Kidney medicine · 2025
Development of a Predictive Tool for Maintenance Dialysis Initiation: A Retrospective Cohort Analysis of 589,284 Participants From the Shizuoka Kokuho Database Study.
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Oshiro Yuri, Satoh Tatsunori, Ohata Emi, Nakatani Eiji, Kaneda Hideaki, Sugawara Akira
Аннотация
RATIONALE & OBJECTIVE: Maintenance dialysis initiation is rare in early-stage chronic kidney disease (CKD), making accurate risk stratification difficult. We sought to build a simple prediction score based on routine health checkup data.
STUDY DESIGN: Retrospective cohort study.
SETTING & PARTICIPANTS: Adults (≥40 years) attending government-mandated health checkups in Shizuoka Prefecture, Japan, 2012-2020 (N = 589,284).
PREDICTORS: Baseline demographics, vital signs, laboratory indices, medication use, and lifestyle factors routinely recorded at health checks.
OUTCOMES: Time from first health checkup to initiation of maintenance dialysis, ascertained from procedure codes; death treated as a competing risk.
ANALYTICAL APPROACH: Two-thirds of participants were randomly assigned to a training cohort (n = 392,856; events = 335) and one-third to a test cohort (n = 196,428; events = 179). Cause-specific Cox models generated hazard ratios that were converted to integer scores (maximum 31). Discrimination was evaluated with Harrell's c-index and calibration with performed with cumulative incidence curves.
RESULTS: During a median follow-up of 5.9 years, 514 participants (0.09%) initiated dialysis. Independent predictors included male sex, body mass index <18.5 kg/m2, higher systolic blood pressure, hemoglobin A1c ≥8 %, lower estimated glomerular filtration rate, proteinuria, aspartate aminotransferase ≥50 IU/L, use of antihypertensive or antidiabetic drugs, and habitual smoking. The score showed excellent discrimination in both training (c-index, 0.916; 95% confidence interval [CI], 0.898-0.934) and test (c-index, 0.916; 95% CI, 0.889-0.943) cohorts. High-risk individuals (score ≥16; 0.3% of the cohort) had a 5-year dialysis incidence of 4.6%, yet 95% remained dialysis-free, underscoring the challenge of predicting this rare outcome.
LIMITATIONS: CKD onset predated cohort entry, prescription data were not analyzed for causal effects, and external validation is pending.
CONCLUSIONS: Our model, leveraging routine health checkup data, accurately identifies persons at elevated risk for future dialysis despite low event rates.
STUDY DESIGN: Retrospective cohort study.
SETTING & PARTICIPANTS: Adults (≥40 years) attending government-mandated health checkups in Shizuoka Prefecture, Japan, 2012-2020 (N = 589,284).
PREDICTORS: Baseline demographics, vital signs, laboratory indices, medication use, and lifestyle factors routinely recorded at health checks.
OUTCOMES: Time from first health checkup to initiation of maintenance dialysis, ascertained from procedure codes; death treated as a competing risk.
ANALYTICAL APPROACH: Two-thirds of participants were randomly assigned to a training cohort (n = 392,856; events = 335) and one-third to a test cohort (n = 196,428; events = 179). Cause-specific Cox models generated hazard ratios that were converted to integer scores (maximum 31). Discrimination was evaluated with Harrell's c-index and calibration with performed with cumulative incidence curves.
RESULTS: During a median follow-up of 5.9 years, 514 participants (0.09%) initiated dialysis. Independent predictors included male sex, body mass index <18.5 kg/m2, higher systolic blood pressure, hemoglobin A1c ≥8 %, lower estimated glomerular filtration rate, proteinuria, aspartate aminotransferase ≥50 IU/L, use of antihypertensive or antidiabetic drugs, and habitual smoking. The score showed excellent discrimination in both training (c-index, 0.916; 95% confidence interval [CI], 0.898-0.934) and test (c-index, 0.916; 95% CI, 0.889-0.943) cohorts. High-risk individuals (score ≥16; 0.3% of the cohort) had a 5-year dialysis incidence of 4.6%, yet 95% remained dialysis-free, underscoring the challenge of predicting this rare outcome.
LIMITATIONS: CKD onset predated cohort entry, prescription data were not analyzed for causal effects, and external validation is pending.
CONCLUSIONS: Our model, leveraging routine health checkup data, accurately identifies persons at elevated risk for future dialysis despite low event rates.