Application of flexible parametric cure model in determination of effective factors on the technique of survival peritoneal dialysis patients in adults of Iran

Abstract

Objective: Peritoneal Dialysis (PD) is one of the most common methods of dialysis performed in patients with kidney failure. This study presents the application of flexible parametric cure models, which enhance fit and interpretability by accommodating various survival distributions and allowing for the estimation of the cured fraction in diverse populations. Thus, the purpose of this study is to determine the risk and demographic factors affecting the survival of non-cured dialysis patients undergoing peritoneal dialysis, as well as to estimate the cured fraction of survival using this method.

Methodology: A total of 4,144 adult patients (aged 20 and above) were included in a retrospective (historical) cohort study from the Iranian PD registry, conducted between 1995 and 2018 across 20 centers nationwide. The study considered important variables such as sex, age, BMI, creatinine, calcium, cholesterol, triglycerides, Low-Density Lipoprotein (LDL), High-Density Lipoprotein (HDL), Erythrocyte Sedimentation Rate (ESR), systolic blood pressure (SBP), and diastolic blood pressure (DBP). A flexible parametric cure model was employed to assess the independent risk factors for PD technique failure.

Findings: The results of the current study showed that the flexible parametric cure Model (AIC = 5735.06) has a better fit compared with another cure model (mixture cure model = 6676.25, non-mixture cure model = 6677.28). Based on the findings of this study, the estimated cure fraction of survival using the PD method was 50% (95% CI: 0.47-0.53). The flexible cure model, demonstrated in a multivariate setting, indicated that variables such as age over 60 years (HR = 2.11, 95% CI: 1.05-1.35), calcium levels above 10.2 (HR = 1.43, 95% CI: 1.30-1.56), LDL levels above 140 (HR = 1.23 95% CI: 1.07-1.42), ESR (HR = 1.006, 95% CI: 1.003-1.01) and DBP (HR = 1.17, 95% CI: 1.16-1.31) significantly impacted the survival of non-cured patients.

Conclusion: The presence of cured individuals in the data flattens the Kaplan-Meier curve. In such cases, employing a flexible parametric cure model is appropriate to separately investigate the effects of various factors on the cure fraction and the survival of susceptible individuals. The findings of this study indicated that age, calcium, LDL, ESR, and DBP significantly influenced the survival of non-cured individuals. Specifically, increases in these variables were associated with a higher risk of technique failure.

Fig 1. Kaplan-Meier estimated survival plot based on years.

Initially, fitting a model without the presence of predictive variables was used to estimate the overall recovery rate. The results of fitting a flexible parametric cure model without predictive variables showed that the cure ratio is 0.50 (95% CI: 0.47–0.53).

Fig 2. Comparison of estimated survival by mixture cure models, non-mixture cure models with Weibull distribution, and flexible parametric cure model against Kaplan-Meier estimated survival.