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.

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.

Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm.

AIM: To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm. MATERIALS AND METHODS: Data from adults and children with type 1 diabetes and more than two diabetes-related visits were analysed from the Diabetes Prospective Follow-up Registry. Q-Finder, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH less than 7.3 during a hospitalization event. RESULTS: Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q-Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast-acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the number of risk profiles matching patients' characteristics. CONCLUSIONS: Q-Finder confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA.

LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning.

INTRODUCTION: Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and extract relevant articles from the pack. This task is critical, as serious Adverse Drug Reactions (ADRs) still account for a large number of hospital admissions each year. OBJECTIVES: The aim of this study is to develop an augmented intelligence methodology for automatically identifying relevant publications mentioning an established link between a Drug and a Serious Adverse Event, according to the European Medicines Agency (EMA) definition of seriousness. METHODS: The proposed pipeline, called LiSA (for Literature Search Application), is based on three independent deep learning models supporting a precise detection of safety signals in the biomedical literature. By combining a Bidirectional Encoder Representations from Transformers (BERT) algorithms and a modular architecture, the pipeline achieves a precision of 0.81 and a recall of 0.89 at sentences level in articles extracted from PubMed (either abstract or full-text). We also measured that by using LiSA, a medical reviewer increases by a factor of 2.5 the number of relevant documents it can collect and evaluate compared to a simple keyword search. In the interest of re-usability, emphasis was placed on building a modular pipeline allowing the insertion of other NLP modules to enrich the results provided by the system, and extend it to other use cases. In addition, a lightweight visualization tool was developed to analyze and monitor safety signal results. CONCLUSIONS: Overall, the generic pipeline and the visualization tool proposed in this article allows for efficient and accurate monitoring of serious adverse drug reactions from the literature and can easily be adapted to similar pharmacovigilance use cases. To facilitate reproducibility and benefit other research studies, we also shared a first benchmark dataset for Serious Adverse Drug Events detection.