Reasons for Discontinuation of Lipid-Lowering Medications in Patients with Chronic Kidney Disease

Morrison, Fritha; Zhang, Huabing; Skentzos, Stephen; Shubina, Maria; Bentley‐Lewis, Rhonda; Turchin, Alexander

doi:10.1159/000368914

Cited by 11 publications

(16 citation statements)

References 38 publications

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“…The data sources for the studies were varied, with most utilizing information from EHRs or other clinical databases. Within these studies, the majority only included structured data in their models (47, 48, 49, 50, 58, 59, 60, 61, 62) while a few incorporated both structured and unstructured data from these resources (45, 43, 44, 63). Another source of data was surveys, questionnaires, or interviews, where again the majority of studies used only structured data in their models (46, 53, 54, 51, 57), with only one study applying NLP methods and used both structured and unstructured data (64).…”

Section: Resultsmentioning

confidence: 99%

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Golder

OConnor

Wang

et al. 2022

Preprint

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A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) "women" or "men" or "sex," (2) "big data" or "artificial intelligence" or "NLP", and (3) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

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Section: Resultsmentioning

confidence: 99%

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Golder

OConnor

Wang

et al. 2022

Preprint

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“…We also found that CKD patients had a high rate of statin discontinuation. According to Morrison and colleagues, the most common reason for long-term discontinuation of lipid-lowering medications in CKD patients is adverse reactions [ 24 ]. However, due to the retrospective nature of our study design, it was not possible to explore the potential reasons behind statin discontinuation in the current research.…”

Section: Discussionmentioning

confidence: 99%

Effects of Long-Term Statin Therapy in Coronary Artery Disease Patients with or without Chronic Kidney Disease

Huang

Chen

et al. 2015

Disease Markers

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Introduction. The effect of long-term statin therapy is essential for secondary prevention of adverse clinical outcomes of coronary artery disease (CAD) patients. No study has compared the effects of long-term statin treatment in CAD patients with or without chronic kidney disease (CKD) and CKD only patients. Methods. We compared the effects of long-term statin therapy (average follow-up time 5.79 years) in terms of major adverse cardiovascular events (MACE), all-cause death, and cardiac death among 570 CAD patients with or without CKD and 147 CKD only patients. Results. The all-cause death and cardiac death of the patients with CAD and CKD (24.4% and 20.4%) doubled those of CAD only patients (10.7% and 9.1%) (P < 0.001). Long-term statin therapy dramatically reduced the rates of both MACE and all-cause death/cardiac death (by 20.5% and 28.6%/27.7%, resp.) in CAD and CKD patients. CKD only patients had no significant adverse clinical outcomes and were not responsive to long-term statin therapy. Conclusion. Chinese CAD patients with CKD had dramatically high rates of adverse clinical outcomes; for them, long-term statin therapies were exceptionally effective in improving morbidity and mortality. CKD patients who had no cardiovascular disease initially can prognose good clinical outcomes and do not require statin treatment.

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“…On the sentence level, only $2% of the sentences from 1867 notes contain DDCs. Therefore, although DDC events are common in patient care, 4,17,18 they are sparsely documented in clinical narratives, making NLP-based DDC detection a challenge.…”

Section: Data Annotation Schema Annotationmentioning

confidence: 99%

“…Existing studies typically group DDC reasons into 4 categories: 1) adverse events (AEs), 2) lack of treatment effectiveness, 3) patient preference/nonadherence, and 4) medication being no longer necessary. 4,5 The various categories contribute differently. Studies on causes of anti-tumor necrosis factor discontinuation, for instance, showed that AEs were responsible for treatment discontinuations in 48.7% of cases, and lack of treatment effectiveness represented the most significant single cause for anti-tumor necrosis factor treatment discontinuation (50%).…”

Section: Introductionmentioning

confidence: 99%

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Learning to detect and understand drug discontinuation events from clinical narratives

Liu

Pradhan

Druhl

et al. 2019

Journal of the American Medical Informatics Association

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Objective Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article, we assessed the ability of natural language processing (NLP) systems to unlock DDC information from clinical narratives automatically. Materials and Methods We collected 1867 de-identified providers’ notes from the University of Massachusetts Medical School hospital electronic health record system. Then 2 human experts chart reviewed those clinical notes to annotate DDC events and their reasons. Using the annotated data, we developed and evaluated NLP systems to automatically identify drug discontinuations and reasons at the sentence level using a novel semantic enrichment-based vector representation (SEVR) method for enhanced feature representation. Results Our SEVR-based NLP system achieved the best performance of 0.785 (AUC-ROC) for detecting discontinuation events and 0.745 (AUC-ROC) for identifying reasons when testing this highly imbalanced data, outperforming 2 state-of-the-art non–SEVR-based models. Compared with a rule-based baseline system for discontinuation detection, our system improved the sensitivity significantly (57.75% vs 18.31%, absolute value) while retaining a high specificity of 99.25%, leading to a significant improvement in AUC-ROC by 32.83% (absolute value). Conclusion Experiments have shown that a high-performance NLP system can be developed to automatically identify DDCs and their reasons from providers’ notes. The SEVR model effectively improved the system performance showing better generalization and robustness on unseen test data. Our work is an important step toward identifying reasons for drug discontinuation that will inform drug safety surveillance and pharmacovigilance.

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Reasons for Discontinuation of Lipid-Lowering Medications in Patients with Chronic Kidney Disease

Cited by 11 publications

References 38 publications

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Effects of Long-Term Statin Therapy in Coronary Artery Disease Patients with or without Chronic Kidney Disease

Learning to detect and understand drug discontinuation events from clinical narratives

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