Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Abstract

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).

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.

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.