Patient-reported outcomes and treatment adherence in type 2 diabetes using natural language processing: Wave 8 of the Observational International Diabetes Management Practices Study.

AIMS/INTRODUCTION: We analyzed patient-reported outcomes of people with type 2 diabetes to better understand perceptions and experiences contributing to treatment adherence. MATERIALS AND METHODS: In the ongoing International Diabetes Management Practices Study, we collected patient-reported outcomes data from structured questionnaires (chronic treatment acceptance questionnaire and Diabetes Self-Management Questionnaire) and free-text answers to open-ended questions to assess perceptions of treatment value and side-effects, as well as barriers to, and enablers for, adherence and self-management. Free-text answers were analyzed by natural language processing. RESULTS: In 2018-2020, we recruited 2,475 patients with type 2 diabetes (43.3% insulin-treated, glycated hemoglobin (HbA1c) 8.0 ± 1.8%; 30.9% with HbA1c <7%) from 13 countries across Africa, the Middle East, Europe, Latin America and Asia. Mean ± standard deviation scores of chronic treatment acceptance questionnaire (acceptance of medication, rated out of 100) and Diabetes Self-Management Questionnaire (self-management, rated out of 10) were 87.8 ± 24.5 and 3.3 ± 0.9, respectively. Based on free-text analysis and coded responses, one in three patients reported treatment non-adherence. Overall, although most patients accepted treatment values and side-effects, self-management was suboptimal. Treatment duration, regimen complexity and disruption of daily routines were major barriers to adherence, whereas habit formation was a key enabler. Treatment-adherent patients were older (60 ± 11.6 vs 55 ± 11.7 years, P < 0.001), and more likely to have longer disease duration (12 ± 8.6 vs 10 ± 7.7 years, P < 0.001), exposure to diabetes education (73.1% vs 67.8%, P < 0.05), lower HbA1c (7.9 ± 1.8% vs 8.3 ± 1.9%, P < 0.001) and attainment of HbA1c <7% (29.7% vs 23.3%, P < 0.01). CONCLUSIONS: Patient perceptions/experiences influence treatment adherence and self-management. Patient-centered education and support programs that consider patient-reported outcomes aimed at promoting empowerment and developing new routines might improve glycemic control.

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.

Qluster: An easy-to-implement generic workflow for robust clustering of health data.

The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.

Rationale and methodology for a European pooled analysis of postmarketing interventional and observational studies of insulin glargine 300 U/mL in diabetes: protocol of REALI project.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a common and heterogeneous disease. Using advanced analytic approaches to explore real-world data may identify different disease characteristics, responses to treatment and progression patterns. Insulin glargine 300 units/mL (Gla-300) is a second-generation basal insulin analogue with preserved glucose-lowering efficacy but reduced risk of hypoglycaemia. The purpose of the REALI pooled analysis described in this paper is to advance the understanding of the effectiveness and real-world safety of Gla-300 based on a large European patient database of postmarketing interventional and observational studies. METHODS AND ANALYSIS: In the current round of pooling, REALI will include data from up to 10 000 subjects with diabetes mellitus (mostly T2DM) from 20 European countries. Outcomes of interest include change from baseline to week 24 in haemoglobin A1c, fasting plasma glucose, self-measured plasma glucose, body weight, insulin dose, incidence and rate of any-time-of-the-day and nocturnal hypoglycaemia. The data pool is being investigated using two complementary methodologies: a conventional descriptive, univariate and multivariable prognostic analysis; and a data-mining approach using subgroup discovery to identify phenotypic clusters of patients who are highly associated with the outcome of interest. By mid-2019, deidentified data of 7584 patients were included in the REALI database, with a further expected increase in patient number in 2020 as a result of pooling additional studies. ETHICS AND DISSEMINATION: The proposed study does not involve collection of primary data. Moreover, all individual study protocols were approved by independent local ethics committees, and all study participants provided written informed consent. Furthermore, patient data is deidentified before inclusion in the REALI database. Hence, there is no requirement for ethical approval. Results will be disseminated via peer-reviewed publications and presentations at international congresses as data are analysed.