kfre: A Python Library for Reproducing Kidney Failure Risk Equations (KFRE)
Creators
Description
kfre is a Python library that leverages the Kidney Failure Risk Equation (KFRE) developed by Tangri et al. to estimate the risk of chronic kidney disease (CKD) progression over two distinct timelines: 2 years and 5 years. The library is designed as a tool for healthcare professionals and researchers to predict CKD risk based on patient data. It supports predictions for both males and females and includes specific adjustments for individuals from North American and non-North American regions, making it a versatile resource in the management and study of CKD.
What's New in kfre 0.1.2
This release, kfre 0.1.2, marks a substantial update from the preliminary alpha versions, introducing significant enhancements and features that elevate the tool's flexibility, accuracy, and ease of use:
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Enhanced Core Functionality: A comprehensive overhaul from earlier minimal viable products to a more robust and feature-rich application.
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New Calculator Function: The introduction of the
kfre_person()function enables risk metrics calculations for individuals one at a time, customizing the analysis to each unique dataset. -
Increased Flexibility: The
add_kfre_risk_col()function now allows for direct execution of kfre without the need to instantiate a class, simplifying the process for users. -
Model Variability: Users can specify models with 4, 6, or 8 variables through the
add_kfre_risk_col()function, adapting to different data requirements. -
Timeframe Options: The function now accommodates specification of projection years (2 or 5 years, or either), providing tailored risk assessments.
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DataFrame Handling: An option to either copy the dataframe or modify it in place when adding kfre columns is now available, offering greater flexibility in data management.
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Formula Correction: The formula for the 6-variable calculation has been updated with the correct coefficients from Tangri et al., enhancing prediction accuracy.
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Conversion Tools: The new
perform_conversions()function facilitates the conversion of relevant clinical metrics, streamlining data preparation for analysis.
This release reflects ongoing efforts to enhance and refine kfre, driven by feedback from users and continuous research into improving its utility and functionality.
Full Changelog:
Files
lshpaner/kfre-0.1.2.zip
Files
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Additional details
Related works
- Is compiled by
- Software: https://pypi.org/project/kfre/ (URL)
- Is supplement to
- Software: https://github.com/lshpaner/kfre/tree/0.1.2 (URL)
Software
- Repository URL
- https://github.com/lshpaner/kfre
- Programming language
- Python
- Development Status
- Active
References
- Tangri N, Grams ME, Levey AS, Coresh J, Appel LJ, Astor BC, Chodick G, Collins AJ, Djurdjev O, Elley CR, Evans M, Garg AX, Hallan SI, Inker LA, Ito S, Jee SH, Kovesdy CP, Kronenberg F, Heerspink HJL, Marks A, Nadkarni GN, Navaneethan SD, Nelson RG, Titze S, Sarnak MJ, Stengel B, Woodward M, Iseki K, for the CKD Prognosis Consortium. (2016). Multinational assessment of accuracy of equations for predicting risk of kidney failure: A meta-analysis. JAMA, 315(2), 164–174. doi: 10.1001/jama.2015.18202.
- Tangri, N., Stevens, L. A., Griffith, J., Tighiouart, H., Djurdjev, O., Naimark, D., Levin, A., & Levey, A. S. (2011). A predictive model for progression of chronic kidney disease to kidney failure. JAMA, 305(15), 1553-1559. doi: 10.1001/jama.2011.451.
- Sumida K, Nadkarni GN, Grams ME, Sang Y, Ballew SH, Coresh J, Matsushita K, Surapaneni A, Brunskill N, Chadban SJ, Chang AR, Cirillo M, Daratha KB, Gansevoort RT, Garg AX, Iacoviello L, Kayama T, Konta T, Kovesdy CP, Lash J, Lee BJ, Major RW, Metzger M, Miura K, Naimark DMJ, Nelson RG, Sawhney S, Stempniewicz N, Tang M, Townsend RR, Traynor JP, Valdivielso JM, Wetzels J, Polkinghorne KR, Heerspink HJL, for the Chronic Kidney Disease Prognosis Consortium. (2020). Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis. Ann Intern Med, 173(6), 426-435. doi: 10.7326/M20-0529.