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

Medication adherence scales in non-communicable diseases: A scoping review of design gaps, constructs and validation processes

INTRODUCTION

NCDs arise from complex interactions of modifiable factors such as unhealthy lifestyles, poor diet, and psychosocial challenges, along with non-modifiable factors like age and genetics. Notably, medication non-adherence is a widespread and growing concern, significantly contributing to disease progression and poor outcomes globally.

OBJECTIVE

This scoping review aims to synthesize evidence on medication adherence scales used for selected non communicable diseases. It examines their development methods, psychometric properties, and assessed domains, while identifying gaps or limitations in their design and application.

MATERIALS AND METHODS

The Joanna Briggs Institute methodological framework guided this scoping review and the protocol was registered prospectively to ensure methodological transparency and rigor. Electronic databases, the reference list of included articles, and grey literature were searched. Studies published in English from January 1950 to June 2024 were included. Two reviewers independently screened all articles, and a third reviewer settled any conflicts between the reviewers. Critical appraisal of the screened-in articles was done using JBI critical appraisal scales. The data was compiled into tables and a narrative summary that is consistent with the review's goal.

RESULTS

Our study included 140 articles, identifying 57 medication adherence scales. These scales, developed using qualitative methods (10.8%), literature review (32.4%), and mixed methods (45.9%), primarily focus on behavior, often neglecting cost-related non-adherence, self-efficacy, and systemic barriers. Psychometric findings varied widely, reflecting heterogeneity in study designs and scale development approaches. Many scales lack validation in diverse settings, underscoring the need for comprehensive, context-sensitive tools.

CONCLUSION

This scoping review highlights gaps in existing medication adherence scales for NCDs, particularly their limited consideration of socioeconomic and cultural factors and incomplete adherence assessment. Future research should focus on developing more holistic, contextually relevant adherence scales that integrate these dimensions. Strengthening adherence measurement methodologies can enhance patient-centered care, inform policy interventions, and improve health outcomes.

Prediction Models of Medication Adherence in Chronic Disease Patients: Systematic Review and Critical Appraisal

AIMS AND OBJECTIVES

To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.

BACKGROUND

Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.

DESIGN

Systematic review.

METHODS

We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.

RESULTS

The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high-risk for bias.

CONCLUSIONS

According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.

RELEVANCE TO CLINICAL PRACTICE

Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high-risk individuals and key factors associated with low medication adherence in chronic diseases.

PATIENT OR PUBLIC CONTRIBUTION

This systematic review was conducted without patient or public participation.

Prognostic nutritional index (PNI) as an influencing factor for in-hospital mortality in patients with stroke-associated pneumonia: a retrospective study

Background

Stroke-associated pneumonia (SAP) significantly increases patients' risk of death after stroke. The identification of patients at high risk for SAP remains difficult. Nutritional assessment is valuable for risk identification in stroke patients. The aim of this study was to evaluate the relationship between prognostic nutritional index (PNI) levels and in-hospital mortality in SAP patients.

Methods

A total of 336 SAP patients who visited the Third People's Hospital of Chengdu from January 2019 to December 2023 were included in this study, and PNI were calculated based on the results of admission examinations. Linear regression was used to analyze the influencing factors of baseline PNI in SAP patients. Logistic regression as well as restricted cubic splines (RCS) were used to analyze the relationship between baseline PNI levels and hospital mortality events in SAP patients. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of PNI for in-hospital mortality by area under the curve (AUC).

Results

Thirty out of 336 SAP patients presented with in-hospital mortality and these patients had significantly lower PNI levels. In our study, PNI levels were influenced by age, body mass index, and total cholesterol. Increased PNI levels are an independent protective factor for the risk of in-hospital mortality in SAP patients (OR: 0.232, 95% CI [0.096-0.561], P = 0.001). There was a nonlinear correlation between PNI and in-hospital mortality events (P for nonlinear <0.001). In terms of predictive effect, PNI levels were more efficacious in predicting in-hospital mortality in SAP patients with higher sensitivity and/or specificity compared to individual indicators (AUC = 0.750, 95% CI [0.641-0.860], P < 0.001).

