Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients

Dino Gibertoni, Paola Rucci, Marcora Mandreoli, Mattia Corradini, Davide Martelli, Giorgia Russo, Elena Mancini, Antonio Santoro

Research output: Contribution to journalArticle

Abstract

Background: A classification tree model (CT-PIRP) was developed in 2013 to predict the annual renal function decline of patients with chronic kidney disease (CKD) participating in the PIRP (Progetto Insufficienza Renale Progressiva) project, which involves thirteen Nephrology Hospital Units in Emilia-Romagna (Italy). This model identified seven subgroups with specific combinations of baseline characteristics that were associated with a differential estimated glomerular filtration rate (eGFR) annual decline, but the model's ability to predict mortality and renal replacement therapy (RRT) has not been established yet. Methods: Survival analysis was used to determine whether CT-PIRP subgroups identified in the derivation cohort (n = 2265) had different mortality and RRT risks. Temporal validation was performed in a matched cohort (n = 2051) of subsequently enrolled PIRP patients, in which discrimination and calibration were assessed using Kaplan-Meier survival curves, Cox regression and Fine & Gray competing risk modeling. Results: In both cohorts mortality risk was higher for subgroups 3 (proteinuric, low eGFR, high serum phosphate) and lower for subgroups 1 (proteinuric, high eGFR), 4 (non-proteinuric, younger, non-diabetic) and 5 (non-proteinuric, younger, diabetic). Risk of RRT was higher for subgroups 3 and 2 (proteinuric, low eGFR, low serum phosphate), while subgroups 1, 6 (non-proteinuric, old females) and 7 (non-proteinuric, old males) showed lower risk. Calibration was excellent for mortality in all subgroups while for RRT it was overall good except in subgroups 4 and 5. Conclusions: The CT-PIRP model is a temporally validated prediction tool for mortality and RRT, based on variables routinely collected, that could assist decision-making regarding the treatment of incident CKD patients. External validation in other CKD populations is needed to determine its generalizability.

Original languageEnglish
Article number177
JournalBMC Nephrology
Volume20
Issue number1
DOIs
Publication statusPublished - May 17 2019

Fingerprint

Renal Replacement Therapy
Chronic Renal Insufficiency
Glomerular Filtration Rate
Mortality
Calibration
Phosphates
Hospital Units
Aptitude
Nephrology
Kaplan-Meier Estimate
Survival Analysis
Serum
Italy
Decision Making
Kidney
Population

Keywords

  • Chronic kidney disease
  • CKD
  • Classification trees
  • Prognostic models
  • Renal disease
  • Renal outcomes
  • RRT inception
  • Temporal validation

ASJC Scopus subject areas

  • Nephrology

Cite this

Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients. / Gibertoni, Dino; Rucci, Paola; Mandreoli, Marcora; Corradini, Mattia; Martelli, Davide; Russo, Giorgia; Mancini, Elena; Santoro, Antonio.

In: BMC Nephrology, Vol. 20, No. 1, 177, 17.05.2019.

Research output: Contribution to journalArticle

Gibertoni, Dino ; Rucci, Paola ; Mandreoli, Marcora ; Corradini, Mattia ; Martelli, Davide ; Russo, Giorgia ; Mancini, Elena ; Santoro, Antonio. / Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients. In: BMC Nephrology. 2019 ; Vol. 20, No. 1.
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T1 - Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients

AU - Gibertoni, Dino

AU - Rucci, Paola

AU - Mandreoli, Marcora

AU - Corradini, Mattia

AU - Martelli, Davide

AU - Russo, Giorgia

AU - Mancini, Elena

AU - Santoro, Antonio

PY - 2019/5/17

Y1 - 2019/5/17

N2 - Background: A classification tree model (CT-PIRP) was developed in 2013 to predict the annual renal function decline of patients with chronic kidney disease (CKD) participating in the PIRP (Progetto Insufficienza Renale Progressiva) project, which involves thirteen Nephrology Hospital Units in Emilia-Romagna (Italy). This model identified seven subgroups with specific combinations of baseline characteristics that were associated with a differential estimated glomerular filtration rate (eGFR) annual decline, but the model's ability to predict mortality and renal replacement therapy (RRT) has not been established yet. Methods: Survival analysis was used to determine whether CT-PIRP subgroups identified in the derivation cohort (n = 2265) had different mortality and RRT risks. Temporal validation was performed in a matched cohort (n = 2051) of subsequently enrolled PIRP patients, in which discrimination and calibration were assessed using Kaplan-Meier survival curves, Cox regression and Fine & Gray competing risk modeling. Results: In both cohorts mortality risk was higher for subgroups 3 (proteinuric, low eGFR, high serum phosphate) and lower for subgroups 1 (proteinuric, high eGFR), 4 (non-proteinuric, younger, non-diabetic) and 5 (non-proteinuric, younger, diabetic). Risk of RRT was higher for subgroups 3 and 2 (proteinuric, low eGFR, low serum phosphate), while subgroups 1, 6 (non-proteinuric, old females) and 7 (non-proteinuric, old males) showed lower risk. Calibration was excellent for mortality in all subgroups while for RRT it was overall good except in subgroups 4 and 5. Conclusions: The CT-PIRP model is a temporally validated prediction tool for mortality and RRT, based on variables routinely collected, that could assist decision-making regarding the treatment of incident CKD patients. External validation in other CKD populations is needed to determine its generalizability.

AB - Background: A classification tree model (CT-PIRP) was developed in 2013 to predict the annual renal function decline of patients with chronic kidney disease (CKD) participating in the PIRP (Progetto Insufficienza Renale Progressiva) project, which involves thirteen Nephrology Hospital Units in Emilia-Romagna (Italy). This model identified seven subgroups with specific combinations of baseline characteristics that were associated with a differential estimated glomerular filtration rate (eGFR) annual decline, but the model's ability to predict mortality and renal replacement therapy (RRT) has not been established yet. Methods: Survival analysis was used to determine whether CT-PIRP subgroups identified in the derivation cohort (n = 2265) had different mortality and RRT risks. Temporal validation was performed in a matched cohort (n = 2051) of subsequently enrolled PIRP patients, in which discrimination and calibration were assessed using Kaplan-Meier survival curves, Cox regression and Fine & Gray competing risk modeling. Results: In both cohorts mortality risk was higher for subgroups 3 (proteinuric, low eGFR, high serum phosphate) and lower for subgroups 1 (proteinuric, high eGFR), 4 (non-proteinuric, younger, non-diabetic) and 5 (non-proteinuric, younger, diabetic). Risk of RRT was higher for subgroups 3 and 2 (proteinuric, low eGFR, low serum phosphate), while subgroups 1, 6 (non-proteinuric, old females) and 7 (non-proteinuric, old males) showed lower risk. Calibration was excellent for mortality in all subgroups while for RRT it was overall good except in subgroups 4 and 5. Conclusions: The CT-PIRP model is a temporally validated prediction tool for mortality and RRT, based on variables routinely collected, that could assist decision-making regarding the treatment of incident CKD patients. External validation in other CKD populations is needed to determine its generalizability.

KW - Chronic kidney disease

KW - CKD

KW - Classification trees

KW - Prognostic models

KW - Renal disease

KW - Renal outcomes

KW - RRT inception

KW - Temporal validation

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