Per Thorgaard

Non-invasive estimation of shunt and ventilation-perfusion mismatch.

ObjectiveTo investigate whether parameters describing pulmonary gas exchange (shunt and ventilation-perfusion mismatch) can be estimated consistently by the use of non-invasive data as input to a mathematical model of oxygen transport.DesignProspective study.SettingInvestigations were carried out in the post-anaesthesia care unit, coronary care unit, and intensive care unit.PatientsData from ninety-five patients and six normal subjects were included for the comparison. The clinical situations differed, ranging from healthy subjects to patients with acute respiratory failure in the intensive care unit.MeasurementsThe experimental procedure involved changing the inspired oxygen fraction (FIO2) in 4–6 steps in order to obtain arterial oxygen saturations (SaO2) in the range from 90–100%. This procedure allows plotting a FIO2/SaO2 or FEO2/SaO2 curve, the shape and position of which was quantified using the mathematical model estimating pulmonary shunt and a measure of ventilation-perfusion mismatch (ΔPO2). This procedure was performed using either arterial blood samples at each FIO2 level (invasive approach) or using values from the pulse oximeter (non-invasive approach).Main resultsThe model provided good fit to data using both the invasive and non-invasive experimental approach. The parameter estimates were linearly correlated with highly significant correlation coefficients; shuntinvasive vs shuntnon-invasive, r2 = 0.74, P <0.01, and ΔPO2invasive vs ΔPO2non-invasive, r2 = 0.97, P <0.001.ConclusionsPulmonary gas exchange can be described equally well using non-invasive data. The simplicity of the non-invasive approach makes the method suitable for large-scale clinical use.

Hypoxemia after coronary bypass surgery modeled by resistance to oxygen diffusion.

OBJECTIVE To evaluate a model describing postoperative hypoxemia after cardiac surgery by using two variables, i.e., shunt and resistance to oxygen diffusion (Rdff). DESIGN Estimation of these two variables in normal subjects and postoperative cardiac patients. SETTING The pulmonary function laboratory for the normal subjects and the intensive care unit for the cardiac patients. PATIENTS/SUBJECTS Nine postoperative cardiac patients and six healthy subjects. INTERVENTIONS Inspired oxygen fraction was varied in normal subjects and in cardiac patients 3-6 hrs after surgery. This variation occurred in four to seven steps to achieve arterial oxygen saturations in the range 0.90-1.00. MEASUREMENTS AND MAIN RESULTS Measurements were taken of arterial oxygen saturation, cardiac output, ventilation, and end-tidal gases at each inspired oxygen fraction. These measurements gave the following estimates for the normal subjects: shunt = 3.9+/-5.4% (mean +/- SD) and Rdiff = -5+/-16 torr/(L/min) [-0.7+/-2.2 kPa/(L/min)]; for the cardiac patients: shunt = 7.7+/-1.8% and Rdiff = 212+/-230 torr/(L/min) [28.2+/-30.6 kPa/(L/min)]. The increase in Rdiff (P = .01) was sufficient to explain the observed hypoxemia in these patients. The value for shunt was not significantly increased in the patients (p = .09). The two-variable model (shunt and Rdff) gave a better prediction of arterial oxygen saturation than a model with shunt as the only variable (p = .02). CONCLUSIONS In cardiac patients requiring supplementary oxygen, the respiratory abnormality could, in our model, be best described by an increased Rdiff, not by an increased shunt value.

A decision support system for suggesting ventilator settings: Retrospective evaluation in cardiac surgery patients ventilated in the ICU

Selecting appropriate ventilator settings decreases the risk of ventilator-induced lung injury. A decision support system (DSS) has been developed based on physiological models, which can advise on setting of tidal volume (Vt), respiratory frequency (f) and fraction of inspired oxygen (FiO2). The aim of this study is to assess the feasibility of the DSS by comparing its advice with the values used in clinical practice. Data from 20 patients following uncomplicated coronary artery bypass grafting (CABG) with cardiopulmonary bypass was used to test the DSS. Ventilator settings suggested by the DSS were compared to the settings selected by the clinician. When compared to the clinician the DSS suggested: lowering FiO2 (by median 7%, range 2-17%) at high SpO2 and increasing FiO2 (by median 2%, range 1-5%) at low SpO2; lowering ventilation volume (by median 0.57 l min(-1), range 0.2-1.1 l min(-1)) at high pHa and increasing ventilation volume (by median 0.4 l min(-1), range 0.1-0.9 l min(-1)) at low pHa. Suggested changes in ventilation volume were such that simulated values of PIP were < or = 22.9 cmH2O and respiratory frequency < or = 18 breaths min(-1). In all cases, computer suggested values of FiO2, Vt or f were consistent with maintaining sufficient oxygenation, normalising pH and obtaining low values of PIP.

