A Stochastic Model to Measure Patient Effects Stemming from Hospital-Acquired Infections

We introduce a Markov chain model to represent a patient's path in terms of the number and type of infections s/he may have acquired during a hospitalization period. The model allows for categories of patient diagnoses, surgery, the four major types of nosocomial (hospital-acquired) infections, and discharge or death Data from a national medical records survey including 58,647 patients enable us to estimate transition probabilities and, ultimately, perform statistical tests of fit, including a validation test. Novel parameterizations (functions of the transition matrix) are introduced to answer research questions on time-dependent infection rates, time to discharge or death as a function of patient diagnostic groups and conditional infection rates reflecting intervening variables (e.g., surgery).