Mark S. Roberts and colleagues from the University of Pittsburgh used data from a large, multi-centre study to develop their dynamic microsimulation model. This included information on admission date, movement between wards, trips to the intensive care unit, discharge, deaths and disease progression from more than 1,800 patients with pneumonia-related sepsis.

They found that their model closely predicts changing health and the pattern and number of discharges and deaths in patients over a 30-day period. There were 1,776 discharges in the original multi-centre study, and based on the precision of its patient-matching algorithms, the model predicted between 1,779 and 1,804. The model forecast between 62 and 84 of the 85 patients who actually died[MSR1]. The researchers also found the simulation model could predict not only the number but also the pattern of events over time, although the ability to predict when deaths and discharges occur over time varies.

The model has certain advantages over predecessors, which assume a constant rate of disease progression, and often don't incorporate past clinical history. The result is a more powerful model that can help predict the individual course and outcome of disease. Sepsis affects approximately 750,000 people in the US alone every year, and around a third of these die. With the ability to influence clinical decisions and patient outcomes, this makes the new simulation technique a powerful tool.

Article: "Predicting disease progression using dynamic microsimulation in pneumonia-related sepsis"  Gorkem Saka, Jennifer E Kreke, Andrew J Schaefer, Chung-Chou H Chang, Mark S Roberts and Derek C Angus, Critical Care (in press)

Note: If no author is given, the source is cited instead.

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