• Gastón Murias, Jaume Montanyà, Encarna Chacón, Anna Estruga, Carles Subirà, Rafael Fernández, Bernat Sales, Candelaria de Haro, Josefina López-Aguilar, Umberto Lucangelo, Jesús Villar, Robert M Kacmarek, and Lluís Blanch.
• Clínica Bazterrica y Clínica Santa Isabel, Departamento de Ciencias Fisiológicas, Farmacológicas y Bioquímicas, Facultad de Medicina, Universidad Favaloro, Buenos Aires, Argentina.
• Crit Care. 2016 Aug 14; 20 (1): 258.

BackgroundExpert systems can help alleviate problems related to the shortage of human resources in critical care, offering expert advice in complex situations. Expert systems use contextual information to provide advice to staff. In mechanical ventilation, it is crucial for an expert system to be able to determine the ventilatory mode in use. Different manufacturers have assigned different names to similar or even identical ventilatory modes so an expert system should be able to detect the ventilatory mode. The aim of this study is to evaluate the accuracy of an algorithm to detect the ventilatory mode in use.MethodsWe compared the results of a two-step algorithm designed to identify seven ventilatory modes. The algorithm was built into a software platform (BetterCare® system, Better Care SL; Barcelona, Spain) that acquires ventilatory signals through the data port of mechanical ventilators. The sample analyzed compared data from consecutive adult patients who underwent >24 h of mechanical ventilation in intensive care units (ICUs) at two hospitals. We used Cohen's kappa statistics to analyze the agreement between the results obtained with the algorithm and those recorded by ICU staff.ResultsWe analyzed 486 records from 73 patients. The algorithm correctly labeled the ventilatory mode in 433 (89 %). We found an unweighted Cohen's kappa index of 84.5 % [CI (95 %) = (80.5 %: 88.4 %)].ConclusionsThe computerized algorithm can reliably identify ventilatory mode.

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