Behnood Gholami1, Timothy S Phan2, Wassim M Haddad3, Andrew Cason4, Jerry Mullis4, Levi Price4, James M Bailey4. 1. Autonomous Healthcare, Inc., Hoboken, NJ, USA. Electronic address: bgholami@autonomoushealthcare.com. 2. Autonomous Healthcare, Inc., Hoboken, NJ, USA. 3. School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA. 4. Northeast Georgia Medical Center, Gainesville, GA, USA.
Abstract
BACKGROUND: - Acute respiratory failure is one of the most common problems encountered in intensive care units (ICU) and mechanical ventilation is the mainstay of supportive therapy for such patients. A mismatch between ventilator delivery and patient demand is referred to as patient-ventilator asynchrony (PVA). An important hurdle in addressing PVA is the lack of a reliable framework for continuously and automatically monitoring the patient and detecting various types of PVA. METHODS: - The problem of replicating human expertise of waveform analysis for detecting cycling asynchrony (i.e., delayed termination, premature termination, or none) was investigated in a pilot study involving 11 patients in the ICU under invasive mechanical ventilation. A machine learning framework is used to detect cycling asynchrony based on waveform analysis. RESULTS: - A panel of five experts with experience in PVA evaluated a total of 1377 breath cycles from 11 mechanically ventilated critical care patients. The majority vote was used to label each breath cycle according to cycling asynchrony type. The proposed framework accurately detected the presence or absence of cycling asynchrony with sensitivity (specificity) of 89% (99%), 94% (98%), and 97% (93%) for delayed termination, premature termination, and no cycling asynchrony, respectively. The system showed strong agreement with human experts as reflected by the kappa coefficients of 0.90, 0.91, and 0.90 for delayed termination, premature termination, and no cycling asynchrony, respectively. CONCLUSIONS: - The pilot study establishes the feasibility of using a machine learning framework to provide waveform analysis equivalent to an expert human.
BACKGROUND: - Acute respiratory failure is one of the most common problems encountered in intensive care units (ICU) and mechanical ventilation is the mainstay of supportive therapy for such patients. A mismatch between ventilator delivery and patient demand is referred to as patient-ventilator asynchrony (PVA). An important hurdle in addressing PVA is the lack of a reliable framework for continuously and automatically monitoring the patient and detecting various types of PVA. METHODS: - The problem of replicating human expertise of waveform analysis for detecting cycling asynchrony (i.e., delayed termination, premature termination, or none) was investigated in a pilot study involving 11 patients in the ICU under invasive mechanical ventilation. A machine learning framework is used to detect cycling asynchrony based on waveform analysis. RESULTS: - A panel of five experts with experience in PVA evaluated a total of 1377 breath cycles from 11 mechanically ventilated critical care patients. The majority vote was used to label each breath cycle according to cycling asynchrony type. The proposed framework accurately detected the presence or absence of cycling asynchrony with sensitivity (specificity) of 89% (99%), 94% (98%), and 97% (93%) for delayed termination, premature termination, and no cycling asynchrony, respectively. The system showed strong agreement with human experts as reflected by the kappa coefficients of 0.90, 0.91, and 0.90 for delayed termination, premature termination, and no cycling asynchrony, respectively. CONCLUSIONS: - The pilot study establishes the feasibility of using a machine learning framework to provide waveform analysis equivalent to an expert human.
Authors: Gregory B Rehm; Sang Hoon Woo; Xin Luigi Chen; Brooks T Kuhn; Irene Cortes-Puch; Nicholas R Anderson; Jason Y Adams; Chen-Nee Chuah Journal: IEEE Pervasive Comput Date: 2020-05-25 Impact factor: 1.603
Authors: Candelaria de Haro; Ana Ochagavia; Josefina López-Aguilar; Sol Fernandez-Gonzalo; Guillem Navarra-Ventura; Rudys Magrans; Jaume Montanyà; Lluís Blanch Journal: Intensive Care Med Exp Date: 2019-07-25
Authors: Francesco Mojoli; Marco Pozzi; Anita Orlando; Isabella M Bianchi; Eric Arisi; Giorgio A Iotti; Antonio Braschi; Laurent Brochard Journal: Crit Care Date: 2022-01-30 Impact factor: 9.097