Medication nonadherence monitoring and management in adult kidney transplantation: A survey of practices and perceptions at <scp>US‐based</scp> transplant programs

Patel, Samir J.; Hofmeyer, Brooke A; Moore, Cody A.; Descourouez, Jillian L.; Nguyen, Duc T.; Graviss, Edward A.; Taber, David J.

doi:10.1002/jac5.1477

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Although the factors have been discussed, many studies still failed to evaluate how risk factors can be utilized to predict the risk of non-adherence behavior. A study conducted in 99 US-based transplant centers showed that 71.1% of health care professionals acknowledged not having a prospective medication non-adherence screening protocol, and little is known about strategies currently utilized by transplant centers to monitor or manage this issue (38). To solve this common problem, some scholars have attempted to establish predictive models of non-adherence in patients after kidney transplantation, such as HBM and TPB models (5,8,20,39).…”

Section: Discussionmentioning

confidence: 99%

Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology

et al. 2022

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ObjectivesPredicting adherence to immunosuppressive medication (IM) is important to improve and design future prospective, personalized interventions in Chinese renal transplant patients (RTPs).MethodsA retrospective, multicenter, cross-sectional study was performed in 1,191 RTPs from October 2020 to February 2021 in China. The BAASIS was used as the standard to determine the adherence of the patients. Variables of the combined theory, including the general data, the HBM, the TPB, the BMQ, the PSSS and the GSES, were used to build the models. The machine learning (ML) models included LR, RF, MLP, SVM, and XG Boost. The SHAP method was used to evaluate the contribution of predictors to predicting the risk of IM non-adherence in RTPs.ResultsThe IM non-adherence rate in the derivation cohort was 38.5%. Ten predictors were screened to build the model based on the database. The SVM model performed better among the five models, with sensitivity of 0.59, specificity of 0.73, and average AUC of 0.75. The SHAP analysis showed that age, marital status, HBM-perceived barriers, use pill box after transplantation, and PSSS-family support were the most important predictors in the prediction model. All of the models had good performance validated by external data.ConclusionsThe IM non-adherence rate of RTPs was high, and it is important to improve IM adherence. The model developed by ML technology could identify high-risk patients and provide a basis for the development of relevant improvement measures.

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Section: Discussionmentioning

confidence: 99%

Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology

et al. 2022

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Electronic health record-enabled routine assessment of medication adherence after solid organ transplantation: the time is now

Leino,

Kaiser,

Khalil

et al. 2024

American Journal of Transplantation

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Program Evaluation of Pharmacist-Performed Medication Adherence Assessments in Candidates for Living Donor Kidney Transplant

Nguyen,

Forte,

Malat

et al. 2024

Prog Transpl

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Introduction: Medication education and adherence assessments are integral to kidney transplant success. This program evaluation aimed to describe candidate-reported findings using a standardized medication adherence assessment in candidates undergoing living-donor kidney transplantation. Design: This was a single-center retrospective description of medication adherence on adult HIV-negative living-donor candidates from July 1, 2018 to December 1, 2018 who had ≥6 months post-operative follow-up. Medication adherence assessments were performed by a pharmacist at the pre-operative visit within 2 weeks prior to transplant. Candidates were considered to (a) have adherence concerns if they reported missed/late medications within 2 weeks of assessment or ever stopped a medication without medical advice and (b) considered using adherence strategies if they reported active use of pill box, method to keep track of refills/auto-refill use, medication list, or medication reminder(s). Missed medication data were collected at 3- and 6-months posttransplant. Results: Among 181 candidates included, 81 (45%) had adherence concerns and 169 (93%) reported using adherence strategies. There were no significant differences with adherence concerns by age ≤ 29 years, sex, race, prior transplant/dialysis, or less than a high school education. More candidates with greater than a high school education used adherence strategies (96% vs 86%, P = .002). Too few candidates had documentation on missing medications at 3 and 6 months. Conclusions: Over 40% of candidates reported characteristics concerning medication nonadherence despite over 90% reporting adherence strategies used. Medication adherence assessments can assist with identification of medication nonadherence and education individualization.

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Medication nonadherence monitoring and management in adult kidney transplantation: A survey of practices and perceptions at US‐based transplant programs

Cited by 3 publications

References 30 publications

Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology

Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology

Electronic health record-enabled routine assessment of medication adherence after solid organ transplantation: the time is now

Program Evaluation of Pharmacist-Performed Medication Adherence Assessments in Candidates for Living Donor Kidney Transplant

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