemental Table 2).Table 1 Patient characteristics of three separate patient cohorts

	MIMIC-ICP with ICP monitors (n = 157)	MIMIC-GENERAL without ICP monitors (n = 1694)	MSH-ICP with ICP monitors (n = 56)	P value	
Amount of data (hours)	436.1	4705.6	155.6		
Mean Age, (SD)	67.7 (13.3)	69.3 (13.8)	54.6 (31.3)	<0.01	
Female, %	40.8	41	39.3		
Self-reported race/ethnicity, %	
 White	60.5	71.0	50.0	< 0.01	
 Black/African American	3	10	16.1	
 Non-White Hispanic	3	2.7	17.8	
 Other	34.5	16.3	14.3	
Insurance, %	
 Medicare	53.3	55.4	28.6	<0.01	
 Private	23.3	30.3	42.9	
 Medicaid	20.0	10.7	28.6	
 Other	3.4	3.6	0	
Measured intracranial pressure in mm of Hg, Mean (SD)	8.9 (7.3)	NA	16.7 (5.2)	<0.01	
Measured intracranial hypertension, %	9.7	NA	8.0	<0.01	
Neurological disease, %	100	52.8	100	<0.01	
Cardiovascular disease, %	19	56.1	100	<0.01	
P values for continuous values are calculated via ANOVA, and for binary variables are calculated via χ2 test. Neurological and Cardiovascular outcomes are defined by ICD-9 codes (Supplementary Table 3).

56 patients were admitted to the Mount Sinai Hospital between 2020 and 2022 with intracranial monitors comprised of MSH-ICP. The average age was 54.6 (SD, 31.3) with 39.3% of patients identifying as female. 50.0% of patients identified as White, 16.1% as Black or African American, 17.8% as Hispanic, with the remaining 14.3% identifying as other. 28.6% of patients were on Medicare, 42.8% on private insurance and 28.6% on Medicaid. This cohort demonstrated an average intracranial pressure of 16.7 mmHg. Elevated ICP was observed for a total of 8% of the total observation period. All patients in this cohort had neurological and cardiovascular comorbidities.

For the clinical association cohort (MIMIC-GENERAL), the average age was 69.3 years and 41% of the patients were female. 71.0% of patients identified as White, 10.0% as Black or African American, 2.7% as Hispanic, and 16.3% as other. 55.4% were on Medicare, 30.3% on private insurance, and 10.7% on Medicaid. 52.8% of patients had an identifiable neurological comorbidity and 56.1% had an identified cardiovascular comorbidity.

aICP performance

First, we assessed the patient-level performance of aICP using the testing set from MIMIC-ICP (Table 2). The Receiver Operating Characteristic (ROC) curve was employed to measure the model’s ability to detect elevated intracranial pressure (ICP > 15 mm Hg), with overall accuracy of 0.97, and an area-under-the-curve (AUC) of 0.91. The sensitivity of the model at a threshold of 0.5 was 0.87 and the specificity was 1.0. At higher cutoff points for elevated ICP (20 mmHg and 22 mmHg), the model had worse performance. This may be due to a reduced number of positive samples or different underlying physiologies (Supplementary Table 3). Our results outperformed those previously reported (Supplementary Table 4). We also benchmarked against other deep learning architectures for time series data (Supplementary Table 5). We note that improvements in model architectures mostly lead to increased sensitivity with little to no difference in specificity.Table 2 Performance of classification model for elevated ICP

	MIMIC-ICP	MSH-ICP	
Accuracy (95% CI)	0.972 (0.971–0.973)	0.738 (0.720–0.756)	
Area Under the Receiver Operating Curve (95% CI)	0.91 (0.90–91)	0.80 (0.79–0.80)	
Area Under the Precision Recall Curve (95% CI)	0.91 (0.91–0.91)	0.93 (0.93–0.93)	
Sensitivity (95% CI)	0.87 (0.86–0.87)	0.74 (0.73–0.75)	
Specificity (95% CI)	1.0 (1.0–1.0)	0.73 (0.72–0.74)	
Performance of patient level classification model on internal and external testing set, evaluated with accuracy (threshold: 0.5), sensitivity (threshold: 0.5), specificity (threshold: 0.5), area under the receiving-operator-curve, and area under the precision-recall curve for ICP elevation.

