Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram

Abstract

Purpose: The aim of this study was to develop and internally validate a medication nonadherence risk nomogram in a Chinese population of patients with inflammatory rheumatic diseases.

Patients and methods: We developed a prediction model based on a training dataset of 244 IRD patients, and data were collected from March 2016 to May 2016. Adherence was evaluated using 19-item Compliance Questionnaire Rheumatology. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the medication nonadherence risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.

Results: Predictors contained in the prediction nomogram included use of glucocorticoid (GC), use of nonsteroidal anti-inflammatory drugs, number of medicine-related questions, education level, and the distance to hospital. The model displayed good discrimination with a C-index of 0.857 (95% confidence interval: 0.807-0.907) and good calibration. High C-index value of 0.847 could still be reached in the interval validation. Decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 14%.

Conclusion: This novel nonadherence nomogram incorporating the use of GC, the use of nonsteroidal anti-inflammatory drugs, the number of medicine-related questions, education level, and distance to hospital could be conveniently used to facilitate the individual medication nonadherence risk prediction in IRD patients.

Keywords: Compliance Questionnaire Rheumatology; inflammatory rheumatic diseases; noadherence; nomogram; predictors.

Figure 1

Demographic and clinical feature selection using the LASSO binary logistic regression model. Notes: (A) Optimal parameter (lambda) selection in the LASSO model used fivefold cross-validation via minimum criteria., The partial likelihood deviance (binomial deviance) curve was plotted versus log(lambda). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 SE of the minimum criteria (the 1-SE criteria). (B) LASSO coefficient profiles of the 22 features. A coefficient profile plot was produced against the log(lambda) sequence. Vertical line was drawn at the value selected using fivefold cross-validation, where optimal lambda resulted in five features with nonzero coefficients. Abbreviations: LASSO, least absolute shrinkage and selection operator; SE, standard error.

Figure 2

Developed medication nonadherence nomogram. Note: The medication nonadherence nomogram was developed in the cohort, with the use of GC, the use of NSAIDs, the number of medicine-related questions, education level, and the distance to hospital incorporated. Abbreviations: GC, glucocorticoid; NSAIDs, nonsteroidal anti-inflammatory drugs.

Figure 3

Calibration curves of the nonadherence nomogram prediction in the cohort. Notes: The x-axis represents the predicted medication nonadherence risk. The y-axis represents the actual diagnosed nonadherence. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction.

Figure 4

Decision curve analysis for the nonadherence nomogram. Notes: The y-axis measures the net benefit. The dotted line represents the medication nonadherence risk nomogram. The thin solid line represents the assumption that all patients are nonadherent to medication. Thin thick solid line represents the assumption that no patients are nonadherent to medication. The decision curve showed that if the threshold probability of a patient and a doctor is >14 and <88%, respectively, using this nonadherence nomogram in the current study to predict medication nonadherence risk adds more benefit than the intervention-all-patients scheme or the intervention-none scheme.