Mortality Risk Prediction Models for People With Kidney Failure
Author Information
Author(s): Jarrar Faisal MD, Pasternak Meghann MD, Harrison Tyrone G. MD PhD, James Matthew T. MD PhD, Quinn Robert R. MD PhD, Lam Ngan N. MD MSc, Donald Maoliosa PhD MSc, Elliott Meghan MD MSc, Lorenzetti Diane L. PhD, Strippoli Giovanni MD, Liu Ping PhD, Sawhney Simon MBChB PhD, Gerds Thomas Alexander PhD, Ravani Pietro MD PhD
Primary Institution: Cumming School of Medicine, University of Calgary
Hypothesis
What are the quality and clinical applicability of existing mortality prediction models for people with kidney failure?
Conclusion
Existing mortality prediction models for people with kidney failure are not suitable for informing clinical decision-making.
Supporting Evidence
- This systematic review identified and evaluated 50 studies.
- Models were found to be at high risk of bias.
- None demonstrated promise in terms of clinical usability.
Takeaway
Doctors need better tools to predict how long people with kidney failure will live, so they can make better treatment choices.
Methodology
This systematic review evaluated 50 studies on mortality prediction models for kidney failure, assessing their quality and applicability.
Potential Biases
High risk of bias due to inadequate selection of study population and flaws in analysis strategy.
Limitations
All models were at high risk of bias and had applicability concerns for clinical practice.
Participant Demographics
Median age of participants was 64 years, with a median proportion of women at 42%.
Digital Object Identifier (DOI)
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