Next, we evaluated the performance on MSH-ICP, external validation test set. The area-under-the-curve on the external validation test set was 0.80, with an accuracy of 0.74, a sensitivity of 0.74 and a specificity of 0.73. The Receiver Operating Curve and Precision-Recall Curves for internal and external validation testing sets showed discriminatory performance of aICP to identify patients with ICP values greater than 15 mmHg (Fig. 2).Fig. 2 Performance of the aICP model.

a AUROC curve on the internal (MIMIC-ICP) and external (MSH-ICP) test cohorts. b AUPRC curve for internal (MIMIC-ICP) and external (MSH-ICP) test cohorts.

To evaluate the association of aICP with relevant clinical outcomes in patients without intracranial pressure monitors, we used MIMIC-GENERAL, a testing set consisting of patients that had extracranial waveforms (ABP, EKG, respiratory and PPG) and linked clinical data. aICP provided a patient level risk score for 1,694 patients. We calculated an odds ratio with respect to a 10 percentile increase in aICP. A ten-percentile increase in aICP was associated with an increased likelihood of stroke (OR = 2.12; 95% CI, 1.27–3.13; P = 4.06 × 10−3), brain malignancy (OR = 1.68; 95% CI, 1.09–2.60; P = 1.93 × 10−2), subdural hemorrhage (OR = 1.66; 95% CI, 1.07–2.57; P = 2.38 × 10−2), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32; P = 1.17 × 10−3). Moreover, when looking at patient procedures done over the course of admission, a ten percentile increase in aICP was associated with a percutaneous brain biopsy (OR = 1.58; 95% CI, 1.15-2.18; P = 4.58 × 10−3), and craniotomy and resection (OR = 1.43; 95% CI, 1.12–1.84; P = 4.10 × 10−3).

To see if the effect of aICP was larger in high-risk groups, we then stratified patients into low and high-risk groups, using the 75th percentile of predicted patient level risk score as the cutoff. We calculated odds ratios and P-values via the Fisher’s Exact Test for pre-determined outcomes. Patients in the top quartile demonstrated increased risk of subdural hemorrhage (OR = 24.2; P = 3.01 × 10−2), traumatic brain injury (OR = 6.04; P = 3.8 × 10−2), intracerebral hemorrhage (OR = 1.85; P = 1.32 × 10−3), and receiving a craniectomy (OR = 7.55; P = 1.58 × 10−2) or a percutaneous brain biopsy (OR = 5.03; P = 2.72 × 10−2).

Explainability behind elevated aICP

We visualized aICP and the corresponding input waveforms with two different patients across (Supplementary Fig. 1). In the first example, a patient demonstrates different slightly elevated aICP. The EKG is normal, the arterial waveform has a flat systolic phase, and the plethysmography is normal. In the second example, a patient with significantly elevated aICP demonstrates prolonged QT-interval, a high dicrotic notch on arterial waveform and bronchospasm on plethysmography. Pathological aICP correlates strongly with pathologies on other waveforms. However, more work needs to be done to highlight the underlying etiologies of intracranial hypertension and their manifestations in waveforms.

We also visualize aICP versus ICP on the scale of 30 minutes for 3 patients in the testing dataset (Supplementary Fig. 2).

Discussion

We introduce a novel deep learning system named aICP, that serves as a digital biomarker of intracranial hypertension. We developed and validated aICP both internally and externally for ICP greater than 15 mmHg in patients with invasive ICP monitors. In patients without ICP monitoring, we show that aICP is strongly associated with relevant neurological phenotypes. Thus, aICP may be able to serve as a screening tool in patients not subjected to intracranial monitoring, but using only standard monitoring equipment. The only other deep-learning based approach to ICP monitoring required transcranial doppler, which is specialized and not widely available21.

aICP demonstrated an AUROC of 80% in MSH-ICP, the external validation cohort. It demonstrated promise as a screening tool, with high sensitivity (99.5%) in the external validation dataset (99.5%) at a threshold of 0.5. The slight drop in performance compared to internal validation cohort may be related to the demographic differences in the cohorts, as well as differences in monitoring devices and neurosurgical practices. The training and validation cohorts exhibited comparable distributions of sex and prevalence of neurological disorders. However, there were significant differences in racial, ethnic, and insurance distributions across the cohorts. Additionally, patients in MSH-ICP demonstrated more disease severity, indicated by a significantly higher average intracranial blood pressure compared to patients in MIMIC-ICP. Moreover, all patients in the MSH-ICP had documented circulatory disorders, in contrast to the MIMIC-ICP cohort, where only 20% of patients had recorded cardiovascular phenotypes. Despite these differences, which may influence EKG and arterial line signals, the performance remained robust in the external validation cohort.

