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Updated: Oct 31, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Multivariable prognostic model for dialysis patients with end stage renal disease: An observational cohort study of
Maryam Siddiqa1, Alan Charles Kimber1, Javid Shabbir1
1International Islamic University (Siddiqa), Islamabad, Pakistan; from the Quaid-i-Azam University (Shabbir), Islamabad, Pakistan; and from the University of Southampton (Kimber), Southampton, United Kingdom.
Objectives:
To develop an externally validated multivariable prognostic model for an underprivileged dialysis population.
Methods:
This was a multicenter retrospective cohort study of 5 years duration from January 2013 to December 2017. A total of 758 patients (37.5% female; mean±SD age, 44.26±14.77 years) were enrolled for construction of the prognostic model. The data were analyzed using a proportional hazards model to identify predictors of survival. Three risk groups were identified at the 25th and 75th percentiles of the resultant prognostic index. The model was externally validated with another dataset of 622 dialysis patients.
Results:
The prognostic index included 5 predictor variables: hemoglobin, serum potassium, interdialytic weight gain, serum albumin, and duration of dialysis, which had good predictive performance on the calibration and discrimination aspects of the model (Harrell's c statistic: 0.748, Gonen and Heller k statistic: 0.647, Somers' D statistic: 0.496, calibration slope: 1.156). There were significant interaction effects between weight and hemoglobin, weight and albumin, albumin and potassium, and albumin and hemoglobin.
Conclusions:
We developed an externally validated model that contained 5 routinely collected prognosticators and confirmed its calibration and discrimination abilities in obtaining reliable prognostic estimates in developing countries. The model will assist clinicians in deciding the prognosis of dialysis patients. The application of this model in different clinical settings of developing countries can indicate interesting findings regarding public health.
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