SAFEHEART-risk equation can now be used in other European population of HeFH patients to predict CV events.• The cholesterol-year-score is a robust predictor of CV events in HeFH patients.• SAFEHEART-RE and the cholesterol-year-score are valid in primary prevention heFH patients.
Background and aims: Cardiovascular risk is high in heterozygous familial hypercholesterolemia (HeFH). The objective of this study was to describe recurrent cardiovascular events in selected patients with HeFH attending lipid clinics in France. Methods: We included 781 patients with a clinical (Dutch Lipid Clinic Network score ≥ 6) or genetic diagnosis of HeFH who had experienced a first cardiovascular event (myocardial infarction, percutaneous coronary intervention or coronary bypass, unstable angina, stroke, peripheral arterial revascularization or cardiovascular death) and were enrolled in the French Familial Hypercholesterolemia Registry (November 2015 to March 2018). Results: The first cardiovascular event occurred at the mean age of 47 years (interquartile range 39-55) in a predominantly male population (72%); 48% of patients were on statin therapy. Overall, 37% of patients had at least one recurrent cardiovascular event (mean of 1.8 events per patient), of which 32% occurred in the 12 months after the index event; 55% of events occurred >3 years after the first event. Mean LDL-C at the last clinic visit was 144±75 mg/dL (132±69 mg/dL for patients on highpotency statin therapy and 223±85 mg/dL for untreated patients). Conclusions: The rate of recurrent cardiovascular events was high in French patients with HeFH in secondary prevention. The detection of FH in the childhood is crucial to prevent CV events at a young age by early initiating statin therapy. There is a clear urgent need to expand the actual very small target population which can be treated with PCSK9 inhibitor in France. Highlights n , French FH Registry group †
Background Innovative provider payment methods that avoid adverse selection and reward performance require accurate prediction of healthcare costs based on individual risk adjustment. Our objective was to compare the performances of a simple neural network (NN) and random forest (RF) to a generalized linear model (GLM) for the prediction of medical cost at the individual level. Methods A 1/97 representative sample of the French National Health Data Information System was used. Predictors selected were: demographic information; pre-existing conditions, Charlson comorbidity index; healthcare service use and costs. Predictive performances of each model were compared through individual-level (adjusted R-squared (adj-R 2 ), mean absolute error (MAE) and hit ratio (HiR)), and distribution-level metrics on different sets of covariates in the general population and by pre-existing morbid condition, using a quasi-Monte Carlo design. Results We included 510,182 subjects alive on 31st December, 2015. Mean annual costs were 1894€ (standard deviation 9326€) (median 393€, IQ range 95€; 1480€), including zero-claim subjects. All models performed similarly after adjustment on demographics. RF model had better performances on other sets of covariates (pre-existing conditions, resource counts and past year costs). On full model, RF reached an adj-R 2 of 47.5%, a MAE of 1338€ and a HiR of 67%, while GLM and NN had an adj-R 2 of 34.7% and 31.6%, a MAE of 1635€ and 1660€, and a HiR of 58% and 55 M, respectively. RF model outperformed GLM and NN for most conditions and for high-cost subjects. Conclusions RF should be preferred when the objective is to best predict medical costs. When the objective is to understand the contribution of predictors, GLM was well suited with demographics, conditions and base year cost.
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