In patients who did not have ICP monitors, aICP demonstrated associations with specific neurologic pathologies and neurosurgical procedures. These associations suggest aICP’s clinical value in identifying high-risk patients who may benefit from enhanced monitoring. Notably, patients in the top quartile exhibited significantly elevated risks for subdural hemorrhage, traumatic brain injury, intracerebral hemorrhage, craniectomy, and percutaneous brain biopsy, indicating the tool’s potential to guide tailored interventions.

Thus, we provide two specific potential clinical use cases for aICP. First, aICP can provide intracranial monitoring to patients that have contraindications to invasive monitoring. For example, external ventricular drain placement is contraindicated in patients on anticoagulation due to bleeding risk. In patients on anticoagulation, aICP can be used in place of an external ventricular drain to monitor for ischemic-to-hemorrhagic conversion of a stroke. Second, because aICP needs only data from routine extracranial monitors, it could be utilized as early as arrival to the emergency department in the context of traumatic brain injury.

Despite its intended narrow use, a comprehensive exploration across non-neurological phenotypes revealed associations between aICP and a spectrum of different phenotypes. For example, elevated ICP was associated with severe renal failure and acute liver failure, which may provide insight into the etiology of hepatic and uremic encephalopathy22,23. Additionally, aICP demonstrated associations with intraocular pressure, which has been previously reported24,25. Moreover, we found that aICP is strongly associated with cardiac arrest. Past studies have demonstrated that changes on an electroencephalogram, transcranial doppler, and pupillometry are linked to prognosis following cardiac arrest26. These findings highlight the multifaceted potential of aICP in providing clinical insights through diverse associations beyond neurovascular domains, making it an asset for comprehensive patient assessment. Nevertheless, as a biomarker for intracranial hypertension, it is sensitive but not specific for underlying etiology. Consequently, it should be used to assist neurocritical care physicians rather than replace their diagnostic abilities.

As with all clinical tools, our method also has limitations. This is, to our knowledge, one of the largest clinical datasets used to train a deep learning algorithm for intracranial pressure estimation. While our clinical validation cohort (MSH-ICP) is very diverse in race and ethnicity, as it is reflective of the current diversity of New York City, it has fewer patients than the MIMIC-ICP (training) cohort. The significant difference in sample distribution between MIMIC-III and MSH-ICP may affect the interpretation of our results. However, awareness of disparities in racial, ethnic, and socioeconomic backgrounds is crucial for understanding how results may generalize to different patient populations. Some patients had to be excluded when we encountered significant patient drift in the measured variables. Prospective studies with large, diverse patient cohorts will be necessary to explore the practical implementation of aICP. Second, diagnoses are not time-locked to the waveform, which is a key limitation to this work. Therefore, predicted aICP can only be associated with phenotypes rather than causally or temporally linked. Future work should evaluate if aICP can be utilized to predict and prevent the development of acute neurological injury. Third, we utilize no electronic medical record data to guide the prediction of intracranial hypertension. While we anticipate that additional variables to waveform data may provide some insight into whether or not an individual patient has intracranial hypertension, it may be more useful in providing insight into the underlying etiology of intracranial hypertension. As a biomarker for intracranial hypertension, the model is sensitive but not specific for underlying etiology. Consequently, it should be in conjunction with variables like lipid levels, age, and gender to guide determining whether the etiology is stroke, tumor, or idiopathic intracranial hypertension. Fourth, we noticed that our architecture leads to general improvements in sensitivity, but less significant changes in specificity. To further improve performance, we intend to include more data modalities such as imaging, as well as introduce longer-term temporal dependencies with attention, which has shown some benefit in forecasting tasks.

Third, the phenome-wide association study between aICP and a wide range of other phenotypes attempts to evaluate its association with other conditions reflective of increased intracranial pressure and subsequent pathology. Nevertheless, we acknowledge that there are many potential confounding factors in waveforms of patients with other diagnoses like ARDS, liver failure, myocardial infarctions because acute interventions may require repositioning the patient, adding and removing arterial lines, and the delivery of life-saving drugs like norepinephrine, which all can have direct effects on aICP estimation. Thus, the phenome wide study should be considered hypothesis generating and significant future work is necessary to evaluate the associations and temporal relationship with other conditions, independent of confounders.

