Postflight Quiet Stance Stability of Astronauts Following Recovery From a Simulated Fall

INTRODUCTION: Astronauts returning from space flight universally present with postural ataxia. Throughout the Space Shuttle Program, measurement of ataxia has concentrated on sway in the anterior-posterior plane. Implementation of an interdisciplinary pre- and postflight study (Functional Task Test, FTT) designed to evaluate both astronaut postflight functional performance and related physiological changes has allowed the investigation of postural instability by characterizing dynamic stabilographic sway patterns. METHODS: Six astronauts from short-duration (Shuttle) and three from long-duration (ISS) flights were required to recover from a simulated fall. Subjects with eyes open, wearing running shoes lay prone on the floor for 2 minutes and then quickly stood up, maintained a quiet stance for 3 minutes, arms relaxed along the side of the body, and feet comfortably placed on the force plate. Crewmembers were tested twice before flight, on landing day (Shuttle only), and 1, 6, and 30 days after flight. Anterior-posterior (AP) and medial-lateral (ML) center-of-pressure (COP) coordinates were calculated from the ground reaction forces collected at 500 Hz. The 3-minute quiet stance trial was broken into three 1-minute segments for stabilogram diffusion analysis. A mean sway speed (rate of change of COP displacement) was also calculated as an additional postural stability parameter. RESULTS/CONCLUSION: While there was considerable variation, most of crewmembers tested exhibited increased stochastic activity evidenced by larger short-term COP diffusion coefficients postflight in both the AP and ML planes, suggesting significant changes in postural control mechanisms, particularly control of lower limb muscle function. As expected, postural instability of ISS astronauts on the first day postflight was similar to that of Shuttle crewmembers on landing day. Recoveries of stochastic activity and mean sway speed to baseline levels were typically observed by the 30th day postflight for both long-duration and short-duration crewmembers. Dynamic postural stability characteristics obtained in this low-impact study complement the data measured with computerized dynamic posturography

Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction

<p>Abstract</p> <p>Background</p> <p>Resting conventional 12-lead ECG has low sensitivity for detection of coronary artery disease (CAD) and left ventricular hypertrophy (LVH) and low positive predictive value (PPV) for prediction of left ventricular systolic dysfunction (LVSD). We hypothesized that a ~5-min resting 12-lead <it>advanced </it>ECG test ("A-ECG") that combined results from both the advanced and conventional ECG could more accurately screen for these conditions than strictly conventional ECG.</p> <p>Methods</p> <p>Results from nearly every conventional and advanced resting ECG parameter known from the literature to have diagnostic or predictive value were first retrospectively evaluated in 418 healthy controls and 290 patients with imaging-proven CAD, LVH and/or LVSD. Each ECG parameter was examined for potential inclusion within multi-parameter A-ECG scores derived from multivariate regression models that were designed to optimally screen for disease in general or LVSD in particular. The performance of the best retrospectively-validated A-ECG scores was then compared against that of optimized pooled criteria from the strictly conventional ECG in a test set of 315 additional individuals.</p> <p>Results</p> <p>Compared to optimized pooled criteria from the strictly conventional ECG, a 7-parameter A-ECG score validated in the training set increased the sensitivity of resting ECG for identifying disease in the test set from 78% (72-84%) to 92% (88-96%) (P < 0.0001) while also increasing specificity from 85% (77-91%) to 94% (88-98%) (P < 0.05). In diseased patients, another 5-parameter A-ECG score increased the PPV of ECG for LVSD from 53% (41-65%) to 92% (78-98%) (P < 0.0001) without compromising related negative predictive value.</p> <p>Conclusion</p> <p>Resting 12-lead A-ECG scoring is more accurate than strictly conventional ECG in screening for CAD, LVH and LVSD.</p

Schematic summarizing all stages of ECG data processing by the system.