Prospective evaluation of a decision support system for setting inspired oxygen in intensive care patients

PURPOSE The aim of the study was to prospectively evaluate a decision support system for its ability to provide appropriate suggestions of inspired oxygen fraction in intensive care patients comparing with levels used by clinicians in attendance. MATERIALS AND METHODS Thirteen mechanically ventilated patients were studied in an intensive care unit where up to 4 experiments were performed during 2 consecutive days. Inspired oxygen fraction was selected in each experiment by both the decision support system and attending clinicians, and each selection was evaluated by measuring arterial oxygen saturation. RESULTS Median (interquartile range [range]) changes in inspired oxygen fraction from baseline level by attending clinicians and the decision support system were 0.00 (-0.05 to 0.00 [-0.10 to 0.05]) and -0.03 (-0.07 to 0.01 [-0.16 to 0.12]), respectively. Clinician ranges of inspired oxygen fraction and arterial oxygen saturation were 0.25 to 0.70 and 0.92 to 0.99, respectively. Decision support system ranges of inspired oxygen fraction and arterial oxygen saturation were 0.26 to 0.54 and 0.94 to 0.99, respectively. CONCLUSIONS The decision support system selects appropriate levels of inspired oxygen fraction in intensive care patients and could be used for automatic frequent assessment of patients, freeing the focus of clinicians to concentrate on more challenging therapy.

Retrospective evaluation of a decision support system for controlled mechanical ventilation

Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired oxygen fraction, tidal volume and respiratory frequency. The DSS was retrospectively evaluated in 16 intensive care patient cases, with physiological models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Sensitivity of the DSS’s advice to variations in cardiac output (CO) was evaluated. Compared to the baseline ventilator settings set as part of routine clinical care, the system suggested lower tidal volumes and inspired oxygen fraction, but higher frequency, with all suggestions and the model simulated outcome comparing well with the respiratory goals of the Acute Respiratory Distress Syndrome Network from 2000. Changes in advice with CO variation of about 20% were negligible except in cases of high oxygen consumption. Results suggest that the DSS provides clinically relevant and rational advice on therapy in agreement with current ‘best practice’, and that the advice is robust to variation in CO.

Acute Favism: Methemoglobinemia May Cause Cyanosis and Low Pulse Oximetry Readings

Persons with glucose-6-phosphate dehydrogenase (G6PD) deficiency may suffer episodes of acute hemolysis triggered by infection, drugs, and chemicals [1]. Acute favism after ingestion of fava beans is a special type [2]. Recently, two cases with clinically manifest methemoglobinemia, a hitherto unrecognized feature, were reported [3, 4]. We saw two similar cases with low-pulse oximeter saturation (SpO2) misinterpreted as hypoxia.

Model-based measurement of gas exchange in healthy subjects using ALPE essential - influence of age, posture and gender

The ALPE Essential device for model-based measurement of pulmonary gas exchange status may be a useful alternative to current methods for diagnosing, monitoring and evaluating treatment related to pulmonary gas exchange. In this study, shunt and ventilation/perfusion mismatch were measured with ALPE Essential in 106 healthy subjects with the aim of investigating the influence of age, posture and gender on gas exchange parameters and evaluating the test-retest reliability of the measurements. Age and gender did not have statistically significant influence on gas exchange parameters, although there was a tendency for poorer matching of ventilation and perfusion with age. Posture was shown to be important when measuring gas exchange parameters. Absolute measurement reliability was acceptable with future studies in patients being necessary for accurate evaluation of relative reliability.

Non-invasive estimation of respiratory depression profiles during robot-assisted laparoscopic surgery using a model-based approach

Introduction: Robot assisted laparoscopic surgeries are becoming the standard procedure for radical prostatectomies (RALRP). General anesthesia, Trendelenburg positioning and capnoperitoneum during RALRP affect patient’ gas exchange, leading to possible complications in the postoperative phase, such as hypoxemia. The aim of this paper is to examine the changes in pulmonary gas exchange through the perioperative period for RALRP using a mathematical model approach.