In summary, we developed aICP to detect intracranial pressure abnormalities demonstrating both strong performance and clinical significance. aICP does not have any non-standard hardware requirements, and consequently, implementation of aICP as a tool for point-of-care bedside monitoring at the start of admission can occur on routine bedside monitors. Moreover, an aICP-generated risk score for each patient over the course of a patient’s hospital stay, could reduce detection times, and improve outcomes, especially given the time-sensitive nature of neurological diseases such as stroke and hemorrhage. Finally, we anticipate that aICP has the potential to detect pathophysiological conditions like hepatic encephalopathy and glaucoma, which are associated with intracranial pressure changes but do not have clear neurovascular etiologies.

Methods

Study design

We conducted a retrospective study using data from two hospitals in two different cities in the United States. This study was approved by the Program for the Protection of Human Subjects at the Icahn School of Medicine at Mount Sinai (STUDY-20-00338).

Patients

We used single-admission data from two distinct sources: (1) the publicly available MIMIC III Waveform Database Matched Subset20,27,28 contains waveform records from bedside monitors for 10,282 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (Boston, MA) between 2001 and 201220, and (2) the MSH Bedmaster Matched Database, a database of waveform recordings for 50,894 patients admitted to the Mount Sinai Hospital (New York, NY) between 2018 and 2022 (Fig. 1ai). The latter was derived from a deployment of the BedmasterTM software. This software is engineered to extract and store real-time patient data obtained from networked General Electric Healthcare bedside patient multi-parameter monitors (Excel Medical Electronics, Jupiter, FL).

We created three cohorts from the two data sources described above: For training and internal validation of aICP, we used part of the MIMIC-III waveform dataset consisting of hospital stays with intracranial waveform recordings (MIMIC-ICP). For external validation of aICP, we used a waveform dataset from Mount Sinai Hospital consisting of hospital stays with intracranial waveform recordings (MSH-ICP). To map aICP to clinical associations, we used another, non-overlapping subset from the MIMIC-III waveform dataset consisting of patients without intracranial waveform recordings (MIMIC-GENERAL) (Fig. 1aii).

Procedures

Preprocessing

Since intracranial pressure recordings in both data sources were recorded at 125 Hz, we up-sampled or down-sampled the extracranial waveforms (ABP, EKG, respiratory and PPG) to 125 Hz. Real-time arterial blood pressure (ABP) was obtained via a continuous arterial sampling from an arterial line. Respiratory waveforms were obtained by plethysmography. We subsequently filtered for quality control (Fig. 1aiii). Patient waveforms were included only if they had no missing data and had a non-zero mean and non-zero standard deviation across all waveforms. Next, we normalized all input variables to values between 0 and 1 before propagating them through the model. All the intracranial pressure waveforms collected were via external ventricular drain measurements in the intraventricular space. For intracranial pressure, we utilized boundaries of 2 mmHg to 45 mmHg and excluded waveforms with standard deviations of zero, which may represent device malfunction. Subsequently, the intracranial waveforms were validated by two neurocritical care attendings. We found that this procedure removed significant portions of missing or physiologically implausible data that occurs during calibration and setup of the various lines. All patients with an intraventricular catheter had periods of elevated ICP > 15 mm Hg. As a result, we conducted weighted sampling of 10,000 1-second segments from each patient in the training dataset to achieve class balance and to avoid over-representing patients with longer stays in the intensive care unit.

After filtering, the MIMIC-ICP dataset was partitioned into subsets, with 100 patients allocated to the training dataset, 20 patients to the validation dataset, and 37 patients to the test dataset. In the training dataset, this amounted to 1,000,000 waveforms containing 277 hours of data, with 200,000 waveforms and 55.5 h in the validation dataset, and 360,000 waveforms with 102.8 h. The MSH-ICP dataset had 56 patients after filtering (Fig. 1aiv) with a total of 560,000 waveforms and 155.6 hours of data. MIMIC-GENERAL had 1694 after filtering waveforms and EHR data for missingness, containing 16,940,000 waveforms and 4,705.6 h of data.

We employed a threshold of 15 mm Hg to define intracranial hypertension, as previously described in literature and to maximize the number of positive samples, given the relative low frequency (8%) in the dataset29,30. While various thresholds in the literature have been reported, our dataset had relatively lower prevalence at higher pressures. We also analyzed and reported performance of aICP at higher ICP thresholds of 20 mmHg and 22 mmHg (Supplementary Table 3).

Model architecture

aICP utilizes a 5D-convolutional neural network which takes as input one second long segments of waveform data31,32. These predictions are aggregated to form patient level predictions. Each input consists of the 128 data points (approximately one second) of each of the five main perioperative waveforms (ABG, EKG leads II and V, respiratory, and PPG). The output for each input is a prediction at each second of whether the patient has ICP > 15 mm of Hg. Implementation of the model architecture is visualized (Fig. 1b). The loss function utilized a Tversky Loss, which is a modified DICE score accounting for the relative class imbalance between positive and negative samples33.

