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Development and Validation of a Risk Prediction Model for Tacrolimus Blood Concentration Failure
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Zeng LI, Zhao YIN, Shuzhang DU*
Chinese Pharmaceutical Journal | 2024, 59(10) : 945 - 950
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Chinese Pharmaceutical Journal | 2024, 59(10): 945-950
Development and Validation of a Risk Prediction Model for Tacrolimus Blood Concentration Failure
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Zeng LI, Zhao YIN, Shuzhang DU*
Affiliations
  • Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
Published: 2024-05-22 doi: 10.11669/cpj.2024.10.012
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OBJECTIVE To analyze the influencing factors of tacrolimus (FK506) blood concentration non-attainment in renal transplant patients and construct a risk prediction column-line diagram model. METHODS Two hundred patients admitted to the hospital from June 2020 to June 2023 who were treated with FK506 after renal transplantation and whose blood concentration was monitored for 30 d were selected for the study, and according to the monitoring results, they were categorized into 126 cases in the group of meeting the standard (5-15 μg·L-1) and 74 cases in the group of failing to meet the standard (<5 μg·L-1 or>15 μg·L-1), and the clinical data were collected, and independent risk factors for failing to meet the standard of FK506 were screened and fit into a predictive model by single-factor and multifactorial logistic regression analysis. Clinical data of the two groups were collected, and the independent risk factors for FK506 blood concentration non-compliance were screened by single-factor and multifactor logistic regression analyses, according to which, a prediction model for the risk of FK506 blood concentration non-compliance was constructed with a line graph and tested for goodness-of-fit. RESULTS Of the 1 200 FK506 concentration monitoring sessions in 200 patients within 30 d after surgery, the number of sessions in which the target concentration of 5-15 μg·L-1 was reached was 756, with an attainment rate of 63.0%. A total of 126 patients reached the target concentration (target group), and 74 patients did not reach the target concentration (non-target group). The proportion of patients with age >60 years, male, intravenous, fasting blood glucose (FPG) >7 mmol·L-1, total bilirubin (TB) >20 μmol·L-1, white blood cell count (WBC)≤4×109·L-1, and blood creatinine (Cr) >133 μmol·L-1 was higher in the non-attainment group than in the attainment group, and the difference was statistically significant (P<0.05). Multifactorial logistic regression analysis was performed, and the results showed that age >60 years, male, FPG>7 mmol·L-1, TB>20 μmol·L-1, and Cr>133 μmol·L-1 were independent risk factors for substandard FK506 blood concentration in renal transplant patients (all P<0.05). The AUC of the constructed column-line graph model reached 0.859 (0.795-0.924), the model calibration curve validation C-index was 0.836, and the H-L deviation test χ2=4.203, P=0.516, the calibration curve was basically the same as the ideal curve trend, with a good degree of precision and differentiation. DCA analysis showed that the column-line graph prediction model displayed a large range of threshold probabilities (0.05 to 0.95), which corresponded to the largest area of the red curve to the gray curve and the horizontal axis, indicating a better clinical utility value of the model. CONCLUSION Age >60 years, male, FPG >7 mmol·L-1, TB >20 μmol·L-1, and Cr >133 μmol·L-1 are the risk factors for substandard FK506 blood concentration in renal transplantation patients, and the column-line graph model constructed accordingly can effectively predict the degree of risk for substandard FK506 blood concentration in renal transplantation patients.

tacrolimus  /  blood concentration  /  risk factor  /  risk prediction modeling
Zeng LI, Zhao YIN, Shuzhang DU. Development and Validation of a Risk Prediction Model for Tacrolimus Blood Concentration Failure[J]. Chinese Pharmaceutical Journal, 2024 , 59 (10) : 945 -950 . DOI: 10.11669/cpj.2024.10.012
Year 2024 volume 59 Issue 10
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doi: 10.11669/cpj.2024.10.012
  • Receive Date:2023-08-02
  • Online Date:2025-11-25
  • Published:2024-05-22
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  • Received:2023-08-02
Affiliations
    Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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表12种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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