Abstract
Background
People with kidney failure have high risk of postoperative morbidity and mortality. Although the Revised Cardiac Risk Index (RCRI) is used to estimate the risk of major postoperative events, it has not been validated in this population. We aimed to externally validate the RCRI and determine whether updating the model improved predictions for people with kidney failure.
Methods
We derived a retrospective, population-based cohort of adults with kidney failure (maintenance dialysis or sustained estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2) who had surgery in Alberta, Canada between 2005 and 2019. We categorized participants based on RCRI variables and assigned risk estimates of death or major cardiac events, and then estimated predictive performance. We re-estimated the coefficients for each RCRI variable and internally validated the updated model. Net benefit was estimated with decision curve analysis.
Results
After 38,541 surgeries, 1,204 (3.1%) events occurred. The estimated C-statistic for the original RCRI was 0.64 (95% confidence interval [CI]: 0.62, 0.65). Examination of calibration revealed significant risk overestimation. In the re-estimated RCRI model, discrimination was marginally different (C-statistic 0.67 [95%CI 0.66, 0.69]), though calibration was improved. There was net benefit when examined with decision curve analysis, while the original RCRI was associated with harm.
Conclusions
The RCRI performed poorly in a Canadian kidney failure cohort and significantly overestimated risk, suggesting RCRI use in similar kidney failure populations should be limited. A re-estimated kidney failure specific RCRI may be promising, though needs external validation. Novel perioperative models for this population are urgently needed.
People with kidney failure have high risk of postoperative morbidity and mortality. Although the Revised Cardiac Risk Index (RCRI) is used to estimate the risk of major postoperative events, it has not been validated in this population. We aimed to externally validate the RCRI and determine whether updating the model improved predictions for people with kidney failure.
Methods
We derived a retrospective, population-based cohort of adults with kidney failure (maintenance dialysis or sustained estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2) who had surgery in Alberta, Canada between 2005 and 2019. We categorized participants based on RCRI variables and assigned risk estimates of death or major cardiac events, and then estimated predictive performance. We re-estimated the coefficients for each RCRI variable and internally validated the updated model. Net benefit was estimated with decision curve analysis.
Results
After 38,541 surgeries, 1,204 (3.1%) events occurred. The estimated C-statistic for the original RCRI was 0.64 (95% confidence interval [CI]: 0.62, 0.65). Examination of calibration revealed significant risk overestimation. In the re-estimated RCRI model, discrimination was marginally different (C-statistic 0.67 [95%CI 0.66, 0.69]), though calibration was improved. There was net benefit when examined with decision curve analysis, while the original RCRI was associated with harm.
Conclusions
The RCRI performed poorly in a Canadian kidney failure cohort and significantly overestimated risk, suggesting RCRI use in similar kidney failure populations should be limited. A re-estimated kidney failure specific RCRI may be promising, though needs external validation. Novel perioperative models for this population are urgently needed.
Original language | English |
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Pages (from-to) | 905-912 |
Journal | CJC Open |
Volume | 4 |
Issue number | 10 |
Early online date | 14 Jul 2022 |
DOIs | |
Publication status | Published - Oct 2022 |
Keywords
- kidney failure
- perioperative
- surgery
- Risk prediction
- RCRI
- validation