The quest for cardiovascular disease risk prediction models in patients with nondialysis chronic kidney disease. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Streja, Elani; Norris, Keith C.; Budoff, Matthew J.; Hashemi, Leila; Akbilgic, Oguz; Kalantar-Zadeh, Kamyar
• Is Part Of: Current opinion in nephrology and hypertension. Volume 30:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: cardiovascular disease -- chronic kidney disease -- machine learning -- pooled cohort equation -- risk prediction; Hypertension -- Periodicals; Nephrology -- Periodicals; Hypertension -- Indexes; Hypertension -- Periodicals; Kidney Diseases -- Indexes; Kidney Diseases -- Periodicals; Nephrology -- Periodicals; 616.132
• DOI: 10.1097/MNH.0000000000000672
• Languages: English
• ISSNs: 1062-4821

Abstract

Abstract : Purpose of review: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). However, traditional CVD risk prediction equations do not work well in patients with CKD, and inclusion of kidney disease metrics such as albuminuria and estimated glomerular filtration rate have a modest to no benefit in improving prediction. Recent findings: As CKD progresses, the strength of traditional CVD risk factors in predicting clinical outcomes weakens. A pooled cohort equation used for CVD risk prediction is a useful tool for guiding clinicians on management of patients with CVD risk, but these equations do not calibrate well in patients with CKD, although a number of studies have developed modifications of the traditional equations to improve risk prediction. The reason for the poor calibration may be related to the fact that as CKD progresses, associations of traditional risk factors such as BMI, lipids and blood pressure with CVD outcomes are attenuated or reverse, and other risk factors may become more important. Summary: Large national cohorts such as the US Veteran cohort with many patients with evolving CKD may be useful resources for the developing CVD prediction models; however, additional considerations are needed for the unique composition of patients receiving care in these healthcare systems, including those with multiple comorbidities, as well as mental health issues, homelessness, posttraumatic stress disorders, frailty, malnutrition and polypharmacy. Machine learning over conventional risk prediction models may be better suited to handle the complexity needed for these CVD prediction models.