Closed-Loop Control for Fluid Resuscitation: Recent Advances and Future Challenges.

Fluid therapy is extensively used to treat traumatized patients as well as patients during surgery. The fluid therapy process is complex due to interpatient variability in response to therapy as well as other complicating factors such as comorbidities and general anesthesia. These complexities can result in under- or over-resuscitation. Given the complexity of the fluid management process as well as the increased capabilities in hemodynamic monitoring, closed-loop fluid management can reduce the workload of the overworked clinician while ensuring specific constraints on hemodynamic endpoints are met with higher accuracy. The goal of this paper is to provide an overview of closed-loop control systems for fluid management and highlight several key steps in transitioning such a technology from bench to the bedside.

Using sensor-fusion and machine-learning algorithms to assess acute pain in non-verbal infants: a study protocol.

INTRODUCTION: Objective pain assessment in non-verbal populations is clinically challenging due to their inability to express their pain via self-report. Repetitive exposures to acute or prolonged pain lead to clinical instability, with long-term behavioural and cognitive sequelae in newborn infants. Strong analgesics are also associated with medical complications, potential neurotoxicity and altered brain development. Pain scores performed by bedside nurses provide subjective, observer-dependent assessments rather than objective data for infant pain management; the required observations are labour intensive, difficult to perform by a nurse who is concurrently performing the procedure and increase the nursing workload. Multimodal pain assessment, using sensor-fusion and machine-learning algorithms, can provide a patient-centred, context-dependent, observer-independent and objective pain measure. METHODS AND ANALYSIS: In newborns undergoing painful procedures, we use facial electromyography to record facial muscle activity-related infant pain, ECG to examine heart rate (HR) changes and HR variability, electrodermal activity (skin conductance) to measure catecholamine-induced palmar sweating, changes in oxygen saturations and skin perfusion, and electroencephalography using active electrodes to assess brain activity in real time. This multimodal approach has the potential to improve the accuracy of pain assessment in non-verbal infants and may even allow continuous pain monitoring at the bedside. The feasibility of this approach will be evaluated in an observational prospective study of clinically required painful procedures in 60 preterm and term newborns, and infants aged 6 months or less. ETHICS AND DISSEMINATION: The Institutional Review Board of the Stanford University approved the protocol. Study findings will be published in peer-reviewed journals, presented at scientific meetings, taught via webinars, podcasts and video tutorials, and listed on academic/scientific websites. Future studies will validate and refine this approach using the minimum number of sensors required to assess neonatal/infant pain. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT03330496).

A Hybrid Radar-Camera Respiratory Monitoring System Based on an Impulse-Radio Ultrawideband Radar.

This paper reports a pilot study of a hybrid radar-camera system that simultaneously monitors the respiration of two subjects. A prototype system was built involving a low-cost impulse-radio ultra-wideband (IR-UWB) radar module and an optical and depth-sensing camera module. The system detects subjects using the camera and utilizes the distance information acquired to guide the signal processing of the radar. This structure simplifies subject identification and tracking for the radar system, provides further context to the radar, and facilitates the extraction of respiration information. Experiments under different scenarios were conducted to evaluate the performance of the system at different distances and angles from subjects. The localization procedure has an average accuracy of 0.1 m. The respiration rates extracted from the radar are comparable with the values from the reference wearable sensor, reporting an average error of 0.79 respirations per minute (RPM) with a standard deviation of 0.71 RPM. The results suggest that the respiration signals from subjects could be accurately monitored with the presented framework. It is also feasible to monitor two subjects at the same time in most scenarios. The proposed framework shows promising potential to work as a ubiquitous monitoring system for multiple subjects.

Validation of an automated system for detecting ineffective triggering asynchronies during mechanical ventilation: a retrospective study.

We compare the sensitivity and specificity of clinician visual waveform analysis against an automated system's waveform analysis in detecting ineffective triggering in mechanically ventilated intensive care unit patients when compared against a reference label set based upon analysis of respiratory muscle activity. Electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure waveforms were available to a single clinician for the generation of a reference label set indicating the ground truth, that is, presence or absence of ineffective triggering, on a breath-by-breath basis. Pressure and flow versus time tracings were made available to (i) a group of three clinicians; and (ii) the automated Syncron-E™ system capable of detecting patient-ventilator asynchrony in real-time, in order to obtain breath-by-breath labels indicating the presence or absence of ineffective triggering. The clinicians and the automated system did not have access to other waveforms such as electrical activity of the diaphragm or esophageal/transdiaphragmatic pressure. In total, 926 breaths were analyzed across the seven patients. Specificity for clinicians and the automated system were high (99.3% for clinician and 98.5% for the automated system). The automated system had a significantly higher sensitivity (83.2%) compared to clinicians (41.1%). Ineffective triggering detected by the automated system, which has access only to airway pressure and flow versus time tracings, is in substantial agreement with a reference detection derived from analysis of invasively measured patient effort waveforms.

