ORIGINAL ARTICLE

Bayes pulmonary embolism assisted diagnosis: a new expert system for clinical use

Davide Luciani¹, Silvio Cavuto¹, Luca Antiga², Massimo Miniati³, Simona Monti³, Massimo Pistolesi⁴, Guido Bertolini⁵

¹ Unit of Clinical Knowledge Engineering, Laboratory of Clinical Epidemiology, "Mario Negri" Institute of Pharmacological Research, Clinical Centre for Rare Diseases Aldo e Cele Daccò, Ranica (Bergamo), Italy
² Laboratory of Biomedical Technologies, "Mario Negri" Institute of Pharmacological Research, Clinical Centre for Rare Diseases Aldo e Cele Daccò, Ranica (Bergamo), Italy
³ Department of Clinical Physiology, Italian National Research Council, Pisa, Italy
⁴ Department of Critical Care, University of Florence, Firenze, Italy
⁵ Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche "Mario Negri", Centro "Aldo e Cele Daccò", Ranica (Bergamo), Italy

Correspondence to:
Correspondence to:
Dr G Bertolini
GiViTI Coordinating Center—Istituto di Ricerche Farmacologiche "Mario Negri", Centro di Ricerche Cliniche per le Malattie Rare Aldo e Cele Daccò, Ranica (Bergamo) 24020, Italy; bertolini{at}marionegri.it

Background: The diagnosis of pulmonary embolism demands flexible decision models, both for the presence of clinical confounders and for the variability of local diagnostic resources. As Bayesian networks fully meet this requirement, Bayes Pulmonary embolism Assisted Diagnosis (BayPAD), a probabilistic expert systems focused on pulmonary embolism, was developed.

Methods: To quantitatively validate and improve BayPAD, the system was applied to 750 patients from a prospective study done in an Italian tertiary hospital where the true pulmonary embolism status was confirmed using pulmonary angiography or ruled out with a lung scan. The proportion of correct diagnoses made by BayPAD (accuracy) and the correctness of the pulmonary embolism probabilities predicted by the model (calibration) were calculated. The calibration was evaluated according to the Cox regression–calibration model.

Results: Before refining the model, accuracy was 88.6%. Once refined, accuracy was 97.2% and 98%, respectively, in the training and validation samples. According to Cox analysis, calibration was satisfactory, despite a tendency to exaggerate the effect of the findings on the probability of pulmonary embolism. The lack of some investigations (like Spiral computed tomographic scan and Lower limbs doppler ultrasounds) in the pool of available data often prevents BayPAD from reaching the diagnosis without invasive procedures.

Conclusions: BayPAD offers clinicians a flexible and accurate strategy to diagnose pulmonary embolism. Simple to use, the system performs case-based reasoning to optimise the use of resources available within a particular hospital. Bayesian networks are expected to have a prominent role in the clinical management of complex diagnostic problems in the near future.

Abbreviations: BayPAD, Bayes Pulmonary embolism Assisted Diagnosis; PISA-PED, Prospective Investigative Study of Acute Pulmonary Embolism Diagnosis

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