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Background: Vasoactive drug infusion rates are titrated to achieve a desired effect, e.g., mean arterial blood pressure (MAP), rather than using infusion rates based on body weight. The purpose of this study is to evaluate a method to automatically identify a patient's sensitivity to sodium-nitroprusside, dobutamine or dopamine and to evaluate, whether an advisory system that predicts MAP 5 min in the future enhances a clinician's ability to titrate sodium-nitroprusside infusions.Methods: We used published models implemented in MATLAB to simulate the response of 100 individual patients to infusions of sodium-nitroprusside, dopamine and dobutamine. The simulated patient's sensitivity to the three drugs was identified using an adaptive filter approach, where MAP was altered in a binary stepwise fashion. Next, 9 nurses were asked to control the MAP of 6 of the simulated patients. For half of the patients, we used the identified sensitivity to predict and display MAP 5 min into the future.Results: Identifying each individual patient's sensitivity improved the accuracy of the MAP prediction by 75% for sodium-nitroprusside, 82% for dopamine and 52% for dobutamine over the MAP prediction based on an "average" patient's sensitivity. The advisory system shortened the median time to reach the desired MAP from 10.2 to 4.1 min, decreased the median number of infusion rate changes from 6 to 4, and resulted in a significant reduction of mental workload and effort.Discussion: Patient-specific drug sensitivity identifi- cation significantly improved the prediction of future MAP. By predicting and displaying the expected MAP 5 min in the future, the advisory system helped nurses titrate faster, reduced their perceived workload and might improve patient safety. [ABSTRACT FROM AUTHOR]

Flow sensors are often sensitive to the presence of volatile anesthetics. However, this sensitivity provides a unique opportunity to combine flow sensors of differing technological principles as an alternative to measuring volatile anesthetic gas concentration, particularly for austere settings. To determine the feasibility of flow sensor fusion for volatile anesthetic concentrations monitoring, eight flow sensors were tested with isoflurane, sevoflurane, and desflurane, ranging in concentrations from 0-4.5%, 0-3.5%, and 0-18%, respectively. Pairs of flow sensors were fit to the volatile anesthetic gas concentration with a leave-one-out cross-validation method to reduce the likelihood of overfitting. Bland-Altman was used for the final evaluation of sensor pair performance. Several sensor pairs yielded limits of agreement comparable to the rated accuracy of a commercial infrared spectrometer. The ultrasonic and orifice-plate flowmeters yielded the most combinations of viable sensor pairs for all three volatile anesthetic gases. Conclusion: Measuring volatile anesthetic gases using flow sensor fusion is a feasible low-cost, low-maintenance alternative to infrared spectroscopy. In this study, testing was done under steady-state conditions in 100% oxygen. Further testing is necessary to ensure sensor fusion performance under conditions that are more reflective of the clinical use case., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Many variants of the spatial QRS-T angle (QRS-Ta) are in use. We aimed to identify the best QRS-Ta for all-cause mortality prediction among different variants., Methods: 6667 individuals from the Inter99 General Population Study were followed for a median of 12.7 years. Vectorcardiograms were calculated using the Kors and Inverse Dower matrices. The QRS-Ta was calculated using both mean and peak vectors of the QRS- and T-loops. Hazard ratios (HR) for abnormal QRS-Tas were calculated using a Cox's Proportional Hazard Model., Results: The highest HR and largest AUC for all-cause mortality was obtained with the Kors matrix and the mean vector (HR = 2.2, 95% confidence interval: [1.38;3.43] p < 0.001, in men). There was interaction with the orientation of the QRS-T plane., Conclusion: For optimal prediction of all-cause mortality, the mean vectors in the QRS- and T-loops of the Kors-derived vectorcardiogram should be used. QRS-T plane orientation affects mortality prediction., (Copyright © 2018 Elsevier Inc. All rights reserved.)

