Carlotta Ginevra Nucci1, Pasquale De Bonis2, Annunziato Mangiola2, Pietro Santini2, Marco Sciandrone3, Arnaldo Risi4, Carmelo Anile2. 1. Institute of Neurosurgery, Catholic University School of Medicine, Largo A. Gemelli 8, Rome, Italy. carlottaginevra@gmail.com. 2. Institute of Neurosurgery, Catholic University School of Medicine, Largo A. Gemelli 8, Rome, Italy. 3. Department of Information Engineering, University of Florence, Via di Santa Marta 3, Florence, Italy. 4. Institute of System Analysis and Informatics, National Research Council, Viale Manzoni 30, Rome, Italy.
Abstract
BACKGROUND: Recently, different software has been developed to automatically analyze multiple intracranial pressure (ICP) parameters, but the suggested methods are frequently complex and have no clinical correlation. The objective of this study was to assess the clinical value of a new morphological classification of the cerebrospinal fluid pulse pressure waveform (CSFPPW), comparing it to the elastance index (EI) and CSF-outflow resistance (Rout), and to test the efficacy of an automatic ICP analysis. METHODS: An artificial neural network (ANN) was trained to classify 60 CSFPPWs in four different classes, according to their morphology, and its efficacy was compared to an expert examiner's classification. The morphology of CSFPPW, recorded in 60 patients at baseline, was compared to EI and Rout calculated at the end of an intraventricular infusion test to validate the utility of the proposed classification in patients' clinical evaluation. RESULTS: The overall concordance in CSFPPW classification between the expert examiner and the ANN was 88.3 %. An elevation of EI was statistically related to morphological class' progression. All patients showing pathological baseline CSFPPW (class IV) revealed an alteration of CSF hydrodynamics at the end of their infusion test. CONCLUSIONS: The proposed morphological classification estimates the global ICP wave and its ability to reflect or predict an alteration in CSF hydrodynamics. An ANN can be trained to efficiently recognize four different CSF wave morphologies. This classification seems helpful and accurate for diagnostic use.
BACKGROUND: Recently, different software has been developed to automatically analyze multiple intracranial pressure (ICP) parameters, but the suggested methods are frequently complex and have no clinical correlation. The objective of this study was to assess the clinical value of a new morphological classification of the cerebrospinal fluid pulse pressure waveform (CSFPPW), comparing it to the elastance index (EI) and CSF-outflow resistance (Rout), and to test the efficacy of an automatic ICP analysis. METHODS: An artificial neural network (ANN) was trained to classify 60 CSFPPWs in four different classes, according to their morphology, and its efficacy was compared to an expert examiner's classification. The morphology of CSFPPW, recorded in 60 patients at baseline, was compared to EI and Rout calculated at the end of an intraventricular infusion test to validate the utility of the proposed classification in patients' clinical evaluation. RESULTS: The overall concordance in CSFPPW classification between the expert examiner and the ANN was 88.3 %. An elevation of EI was statistically related to morphological class' progression. All patients showing pathological baseline CSFPPW (class IV) revealed an alteration of CSF hydrodynamics at the end of their infusion test. CONCLUSIONS: The proposed morphological classification estimates the global ICP wave and its ability to reflect or predict an alteration in CSF hydrodynamics. An ANN can be trained to efficiently recognize four different CSF wave morphologies. This classification seems helpful and accurate for diagnostic use.
Authors: V R Bollela; G Frigieri; F C Vilar; D L Spavieri; F J Tallarico; G M Tallarico; R A P Andrade; T M de Haes; O M Takayanagui; A M Catai; S Mascarenhas Journal: Braz J Med Biol Res Date: 2017-08-07 Impact factor: 2.590