Association Between Regulatory Emotional Self-Efficacy and Immunosuppressive Medication Adherence in Renal Transplant Recipients:Does Medication Belief Act as a Mediator?

Liu, Jia; Zhu, Xiao; Yan, Jin; Gong, Ling; Wu, Xiaoxia; Liu, Min; Mao, Ping

doi:10.3389/fphar.2021.559368

Cited by 7 publications

(10 citation statements)

References 46 publications

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“…As stated in the Introduction, many studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ] have analyzed patient self-reporting results by focusing on “whether any immunosuppression-taking NA has occurred” (with “no” implying perfect adherence). In fact, the ordinal scale portion of the self-reporting questionnaire (BAASIS, ITAS, Morisky, or some other), which attempts to measure the extent of a patient’s NA behavior, has often not been more fully utilized.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, in some of these NA studies, the self-reporting questionnaire was only given to patients during the first 12–24 months post-transplant, and this study along with previous reports [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] have shown that both NA and GFNA are more likely to occur late, i.e., beyond 24 months, in the patient’s post-transplant follow-up. While use of a patient self-reporting questionnaire, as highlighted in these studies, [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ] provides a simple snapshot of patient NA behavior, usually within the most recent 4 weeks (range: 1 week–3 months), achieving an accurate picture of each patient’s NA behavior over time would most likely require serial use of patient self-reporting throughout post-transplant follow-up.…”

Section: Discussionmentioning

confidence: 99%

“…Many reports of patient nonadherence (NA) in the literature have focused on analyzing the results of patient self-reporting using the strict dichotomy, “whether any immunosuppression-taking NA has occurred”, with “no” implying perfect adherence, and “yes” implying any evidence of nonadherent (NA) behavior [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. The main problem with using such a strict dichotomy of “perfect adherence” vs. “any type of NA behavior” is the fact that this NA definition includes all levels of minor/subclinical NA behavior, including patients who (to a small degree) mistime their dose-taking, take less than the prescribed dose, and/or even skip taking some doses.…”

Section: Introductionmentioning

confidence: 99%

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Graft Failure Due to Nonadherence among 150 Prospectively-Followed Kidney Transplant Recipients at 18 Years Post-transplant: Our Results and Review of the Literature

Gaynor

Guerra

Roth

et al. 2022

JCM

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Background: We previously reported that graft failure due to nonadherence (GFNA) was a major cause of graft loss in kidney transplantation. Here, among 150 prospectively-followed kidney transplant recipients at 18 years post-transplant, we provide: updated (longer-term) estimates of cause-specific graft loss probabilities, risk factors for developing GFNA, and detailed characterizations of patients’ overt nonadherent (NA) behavior, including timing, extent, and clinical consequences. Methods: Determination of the patient becoming NA in taking his/her immunosuppressive medications, and the underlying cause of graft loss, were determined prospectively by the attending physicians. For never-functioning-graft, GFNA, GF due to causes other than NA (Other GF), and death with a functioning graft (DWFG), cumulative incidence functions were used to estimate the cumulative probabilities of cause-specific graft loss. Cox stepwise regression was used to determine significant multivariable predictors for the hazard rate of developing GFNA. Results: GFNA was a major cause of graft loss (22/150 patients), particularly among African-American and Hispanic recipients <50 years of age-at-transplant (20/56 experienced GFNA), with estimated percentages of such patients ever developing GFNA ranging between 36.9 and 41.5%. These patients were also at a higher risk of developing Other GF. For the remaining patients (2/94 experienced GFNA), estimated percentages of ever-developing GFNA were much lower (range: 0.0–6.7%). The major cause of graft loss among recipients ≥50 years of age was DWFG; GFNA rarely occurred among older recipients. In 21/22 GFNA patients, NA behavior lasted continuously from the time of developing NA until GFNA. In total, 28/150 patients became NA, and 67.9% (19/28) occurred beyond 36 months post-transplant. A total of 25 of 28 NA patients (89.3%) developed biopsy-proven acute rejection and/or chronic rejection that was directly attributed to the NA behavior. Lastly, 25/28 admitted to NA behavior, with financial and psychological components documented in 71.4% (20/28) and 96.4% (27/28) of NA cases, respectively. Conclusions: These results highlight the importance of performing serial monitoring of patients for overt NA behavior throughout their post-transplant follow-up. Financial and psychological components to NA behavior need to be simultaneously addressed with the goal of achieving complete avoidance/elimination of NA behavior among higher risk patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graft Failure Due to Nonadherence among 150 Prospectively-Followed Kidney Transplant Recipients at 18 Years Post-transplant: Our Results and Review of the Literature

