Automated Closed Loop Control of Inspired Oxygen Concentration
Division of Neonatology, Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida. Respiratory care
(Impact Factor: 1.84).
01/2013; 58(1):151-61. DOI: 10.4187/respcare.01955
Oxygen therapy is extensively used in premature infants and adults with respiratory insufficiency. In the premature infant the goal during manual control of the F(IO(2)) is to maintain adequate oxygenation and to minimize the exposure to hypoxemia, hyperoxemia, and oxygen. However, this is frequently not achieved during routine care, which increases the risks of associated side effects affecting the eye, lungs, and central nervous system. In the adult the primary goal is to avoid hypoxemia, but conventional methods of oxygen supplementation may fall short during periods of increased demand. On the other hand, there are growing concerns related to unnecessarily high F(IO(2)) levels that increase the exposure to hyperoxemia and excessive oxygen use in settings where resources are limited. Systems for automated closed loop control of F(IO(2)) have been developed for use in neonates and adults. This paper will give an overview of the rationale for the development of these systems, present the evidence, and discuss important advantages and limitations.
Available from: ojs.uac.edu.co
- "Estudios recientes han demostrado que en los recién nacidos prematuros, un excesivo y prolongado uso del oxígeno ha sido asociado con complicaciones a largo plazo que afectan a los ojos, los pulmones y el sistema nervioso central  . Por otra parte, la oxigenación insuficiente también ha sido asociada con efectos perjudiciales en el cerebro, problemas pulmonares y permeabilidad del ducto arterioso  . "
Available from: Kaouther Saihi
- "During all study periods, the SpO2 target range was 92% to 96%. This range was consistent with previous clinical publications on automatic FiO2 controllers [13,14,16,20]. It was considered a reasonable compromise that combines safety (limiting risk of hypoxemia) and efficacy to limit FiO2 in comparison to usual care, and which was also used in the control ICU. "
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ABSTRACT: Hypoxemia and high fractions of inspired oxygen (FiO2) are concerns in critically ill patients. An automated FiO2 controller based on continuous oxygen saturation (SpO2) measurement was tested. Two different SpO2-FiO2 feedback open loops designed to react differently based on the level of hypoxemia were compared. The results of the FiO2 controller were also compared to an historical control group.
The system measures SpO2, compares to a target range (92% - 96%) and proposes in real time FiO2 settings to maintain SpO2 within target. In 20 patients under mechanical ventilation, two different FiO2-SpO2 open loops were applied by a dedicated research nurse during 3 hours each in random order. The times spent in and outside the target SpO2 were measured. The results of the automatic controller were then compared to a retrospective control group of 30 ICU patients. SpO2-FiO2 values of the control group were collected over 3 different periods of 6 hours.
Time in the target range was higher than 95% with the controller. When the 20 patients were separated according to the median PaO2/FiO2 (160(133-176) mmHg vs. 239(201-285)), the loop with the highest slope was slightly better (P = 0.047) for the more hypoxemic patients. Hyperoxemia and hypoxemia durations were significantly shorter with the controller compared to usual care: SpO2 target range was reached 90% versus 24%, 27% and 32% (P < .001) with the controller compared to three historical control group periods.
A specific FiO2 controller is able to reliably maintain SpO2 within a predefined target range. Two different feedback loops can be used depending on the initial PaO2/FiO2; with both, the automatic controller showed excellent performance when compared to usual care.
Available from: Siddharth Dani
- "Algorithms for parameter adjustment might provide an opportunity to improve efficiency, efficacy, and access to parameter modifications in neurostimulation. Examples of algorithmic automation concepts in healthcare devices can be found in cardiac pacing , diabetes , and respiration . The exploration of closed-loop therapies is motivated by a number of potential advantages: 1. Reduced intervention response time: This is the ability to respond to an episodic disease state (e.g., seizure onset in epilepsy) without manual intervention 2. Personalized parameter adjustment: This is the use of the subject's particular natural history of disease to drive intervention rather than relying on potentially imperfect All authors: Medtronic Neuromodulation, Minneapolis, MN 55432 USA (phone: email: ) "
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ABSTRACT: Architectures capable of using an algorithm to modify actuation based on measured signals are often called "closed-loop" systems. While such systems are traditionally thought to rely on algorithms residing in device firmware, these may also reside outside the device in a host processor located physically nearby, or on a cloud-based architecture. In order to serve the potentially broad array of data processing modalities, we have developed an application programming interface (API). The API enables access to the sensing and stimulation capabilities of an implantable bi-directional neural interface. Systems using the API on different hardware/software platforms could measure neural signals, process signals in real-time, and modulate stimulation parameters using a variety of algorithms. This flexibility allows increased algorithm access and enables rapid prototyping for potentially improved technology solutions. The system performance was characterized using a signal generator to input square wave pulses to a Simulink model via the API. Closed-loop stimulation latencies of around 600ms were achieved. I. NEED FOR PROTOTYPING SYSTEMS FOR CLOSED-LOOP NEUROMODULATION Neurostimulation is used to treat a variety of neurological diseases such as Parkinson's disease, essential tremor, urinary incontinence, and chronic pain. To function properly, these technologies require both accurate hardware placement (e.g., placing leads in the correct nervous system location) as well as therapy parameter setting optimization (e.g. electrode selection, stimulation amplitude, pulse width, and frequency). Selection of optimal parameters is largely an empirical process that may involve multiple, time-consuming device programming sessions spread apart by weeks or months. Outside of these sessions, the ability to make stimulation parameter adjustments is generally limited.
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