Fluid responsiveness in patients under mechanical ventilation and low tidal volume ventilation through the pulse pressure variation after tidal volume challenge in the post-operative cardiac surgery

Background It is known that in the immediate postoperative period of cardiac surgery, strict control of hemodynamic variables and blood volume is necessary, since there is an imbalance between oxygen supply and consumption. Thus, the present study seeks to validate methods previously used in different clinical situations to predict fluid responsiveness, in the current scenario of the immediate postoperative period of cardiac surgery. Purpose To evaluate the influence of “tidal volume challenge” from 6 ml / kg to 8 ml / kg of the predicted body weight (PBW) in conjunction with the end expiratory occlusion test (EEOT) in the variation of pulse pressure to predict fluid responsiveness in the immediate postoperative period of cardiac surgery. Methodology This prospective study included 30 patients after cardiac surgery. Hemodynamic and ventilatory parameters were initially recorded in mechanical ventilation at 6 ml/kg and after tidial volume challenge and with the EEOT at 8 ml/kg of predicted body weight (PBW). After recorded the intervention data, there was a return to ventilation at 6 ml/kg and a saline infusion of 500 ml was performed for 15 minutes. Fluid responsiveness was defined for patients who had an increase of 10% or more in velocity time integral (VTI) by echocardiogram after volume expansion compared to baseline value. Multivariate analysis was used to identify independent predictors of fluid response status. Sensitivity and specificity analyzes were performed to determine the predictive precision of each parameter. Results The main result of our study is that, when the tidal volume is increased from 6 to 8 ml/kg of PBW, the relative increase in pulse pressure variation (%ΔPPV6–8) predicts with excellent accuracy responsiveness to fluids with cut-off values of 18.3%, with sensitivity of 92.9% and specificity of 84% (P=0.019). Although changing PPV6, EEOT6 and EEOT8 are not reliable in predicting fluid responsiveness, they still require additional calculations. PPV8 also discriminates between responders and non-responders; however, with sensitivity (78.6%) and specificity (66.6%) when the value found in the PPV8 is up 8.5, but without statistical significance (figure). Conclusion The challenge of tidal volume and its influence on the ΔPP variation has excellent accuracy to predict fluid responsiveness in the immediate postoperative period of cardiac surgery. EEOT did not present good accuracy to predict fluid responsiveness in patients in the immediate postoperative period of cardiac surgery. FUNDunding Acknowledgement Type of funding sources: None.

Identifying the Location of an Accessory Pathway in Pre-Excitation Syndromes Using an Artificial Intelligence-Based Algorithm

(1) Background: The exact anatomic localization of the accessory pathway (AP) in patients with Wolff–Parkinson–White (WPW) syndrome still relies on an invasive electrophysiologic study, which has its own inherent risks. Determining the AP localization using a 12-lead ECG circumvents this risk but is of limited diagnostic accuracy. We developed and validated an artificial intelligence-based algorithm (location of accessory pathway artificial intelligence (locAP AI)) using a neural network to identify the AP location in WPW syndrome patients based on the delta-wave polarity in the 12-lead ECG. (2) Methods: The study included 357 consecutive WPW syndrome patients who underwent successful catheter ablation at our institution. Delta-wave polarity was assessed by four independent electrophysiologists, unaware of the site of successful catheter ablation. LocAP AI was trained and internally validated in 357 patients to identify the correct AP location among 14 possible locations. The AP location was also determined using three established tree-based, ECG-based algorithms (Arruda, Milstein, and Fitzpatrick), which provide limited resolutions of 10, 5, and 8 AP locations, respectively. (3) Results: LocAP AI identified the correct AP location with an accuracy of 85.7% (95% CI 79.6–90.5, p < 0.0001). The algorithms by Arruda, Milstein, and Fitzpatrick yielded a predictive accuracy of 53.2%, 65.6%, and 44.7%, respectively. At comparable resolutions, the locAP AI achieved a predictive accuracy of 95.0%, 94.9%, and 95.6%, respectively (p < 0.001 for differences). (4) Conclusions: Our AI-based algorithm provided excellent accuracy in predicting the correct AP location. Remarkably, this accuracy is achieved at an even higher resolution of possible anatomical locations compared to established tree-based algorithms.

Predicting quantum potentials by deep neural network and metropolis sampling

The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields. Inspired by quantum potential neural network, we here propose to solve the potential in the Schrödinger equation provided the eigenstate, by combining Metropolis sampling with deep neural network, which we dub as Metropolis potential neural network (MPNN). A loss function is proposed to explicitly involve the energy in the optimization for its accurate evaluation. Benchmarking on the harmonic oscillator and hydrogen atom, MPNN shows excellent accuracy and stability on predicting not just the potential to satisfy the Schrödinger equation, but also the eigen-energy. Our proposal could be potentially applied to the ab-initio simulations, and to inversely solving other partial differential equations in physics and beyond.

Estimation of parameters of a sum of sinusoids sampled below the Nyquist rate with frequencies away from the grid

We are witnessing a growing interest in processing signals sampled below the Nyquist rate. The main limitation of current approaches considering estimation of multicomponent sinusoids parameters is the assumption of frequencies on the frequency grid. The sinusoids away from the frequency grid are considered in this paper. The proposed procedure has three stages. In the first two, a rough estimation of signal components is performed while in the third refinement in estimation is achieved in a component-by-component manner. We have tested the developed technique on an extended set of simulation examples showing excellent accuracy. Three scenarios are considered in experiments: missing samples, noisy environment, and non-uniform sampling below the Nyquist rate.