<p>Schematic summarizing all stages of ECG data processing by the system.</p

New System for Digital to Analog Transformation and Reconstruction of 12-Lead ECGs

<div><p>Introduction</p><p>We describe initial validation of a new system for digital to analog conversion (DAC) and reconstruction of 12-lead ECGs. The system utilizes an open and optimized software format with a commensurately optimized DAC hardware configuration to accurately reproduce, from digital files, the original analog electrocardiographic signals of previously instrumented patients. By doing so, the system also ultimately allows for transmission of data collected on one manufacturer's 12-lead ECG hardware/software into that of any other.</p><p>Materials and Methods</p><p>To initially validate the system, we compared original and post-DAC re-digitized 12-lead ECG data files (∼5-minutes long) in two types of validation studies in 10 patients. The first type <i>quantitatively</i> compared the total waveform voltage differences between the original and re-digitized data while the second type <i>qualitatively</i> compared the automated electrocardiographic diagnostic statements generated by the original versus re-digitized data.</p><p>Results</p><p>The grand-averaged difference in root mean squared voltage between the original and re-digitized data was 20.8 µV per channel when re-digitization involved the same manufacturer's analog to digital converter (ADC) as the original digitization, and 28.4 µV per channel when it involved a different manufacturer's ADC. Automated diagnostic statements generated by the original versus reconstructed data did not differ when using the diagnostic algorithm from the same manufacturer on whose device the original data were collected, and differed only slightly for just 1 of 10 patients when using a third-party diagnostic algorithm throughout.</p><p>Conclusion</p><p>Original analog 12-lead ECG signals can be reconstructed from digital data files with accuracy sufficient for clinical use. Such reconstructions can readily enable automated second opinions for difficult-to-interpret 12-lead ECGs, either locally or remotely through the use of dedicated or cloud-based servers.</p></div

Automated clinical diagnostic statements outputted by the Cardiax algorithm for the original versus re-digitized files when both files were collected on the same model of Cardiax ADC.

<p>H and D: Healthy and Diseased patients, respectively.</p

“Worst case” result from a qualitative standpoint.

<p>Original (<b>A</b>) and re-digitized (<b>B</b>) 12-lead ECG tracings from patient 2H as interpreted by the Leuven automated diagnostic algorithm when a Cardiax ADC was used to collect the original data and a CorScience ADC the re-digitized data. This was the only file amongst the 10 tested wherein a minor change was elicited in the automated interpretation of the re-digitized compared to the original file. This minor change occurred only when using the Leuven algorithm (a corresponding change did not occur for the automated interpretation when using the Cardiax algorithm under any circumstances), and occurred regardless of whether the re-digitized data were collected on a CorScience or Cardiax ADC. Note also the modest change in DC offset (which may have been a key contributor to the slight change in the automated interpretation) as well as the very minor differences between (A) and (B) in some intervals, axes and voltages as automatically determined.</p

RMS difference values for all 10 patients' original versus re-digitized files when both the original and re-digitized files were collected on the same model of Cardiax ADC.

<p>RMS: Root mean square, with RMS difference values expressed in analog to digital converter (ADC) counts, and with 1 ADC count = 2.44 µV.</p><p>Channel: the equivalent of leads I, II and the precordial electrodes as referenced to the right arm electrode (CR1-CR6).</p><p>H and D: Healthy and Diseased patients, respectively.</p><p>LBBB and RBBB: left and right bundle branch block (BBB), respectively.</p

RMS difference values for all 10 patients' original versus re-digitized files when the original files were collected on a Cardiax ADC and the re-digitized files on a CorScience ADC.

<p>RMS: Root mean square, with RMS difference values expressed in analog to digital converter (ADC) counts, and with 1 ADC count = 2.44 µV.</p><p>Channel: the equivalent of leads I, II and the precordial electrodes as referenced to the right arm electrode (CR1-CR6).</p><p>H and D: Healthy and Diseased patients, respectively.</p><p>LBBB and RBBB: left and right bundle branch block (BBB), respectively.</p

Automated clinical diagnostic statement(s) outputted by the Leuven algorithm for the original vs. re-digitized files when the original file was collected on a Cardiax ADC and the re-digitized file on either a Cardiax or CorScience ADC.

<p>H and D: Healthy and Diseased patients, respectively. The single difference noted in the automated diagnostic interpretations (original versus re-digitized) is shown in italics (file 2H).</p