Cluster analysis of kidney function decline among males with Fabry disease in a large United States electronic health records database.

BACKGROUND: Fabry disease (FD) is an X-linked lysosomal storage disorder caused by deficient α-galactosidase A activity. The spectrum of disease includes phenotypes ranging from "classic" to "later-onset," with varying kidney disease progression. Identifying patterns of declining kidney function and involvement of other major organs in patients with FD is important to guide therapy decisions. METHODS: Clusters of patients with FD and similar estimated glomerular filtration rate (eGFR) decline and age were created using agglomerative clustering of data captured between 2007 and 2020 in the United States Optum Market Clarity database. Male patients with a diagnosis of FD and two or more eGFR values ≥6 months apart were included. Disease progression was compared with a control cohort of patients without an FD diagnosis. RESULTS: eGFR values from 234 male patients with FD were analysed, yielding seven clusters. Five clusters demonstrated disease progression from "natural" eGFR decline, with a slight decrease in kidney function and eGFR usually within the normal range, to rapid, early decline in eGFR and cardiac complications. When compared with the control cohort, a more rapid decline and a higher percentage of cardiac hypertrophy, heart failure, arrhythmias and stroke were noted in the study group. An inflection point was observed in each cluster when deterioration of kidney function accelerated. CONCLUSIONS: Clustering of male patients with FD by decline in kidney function, organ involvement and phenotype through analysis of real-world data provides a reference that could help determine the optimal time for initiation of FD-specific treatment and facilitate management decisions made by healthcare professionals.

Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States.

BACKGROUND: Early diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records. METHODS: We utilized Optum's de-identified Integrated Claims-Clinical dataset (2007-2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients. RESULTS: Splenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients. CONCLUSIONS: The age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10-20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients.

Epidemiology, treatment patterns, clinical outcomes, and disease burden among patients with immune-mediated thrombotic thrombocytopenic purpura in the United States.

Background: Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is a life-threatening thrombotic microangiopathy. Due to its rarity, epidemiology and real-world outcomes data are scarce. Objectives: The aim was to assess epidemiology, treatment patterns, clinical outcomes, and disease burden in patients with iTTP in the United States. Methods: This longitudinal, retrospective observational study of the Optum-Humedica database included patients with an iTTP diagnosis (≥1 documented ADAMTS13 activity less than 10% or one or more iTTP episodes) from January 2007 to December 2019. Results: Of 666 patients with an iTTP diagnosis between October 2015 and December 2019, 302 (45%) had one or more iTTP episodes. The pooled annual incidence of documented iTTP during this period was 3.43/million, and the annual incidence of one or more iTTP episodes was 1.81/million. Patients with one or more iTTP episodes received a median of six therapeutic plasma exchange (TPE) sessions per episode; 86% received corticosteroids, and 59% received rituximab. Exacerbations occurred in 17% (52/302) and relapse in 11% (34/302); 34% (103/302) had one or more thromboembolic events. Mortality rates during the study period were 25% (167/666) among all patients with iTTP diagnosis, and 14% (41/302) among patients with one or more iTTP episodes. In the assessment of disease burden (January 2007 to September 2019), patients in the iTTP cohort (n = 514) presented with a mean of 14 comorbidities, compared with 3 in a matched non-iTTP cohort (n = 2570). In a cluster analysis, duration of iTTP episode and mortality rate were greater in older versus younger patients. Conclusions: Despite treatment with TPE and immunosuppressants, patients with iTTP have high risk of morbidity and mortality, demonstrating the need for more effective therapies.