Time series activity classification using gated recurrent units

<span>The population of elderly is growing and is projected to outnumber the youth in the future. Many researches on elderly assisted living technology were carried out. One of the focus areas is activity monitoring of the elderly. AReM dataset is a time series activity recognition dataset for seven different types of activities, which are bending 1, bending 2, cycling, lying, sitting, standing and walking. In the original paper, the author used a many-to-many Recurrent Neural Network for activity recognition. Here, we introduced a time series classification method where Gated Recurrent Units with many-to-one architecture were used for activity classification. The experimental results obtained showed an excellent accuracy of 97.14%.</span>

Combined detection of peripheral blood VEGF and inflammation biomarkers to evaluate the clinical response and prognostic prediction of non-operative ESCC

An association between angiogenesis/inflammation status and tumor has been reported in various types of cancer. This study sought to assess the role of peripheral blood VEGF and some inflammation biomarkers in evaluating clinical response and prognosis in patients with non-operative esophageal squamous cell carcinoma (ESCC). Peripheral blood of 143 patients with non-operative ESCC at our institute was dynamically collected at 5 time points including 1 day before radiotherapy, during radiotherapy (15f), at the end of radiotherapy, 1 month after radiotherapy, and 3 months after radiotherapy. VEGF expression in the peripheral blood was detected and related inflammation biomarkers such as GPS, CAR and CLR were counted. Logistic regression and Cox regression were implemented respectively to analyze the correlation of each predictor with clinical response and prognosis. The performance of combined testing was estimated using AUCs. Based on independent predictors, a nomogram prediction model was established to predict the probabilities of 1- and 2-year PFS of patients. The effectiveness of the nomogram model was characterized by C-index, AUC, calibration curves and DCA. VEGF and CLR levels at the end of radiotherapy were independent predictors of clinical response, while VEGF and GPS levels at 3 months after radiotherapy were independent prognostic predictors. The efficacy of combined detection of VEGF and CLR is superior to the single detection in evaluating clinical response and prognosis. The nomogram showed excellent accuracy in predicting PFS. The combined detection of VEGF and CLR at the end of radiotherapy can be used to evaluate the clinical response of patients with non-operative ESCC, and the combined detection of VEGF and GPS 3 months after radiotherapy can be used to predict the prognosis. Implemented by nomogram model, it is expected to provide practical and reliable method to evaluate the clinical response and prognosis of patients with non-operative ESCC tool.

Clinical accuracy of non-contact forehead infrared thermometer and infrared tympanic thermometer in postoperative adult patients: A comparative study.

Core temperature monitoring is important for the assessment and prevention of possible postoperative complications. The aim of the present study was to examine the agreement between the core temperature values and the forehead, tympanic membrane and axillary values in postoperative adult patients in clinical practice. The study measured the core temperature of 65 patients undergoing scheduled abdominal surgery using SpotOn™ and compared these with those obtained using non-contact forehead infrared, infrared tympanic and axillary thermometers. Correlation and Bland–Altman analyses were conducted for these comparisons. All temperatures were recorded at 4h intervals after postoperative arrival to the ward. Forehead temperature recordings showed a good correlation with the core temperature with excellent accuracy and was comparable to the tympanic temperature. Both forehead and tympanic thermometers can rapidly and effectively measure the core temperature during early postoperative period. Considering patients’ safety, non-contact forehead infrared thermometers may be useful for postoperative monitoring.

Experimental study on the factors affecting torque of beam-type implant torque wrenches

Background Screw breakage and loosening are the most common mechanical complications associated with implant treatment, and they may occur due to excess or inadequate screw tightening torque. When fastening and fixing the implant superstructure, screws are tightened using a torque wrench, which is essential for an accurate tightening force. However, the characteristics of the torque wrench have not been fully verified. Therefore, we aimed to clarify the factors affecting the torque with a focus on beam-type torque wrenches, which are the main types of wrenches. Methods The torque values generated by beam-type torque wrenches from eight manufacturers were measured using a torque gauge. To investigate the influence of the location of the beam relative to the scale, measurements were performed with a scale aligned with the trailing edge, center, and leading edge of the beam respectively. Additionally, measurements were taken at 90°, 60°, and 30° to examine the effect of the angle at which the examiner read the torque value. Under each condition, a single examiner applied the recommended torque to each manufacturer's screws five times in a clockwise direction. The average measured torque, standard deviation, bias, and coefficient of variation were calculated and compared accordingly. Results Wrenches from six manufacturers demonstrated excellent accuracy for measurements at the center of the beam (bias within ± 4%). For measurements at 90°, equipments from five manufacturers displayed excellent accuracy (bias within ± 7%), and seven showed excellent repeatability (coefficient of variation ≤ 2%). Conclusion The scale should be aligned with the center of the beam and read from 90° while using a torque wrench. The accuracy and repeatability torques generated by the wrenches differed according to the manufacturer, scale width, scale line width, beam width, and distance between the scale and beam center. Based on these results, we suggest that a torque wrench must be selected after determining the difference in the structure of the torque wrench.