Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4809
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Item Tacrolimus intrapatient variability in BK virus nephropathy and chronic calcineurin toxicity in kidney transplantation(2021) Turgut, Didem; Sayin, Burak; Soy, Ebru Ayvazoglu; Topcu, Deniz İlhan; Ozdemir, Binnaz Handan; Haberal, Mehmet; 0000-0002-3462-7632; 0000-0002-0993-9917; 35017328; AAJ-8097-2021; AAC-5566-2019Intrapatient variability (IPV) in tacrolimus has been increasingly acknowledged as a risk factor for poor graft survival after kidney transplantation. Although past studies have mainly accounted for IPV in acute or chronic rejection states as due to underimmunosuppression, this is not yet clear. So far, tacrolimus IPV for BK virus-associated nephropathy (BKVN) and chronic calcineurin inhibitor toxicity (CNIT) has not been investigated. Here, we evaluated IPV in tacrolimus for BKVN and chronic CNIT, which are mainly considered as overimmunosuppression states. In this caseucontrol study, kidney allograft biopsies conducted between 1998 and 2018 were included, with patients grouped by biopsy results as BKVN alone group, CNIT alone group, and normal graft function (control group). IPV was estimated as mean absolute deviation. Our study groups included 25 kidney transplant recipients with BKVN alone, 91 patients with CNIT alone, and 60 patients with normal 5-year graft survival (control group). In analyses of IPV in tacrolimus six months before graft biopsy, IPV was highest in the BKVN group (P = 0.001). The BKVN group also had the highest IPV in tacrolimus at 12 months after biopsy (P = 0.001), with all pairwise comparisons statistically different between groups. At 12 months after biopsy, five patients (20%) in the BKVN group and 10 patients (10.9%) in the CNIT group had graft loss. Among other risk factors, BKVN and chronic CNIT are consequences related to high IPV. Quantification of IVP for tacrolimus in clinical practice would help to optimize kidney transplant outcomes.Item Estimation of Low-Density Lipoprotein Cholesterol Concentration Using Machine Learning(2021) Cubukcu, Hikmet Can; Topcu, Deniz İlhan; 0000-0002-1219-6368; 34635916; E-3717-2019Objective Low-density lipoprotein cholesterol (LDL-C) can be estimated using the Friedewald and Martin-Hopkins formulas. We developed LDL-C prediction models using multiple machine learning methods and investigated the validity of the new models along with the former formulas. Methods Laboratory data (n = 59,415) on measured LDL-C, high-density lipoprotein cholesterol, triglycerides (TG), and total cholesterol were partitioned into training and test data sets. Linear regression, gradient-boosted trees, and artificial neural network (ANN) models were formed based on the training data. Paired-group comparisons were performed using a t-test and the Wilcoxon signed-rank test. We considered P values .2 to be statistically significant. Results For TG >= 177 mg/dL, the Friedewald formula underestimated and the Martin-Hopkins formula overestimated the LDL-C (P <.001), which was more significant for LDL-C <70 mg/dL. The linear regression, gradient-boosted trees, and ANN models outperformed the aforementioned formulas for TG >= 177 mg/dL and LDL-C <70 mg/dL based on a comparison with a homogeneous assay (P >.001 vs. P <.001) and classification accuracy. Conclusion Linear regression, gradient-boosted trees, and ANN models offer more accurate alternatives to the aforementioned formulas, especially for TG 177 to 399 mg/dL and LDL-C <70 mg/dL.