Clinical refinement of the automatic lung parameter estimator (ALPE).

The automatic lung parameter estimator (ALPE) method was developed in 2002 for bedside estimation of pulmonary gas exchange using step changes in inspired oxygen fraction (FIO2). Since then a number of studies have been conducted indicating the potential for clinical application and necessitating systems evolution to match clinical application. This paper describes and evaluates the evolution of the ALPE method from a research implementation (ALPE1) to two commercial implementations (ALPE2 and ALPE3). A need for dedicated implementations of the ALPE method was identified: one for spontaneously breathing (non-mechanically ventilated) patients (ALPE2) and one for mechanically ventilated patients (ALPE3). For these two implementations, design issues relating to usability and automation are described including the mixing of gasses to achieve FIO2 levels, and the automatic selection of FIO2. For ALPE2, these improvements are evaluated against patients studied using the system. The major result is the evolution of the ALPE method into two dedicated implementations, namely ALPE2 and ALPE3. For ALPE2, the usability and automation of FIO2 selection has been evaluated in spontaneously breathing patients showing that variability of gas delivery is 0.3 % (standard deviation) in 1,332 breaths from 20 patients. Also for ALPE2, the automated FIO2 selection method was successfully applied in 287 patient cases, taking 7.2 ± 2.4 min and was shown to be safe with only one patient having SpO2 < 86 % when the clinician disabled the alarms. The ALPE method has evolved into two practical, usable systems targeted at clinical application, namely ALPE2 for spontaneously breathing patients and ALPE3 for mechanically ventilated patients. These systems may promote the exploration of the use of more detailed descriptions of pulmonary gas exchange in clinical practice.

Decision support of inspired oxygen selection based on Bayesian learning of pulmonary gas exchange parameters.

OBJECTIVE: To investigate if the real-time Bayesian learning of physiological model parameters can be used to support and improve the selection of inspired oxygen fraction. METHODS AND MATERIAL: Supporting the selection of inspired oxygen fraction relies on predictions of arterial oxygen saturation. The efficacy of using these predictions to select inspired oxygen was tested retrospectively in a system for estimating gas exchange parameters of the lung (Automatic Lung Parameter Estimator, ALPE). For the predictions to offer effective decision support they need to be accurate and above all safe. These qualities were tested with data from 16 post-operative cardiac patients, using two different tests. The aim of the first test was to assess retrospectively if the predictions could have supported clinical decisions. The second test sought to establish if the predictions could support improving the efficiency of inspired oxygen selection during an ALPE oxygen titration. RESULTS: The predictions were found to be reasonably accurate, and most importantly safe in both of the tests. CONCLUSION: The method described can be used to support the selection of inspired oxygen fraction, and it has the potential to improve the efficiency of inspired oxygen selection during an oxygen titration.

Design, implementation and preliminary evaluation of a telemedicine system for home haemodialysis.

A telemedicine system for home haemodialysis was designed using a systems approach and a feedback model to produce the hardware and software specifications. Preliminary clinical trials at four European locations involved 29 patients and 305 sessions of haemodialysis. The evaluation included an evaluability assessment and formative evaluation. Central to the methodology was the detailed specification of a stakeholder/evaluation criterion matrix. Preliminary results indicated that the telemedicine system was capable of satisfying the requirements of formative evaluation as a precursor to evaluating its overall worth.