Continuous medication monitoring: A clinical model to predict adherence to medications among chronic kidney disease patients

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.

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

OBJECTIVE

The high prevalence of medication non-adherence in patients with chronic kidney disease places a tremendous burden on healthcare resources. The study was designed to develop and validate a nomogram model of medication non-adherence in patients with chronic kidney disease in China.

METHODS

A multicenter cross-sectional study was conducted. 1206 chronic kidney disease patients were consecutively enrolled from Be Resilient to Chronic Kidney Disease (registration number: ChiCTR2200062288) between September 2021 and October 2022 in four tertiary hospitals in China. The Chinese version of four-item Morisky Medication Adherence Scale was used to assess the medication adherence of the patients and associated factors consisted of socio-demographic information, self-designed medication knowledge questionnaire, the 10-item Connor-Davidson Resilience Scale, the Beliefs about Medicine questionnaire, the Acceptance Illness Scale, and the Family Adaptation Partnership Growth and Resolve Index. Least Absolute Shrinkage and Selection Operator regression was performed to select significant factors. Concordance index, Hosmer-Lemeshow test and decision curve analysis were estimated.

RESULTS

The prevalence of medication non-adherence was 63.8%. Area under the curves ranged from 0.72 to 0.96 in internal and external validation sets. The predicted probabilities of the model were consistent with those of the actual observations by Hosmer-Lemeshow test (all P > .05). The final model included educational level, occupational status, duration of chronic kidney disease, medication beliefs (perceptions of the need to take medications and concerns about adverse effects), and illness acceptance (adaptation and acceptance of the disease).

CONCLUSIONS

There is a high prevalence of medication non-adherence among Chinese patients with chronic kidney disease. A nomogram model based on five factors has been successfully developed and validated and could be incorporated into long-term medication management.

The Pharmacist's Role in Managing COVID-19 in Chronic Kidney Disease Patients: A Review of Existing Strategies and Future Implications

The global burden of the COVID-19 pandemic has not only disrupted healthcare delivery but has also compromised patients’ access to healthcare on account of the scarcity of medications and trained healthcare professionals. COVID-19 has been particularly challenging for patient subpopulations constituting immunocompromised individuals, geriatric patients, and those afflicted by chronic ailments. Reports indicate that diminished kidney function in chronic kidney disease (CKD) renders patients highly susceptible to complications during COVID-19 treatment. Pharmacists, being medication experts, have a significant role in making treatment decisions during COVID-19 infection. This article describes pharmacists’ interventions for monitoring and managing COVID-19 in patients with CKD. Given the massive increase in off-label use of medications to treat COVID-19, pharmacists can contribute substantially towards dosing decisions, reporting adverse medication events, and managing drug–drug interactions in COVID-19 patients suffering from CKD. In addition to traditional methods of delivering their services, the pharmacist should also adopt innovative tele-health systems to optimize patient care and ensure that patients receive safe and effective therapy during the pandemic.

Accuracy versus reliability-based modelling approaches for medical decision making

Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.

Effects of CYP3A5 Polymorphism on Rapid Progression of Chronic Kidney Disease: A Prospective, Multicentre Study

Personalised medicine is potentially useful to delay the progression of chronic kidney disease (CKD). The aim of this study was to determine the effects of CYP3A5 polymorphism in rapid CKD progression. This multicentre, observational, prospective cohort study was performed among adult CKD patients (≥18 years) with estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73 m2, who had ≥4 outpatient, non-emergency eGFR values during the three-year study period. The blood samples collected were analysed for CYP3A5*3 polymorphism. Rapid CKD progression was defined as eGFR decline of >5 mL/min/1.73 m2/year. Multiple logistic regression was then performed to identify the factors associated with rapid CKD progression. A total of 124 subjects consented to participate. The distribution of the genotypes adhered to the Hardy-Weinberg equilibrium (X2 = 0.237, p = 0.626). After adjusting for potential confounding factors via multiple logistic regression, the factors associated with rapid CKD progression were CYP3A5*3/*3 polymorphism (adjusted Odds Ratio [aOR] 4.190, 95% confidence interval [CI]: 1.268, 13.852), adjustments to antihypertensives, young age, dyslipidaemia, smoking and use of traditional/complementary medicine. CKD patients should be monitored closely for possible factors associated with rapid CKD progression to optimise clinical outcomes. The CYP3A5*3/*3 genotype could potentially be screened among CKD patients to offer more individualised management among these patients.