Conclusion

PNI levels in SAP patients were associated with the short-term prognosis of patients, and SAP patients with elevated PNI levels had a reduced risk of in-hospital mortality.

Developing and Validating a Nomogram for Non-Adherence to Inhaler Therapy Among Elderly Chronic Obstructive Pulmonary Disease Patients Based on the Social Ecological Model

Purpose

This study aimed to identify the risk predictors of non-adherence to inhaler therapy and construct a nomogram prediction model for use in Chinese elderly patients with chronic obstructive pulmonary disease (COPD).

Patients and Methods

A cross-sectional study was conducted with 305 participants recruited from a tertiary care hospital in Anhui, China. Adherence was analyzed using the Test of Adherence to Inhalers. Potential predictive factors were incorporated based on the social ecological model, and data were collected through a questionnaire method. R version 4.3.3 was utilized to perform the least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis to identify risk factors and establish a nomogram prediction model.

Results

The results of the multivariable analysis revealed that medication beliefs, illness perception, the COPD Assessment Test score, smoking status, and education level were significant risk factors for non-adherence to inhaler therapy in elderly COPD patients (all P < 0.05). The nomogram prediction model for non-adherence to inhaler therapy in elderly COPD patients demonstrated a good discriminative ability, with an area under the receiver operating characteristic curve of 0.912. The C-index was 0.922 (95% CI: 0.879 to 0.965), and the Brier value was 0.070, indicating good consistency and calibration. Decision curve analysis indicated that the use of the nomogram would be more beneficial in clinical practice when the threshold probability of non-adherence exceeds 17%.

Conclusion

This study identified predictive factors regarding non-adherence among elderly patients with COPD and constructed a predictive nomogram. By utilizing the nomogram model healthcare professionals could swiftly calculate and comprehend the non-compliance level of COPD patients, thus guiding the development of personalized interventions in clinical practice.

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

Purpose

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.

A nomogram based on nutritional status and A2DS2 score for predicting stroke-associated pneumonia in acute ischemic stroke patients with type 2 diabetes mellitus: A retrospective study

Background

Stroke-associated pneumonia (SAP) commonly complicates acute ischemic stroke (AIS) and significantly worsens outcomes. Type 2 diabetes mellitus (T2DM) may contribute to malnutrition, impair innate immunity function, and increase the probability of SAP occurrence in AIS patients. We aimed to determine early predictors of SAP in AIS patients with T2DM and to construct a nomogram specifically for predicting SAP in this population by combining the A²DS² score with available nutrition-related parameters.

Methods

A total of 1,330 consecutive AIS patients with T2DM were retrospectively recruited. The patients were randomly allocated to the training (n = 887) and validation groups (n = 443). Univariate and multivariate binary logistic regression analyses were applied to determine the predictors of SAP in the training group. A nomogram was established according to the identified predictors. The areas under the receiver operating characteristic curve (AUROC) and calibration plots were performed to access the predictive values of the nomogram. The decision curve was applied to evaluate the net benefits of the nomogram.

Results

The incidence of SAP was 9% and 9.7% in the training and validation groups, respectively. The results revealed that the A²DS² score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP. A novel nomogram, A²DS²-Nutrition, was constructed based on these five predictors. The AUROC for A²DS²-Nutrition (0.820, 95% CI: 0.794–0.845) was higher than the A²DS² score (0.691, 95% CI: 0.660–0.722) in the training group. Similarly, it showed a better predictive performance than the A²DS² score [AUROC = 0.864 (95% CI: 0.828–0.894) vs. AUROC = 0.763 (95% CI: 0.720–0.801)] in the validation group. These results were well calibrated in the two groups. Moreover, the decision curve revealed that the A²DS²-Nutrition provided an additional net benefit to the AIS patients with T2DM compared to the A²DS² score in both groups.

Conclusion

The A²DS² score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP in AIS patients with T2DM. Thus, the proposed A²DS²-Nutrition may be a simple and reliable prediction model for SAP occurrence in AIS patients with T2DM.