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

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.