Health Disparities in Kidney Failure Among Patients With Autosomal Dominant Polycystic Kidney Disease: A Cross-Sectional Study

Harrison, Teresa N.; Chen, Qiaoling; Lee, Min Young; Munis, Mercedes A.; Morrissette, Kerresa; Sundar, Shirin V.; Pareja, Kristin; Nourbakhsh, Ali; Shu, Yu-Shiang; Willey, Cynthia; Sim, John J.

doi:10.1016/j.xkme.2022.100577

Cited by 2 publications

(4 citation statements)

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“…The study population from which this cohort was identified has been previously described [ 4 , 14 , 27 ]. In brief, the study population included patients of any age with a minimum of 1-year continuous membership in the health plan.…”

Section: Methodsmentioning

confidence: 99%

“…There is interfamilial and intrafamilial variability in the natural course of ADPKD [ 9 – 13 ]. In addition, we previously described differences in prevalence of and also progression to ESKD among different race/ethnic patients with ADPKD by race and ethnicity [ 4 , 14 ]. While 50% of patients progress to ESKD by their sixth decade of life, the decline in eGFR typically occurs rapidly later in adulthood due to initial compensatory glomerular hyperfiltration.…”

Section: Introductionmentioning

confidence: 99%

“…Both the MIC and the PROPKD score were derived from primarily Caucasian populations and thus may be limited in generalizability [ 7 , 8 , 19 ]. We previously characterized and described an ADPKD cohort and observed racial/ethnic differences in the proportion of patients with kidney failure, age of kidney failure onset, and likelihood of having had kidney transplantation [ 14 ]. Thus, approaches to identify rapid progressors early in the disease course with readily available clinical data and considering population diversity are warranted.…”

Section: Introductionmentioning

confidence: 99%

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Data driven approach to characterize rapid decline in autosomal dominant polycystic kidney disease

Sim,

Shu,

Bhandari

et al. 2024

PLoS ONE

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Autosomal dominant polycystic kidney disease (ADPKD) is a genetic kidney disease with high phenotypic variability. Furthering insights into patients’ ADPKD progression could lead to earlier detection, management, and alter the course to end stage kidney disease (ESKD). We sought to identify patients with rapid decline (RD) in kidney function and to determine clinical factors associated with RD using a data-driven approach. A retrospective cohort study was performed among patients with incident ADPKD (1/1/2002-12/31/2018). Latent class mixed models were used to identify RD patients using differences in eGFR trajectories over time. Predictors of RD were selected based on agreements among feature selection methods, including logistic, regularized, and random forest modeling. The final model was built on the selected predictors and clinically relevant covariates. Among 1,744 patients with incident ADPKD, 125 (7%) were identified as RD. Feature selection included 42 clinical measurements for adaptation with multiple imputations; mean (SD) eGFR was 85.2 (47.3) and 72.9 (34.4) in the RD and non-RD groups, respectively. Multiple imputed datasets identified variables as important features to distinguish RD and non-RD groups with the final prediction model determined as a balance between area under the curve (AUC) and clinical relevance which included 6 predictors: age, sex, hypertension, cerebrovascular disease, hemoglobin, and proteinuria. Results showed 72%-sensitivity, 70%-specificity, 70%-accuracy, and 0.77-AUC in identifying RD. 5-year ESKD rates were 38% and 7% among RD and non-RD groups, respectively. Using real-world routine clinical data among patients with incident ADPKD, we observed that six variables highly predicted RD in kidney function.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data driven approach to characterize rapid decline in autosomal dominant polycystic kidney disease

Sim,

Shu,

Bhandari

et al. 2024

PLoS ONE

Self Cite

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show abstract

“…8, 19 We previously characterized and described an ADPKD cohort and observed racial/ethnic differences in the proportion of patients with kidney failure, age of kidney failure onset, and likelihood of having had kidney transplantation. 20 Thus, approaches to identify rapid progressors early in the disease course with readily available clinical data and considering population diversity are warranted.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Approach to Characterize Rapid Decline in Autosomal Dominant Polycystic Kidney Disease

Sim,

Shu,

Bhandari

et al. 2024

Preprint

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Background Autosomal dominant polycystic kidney disease (ADPKD) is a genetic kidney disease with high phenotypic variability. Insights into ADPKD progression could lead to earlier detection and management prior to end stage kidney disease (ESKD). We sought to identify patients with rapid decline (RD) in kidney function and to determine clinical factors associated with RD using a data-driven approach. Methods A retrospective cohort study was performed among patients with incident ADPKD (1/1/2002-12/31/2018). Latent class mixed models were used to identify RD patients using rapidly declining eGFR trajectories over time. Predictors of RD were selected based on agreements among feature selection methods, including logistic, regularized, and random forest modeling. The final model was built on the selected predictors and clinically relevant covariates. Results Among 1,744 patients with incident ADPKD, 125 (7%) were identified as RD. Feature selection included 42 clinical measurements for adaptation with multiple imputations; mean (SD) eGFR was 85.2 (47.3) and 72.9 (34.4) in the RD and non-RD groups, respectively. Multiple imputed datasets identified variables as important features to distinguish RD and non-RD groups with the final prediction model determined as a balance between area under the curve (AUC) and clinical relevance which included 6 predictors: age, sex, hypertension, cerebrovascular disease, hemoglobin, and proteinuria. Results showed 72%-sensitivity, 70%-specificity, 70%-accuracy, and 0.77-AUC in identifying RD. 5-year ESKD rates were 38% and 7% among RD and non-RD groups, respectively. Conclusion Using real-world routine clinical data among patients with incident ADPKD, we observed that six variables highly predicted RD in kidney function.

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Health Disparities in Kidney Failure Among Patients With Autosomal Dominant Polycystic Kidney Disease: A Cross-Sectional Study

Cited by 2 publications

References 42 publications

Data driven approach to characterize rapid decline in autosomal dominant polycystic kidney disease

Data driven approach to characterize rapid decline in autosomal dominant polycystic kidney disease

Data Driven Approach to Characterize Rapid Decline in Autosomal Dominant Polycystic Kidney Disease

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