Briefly, the architecture follows a modified U-Net, a popular neural network design for semantic segmentation tasks, with a modified focus on handling input data with five channels rather than the traditional two or three34. It is composed of two convolutional layers with batch normalization and rectified linear unit (ReLU) activation functions, aiming to capture non-linear temporal features within the input data. We add skip connections to handle the high dimensionality and large number of channels.

Slices within a given time series were labeled using the slice-level prediction model and then transformed into a sequence. The patient-level model aggregated these individual second predictions, generating a unified risk score reflecting the patient’s frequency of dysregulation in intracranial pressure throughout their hospital stay. We utilize no database-related variables to generate the model architecture.

Model development and validation

The aICP model has approximately 10.4 million parameters and was trained on a single NVIDIA A100 GPU over the span of 3 days and 30 epochs. The segmentation performance was optimized using a modified Dice coefficient. The optimal model was selected and calibrated based on its performance on the validation dataset. Additionally, we employed hyperparameter optimization, early stopping and an Adam optimizer within the PyTorch Lightning framework.

Model performance metrics

We assessed the classification performance of time series and patient-level data using five key metrics: three threshold-dependent metrics: accuracy, sensitivity, specificity, and two threshold-independent metrics: area-under-the-receiver-operator-curve (AUROC) and area-under-the-precision-recall curve (AUPRC). We specifically report the AUPRC curve due to high rates of class imbalance in the internal and external test dataset. Receiver operating curves (ROCs) and confusion matrices evaluated the patient-level model performance in each classification task. The threshold was calibrated on the internal validation set, and then applied to the internal test set and external validation set.

We conducted a benchmark comparison against standard time series classification models. They included Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Temporal Convolutional Neural Networks (TCNs), which are state-of-the-art12.

Association with phenotypes and outcomes

To evaluate the association of aICP with relevant clinical outcomes in patients without intracranial pressure monitors, we used a testing set consisting of patients who had only extracranial waveforms (ABP, EKG, respiratory, PPG) in addition to clinical data from the electronic health record. aICP provided a patient-level risk score for 1694 patients. We also ran a phenome-wide association scan.

Statistical analysis

We calculated descriptive statistics for each cohort by mapping waveform IDs to electronic health records for available patients.

All experiments were seeded, to ensure reproducibility. Our models underwent consistent training, validation, and testing on identical datasets (Supplemental Table 1). To quantify the uncertainty in experimental results, confidence intervals were computed using non-parametric bootstrapping 50 times. All evaluation and statistical metrics were computed utilizing the torchmetrics and statsmodels packages35,36. Odds ratios were calculated by logistic regression and Fisher’s Exact Test.

To evaluate the significance of association of aICP with clinical phenotypes, we calculated an odds ratio with respect to a ten percentile increase in aICP. We report unadjusted P-values and as well as the adjusted threshold for correlated phenotypes (P < 3.33 × 10−4). We then stratified patients into low- and high-risk groups using the 75th percentile of predicted patient level risk score as the cutoff, and calculated odds ratios for pre-determined outcomes. P-value was calculated using the Fisher’s Exact Test.

We additionally fill out the TRIPOD reporting guideline and include it in the Supplement (Supplementary Fig. 4).

Supplementary information

Supplement

Supplementary information

The online version contains supplementary material available at 10.1038/s41746-024-01227-0.

Acknowledgements

This work was supported in part through the computational and data resources and staff expertize provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders played no role in study design, data collction, analysis and interpretation of data, or the writing of this manuscript.

Author contributions

Conceptualization – F.G., N.S.D., and G.N.N. Data curation – F.G., P.J., A.S.S., and B.F. Formal analysis – F.G., J.D., B.F., A.C., B.Y.S., N.A., S.Q.D., and A.V. Funding acquisition – P.K., A.W.C., and G.N.N. Methodology – F.G., A.S.R., A.V., I.S.H., A.S., N.S.D., and G.N.N. Resources – P.K., R.F., D.S.R., A.W.C., and G.N.N. Supervision – A.S.S., A.S., N.S.D., and G.N.N. Writing – original draft – F.G. and P.J. Writing – review & editing – F.G., A.S.S., I.S.H., A.S., and G.N.N. All authors had full access to the data in the study and had final responsibility for the decision to submit for publication. F.G. and A.S. have verified the data.

