Prediction Model and Risk Stratification Tool for Survival in Patients With CKD
Because chronic kidney disease (CKD) adversely affects survival, prediction of mortality risk should help to identify individuals requiring therapeutic intervention. The goal of this project was to construct and to validate a risk scoring system and prediction model of the probability of 2-year mort...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-03-01
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| Series: | Kidney International Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468024917304461 |
| _version_ | 1819096918458892288 |
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| author | Alexander S. Goldfarb-Rumyantzev Shiva Gautam Ning Dong Robert S. Brown |
| author_facet | Alexander S. Goldfarb-Rumyantzev Shiva Gautam Ning Dong Robert S. Brown |
| author_sort | Alexander S. Goldfarb-Rumyantzev |
| collection | DOAJ |
| description | Because chronic kidney disease (CKD) adversely affects survival, prediction of mortality risk should help to identify individuals requiring therapeutic intervention. The goal of this project was to construct and to validate a risk scoring system and prediction model of the probability of 2-year mortality in a CKD population.
Methods: We applied the Woodpecker approach to develop prediction equations using linear, exponential, and combined models. A risk indicator R on a scale of 0 to 10 was calculated as follows: starting with 0, add 0.048 for each year of age above 20, 0.45 for male sex, 0.49 for each stage of CKD over stage 2, 1.04 for proteinuria, 0.72 for smoking history, and 0.49 for each significant comorbidity up to 5.
Results: Using R to predict 2-year mortality, the model yielded an area under the receiver operating characterisic curve of 0.83 (95% confidence interval = 0.81−0.86) with 5062 subjects with CKD ≥stage 2 from a National Health and Nutrition Examination Survey cohort (1999−2004) having a 3.2% 2-year mortality. The combined expression offered results closest to most actual outcomes for the entire population and for each CKD stage. For those patients with higher risk (R ≥ 4−5, >5−6, and >6), the predicted 2-year mortality rates were 3.8%, 6.4%, and 13.0%, respectively, compared to observed mortality rates of 2.7%, 4.5%, and 13.3%.
Conclusion: The risk stratification tool and prediction model of 2-year mortality demonstrated good performance and may be used in clinical practice to quantify the risk of death for individual patients with CKD. |
| first_indexed | 2024-12-22T00:06:50Z |
| format | Article |
| id | doaj.art-74fb41c0acd541f996f814d7ef77bbb0 |
| institution | Directory Open Access Journal |
| issn | 2468-0249 |
| language | English |
| last_indexed | 2024-12-22T00:06:50Z |
| publishDate | 2018-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kidney International Reports |
| spelling | doaj.art-74fb41c0acd541f996f814d7ef77bbb02022-12-21T18:45:32ZengElsevierKidney International Reports2468-02492018-03-013241742510.1016/j.ekir.2017.11.010Prediction Model and Risk Stratification Tool for Survival in Patients With CKDAlexander S. Goldfarb-Rumyantzev0Shiva Gautam1Ning Dong2Robert S. Brown3Division of Nephrology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Biostatistics, University of Florida, Gainesville, Florida, USADepartment of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USADivision of Nephrology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USABecause chronic kidney disease (CKD) adversely affects survival, prediction of mortality risk should help to identify individuals requiring therapeutic intervention. The goal of this project was to construct and to validate a risk scoring system and prediction model of the probability of 2-year mortality in a CKD population. Methods: We applied the Woodpecker approach to develop prediction equations using linear, exponential, and combined models. A risk indicator R on a scale of 0 to 10 was calculated as follows: starting with 0, add 0.048 for each year of age above 20, 0.45 for male sex, 0.49 for each stage of CKD over stage 2, 1.04 for proteinuria, 0.72 for smoking history, and 0.49 for each significant comorbidity up to 5. Results: Using R to predict 2-year mortality, the model yielded an area under the receiver operating characterisic curve of 0.83 (95% confidence interval = 0.81−0.86) with 5062 subjects with CKD ≥stage 2 from a National Health and Nutrition Examination Survey cohort (1999−2004) having a 3.2% 2-year mortality. The combined expression offered results closest to most actual outcomes for the entire population and for each CKD stage. For those patients with higher risk (R ≥ 4−5, >5−6, and >6), the predicted 2-year mortality rates were 3.8%, 6.4%, and 13.0%, respectively, compared to observed mortality rates of 2.7%, 4.5%, and 13.3%. Conclusion: The risk stratification tool and prediction model of 2-year mortality demonstrated good performance and may be used in clinical practice to quantify the risk of death for individual patients with CKD.http://www.sciencedirect.com/science/article/pii/S2468024917304461CKDepidemiologymortalityoutcomepredictionsurvival |
| spellingShingle | Alexander S. Goldfarb-Rumyantzev Shiva Gautam Ning Dong Robert S. Brown Prediction Model and Risk Stratification Tool for Survival in Patients With CKD Kidney International Reports CKD epidemiology mortality outcome prediction survival |
| title | Prediction Model and Risk Stratification Tool for Survival in Patients With CKD |
| title_full | Prediction Model and Risk Stratification Tool for Survival in Patients With CKD |
| title_fullStr | Prediction Model and Risk Stratification Tool for Survival in Patients With CKD |
| title_full_unstemmed | Prediction Model and Risk Stratification Tool for Survival in Patients With CKD |
| title_short | Prediction Model and Risk Stratification Tool for Survival in Patients With CKD |
| title_sort | prediction model and risk stratification tool for survival in patients with ckd |
| topic | CKD epidemiology mortality outcome prediction survival |
| url | http://www.sciencedirect.com/science/article/pii/S2468024917304461 |
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