ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on Twitter

Kidney Week

Abstract: PO1318

Association Between Longitudinal Plasma and Urine Oxalate and Time-to-Kidney Failure in Primary Hyperoxaluria Using Joint Models

Session Information

Category: Genetic Diseases of the Kidneys

  • 1002 Genetic Diseases of the Kidneys: Non-Cystic

Authors

  • Vaughan, Lisa E., Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Lieske, John C., Mayo Clinic Minnesota, Rochester, Minnesota, United States
  • Schulte, Phillip, Mayo Clinic Minnesota, Rochester, Minnesota, United States
Background

The association between plasma oxalate (POX), urine oxalate (UOX) and the risk of kidney failure (KF) in patients with the rare disease primary hyperoxaluria (PH) is challenging to study due to small sample sizes and correlations among POX, UOX, and eGFR. To develop better KF models that simultaneously account for all 3 variables we used a novel statistical approach, joint models, modeling longitudinal biomarker processes (eGFR POX, UOX) and survival process (time-to-KF) jointly, using retrospective data from the Rare Kidney Stone Consortium PH registry.

Methods

Repeated eGFR, UOX, and POX after PH diagnosis were obtained from the registry. Time-to-KF was defined as time between PH diagnosis until transplantation, dialysis or eGFR<15 mL/min/1.73m2. A multivariate joint model was fit with longitudinal sub-models for each biomarker and a survival sub-model for KF. Longitudinal sub-models employed linear mixed effects models with biomarkers on the log scale. Joint models share information between longitudinal and survival sub-models such that eGFR, UOX, and POX were time-dependent variables in the survival sub-model, specifically using subject-specific mean biomarker values. Models were adjusted for age and sex at diagnosis. Results were compared to last observation carried forward (LOCF) analyses.

Results

A total 166 patients (mean 5 POX and 7 UOX per patient) with 60 KF events during follow up were included. With LOCF, POX positively associated with KF risk, both unadjusted and adjusted for other biomarkers (hazard ratio (HR) = 1.14 per umol/L, 95%CI = 1.07, 1.22, p<0.001), while UOX was not associated with KF after adjustment. With joint modeling, POX and KF were not significantly associated after adjustment (HR = 1.12, 95%CI = 0.99, 1.30, p=0.08), while higher UOX was associated with lower KF risk (HR = 0.30 per mmol/1.73m2/24h, 95%CI = 0.07, 0.92, p=0.04).

Conclusion

When modeling unevenly spaced longitudinal biomarkers and their association with KF, the LOCF time-dependent model makes implausible assumptions about steady-state biomarkers between observations. Implementation of a joint modeling framework allows flexible estimation of the association, which may impact conclusions. These novel methods can be used to inform patient-specific decisions about future KF risk, and the risk-benefit of novel treatment approaches.

Funding

  • NIDDK Support