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Are there tools to help estimate prognosis of kidney disease?
Validated prognostication tools have been described in the literature. It is important to emphasize that these tools share the same limitations as any prediction algorithm; the accuracy of their results tends to be highest at an aggregate, population level. Their accuracy is limited in evaluating individual patients.
Do patients want to know their prognosis of CKD?
Based on data from a Canadian study involving 584 patients with advanced CKD, more than 90% reported that it was somewhat or extremely important for them to be informed about their prognosis.
The below table illustrates some of these prediction models.
Studies Examining Survival Prediction in Kidney Disease
| PROGN OSTIC MODEL | VALIDATION COHORT | USEFUL FOR | DESCRIPTION |
|---|---|---|---|
| Cohen LM et al. a | Patients on maintenance hemodialysis at outpatient centers in the northeastern United States | Estimating chance of survival at 6 months | An integrated model that uses five components:1. Advanced age2. Dementia3. Peripheral vascular disease4. Decreased albumin5. Positive “surprise question” (“Would I be surprised if the patient died within the next 6 months?”) |
| Moss AH et al. “Surprise question” | Patients on maintenance hemodialysis at outpatient centers in West Virginia | Identifying patients who may benefit from palliative care intervention | Nurse practitioners were asked if they would be surprised if the patient died in the next 12 months. The odds ratio for death in 12 months in the “no” group versus the “yes” group was 3.5 (95% CI, 1.4–9.1) b |
| Thamer M et al. | Retrospective observational cohort study of 69,441 patients who started dialysis in the United States with an analysis of pre-ESKD Medicare claims to build a prediction model | Estimating risk of 3- and 6-month mortality for incident dialysis patients | A simple risk score is computed from common clinical characteristics. Scores range from 0 to 9, with higher scores correlating with shortened survival. |
| Couchoud CG et al. a | Retrospective observational cohort study of 24,348 patients who started dialysis in France | Estimating risk of 3-month mortality for incident dialysis patients | A risk score is computed from common clinical characteristics. Scores range from 0 to 25, with higher scores correlating with shortened survival. |
a Denotes that an online calculator is available at https://www.qxmd.com/calculate/ .
b Denotes confidence interval.
Sources
Data from Cohen, L. M., Ruthazer, R., Moss, A. H. & Germain, M. J. (2010.) Predicting six-month mortality for patients who are on maintenance hemodialysis. Clin J Am Soc Nephrol 5, 72–79; Moss, A. H. et al. (2008.) Utility of the ‘surprise’ question to identify dialysis patients with high mortality. Clin J Am Soc Nephrol 3, 1379–1384; Thamer, M. et al. (2015.) Predicting Early Death Among Elderly Dialysis Patients: Development and Validation of a Risk Score to Assist Shared Decision Making for Dialysis Initiation. Am. J. Kidney Dis. 66, 1024–1032; Couchoud, C. G. G., Beuscart, J.-B. R. B., Aldigier, J.-C. C., Brunet, P. J. & Moranne, O. P. (2015). Development of a risk stratification algorithm to improve patient-centered care and decision making for incident elderly patients with end-stage renal disease. Kidney Int. 88, 1178–1186.
