&lt;p&gt;Derivation and Validation of a Risk Prediction Model for Vancomycin-Associated Acute Kidney Injury in Chinese Population&lt;/p&gt;

Xu, Nana; Zhang, Qiao; Wu, Guolan; Lv, Duo; Zheng, Yang

doi:10.2147/tcrm.s253587

Cited by 7 publications

(16 citation statements)

References 29 publications

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“…Among these, 261 studies were excluded for the following reasons: evaluating antibiotics other than vancomycin ( n = 104), evaluating outcomes other than AKI ( n = 45), not assessing risk factors ( n = 68), not original articles ( n = 4), involving paediatric patients ( n = 8), involving nonhuman subjects ( n = 12), overlapping study population ( n = 3), not a patient‐level analysis ( n = 1), involving patients who underwent renal replacement therapy ( n = 4), and data extraction not possible or feasible ( n = 12). Finally, 53 studies were included in the meta‐analysis 6–8,10–12,21–67 . A flow diagram summarizing the study selection process is shown in Figure 1.…”

Section: Resultsmentioning

confidence: 99%

Risk factors for vancomycin‐associated acute kidney injury: A systematic review and meta‐analysis

Kim

Yee

Yoon

et al. 2022

Brit J Clinical Pharma

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and obesity (OR 1.46, 95% CI: 1.12-1.90) were associated with an increase in vancomycin-associated AKIs. In terms of vancomycin-related factors, longer treatment duration (>14 d; OR 1.73, 95% CI: 1.06-2.83), serum vancomycin trough level >15 μg/mL (OR 2.10, 95% CI: 1.43-3.07) and vancomycin trough level >20 μg/mL (OR 2.84, 95% CI: 1.48-5.44) increased the risks of vancomycin-associated AKI. For comorbidities and clinical factors, renal disease (OR 2.19, 95% CI: 1.51-3.17) showed the highest odds of vancomycin-associated AKI, followed by hepatic disease, intensive care unit admission, heart failure, sepsis, coronary heart disease and diabetes mellitus. For concomitant nephrotoxic drugs, amphotericin B (OR 5.21, 95% CI: 3.44-7.87) showed the highest odds of vancomycin-associated AKI, followed by acyclovir (OR 3.22, 95% CI: 1.39-7.46), vasopressors, loop diuretics, piperacillintazobactam and aminoglycoside. The use of any concomitant nephrotoxic agent (OR 1.74, 95% CI: 1.17-2.58) increased the odds of vancomycin-associated AKI. Conclusion:Our results may help predict the risk of vancomycin-associated AKI in the clinical setting. K E Y W O R D S acute kidney injury, meta-analysis, risk factors, systematic review, vancomycin 1 | INTRODUCTION Vancomycin is a glycopeptide antibiotic agent used to treat severe infections, such as sepsis, osteomyelitis or pneumonia caused by methicillin-resistant Staphylococcus aureus. 1 Acute kidney injury (AKI) is a well-known adverse drug reaction of vancomycin. Estimates of the incidence of vancomycin-associated AKI vary from 5 to 43%. 2Since AKI has a significant impact on clinical outcomes, such as length of hospital stay or overall mortality, it is crucial to decrease the AKI incidence in hospitals. [3][4][5] Several studies have reported risk factors for vancomycinassociated AKI. [6][7][8][9][10][11][12] The reported risk factors are diverse, including patient factors (e.g., sex, body weight and race), drug-related factors (e.g., dose and duration of vancomycin) and concomitant drug factors

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Section: Resultsmentioning

confidence: 99%

Risk factors for vancomycin‐associated acute kidney injury: A systematic review and meta‐analysis

Kim

Yee

Yoon

et al. 2022

Brit J Clinical Pharma

View full text Add to dashboard Cite

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“…The Table shows a comparison of the AUROC for the models in 20 well-studied AKI subgroups from the literature (eTable 13 in the Supplement ). 17 , 20 , 22 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 The personalized model with transfer learning was superior to each of the current models, significantly outperforming the global model in 16 subgroups, the subgroup model in 11 subgroups, and the subgroup model with transfer learning in 9 subgroups. For example, among patients older than 65 years, AUROC was 0.76 (95% CI, 0.74-0.77) for the personalized model with transfer learning, 0.73 (95% CI, 0.72-0.75; P < .001) for the global model, 0.71 (95% CI, 0.70-0.72; P < .001) for the subgroup model, and 0.73 (95% CI, 0.72-0.75; P < .001) for the subgroup model with transfer learning.…”

Section: Resultsmentioning

confidence: 99%

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

et al. 2022

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IMPORTANCEAcute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes. OBJECTIVE To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study analyzed EHR data from 1 tertiary care hospital and used machine learning and logistic regression to develop and validate global, subgroup, and personalized risk estimation models. Transfer learning was implemented to enhance the personalized model. Predictor outcomes across subpopulations were analyzed, and metaregression was used to explore predictor interactions. Adults who were hospitalized for 2 or more days from November 1, 2007, to December 31, 2016, were included in the analysis. Patients with moderate or severe kidney dysfunction at admission were excluded. Data were analyzed

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“…The study flowchart according to the PRISMA statement is shown in Figure 1. The review included 150 studies (encompassing 14.4 million patients) with requisite data to enable sROC curves in 103 studies. The median duration of study data collection was 4 years (range, 3 months to 17 years).…”

Section: Resultsmentioning

confidence: 99%

“…Most studies reported prospective data collection (124 [82.7%]) . Based on prespecified clinical scenario subgroups, 29 studies assessed AKI associated with nephrotoxic agents, including 26 studies examining radio-contrast medium associated AKI and 3 studies that reported AKI associated with other nephrotoxins; 64 studies assessed AKI prediction in the postoperative setting; 21 studies assessed AKI prediction in intensive care units (ICU); 35 studies assessed AKI in general hospitalizations…”

Section: Resultsmentioning

confidence: 99%

Characterization of Risk Prediction Models for Acute Kidney Injury

et al. 2023

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ImportanceDespite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.ObjectiveTo systematically review published AKI prediction models across all clinical subsettings.Data SourcesMEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.Study SelectionAll studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.Data Extraction and SynthesisTwo authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.Main Outcomes and MeasuresC statistic was used to measure the discrimination of prediction models.ResultsOf the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium–associated AKI: I2 = 99.9%; P &lt; .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.Conclusions and RelevanceIn this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

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<p>Derivation and Validation of a Risk Prediction Model for Vancomycin-Associated Acute Kidney Injury in Chinese Population</p>

Cited by 7 publications

References 29 publications

Risk factors for vancomycin‐associated acute kidney injury: A systematic review and meta‐analysis

Risk factors for vancomycin‐associated acute kidney injury: A systematic review and meta‐analysis

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

Characterization of Risk Prediction Models for Acute Kidney Injury

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