kfre 0.1.5

A Python library for estimating kidney failure risk using the KFRE model developed by Tangri et al.

kfre is a Python library designed to estimate the risk of chronic kidney disease (CKD) progression using the Kidney Failure Risk Equation (KFRE) developed by Tangri et al. It provides risk assessments over two distinct timelines: 2 years and 5 years. The library is tailored for healthcare professionals and researchers, enabling precise CKD risk predictions based on patient data. It supports predictions for both males and females and includes adjustments for individuals from North American and non-North American regions.

Prerequisites

Before you install kfre, ensure you have the following:

• Python: Python 3.6 or higher is required to run kfre.

Additionally, kfre has the following package dependencies:

• NumPy: Version 1.18.5 or higher
• Pandas: Version 1.0.5 or higher

Installation

You can install kfre directly from PyPI:

pip install kfre

📄 Official Documentation

https://lshpaner.github.io/kfre_docs

🌐 Author Website

https://www.leonshpaner.com

⚖️ License

kfre is distributed under the MIT License. See LICENSE for more information.

📚 Citing kfre

If you use kfre in your research or projects, please consider citing it.

    @software{shpaner_2024_11100222,
      author       = {Shpaner, Leonid},
      title        = {{kfre: A Python Library for Reproducing Kidney 
                       Failure Risk Equations (KFRE)}},
      month        = may,
      year         = 2024,
      publisher    = {Zenodo},
      version      = {0.1.5},
      doi          = {10.5281/zenodo.11100222},
      url          = {https://doi.org/10.5281/zenodo.11100222}
    }

Support

If you have any questions or issues with kfre, please open an issue on this GitHub repository.

Acknowledgements

The KFRE model developed by Tangri et al. has made significant contributions to kidney disease research.

The kfre library is based on the risk prediction models developed in the studies referenced below. Please refer to these studies for an in-depth understanding of the kidney failure risk prediction models used within this library.

References

Sumida, K., Nadkarni, G. N., Grams, M. E., Sang, Y., Ballew, S. H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S. J., Chang, A. R., Cirillo, M., Daratha, K. B., Gansevoort, R. T., Garg, A. X., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C. P., Lash, J., Lee, B. J., Major, R. W., Metzger, M., Miura, K., Naimark, D. M. J., Nelson, R. G., Sawhney, S., Stempniewicz, N., Tang, M., Townsend, R. R., Traynor, J. P., Valdivielso, J. M., Wetzels, J., Polkinghorne, K. R., & Heerspink, H. J. L. (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. Annals of Internal Medicine, 173(6), 426-435. https://doi.org/10.7326/M20-0529

Tangri, N., Grams, M. E., Levey, A. S., Coresh, J., Appel, L. J., Astor, B. C., Chodick, G., Collins, A. J., Djurdjev, O., Elley, C. R., Evans, M., Garg, A. X., Hallan, S. I., Inker, L. A., Ito, S., Jee, S. H., Kovesdy, C. P., Kronenberg, F., Heerspink, H. J. L., Marks, A., Nadkarni, G. N., Navaneethan, S. D., Nelson, R. G., Titze, S., Sarnak, M. J., 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. https://doi.org/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. https://doi.org/10.1001/jama.2011.451