Systematic review of prognostic prediction models for acute kidney injury in general hospital populations : methodology /
Acute kidney injury (AKI) is an area of increasing interest, in part due to a recognition of its association with adverse outcomes and also secondary to the relatively new and evolving definitions of this syndrome. Consensus definitions for AKI serve as a useful platform for unifying research into e...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
London :
SAGE Publications Ltd,
2020.
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| Series: | SAGE Research Methods Cases : Medicine and Health.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | Acute kidney injury (AKI) is an area of increasing interest, in part due to a recognition of its association with adverse outcomes and also secondary to the relatively new and evolving definitions of this syndrome. Consensus definitions for AKI serve as a useful platform for unifying research into etiology, treatment, and prognosis of AKI. Numerous prediction models exist for predicting the development of AKI; however, the generalizability and validation of such models are less well defined, particularly for de novo AKI in non-specialist general hospital patients. Our systematic review sought to extract the available data for such cohorts using dedicated systematic review frameworks to compare, contrast, and score these models in terms of their performance and risk of bias. Set standards exist for what should be incorporated into a prediction model, including clear definitions of outcomes and predictors, data handling (including missing data where applicable), internal and external validation, blinding of predictors or outcomes, and measures of model performance. This review aims to delineate the approach we took, including key difficulties we faced and key learning points. |
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| Physical Description: | 1 online resource. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781529740226 1529740223 |