A model for diagnosis of pulmonary infections in solid-organ transplant recipients

Opportunistic pulmonary infections are a major cause of morbidity and mortality among solid organ transplant recipients. The diagnosis of these infections is challenging because of the broad spectrum of bacteria, fungi and viruses affecting these patients and the lack of specific signs and symptoms. Treatment directed at the offending organism started as soon as possible improves survival.

To develop a decision support system for the diagnosis of pulmonary infections in solid-organ transplant recipients. The model's goal is to improve the accuracy of the diagnosis and thus the appropriateness of empirical treatment.

The model is built using a Bayesian network (also known as causal probabilistic network). The network is based on pathogen segments which are the main building blocks of the model. Segments share common risk factors, such as time after transplantation, latent infections of donor/recipient and organ transplanted. The segments are linked at symptoms, signs and diagnostic tests common to all pathogens. The outputs of the model are predicted probabilities of infectious pathogens. To populate the model with data we have mainly abstracted data from the literature, using a systematic approach. The structure of the model and its adaptation for decision support will be presented.

The first evaluation phase assessed the model's diagnosis in a series of 20 representative cases of opportunistic infections. A match between the case's diagnosis and the model's prediction was achieved in 17/20 of cases. The next evaluation phase will consist of a prospective observational study comparing the accuracy of the model's diagnosis vs. that of the physician within 24h of episode onset, as compared with a gold-standard diagnosis ascribed to the patients at the end of the infectious episode by two independent experts. Data for this phase are currently collected prospectively.