The Intelligent Ventilator project: application of physiological models in decision support

This paper describes progress in a model-based approach to building a decision support system for mechanical ventilation. It highlights that the process of building models promotes generation of ideas and describes three systems resulting from this process, i.e. for assessing pulmonary gas exchange, calculating arterial acid-base status; and optimizing mechanical ventilation. Each system is presented and its current status and impact reviewed.

Use of the invent system for standardized quantification of clinical preferences towards mechanical ventilator settings

for ESCTAIC 2010 “Glucosafe A model-based medical decision support system for tight glycemic control in critical care” Ulrike Pielmeier a a Center for Model-based Medical Decision Support, Aalborg University, Fredrik-Bajers-Vej 7, 9220 Aalborg, Denmark Introduction Hyperglycemia during critical illness is common and is associated with increased mortality, morbidity and prolonged stay in intensive care [1][2]. The past decade has seen many attempts to improve survival by regulating blood glucose using intensive insulin therapy (IIT) protocols [3][4]. However, consistent control has proven elusive, not least because typical IIT protocols ignore the carbohydrate intake of patients [5]. An effective method that achieves and maintains “tight” blood glucose levels (i.e. in the range from 4 to 6 mmol/l) without high glucose variances and without increasing insulin-induced severe hypoglycemia (<2.2 mmol/l) has yet to emerge [6]. This work assesses the effectiveness of the computerized decision support system “Glucosafe” for tight glycemic control in critical care. This system advises insulin therapy and infusion rates of enteral and parenteral nutrition, based on blood glucose predictions with a physiological insulin-glucose model and patient-specific data [7]. Pilot testing shows significant improvements of glycemic control in a prospectively controlled cohort of intensive care patients [8]. In a retrospective analysis of the pilot study data the model is assessed with regard to how accurately blood glucose was predicted, and whether the predictive accuracy can be improved by two physiological model extensions, regarding the decreased delivery rate of nutrients that is often observed in critical care patients with delayed gastric emptying [9], and the dependency of pancreatic insulin secretion on the blood glucose level [10]. Methods The blood glucose concentrations of 10 hyperglycemic patients admitted to a neuroand trauma intensive care unit were retrospectively predicted using a) the original Glucosafe model [7] b) the Glucosafe model including a feedback loop between blood glucose and pancreatic insulin secretion rate c) the Glucosafe model and a reduced rate of appearance of enterally administered nutrition in the intestinal reservoir d) both extensions as described in b) and c). Prediction errors were expressed as absolute percent error (APE) from measured concentrations; the comparison was based on median APEs for different prediction time lengths, reflecting intervals between measurements of up to 5 hours. Results The model predictive accuracy improved modestly for each one of the two model extensions. The greatest reduction in prediction error was achieved when both model extensions were included in the Glucosafe model. For predictions time lengths (in hours) of 0.5-1.5h, 1.5-2.5h, 2.5-3.5h, 3.5-4.5h and 4.5-5.5h, the median APE was 9.7%, 11.2%, 14.8%, 15.1% and 17.7% with the Glucosafe model, compared to 9.2%, 10.1%, 12.3%, 13.2% and 16.6% with both of the model extensions included, for the same prediction time lengths. Discussion Predicted blood glucose concentrations with the Glucosafe model in its original form [7] are sufficiently accurate for typical time intervals between two measurements. The pilot trial results [8] showed that glycemic control was significantly improved, while no hypoglycemic event was observed. Thus, model-based predictive control based on the Glucosafe model may be a step towards a consistent reduction of elevated blood glucose levels. This retrospective analysis also explored two physiological model extensions, which modestly improved the model’s predictive accuracy. However, as the data used in this study were from a small cohort of patients with similar admission