A Fast Parameter Identification Framework for Personalized Pharmacokinetics.

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.

A pilot study evaluating adaptive closed-loop fluid resuscitation during states of absolute and relative hypovolemia in dogs.

OBJECTIVE: To evaluate and determine the performance of a partially automated as well as a fully automated closed-loop fluid resuscitation system during states of absolute and relative hypovolemia. DESIGN: Prospective experimental trial. SETTING: Research laboratory. ANIMALS: Five adult Beagle dogs. METHODS: Isoflurane anesthetized mechanically ventilated dogs were subjected to absolute hypovolemia (controlled: 2 trials; uncontrolled: 3 trials), relative hypovolemia (2 trials), and the combination of relative and absolute controlled hypovolemia (2 trials). Controlled and uncontrolled hypovolemia were produced by withdrawing blood from the carotid or femoral artery. Relative hypovolemia was produced by increasing the isoflurane concentration (1 trial) or by infusion of intravenous sodium nitroprusside (1 trial). Relative hypovolemia combined with controlled absolute hypovolemia was produced by increasing the isoflurane concentration (1 trial) and infusion of IV sodium nitroprusside (1 trial). Hemodynamic parameters including stroke volume variation (SVV) were continuously monitored and recorded in all dogs. A proprietary closed-loop fluid administration system based on fluid distribution and compartmental dynamical systems administered a continuous infusion of lactated Ringers solution in order to restore and maintain SVV to a predetermined target value. MEASUREMENTS AND MAIN RESULTS: A total of 9 experiments were performed on 5 dogs. Hemodynamic parameters deteriorated and SVV increased during controlled or uncontrolled hypovolemia, relative hypovolemia, and during relative hypovolemia combined with controlled hypovolemia. Stroke volume variation was restored to baseline values during closed-loop fluid infusion. CONCLUSIONS: Closed-loop fluid administration based on IV fluid distribution and compartmental dynamical systems can be used to provide goal directed fluid therapy during absolute or relative hypovolemia in mechanically ventilated isoflurane anesthetized dogs.

Replicating human expertise of mechanical ventilation waveform analysis in detecting patient-ventilator cycling asynchrony using machine learning.

BACKGROUND: - Acute respiratory failure is one of the most common problems encountered in intensive care units (ICU) and mechanical ventilation is the mainstay of supportive therapy for such patients. A mismatch between ventilator delivery and patient demand is referred to as patient-ventilator asynchrony (PVA). An important hurdle in addressing PVA is the lack of a reliable framework for continuously and automatically monitoring the patient and detecting various types of PVA. METHODS: - The problem of replicating human expertise of waveform analysis for detecting cycling asynchrony (i.e., delayed termination, premature termination, or none) was investigated in a pilot study involving 11 patients in the ICU under invasive mechanical ventilation. A machine learning framework is used to detect cycling asynchrony based on waveform analysis. RESULTS: - A panel of five experts with experience in PVA evaluated a total of 1377 breath cycles from 11 mechanically ventilated critical care patients. The majority vote was used to label each breath cycle according to cycling asynchrony type. The proposed framework accurately detected the presence or absence of cycling asynchrony with sensitivity (specificity) of 89% (99%), 94% (98%), and 97% (93%) for delayed termination, premature termination, and no cycling asynchrony, respectively. The system showed strong agreement with human experts as reflected by the kappa coefficients of 0.90, 0.91, and 0.90 for delayed termination, premature termination, and no cycling asynchrony, respectively. CONCLUSIONS: - The pilot study establishes the feasibility of using a machine learning framework to provide waveform analysis equivalent to an expert human.

A Microsoft Kinect-Based Point-of-Care Gait Assessment Framework for Multiple Sclerosis Patients.

