Dataset Information

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.

ABSTRACT:

Importance

Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring.

Objective

Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data.

Design

Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes.

Setting

MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation.

Participants

Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694).

Exposures

Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO₂.

Main outcomes and measures

Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association.

Results

The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all).

Conclusions and relevance

aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.

SUBMITTER: Gulamali F

PROVIDER: S-EPMC10863000 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.

Gulamali Faris F Jayaraman Pushkala P Sawant Ashwin S AS Desman Jacob J Fox Benjamin B Chang Annie A Soong Brian Y BY Arivazaghan Naveen N Reynolds Alexandra S AS Duong Son Q SQ Vaid Akhil A Kovatch Patricia P Freeman Robert R Hofer Ira S IS Sakhuja Ankit A Dangayach Neha S NS Reich David S DS Charney Alexander W AW Nadkarni Girish N GN

medRxiv : the preprint server for health sciences 20240130

<h4>Importance</h4>Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring.<h4>Objective</h4>Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data.<h4>Design</h4>Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associa ...[more]

PMID: 38352556

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Project description:BackgroundCentral blood pressure (cBP) is a better indicator of cardiovascular morbidity and mortality than peripheral BP (pBP). However, direct cBP measurement requires invasive techniques and indirect cBP measurement is based on rigid and empirical transfer functions applied to pBP. Thus, development of a personalized and well-validated method for non-invasive derivation of cBP from pBP is necessary to facilitate the clinical routine. The purpose of the present study was to develop a novel blind source separation tool to separate a single recording of pBP into their pressure waveforms composing its dynamics, to identify the compounds that lead to pressure waveform distortion at the periphery, and to estimate the cBP. The approach is patient-specific and extracts the underlying blind pressure waveforms in pBP without additional brachial cuff calibration or any a priori assumption on the arterial model.MethodsThe intra-arterial femoral BPfe and intra-aortic pressure BPao were anonymized digital recordings from previous routine cardiac catheterizations of eight patients at the German Heart Centre Berlin. The underlying pressure waveforms in BPfe were extracted by the single-channel independent component analysis (SCICA). The accuracy of the SCICA model to estimate the whole cBP waveform was evaluated by the mean absolute error (MAE), the root mean square error (RMSE), the relative RMSE (RRMSE), and the intraclass correlation coefficient (ICC). The agreement between the intra-aortic and estimated parameters including systolic (SBP), diastolic (DBP), mean arterial pressure (MAP), and pulse pressure (PP) was evaluated by the regression and Bland-Altman analyses.ResultsThe SCICA tool estimated the cBP waveform non-invasively from the intra-arterial BPfe with an MAE of 0.159 ± 1.629, an RMSE of 5.153 ± 0.957 mmHg, an RRMSE of 5.424 ± 1.304%, and an ICC of 0.94, as well as two waveforms contributing to morphological distortion at the femoral artery. The regression analysis showed a strong linear trend between the estimated and intra-aortic SBP, DBP, MAP, and PP with high coefficient of determination R2 of 0.98, 0.99, 0.99, and 0.97 respectively. The Bland-Altman plots demonstrated good agreement between estimated and intra-aortic parameters with a mean error and a standard deviation of difference of -0.54 ± 2.42 mmHg [95% confidence interval (CI): -5.28 to 4.20] for SBP, -1.97 ± 1.62 mmHg (95% CI: -5.14 to 1.20) for DBP, -1.49 ± 1.40 mmHg (95% CI: -4.25 to 1.26) for MAP, and 1.43 ± 2.79 mmHg (95% CI: -4.03 to 6.90) for PP.ConclusionsThe SCICA approach is a powerful tool that identifies sources contributing to morphological distortion at peripheral arteries and estimates cBP.

Dataset Information

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.

Importance

Objective

Design

Setting

Participants

Exposures

Main outcomes and measures

Results

Conclusions and relevance

Publications

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.

Similar Datasets

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