Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2023
ReviewThe impact of continuous wireless monitoring on adverse device effects in medical and surgical wards: a review of current evidence.
Novel technologies allow continuous wireless monitoring systems (CWMS) to measure vital signs and these systems might be favorable compared to intermittent monitoring regarding improving outcomes. However, device safety needs to be validated because uncertain evidence challenges the clinical implementation of CWMS. This review investigates the frequency of device-related adverse events in patients monitored with CWMS in general hospital wards. ⋯ The studies of the SensiumVitals® patch, the iThermonitor, and the ViSi Mobile® device reported 28 (9%), 25 (5%), and 1 (3%) ADEs, respectively. No ADEs were reported using the HealthPatch, WARD 24/7 system, or Coviden Alarm Management. Current evidence suggests that CWMS are safe to use but systematic reporting of all adverse device effects is warranted.
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J Clin Monit Comput · Feb 2023
Quantification of respiratory sounds by a continuous monitoring system can be used to predict complications after extubation: a pilot study.
To show that quantification of abnormal respiratory sounds by our developed device is useful for predicting respiratory failure and airway problems after extubation. A respiratory sound monitoring system was used to collect respiratory sounds in patients undergoing extubation. The recorded respiratory sounds were subsequently analyzed. ⋯ For bilateral lateral thoracic sounds, the QV of fine crackles was significantly higher in the outcome group vs the non-outcome group. Cervical inspiratory sounds volume (average of five breaths) immediately after extubation was significantly louder in the outcome group vs non-outcome group (63.3 dB vs 54.3 dB, respectively; p < 0.001). Quantification of abnormal respiratory sounds and respiratory volume may predict respiratory failure and airway problems after extubation.
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J Clin Monit Comput · Feb 2023
Observational StudyRelationship between ANI and qNOX and between MAC and qCON during outpatient laparoscopic cholecystectomy using remifentanil and desflurane without muscle relaxants: a prospective observational preliminary study.
This study was designed to investigate qCON and qNOX variations during outpatient laparoscopic cholecystectomy using remifentanil and desflurane without muscle relaxants and compare these indices with ANI and MAC. Adult patients undergoing outpatient laparoscopic cholecystectomy were included in this prospective observational study. Maintenance of anesthesia was performed using remifentanil targeted to ANI 50-80 and desflurane targeted to MAC 0.8-1.2 without muscle relaxants. ⋯ While qCON correlated with MAC, the correlation of overall qCON and ANI was poor but significant. Additionally, the qNOX weakly correlated with the remifentanil infusion rate. This observational study suggests that the proposed ranges of 40-60 for both indexes may correspond to adequate levels of hypnosis and analgesia during general anesthesia, although this should be confirmed by further research.
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J Clin Monit Comput · Feb 2023
Development and validation of a model to calculate anesthetic agent consumption from inspired and end-expired concentrations, minute ventilation, fresh gas flow and dead space ventilation.
Anesthetic agent consumption is often calculated as the product of fresh gas flow (FGF) and vaporizer dial setting (FVAP). Because FVAP of conventional vaporizers is not registered in automated anesthesia records, retrospective agent consumption studies are hampered. The current study examines how FVAP can be retrospectively calculated from the agent's inspired (FIN) and end-expired concentration (FET), FGF, and minute ventilation (MV). ⋯ The model predicted dialed FVAP well, with a MDPE of -1 (-11, 6) % and MDAPE of 8 (4, 17) %. FVAP can be retrospectively calculated from FIN, FET, FGF, and MV plus an agent specific dead space fraction factor with a degree of error that we believe suffices for retrospective sevoflurane consumption analyses. Performance with other agents and N2O awaits further validation.
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J Clin Monit Comput · Feb 2023
Pulmonary gas exchange evaluated by machine learning: a computer simulation.
Using computer simulation we investigated whether machine learning (ML) analysis of selected ICU monitoring data can quantify pulmonary gas exchange in multi-compartment format. A 21 compartment ventilation/perfusion (V/Q) model of pulmonary blood flow processed 34,551 combinations of cardiac output, hemoglobin concentration, standard P50, base excess, VO2 and VCO2 plus three model-defining parameters: shunt, log SD and mean V/Q. From these inputs the model produced paired arterial blood gases, first with the inspired O2 fraction (FiO2) adjusted to arterial saturation (SaO2) = 0.90, and second with FiO2 increased by 0.1. 'Stacked regressor' ML ensembles were trained/validated on 90% of this dataset. ⋯ Single-point estimates were less accurate: R2 = 0.77-0.89, slope = 0.991-0.993, intercept = 0.009-0.334. ML applications using blood gas, indirect calorimetry, and cardiac output data can quantify pulmonary gas exchange in terms describing a 20 compartment V/Q model of pulmonary blood flow. High fidelity reports require data from two FiO2 settings.