There are a limited number of methods to guide and confirm the placement of a peripherally inserted central catheter (PICC) at the cavoatrial junction. The aim of this study was to design, test and validate a dual-wavelength, diode laser-based, single optical fiber instrument that would accurately confirm PICC tip location at the cavoatrial junction of an animal heart, in vivo. This was accomplished by inserting the optical fiber into a PICC and ratiometrically comparing simultaneous visible and near-infrared reflection intensities of venous and atrial tissues found near the cavoatrial junction. The system was successful in placing the PICC line tip within 5 mm of the cavoatrial junction., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Although nurses perform the majority of the clinical tasks in an intensive care unit, current patient monitors were not designed to support a nurse's workflow. Nurses constantly triage patients, deciding which patient is currently in the most need of care. To make this decision, nurses must observe the patient's vital signs and therapeutic device information from multiple sources. To obtain this information, they often have to enter the patient's room. This study addresses 3 hypotheses. Information provided by far-view monitoring displays (1) reduces the amount of time to determine which patient needs care first, (2) increases the accuracy of assigning priority to the right patient, and (3) reduces nurses mental workload. We developed 2 far-view displays to be read from a distance of 3 to 5 m without entering the patient's room. Both display vital signs, trends, alarms, infusion pump status, and therapy support indicators. To evaluate the displays, nurses were asked to use the displays to decide which of 2 patients required their attention first. They made 60 decisions: 20 with each far-view display and 20 decisions with a standard patient monitor next to an infusion pump. Sixteen nurses (median age of 27.5 years with 2.75 years of experience) participated in the study. Using the 2 far-view displays, nurses more accurately and rapidly identified stable patients and syringe pumps that were nearly empty. Median decision times were 11.3 and 12.4 seconds for the 2 far-view displays and 17.2 seconds for the control display. The 2 far-view displays reduced median decision-making times by 4.8 to 5.9 seconds, increased accuracy in assignment of priority in 2 of 7 patient conditions, and reduced nurses' frustration with the triaging task. In a clinical setting, the proposed far-view display might reduce nurses' mental workload and thereby increase patient safety.

When secreted from malignant cells, hyaluronan facilitates tumor invasion and metastasis, as inhibition of its export by zaprinast inhibited metastasis formation in mice. However, the precise steps of the metastatic cascade, which were influenced by zaprinast, have not been identified as yet. Here we analyzed the cell biological effects of the inhibitor on three human melanoma cell lines that differed in their hyaluronan production and their metastatic capability when xenografted into SCID mice. We measured the influence of zaprinast on cellular hyaluronan export, surface coat formation, proliferation, random migration, colony formation in soft agar, adhesion, and transepithelial resistance. Concentrations of zaprinast not affecting cell proliferation, adhesion and transepithelial resistance, nevertheless reduced hyaluronan export by 50%, surface coat formation, random migration, and colony formation in soft agar. These results indicate that hyaluronan enhances metastasis formation primarily in those steps of the metastatic cascade, which involves tumor cell migration.

A competent breathing circuit is mandatory to the safe and effective delivery of oxygen and anesthetic gases to the patient. Studies have shown that failures in the circuit are the most likely causes of anesthetic mishaps. Unfortunately, the complexity of the system renders traditional monitoring methods ineffective. We have developed a hierarchical artificial neural network monitor that is capable of examining ventilator signals. It was trained to identify 23 faults in the breathing circuit during ventilator controlled breathing and 21 faults during spontaneous breathing. The networks correctly identified a fault condition in 92% and 83% of cases for ventilator and spontaneous data, respectively. The correct fault type was found in 76% and 68% of cases for ventilator and spontaneous data, respectively. Results show that the network met our criteria for a holistic, specific, and vigilant monitoring system.

We have developed a neural network based alarm system that identifies 19 specific faults in the anesthesia breathing circuit, such as "Inspiratory Hose Leak," or "Y-Piece Disconnection." CO2, pressure, and expired flow waveforms, along with ventilator settings, were sampled by a personal computer. Fifty-two features, such as "maximum CO2" or "minimum pressure", were extracted from each breath, converted to "differential" features, normalized, and used as the inputs of a three layered feed-forward neural network. The network was trained, using backward error propagation with momentum, to classify each breath as normal or containing one of 19 faults. To collect the neutral network training set, seven dogs were anesthetized and ventilated using controlled ventilation. Each of 19 faults were created over a range of ventilator settings and fresh gas flows. The neural network correctly identified 83.1% of 550 events presented to it during testing. These preliminary results are an encouraging example of neural network applications in the field of clinical monitoring.