PurposeOxygen delivery (DO2) during cardiopulmonary bypass (CPB) is critical in preventing postoperative complications in adult cardiac surgery. This systematic review aimed to assess the relationship between intraoperative DO2 during CPB, particularly within Goal-directed Perfusion (GDP) strategies, and associated clinical outcomes.MethodsA systematic search of MEDLINE, Embase, Web of Science, PsycINFO, CINAHL, PROSPERO, and Cochrane was conducted from database inception through December 2024, adhering to PRISMA 2020 guidelines. Studies reported intraoperative DO2 measurements and their relationship with clinical outcomes among adults undergoing cardiac surgery with CPB. Data extraction and quality assessment were performed independently by two reviewers.ResultsThirty-nine studies (71,050 patients) were included, with acute kidney injury (AKI) being the most frequently studied outcome (84.6% of studies). A consistent association was found between lower intraoperative DO2 and increased risk of AKI, intraoperative lactate elevations, and prolonged mechanical ventilation. Five randomized controlled trials (RCTs) demonstrated that maintaining DO2 levels, indexed to body surface area (iDO2), above a threshold of 270-300 mL/min/m2 significantly reduced the risk of postoperative AKI. However, evidence linking DO2 management directly to reductions in mortality or neurologic complications remains limited, as well as studies reporting compliance with GDP strategies.ConclusionMaintaining adequate iDO2 during CPB significantly reduces postoperative complications, especially AKI. These findings underscore the clinical relevance of GDP strategies, highlighting the importance of individualized perfusion management to optimize outcomes. Further large-scale RCTs are needed to confirm these benefits, standardize specific iDO2 threshold levels that are beneficial, and to explore strategies that impact mortality and neurologic outcomes, as well as investigate the role that temperature management plays in DO2 threshold determination.

Managing delivery of complex multidrug infusions in anesthesia and critical care presents a significant clinical challenge. Current practices relying on manual control of infusion pumps often result in unpredictable drug delivery profiles and dosing errors-key issues highlighted by the United States Food and Drug Administration (FDA). To address these issues, we introduce the SMART (synchronized-pump management algorithms for reliable therapies) framework, a novel approach that leverages low Reynolds number drug transport physics and machine learning to accurately manage multidrug infusions in real-time. SMART is activated based on the Shafer number ( Sh ), a novel non-dimensional number that quantifies the relative magnitude of a drug's therapeutic action timescale to its transport timescale within infusion manifolds. SMART is useful when Sh < 1 , where drug transport becomes the rate limiting step in achieving the desired therapeutic effects. When activated, SMART monitors multidrug concentrations within infusion manifolds and leverages this information to perform end-to-end management of drug delivery using an ensemble of deterministic and deep reinforcement learning (RL) decision networks. Notably, SMART RL networks employ differentially sampled split buffer architecture that accelerates learning and improves performance by seamlessly combining deterministic predictions with RL experience during training. SMART deployed in standalone infusion pumps under simulated clinical conditions outperformed state-of-the-art manual control protocols. This framework has the potential to revolutionize critical care by enhancing accuracy of medication delivery and reducing cognitive workloads. Beyond critical care, the ability to accurately manage multi-liquid delivery via complex manifolds will have important bearings for manufacturing and process control.

INTRODUCTION:The need for remote ventilator control has been highlighted by the COVID-19 Public Health Emergency. Remote ventilator control from outside a patient's room can improve response time to patient needs, protect health care workers, and reduce personal protective equipment (PPE) consumption. Extending remote control to distant locations can expand the capabilities of frontline health care workers by delivering specialized clinical expertise to the point of care, which is much needed in diverse health care settings, such as tele-critical care and military medicine. However, the safety and effectiveness of remote ventilator control can be affected by many risk factors, including communication failures and network disruptions. Consensus safety requirements and test methods are needed to assess the resilience and safety of remote ventilator control under communication failures and network disruptions. MATERIALS AND METHODS:We designed two test methods to assess the robustness, usability, and safety of a remote ventilator control prototype system jointly developed by Nihon Kohden OrangeMed, Inc. and DocBox, Inc. ("the NK-DocBox system") to control the operation of an NKV-550 critical care ventilator under communication failures and network disruptions. First, the robustness of the NKV-550 ventilator was tested using a remote-control application developed on OpenICE - an open-source medical device interoperability platform - to transmit customized high-frequency and erroneous remote-control commands that could be caused by communication failures in a real-world environment. The second method utilized a network emulator to create different types and severity of network quality of service (QoS) degradation, including bandwidth throttling, network delay and jitter, packet drop and reordering, and bit errors, in the NKV-DocBox system to quantitatively assess the impact on system usability and safety. RESULTS:The NKV-550 ventilator operated as expected when remote-control commands arrived as fast as once per second. It ignored erroneous commands attempting to adjust invalid ventilation parameters. When facing commands that set the ventilation mode and parameters to invalid values, it reset the ventilation mode or parameters to default values, the safety implication of which may merit further evaluation. When any network QoS attribute (except for packet reordering) started to degrade, the NK-DocBox System experienced interference to its remote-control function, such as delays in the transmission of ventilator data and remote-control commands within the system. When the network QoS was worse than 500 ms network delay, 100 ms network jitter, 1% data drop rate, 12 Mbps minimal bandwidth, or 1e-6 bit error rate, the system became unsafe to use. For example, ventilator waveforms visualized on the remote-control application demonstrated freezes, out-of-synchronization, and moving backwards; and the connection between the ventilator and the remote-control application became unstable. CONCLUSION:The presented test methods confirmed the robustness of the NKV-550 ventilator against high-frequency and erroneous remote control, quantified the impact of network disruptions on the usability, reliability, and safety of the NK-DocBox system and identified the minimum network QoS requirements for it to function safely. These generalizable test methods can be customized to evaluate other remote ventilator control technologies and remote control of other types of medical devices against communication failures and network disruptions.