Gaynor

Guerra

Roth

et al. 2022

JCM

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“…A recent study showed that IM nonadherence could lead to increasing risk of cancer over the long term in renal transplant patients (RTPs) (Yadav et al, 2019). However, research reports indicated that the prevalence rate of IM nonadherence in China ranges from 23.2 to 54.9% in RTPs (Teng et al, 2015;Xia et al, 2019;Liu et al, 2021), which was almost consistent with a worldwide incidence of 20-50% (Germani et al, 2011). Therefore, identifying the predictive factors with IM nonadherence would allow us to evaluate patients' need for interventions to improve adherence, which could reduce the acute rejection rate and transplant graft loss to improve transplant outcomes.…”

Section: Introductionmentioning

confidence: 99%

Identification of Immunosuppressive Medication Nonadherence Factors Through a Combined Theory Model in Renal Transplant Recipients

et al. 2021

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Background: Immunosuppressive medication (IM) nonadherence is associated with poor transplant outcomes. Therefore, it is of great importance to identify predictive factors with IM nonadherence. We aimed to improve the predicted capacity of the theory of planned behavior (TPB) by adding health belief model’s (HBM) variables in renal transplant patients (RTPs).Methods: This cross-sectional study distributed questionnaires to patients who had undergone renal transplant and follow-up regularly in the transplant center of Third Xiangya Hospital in China. The self-developed questionnaire collected data in three aspects: general data questionnaire, TPB, HBM-specific questionnaire, and Basel Assessment of Adherence to Immunosuppressive Medications scale.Results: A total of 1,357 of 1,480 patients completed the survey, with a participation rate of 91.69% and IM nonadherence rate of 33.53%. The marital status, household income, preoperative drinking history, the time after transplantation, and religion showed independent predictive factors with IM nonadherence (p < 0.05). Strikingly, adding HBM variables to the TPB theory model significantly increased its prediction ability to IM nonadherence (52%). Also, HBM manifested the highest coefficient of effect (−0.620). Particularly, perceived barriers and perceived seriousness, the variables of the HBM model, played a vital influence on medication nonadherence (−0.284 and 0.256).Conclusion: Our study here reveals the first investigation of the combined effects of the TPB and HBM model on IM nonadherence in Chinese RTPs, which could significantly improve the predictive ability of any single model. Meanwhile, future interventions should be conducted to both increase perceived seriousness and reduce perceived barriers for taking IM, which will effectively decrease IM nonadherence rates and improve transplant outcomes.

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“…Successful long-term kidney graft outcomes remain suboptimal, with IM non-adherence considered as an important contributing factor. Nevertheless, non-adherence is common, occurring in 23.21-44.2% of Chinese renal transplant recipients in our previous studies (4)(5)(6). IM nonadherence is a major issue among transplant recipients that can lead to misdiagnosis, rejection, graft loss or death.…”

Section: Introductionmentioning

confidence: 91%

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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Association Between Regulatory Emotional Self-Efficacy and Immunosuppressive Medication Adherence in Renal Transplant Recipients:Does Medication Belief Act as a Mediator?

Cited by 7 publications

References 46 publications

Graft Failure Due to Nonadherence among 150 Prospectively-Followed Kidney Transplant Recipients at 18 Years Post-transplant: Our Results and Review of the Literature

Graft Failure Due to Nonadherence among 150 Prospectively-Followed Kidney Transplant Recipients at 18 Years Post-transplant: Our Results and Review of the Literature

Identification of Immunosuppressive Medication Nonadherence Factors Through a Combined Theory Model in Renal Transplant Recipients

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

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