Risk factors and clinical characteristics of tacrolimus-induced acute nephrotoxicity in children with nephrotic syndrome: a retrospective case-control study

Gao, Ping; Guan, Xin-Lei; Huang, Rui; Shangguan, Xiaofang; Luan, Jiangwei; Liu, Mao-Chang; Xu, Huibi; Wang, Xiaowen

doi:10.1007/s00228-019-02781-3

Cited by 9 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we first comprehensively examined the effect of the above gene polymorphisms and clinical features on TAC-induced nephrotoxicity in the NS population. Unlike other studies (Morgan et al, 2011;Sinha et al, 2013;Gao et al, 2020), no clinical variables were excavated to have a significant effect on TAC-induced renal tubular toxicity (NAG) in our study. Although the URBC0 significantly affected NAG in the univariate analysis, it was not included in our final model after screening by machine learning algorithms.…”

Section: Discussionmentioning

confidence: 67%

“…2011; Sinha et al, 2013;Gao et al, 2020). For example, Gao et al (2020). established a model using LR to evaluate the risk factors of TAC-induced acute nephrotoxicity.…”

Section: Discussionmentioning

confidence: 99%

“…Tacrolimus, an important calcineurin inhibitor (CNI), is widely used in kidney transplantation and NS. However, there are only few reports focused on the risk factors of TAC-induced nephrotoxicity in NS (Morgan et al, 2011;Sinha et al, 2013;Gao et al, 2020). The risk factors excavated by these models include TAC trough concentration, diarrhea, proteinuria duration, high blood pressure, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The risk factors excavated by these models include TAC trough concentration, diarrhea, proteinuria duration, high blood pressure, etc. However, these studies have certain limitations: First, these risk-factor models were generated using traditional logistic regression (LR), which may not be the optimal method because of its low prediction performance; Second, Gao et al (2020) used serum creatinine as the indicator of nephrotoxicity , which may not sensitively reflect kidney damage. Studies have shown that a significant increase in serum creatinine can only be seen when kidney function drops to 50% of normal levels (Price, 1992).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning

Chen

Ieong

et al. 2021

Front. Pharmacol.

View full text Add to dashboard Cite

Background and Aims: Tacrolimus(TAC)-induced nephrotoxicity, which has a large individual variation, may lead to treatment failure or even the end-stage renal disease. However, there is still a lack of effective models for the early prediction of TAC-induced nephrotoxicity, especially in nephrotic syndrome(NS). We aimed to develop and validate a predictive model of TAC-induced tubular toxicity in children with NS using machine learning based on comprehensive clinical and genetic variables.Materials and Methods: A retrospective cohort of 218 children with NS admitted between June 2013 and December 2018 was used to establish the models, and 11 children were prospectively enrolled for external validation. We screened 47 clinical features and 244 genetic variables. The changes in urine N- acetyl- β-D- glucosaminidase(NAG) levels before and after administration was used as an indicator of renal tubular toxicity.Results: Five machine learning algorithms, including extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), extremely random trees (ET), random forest (RF), and logistic regression (LR) were used for model generation and validation. Four genetic variables, including TRPC6 rs3824934_GG, HSD11B1 rs846910_AG, MAP2K6 rs17823202_GG, and SCARB2 rs6823680_CC were incorporated into the final model. The XGBoost model has the best performance: sensitivity 75%, specificity 77.8%, accuracy 77.3%, and AUC 78.9%.Conclusion: A pre-administration model with good performance for predicting TAC-induced nephrotoxicity in NS was developed and validated using machine learning based on genetic factors. Physicians can estimate the possibility of nephrotoxicity in NS patients using this simple and accurate model to optimize treatment regimen before administration or to intervene in time after administration to avoid kidney damage.

show abstract

Section: Discussionmentioning

confidence: 67%

“…2011; Sinha et al, 2013;Gao et al, 2020). For example, Gao et al (2020). established a model using LR to evaluate the risk factors of TAC-induced acute nephrotoxicity.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning

Chen

Ieong

et al. 2021

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

“…To contain the COVID-19 pandemic, there is a need to develop a rapid and easily available diagnostic and screening tool [17] . Currently, the virus is detected using RT-PCR, which has varying diagnostic accuracies depending on the kits [18] . To increase the accuracy of diagnostic tests, the samples must be collected from both the upper and lower respiratory tracts [19] .…”

Section: Covid-19: Major Problems In Developing Therapiesmentioning

confidence: 99%

A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage

Ishay¹,

Potruch²,

Schwartz³

et al. 2021

Biomedicine & Pharmacotherapy

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19), which is a respiratory illness associated with high mortality, has been classified as a pandemic. The major obstacles for the clinicians to contain the disease are limited information availability, difficulty in disease diagnosis, predicting disease prognosis, and lack of disease monitoring tools. Additionally, the lack of valid therapies has further contributed to the difficulties in containing the pandemic. Recent studies have reported that the dysregulation of the immune system leads to an ineffective antiviral response and promotes pathological immune response, which manifests as ARDS, myocarditis, and hepatitis. In this study, a novel platform has been described for disseminating information to physicians for the diagnosis and monitoring of patients with COVID-19. An adjuvant approach using compounds that can potentiate antiviral immune response and mitigate COVID-19-induced immune-mediated target organ damage has been presented. A prolonged beneficial effect is achieved by implementing algorithm-based individualized variability measures in the treatment regimen.

show abstract

Optimal Trough Concentration of Tacrolimus in Pediatric Patients With Primary Nephrotic Syndrome

Wang,

Liu,

Wang

et al. 2024

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

The trough concentration (C0) of tacrolimus in children with nephrotic syndrome (NS) has rarely been explored, so its target level was based on transplant research. This study aimed to determine the optimal tacrolimus C0 in NS children. Data from primary NS children treated with tacrolimus at Wuhan Children's Hospital in the last 10 years were retrospectively collected. According to the cutoff C0 analyzed by receiver‐operator characteristics (ROC) analysis, patients were divided into very low‐ (< 4 ng/mL), low‐ (4–5 ng/mL), medium‐ (5–7 ng/mL), and high‐concentration (7–10 ng/mL) groups. A total of 196 patients were enrolled for primary outcome analysis. Compared to medium‐concentration group, only the very low‐concentration group obtained significant inferior primary outcomes, including overall remission rate, relapse‐free survival rate, and relapse rate at 6 months. For secondary outcomes, the very low‐concentration group experienced more frequent treatment failure in 12 months, whereas the high‐concentration group suffered a higher risk of adverse events than the medium‐concentration group. For steroid‐resistant NS, very low‐ and low‐concentration groups required longer time to achieve remission compared to medium‐concentration group. For steroid‐sensitive NS, the very low‐concentration group suffered a higher relapse frequency than medium‐concentration group. Lastly, the dose of tacrolimus required for children with different CYP3A5 genotypes with or without Wuzhi capsules was analyzed. In conclusion, tacrolimus may be targeted to C0 of 4–7 ng/mL during the first 6 months in children with NS. For steroid‐resistant NS, C0 of 5–7 ng/mL can achieve a rapid remission.

show abstract

Risk factors and clinical characteristics of tacrolimus-induced acute nephrotoxicity in children with nephrotic syndrome: a retrospective case-control study

Cited by 9 publications

References 36 publications

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning

A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage

Optimal Trough Concentration of Tacrolimus in Pediatric Patients With Primary Nephrotic Syndrome

Contact Info

Product

Resources

About