Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2022
Modeling acid-base balance during continuous kidney replacement therapy.
Clinical studies have suggested that use of bicarbonate-containing substitution and dialysis fluids during continuous kidney replacement therapy may result in excessive increases in the carbon dioxide concentration of blood; however, the technical parameters governing such changes are unclear. The current work used a mathematical model of acid-base chemistry of blood to predict its composition within and exiting the extracorporeal circuit during continuous veno-venous hemofiltration (CVVH) and continuous veno-venous hemodiafiltration (CVVHDF). Model predictions showed that a total substitution fluid infusion rate of 2 L/h (33% predilution) with a bicarbonate concentration of 32 mEq/L during CVVH at a blood flow rate of 200 mL/min resulted in only modest increases in plasma bicarbonate concentration by 2.0 mEq/L and partial pressure of dissolved carbon dioxide by 4.4 mmHg in blood exiting the extracorporeal circuit. ⋯ The changes in plasma acid-base levels were larger with a higher infusion rate of substitution fluid but smaller with a higher blood flow rate or use of substitution fluid with a lower bicarbonate concentration (22 mEq/L). Under comparable flow conditions and substitution fluid composition, model predicted changes in acid-base levels during CVVHDF were similar, but smaller, than those during CVVH. The described mathematical model can predict the effect of operating conditions on acid-base balance within and exiting the extracorporeal circuit during continuous kidney replacement therapy.
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J Clin Monit Comput · Feb 2022
Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema.
Discriminating acute respiratory distress syndrome (ARDS) from acute cardiogenic pulmonary edema (CPE) may be challenging in critically ill patients. Aim of this study was to investigate if gray-level co-occurrence matrix (GLCM) analysis of lung ultrasound (LUS) images can differentiate ARDS from CPE. The study population consisted of critically ill patients admitted to intensive care unit (ICU) with acute respiratory failure and submitted to LUS and extravascular lung water monitoring, and of a healthy control group (HCG). ⋯ HCG a statistical significance occurred only in two matrix features (correlation: P = 0.005; homogeneity: P = 0.048). The quantitative method proposed has shown high diagnostic accuracy in differentiating normal lung from ARDS or CPE, and good diagnostic accuracy in differentiating CPE and ARDS. Gray-level co-occurrence matrix analysis of LUS images has the potential to aid pulmonary edemas differential diagnosis.
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J Clin Monit Comput · Feb 2022
Detection of arterial pressure waveform error using machine learning trained algorithms.
In critically ill and high-risk surgical room patients, an invasive arterial catheter is often inserted to continuously measure arterial pressure (AP). The arterial waveform pressure measurement, however, may be compromised by damping or inappropriate reference placement of the pressure transducer. Clinicians, decision support systems, or closed-loop applications that rely on such information would benefit from the ability to detect error from the waveform alone. ⋯ A total of 40 h of monitoring time was recorded with approximately 120,000 heart beats featurized. For all error states, ROC AUCs for algorithm performance on classification of the state were greater than 0.9; when using patient-specific calibrated data AUCs were 0.94, 0.95, and 0.99 for the transducer low, transducer high, and damped conditions respectively. Machine-learning trained algorithms were able to discriminate arterial line transducer error states from the waveform alone with a high degree of accuracy.
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J Clin Monit Comput · Feb 2022
Efficacy of continuous monitoring of maternal temperature during labor using wireless axillary sensors.
Neonatal early onset sepsis (EOS) occurs in 0.5-0.8/1000 live births and is a major cause of morbidity and mortality. Its presenting signs in newborns are non-specific, so risk assessment before birth is essential. Maternal fever during labor is the strongest predictor of EOS, but the current standard is for infrequent manual determinations of temperature. ⋯ Manual measurements missed 32 fevers > 38 °C and 13 fevers > 38.5 °C that were identified by continuous. Continuous measurement of maternal temperature for the duration of labor is practical and accurate. It may be more sensitive for identifying infants at risk for EOS than the current practice, enabling earlier and more effective targeted treatment of affected infants.