Size and Scope

1,186 patients | 11 columns

Dataset Preparation

• All coding done in Python 3.10.12 using the following libraries:
  • Pandas, NumPy, LifeLines, Matplotlib, Seaborn, Shap, and Scikit-Learn

Basic Data Exploration

Outcome	Lived	Died	Total Count	Lived (%)	Died (%)	Total (%)
0-1_years	12	8	20	60	40	100
1-2_years	12	14	26	46.15	53.85	100
2-3_years	14	12	26	53.85	46.15	100
3-4_years	24	11	35	68.57	31.43	100
4-5_years	31	11	42	73.81	26.19	100
5-6_years	39	10	49	79.59	20.41	100
6-7_years	68	19	87	78.16	21.84	100
7-8_years	101	9	110	91.82	8.18	100
8-9_years	305	14	319	95.61	4.39	100
9-10_years	445	4	449	99.11	0.89	100
10_years_plus	23	0	23	100	0	100
Total   Count	1,074	112	1,186	90.56	9.44	100

Outcomes by Risk Factors/Comorbidities

Cox Proportional-Hazards Model

Model Calibration

Model Prediction Comparisons

AUROC Curves

Confusion Matrix

Decision Tree from Random Forest Model

Feature Importance

Partial Dependence (Creatinine vs. Time)

References

• Al-Shamsi, S., Govender, R. D., & King, J. (2021). Predictive value of creatinine-based equations of kidney function in the long-term prognosis of United Arab Emirates patients with vascular risk. Oman medical journal, 36(1), e217. https://doi.org/10.5001/omj.2021.07


• Al-Shamsi, S., Govender, R. D., & King, J. (2019). Predictive value of creatinine-based equations of kidney function in the long-term prognosis of United Arab Emirates patients with vascular risk [Dataset]. Mendeley Data, V1. https://data.mendeley.com/datasets/ppfwfpprbc/1

Thank you!

Questions?

Email: Lshpaner@ucla.edu