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions.

The cardiac operating room (OR) is a high-risk, high-stakes environment inserted into a complex socio-technical healthcare system. During cardiopulmonary bypass (CPB), the most critical phase of cardiac surgery, the perfusionist has a crucial role within the interprofessional OR team, being responsible for optimizing patient perfusion while coordinating other tasks with the surgeon, anesthesiologist, and nurses. The aim of this study was to investigate objective digital biomarkers of perfusionists' workload and stress derived from heart rate variability (HRV) metrics captured via a wearable physiological sensor in a real cardiac OR. We explored the relationships between several HRV parameters and validated self-report measures of surgical task workload (SURG-TLX) and acute stress (STAI-SF), as well as surgical processes and outcome measures. We found that the frequency-domain HRV parameter HF relative power - FFT (%) presented the strongest association with task workload (correlation coefficient: -0.491, p-value: 0.003). We also found that the time-domain HRV parameter RMSSD (ms) presented the strongest correlation with perfusionists' acute stress (correlation coefficient: -0.489, p-value: 0.005). A few workload and stress biomarkers were also associated with bypass time and patient length of stay in the hospital. The findings from this study will inform future research regarding which HRV-based biomarkers are best suited for the development of cognitive support systems capable of monitoring surgical workload and stress in real time.

Excessive intra-operative noise in cardiac surgery has the potential to serve as source of distraction and additional cognitive workload for the surgical team, and may interfere with optimal performance. The separation from bypass phase is a technically complex phase of surgery, making it highly susceptible to communication breakdowns due to high cognitive demands and requiring tightly coupled team coordination. The objective of this study was to investigate team cognitive workload levels and communication in relation to intra-operative time periods representative of infrequent vs. frequent peaks in ambient noise. Compared to 5-minute segments with no peaks in noise at all, segments with the highest percentage of noise peaks (≥10%) were significantly associated with higher team members' heart rate before, during, and after noise segments analyzed. These noisier segments were also associated with a significantly higher level of case-irrelevant communication events. These data suggest that case-irrelevant conversations associated with a greater degree of excessive peaks in noise may be associated with team workload levels, warranting further investigation into efforts to standardize communication during critical surgical phases.

Time lags between the initiation of a continuous drug infusion and achievement of a steady state delivery rate present an important safety concern. At least 3 factors contribute to these time lags: (1) dead volume size, (2) the ratio between total system flow and dead volume, and (3) startup delay. While clinicians employ both peristaltic pumps and syringe pumps to propel infusions, there has been no head-to-head comparison of drug delivery between commercially available infusion pumps with these distinct propulsion mechanisms. We quantified the delivery of a model drug by peristaltic and syringe pumps at clinically relevant flow rates using spectrophotometric absorbance. Delivery curves were modeled and compared, and the time required to reach 5% (T5), 50% (T50), and 95% (T95) of the intended delivery rate was reported. The ability to overcome the combined effects of startup delay and dead volume differed between syringe and peristaltic pumps. T5, T50, and T95 were shorter for the peristaltic pump at higher flow rates. T50 and T95 were shorter for the syringe pump at lower flow rates. The ability to overcome the effects of dead volume was overall similar between the syringe and peristaltic pumps, as was the response to consecutive changes in drug infusion rates. Startup delay and dead volume in carrier-based infusion systems cause substantial time lags to reaching intended delivery rates. Peristaltic and syringe pumps are similarly susceptible to dead volume effects. Startup performance differed between peristaltic and syringe pumps; their relative performance may be dependent on flow rate.

The cardiac surgery operating room is a high-risk and complex environment in which multiple experts work as a team to provide safe and excellent care to patients. During the cardiopulmonary bypass phase of cardiac surgery, critical decisions need to be made and the perfusionists play a crucial role in assessing available information and taking a certain course of action. In this paper, we report the findings of a simulation-based study using machine learning to build predictive models of perfusionists' decision-making during critical situations in the operating room (OR). Performing 30-fold cross-validation across 30 random seeds, our machine learning approach was able to achieve an accuracy of 78.2% (95% confidence interval: 77.8% to 78.6%) in predicting perfusionists' actions, having access to only 148 simulations. The findings from this study may inform future development of computerised clinical decision support tools to be embedded into the OR, improving patient safety and surgical outcomes.

OBJECTIVE:In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications.METHODS:We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles.RESULTS:The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system.CONCLUSION:These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles.SIGNIFICANCE:This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications.

If a perfusionist weans a patient off the heart lung machine (HLM) and the anesthesiologist has not re-started the ventilator, the patient will become hypoxic. The objective of this project was to create a redundant safety system of verbal and electronic communication to prevent failure to ventilate errors after cardiopulmonary bypass. This objective could be realized by building an electronic communication bridge directly between the HLM and ventilator. A software application was created to retrieve and interpret data from the pump and ventilator and trigger a programmed smart alarm. The software is able to interpret data from the pump and ventilator. When both are off simultaneously (defined as a pump flow of 0 L/min with a respiratory rate of 0 breaths/min), the application will raies an alarm. Communication between a pump and ventilator is possible, enabling the deployment of a safety system that could exist in the operating room (OR) as a standalone alarm. A device dataset can be used to optimize clinical performance of the alarm. The application could also be integrated into smart checklists and computer-assisted OR process models that are currently in development.