Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.

AIMS: The use of large medical or healthcare claims databases is very useful for population-based studies on the burden of heart failure (HF). Clinical characteristics and management of HF patients differ according to categories of left ventricular ejection fraction (LVEF), but this information is often missing in such databases. We aimed to develop and validate algorithms to identify LVEF in healthcare databases where the information is lacking. METHODS AND RESULTS: Algorithms were built by machine learning with a random forest approach. Algorithms were trained and reinforced using the French national claims database [Système National des Données de Santé (SNDS)] and a French HF registry. Variables were age, gender, and comorbidities, which could be identified by medico-administrative code-based proxies, Anatomical Therapeutic Chemical codes for drug delivery, International Classification of Diseases (Tenth Revision) coding for hospitalizations, and administrative codes for any other type of reimbursed care. The algorithms were validated by cross-validation and against a subset of the SNDS that includes LVEF information. The areas under the receiver operating characteristic curve were 0.84 for the algorithm identifying LVEF ≤ 40% and 0.79 for the algorithms identifying LVEF < 50% and ≥50%. For LVEF ≤ 40%, the reinforced algorithm identified 50% of patients in the validation dataset with a positive predictive value of 0.88 and a specificity of 0.96. The most important predictive variables were delivery of HF medication, sex, age, hospitalization, and testing for natriuretic peptides with different orders of positive or negative importance according to the LVEF category. CONCLUSIONS: The algorithms identify reduced or preserved LVEF in HF patients within a nationwide healthcare claims database with high positive predictive value and low rates of false positives.

Clinical effects of permanent pacemaker implantation after transcatheter aortic valve implantation: Insights from the nationwide FRANCE-TAVI registry.

BACKGROUND: The influence of permanent pacemaker implantation upon outcomes after transcatheter aortic valve implantation (TAVI) remains controversial. AIMS: To evaluate the impact of permanent pacemaker implantation after TAVI on short- and long-term mortality, and on the risk of hospitalization for heart failure. METHODS: Data from the large FRANCE-TAVI registry, linked to the French national health single-payer claims database, were analysed to compare 30-day and long-term mortality rates and hospitalization for heart failure rates among patients with versus without permanent pacemaker implantation after TAVI. Multivariable regressions were performed to adjust for confounders. RESULTS: A total of 36,549 patients (mean age 82.6years; 51.6% female) who underwent TAVI from 2013 to 2019 were included in the present analysis. Among them, 6999 (19.1%) received permanent pacemaker implantation during the index hospitalization, whereas 232 (0.6%) underwent permanent pacemaker implantation between hospital discharge and 30days after TAVI, at a median of 11 (interquartile range: 7-18) days. In-hospital permanent pacemaker implantation was not associated with an increased risk of death between discharge and 30days (adjusted odds ratio: 0.91, 95% confidence interval: 0.64-1.29). At 5years, the incidence of all-cause death was higher among patients with versus without permanent pacemaker implantation within 30days of the procedure (adjusted hazard ratio: 1.13, 95% confidence interval: 1.07-1.19). Permanent pacemaker implantation within 30days of TAVI was also associated with a higher 5-year rate of hospitalization for heart failure (adjusted subhazard ratio: 1.17, 95% confidence interval: 1.11-1.23). CONCLUSIONS: Permanent pacemaker implantation after TAVI is associated with an increased risk of long-term hospitalization for heart failure and all-cause mortality. Further research to mitigate the risk of postprocedural permanent pacemaker implantation is needed as TAVI indications expand to lower-risk patients.

Long-Term Prognosis Value of Paravalvular Leak and Patient-Prosthesis Mismatch following Transcatheter Aortic Valve Implantation: Insight from the France-TAVI Registry.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is the preferred treatment for symptomatic severe aortic stenosis (AS) in a majority of patients across all surgical risks. PATIENTS AND METHODS: Paravalvular leak (PVL) and patient-prosthesis mismatch (PPM) are two frequent complications of TAVI. Therefore, based on the large France-TAVI registry, we planned to report the incidence of both complications following TAVI, evaluate their respective risk factors, and study their respective impacts on long-term clinical outcomes, including mortality. RESULTS: We identified 47,494 patients in the database who underwent a TAVI in France between 1 January 2010 and 31 December 2019. Within this population, 17,742 patients had information regarding PPM status (5138 with moderate-to-severe PPM, 29.0%) and 20,878 had information regarding PVL (4056 with PVL ≥ 2, 19.4%). After adjustment, the risk factors for PVL ≥ 2 were a lower body mass index (BMI), a high baseline mean aortic gradient, a higher body surface area, a lower ejection fraction, a smaller diameter of TAVI, and a self-expandable TAVI device, while for moderate-to-severe PPM we identified a younger age, a lower BMI, a larger body surface area, a low aortic annulus area, a low ejection fraction, and a smaller diameter TAVI device (OR 0.85; 95% CI, 0.83-0.86) as predictors. At 6.5 years, PVL ≥ 2 was an independent predictor of mortality and was associated with higher mortality risk. PPM was not associated with increased risk of mortality. CONCLUSIONS: Our analysis from the France-TAVI registry showed that both moderate-to-severe PPM and PVL ≥ 2 continue to be frequently observed after the TAVI procedure. Different risk factors, mostly related to the patient's anatomy and TAVI device selection, for both complications have been identified. Only PVL ≥ 2 was associated with higher mortality during follow-up.

Successful linkage of French large-scale national registry populations to national reimbursement data: Improved data completeness and minimized loss to follow-up.

BACKGROUND: Registries, a cornerstone of contemporary medicine, frequently suffer from incomplete documentation and losses to follow-up. By linking data to a single-payer national claims database, national registries may be enriched and the quality enhanced. AIMS: To explore the value of data from the French Système National des Données de Santé (SNDS) as a resource to enhance the quality of registries when combined with data from electronic case report forms, and to assess the power to minimize data gaps and losses to follow-up. METHODS: A probabilistic algorithm was developed to link and match records in the SNDS with patient data from the electronic case report forms of two registries on transcatheter aortic valve implantation: FRANCE-2 and FRANCE-TAVI. The algorithm created patient profiles from transcatheter aortic valve implantation procedures in the SNDS, matching them as closely as possible to the profiles in the registry databases. The objective was to achieve 90% linkage of the populations. The linked database was analysed for completeness and loss to follow-up. For validation, mortality curves for the linked registry cohorts were compared with those for the original populations. RESULTS: A total of 34,397 unique registries entries were identified, and 89.9% of patients in the SNDS could be linked. Rates of losses to follow-up over 2 years were 1.0% in the linked FRANCE-TAVI population compared with 40.3% based on electronic case report form documentation. For FRANCE-2, 3-year rates of losses to follow-up were 1.7% and 6.1%, respectively. Mortality curves for populations based on SNDS and electronic case report form data were practically superimposable. CONCLUSIONS: Linking data from a single-payer national claims database to national registries using a probabilistic approach is feasible and can close data gaps and practically abolish losses to follow-up in the registry population.