Data availability

MIMIC-GENERAL and MIMIC-ICP data can be obtained via the MIMIC-III online repository. MSH-ICP data is from the Mount Sinai Data Warehouse but contains private health information. We do not make this data publicly available due to concerns for healthcare data privacy.

Code availability

Code for aICP will be shared upon reasonable requests to the corresponding author.

Competing interests

aICP is the subject of a provisional patent application (Application No. 63/626,051) filed with the United States Patents and Trademarks Office, in which F.G., A.S., N.D., I.S.H., and G.N.N. are named inventors. Dr Girish Nadkarni is an Associate Editor for npj Digital Medicine. He had no role in editorial decisions about this manuscript. The other authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Fernando SM Diagnosis of elevated intracranial pressure in critically ill adults: systematic review and meta-analysis BMJ 2019 366 l4225 10.1136/bmj.l4225 31340932
Fernando, S. M. et al. Diagnosis of elevated intracranial pressure in critically ill adults: systematic review and meta-analysis. BMJ 366, l4225 (2019).31340932 10.1136/bmj.l4225
2. Hawryluk GWJ Intracranial pressure: current perspectives on physiology and monitoring Intensive Care Med. 2022 48 1471 1481 10.1007/s00134-022-06786-y 35816237
Hawryluk, G. W. J. et al. Intracranial pressure: current perspectives on physiology and monitoring. Intensive Care Med. 48, 1471–1481 (2022).35816237 10.1007/s00134-022-06786-y
3. Le Roux P Consensus summary statement of the international multidisciplinary consensus conference on multimodality monitoring in neurocritical care Neurocrit. Care 2014 21 1 26 10.1007/s12028-014-0041-5
Le Roux, P. et al. Consensus summary statement of the international multidisciplinary consensus conference on multimodality monitoring in neurocritical care. Neurocrit. Care 21, 1–26 (2014).10.1007/s12028-014-0041-5
4. Robba C Multimodal non-invasive assessment of intracranial hypertension: an observational study Crit. Care 2020 24 1 10 10.1186/s13054-020-03105-z 31898531
Robba, C. et al. Multimodal non-invasive assessment of intracranial hypertension: an observational study. Crit. Care 24, 1–10 (2020).31898531 10.1186/s13054-020-03105-z
5. Müller SJ Non-invasive intracranial pressure monitoring J. Clin. Med. Res. 2023 12 2209
Müller, S. J. et al. Non-invasive intracranial pressure monitoring. J. Clin. Med. Res. 12, 2209 (2023).
6. Dubourg J Javouhey E Geeraerts T Messerer M Kassai B Ultrasonography of optic nerve sheath diameter for detection of raised intracranial pressure: a systematic review and meta-analysis Intensive Care Med. 2011 37 1059 1068 10.1007/s00134-011-2224-2 21505900
Dubourg, J., Javouhey, E., Geeraerts, T., Messerer, M. & Kassai, B. Ultrasonography of optic nerve sheath diameter for detection of raised intracranial pressure: a systematic review and meta-analysis. Intensive Care Med. 37, 1059–1068 (2011).21505900 10.1007/s00134-011-2224-2
7. Flower, L. & Madhivathanan, P. Point-of-Care Ultrasound in Critical Care (Scion Publishing Ltd, 2022).
8. Chen W Zhang X Ye X Ying P Diagnostic accuracy of optic nerve sheath diameter on ultrasound for the detection of increased intracranial pressure in patients with traumatic brain injury: a systematic review and meta‑analysis Biomed. Rep. 2023 19 103 10.3892/br.2023.1685 38025834
Chen, W., Zhang, X., Ye, X. & Ying, P. Diagnostic accuracy of optic nerve sheath diameter on ultrasound for the detection of increased intracranial pressure in patients with traumatic brain injury: a systematic review and meta‑analysis. Biomed. Rep. 19, 103 (2023).38025834 10.3892/br.2023.1685
9. Chesnut RM A trial of intracranial-pressure monitoring in traumatic brain injury N. Engl. J. Entrep. 2013 367 2471 10.1056/NEJMoa1207363
Chesnut, R. M. A trial of intracranial-pressure monitoring in traumatic brain injury. N. Engl. J. Entrep. 367, 2471 (2013).10.1056/NEJMoa1207363
