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Follow-up healthcare services were used by 35% of participants. This was found to be independent of age, sex or smoking history, but individuals with lower FEV1% were observed to utilize services more often than their peers. The model that used clinical data, pulmonary function tests and CT measurements was found to be the most accurate in predicting utilization, with an accuracy of 80%.

We found that adding imaging predictors to conventional measurements resulted in a 15% increase for correct classification, corresponding author MirandaKirby,PhD, of the Department of Physics at Toronto Metropolitan University, and co-authors wrote. Although this increase may seem small, identifying high risk patients could lead to healthcare utilization prevention through earlier treatment initiation or more careful monitoring.

The authors suggested that even small increases in prediction accuracy could translate into preventing a large number of hospitalizations at the population level.

The full study can be viewed here.

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Predicting healthcare utilization in COPD patients using CT and machine learning - Health Imaging