Comparing the Predictive Power of Heart Failure Hospitalisation Risk Scores in the Diabetic Outpatient Clinic and Primary Care Settings

The organisation of care at diabetes outpatient clinics is typically different from that delivered by general practitioners, it is thus of interest to assess whether there is also a difference in the predictive power of heart failure hospitalisation risk scores developed independently for each subpopulation. To such a purpose, a diabetes outpatient clinic dataset and a primary care dataset were considered. A Cox proportional hazard model, an accelerated failure time model, a logistic regression, a random forest, and a K-nearest neighbours model were trained in each dataset and tested on both. The UK Prospective Diabetes Study (UKPDS) risk engine was used as benchmark. Results show that models developed using primary care data performed well on the corresponding test set but poorly when used in the diabetes outpatient clinic setting (best C-Index = 0.759vs. 0.615, best AUROC = 0.757vs. 0.598). Models trained on the diabetes outpatient clinic data performed well on the corresponding test set, and their predictive power in the primary care setting was not statistically different from the one of models developed using primary care data (best C-Index = 0.814 vs 0.740, best AUROC = 0.812 vs 0.750). In both settings UKPDS had lower predictive power than the best newly-developed models. Different care setting led to a difference in the predictive power of heart failure hospitalisation risk scores that depended on both the data used for training and the methodological approach chosen. This suggests the need to consider these factors when applying risk scores to a target population where the expected incidence of the outcome and the distribution of baseline covariates differ from those of the population for which scores were proposed.