Quantitative Ventricular Fibrillation Metrics in a Biosignal Guided Cardiopulmonary Resuscitation Device for Cardiac Arrest and Their Translation to Clinical Data

Out of hospital cardiac arrest is a major cause of mortality with an estimated yearly incidence of 350,000 in the United States alone. Cardiopulmonary resuscitation (CPR) is a treatment for cardiac arrest involving chest compressions and rescues breaths that can save lives but is limited by the fact that it currently treats all patients in a 'one size fits all' approach. This work describes an adaptive approach to chest compressions controlled by a mechanical device that receives biosignals from the patient it treats. The device is capable of adjusting its chest compression parameters such as rate and depth in response to the biosignals it receives. We focused on integrating the quantitative electrocardiogram (QECG) of the ventricular fibrillation signal, a biosignal shown to respond to increased perfusion of the myocardium during CPR, into a chest compression algorithm controlled by the adaptive chest compression device. QECG is readily available for cardiac arrest patients since ECG analysis is standard of care in cardiac arrest. In our first aim we developed the adaptive chest compression device and tested it in animal feasibility studies which demonstrated that it responded appropriately to the biosignals it received. Next, in a computational model of adaptive chest compressions, adjustments in chest compression depth yielded the largest increase in cardiac output in patients with simulated variable physiology. In follow-up animal studies, select QECG measures responded to changes in chest compression parameters which demonstrated the initial feasibility of QECG measures as a potential biosignal in this model. We found that the QECG measures of median slope, centroid frequency, and log of the absolute correlation responded to changes in chest compression rate in the early phase of chest compressions. We found that in late phases of chest compressions the QECG measure median slope responded to chest compression rate changes and the QECG measure AMSA responded to chest compression duty cycle changes. Our second aim sought to retrospectively translate the findings in the first aim animal studies to human clinical data in the continuous chest compression trial of the Resuscitation Outcomes Consortium (ROC). The clinical trial provided us with ECG and compression data in covering thousands of cardiac arrest events. All QECG metrics in the clinical data set was predictive of shock outcome and chest compression rate along with chest compression bout duration were predictive of survival. However, when controlled for the presenting first rhythm status and demographic variables, only chest compression bout duration was predictive of survival. In addition to the predictive value of chest compression parameters and QECG measures, associations were found between varying chest compression parameters averaged across bouts of compressions with change in QECG values (dQECG) in the clinical data. Chest compression rate was found to be predictive of the dQECG metric median slope (dMS) and the dQECG metric (dAMSA). Dosed compression rate was found to be predictive of the dQECG metric dMS as well. dCF responded to changes in chest compression duty cycle. These findings provide a foundation for delivering adaptive chest compressions with the potential of improving survival outcomes to cardiac arrest

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 167)

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1977

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

From Benchtop to Beside: Patient-specific Outcomes Explained by Invitro Experiment

Study: Recent analyses show that females have higher early postoperative (PO) mortality and right ventricular failure (RVF) than males after left ventricular assist device (LVAD) implantation; and that this association is partially mediated by smaller LV size in females. Benchtop experiments allow us to investigate patient-specific (PS) characteristics in a reproducible way given the fact that the PS anatomy and physiology is mimicked accurately. With multiple heart models of varying LV size, we can directly study the individual effects of titrating the LVAD speed and the resulting bi-ventricular volumes, shedding light on the interplay between LV and RV as well as resulting inter-ventricular septum (IVS) positions, which may cause the different outcomes pertaining to sex. Methods: In vitro, we studied the impact of the heart size to IVS position using two smaller and two larger sized PS silicone heart phantoms derived from clinical CT images (Fig. 1A). With ultrasound crystals that were integrated on a placeholder inflow cannula, the IVS position was measured during LV and RV volume changes (dV) mimicking varying ventricular loading states (Fig. 1B). Figure 1 A Two small (blue) and two large PS heart phantoms (orange) on B benchtop. C Median septum curvature results. LVEDD/LVV/RVV: LV enddiastolic diameter/LV and RV volume. Results: Going from small to large dV, at zero curvature, the septum starts to shift towards the left; for smaller hearts at dV = -40 mL and for larger hearts at dV = -50 mL (Fig. 1C). This result indicates that smaller hearts are more prone to an IVS shift to the left than larger hearts. We conclude that smaller LV size may therefore mediate increased early PO LVAD mortality and RVF observed in females compared to males. Novel 3D silicone printing technology enables us to study accurate, PS heart models across a heterogeneous patient population. PS relationships can be studied simultaneously to clinical assessments and support the decision-making prior to LVAD implantation

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 375)

This bibliography lists 212 reports, articles, and other documents recently introduced into the NASA Scientific and Technical Information System database. Subject coverage includes the following: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance