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 language | English |
|---|---|
| Article number | 177 |
| Journal | BMC Nephrology |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - May 17 2019 |
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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 journal › Article
}
TY - JOUR
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
UR - http://www.scopus.com/inward/record.url?scp=85065910288&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065910288&partnerID=8YFLogxK
U2 - 10.1186/s12882-019-1345-7
DO - 10.1186/s12882-019-1345-7
M3 - Article
AN - SCOPUS:85065910288
VL - 20
JO - BMC Nephrology
JF - BMC Nephrology
SN - 1471-2369
IS - 1
M1 - 177
ER -