Development and validation of a nomogram model for medication non-adherence in patients with chronic kidney disease

Wu, Xiaona; Tang, Fang; Li, Huanhuan; Chen, Cuiqing; Zhang, Haiyan; Liu, X.; Lai, Huijing; Li, Qiang; Deng, Lili; Ye, Zeng Jie

doi:10.1016/j.jpsychores.2023.111385

Cited by 5 publications

(2 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of DCA, clinical impact, calibration curves and ROC curves indicated that this model was precise and reliable. The nomogram was a widely used predictive model, and it was reported in the prediction of non-adherence in chronic kidney disease, 53 hemodialysis, 54 type 2 diabetes 55 and rheumatoid arthritis. 56 Our model had a higher AUC value than other disease models, indicating that the patients might benefit more from this model.…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Identifying Low Medication Adherence Among Patients with Cirrhosis

Wang,

Li,

Suo

et al. 2023

PPA

View full text Add to dashboard Cite

This study aims to identify the novel risk predictors of low medication adherence of cirrhosis patients in a large cohort and construct an applicable predictive model to provide clinicians with a simple and precise personalized prediction tool. Patients and Methods: Patients with cirrhosis were recruited from the inpatient populations at the Department of Infectious Diseases of Tangdu Hospital. Patients who did not meet the inclusion criteria were excluded. The primary outcome was medication adherence, which was analyzed by the medication possession ratio (MPR). Potential predictive factors, including demographics, the severity of cirrhosis, knowledge of disease and medical treatment, social support, self-care agency and pill burdens, were collected by questionnaires. Predictive factors were selected by univariable and multivariable logistic regression analysis. Then, a nomogram was constructed. The decision curve analysis (DCA), clinical application curve analysis, ROC curve analysis, Brier score and mean squared error (MSE) score were utilized to assess the performance of the model. In addition, the bootstrapping method was used for internal validation. Results: Among the enrolled patients (460), most had good or moderate (344, 74.78%) medical adherence. The main risk factors for non-adherence include young age (≤50 years), low education level, low income, short duration of disease (<10 years), low Child-Plush class, poor knowledge of disease and medical treatment, poor social support, low self-care agency and high pill burden. The nomogram comprised these factors showed good calibration and good discrimination (AUC = 0.938, 95% CI = 0.918-0.956; Brier score = 0.14). In addition, the MSE value was 0.03, indicating no overfitting. Conclusion:This study identified predictive factors regarding low medication adherence among patients with cirrhosis, and a predictive nomogram was constructed. This model could help clinicians identify patients with a high risk of low medication adherence and intervention measures can be taken in time.

show abstract

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Identifying Low Medication Adherence Among Patients with Cirrhosis

Wang,

Li,

Suo

et al. 2023

PPA

View full text Add to dashboard Cite

show abstract

“…Furthermore, the nomogram model construction process is both faster and less reliant on extensive training data, which is very beneficial to our model construction. The nomogram model is a reliable and convenient tool widely used in the medical field [19][20][21][22][23]. In the field of agronomy, its application is relatively rare, but because of its intuitive, rapid construction and reliability, it is considered to be a potential tool.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Wang,

Yang,

Zhang

et al. 2023

Agronomy

View full text Add to dashboard Cite

To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.

show abstract

Investigating the risk factors for nonadherence to analgesic medications in cancer patients: Establishing a nomogram model

Wang,

Hu,

et al. 2024

Heliyon

View full text Add to dashboard Cite

Development and validation of a nomogram model for medication non-adherence in patients with chronic kidney disease

Cited by 5 publications

References 81 publications

A Predictive Model for Identifying Low Medication Adherence Among Patients with Cirrhosis

A Predictive Model for Identifying Low Medication Adherence Among Patients with Cirrhosis

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Investigating the risk factors for nonadherence to analgesic medications in cancer patients: Establishing a nomogram model

Contact Info

Product

Resources

About