Gait impairment is a prevalent and important difficulty for patients with multiple sclerosis (MS), a common neurological disorder. An easy to use tool to objectively evaluate gait in MS patients in a clinical setting can assist clinicians to perform an objective assessment. The overall objective of this study is to develop a framework to quantify gait abnormalities in MS patients using the Microsoft Kinect for the Windows sensor; an inexpensive, easy to use, portable camera. Specifically, we aim to evaluate its feasibility for utilization in a clinical setting, assess its reliability, evaluate the validity of gait indices obtained, and evaluate a novel set of gait indices based on the concept of dynamic time warping. In this study, ten ambulatory MS patients, and ten age and sex-matched normal controls were studied at one session in a clinical setting with gait assessment using a Kinect camera. The expanded disability status scale (EDSS) clinical ambulation score was calculated for the MS subjects, and patients completed the Multiple Sclerosis walking scale (MSWS). Based on this study, we established the potential feasibility of using a Microsoft Kinect camera in a clinical setting. Seven out of the eight gait indices obtained using the proposed method were reliable with intraclass correlation coefficients ranging from 0.61 to 0.99. All eight MS gait indices were significantly different from those of the controls (p-values less than 0.05). Finally, seven out of the eight MS gait indices were correlated with the objective and subjective gait measures (Pearson's correlation coefficients greater than 0.40). This study shows that the Kinect camera is an easy to use tool to assess gait in MS patients in a clinical setting.

A statistical modeling approach for tumor-type identification in surgical neuropathology using tissue mass spectrometry imaging.

Current clinical practice involves classification of biopsied or resected tumor tissue based on a histopathological evaluation by a neuropathologist. In this paper, we propose a method for computer-aided histopathological evaluation using mass spectrometry imaging. Specifically, mass spectrometry imaging can be used to acquire the chemical composition of a tissue section and, hence, provides a framework to study the molecular composition of the sample while preserving the morphological features in the tissue. The proposed classification framework uses statistical modeling to identify the tumor type associated with a given sample. In addition, if the tumor type for a given tissue sample is unknown or there is a great degree of uncertainty associated with assigning the tumor type to one of the known tumor models, then the algorithm rejects the given sample without classification. Due to the modular nature of the proposed framework, new tumor models can be added without the need to retrain the algorithm on all existing tumor models.

Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems.

Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in [1] to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations.

Recursive feature elimination for brain tumor classification using desorption electrospray ionization mass spectrometry imaging.

The metabolism and composition of lipids is of increasing interest for understanding and detecting disease processes. Lipid signatures of tumor type and grade have been demonstrated using magnetic resonance spectroscopy. Clinical management and ultimate prognosis of brain tumors depend largely on the tumor type, subtype, and grade. Mass spectrometry, a well-known analytical technique used to identify molecules in a given sample based on their mass, can significantly improve the problem of tumor type classification. This work focuses on the problem of identifying lipid features to use as input for classification. Feature selection could result in improvements in classifier performance, discovery of biomarkers, improved data interpretation, and patient treatment.

Classification of astrocytomas and oligodendrogliomas from mass spectrometry data using sparse kernel machines.

Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample's histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry.

A compressive sensing approach for glioma margin delineation using mass spectrometry.

Surgery, and specifically, tumor resection, is the primary treatment for most patients suffering from brain tumors. Medical imaging techniques, and in particular, magnetic resonance imaging are currently used in diagnosis as well as image-guided surgery procedures. However, studies show that computed tomography and magnetic resonance imaging fail to accurately identify the full extent of malignant brain tumors and their microscopic infiltration. Mass spectrometry is a well-known analytical technique used to identify molecules in a given sample based on their mass. In a recent study, it is proposed to use mass spectrometry as an intraoperative tool for discriminating tumor and non-tumor tissue. Integration of mass spectrometry with the resection module allows for tumor resection and immediate molecular analysis. In this paper, we propose a framework for tumor margin delineation using compressive sensing. Specifically, we show that the spatial distribution of tumor cell concentration can be efficiently reconstructed and updated using mass spectrometry information from the resected tissue. In addition, our proposed framework is model-free, and hence, requires no prior information of spatial distribution of the tumor cell concentration.

Relevance vector machine learning for neonate pain intensity assessment using digital imaging.

Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent "pure" facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners.

Agitation and pain assessment using digital imaging.

Pain assessment in patients who are unable to verbally communicate with medical staff is a challenging problem in patient critical care. The fundamental limitations in sedation and pain assessment in the intensive care unit (ICU) stem from subjective assessment criteria, rather than quantifiable, measurable data for ICU sedation and analgesia. This often results in poor quality and inconsistent treatment of patient agitation and pain from nurse to nurse. Recent advancements in pattern recognition techniques using a relevance vector machine algorithm can assist medical staff in assessing sedation and pain by constantly monitoring the patient and providing the clinician with quantifiable data for ICU sedation. In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners.