A simple gas exchange model predicting arterial oxygen content for various FiO&lt;inf&gt;2&lt;/inf&gt; levels

Kretschmer, Jörn; Becher, Tobias; Riedlinger, Axel; Schädler, Dirk; Weiler, Norbert; Möller, Knut

doi:10.1109/embc.2013.6609537

Cited by 10 publications

(14 citation statements)

References 8 publications

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“…We develop a minimal model, building on results from the work of Kretschmer et al [12] on estimating P a O 2 and from our previous work [9] on detecting drops in the O 2 concentration. Our model is approximate in the sense that it captures general trends and relationships among the variables in order to reduce the number of unidentifiable parameters.…”

Section: Physiological Modelmentioning

confidence: 99%

“…Thus, we adopt an approach used in prior work [12] for the estimation of f . This requires an initializing measurement of P a O 2 through bloodgas analysis.…”

Section: A System Dynamicsmentioning

confidence: 99%

“…Figure 4 presents the absolute errors of the context-aware filter, with all measurements from all patients stacked together. For better evaluation, we compare the filter with a P a O 2 estimation algorithm developed in previous work that uses a similar model and also requires one blood-gas measurement for initialization [12]; this algorithm is named here "F i O 2 -based estimator". As can be seen in the Figure, the context-aware filter eliminates all of the F i O 2 -based estimator outliers except for one (discussed below).…”

Section: Case Studymentioning

confidence: 99%

“…On the other hand, when models are parameterized by patient-specific parameters, one may use parameter-invariant detectors in order to achieve the same detection performance regardless of the values of these parameters [9], [22]. Finally, estimation is more challenging as most models are unidentifiable given current measurements; in some applications, it is possible to use invasive measurements to learn the parameters [12] or to use machine learning when sufficient data is available [5], [16]. Since the problem addressed in our paper does not have rich data or frequent invasive measurements, we generate context measurements based on other available data.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Blood Oxygen Content Using Context-Aware Filtering

Ivanov

Atanasov

Weimer

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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In this paper we address the problem of estimating the blood oxygen concentration in children during surgery.Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children's Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables. Abstract-In this paper we address the problem of estimating the blood oxygen concentration in children during surgery. Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children's Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables. Disciplines Computer Engineering | Computer Sciences

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Section: Physiological Modelmentioning

confidence: 99%

“…Thus, we adopt an approach used in prior work [12] for the estimation of f . This requires an initializing measurement of P a O 2 through bloodgas analysis.…”

Section: A System Dynamicsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Blood Oxygen Content Using Context-Aware Filtering

Ivanov

Atanasov

Weimer

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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“…Das Modell mit einem Alveolarkompartiment wird kalibriert, indem die Größe des Shunts aus den Messwerten einer Blutgasanalyse (BGA) berechnet wird. Tests mit synthetischen und realen Patientendaten zeigten eine gute Abbildung der gemessenen PaO 2 -Werte [33] …”

Section: Gasaustauschunclassified

Komplexe Modellsysteme in der automatisierten Beatmung

Kretschmer¹,

Schranz²,

Riedlinger³

et al. 2015

At - Automatisierungstechnik

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Zusammenfassung: Die künstliche Beatmung stellt eine etablierte Therapie in der Intensivmedizin dar. Trotz der routinemäßigen Anwendung kann es bei schwerkranken Patienten zu Komplikationen kommen. Die modellbasierte Entscheidungsunterstützung erlaubt eine Verbesserung der Therapie, indem an den Patienten angepasste Modelle für Prädiktionen genutzt und damit Therapieeinstellungen individuell optimiert werden. Die eingesetzten Modelle sollten dabei an den Krankheitszustand des Patienten, an die vorliegenden Daten sowie an die aktuelle klinische Fragestellung anpassbar sein und neben der Atemmechanik ebenfalls die wichtigen Bereiche Gasaustausch und Hämodynamik umfassen. Die gezeigten hierarchisch geordneten Modellfamilien erlauben eine flexible Anpassung an die aktuelle klinische Situation, eine robuste Identifikation der Modellparameter sowie eine umfassende Abbildung des Patienten.Schlüsselwörter: Physiologische Modellbildung, Medizinische Entscheidungsunterstützung, Modellhierarchie, Beatmungstherapie.Abstract: Artificial ventilation is a well established therapy in the intensive care unit. Despite its regular use it may harm critically ill patients if not applied adequately. Model based decision support enables the optimization of clinical therapy by exploiting individualized models for predictions of optimized ventilator settings. The implemented models should be adaptable to the patient's disease state as well as the available data at the bedside and the current clinical treatment goal. Moreover, besides respiratory mechanics, they should include gas exchange and cardiovasChristoph Schranz: Hamilton Medical, Bonaduz, Schweiz cular dynamics. The hierarchically structured model families presented in this paper allow a flexible adaption to the current clinical situation, a robust identification of model parameters as well as a global and numerically efficient representation of the patient's physiological properties.

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Variability in estimating shunt from single pulse oximetry measurements

et al. 2015

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Virtual shunt describes the overall loss of O2 content between the alveolar gas and arterial blood. Clinicians indirectly estimate the magnitude of the virtual shunt by monitoring peripheral blood oxygen saturation (SpO2) using non-invasive pulse oximetry. An inherent limitation of this method is the variable precision of pulse oximeters and the non-linear relationship between virtual shunt and SpO2 which is rarely depicted.We propose a model using a combination of basic physiological equations to analyze the estimation of virtual shunt from inspired oxygen (FiO2) and SpO2. The model emphasizes the effect of the non-linearity of the Hb-O2 dissociation curve. Furthermore, it accounts for the variability in SpO2 measurements due to the precision of pulse oximeters.The model was validated with experiments conducted on healthy subjects in a normobaric hypoxia chamber comparing the simultaneous readings from two different commercial pulse oximeters at FiO2 = 21% and 17%. SpO2 probability distributions calculated from the model were estimated. Although a variable bias (1.2-2.1%) in SpO2 between the pulse oximeter brands was observed, the tested pulse oximeters were both within tolerance specified by the manufacturers and matched the probability distributions from the model.The theoretical and experimental findings show that the estimation of virtual shunt is challenging with a single SpO2 measurement using pulse oximeters with tolerances of 2%. Clinical decisions must be based on an appreciation of these limitations.

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A simple gas exchange model predicting arterial oxygen content for various FiO<inf>2</inf> levels

Cited by 10 publications

References 8 publications

Estimation of Blood Oxygen Content Using Context-Aware Filtering

Estimation of Blood Oxygen Content Using Context-Aware Filtering

Komplexe Modellsysteme in der automatisierten Beatmung

Variability in estimating shunt from single pulse oximetry measurements

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