10. Nattino G Comparative effectiveness of intracranial pressure monitoring on 6-month outcomes of critically Ill patients with traumatic brain injury JAMA Netw. Open 2023 6 e2334214 10.1001/jamanetworkopen.2023.34214 37755832
Nattino, G. et al. Comparative effectiveness of intracranial pressure monitoring on 6-month outcomes of critically Ill patients with traumatic brain injury. JAMA Netw. Open 6, e2334214 (2023).37755832 10.1001/jamanetworkopen.2023.34214
11. Robba C Intracranial pressure monitoring in patients with acute brain injury in the intensive care unit (SYNAPSE-ICU): an international, prospective observational cohort study Lancet Neurol. 2021 20 548 558 10.1016/S1474-4422(21)00138-1 34146513
Robba, C. et al. Intracranial pressure monitoring in patients with acute brain injury in the intensive care unit (SYNAPSE-ICU): an international, prospective observational cohort study. Lancet Neurol. 20, 548–558 (2021).34146513 10.1016/S1474-4422(21)00138-1
12. Nair, S. S. et al. A real-time deep learning approach for inferring intracranial pressure from routinely measured extracranial waveforms in the Intensive Care Unit. bioRxiv10.1101/2023.05.16.23289747 (2023).
13. Brasil S A novel noninvasive technique for intracranial pressure waveform monitoring in critical care J. Pers. Med. 2021 11 1302 10.3390/jpm11121302 34945774
Brasil, S. et al. A novel noninvasive technique for intracranial pressure waveform monitoring in critical care. J. Pers. Med. 11, 1302 (2021).34945774 10.3390/jpm11121302
14. Megjhani M A deep learning framework for deriving noninvasive intracranial pressure waveforms from transcranial doppler Ann. Neurol. 2023 94 196 202 10.1002/ana.26682 37189299
Megjhani, M. et al. A deep learning framework for deriving noninvasive intracranial pressure waveforms from transcranial doppler. Ann. Neurol. 94, 196–202 (2023).37189299 10.1002/ana.26682
15. Lazaridis C Prediction of intracranial hypertension and brain tissue hypoxia utilizing high-resolution data from the BOOST-II clinical trial Neurotrauma Rep. 2022 3 473 478 10.1089/neur.2022.0055 36337077
Lazaridis, C. et al. Prediction of intracranial hypertension and brain tissue hypoxia utilizing high-resolution data from the BOOST-II clinical trial. Neurotrauma Rep. 3, 473–478 (2022).36337077 10.1089/neur.2022.0055
16. Lei, X. et al. An end-to-end deep learning framework for accurate estimation of intracranial pressure waveform characteristics. Eng. Appl. Artif. Intell. 130, 107686 (2024).
17. Godoy DA Robba C Paiva WS Rabinstein AA Acute intracranial hypertension during pregnancy: special considerations and management adjustments Neurocrit. Care 2022 36 302 316 10.1007/s12028-021-01333-x 34494211
Godoy, D. A., Robba, C., Paiva, W. S. & Rabinstein, A. A. Acute intracranial hypertension during pregnancy: special considerations and management adjustments. Neurocrit. Care 36, 302–316 (2022).34494211 10.1007/s12028-021-01333-x
18. Maslove DM Redefining critical illness Nat. Med. 2022 28 1141 1148 10.1038/s41591-022-01843-x 35715504
Maslove, D. M. et al. Redefining critical illness. Nat. Med. 28, 1141–1148 (2022).35715504 10.1038/s41591-022-01843-x
19. Battaglini D Pelosi P Robba C The importance of neuromonitoring in non brain injured patients Crit. Care 2022 26 78 10.1186/s13054-022-03914-4 35337357
Battaglini, D., Pelosi, P. & Robba, C. The importance of neuromonitoring in non brain injured patients. Crit. Care 26, 78 (2022).35337357 10.1186/s13054-022-03914-4
20. Goldberger AL PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 101 E215 20 10.1161/01.CIR.101.23.e215 10851218
Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215–20 (2000).10851218 10.1161/01.CIR.101.23.e215
21. Lau VI Arntfield RT Point-of-care transcranial Doppler by intensivists Crit. Ultrasound J. 2017 9 21 10.1186/s13089-017-0077-9 29030715
Lau, V. I. & Arntfield, R. T. Point-of-care transcranial Doppler by intensivists. Crit. Ultrasound J. 9, 21 (2017).29030715 10.1186/s13089-017-0077-9
22. Kamat P Invasive intracranial pressure monitoring is a useful adjunct in the management of severe hepatic encephalopathy associated with pediatric acute liver failure Pediatr. Crit. Care Med. 2012 13 e33 8 10.1097/PCC.0b013e31820ac08f 21263362
