in the previous issue of critical care , saka and colleagues describe one of the latest developments of complex microsimulation . in social sciences , in economy , and in technical areas , the technique of simulation is widely accepted as an important intervention that can prevent adverse events . simulation is the concept of building an environment or part of the environment resembling a real - life environment in appearance and behavior . basically , there are three main reasons for using simulation : as a method for quality control , as a teaching tool , and for the prediction of time courses . simulation for quality control is the domain of technical areas . in major industry , no production line , no large software , no novel technical concept would ever be possible today without previous testing with simulation programs to exclude major flaws . in social sciences , education and prediction macrosimulators have a physical component , usually a mannequin or a body - part module , and microsimulators are purely computer - based . both types can be further regarded as either simple or complex , depending on the complexity of the topic to be learned . this results in four possible subtypes of simulator . although the micro / macro differentiation is discrete , the simplicity / complexity aspect is not . simple simulators , often called part - task simulators , teach simple algorithms or procedures , involving only a few aspects of a problem . simple microsimulators have been known since the 1960s : kelman developed a ' digital computer subroutine ' to translate a measured oxygen tension into a predicted saturation . in the following 5 years , this was also practiced for carbon dioxide content , and ended up with the development of an artificial ' lung model ' in 1970 . with regard to the technical equipment available in these days , kelman 's work has to be considered a milestone of simulation , and was definitely one of the first crucial steps in the development of mathematical models for human physiology . meanwhile , these mathematical models are further differentiated into ' forward ' and ' backward ' ( or inverse ) processes . this subtyping depends on the characterization of input and output variables of the mathematical model . for a forward process , the input variables are theoretical inputs , whereas the outputs are physiological parameters , which are usually measured . this forward process is the most common way in which biomathematical models have been used in medicine . the backward processes are much more complicated : these models take measured variables such as blood gases as inputs , and seek to provide an inverse description ; an example of the latter is the anatomy of the lung . in fact , computed tomography scans are based on inverse mathematical processing . in the paper by saka and colleagues , the investigators took data from a large cohort of 1,888 patients with severe sepsis based on community - acquired pneumonia . in contrast to former forward models , which were developed to predict outcome in a stationary manner , namely using predefined states such as septic , severely septic , or dead , this project was aimed at simulating dynamic disease progression ( or recovery ) by using time - dependent inputs . in a recent paper a similar approach was chosen , also using the daily sepsis - related organ failure assessment ( sofa ) score . the paper by saka and colleagues , however , included additional data from the clinical course before they entered the intensive care unit ( age , gender , and ethnicity ) , thus ' recreating individual patients ' whose entire disease course reproduced the rate of change of severity of illness . the investigators tested eight different algorithms with different sets of input variables , and the ability to predict the clinical course by some of these models is amazing , as seen in fig . moreover , the analysis nicely demonstrates that the quality of prediction is dependent on the quality of input data : the more restrictive the criteria are , the more closely the model can predict actual experience . for instance , the ' real ' data contained 1,787 discharges from the intensive care unit ; the simulation algorithms predicted between 1,779 and 1,804 discharges ! the best results were seen when the model also incorporated information on the duration of illness and the direction of progression . although the approach presented is highly data - intensive , the model demonstrates that cohort - level characteristics can be reproduced in a dynamic manner . the use of such a dynamic microsimulation can not be foreseen , but it may have enormous potential for the future : teaching tools , feedback strategies ( ' briefing ' and ' debriefing ' after treatment ) , protocol development , and cost - effectiveness analysis are just a few approaches . this example of current developments of complex microsimulators may be considered as ' just a footprint ' , but even if we do not know what microsimulation will look like in 5 to 10 years , the direction is clear : education of health care professionals is changing markedly , and there are many more opportunities across various medical fields for expanding the use of simulations . microsimulation in critical care is no experiment , no hobby ; it is present and part of the very near future .
today , computer - aided strategies in social sciences are an indispensable component of teaching programs . in recent years , microsimulation modeling has gained attention in its ability to represent predicted physiological developments visually , thus providing the user with a full understanding of the impacts of a proposed scheme . 
 there are several microsimulation models in human medicine , and they can be either dynamic or static . if the model is dynamic the course of variables changes over time ; in contrast , in the static case time constancy is assumed . in critical care there have been several approaches to implement microsimulation models to predict outcome . 
 this commentary describes current approaches for predicting disease progression by using dynamic microsimulation in pneumonia - related sepsis .