Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Yu, Chengxuan; Guo, Daihong; Yan, Chong; Zhu, Yu; Liu, Siyuan; Kong, Xiangru

doi:10.3389/fphar.2021.657853

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…Some authors are already studying and developing predictive models to forecast acute kidney injury in short time frames, allowing for the identification of high-risk patients and risk factors and the implementation of preventive strategies [ 27 ]. Other authors have been advancing in the identification of biomarkers that enable early recognition of this type of injury, culminating in the early cessation of the suspected agent [ 27 ], while the development of propensity scores has been another explored approach [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Drug-Associated Acute Kidney Disease – Data Ffrom a World Pharmacovigilance Database

Baptista,

Marreiros,

Macedo

et al. 2024

Cureus

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Background Drugs are a frequent cause of nephrotoxicity, especially in the context of acute kidney disease (AKD), with a significant number of cases being drug-associated. The WHO's VigiBase is a powerful tool for identifying drugs described and associated with the development of AKD. Methods We retrieved data from the period 1968 to 2022 regarding notifications of adverse drug reactions (ADR). The extracted medications were evaluated for their nephrotoxicity based on the bibliographic score (BS) developed through pre-selected references. The main medications involved were classified as 'non-nephrotoxic', 'potentially nephrotoxic', and 'nephrotoxic'. We utilized the IC 025 and reporting odds ratio (ROR) disproportionality indexes to study the relationship between medications and the odds of being included in an AKD notification. Results During the period, a total of 33,932,051 notifications were obtained, revealing 435,677 cases related to drug-associated AKD following MedDRA term filtering, predominantly affecting males aged 45-64. We identified 8,991 active ingredients or suspected combinations associated with AKD development, with the ATC class A - Alimentary Tract and Metabolism being the most frequently described. Among the medications most strongly associated with this phenotype, classes J and N stood out. Among the most notable medications collected, 8.3% were classified as "non-nephrotoxic," 16.7% as "potentially nephrotoxic," and 75% as "known nephrotoxic." Notable active ingredients included cobicistat + elvitegravir + emtricitabine + tenofovir disoproxil (IC 025 8.7; ROR 786.96), inotersen (IC 025 7.7; ROR 604.57), emtricitabine + tenofovir disoproxil (IC 025 7.9; ROR 432.36), esomeprazole (IC 025 6.8; ROR 184.23), and pantoprazole (IC 025 6.3; ROR 109.86), with proton pump inhibitors dominating the top four positions among the most frequently involved medications. Conclusion AKD is a frequent adverse reaction in VigiBase, with a significantly high reported mortality rate. Evaluation of the notifications revealed medications with a high disproportionality index and a strong association with AKD. We also highlight the potential nephrotoxic role of less suspected medications. This study emphasizes the need to consider AKD as a condition potentially associated with iatrogenic etiology, highlighting various medications and their respective involvement in the various possible manifestations of AKD.

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Section: Discussionmentioning

confidence: 99%

Drug-Associated Acute Kidney Disease – Data Ffrom a World Pharmacovigilance Database

Baptista,

Marreiros,

Macedo

et al. 2024

Cureus

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“…Therefore, we developed and validated a nomogram for risk assessment of AKI that included these biomarkers. Several nomogram models for predicting AKI have been reported in previous studies [25][26][27][28][29]. However, previous studies only focused on the risk assessment of AKI after the first use of cisplatin.…”

Section: Discussionmentioning

confidence: 99%

Acute Kidney Injury after Multiple Cycles of Cisplatin Chemotherapy: A Nomogram for Risk Assessment

