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Cardiac output (CO) measurement is crucial for the guidance of therapeutic decisions in critically ill and high-risk surgical patients. Newly developed completely non-invasive CO technologies are commercially available; however, their accuracy and precision have not recently been evaluated in a meta-analysis. We conducted a systematic search using PubMed, Cochrane Library of Clinical Trials, Scopus, and Web of Science to review published data comparing CO measured by bolus thermodilution with commercially available non-invasive technologies including pulse wave transit time, non-invasive pulse contour analysis, thoracic electrical bioimpedance/bioreactance, and CO2 rebreathing. The non-invasive CO technology was considered acceptable if the pooled estimate of percentage error was

Objective: The 2018 National NLP Clinical Challenge (2018 n2c2) focused on the task of cohort selection for clinical trials, where participating systems were tasked with analyzing longitudinal patient records to determine if the patients met or did not meet any of the 13 selection criteria. This article describes our participation in this shared task., Materials and Methods: We followed a hybrid approach combining pattern-based, knowledge-intensive, and feature weighting techniques. After preprocessing the notes using publicly available natural language processing tools, we developed individual criterion-specific components that relied on collecting knowledge resources relevant for these criteria and pattern-based and weighting approaches to identify "met" and "not met" cases., Results: As part of the 2018 n2c2 challenge, 3 runs were submitted. The overall micro-averaged F1 on the training set was 0.9444. On the test set, the micro-averaged F1 for the 3 submitted runs were 0.9075, 0.9065, and 0.9056. The best run was placed second in the overall challenge and all 3 runs were statistically similar to the top-ranked system. A reimplemented system achieved the best overall F1 of 0.9111 on the test set., Discussion: We highlight the need for a focused resource-intensive effort to address the class imbalance in the cohort selection identification task., Conclusion: Our hybrid approach was able to identify all selection criteria with high F1 performance on both training and test sets. Based on our participation in the 2018 n2c2 task, we conclude that there is merit in continuing a focused criterion-specific analysis and developing appropriate knowledge resources to build a quality cohort selection system., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

To assess the relationship between the addition of advanced monitoring variables and changes in clinical decision-making. A 15-questions survey was anonymously emailed to international experts and physician members of five anesthesia societies which focused on assessing treatment decisions of clinicians during three realistic clinical scenarios measured at two distinct time points. The first is when typical case information and basic monitoring (T1) were provided, and then once again after the addition of advanced monitoring variables (T2). We hypothesized that the addition of advanced variables would increase the incidence of an optimal therapeutic decision (a priori defined as the answer with the highest percentage of expert agreement) and decrease the variability among the physician's suggested treatments. The survey was completed by 18 experts and 839 physicians. Overall, adding advanced monitoring did not significantly increase physician response accuracy, with the least substantial changes noted on questions related to volume expansion or vasopressor administration. Moreover, advanced monitoring data did not significantly decrease the high level of initial practice variability in physician suggested treatments (P = 0.13), in contrast to the low variability observed within the expert group (P = 0.039). Additionally, 5-10 years of practice (P < 0.0001) and a cardiovascular subspecialty (P = 0.048) were both physician characteristics associated with a higher rate of optimal therapeutic decisions. The addition of advanced variables was of limited benefit for most physicians, further indicating the need for more in depth education on the clinical value and technical understanding of such variables.

Background: Pulse pressure variation (PPV) can be used to assess fluid status in the operating room. This measurement, however, is time consuming when done manually and unreliable through visual assessment. Moreover, its continuous monitoring requires the use of expensive devices. Capstesia™ is a novel Android™/iOS™ application, which calculates PPV from a digital picture of the arterial pressure waveform obtained from any monitor. The application identifies the peaks and troughs of the arterial curve, determines maximum and minimum pulse pressures, and computes PPV. In this study, we compared the accuracy of PPV generated with the smartphone application Capstesia (PPVapp) against the reference method that is the manual determination of PPV (PPVman)., Methods: The Capstesia application was loaded onto a Samsung Galaxy S4 phone. A physiologic simulator including PPV was used to display arterial waveforms on a computer screen. Data were obtained with different sweep speeds (6 and 12 mm/s) and randomly generated PPV values (from 2% to 24%), pulse pressure (30, 45, and 60 mm Hg), heart rates (60-80 bpm), and respiratory rates (10-15 breaths/min) on the simulator. Each metric was recorded 5 times at an arterial height scale X1 (PPV5appX1) and 5 times at an arterial height scale X3 (PPV5appX3). Reproducibility of PPVapp and PPVman was determined from the 5 pictures of the same hemodynamic profile. The effect of sweep speed, arterial waveform scale (X1 or X3), and number of images captured was assessed by a Bland-Altman analysis. The measurement error (ME) was calculated for each pair of data. A receiver operating characteristic curve analysis determined the ability of PPVapp to discriminate a PPVman > 13%., Results: Four hundred eight pairs of PPVapp and PPVman were analyzed. The reproducibility of PPVapp and PPVman was 10% (interquartile range, 7%-14%) and 6% (interquartile range, 3%-10%), respectively, allowing a threshold ME of 12%. The overall mean bias for PPVappX1 was 1.1% within limits of -1.4% (95% confidence interval [CI], -1.7 to -1.1) to +3.5% (95% CI, +3.2 to +3.8). Averaging 5 values of PPVappX1 with a sweep speed of 12 mm/s resulted in the smallest bias (+0.6%) and the best limits of agreement (±1.3%). ME of PPVapp was <12% whenever 3, 4, or 5 pictures were taken to average PPVapp. The best predictive value for PPVapp to detect a PPVman > 13% was obtained for PPVappX1 by averaging 5 pictures showing a PPVapp threshold of 13.5% (95% CI, 12.9-15.2) and a receiver operating characteristic curve area of 0.989 (95% CI, 0.963-0.998) with a sensitivity of 97% and a specificity of 94%., Conclusions: Our findings show that the Capstesia PPV calculation is a dependable substitute for standard manual PPV determination in a highly controlled environment (simulator study). Further studies are warranted to validate this mobile feature extraction technology to predict fluid responsiveness in real conditions.