Kamat, P. et al. Invasive intracranial pressure monitoring is a useful adjunct in the management of severe hepatic encephalopathy associated with pediatric acute liver failure. Pediatr. Crit. Care Med. 13, e33–8 (2012).21263362 10.1097/PCC.0b013e31820ac08f
23. Sepehrinezhad A Zarifkar A Namvar G Shahbazi A Williams R Astrocyte swelling in hepatic encephalopathy: molecular perspective of cytotoxic edema Metab. Brain Dis. 2020 35 559 578 10.1007/s11011-020-00549-8 32146658
Sepehrinezhad, A., Zarifkar, A., Namvar, G., Shahbazi, A. & Williams, R. Astrocyte swelling in hepatic encephalopathy: molecular perspective of cytotoxic edema. Metab. Brain Dis. 35, 559–578 (2020).32146658 10.1007/s11011-020-00549-8
24. Baneke AJ Aubry J Viswanathan AC Plant GT The role of intracranial pressure in glaucoma and therapeutic implications Eye 2020 34 178 191 10.1038/s41433-019-0681-y 31776450
Baneke, A. J., Aubry, J., Viswanathan, A. C. & Plant, G. T. The role of intracranial pressure in glaucoma and therapeutic implications. Eye 34, 178–191 (2020).31776450 10.1038/s41433-019-0681-y
25. Rasulo FA Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study Crit. Care 2022 26 1 12 10.1186/s13054-022-03978-2 34980198
Rasulo, F. A. et al. Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study. Crit. Care 26, 1–12 (2022).34980198 10.1186/s13054-022-03978-2
26. A comparison of non-invasive versus invasive measures of intracranial pressure in hypoxic ischaemic brain injury after cardiac arrest. Resuscitation 137, 221–228 (2019).
27. Moody, B. et al. The MIMIC-III Waveform Database Matched Subset, physionet. org, (2017).
28. Johnson AEW MIMIC-III, a freely accessible critical care database Sci. Data 2016 3 160035 10.1038/sdata.2016.35 27219127
Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016).27219127 10.1038/sdata.2016.35
29. Hawryluk GWJ Analysis of normal high-frequency intracranial pressure values and treatment threshold in neurocritical care patients: insights into normal values and a potential treatment threshold JAMA Neurol. 2020 77 1150 1158 10.1001/jamaneurol.2020.1310 32539101
Hawryluk, G. W. J. et al. Analysis of normal high-frequency intracranial pressure values and treatment threshold in neurocritical care patients: insights into normal values and a potential treatment threshold. JAMA Neurol. 77, 1150–1158 (2020).32539101 10.1001/jamaneurol.2020.1310
30. Wijdicks EFM 10 or 15 or 20 or 40 mmHg? What is increased intracranial pressure and who said so? Neurocrit. Care 2022 36 1022 1026 10.1007/s12028-021-01438-3 35141861
Wijdicks, E. F. M. 10 or 15 or 20 or 40 mmHg? What is increased intracranial pressure and who said so? Neurocrit. Care 36, 1022–1026 (2022).35141861 10.1007/s12028-021-01438-3
31. Chen Z Post-processing refined ECG delineation based on 1D-UNet Biomed. Signal Process. Control 2023 79 104106 10.1016/j.bspc.2022.104106
Chen, Z. et al. Post-processing refined ECG delineation based on 1D-UNet. Biomed. Signal Process. Control 79, 104106 (2023).10.1016/j.bspc.2022.104106
32. Yan J Meng J Zhao J Bottom detection from backscatter data of conventional side scan sonars through 1D-UNet Remote Sens. 2021 13 1024 10.3390/rs13051024
Yan, J., Meng, J. & Zhao, J. Bottom detection from backscatter data of conventional side scan sonars through 1D-UNet. Remote Sens. 13, 1024 (2021).10.3390/rs13051024
33. Salehi, S. S. M., Erdogmus, D. & Gholipour, A. Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. in Machine Learning in Medical Imaging 379–387 (Springer International Publishing, 2017).
34. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 234–241 (Springer International Publishing, 2015).
35. Detlefsen N TorchMetrics - measuring reproducibility in PyTorch J. Open Source Softw. 2022 7 4101 10.21105/joss.04101
Detlefsen, N. et al. TorchMetrics - measuring reproducibility in PyTorch. J. Open Source Softw. 7, 4101 (2022).10.21105/joss.04101
36. Seabold, S. & Perktold, J. Statsmodels: Econometric And Statistical Modeling With Python (2010).