Zhu¹,

Li²,

Tang³

et al. 2023

Kidney Blood Press Res

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Introduction: Acute kidney injury (AKI) caused by cisplatin is common and has a higher incidence of multiple use, resulting in a poor short- and long-term prognosis for patients. There is currently no good premedication AKI risk assessment tool. The aim of this study was to establish an AKI risk assessment nomogram for patients with multiple cisplatin applications. Methods: This study was a retrospective analysis of patients who were treated with non-first-time cisplatin chemotherapy regimen at Changzhou Second People’s Hospital affiliated to Nanjing Medical University from January 2016 to January 2022. All data from the development group were used to screen the impact factors of AKI via univariate and multivariate analyses. A nomogram was developed based on these impact factors and verified with verification group. The area under the curve (AUC) of receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs) were used to evaluate the nomogram. Results: Among the 256 patients enrolled in 450 cycles of chemotherapy, 282 were in the development cohort (97 AKI), and 168 were in the validation cohort (61 AKI). Multivariate logistic regression revealed that age, hypertension, diabetes, serum cystatin C (sCysC), urinary kidney injury molecule-1 (uKim1), and a single dose of cisplatin were independently associated with AKI. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.887 and 0.906 using the development group and on verification group. The calibration plots and DCA showed the superior clinical applicability of the nomogram. These results were verified in the validation cohort. Conclusion: A nomogram combining functional (sCysC) and tubular (uKim1) injury biomarkers with conventional clinical factors might assess the risk of AKI after multiple cycles of cisplatin chemotherapy.

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“…In 2020, Xu N. et al developed a nomogram to predict vancomycin related nephrotoxicity in hospitalized patients [ 36 ]. In 2021, Yu C. et al developed a nomogram to predict drug-induced acute renal injury in hospitalized patients [ 37 ]. In this study, a predictive model of voriconazole-related liver injury with a nomogram was constructed, and the model was verified internally and externally.…”

Section: Discussionmentioning

confidence: 99%

The Development and Validation of a Predictive Model for Voriconazole-Related Liver Injury in Hospitalized Patients in China

Xiao

Liu

Chen

et al. 2023

JCM

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Voriconazole is widely used in the treatment and prevention of invasive fungal diseases. Common drug-induced liver injuries increase the economic burdens and the risks of premature drug withdrawal and disease recurrence. This study estimated the disposal cost of voriconazole-related liver injury, explored the risk factors of voriconazole-related liver injury in hospitalized patients, and established a predictive model of liver injury to assist clinicians and pharmacists in estimating the probability or risk of liver injury after voriconazole administration to allow for early identification and intervention in patients at high risk of liver injury. A retrospective study was conducted on the selected inpatients whose blood concentration of voriconazole was measured in the West China Hospital of Sichuan University from September 2016 to June 2020. The incidence and disposal cost of voriconazole-related liver injuries were calculated. The incidence of voriconazole-related liver injury was 15.82% (217/1372). The disposal cost has been converted to 2023 at a discount rate of 5%. The median (P25, P75) disposal cost of severe liver injury (n = 42), general liver injury (n = 175), and non-liver injury (n = 1155) was 993.59 (361.70, 1451.76) Chinese yuan, 0.00 (0.00, 410.48) yuan, and 0.00 (0.00, 0.00) yuan, respectively, with a statistically significant difference (p < 0.001). Single factor analysis and multiple factor logistic regression were used to analyze the risk factors of voriconazole-related liver injury. The voriconazole-related liver injury was related to the trough concentration (Cmin, OR 1.099, 95% CI 1.058–1.140), hypoproteinemia (OR 1.723, 95% CI 1.126–2.636), and transplantation status (OR 0.555, 95% CI 0.325–0.948). The prediction model of liver injury was Logit (P)= −2.219 + 0.094 × Cmin + 0.544 × Hydroproteinemia − 0.589 × Transplantation, and the prediction model nomogram was established. The model validation results showed that the C-index of the derivation set and validation set was 0.706 and 0.733, respectively. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.705 and 0.733, respectively, indicating that the model had good prediction ability. The prediction model will be helpful to develop clinical individualized medication of voriconazole and to identify and intervene in the cases of patients at high risk of voriconazole-related liver injury early on, in order to reduce the incidence of voriconazole-related liver injuries and the cost of treatment.

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Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching

Cited by 8 publications

References 41 publications

Drug-Associated Acute Kidney Disease – Data Ffrom a World Pharmacovigilance Database

Drug-Associated Acute Kidney Disease – Data Ffrom a World Pharmacovigilance Database

Acute Kidney Injury after Multiple Cycles of Cisplatin Chemotherapy: A Nomogram for Risk Assessment

The Development and Validation of a Predictive Model for Voriconazole-Related Liver Injury in Hospitalized Patients in China

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