Dataset Information

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

ABSTRACT:

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 (HbA_1c) 8.0 ± 1.8%; 30.9% with HbA_1c <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 HbA_1c (7.9 ± 1.8% vs 8.3 ± 1.9%, P < 0.001) and attainment of HbA_1c <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.

SUBMITTER: Chan JC

PROVIDER: S-EPMC11363103 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Chan Juliana Cn JC Mbanya Jean Claude JC Chantelot Jean-Marc JM Shestakova Marina M Ramachandran Ambady A Ilkova Hasan H Deplante Lucille L Rollot Melissa M Melas-Melt Lydie L Gagliardino Juan Jose JJ Aschner Pablo P

Journal of diabetes investigation 20240605 9

<h4>Aims/introduction</h4>We analyzed patient-reported outcomes of people with type 2 diabetes to better understand perceptions and experiences contributing to treatment adherence.<h4>Materials and methods</h4>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 perce ...[more]

PMID: 38840439

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Project description:BackgroundAssessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship.ObjectiveThis study aims to test the validity of natural language processing (NLP) and machine learning (ML) algorithms in identifying different attributes of pain interference and fatigue symptoms experienced by child and adolescent survivors of cancer versus the judgment by PRO content experts as the gold standard to validate NLP/ML algorithms.MethodsThis cross-sectional study focused on child and adolescent survivors of cancer, aged 8 to 17 years, and caregivers, from whom 391 meaning units in the pain interference domain and 423 in the fatigue domain were generated for analyses. Data were collected from the After Completion of Therapy Clinic at St. Jude Children's Research Hospital. Experienced pain interference and fatigue symptoms were reported through in-depth interviews. After verbatim transcription, analyzable sentences (ie, meaning units) were semantically labeled by 2 content experts for each attribute (physical, cognitive, social, or unclassified). Two NLP/ML methods were used to extract and validate the semantic features: bidirectional encoder representations from transformers (BERT) and Word2vec plus one of the ML methods, the support vector machine or extreme gradient boosting. Receiver operating characteristic and precision-recall curves were used to evaluate the accuracy and validity of the NLP/ML methods.ResultsCompared with Word2vec/support vector machine and Word2vec/extreme gradient boosting, BERT demonstrated higher accuracy in both symptom domains, with 0.931 (95% CI 0.905-0.957) and 0.916 (95% CI 0.887-0.941) for problems with cognitive and social attributes on pain interference, respectively, and 0.929 (95% CI 0.903-0.953) and 0.917 (95% CI 0.891-0.943) for problems with cognitive and social attributes on fatigue, respectively. In addition, BERT yielded superior areas under the receiver operating characteristic curve for cognitive attributes on pain interference and fatigue domains (0.923, 95% CI 0.879-0.997; 0.948, 95% CI 0.922-0.979) and superior areas under the precision-recall curve for cognitive attributes on pain interference and fatigue domains (0.818, 95% CI 0.735-0.917; 0.855, 95% CI 0.791-0.930).ConclusionsThe BERT method performed better than the other methods. As an alternative to using standard PRO surveys, collecting unstructured PROs via interviews or conversations during clinical encounters and applying NLP/ML methods can facilitate PRO assessment in child and adolescent cancer survivors.

Dataset Information

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

Materials and methods

Results

Conclusions

Publications

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

Similar Datasets

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