title: Clinical refinement of the automatic lung parameter estimator (ALPE).
abstract: 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.
authors: Department of Health Science and Technology, Center for Model-based Medical Decision Support, Aalborg University, Fredrik Bajers Vej 7E, 9220 Aalborg East, Denmark. lpt@hst.aau.dk
authors: Lars P
authors: LP
authors: Thomsen
authors: Dan S
authors: DS
authors: Karbing
authors: Bram W
authors: BW
authors: Smith
authors: David
authors: D
authors: Murley
authors: Ulla M
authors: UM
authors: Weinreich
authors: Søren
authors: S
authors: Kjærgaard
authors: Egon
authors: E
authors: Toft
authors: Per
authors: P
authors: Thorgaard
authors: Steen
authors: S
authors: Andreassen
authors: Stephen E
authors: SE
authors: Rees
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004867:Equipment Design; D006801:Humans; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000706:statistics & numerical data; D010100:Oxygen / Q000502:physiology; D011659:Pulmonary Gas Exchange / Q000502:physiology*; D012121:Respiration, Artificial; D012129:Respiratory Function Tests / Q000295:instrumentation* / Q000706:statistics & numerical data; D014692:Ventilation-Perfusion Ratio / Q000502:physiology
chemical_list: D010100:Oxygen