diagnosis, groups of other patients with a different disease background should be used to verify these preliminary results. References [1] Falciglia M, Freyberg RW, Almenoff PL, et al. Hyperglycemia-related mortality in critically ill patients varies with admission diagnosis. Crit Care Med 37 (12): 3001-3009, 2009. [2] Krinsley JS: Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clinic Proceedings 78 (12): 1471-1478, 2003. [3] Meijering S, Corstjens AM, Tulleken JE, et al.Towards a feasible algorithm for tight glycaemic control in critically ill patients: a systematic review of the literature. Critical Care 10 (1): R19, 2006. [4] Chase JG, Hann CE, Shaw GM, et al. An overview of glycemic control in critical care relating performance and clinical results. Journal of Diabetes Science and Technology 1 (1): 82-91, 2007. [5] Kalfon P, Preiser JC. Tight glucose control: should we move from intensive insulin therapy alone to modulation of insulin and nutritional inputs? Crit Care 12:156, 2008. [6] Dossett LA, Collier B, Donahue R, et al. Intensive insulin therapy in practice: Can we do it? J Parenter Enteral Nutr 33 (1): 14-20, 2009. [7] Pielmeier U, Andreassen S, Nielsen BS, et al. A simulation model of insulin saturation and glucose balance for glycemic control in ICU patients. Computer Methods and Programs in Biomedicine 97 (3): 211-222, 2010. [8] Pielmeier U, Andreassen S, Juliussen B, et al. The Glucosafe system for tight glycemic control in critical care: A pilot evaluation study. J Crit Care 25 (1): 97-104, 2010. [9] Chapman M, Fraser R, Matthews G, et al. Glucose absorption and gastric emptying in critical illness. Crit Care 13 (4): R140, 2009. [10] Polonsky KS, Given BD, Van Cauter E. Twenty-four-hour profiles and pulsatile patterns of insulin secretion in normal and obese subjects. J Clin Invest 81: 442-448, 1988.SELECTED ABSTRACTS PRESENTED AT THE 21ST MEETING OF THE EUROPEAN SOCIETY FOR COMPUTING AND TECHNOLOGY IN ANAESTHESIA AND INTENSIVE CARE (ESCTAIC) Amsterdam, The Netherlands, 6th–9th October, 2010 Edited by: A. A. van Dusseldorp, C. Boer, D. S. Karbing, L. Krummreich, S. E. Rees, S. A. Loer LIST OF ABSTRACTS Soraya Abbasi: The Role of Physiological Models in Critiquing Mechanical Ventilation Treatments Gracee Agrawal: Real-time Detection of Suppression in EEG Christa Boer: Clinical Experience with Perioperative Non-invasive Beat-to-beat Arterial Blood Pressure Monitoring Nadja Bressan: Infusion Rate Control Algorithm for Target Control Infusion using Optimal Control Chih-Yen Chiang: Rule-based Evaluation for the Patientcontrolled Analgesia Clinical Effectiveness Wolfgang Friesdorf: Professional Design of Clinical Working Systems According to Human Factors Fred de Geus: CAROLA: An Open Source PDMS, After 25 Years Still Experimental? Yori Gidron: The Effects of Stress and Hemispheric Lateralization on Managerial Decisions Johan Groeneveld: Value of Central or Mixed Venous O2 Saturation in Guiding Treatment in the Intensive Care Unit Gabriel M. Gurman: Professional Stress and the Anesthesiologist-how Evident is it? Eliahu Heldman: Salivary Cortisol as a Measure of Professional Stress; An Overview and a Description of a Study with Paramedics Martin Hurrell: Implementation of a Standards-based, CDA-Compliant Anesthesia Record Mathieu Jeanne: Analgesia Nociception Index Online Computation and Preliminary Clinical Test During Cholecystectomy Under Remifentanil-Propofol Anaesthesia Christian Jeleazcov: Pharmacodynamic Modeling of Changes in Pulse Waveform During Induction of Propofol Anaesthesia in Volunteers: Comparison between Invasive and Continuous Non-invasive Measurements of Pulse Pressure Pierre Kalfon: Assessing Performances of Glucose Control Algorithms on a set of Virtual ICU Patients Cor Kalkman: Automation and Automation Surprises: Lessons from Aviation. Should Health Care Brace Itself. Dan S Karbing: Use of the INVENT System for Standardized Quantification of Clinical Preferences Towards Mechanical Ventilator Settings Talma Kushnir: Moods and Burnout Among Physicians: Associations with Prescribing Medications Communicating with Patients, and Referrals for Specialists and Diagnostic Tests Johannes J van Lieshout: Non-invasive Pulse Contour Cardiac Output by Nexfin Technol Egbert Mik: Monitoring Mitochondrial Oxygenation Journal of Clinical Monitoring and Computing (2011) 25:3–43 DOI: 10.1007/s10877-011-9276-2 Springer 2011