Journal of clinical monitoring and computing
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J Clin Monit Comput · Jun 2017
Comparative StudyComputerised respiratory sounds can differentiate smokers and non-smokers.
Cigarette smoking is often associated with the development of several respiratory diseases however, if diagnosed early, the changes in the lung tissue caused by smoking may be reversible. Computerised respiratory sounds have shown to be sensitive to detect changes within the lung tissue before any other measure, however it is unknown if it is able to detect changes in the lungs of healthy smokers. This study investigated the differences between computerised respiratory sounds of healthy smokers and non-smokers. ⋯ Significant differences between computerised respiratory sounds of smokers and non-smokers have been found. Changes in respiratory sounds are often the earliest sign of disease. Thus, computerised respiratory sounds might be a promising measure to early detect smoking related respiratory diseases.
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J Clin Monit Comput · Jun 2017
Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks.
Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEPapp) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEPtot) is the sum of PEEPappand intrinsic PEEP (PEEPi), a hidden component. To measure PEEPtot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). ⋯ ANN agreement with reference PEEPtotwas assessed with the Bland-Altman method. Bland Altman analysis of estimation error by ANN showed -0.40 ± 2.84 (expressed as bias ± precision) and ±5.58 as limits of agreement (data expressed as cmH2O). The ANNs estimated the PEEPtotwell at different levels of PEEPappunder dynamic conditions, opening up new possibilities in monitoring PEEPiin critically ill patients who require ventilator treatment.
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Understanding the use of patient monitoring systems in emergency and acute facilities may help to identify reasons for failure to identify risk patients in these settings. Hence, we investigate factors related to the utilization of automated monitoring for patients admitted to an acute admission unit by introducing monitor load as the proportion between monitored time and length of stay. A cohort study of patients admitted and registered to patient monitors in the period from 10/10/2013 to 1/10/2014 at the acute admission unit of Odense University Hospital in Denmark. ⋯ Higher levels of severity were related to higher degrees of monitoring, but being admitted to the surgical wing reduce how much patients were monitored, and periods with many concurrent patients lead to a small increase in monitoring. We found a significant variation concerning how much patients were monitored during admission to an acute admission unit. Our results point to potential patient safety improvements in clinical procedures, and advocate an awareness of how patient monitoring systems are utilized.