Dataset Information

Predicting population: development and validation of a new predictive nomogram for evaluating medication nonadherence risk in a type 2 diabetes.

ABSTRACT:

Background

Diabetes mellitus is a growing global health challenge and affects patients of all ages. Treatment aims to keep blood glucose levels close to normal and to prevent or delay complications. However, adherence to antidiabetic medicines is often unsatisfactory.

Purpose

Here, we established and internally validated a medication nonadherence risk nomogram for use in Chinese type 2 diabetes mellitus (T2DM) patients.

Methods

This cross-sectional study was carried out from July-December 2020 on randomly selected T2DM patients visiting a diabetes clinic and included 753 participants. Adherence was analyzed based on an eight-item Morisky Medication Adherence Scale (MMAS-8). Other data, including patient demographics, treatment, complications, and comorbidities, were also collected on questionnaires. Optimization of feature selection to develop the medication nonadherence risk model was achieved using the least absolute shrinkage and selection operator regression model (LASSO). A prediction model comprising features selected from LASSO model was designed by applying multivariable logistic regression analysis. The decision curve analysis, calibration plot, and C-index were utilized to assess the performance of the model in terms of discrimination, calibration, and clinical usefulness. Bootstrapping validation was applied for internal validation.

Results

The prediction nomogram comprised several factors including sex, marital status, education level, employment, distance, self-monitoringofbloodglucose, disease duration, and dosing frequency of daily hypoglycemics (pills, insulin, or glucagon-like peptide-1). The model exhibited good calibration and good discrimination (C-index = 0.79, 95% CI [0.75-0.83]). In the validation samples, a high C-index (0.75) was achieved. Results of the decision curve analysis revealed that the nonadherence nomogram could be applied in clinical practice in cases where the intervention is decided at a nonadherence possibility threshold of 12%.

Conclusion

The number of patients who adhere to anti-diabetes therapy was small. Being single male, having no formal education, employed, far from hospital, long disease duration, and taking antidiabetics twice or thrice daily, had significant negative correlation with medication adherence. Thus, strategies for improving adherence are urgently needed.

SUBMITTER: QiMuge N

PROVIDER: S-EPMC8932313 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Predicting population: development and validation of a new predictive nomogram for evaluating medication nonadherence risk in a type 2 diabetes.

QiMuge NaRen N Fang Xu X Chang Baocheng B Li Dong Mei DM Li Yuanyuan Y

PeerJ 20220315

<h4>Background</h4>Diabetes mellitus is a growing global health challenge and affects patients of all ages. Treatment aims to keep blood glucose levels close to normal and to prevent or delay complications. However, adherence to antidiabetic medicines is often unsatisfactory.<h4>Purpose</h4>Here, we established and internally validated a medication nonadherence risk nomogram for use in Chinese type 2 diabetes mellitus (T2DM) patients.<h4>Methods</h4>This cross-sectional study was carried out fro ...[more]

PMID: 35310157

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Project description:BackgroundThe risk of liver metastasis (LM) may be estimated using predictive nomograms. While the nomogram has recently been applied in oncology, there are relatively few studies concentrating on predicting LM in patients with early-onset colon cancer. We aimed to identify independent risk factors for LM in patients with early-onset colon cancer and develop a nomogram for predicting the probability of LM in these patients.MethodsOur study encompassed 4,890 early-onset colon cancer patients with LM who were registered in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. These patients were randomly allocated into training and validation cohorts at a ratio of 7:3. Univariate and multivariate logistic regression analyses were conducted to identify the independent risk factors for LM, and a nomogram was developed using these factors. The model's discriminatory power, accuracy, and clinical utility were evaluated using receiver operating characteristics (ROC), calibration, and decision curve analyses.ResultsOverall, 4,890 patients with early-onset colon cancer and LM were selected from the SEER database. LM incidence in these patients was 18.4%. Univariate and multivariate analyses revealed histological type, T stage, N stage, and carcinoembryonic antigen (CEA) level as independent risk factors. ROC curve analysis revealed that the predictive nomogram for LM risk had an area under the curve of 0.812 [95% confidence interval (CI): 0.795-0.829] and 0.809 (95% CI: 0.784-0.834) in the training and validation sets, respectively, demonstrating good discriminatory ability of the model. Calibration curve analysis showed good agreement between predicted values from the nomogram and actual observations, and the decision curve analysis (DCA) demonstrated the high clinical utility of the nomogram.ConclusionsLM incidence was higher in patients with early-onset colon cancer. Our nomogram demonstrates a high level of efficacy in predicting the risk of LM in patients with early-onset colon cancer, thereby assisting clinicians in making well-informed treatment decisions prior to further intervention.

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Dataset Information

Predicting population: development and validation of a new predictive nomogram for evaluating medication nonadherence risk in a type 2 diabetes.

Background

Purpose

Methods

Results

Conclusion

Publications

Predicting population: development and validation of a new predictive nomogram for evaluating medication nonadherence risk in a type 2 diabetes.

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