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Risk prediction models for acute kidney injury in adults: An overview of systematic reviews

Paulien Van Acker, Wim Van Biesen (UGent) , Evi Nagler (UGent) , Muguet Koobasi (UGent) , Nic Veys (UGent) and Jill Vanmassenhove (UGent)
(2021) PLOS ONE. 16(4).
Author
Organization
Abstract
Background The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI. Methods The protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs. Results Eight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report. Conclusion The methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient.
Keywords
General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine

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MLA
Van Acker, Paulien, et al. “Risk Prediction Models for Acute Kidney Injury in Adults: An Overview of Systematic Reviews.” PLOS ONE, edited by Michele Andreucci, vol. 16, no. 4, 2021, doi:10.1371/journal.pone.0248899.
APA
Van Acker, P., Van Biesen, W., Nagler, E., Koobasi, M., Veys, N., & Vanmassenhove, J. (2021). Risk prediction models for acute kidney injury in adults: An overview of systematic reviews. PLOS ONE, 16(4). https://doi.org/10.1371/journal.pone.0248899
Chicago author-date
Van Acker, Paulien, Wim Van Biesen, Evi Nagler, Muguet Koobasi, Nic Veys, and Jill Vanmassenhove. 2021. “Risk Prediction Models for Acute Kidney Injury in Adults: An Overview of Systematic Reviews.” Edited by Michele Andreucci. PLOS ONE 16 (4). https://doi.org/10.1371/journal.pone.0248899.
Chicago author-date (all authors)
Van Acker, Paulien, Wim Van Biesen, Evi Nagler, Muguet Koobasi, Nic Veys, and Jill Vanmassenhove. 2021. “Risk Prediction Models for Acute Kidney Injury in Adults: An Overview of Systematic Reviews.” Ed by. Michele Andreucci. PLOS ONE 16 (4). doi:10.1371/journal.pone.0248899.
Vancouver
1.
Van Acker P, Van Biesen W, Nagler E, Koobasi M, Veys N, Vanmassenhove J. Risk prediction models for acute kidney injury in adults: An overview of systematic reviews. Andreucci M, editor. PLOS ONE. 2021;16(4).
IEEE
[1]
P. Van Acker, W. Van Biesen, E. Nagler, M. Koobasi, N. Veys, and J. Vanmassenhove, “Risk prediction models for acute kidney injury in adults: An overview of systematic reviews,” PLOS ONE, vol. 16, no. 4, 2021.
@article{8711005,
  abstract     = {{Background
The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. One way to reduce the burden associated with AKI is to prevent the event altogether. An important step in prevention lies in AKI risk prediction. Due to the increasing number of available risk prediction models (RPMs) clinicians need to be able to rely on systematic reviews (SRs) to provide an objective assessment on which RPM can be used in a specific setting. Our aim was to assess the quality of SRs of RPMs in AKI.

Methods
The protocol for this overview was registered in PROSPERO. MEDLINE and Embase were searched for SRs of RPMs of AKI in any setting from 2003 till August 2020. We used the ROBIS tool to assess the methodological quality of the retrieved SRs.

Results
Eight SRs were retrieved. All studies were assessed as being at high risk for bias using the ROBIS tool. Eight reviews had a high risk of bias in study eligibility criteria (domain 1), five for study identification and selection (domain 2), seven for data collection and appraisal (domain 3) and seven for synthesis and findings (domain 4). Five reviews were scored at high risk of bias across all four domains. Risk of bias assessment with a formal risk of bias tool was only performed in five reviews. Primary studies were heterogeneous and used a wide range of AKI definitions. Only 19 unique RPM were externally validated, of which 11 had only 1 external validation report.

Conclusion
The methodological quality of SRs of RPMs of AKI is inconsistent. Most SRs lack a formal risk of bias assessment. SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient.}},
  articleno    = {{e0248899}},
  author       = {{Van Acker, Paulien and Van Biesen, Wim and Nagler, Evi and Koobasi, Muguet and Veys, Nic and Vanmassenhove, Jill}},
  editor       = {{Andreucci, Michele}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  keywords     = {{General Biochemistry,Genetics and Molecular Biology,General Agricultural and Biological Sciences,General Medicine}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{14}},
  title        = {{Risk prediction models for acute kidney injury in adults: An overview of systematic reviews}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0248899}},
  volume       = {{16}},
  year         = {{2021}},
}

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