BACKGROUND: Secondary insults such as hypotension, hypoxia, cerebral hypoperfusion, and intracranial hypertension are associated with poor outcome following severe traumatic brain injury (TBI). Preventing and minimizing the effect of secondary insults are essential in the management of severe TBI. At present, clinicians have no way to predict the development of these events, limiting their ability to plan appropriate timing of interventions. We hypothesized that processing continuous vital signs (VS) data using machine learning methods could predict the development of future intracranial hypertension. METHODS: Continuous VS including intracranial pressure (ICP), heart rate, systolic blood pressure, and mean arterial pressure data were collected from adult patients admitted to a single Level I trauma center requiring an ICP monitor. We tested the ability of Nearest Neighbor Regression (NNR) to predict changes in ICP by algorithmically learning from the patients' past physiology. RESULTS: Continuous VS were collected on 132 adult patients over a minimum of 3 hours per patient (5,466 hours total; 65,600 data points). Bland-Altman plots show that NNR provides good agreement in predicting actual ICP with a bias of 0.02 (±2 SD = 4 mm Hg) for the subsequent 5 minutes and -0.02 (±2 SD = 10 mm Hg) for the subsequent 2 hours. CONCLUSION: We have demonstrated that with the use of physiologic data, it is possible to predict with reasonable accuracy future ICP levels following severe TBI. NNR predicts ICP changes in clinically useful time frames. This ability to predict events may allow clinicians to make better decisions about the timing of necessary interventions, and this method could support the future development of minimally invasive ICP monitoring systems, which may lead to better overall clinical outcomes after severe TBI. LEVEL OF EVIDENCE: Prognostic study, level III.
BACKGROUND: Secondary insults such as hypotension, hypoxia, cerebral hypoperfusion, and intracranial hypertension are associated with poor outcome following severe traumatic brain injury (TBI). Preventing and minimizing the effect of secondary insults are essential in the management of severe TBI. At present, clinicians have no way to predict the development of these events, limiting their ability to plan appropriate timing of interventions. We hypothesized that processing continuous vital signs (VS) data using machine learning methods could predict the development of future intracranial hypertension. METHODS: Continuous VS including intracranial pressure (ICP), heart rate, systolic blood pressure, and mean arterial pressure data were collected from adult patients admitted to a single Level I trauma center requiring an ICP monitor. We tested the ability of Nearest Neighbor Regression (NNR) to predict changes in ICP by algorithmically learning from the patients' past physiology. RESULTS: Continuous VS were collected on 132 adult patients over a minimum of 3 hours per patient (5,466 hours total; 65,600 data points). Bland-Altman plots show that NNR provides good agreement in predicting actual ICP with a bias of 0.02 (±2 SD = 4 mm Hg) for the subsequent 5 minutes and -0.02 (±2 SD = 10 mm Hg) for the subsequent 2 hours. CONCLUSION: We have demonstrated that with the use of physiologic data, it is possible to predict with reasonable accuracy future ICP levels following severe TBI. NNR predicts ICP changes in clinically useful time frames. This ability to predict events may allow clinicians to make better decisions about the timing of necessary interventions, and this method could support the future development of minimally invasive ICP monitoring systems, which may lead to better overall clinical outcomes after severe TBI. LEVEL OF EVIDENCE: Prognostic study, level III.
Authors: Shara I Feld; Daniel S Hippe; Ljubomir Miljacic; Nayak L Polissar; Shu-Fang Newman; Bala G Nair; Monica S Vavilala Journal: J Neurosurg Anesthesiol Date: 2021-11-11 Impact factor: 3.969